Sensors

Research

47 pages, 35624 KiB

Open AccessArticle

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

by Elena Geangu, William A. P. Smith, Harry T. Mason, Astrid Priscilla Martinez-Cedillo, David Hunter, Marina I. Knight, Haipeng Liang, Maria del Carmen Garcia de Soria Bazan, Zion Tsz Ho Tse, Thomas Rowland, Dom Corpuz, Josh Hunter, Nishant Singh, Quoc C. Vuong, Mona Ragab Sayed Abdelgayed, David R. Mullineaux, Stephen Smith and Bruce R. Muller

Sensors 2023, 23(18), 7930; https://doi.org/10.3390/s23187930 - 16 Sep 2023

Cited by 3 | Viewed by 4496

Abstract

There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no [...] Read more.

There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

13 pages, 1818 KiB

Open AccessArticle

Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors

by Antonino Casile, Giulia Fregna, Vittorio Boarini, Chiara Paoluzzi, Fabio Manfredini, Nicola Lamberti, Andrea Baroni and Sofia Straudi

Sensors 2023, 23(18), 7906; https://doi.org/10.3390/s23187906 - 15 Sep 2023

Cited by 4 | Viewed by 1815

Abstract

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients’ movements in ecological scenarios. These systems are, however, very expensive and not readily available for many [...] Read more.

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients’ movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest’s and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

13 pages, 1209 KiB

Open AccessArticle

Smartphone- and Paper-Based Delivery of Balance Intervention for Older Adults Are Equally Effective, Enjoyable, and of High Fidelity: A Randomized Controlled Trial

by Vipul Lugade, Molly Torbitt, Suzanne R. O’Brien and Patima Silsupadol

Sensors 2023, 23(17), 7451; https://doi.org/10.3390/s23177451 - 27 Aug 2023

Viewed by 1659

Abstract

Home-based rehabilitation programs for older adults have demonstrated effectiveness, desirability, and reduced burden. However, the feasibility and effectiveness of balance-intervention training delivered through traditional paper-versus novel smartphone-based methods is unknown. Therefore, the purpose of this study was to evaluate if a home-based balance-intervention [...] Read more.

Home-based rehabilitation programs for older adults have demonstrated effectiveness, desirability, and reduced burden. However, the feasibility and effectiveness of balance-intervention training delivered through traditional paper-versus novel smartphone-based methods is unknown. Therefore, the purpose of this study was to evaluate if a home-based balance-intervention program could equally improve balance performance when delivered via smartphone or paper among adults over the age of 65. A total of 31 older adults were randomized into either a paper or phone group and completed a 4-week asynchronous self-guided balance intervention across 12 sessions for approximately 30 min per session. Baseline, 4-week, and 8-week walking and standing balance evaluations were performed, with exercise duration and adherence recorded. Additional self-reported measures were collected regarding the enjoyment, usability, difficulty, and length of the exercise program. Twenty-nine participants completed the balance program and three assessments, with no group differences found for any outcome measure. Older adults demonstrated an approximately 0.06 m/s faster gait velocity and modified balance strategies during walking and standing conditions following the intervention protocol. Participants further self-reported similar enjoyment, difficulty, and exercise effectiveness. Results of this study demonstrated the potential to safely deliver home-based interventions as well as the feasibility and effectiveness of delivering balance intervention through a smartphone-based application. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 2072 KiB

Open AccessArticle

A Continuously Worn Dual Temperature Sensor System for Accurate Monitoring of Core Body Temperature from the Ear Canal

by Kyle D. Olson, Parker O’Brien, Andy S. Lin, David A. Fabry, Steve Hanke and Mark J. Schroeder

Sensors 2023, 23(17), 7323; https://doi.org/10.3390/s23177323 - 22 Aug 2023

Cited by 1 | Viewed by 3042

Abstract

The objective of this work was to develop a temperature sensor system that accurately measures core body temperature from an ear-worn device. Two digital temperature sensors were embedded in a hearing aid shell along the thermal gradient of the ear canal to form [...] Read more.

The objective of this work was to develop a temperature sensor system that accurately measures core body temperature from an ear-worn device. Two digital temperature sensors were embedded in a hearing aid shell along the thermal gradient of the ear canal to form a linear heat balance relationship. This relationship was used to determine best fit parameters for estimating body temperature. The predicted body temperatures resulted in intersubject limits of agreement (LOA) of ±0.49 °C over a range of physiologic and ambient temperatures without calibration. The newly developed hearing aid-based temperature sensor system can estimate core body temperature at an accuracy level equal to or better than many devices currently on the market. An accurate, continuously worn, temperature monitoring and tracking device may help provide early detection of illnesses, which could prove especially beneficial during pandemics and in the elderly demographic of hearing aid wearers. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

21 pages, 817 KiB

Open AccessArticle

Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

by Muhammad Waleed, Tariq Kamal, Tai-Won Um, Abdul Hafeez, Bilal Habib and Knud Erik Skouby

Sensors 2023, 23(15), 6760; https://doi.org/10.3390/s23156760 - 28 Jul 2023

Cited by 9 | Viewed by 5450

Abstract

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote [...] Read more.

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

14 pages, 2737 KiB

Open AccessArticle

A Single-Sensor Approach to Quantify Gait in Patients with Hereditary Spastic Paraplegia

by Linda M. A. van Gelder, Tecla Bonci, Ellen E. Buckley, Kathryn Price, Francesca Salis, Marios Hadjivassiliou, Claudia Mazzà and Channa Hewamadduma

Sensors 2023, 23(14), 6563; https://doi.org/10.3390/s23146563 - 20 Jul 2023

Viewed by 1580

Abstract

Hereditary spastic paraplegia (HSP) is characterised by progressive lower-limb spasticity and weakness resulting in ambulation difficulties. During clinical practice, walking is observed and/or assessed by timed 10-metre walk tests; time, feasibility, and methodological reliability are barriers to detailed characterisation of patients’ walking abilities [...] Read more.

Hereditary spastic paraplegia (HSP) is characterised by progressive lower-limb spasticity and weakness resulting in ambulation difficulties. During clinical practice, walking is observed and/or assessed by timed 10-metre walk tests; time, feasibility, and methodological reliability are barriers to detailed characterisation of patients’ walking abilities when instrumenting this test. Wearable sensors have the potential to overcome such drawbacks once a validated approach is available for patients with HSP. Therefore, while limiting patients’ and assessors’ burdens, this study aims to validate the adoption of a single lower-back wearable inertial sensor approach for step detection in HSP patients; this is the first essential algorithmic step in quantifying most gait temporal metrics. After filtering the 3D acceleration signal based on its smoothness and enhancing the step-related peaks, initial contacts (ICs) were identified as positive zero-crossings of the processed signal. The proposed approach was validated on thirteen individuals with HSP while they performed three 10-metre tests and wore pressure insoles used as a gold standard. Overall, the single-sensor approach detected 794 ICs (87% correctly identified) with high accuracy (median absolute errors (mae): 0.05 s) and excellent reliability (ICC = 1.00). Although about 12% of the ICs were missed and the use of walking aids introduced extra ICs, a minor impact was observed on the step time quantifications (mae 0.03 s (5.1%), ICC = 0.89); the use of walking aids caused no significant differences in the average step time quantifications. Therefore, the proposed single-sensor approach provides a reliable methodology for step identification in HSP, augmenting the gait information that can be accurately and objectively extracted from patients with HSP during their clinical assessment. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 5300 KiB

Open AccessArticle

Exploring Teslasuit’s Potential in Detecting Sequential Slip-Induced Kinematic Changes among Healthy Young Adults

by Jacob Hepp, Michael Shiraishi, Michelle Tran, Emmy Henson, Mira Ananthanarayanan and Rahul Soangra

Sensors 2023, 23(14), 6258; https://doi.org/10.3390/s23146258 - 9 Jul 2023

Cited by 1 | Viewed by 1357

Abstract

This study aimed to assess whether the Teslasuit, a wearable motion-sensing technology, could detect subtle changes in gait following slip perturbations comparable to an infrared motion capture system. A total of 12 participants wore Teslasuits equipped with inertial measurement units (IMUs) and reflective [...] Read more.

