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Emerging Wearable Sensor Technology in Healthcare

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 55753

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Prof. Dr. Alessandro Tognetti

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Information Engineering Department, University of Pisa and Research Center "E. Piaggio” Largo L. Lazzarino 1, 56122 Pisa, Italy
Interests: biomedical engineering; sensing technologies; soft sensors; motion capture; data fusion; biomechanics; rehabilitation; wearable sensors and technologies
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Dr. Simone Genovesi

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Dipartimento di Ingegneria Dell’Informazione, Università di Pisa, via Caruso 16, 56122 Pisa, Italy
Interests: metamaterials; chipless RFID and sensors; characteristic modes; antenna design; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing cost of healthcare, the progressive aging of the population, and the spread of chronic diseases is shaping a new healthcare system focused on the remote assistance of the person outside the hospital, tailored for a personalized disease management and aiming at stress-less health prevention methods and caring of the general wellbeing. Wearable sensors are undoubtedly the key elements of this new paradigm. One promising direction for the development of this new path is led by personal mobile devices such as smartphones and tablets, which provide a widespread platform that cannot only manage a person-centered care, but also serve as a means of sensing.

Wearable sensors are still facing several challenges. Body-worn devices need to be adapted to the body of the user in order to provide a comfortable fit. Current wearables based on solid-state components are not suitable to adapt to the deformable nature of the human body. To solve this issue, emerging technologies should enable the development of soft, unobtrusive, compliant, and lightweight devices that have the potential to enable innovative mobile health applications.

Examples of such technology are e-textiles and flexible or stretchable devices that can employed for on-body, unobtrusive, and ambulatory biomedical parameter monitoring or patient treatment. By exploiting these emerging technologies, sensors and devices can be embedded in common objects such as clothes, with the potential to enable daily life monitoring systems and wearable and/or implantable devices for a wide range of applications: physiological sensing, human motion analysis, gait analysis, electrical stimulation, biomedical sensing, home-based rehabilitation, biofeedback, biochemical sensors, etc.

From the communication perspective, new devices that interact with the person and connect to the data collection hub are needed. This requires small and highly integrated RF systems. Especially, for the antenna design this often leads to demanding requirements on dimension, efficiency, and isolation from the body. Not only the communication part is relevant; the sensing part can be involved as well. Wireless radio frequency signal can also be exploited for sensing, and new sensor configurations can be offered by on-body as well as intra-body communications.

In this context, the purpose of this Special Issue is to connect researchers in the field of emerging wearable technologies for healthcare, to share their ideas and conceptual approaches, and to discuss the recent advances in this field, addressing innovative solutions, paradigms, and emerging issues.

Prof. Dr. Alessandro Tognetti
Prof. Dr. Simone Genovesi
Guest Editors

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Keywords

unobtrusive monitoring of physiological and biomechanical parameters
biomonitoring
wearable sensors
body area network
on body communication
intra-body communication
skin electronic
electromagnetic propagation
antenna on body
smart textiles
smart materials
flexible and stretchable biosensing
implantable devices
additive manufacturing
printed sensors
wireless sensors
energy harvesting/scavenging
data processing
data fusion

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Published Papers (11 papers)

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Research

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15 pages, 3729 KiB

Open AccessArticle

Towards Motor-Based Early Detection of Autism Red Flags: Enabling Technology and Exploratory Study Protocol

by Mariasole Bondioli, Stefano Chessa, Antonio Narzisi, Susanna Pelagatti and Michele Zoncheddu

Sensors 2021, 21(6), 1971; https://doi.org/10.3390/s21061971 - 11 Mar 2021

Cited by 7 | Viewed by 3066

Abstract

Observing how children manipulate objects while they are playing can help detect possible autism spectrum disorders (ASD) at an early stage. For this purpose, specialists seek the so-called “red-flags” of motor signature of ASD for more precise diagnostic tests. However, a significant drawback to achieve this is that the observation of object manipulation by the child very often is not naturalistic, as it involves the physical presence of the specialist and is typically performed in hospitals. In this framework, we present a novel Internet of Things support in the form factory of a smart toy that can be used by specialists to perform indirect and non-invasive observations of the children in naturalistic conditions. While they play with the toy, children can be observed in their own environment and without the physical presence of the specialist. We also present the technical validation of the technology and the study protocol for the refinement of the diagnostic practice based on this technology. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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16 pages, 6018 KiB

Open AccessArticle

Smart Patch for Skin Temperature: Preliminary Study to Evaluate Psychometrics and Feasibility

by Heejung Kim, Sunkook Kim, Mingoo Lee, Yumie Rhee, Sungho Lee, Yi-Rang Jeong, Sunju Kang, Muhammad Naqi and Soyun Hong

Sensors 2021, 21(5), 1855; https://doi.org/10.3390/s21051855 - 6 Mar 2021

Cited by 11 | Viewed by 4581

Abstract

There is a need for continuous, non-invasive monitoring of biological data to assess health and wellbeing. Currently, many types of smart patches have been developed to continuously monitor body temperature, but few trials have been completed to evaluate psychometrics and feasibility for human subjects in real-life scenarios. The aim of this feasibility study was to evaluate the reliability, validity and usability of a smart patch measuring body temperature in healthy adults. The smart patch consisted of a fully integrated wearable wireless sensor with a multichannel temperature sensor, signal processing integrated circuit, wireless communication feature and a flexible battery. Thirty-five healthy adults were recruited for this test, carried out by wearing the patches on their upper chests for 24 h and checking their body temperature six times a day using infrared forehead thermometers as a gold standard for testing validity. Descriptive statistics, one-sampled and independent t-tests, Pearson’s correlation coefficients and Bland-Altman plot were examined for body temperatures between two measures. In addition, multiple linear regression, receiver operating characteristic (ROC) and qualitative content analysis were conducted. Among the 35 participants, 29 of them wore the patch for over 19 h (dropout rate: 17.14%). Mean body temperature measured by infrared forehead thermometers and smart patch ranged between 32.53 and 38.2 °C per person and were moderately correlated (r = 0.23–0.43) overall. Based on a Bland-Altman plot, approximately 94% of the measurements were located within one standard deviation (upper limit = 4.52, lower limit = −5.82). Most outliers were identified on the first measurement and were located below the lower limit. It is appropriate to use 37.5 °C in infrared forehead temperature as a cutoff to define febrile conditions. Users’ position while checking and ambient temperature and humidity are not affected to the smart patch body temperature. Overall, the participants showed high usability and satisfaction on the survey. Few participants reported discomfort due to limited daily activity, itchy skin or detaching concerns. In conclusion, epidermal electronic sensor technologies provide a promising method for continuously monitoring individuals’ body temperatures, even in real-life situations. Our study findings show the potential for smart patches to monitoring non-febrile condition in the community. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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15 pages, 10788 KiB

Open AccessFeature PaperArticle

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

by Marco Laurino, Danilo Menicucci, Angelo Gemignani, Nicola Carbonaro and Alessandro Tognetti

Sensors 2020, 20(18), 5438; https://doi.org/10.3390/s20185438 - 22 Sep 2020

Cited by 9 | Viewed by 3866

Abstract

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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13 pages, 2104 KiB

Open AccessArticle

A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist

by Daniel M. Hochman, Sevda Gharehbaghi, Daniel C. Whittingslow and Omer T. Inan

Sensors 2020, 20(15), 4240; https://doi.org/10.3390/s20154240 - 30 Jul 2020

Cited by 7 | Viewed by 2768

Abstract

Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist—another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz–20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen–Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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13 pages, 3316 KiB

Open AccessArticle

Vibration Characterization of the Human Knee Joint in Audible Frequencies

by Mohsen Safaei, Nicholas B. Bolus, Alper Erturk and Omer T. Inan

Sensors 2020, 20(15), 4138; https://doi.org/10.3390/s20154138 - 25 Jul 2020

Cited by 8 | Viewed by 3331

Abstract

Injuries and disorders affecting the knee joint are very common in athletes and older individuals. Passive and active vibration methods, such as acoustic emissions and modal analysis, are extensively used in both industry and the medical field to diagnose structural faults and disorders. To maximize the diagnostic potential of such vibration methods for knee injuries and disorders, a better understanding of the vibroacoustic characteristics of the knee must be developed. In this study, the linearity and vibration transmissibility of the human knee were investigated based on measurements collected on healthy subjects. Different subjects exhibit a substantially different transmissibility behavior due to variances in subject-specific knee structures. Moreover, the vibration behaviors of various subjects’ knees at different leg positions were compared. Variation in sagittal-plane knee angle alters the transmissibility of the joint, while the overall shape of the transmissibility diagrams remains similar. The results demonstrate that an adjusted stimulation signal at frequencies higher than 3 kHz has the potential to be employed in diagnostic applications that are related to knee joint health. This work can pave the way for future studies aimed at employing acoustic emission and modal analysis approaches for knee health monitoring outside of clinical settings, such as for field-deployable diagnostics. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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16 pages, 2225 KiB

Open AccessArticle

Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

by Toshitaka Yamakawa, Miho Miyajima, Koichi Fujiwara, Manabu Kano, Yoko Suzuki, Yutaka Watanabe, Satsuki Watanabe, Tohru Hoshida, Motoki Inaji and Taketoshi Maehara

Sensors 2020, 20(14), 3987; https://doi.org/10.3390/s20143987 - 17 Jul 2020

Cited by 33 | Viewed by 9711

Abstract

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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15 pages, 3237 KiB