This study aimed to assess whether the Teslasuit, a wearable motion-sensing technology, could detect subtle changes in gait following slip perturbations comparable to an infrared motion capture system. A total of 12 participants wore Teslasuits equipped with inertial measurement units (IMUs) and reflective markers. The experiments were conducted using the Motek GRAIL system, which allowed for accurate timing of slip perturbations during heel strikes. The data from Teslasuit and camera systems were analyzed using statistical parameter mapping (SPM) to compare gait patterns from the two systems and before and after slip. We found significant changes in ankle angles and moments before and after slip perturbations. We also found that step width significantly increased after slip perturbations (p = 0.03) and total double support time significantly decreased after slip (p = 0.01). However, we found that initial double support time significantly increased after slip (p = 0.01). However, there were no significant differences observed between the Teslasuit and motion capture systems in terms of kinematic curves for ankle, knee, and hip movements. The Teslasuit showed promise as an alternative to camera-based motion capture systems for assessing ankle, knee, and hip kinematics during slips. However, some limitations were noted, including kinematics magnitude differences between the two systems. The findings of this study contribute to the understanding of gait adaptations due to sequential slips and potential use of Teslasuit for fall prevention strategies, such as perturbation training. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 1507 KiB

Open AccessArticle

Assessing the Feasibility of Replacing Subjective Questionnaire-Based Sleep Measurement with an Objective Approach Using a Smartwatch

by Maksym Gaiduk, Ralf Seepold, Natividad Martínez Madrid and Juan Antonio Ortega

Sensors 2023, 23(13), 6145; https://doi.org/10.3390/s23136145 - 4 Jul 2023

Cited by 3 | Viewed by 2176

Abstract

In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary [...] Read more.

In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

9 pages, 1442 KiB

Open AccessCommunication

Validity and Reliability of a Load Cell Sensor-Based Device for Assessment of the Isometric Mid-Thigh Pull Test

by Raynier Montoro-Bombú, Beatriz Branquinho Gomes, Amândio Santos and Luis Rama

Sensors 2023, 23(13), 5832; https://doi.org/10.3390/s23135832 - 22 Jun 2023

Cited by 4 | Viewed by 2195

Abstract

In recent years, there has been an exponential increase in the number of devices developed to measure or estimate physical exercise. However, before these devices can be used in a practical and research environment, it is necessary to determine their validity and reliability. [...] Read more.

In recent years, there has been an exponential increase in the number of devices developed to measure or estimate physical exercise. However, before these devices can be used in a practical and research environment, it is necessary to determine their validity and reliability. The purpose of this study is to test the validity and reliability of a load cell sensor-based device (LC) for measuring the peak force (PFr) and the rate of force development (RFD) during the isometric mid-thigh pull (IMTP) test, using a force plate (FP) as the gold standard. Forty-two undergraduate sport science students (male and female) participated in this study. In a single session, they performed three repetitions of the IMTP test, being tested simultaneously with an LC device and a Kistler force platform (FP). The PFr and RFD data were obtained from the force-time curve of the FP and compared with the LC data, provided automatically by the software of the device (Smart Traction device©). The mean difference between the results obtained by the LC device and the gold-standard equipment (FP) was not significantly different (p > 0.05), for both PFr and RFD, which suggests the validity of the ST results. Bland–Altman analysis showed a small mean difference in PFr = 1.69 N, upper bound = 47.88 N, and lower bound = −51.27 N. RFD showed that the mean difference was −5.27 N/s, upper limit = 44.36 N/s, and lower limit = −54.91 N/s. Our results suggest that the LC device can be used in the assessment of the isometric-mid-thigh-pull test as a valid and reliable tool. It is recommended that this device’s users consider these research results before putting the ST into clinical practice. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

23 pages, 3186 KiB

Open AccessArticle

The Analytical and Clinical Validity of the pfSTEP Digital Biomarker of the Susceptibility/Risk of Declining Physical Function in Community-Dwelling Older Adults

by Alexander Schoenfelder, Brad Metcalf, Joss Langford, Afroditi Stathi, Max J. Western and Melvyn Hillsdon

Sensors 2023, 23(11), 5122; https://doi.org/10.3390/s23115122 - 27 May 2023

Cited by 4 | Viewed by 3316

Abstract

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are [...] Read more.

Measures of stepping volume and rate are common outputs from wearable devices, such as accelerometers. It has been proposed that biomedical technologies, including accelerometers and their algorithms, should undergo rigorous verification as well as analytical and clinical validation to demonstrate that they are fit for purpose. The aim of this study was to use the V3 framework to assess the analytical and clinical validity of a wrist-worn measurement system of stepping volume and rate, formed by the GENEActiv accelerometer and GENEAcount step counting algorithm. The analytical validity was assessed by measuring the level of agreement between the wrist-worn system and a thigh-worn system (activPAL), the reference measure. The clinical validity was assessed by establishing the prospective association between the changes in stepping volume and rate with changes in physical function (SPPB score). The agreement of the thigh-worn reference system and the wrist-worn system was excellent for total daily steps (CCC = 0.88, 95% CI 0.83–0.91) and moderate for walking steps and faster-paced walking steps (CCC = 0.61, 95% CI 0.53–0.68 and 0.55, 95% CI 0.46–0.64, respectively). A higher number of total steps and faster paced-walking steps was consistently associated with better physical function. After 24 months, an increase of 1000 daily faster-paced walking steps was associated with a clinically meaningful increase in physical function (0.53 SPPB score, 95% CI 0.32–0.74). We have validated a digital susceptibility/risk biomarker—pfSTEP—that identifies an associated risk of low physical function in community-dwelling older adults using a wrist-worn accelerometer and its accompanying open-source step counting algorithm. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 5924 KiB

Open AccessArticle

A Workflow for Studying the Stump–Socket Interface in Persons with Transtibial Amputation through 3D Thermographic Mapping

by Andrea Giovanni Cutti, Federico Morosato, Cosimo Gentile, Francesca Gariboldi, Giovanni Hamoui, Maria Grazia Santi, Gregorio Teti and Emanuele Gruppioni

Sensors 2023, 23(11), 5035; https://doi.org/10.3390/s23115035 - 24 May 2023

Cited by 2 | Viewed by 1630

Abstract

The design and fitting of prosthetic sockets can significantly affect the acceptance of an artificial limb by persons with lower limb amputations. Clinical fitting is typically an iterative process, which requires patients’ feedback and professional assessment. When feedback is unreliable due to the [...] Read more.

The design and fitting of prosthetic sockets can significantly affect the acceptance of an artificial limb by persons with lower limb amputations. Clinical fitting is typically an iterative process, which requires patients’ feedback and professional assessment. When feedback is unreliable due to the patient’s physical or psychological conditions, quantitative measures can support decision-making. Specifically, monitoring the skin temperature of the residual limb can provide valuable information regarding unwanted mechanical stresses and reduced vascularization, which can lead to inflammation, skin sores and ulcerations. Multiple 2D images to examine a real-life 3D limb can be cumbersome and might only offer a partial assessment of critical areas. To overcome these issues, we developed a workflow for integrating thermographic information on the 3D scan of a residual limb, with intrinsic reconstruction quality measures. Specifically, workflow allows us to calculate a 3D thermal map of the skin of the stump at rest and after walking, and summarize this information with a single 3D differential map. The workflow was tested on a person with transtibial amputation, with a reconstruction accuracy lower than 3 mm, which is adequate for socket adaptation. We expect the workflow to improve socket acceptance and patients’ quality of life. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 6730 KiB

Open AccessArticle

Comparison between Speckle Plethysmography and Photoplethysmography during Cold Pressor Test Referenced to Finger Arterial Pressure

by Jorge Herranz Olazabal, Ilde Lorato, Jesse Kling, Marc Verhoeven, Fokko Wieringa, Chris Van Hoof, Willem Verkruijsse and Evelien Hermeling

Sensors 2023, 23(11), 5016; https://doi.org/10.3390/s23115016 - 24 May 2023

Cited by 6 | Viewed by 2625

Abstract

Speckle Plethysmography (SPG) and Photoplethysmography (PPG) are different biophotonics technologies that allow for measurement of haemodynamics. As the difference between SPG and PPG under low perfusion conditions is not fully understood, a Cold Pressor Test (CPT—60 s full hand immersion in ice water), [...] Read more.

Speckle Plethysmography (SPG) and Photoplethysmography (PPG) are different biophotonics technologies that allow for measurement of haemodynamics. As the difference between SPG and PPG under low perfusion conditions is not fully understood, a Cold Pressor Test (CPT—60 s full hand immersion in ice water), was used to modulate blood pressure and peripheral circulation. A custom-built setup simultaneously derived SPG and PPG from the same video streams at two wavelengths (639 nm and 850 nm). SPG and PPG were measured at the right index finger location before and during the CPT using finger Arterial Pressure (fiAP) as a reference. The effect of the CPT on the Alternating Component amplitude (AC) and Signal-to-Noise Ratio (SNR) of dual-wavelength SPG and PPG signals was analysed across participants. Furthermore, waveform differences between SPG, PPG, and fiAP based on frequency harmonic ratios were analysed for each subject (n = 10). Both PPG and SPG at 850 nm show a significant reduction during the CPT in both AC and SNR. However, SPG showed significantly higher and more stable SNR than PPG in both study phases. Harmonic ratios were found substantially higher in SPG than PPG. Therefore, in low perfusion conditions, SPG seems to offer a more robust pulse wave monitoring with higher harmonic ratios than PPG. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Graphical abstract

9 pages, 514 KiB

Open AccessCommunication

Prediction of the Physical Activity Level of Community-Dwelling Older Japanese Adults with a Triaxial Accelerometer Containing a Classification Algorithm for Ambulatory and Non-Ambulatory Activities

by Shigeho Tanaka, Kazuko Ishikawa-Takata, Satoshi Nakae and Satoshi Sasaki

Sensors 2023, 23(10), 4960; https://doi.org/10.3390/s23104960 - 22 May 2023

Cited by 3 | Viewed by 1553

Abstract

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed [...] Read more.

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed correction formulae for such populations in Japan. Data for 69 Japanese community-dwelling adults aged 65 to 85 years were used. The total energy expenditure in free-living conditions was measured with the doubly labeled water method and the measured basal metabolic rate. The PAL was also estimated from metabolic equivalent (MET) values obtained with the activity monitor. Adjusted MET values were also calculated with the regression equation of Nagayoshi et al. (2019). The observed PAL was underestimated, but significantly correlated, with the PAL from the ASP. When adjusted using the Nagayoshi et al. regression equation, the PAL was overestimated. Therefore, we developed regression equations to estimate the actual PAL (Y) from the PAL obtained with the ASP for young adults (X) as follows: women: Y = 0.949 × X + 0.205, mean ± standard deviation of the prediction error = 0.00 ± 0.20; men: Y = 0.899 × X + 0.371, mean ± standard deviation of the prediction error = 0.00 ± 0.17. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 5041 KiB

Open AccessCommunication

Monitoring Knee Contact Force with Force-Sensing Insoles

by Alex Spencer, Michael Samaan and Brian Noehren

Sensors 2023, 23(10), 4900; https://doi.org/10.3390/s23104900 - 19 May 2023

Viewed by 1715

Abstract

Numerous applications exist for monitoring knee contact force (KCF) throughout activities of daily living. However, the ability to estimate these forces is restricted to a laboratory setting. The purposes of this study are to develop KCF metric estimation models and explore the feasibility [...] Read more.

Numerous applications exist for monitoring knee contact force (KCF) throughout activities of daily living. However, the ability to estimate these forces is restricted to a laboratory setting. The purposes of this study are to develop KCF metric estimation models and explore the feasibility of monitoring KCF metrics via surrogate measures derived from force-sensing insole data. Nine healthy subjects (3F, age 27 ± 5 years, mass 74.8 ± 11.8 kg, height 1.7 ± 0.08 m) walked at multiple speeds (0.8–1.6 m/s) on an instrumented treadmill. Thirteen insole force features were calculated as potential predictors of peak KCF and KCF impulse per step, estimated with musculoskeletal modeling. The error was calculated with median symmetric accuracy. Pearson product-moment correlation coefficients defined the relationship between variables. Models develop per-limb demonstrated lower prediction error than those developed per-subject (KCF impulse: 2.2% vs 3.4%; peak KCF: 3.50% vs. 6.5%, respectively). Many insole features are moderately to strongly associated with peak KCF, but not KCF impulse across the group. We present methods to directly estimate and monitor changes in KCF using instrumented insoles. Our results carry promising implications for internal tissue loads monitoring outside of a laboratory with wearable sensors. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

13 pages, 4766 KiB

Open AccessArticle

Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum

by António Real, Pedro Morais, Bruno Oliveira, Helena R. Torres and João L. Vilaça

Sensors 2023, 23(9), 4427; https://doi.org/10.3390/s23094427 - 30 Apr 2023

Cited by 1 | Viewed by 1557

Abstract

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of [...] Read more.

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors’ response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis’s applied forces, corroborating its potential for clinical practice. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 704 KiB

Open AccessArticle

Wearable Activity Trackers Objectively Measure Incidental Physical Activity in Older Adults Undergoing Aortic Valve Replacement

by Nicola Straiton, Matthew Hollings, Janice Gullick and Robyn Gallagher

Sensors 2023, 23(6), 3347; https://doi.org/10.3390/s23063347 - 22 Mar 2023

Cited by 1 | Viewed by 2122

Abstract

Background: For older adults with severe aortic stenosis (AS) undergoing aortic valve replacement (AVR), recovery of physical function is important, yet few studies objectively measure it in real-world environments. This exploratory study explored the acceptability and feasibility of using wearable trackers to measure [...] Read more.

Background: For older adults with severe aortic stenosis (AS) undergoing aortic valve replacement (AVR), recovery of physical function is important, yet few studies objectively measure it in real-world environments. This exploratory study explored the acceptability and feasibility of using wearable trackers to measure incidental physical activity (PA) in AS patients before and after AVR. Methods: Fifteen adults with severe AS wore an activity tracker at baseline, and ten at one month follow-up. Functional capacity (six-minute walk test, 6MWT) and HRQoL (SF 12) were also assessed. Results: At baseline, AS participants (n = 15, 53.3% female, mean age 82.3 ± 7.0 years) wore the tracker for four consecutive days more than 85% of the total prescribed time, this improved at follow-up. Before AVR, participants demonstrated a wide range of incidental PA (step count median 3437 per day), and functional capacity (6MWT median 272 m). Post-AVR, participants with the lowest incidental PA, functional capacity, and HRQoL at baseline had the greatest improvements within each measure; however, improvements in one measure did not translate to improvements in another. Conclusion: The majority of older AS participants wore the activity trackers for the required time period before and after AVR, and the data attained were useful for understanding AS patients’ physical function. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

23 pages, 7573 KiB

Open AccessArticle

Quantifying States and Transitions of Emerging Postural Control for Children Not Yet Able to Sit Independently

by Patricia Mellodge, Sandra Saavedra, Linda Tran Poit, Kristamarie A. Pratt and Adam D. Goodworth

Sensors 2023, 23(6), 3309; https://doi.org/10.3390/s23063309 - 21 Mar 2023

Viewed by 2205

Abstract

Objective, quantitative postural data is limited for individuals who are non-ambulatory, especially for those who have not yet developed trunk control for sitting. There are no gold standard measurements to monitor the emergence of upright trunk control. Quantification of intermediate levels of postural [...] Read more.

Objective, quantitative postural data is limited for individuals who are non-ambulatory, especially for those who have not yet developed trunk control for sitting. There are no gold standard measurements to monitor the emergence of upright trunk control. Quantification of intermediate levels of postural control is critically needed to improve research and intervention for these individuals. Accelerometers and video were used to record postural alignment and stability for eight children with severe cerebral palsy aged 2 to 13 years, under two conditions, seated on a bench with only pelvic support and with additional thoracic support. This study developed an algorithm to classify vertical alignment and states of upright control; Stable, Wobble, Collapse, Rise and Fall from accelerometer data. Next, a Markov chain model was created to calculate a normative score for postural state and transition for each participant with each level of support. This tool allowed quantification of behaviors previously not captured in adult-based postural sway measures. Histogram and video recordings were used to confirm the output of the algorithm. Together, this tool revealed that providing external support allowed all participants: (1) to increase their time spent in the Stable state, and (2) to reduce the frequency of transitions between states. Furthermore, all participants except one showed improved state and transition scores when given external support. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

26 pages, 7448 KiB

Open AccessArticle

NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform

by Konstantinos Mitsopoulos, Vasiliki Fiska, Konstantinos Tagaras, Athanasios Papias, Panagiotis Antoniou, Konstantinos Nizamis, Konstantinos Kasimis, Paschalina-Danai Sarra, Diamanto Mylopoulou, Theodore Savvidis, Apostolos Praftsiotis, Athanasios Arvanitidis, George Lyssas, Konstantinos Chasapis, Alexandros Moraitopoulos, Alexander Astaras, Panagiotis D. Bamidis and Alkinoos Athanasiou

Sensors 2023, 23(6), 3281; https://doi.org/10.3390/s23063281 - 20 Mar 2023

Cited by 5 | Viewed by 3796

Abstract

Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The [...] Read more.

Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Results: Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Conclusions: Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 3166 KiB

Open AccessArticle

Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury for Workers in the Healthcare Industry

by Xiaoxu Ji, Ranuki O. Hettiarachchige, Alexa L. E. Littman and Davide Piovesan

Sensors 2023, 23(5), 2781; https://doi.org/10.3390/s23052781 - 3 Mar 2023

Cited by 7 | Viewed by 4145

Abstract

Background: Hospital nurses and caregivers are reported to have the highest number of workplace injuries every year, which directly leads to missed days of work, a large amount of compensation costs, and staff shortage issues in the healthcare industry. Hence, this research study [...] Read more.

Background: Hospital nurses and caregivers are reported to have the highest number of workplace injuries every year, which directly leads to missed days of work, a large amount of compensation costs, and staff shortage issues in the healthcare industry. Hence, this research study provides a new technique to evaluate the risk of injuries for healthcare workers using a combination of unobtrusive wearable devices and digital human technology. The seamless integration of JACK Siemens software and the Xsens motion tracking system was used to determine awkward postures adopted for patient transfer tasks. This technique allows for continuous monitoring of the healthcare worker’s movement which can be obtained in the field. Methods: Thirty-three participants underwent two common tasks: moving a patient manikin from a lying position to a sitting position in bed and transferring the manikin from a bed to a wheelchair. By identifying, in these daily repetitive patient-transfer tasks, potential inappropriate postures that can be conducive to excessive load on the lumbar spine, a real-time monitoring process can be devised to adjust them, accounting for the effect of fatigue. Experimental Result: From the results, we identified a significant difference in spinal forces exerted on the lower back between genders at different operational heights. Additionally, we revealed the main anthropometric variables (e.g., trunk and hip motions) that are having a large impact on potential lower back injury. Conclusions: These results will lead to implementation of training techniques and improvements in working environment design to effectively reduce the number of healthcare workers experiencing lower back pain, which can be conducive to fewer workers leaving the healthcare industry, better patient satisfaction and reduction of healthcare costs. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

11 pages, 2418 KiB

Open AccessArticle

Characterization and Validation of Flexible Dry Electrodes for Wearable Integration

by Tiago Nunes and Hugo Plácido da Silva

Sensors 2023, 23(3), 1468; https://doi.org/10.3390/s23031468 - 28 Jan 2023

Cited by 6 | Viewed by 3655

Abstract

When long-term biosignal monitoring is required via surface electrodes, the use of conventional silver/silver chloride (Ag/AgCl) gelled electrodes may not be the best solution, as the gel in the electrodes tends to dry out over time. In this work, the electrical behaviour and [...] Read more.

When long-term biosignal monitoring is required via surface electrodes, the use of conventional silver/silver chloride (Ag/AgCl) gelled electrodes may not be the best solution, as the gel in the electrodes tends to dry out over time. In this work, the electrical behaviour and performance of dry electrodes for biopotential monitoring was assessed. Three materials were investigated and compared against the gold-standard Ag/AgCl gelled electrodes. To characterize their electrical behaviour, the impedance response over the frequency was evaluated, as well as its signal to noise ratio. The electrodes’ performance was evaluated by integrating them in a proven electrocardiogram (ECG) acquisition setup where an ECG signal was acquired simultaneously with a set of dry electrodes and a set of standard Ag/AgCl gelled electrodes as reference. The obtained results were morphologically compared using the Normalised Root Mean Squared Error (nRMSE) and the Cosine Similarity (CS). The findings of this work suggest that the use of dry electrodes for biopotential monitoring is a suitable replacement for the conventional Ag/AgCl gelled electrodes. The signal obtained with dry electrodes is comparable to the one obtained with the gold standard, with the advantage that these do not require the use of gel and can be easily integrated into fabric to facilitate their use in long-term monitoring scenarios. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

19 pages, 2433 KiB

Open AccessArticle

Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6

by Prashanth Shyam Kumar, Mouli Ramasamy, Kamala Ramya Kallur, Pratyush Rai and Vijay K. Varadan

Sensors 2023, 23(3), 1389; https://doi.org/10.3390/s23031389 - 26 Jan 2023

Cited by 5 | Viewed by 3259

Abstract

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads [...] Read more.

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R², and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R²-72.05, ρ-0.88) and (RMSE-26.27 µV, R²-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 1697 KiB

Open AccessArticle

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

by Jacob L. Griffith, Kim Cluff, Grant M. Downes, Brandon Eckerman, Subash Bhandari, Benjamin E. Loflin, Ryan Becker, Fayez Alruwaili and Noor Mohammed

Sensors 2023, 23(2), 985; https://doi.org/10.3390/s23020985 - 14 Jan 2023

Viewed by 2005

Abstract

The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable [...] Read more.

The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable electromagnetic resonant skin patch sensor during simulated physiological changes observed in aerospace applications. Simulated microgravity was induced in eight healthy human participants (n = 8), and the implementation of lower body negative pressure (LBNP) countermeasures was induced in four healthy human participants (n = 4). The average shift in resonant frequency was −13.76 ± 6.49 MHz for simulated microgravity with a shift in intracranial pressure (ICP) of 9.53 ± 1.32 mmHg, and a shift of 8.80 ± 5.2097 MHz for LBNP with a shift in ICP of approximately −5.83 ± 2.76 mmHg. The constructed regression model to explain the variance in shifts in ICP using the shifts in resonant frequency (R² = 0.97) resulted in a root mean square error of 1.24. This work demonstrates a strong correlation between sensor signal response and shifts in ICP. Furthermore, this study establishes a foundation for future work integrating wearable sensors with alert systems and countermeasure recommendations for pilots and astronauts. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

23 pages, 938 KiB

Open AccessArticle

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

by Abdolrahman Peimankar, Trine Straarup Winther, Ali Ebrahimi and Uffe Kock Wiil

Sensors 2023, 23(2), 679; https://doi.org/10.3390/s23020679 - 6 Jan 2023

Cited by 7 | Viewed by 3075

Abstract

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality [...] Read more.

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer’s disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F₁-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

10 pages, 896 KiB

Open AccessArticle

“Dispatcher, Can You Help Me? A Woman Is Giving Birth”. A Pilot Study of Remote Video Assistance with Smart Glasses

by Silvia Aranda-García, Myriam Santos-Folgar, Felipe Fernández-Méndez, Roberto Barcala-Furelos, Manuel Pardo Ríos, Encarna Hernández Sánchez, Lucía Varela-Varela, Silvia San Román-Mata and Antonio Rodríguez-Núñez

Sensors 2023, 23(1), 409; https://doi.org/10.3390/s23010409 - 30 Dec 2022

Cited by 10 | Viewed by 3209

Abstract

Smart glasses (SG) could be a breakthrough in emergency situations, so the aim of this work was to assess the potential benefits of teleassistance with smart glasses (SG) from a midwife to a lifeguard in a simulated, unplanned, out-of-hospital birth (OHB). Thirty-eight lifeguards [...] Read more.

Smart glasses (SG) could be a breakthrough in emergency situations, so the aim of this work was to assess the potential benefits of teleassistance with smart glasses (SG) from a midwife to a lifeguard in a simulated, unplanned, out-of-hospital birth (OHB). Thirty-eight lifeguards were randomized into SG and control (CG) groups. All participants were required to act in a simulated imminent childbirth with a maternal–fetal simulator (PROMPT Flex, Laerdal, Norway). The CG acted autonomously, while the SG group was video-assisted by a midwife through SG (Vuzix Blade, New York, NY, USA). The video assistance was based on the OHB protocol, speaking and receiving images on the SG. The performance time, compliance with the protocol steps, and perceived performance with the SG were evaluated. The midwife’s video assistance with SG allowed 35% of the SG participants to perform the complete OHB protocol. No CG participant was able to perform it (p = 0.005). All OHB protocol variables were significantly better in the SG group than in the CG (p < 0.05). Telemedicine through video assistance with SG is feasible so that a lifeguard with no knowledge of childbirth care can act according to the recommendations in a simulated, unplanned, uncomplicated OHB. Communication with the midwife by speaking and sending images to the SG is perceived as an important benefit to the performance. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 3225 KiB

Open AccessArticle

Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

by Carla Taramasco, Carla Rimassa and Felipe Martinez

Sensors 2023, 23(1), 268; https://doi.org/10.3390/s23010268 - 27 Dec 2022

Cited by 7 | Viewed by 2863

Abstract

In Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and [...] Read more.

In Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and evaluation of the Quida platform, which consists of a network of unintrusive sensors installed in the houses of elderly participants to monitor their activities and provide assistance. Sixty-nine elderly participants were included. A significant increase in overall QoL was observed amongst participants allocated to the interventional arm (p < 0.02). While some studies point out difficulties monitoring users at home, Quida demonstrates that it is possible to detect presence and movement to identify patterns of behavior in the sample studied, allowing us to visualize the behavior of older adults at different time intervals to support their medical evaluation. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

12 pages, 3114 KiB

Open AccessArticle

Wearable Soft Microtube Sensors for Quantitative Home-Based Erectile Dysfunction Monitoring

by Chee Ming Noel Sng, Li Min Camillus Wee, Kum Cheong Tang, King Chien Joe Lee, Qing Hui Wu, Joo Chuan Yeo and Ali Asgar S. Bhagat

Sensors 2022, 22(23), 9344; https://doi.org/10.3390/s22239344 - 30 Nov 2022

Cited by 8 | Viewed by 6441

Abstract

Quantifiable erectile dysfunction (ED) diagnosis involves the monitoring of rigidity and tumescence of the penile shaft during nocturnal penile tumescence (NPT). In this work, we introduce Erectile Dysfunction SENsor (EDSEN), a home-based wearable device for quantitative penile health monitoring based on stretchable microtubular [...] Read more.

Quantifiable erectile dysfunction (ED) diagnosis involves the monitoring of rigidity and tumescence of the penile shaft during nocturnal penile tumescence (NPT). In this work, we introduce Erectile Dysfunction SENsor (EDSEN), a home-based wearable device for quantitative penile health monitoring based on stretchable microtubular sensing technology. Two types of sensors, the T- and R-sensors, are developed to effectively measure penile tumescence and rigidity, respectively. Conical models mimicking penile shaft were fabricated with polydimethylsiloxane (PDMS) material, using different base to curing agent ratios to replicate the different hardness properties of a penile shaft. A theoretical buckling force chart for the different penile models is generated to determine sufficiency criteria for sexual intercourse. An average erect penile length and circumference requires at least a Young’s modulus of 179 kPa for optimal buckling force required for satisfactory sexual intercourse. The conical penile models were evaluated using EDSEN. Our results verified that the circumference of a penile shaft can be accurately measured by T-sensor and rigidity using the R-sensor. EDSEN provides a private and quantitative method to detect ED within the comfortable confines of the user’s home. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

11 pages, 466 KiB

Open AccessArticle

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals

by Jad Botros, Farah Mourad-Chehade and David Laplanche

Sensors 2022, 22(23), 9190; https://doi.org/10.3390/s22239190 - 26 Nov 2022

Cited by 12 | Viewed by 2604

Abstract

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test [...] Read more.

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

18 pages, 4220 KiB

Open AccessArticle

A Flexible Near-Field Biosensor for Multisite Arterial Blood Flow Detection

by Noor Mohammed, Kim Cluff, Mark Sutton, Bernardo Villafana-Ibarra, Benjamin E. Loflin, Jacob L. Griffith, Ryan Becker, Subash Bhandari, Fayez Alruwaili and Jaydip Desai

Sensors 2022, 22(21), 8389; https://doi.org/10.3390/s22218389 - 1 Nov 2022

Cited by 4 | Viewed by 3833

Abstract

Modern wearable devices show promising results in terms of detecting vital bodily signs from the wrist. However, there remains a considerable need for a device that can conform to the human body’s variable geometry to accurately detect those vital signs and to understand [...] Read more.

Modern wearable devices show promising results in terms of detecting vital bodily signs from the wrist. However, there remains a considerable need for a device that can conform to the human body’s variable geometry to accurately detect those vital signs and to understand health better. Flexible radio frequency (RF) resonators are well poised to address this need by providing conformable bio-interfaces suitable for different anatomical locations. In this work, we develop a compact wearable RF biosensor that detects multisite hemodynamic events due to pulsatile blood flow through noninvasive tissue–electromagnetic (EM) field interaction. The sensor consists of a skin patch spiral resonator and a wearable transceiver. During resonance, the resonator establishes a strong capacitive coupling with layered dielectric tissues due to impedance matching. Therefore, any variation in the dielectric properties within the near-field of the coupled system will result in field perturbation. This perturbation also results in RF carrier modulation, transduced via a demodulator in the transceiver unit. The main elements of the transceiver consist of a direct digital synthesizer for RF carrier generation and a demodulator unit comprised of a resistive bridge coupled with an envelope detector, a filter, and an amplifier. In this work, we build and study the sensor at the radial artery, thorax, carotid artery, and supraorbital locations of a healthy human subject, which hold clinical significance in evaluating cardiovascular health. The carrier frequency is tuned at the resonance of the spiral resonator, which is 34.5 ± 1.5 MHz. The resulting transient waveforms from the demodulator indicate the presence of hemodynamic events, i.e., systolic upstroke, systolic peak, dicrotic notch, and diastolic downstroke. The preliminary results also confirm the sensor’s ability to detect multisite blood flow events noninvasively on a single wearable platform. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

18 pages, 2599 KiB

Open AccessArticle

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

by Emilio Andreozzi, Riccardo Sabbadini, Jessica Centracchio, Paolo Bifulco, Andrea Irace, Giovanni Breglio and Michele Riccio

Sensors 2022, 22(19), 7566; https://doi.org/10.3390/s22197566 - 6 Oct 2022

Cited by 15 | Viewed by 3939

Abstract

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels’ lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for [...] Read more.

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels’ lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical–optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland–Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 4212 KiB

Open AccessArticle

Investigating Stroke Effects on Respiratory Parameters Using a Wearable Device: A Pilot Study on Hemiplegic Patients

by Joshua Di Tocco, Daniela Lo Presti, Martina Zaltieri, Marco Bravi, Michelangelo Morrone, Silvia Sterzi, Emiliano Schena and Carlo Massaroni

Sensors 2022, 22(17), 6708; https://doi.org/10.3390/s22176708 - 5 Sep 2022

Cited by 2 | Viewed by 2261

Abstract

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, [...] Read more.

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

24 pages, 3579 KiB

Open AccessArticle

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing

by Titus Jayarathna, Gaetano D. Gargiulo, Gough Y. Lui and Paul P. Breen

Sensors 2022, 22(17), 6689; https://doi.org/10.3390/s22176689 - 4 Sep 2022

Cited by 3 | Viewed by 2644

Abstract

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise [...] Read more.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and

0.86 \pm 0.61

beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40–140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

11 pages, 3810 KiB

Open AccessArticle

Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization

by Aoxin Ni, Sara Akbarzadeh, Edward Lobarinas and Nasser Kehtarnavaz

Sensors 2022, 22(16), 6033; https://doi.org/10.3390/s22166033 - 12 Aug 2022

Cited by 4 | Viewed by 2389

Abstract

Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with [...] Read more.

Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting. The personalization was carried out based on the user preference model within the framework of maximum likelihood inverse reinforcement learning. The testing of ten subjects with hearing loss was conducted, which indicated that the personalized ADRO was preferred over the standard ADRO on average by about 10 times. Furthermore, a word recognition experiment was conducted, which showed that the personalized ADRO had no adverse impact on speech understanding as compared to the standard ADRO. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

14 pages, 2320 KiB

Open AccessArticle

Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques

by Mohamed Bahache, Abdou El Karim Tahari, Jorge Herrera-Tapia, Nasreddine Lagraa, Carlos Tavares Calafate and Chaker Abdelaziz Kerrache

Sensors 2022, 22(15), 5893; https://doi.org/10.3390/s22155893 - 7 Aug 2022

Cited by 4 | Viewed by 2135

Abstract

Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can [...] Read more.

Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 3202 KiB

Open AccessEditor’s ChoiceArticle

Virtual Reality for Shoulder Rehabilitation: Accuracy Evaluation of Oculus Quest 2

by Arianna Carnevale, Ilaria Mannocchi, Mohamed Saifeddine Hadj Sassi, Marco Carli, Giovanna De Luca, Umile Giuseppe Longo, Vincenzo Denaro and Emiliano Schena

Sensors 2022, 22(15), 5511; https://doi.org/10.3390/s22155511 - 23 Jul 2022

Cited by 43 | Viewed by 7152

Abstract

Virtual reality (VR) systems are becoming increasingly attractive as joint kinematics monitoring systems during rehabilitation. This study aimed to evaluate the accuracy of the Oculus Quest 2 in measuring translational and rotational displacements. As the Oculus Quest 2 was chosen for future applications [...] Read more.

Virtual reality (VR) systems are becoming increasingly attractive as joint kinematics monitoring systems during rehabilitation. This study aimed to evaluate the accuracy of the Oculus Quest 2 in measuring translational and rotational displacements. As the Oculus Quest 2 was chosen for future applications in shoulder rehabilitation, the translation range (minimum: ~200 mm, maximum: ~700 mm) corresponded to the forearm length of the 5th percentile female and the upper limb length of the 95th percentile male. The controller was moved on two structures designed to allow different translational displacements and rotations in the range 0–180°, to cover the range of motion of the upper limb. The controller measures were compared with those of a Qualisys optical capture system. The results showed a mean absolute error of 13.52 ± 6.57 mm at a distance of 500 mm from the head-mounted display along the x-direction. The maximum mean absolute error for rotational displacements was found to be 1.11 ± 0.37° for a rotation of 40° around the z-axis. Oculus Quest 2 is a promising VR tool for monitoring shoulder kinematics during rehabilitation. The inside-out movement tracking makes Oculus Quest 2 a viable alternative to traditional motion analysis systems. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

17 pages, 738 KiB

Open AccessArticle

Depressed Mood Prediction of Elderly People with a Wearable Band

by Jinyoung Choi, Soomin Lee, Seonyoung Kim, Dongil Kim and Hyungshin Kim

Sensors 2022, 22(11), 4174; https://doi.org/10.3390/s22114174 - 31 May 2022

Cited by 15 | Viewed by 4127

Abstract

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a [...] Read more.

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 7690 KiB

Open AccessArticle

Smartphone-Based Hearing Aid Compression and Noise Reduction

by Aoxin Ni and Nasser Kehtarnavaz

Sensors 2022, 22(9), 3306; https://doi.org/10.3390/s22093306 - 26 Apr 2022

Cited by 2 | Viewed by 2587

Abstract

This paper presents an assistive hearing smartphone app mimicking the two main functions of hearing aids, consisting of compression and noise reduction. The app is designed to run in real time on smartphones or tablets. Appropriate levels of amplification or gain are activated [...] Read more.

This paper presents an assistive hearing smartphone app mimicking the two main functions of hearing aids, consisting of compression and noise reduction. The app is designed to run in real time on smartphones or tablets. Appropriate levels of amplification or gain are activated by selecting a filter from a filter bank for six audio environment situations covering three sound pressure levels of speech and two sound pressure levels of noise. The results of this smartphone app for real-world audio environments are provided, indicating its effectiveness as a real-time platform for studying compression and noise reduction algorithms in the field or in realistic audio environments. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

11 pages, 450 KiB

Open AccessArticle

Comparison of Different Physical Activity Measures in a Cardiac Rehabilitation Program: A Prospective Study

by Muaddi Alharbi, Adrian Bauman, Mohammed Alabdulaali, Lis Neubeck, Sidney Smith, Sharon Naismith, Yun-Hee Jeon, Geoffrey Tofler, Atef Surour and Robyn Gallagher

Sensors 2022, 22(4), 1639; https://doi.org/10.3390/s22041639 - 19 Feb 2022

Cited by 3 | Viewed by 2953

Abstract

Concordant assessments of physical activity (PA) and related measures in cardiac rehabilitation (CR) is essential for exercise prescription. This study compared exercise measurement from an in-person walk test; wearable activity tracker; and self-report at CR entry, completion (8-weeks) and follow-up (16-weeks). Forty patients [...] Read more.

Concordant assessments of physical activity (PA) and related measures in cardiac rehabilitation (CR) is essential for exercise prescription. This study compared exercise measurement from an in-person walk test; wearable activity tracker; and self-report at CR entry, completion (8-weeks) and follow-up (16-weeks). Forty patients beginning CR completed the Six-Minute Walk Test (6MWT), Physical Activity Scale for the Elderly (PASE), and wore Fitbit-Flex for four consecutive days including two weekend days. The sample mean age was 66 years; 67% were male. Increased exercise capacity at CR completion and follow-up was detected by a 6MWT change in mean distance (39 m and 42 m; p = 0.01, respectively). Increased PA participation at CR completion was detected by Fitbit-Flex mean change in step counts (1794; p = 0.01). Relative changes for Fitbit-Flex step counts and a 6MWT were consistent with previous research, demonstrating Fitbit-Flex’s potential as an outcome measure. With four days of data, Fitbit-Flex had acceptable ICC values in measuring step counts and MVPA minutes. Fitbit-Flex steps and 6MWT meters are more responsive to changes in PA patterns following exposure to a cardiac rehabilitation program than Fitbit-Flex or PASE-estimated moderate–vigorous PA (MVPA) minutes. Fitbit-Flex step counts provide a useful additional measure for assessing PA outside of the CR setting and accounts for day-to-day variations. Two weekend days and two weekdays are needed for Fitbit-Flex to estimate PA levels more precisely. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

20 pages, 15524 KiB

Open AccessArticle

Measurement System for Unsupervised Standardized Assessments of Timed Up and Go Test and 5 Times Chair Rise Test in Community Settings—A Usability Study

by Sebastian Fudickar, Alexander Pauls, Sandra Lau, Sandra Hellmers, Konstantin Gebel, Rebecca Diekmann, Jürgen M. Bauer, Andreas Hein and Frauke Koppelin

Sensors 2022, 22(3), 731; https://doi.org/10.3390/s22030731 - 19 Jan 2022

Cited by 5 | Viewed by 3389

Abstract

Comprehensive measurements are needed in older populations to detect physical changes, initiate prompt interventions, and prevent functional decline. While established instruments such as the Timed Up and Go (TUG) and 5 Times Chair Rise Test (5CRT) require trained clinicians to assess corresponding functional [...] Read more.

Comprehensive measurements are needed in older populations to detect physical changes, initiate prompt interventions, and prevent functional decline. While established instruments such as the Timed Up and Go (TUG) and 5 Times Chair Rise Test (5CRT) require trained clinicians to assess corresponding functional parameters, the unsupervised screening system (USS), developed in a two-stage participatory design process, has since been introduced to community-dwelling older adults. In a previous article, we investigated the USS’s measurement of the TUG and 5CRT in comparison to conventional stop-watch methods and found a high sensitivity with significant correlations and coefficients ranging from 0.73 to 0.89. This article reports insights into the design process and evaluates the usability of the USS interface. Our analysis showed high acceptance with qualitative and quantitative methods. From participant discussions, suggestions for improvement and functions for further development could be derived and discussed. The evaluated prototype offers a high potential for early detection of functional limitations in elderly people and should be tested with other target groups in other locations. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

10 pages, 244 KiB

Open AccessArticle

Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables

by Taryn Chalmers, Blake Anthony Hickey, Phillip Newton, Chin-Teng Lin, David Sibbritt, Craig S. McLachlan, Roderick Clifton-Bligh, John Morley and Sara Lal

Sensors 2022, 22(1), 151; https://doi.org/10.3390/s22010151 - 27 Dec 2021

Cited by 36 | Viewed by 12151

Abstract

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated [...] Read more.

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

15 pages, 5112 KiB

Open AccessEditor’s ChoiceArticle

A Wearable Electrochemical Gas Sensor for Ammonia Detection

by Martina Serafini, Federica Mariani, Isacco Gualandi, Francesco Decataldo, Luca Possanzini, Marta Tessarolo, Beatrice Fraboni, Domenica Tonelli and Erika Scavetta

Sensors 2021, 21(23), 7905; https://doi.org/10.3390/s21237905 - 27 Nov 2021

Cited by 35 | Viewed by 6465

Abstract

The next future strategies for improved occupational safety and health management could largely benefit from wearable and Internet of Things technologies, enabling the real-time monitoring of health-related and environmental information to the wearer, to emergency responders, and to inspectors. The aim of this [...] Read more.