Open AccessArticle

Proposal of a Lab Bench for the Unobtrusive Monitoring of the Bladder Fullness with Bioimpedance Measurements

by Valentin Gaubert, Hayriye Gidik and Vladan Koncar

Sensors 2020, 20(14), 3980; https://doi.org/10.3390/s20143980 - 17 Jul 2020

Cited by 8 | Viewed by 3321

Abstract

(1) Background: millions of people, from children to the elderly, suffer from bladder dysfunctions all over the world. Monitoring bladder fullness with appropriate miniaturized textile devices can improve, significantly, their daily life quality, or even cure them. Amongst the existing bladder sensing technologies, bioimpedance spectroscopy seems to be the most appropriate one to be integrated into textiles. (2) Methods: to assess the feasibility of monitoring the bladder fullness with textile-based bioimpedance spectroscopy; an innovative lab-bench has been designed and fabricated. As a step towards obtaining a more realistic pelvic phantom, ex vivo pig’s bladder and skin were used. The electrical properties of the fabricated pelvic phantom have been compared to those of two individuals with tetrapolar impedance measurements. The measurements’ reproducibility on the lab bench has been evaluated and discussed. Moreover, its suitability for the continuous monitoring of the bladder filling has been investigated. (3) Results: although the pelvic phantom failed in reproducing the frequency-dependent electrical properties of human tissues, it was found to be suitable at 5 kHz to record bladder volume change. The resistance variations recorded are proportional to the conductivity of the liquid filling the bladder. A 350 mL filling with artificial urine corresponds to a decrease in resistance of 7.2%, which was found to be in the same range as in humans. (4) Conclusions: based on that resistance variation; the instantaneous bladder fullness can be extrapolated. The presented lab-bench will be used to evaluate the ability of textiles electrodes to unobtrusively monitor the bladder volume. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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20 pages, 3926 KiB

Open AccessArticle

SnapKi—An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients

by Ana Oliveira, Duarte Dias, Elodie Múrias Lopes, Maria do Carmo Vilas-Boas and João Paulo Silva Cunha

Sensors 2020, 20(14), 3875; https://doi.org/10.3390/s20143875 - 11 Jul 2020

Cited by 5 | Viewed by 4278

Abstract

The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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12 pages, 2325 KiB

Open AccessArticle

Modeling Fabric Movement for Future E-Textile Sensors

by Roope Ketola, Vigyanshu Mishra and Asimina Kiourti

Sensors 2020, 20(13), 3735; https://doi.org/10.3390/s20133735 - 3 Jul 2020

Cited by 5 | Viewed by 4249

Abstract

Studies with e-textile sensors embedded in garments are typically performed on static and controlled phantom models that do not reflect the dynamic nature of wearables. Instead, our objective was to understand the noise e-textile sensors would experience during real-world scenarios. Three types of sleeves, made of loose, tight, and stretchy fabrics, were applied to a phantom arm, and the corresponding fabric movement was measured in three dimensions using physical markers and image-processing software. Our results showed that the stretchy fabrics allowed for the most consistent and predictable clothing-movement (average displacement of up to −2.3 ± 0.1 cm), followed by tight fabrics (up to −4.7 ± 0.2 cm), and loose fabrics (up to −3.6 ± 1.0 cm). In addition, the results demonstrated better performance of higher elasticity (average displacement of up to −2.3 ± 0.1 cm) over lower elasticity (average displacement of up to −3.8 ± 0.3 cm) stretchy fabrics. For a case study with an e-textile sensor that relies on wearable loops to monitor joint flexion, our modeling indicated errors as high as 65.7° for stretchy fabric with higher elasticity. The results from this study can (a) help quantify errors of e-textile sensors operating “in-the-wild,” (b) inform decisions regarding the optimal type of clothing-material used, and (c) ultimately empower studies on noise calibration for diverse e-textile sensing applications. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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15 pages, 2657 KiB

Open AccessArticle

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure

by Sean Pham, Danny Yeap, Gisela Escalera, Rupa Basu, Xiangmei Wu, Nicholas J. Kenyon, Irva Hertz-Picciotto, Michelle J. Ko and Cristina E. Davis

Sensors 2020, 20(3), 855; https://doi.org/10.3390/s20030855 - 6 Feb 2020

Cited by 52 | Viewed by 9644

Abstract

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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Review

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18 pages, 1079 KiB

Open AccessReview

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

by Thomas Davergne, Antsa Rakotozafiarison, Hervé Servy and Laure Gossec

Sensors 2020, 20(17), 4797; https://doi.org/10.3390/s20174797 - 25 Aug 2020

Cited by 25 | Viewed by 5148

Abstract

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers. Full article

(This article belongs to the Special Issue Emerging Wearable Sensor Technology in Healthcare)

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