The next future strategies for improved occupational safety and health management could largely benefit from wearable and Internet of Things technologies, enabling the real-time monitoring of health-related and environmental information to the wearer, to emergency responders, and to inspectors. The aim of this study is the development of a wearable gas sensor for the detection of NH₃ at room temperature based on the organic semiconductor poly(3,4-ethylenedioxythiophene) (PEDOT), electrochemically deposited iridium oxide particles, and a hydrogel film. The hydrogel composition was finely optimised to obtain self-healing properties, as well as the desired porosity, adhesion to the substrate, and stability in humidity variations. Its chemical structure and morphology were characterised by infrared spectroscopy and scanning electron microscopy, respectively, and were found to play a key role in the transduction process and in the achievement of a reversible and selective response. The sensing properties rely on a potentiometric-like mechanism that significantly differs from most of the state-of-the-art NH₃ gas sensors and provides superior robustness to the final device. Thanks to the reliability of the analytical response, the simple two-terminal configuration and the low power consumption, the PEDOT:PSS/IrOx Ps/hydrogel sensor was realised on a flexible plastic foil and successfully tested in a wearable configuration with wireless connectivity to a smartphone. The wearable sensor showed stability to mechanical deformations and good analytical performances, with a sensitivity of 60 ± 8 μA decade⁻¹ in a wide concentration range (17–7899 ppm), which includes the safety limits set by law for NH₃ exposure. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Graphical abstract

15 pages, 2819 KiB

Open AccessArticle

Analysis of Heart Rate Variability in Response to Serious Games in Elderly People

by Chun-Ju Hou, Yen-Ting Chen, Mycel Capilayan, Yu-Sian Lin, Min-Wei Huang and Ji-Jer Huang

Sensors 2021, 21(19), 6549; https://doi.org/10.3390/s21196549 - 30 Sep 2021

Cited by 6 | Viewed by 2703

Abstract

As the proportion of elderly people continues to grow, so does the concern about age-related cognitive decline. Serious games have been developed for cognitive training or treatment, but measuring the activity of the autonomic nervous system (ANS) has not been taken to account. [...] Read more.

As the proportion of elderly people continues to grow, so does the concern about age-related cognitive decline. Serious games have been developed for cognitive training or treatment, but measuring the activity of the autonomic nervous system (ANS) has not been taken to account. However, cognitive functioning has been known to be heavily influenced by the autonomic nervous system (ANS), and ANS activity can be quantified using heart rate variability (HRV). This paper aims to analyze the physiological response in normal elderly people as they play two types of serious games using HRV features from electrocardiography (ECG). A wearable device designed in-house was used to measure ECG, and the data from this device was pre-processed using digital signal processing techniques. Ten HRV features were extracted, including time-domain, nonlinear, and frequency-domain features. The experiment proceeds as follows: rest for three minutes, play a cognitive aptitude game, rest for another three minutes, followed by two reaction time games. Data from thirty older adults (age: 65.9 ± 7.34; male: 15, female: 15) were analyzed. The statistical results show that there was a significant difference in the HRV between the two types of games. From this, it can be concluded that the type of game has a significant effect on the ANS response. This can be further used in designing games for the elderly, either for training or mood management. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 1253 KiB

Open AccessArticle

Hyperglycemia Identification Using ECG in Deep Learning Era

by Renato Cordeiro, Nima Karimian and Younghee Park

Sensors 2021, 21(18), 6263; https://doi.org/10.3390/s21186263 - 18 Sep 2021

Cited by 23 | Viewed by 5753

Abstract

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers [...] Read more.

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

17 pages, 6062 KiB

Open AccessArticle

Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device

by Vincenzo Randazzo, Jacopo Ferretti and Eros Pasero

Sensors 2021, 21(18), 6036; https://doi.org/10.3390/s21186036 - 9 Sep 2021

Cited by 17 | Viewed by 4736

Abstract

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds [...] Read more.

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

15 pages, 3856 KiB

Open AccessArticle

Skin Strain Analysis of the Scapular Region and Wearables Design

by Arianna Carnevale, Emiliano Schena, Domenico Formica, Carlo Massaroni, Umile Giuseppe Longo and Vincenzo Denaro

Sensors 2021, 21(17), 5761; https://doi.org/10.3390/s21175761 - 26 Aug 2021

Cited by 12 | Viewed by 4809

Abstract

Monitoring scapular movements is of relevance in the contexts of rehabilitation and clinical research. Among many technologies, wearable systems instrumented by strain sensors are emerging in these applications. An open challenge for the design of these systems is the optimal positioning of the [...] Read more.

Monitoring scapular movements is of relevance in the contexts of rehabilitation and clinical research. Among many technologies, wearable systems instrumented by strain sensors are emerging in these applications. An open challenge for the design of these systems is the optimal positioning of the sensing elements, since their response is related to the strain of the underlying substrates. This study aimed to provide a method to analyze the human skin strain of the scapular region. Experiments were conducted on five healthy volunteers to assess the skin strain during upper limb movements in the frontal, sagittal, and scapular planes at different degrees of elevation. A 6 × 5 grid of passive markers was placed posteriorly to cover the entire anatomic region of interest. Results showed that the maximum strain values, in percentage, were 28.26%, and 52.95%, 60.12% and 60.87%, 40.89%, and 48.20%, for elevation up to 90° and maximum elevation in the frontal, sagittal, and scapular planes, respectively. In all cases, the maximum extension is referred to the pair of markers placed horizontally near the axillary fold. Accordingly, this study suggests interesting insights for designing and positioning textile-based strain sensors in wearable systems for scapular movements monitoring. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Graphical abstract

31 pages, 2027 KiB

Open AccessArticle

Controlling a Mouse Pointer with a Single-Channel EEG Sensor

by Alberto J. Molina-Cantero, Juan A. Castro-García, Fernando Gómez-Bravo, Rafael López-Ahumada, Raúl Jiménez-Naharro and Santiago Berrazueta-Alvarado

Sensors 2021, 21(16), 5481; https://doi.org/10.3390/s21165481 - 14 Aug 2021

Cited by 7 | Viewed by 4884

Abstract

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to [...] Read more.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 5957 KiB

Open AccessEditor’s ChoiceArticle

Non-Contact Respiratory Monitoring Using an RGB Camera for Real-World Applications

by Chiara Romano, Emiliano Schena, Sergio Silvestri and Carlo Massaroni

Sensors 2021, 21(15), 5126; https://doi.org/10.3390/s21155126 - 29 Jul 2021

Cited by 40 | Viewed by 5461

Abstract

Respiratory monitoring is receiving growing interest in different fields of use, ranging from healthcare to occupational settings. Only recently, non-contact measuring systems have been developed to measure the respiratory rate (

f_{R}

) over time, even in unconstrained environments. Promising methods rely [...] Read more.

Respiratory monitoring is receiving growing interest in different fields of use, ranging from healthcare to occupational settings. Only recently, non-contact measuring systems have been developed to measure the respiratory rate (

f_{R}

) over time, even in unconstrained environments. Promising methods rely on the analysis of video-frames features recorded from cameras. In this work, a low-cost and unobtrusive measuring system for respiratory pattern monitoring based on the analysis of RGB images recorded from a consumer-grade camera is proposed. The system allows (i) the automatized tracking of the chest movements caused by breathing, (ii) the extraction of the breathing signal from images with methods based on optical flow (FO) and RGB analysis, (iii) the elimination of breathing-unrelated events from the signal, (iv) the identification of possible apneas and, (v) the calculation of

f_{R}

value every second. Unlike most of the work in the literature, the performances of the system have been tested in an unstructured environment considering user-camera distance and user posture as influencing factors. A total of 24 healthy volunteers were enrolled for the validation tests. Better performances were obtained when the users were in sitting position. FO method outperforms in all conditions. In the

f_{R}

range 6 to 60 breaths/min (bpm), the FO allows measuring

f_{R}

values with bias of −0.03 ± 1.38 bpm and −0.02 ± 1.92 bpm when compared to a reference wearable system with the user at 2 and 0.5 m from the camera, respectively. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

10 pages, 4326 KiB

Open AccessCommunication

Mask-Type Sensor for Pulse Wave and Respiration Measurements and Eye Blink Detection

by Thanh-Vinh Nguyen and Masaaki Ichiki

Sensors 2021, 21(14), 4895; https://doi.org/10.3390/s21144895 - 19 Jul 2021

Cited by 10 | Viewed by 2825

Abstract

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical [...] Read more.

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

17 pages, 4306 KiB

Open AccessArticle

Validity Analysis of WalkerView^TM Instrumented Treadmill for Measuring Spatiotemporal and Kinematic Gait Parameters

by Marco Bravi, Carlo Massaroni, Fabio Santacaterina, Joshua Di Tocco, Emiliano Schena, Silvia Sterzi, Federica Bressi and Sandra Miccinilli

Sensors 2021, 21(14), 4795; https://doi.org/10.3390/s21144795 - 14 Jul 2021

Cited by 11 | Viewed by 3368

Abstract

The detection of gait abnormalities is essential for professionals involved in the rehabilitation of walking disorders. Instrumented treadmills are spreading as an alternative to overground gait analysis. To date, the use of these instruments for recording kinematic gait parameters is still limited in [...] Read more.

The detection of gait abnormalities is essential for professionals involved in the rehabilitation of walking disorders. Instrumented treadmills are spreading as an alternative to overground gait analysis. To date, the use of these instruments for recording kinematic gait parameters is still limited in clinical practice due to the lack of validation studies. This study aims to investigate the performance of a multi-sensor instrumented treadmill (i.e., WalkerView^TM, WV) for performing gait analysis. Seventeen participants performed a single gait test on the WV at three different speeds (i.e., 3 km/h, 5 km/h, and 6.6 km/h). In each trial, spatiotemporal and kinematic parameters were recorded simultaneously by the WV and by a motion capture system used as the reference. Intraclass correlation coefficient (ICC) of spatiotemporal parameters showed fair to excellent agreement at the three walking speeds for steps time, cadence, and step length (range 0.502–0.996); weaker levels of agreement were found for stance and swing time at all the tested walking speeds. Bland–Altman analysis of spatiotemporal parameters showed a mean of difference (MOD) maximum value of 0.04 s for swing/stance time and WV underestimation of 2.16 cm for step length. As for kinematic variables, ICC showed fair to excellent agreement (ICC > 0.5) for total range of motion (ROM) of hip at 3 km/h (range 0.579–0.735); weaker levels of ICC were found at 5 km/h and 6.6 km/h (range 0.219–0.447). ICC values of total knee ROM showed poor levels of agreement at all the tested walking speeds. Bland–Altman analysis of hip ROM revealed a higher MOD value at higher speeds up to 3.91°; the MOD values of the knee ROM were always higher than 7.67° with a 60° mean value of ROM. We demonstrated that the WV is a valid tool for analyzing the spatiotemporal parameters of walking and assessing the hip’s total ROM. Knee total ROM and all kinematic peak values should be carefully evaluated, having shown lower levels of agreement. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

22 pages, 3490 KiB

Open AccessArticle

SleepPos App: An Automated Smartphone Application for Angle Based High Resolution Sleep Position Monitoring and Treatment

by Ignasi Ferrer-Lluis, Yolanda Castillo-Escario, Josep Maria Montserrat and Raimon Jané

Sensors 2021, 21(13), 4531; https://doi.org/10.3390/s21134531 - 1 Jul 2021

Cited by 11 | Viewed by 4783

Abstract

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is [...] Read more.

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

21 pages, 2133 KiB

Open AccessArticle

A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

by Francisco Javier González-Cañete and Eduardo Casilari

Sensors 2021, 21(6), 2254; https://doi.org/10.3390/s21062254 - 23 Mar 2021

Cited by 25 | Viewed by 11184

Abstract

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs [...] Read more.

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

Review

Jump to: Research, Other

22 pages, 2073 KiB

Open AccessReview

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing

by Kira Flanagan and Manob Jyoti Saikia

Sensors 2023, 23(20), 8482; https://doi.org/10.3390/s23208482 - 15 Oct 2023

Cited by 6 | Viewed by 4125

Abstract

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, [...] Read more.

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

16 pages, 2367 KiB

Open AccessReview

Pain Management Mobile Applications: A Systematic Review of Commercial and Research Efforts

by Yiannis Koumpouros and Aggelos Georgoulas

Sensors 2023, 23(15), 6965; https://doi.org/10.3390/s23156965 - 5 Aug 2023

Cited by 4 | Viewed by 2208

Abstract

Shared decision making is crucial in the pain domain. The subjective nature of pain demands solutions that can facilitate pain assessment and management. The aim of the current study is to review the current trends in both the commercial and the research domains [...] Read more.

Shared decision making is crucial in the pain domain. The subjective nature of pain demands solutions that can facilitate pain assessment and management. The aim of the current study is to review the current trends in both the commercial and the research domains in order to reveal the key issues and guidelines that could further help in the effective development of pain-focused apps. We searched for scientific publications and commercial apps in 22 databases and the two major app stores. Out of 3612 articles and 336 apps, 69 met the requirements for inclusion following the PRISMA guidelines. An analysis of their features (technological approach, design methodology, evaluation strategy, and others) identified critical points that have to be taken into consideration in future efforts. For example, commercial and research efforts target different types of pain, while no participatory design is followed in the majority of the cases examined. Moreover, the evaluation of the final apps remains a challenge that hinders their success. The examined domain is expected to experience a substantial increase. More research is needed towards the development of non-intrusive wearables and sensors for pain detection and assessment, along with artificial intelligence techniques and open data. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

26 pages, 715 KiB

Open AccessReview

Temperature Monitoring in Hyperthermia Treatments of Bone Tumors: State-of-the-Art and Future Challenges

by Francesca De Tommasi, Carlo Massaroni, Rosario Francesco Grasso, Massimiliano Carassiti and Emiliano Schena

Sensors 2021, 21(16), 5470; https://doi.org/10.3390/s21165470 - 13 Aug 2021

Cited by 16 | Viewed by 4820

Abstract

Bone metastases and osteoid osteoma (OO) have a high incidence in patients facing primary lesions in many organs. Radiotherapy has long been the standard choice for these patients, performed as stand-alone or in conjunction with surgery. However, the needs of these patients have [...] Read more.

Bone metastases and osteoid osteoma (OO) have a high incidence in patients facing primary lesions in many organs. Radiotherapy has long been the standard choice for these patients, performed as stand-alone or in conjunction with surgery. However, the needs of these patients have never been fully met, especially in the ones with low life expectancy, where treatments devoted to pain reduction are pivotal. New techniques as hyperthermia treatments (HTs) are emerging to reduce the associated pain of bone metastases and OO. Temperature monitoring during HTs may significantly improve the clinical outcomes since the amount of thermal injury depends on the tissue temperature and the exposure time. This is particularly relevant in bone tumors due to the adjacent vulnerable structures (e.g., spinal cord and nerve roots). In this Review, we focus on the potential of temperature monitoring on HT of bone cancer. Preclinical and clinical studies have been proposed and are underway to investigate the use of different thermometric techniques in this scenario. We review these studies, the principle of work of the thermometric techniques used in HTs, their strengths, weaknesses, and pitfalls, as well as the strategies and the potential of improving the HTs outcomes. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

20 pages, 14316 KiB

Open AccessReview

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

by Fangfang Jiang, Yihan Zhou, Tianyi Ling, Yanbing Zhang and Ziyu Zhu

Sensors 2021, 21(11), 3814; https://doi.org/10.3390/s21113814 - 31 May 2021

Cited by 12 | Viewed by 3938

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised [...] Read more.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

21 pages, 4688 KiB

Open AccessEditor’s ChoiceReview

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

by Aoxin Ni, Arian Azarang and Nasser Kehtarnavaz

Sensors 2021, 21(11), 3719; https://doi.org/10.3390/s21113719 - 27 May 2021

Cited by 66 | Viewed by 10508

Abstract

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of [...] Read more.

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

Other

Jump to: Research, Review

26 pages, 697 KiB

Open AccessSystematic Review

Technological Solutions for Human Movement Analysis in Obese Subjects: A Systematic Review

by Riccardo Monfrini, Gianluca Rossetto, Emilia Scalona, Manuela Galli, Veronica Cimolin and Nicola Francesco Lopomo

Sensors 2023, 23(6), 3175; https://doi.org/10.3390/s23063175 - 16 Mar 2023

Cited by 9 | Viewed by 2639

Abstract

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From [...] Read more.

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques—such as Magnetic Resonance Imaging (MRI) and biplane radiography—should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Wearable and Unobtrusive Technologies for Healthcare Monitoring

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (56 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI