Sensors

2023

Jump to: 2022, 2021

10 pages, 3549 KiB

Open AccessCommunication

Validation of Alogo Move Pro: A GPS-Based Inertial Measurement Unit for the Objective Examination of Gait and Jumping in Horses

by Kévin Cédric Guyard, Stéphane Montavon, Jonathan Bertolaccini and Michel Deriaz

Sensors 2023, 23(9), 4196; https://doi.org/10.3390/s23094196 - 22 Apr 2023

Cited by 7 | Viewed by 3558

Abstract

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse’s jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical [...] Read more.

Quantitative information on how well a horse clears a jump has great potential to support the rider in improving the horse’s jumping performance. This study investigated the validation of a GPS-based inertial measurement unit, namely Alogo Move Pro, compared with a traditional optical motion capture system. Accuracy and precision of the three jumping characteristics of maximum height (Zmax), stride/jump length (lhorz), and mean horizontal speed (vhorz) were compared. Eleven horse–rider pairs repeated two identical jumps (an upright and an oxer fence) several times (n = 6 to 10) at different heights in a 20 × 60 m tent arena. The ground was a fiber sand surface. The 24 OMC (Oqus 7+, Qualisys) cameras were rigged on aluminum rails suspended 3 m above the ground. The Alogo sensor was placed in a pocket on the protective plate of the saddle girth. Reflective markers placed on and around the Alogo sensor were used to define a rigid body for kinematic analysis. The Alogo sensor data were collected and processed using the Alogo proprietary software; stride-matched OMC data were collected using Qualisys Track Manager and post-processed in Python. Residual analysis and Bland–Altman plots were performed in Python. The Alogo sensor provided measures with relative accuracy in the range of 10.5–20.7% for stride segments and 5.5–29.2% for jump segments. Regarding relative precision, we obtained values in the range of 6.3–14.5% for stride segments and 2.8–18.2% for jump segments. These accuracy differences were deemed good under field study conditions where GPS signal strength might have been suboptimal. Full article

► Show Figures

Figure 1

2022

Jump to: 2023, 2021

13 pages, 3103 KiB

Open AccessArticle

Extraction of Lumbar Spine Motion Using a 3-IMU Wearable Cluster

by Kee S. Moon, Sara P. Gombatto, Kim Phan and Yusuf Ozturk

Sensors 2023, 23(1), 182; https://doi.org/10.3390/s23010182 - 24 Dec 2022

Cited by 4 | Viewed by 2898

Abstract

Spine movement is a daily activity that can indicate health status changes, including low back pain (LBP) problems. Repetitious and continuous movement on the spine and incorrect postures during daily functional activities may lead to the potential development and persistence of LBP problems. [...] Read more.

Spine movement is a daily activity that can indicate health status changes, including low back pain (LBP) problems. Repetitious and continuous movement on the spine and incorrect postures during daily functional activities may lead to the potential development and persistence of LBP problems. Therefore, monitoring of posture and movement is essential when designing LBP interventions. Typically, LBP diagnosis is facilitated by monitoring upper body posture and movement impairments, particularly during functional activities using body motion sensors. This study presents a fully wireless multi-sensor cluster system to monitor spine movements. The study suggests an attempt to develop a new method to monitor the lumbopelvic movements of interest selectively. In addition, the research employs a custom-designed robotic lumbar spine simulator to generate the ideal lumbopelvic posture and movements for reference sensor data. The mechanical motion templates provide an automated sensor pattern recognition mechanism for diagnosing the LBP. Full article

► Show Figures

Figure 1

18 pages, 1391 KiB

Open AccessArticle

Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

by Karin C. Knudson and Anoopum S. Gupta

Sensors 2022, 22(23), 9454; https://doi.org/10.3390/s22239454 - 3 Dec 2022

Cited by 4 | Viewed by 1510

Abstract

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In [...] Read more.

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity. Full article

► Show Figures

Figure 1

17 pages, 2421 KiB

Open AccessArticle

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

by Ellen C. Ketola, Mikenzie Barankovich, Stephanie Schuckers, Aratrika Ray-Dowling, Daqing Hou and Masudul H. Imtiaz

Sensors 2022, 22(23), 9156; https://doi.org/10.3390/s22239156 - 25 Nov 2022

Cited by 6 | Viewed by 2848

Abstract

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, [...] Read more.

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32–64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks. Full article

► Show Figures

Figure 1

21 pages, 2084 KiB

Open AccessArticle

Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring

by Elham Rastegari, Hesham Ali and Vivien Marmelat

Sensors 2022, 22(23), 9122; https://doi.org/10.3390/s22239122 - 24 Nov 2022

Cited by 3 | Viewed by 2277

Abstract

Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of [...] Read more.

Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection. Full article

► Show Figures

Figure 1

15 pages, 20873 KiB

Open AccessArticle

Test-Retest Reliability of Acoustic Emission Sensing of the Knee during Physical Tasks

by Liudmila Khokhlova, Dimitrios-Sokratis Komaris, Salvatore Tedesco and Brendan O’Flynn

Sensors 2022, 22(23), 9027; https://doi.org/10.3390/s22239027 - 22 Nov 2022

Cited by 4 | Viewed by 1883

Abstract

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the [...] Read more.

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises. Full article

► Show Figures

Figure 1

30 pages, 1755 KiB

Open AccessReview

Wireless Body Area Sensor Networks: Survey of MAC and Routing Protocols for Patient Monitoring under IEEE 802.15.4 and IEEE 802.15.6

by Muhammad Sajjad Akbar, Zawar Hussain, Michael Sheng and Rajan Shankaran

Sensors 2022, 22(21), 8279; https://doi.org/10.3390/s22218279 - 28 Oct 2022

Cited by 15 | Viewed by 5058

Abstract

Wireless body area sensor networks (WBASNs) have received growing attention from industry and academia due to their exceptional potential for patient monitoring systems that are equipped with low-power wearable and implantable biomedical sensors under communications standards such as IEEE 802.15.4-2015 and IEEE 802.15.6-2012. [...] Read more.

Wireless body area sensor networks (WBASNs) have received growing attention from industry and academia due to their exceptional potential for patient monitoring systems that are equipped with low-power wearable and implantable biomedical sensors under communications standards such as IEEE 802.15.4-2015 and IEEE 802.15.6-2012. The goal of WBASNs is to enhance the capabilities of wireless patient monitoring systems in terms of data accuracy, reliability, routing, channel access, and the data communication of sensors within, on and around the human body. The huge scope of challenges related to WBASNs has led to various research publications and industrial experiments. In this paper, a survey is conducted for the recent state-of-art in the context of medium access control (MAC) and routing protocols by considering the application requirements of patient monitoring systems. Moreover, we discuss the open issues, lessons learned, and challenges for these layers to provide a source of motivation for the upcoming design and development in the domain of WBASNs. This survey will be highly useful for the 6th generation (6G) networks; it is expected that 6G will provide efficient and ubiquitous connectivity to a huge number of IoT devices, and most of them will be sensor-based. This survey will further clarify the QoS requirement part of the 6G networks in terms of sensor-based IoT. Full article

► Show Figures

Figure 1

9 pages, 1315 KiB

Open AccessArticle

Physical Load While Using a Tablet at Different Tilt Angles during Sitting and Standing

by Yosuke Tomita, Yoshitaka Suzuki, Akari Shibagaki, Shingo Takahashi and Yoshizo Matsuka

Sensors 2022, 22(21), 8237; https://doi.org/10.3390/s22218237 - 27 Oct 2022

Viewed by 2342

Abstract

Few standards and guidelines to prevent health problems have been associated with tablet use. We estimated the effects of posture and tablet tilt angle on muscle activity and posture in healthy young adults. Seventeen healthy young adults (age: 20.5 ± 3 years) performed [...] Read more.

Few standards and guidelines to prevent health problems have been associated with tablet use. We estimated the effects of posture and tablet tilt angle on muscle activity and posture in healthy young adults. Seventeen healthy young adults (age: 20.5 ± 3 years) performed a cognitive task using a tablet in two posture (sitting and standing) and tablet tilt angle (0 degrees and 45 deg) conditions. Segment and joint kinematics were evaluated using 16 inertial measurement unit sensors. Neck, trunk, and upper limb electromyography (EMG) activities were monitored using 12 EMG sensors. Perceived discomfort, kinematics, and EMG activities were compared between conditions using the Friedman test. The perceived discomfort in the standing-0 deg condition was significantly higher than in the remaining three conditions. Standing posture and tablet inclination significantly reduced the sagittal segment and joint angles of the spine, compared with sitting and flat tablet conditions. Similarly, standing posture and tablet inclination significantly reduced EMG activities of the dorsal neck, upper, and lower trunk muscles, while increasing EMG activity of shoulder flexors. Standing posture and tablet inclination reduced the sagittal flexion angle, and dorsal neck, upper, and lower trunk muscle activities, while potentially increasing the muscle activity of arm flexors. Full article

► Show Figures

Figure 1

11 pages, 1450 KiB

Open AccessArticle

Comparative Effectiveness of Artificial Intelligence-Based Interactive Home Exercise Applications in Adolescents with Obesity

by Wonjun Oh, Yeongsang An, Seunghwa Min and Chanhee Park

Sensors 2022, 22(19), 7352; https://doi.org/10.3390/s22197352 - 28 Sep 2022

Cited by 8 | Viewed by 3478

Abstract

The rate of obesity in adolescents has increased due to social distancing measures and school closures caused by the COVID-19 pandemic. These issues have caused adolescents to change their lifestyles and eating habits. Furthermore, the growth in inactive behavior and computer screen or [...] Read more.

The rate of obesity in adolescents has increased due to social distancing measures and school closures caused by the COVID-19 pandemic. These issues have caused adolescents to change their lifestyles and eating habits. Furthermore, the growth in inactive behavior and computer screen or watching TV time, as well as the reduction in physical activity, could similarly be related with obesity. To overcome this problem, we recently developed an artificial intelligence (AI)-based gesture recognition game application called Super Kids Adventure (SUKIA, Funrehab, Daejeon, Korea), which provides inexpensive and motivational game applications. This research is designed to assess the effects of SUKIA and Nintendo Switch (NINS) on calorie consumption, VO2 max, 6-minute walking test (6MWT) as well as body mass index (BMI), and the Borg rating of perceived exertion scale (RPE) in adolescents with obesity. A convenience sample of 24 adolescents with obesity were randomized into either the NINS or SUKIA groups 5 days/week for 3 weeks. Analysis of variance (ANOVA) and independent t-tests were presented with significant level at p < 0.05, and the analysis indicated that SUKIA showed superior effects on calorie consumption, VO2 max, and RPE compared to NINS. Our results provide evidence that SUKIA can more effectively improve cardiopulmonary function and calorie consumption than NINS in adolescents with obesity during COVID-19. Full article

► Show Figures

Figure 1

12 pages, 8829 KiB

Open AccessArticle

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings

by Roushanak Haji Hassani, Romina Willi, Georg Rauter, Marc Bolliger and Thomas Seel

Sensors 2022, 22(11), 4237; https://doi.org/10.3390/s22114237 - 2 Jun 2022

Cited by 6 | Viewed by 2683

Abstract

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate [...] Read more.

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was

5.00 °

,

5.02 °

,

5.26 °

, and

3.72 °

for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was

1.7

%, and the average cadence error was

0.09

steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings. Full article

► Show Figures

Figure 1

52 pages, 77579 KiB

Open AccessArticle

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

by Miao Feng and Jean Meunier

Sensors 2022, 22(6), 2091; https://doi.org/10.3390/s22062091 - 8 Mar 2022

Cited by 32 | Viewed by 9566

Abstract

Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton [...] Read more.

Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested. Full article

► Show Figures

Figure 1

17 pages, 3385 KiB

Open AccessArticle

A Novel Methodology for the Synchronous Collection and Multimodal Visualization of Continuous Neurocardiovascular and Neuromuscular Physiological Data in Adults with Long COVID

by Feng Xue, Ann Monaghan, Glenn Jennings, Lisa Byrne, Tim Foran, Eoin Duggan and Roman Romero-Ortuno

Sensors 2022, 22(5), 1758; https://doi.org/10.3390/s22051758 - 24 Feb 2022

Cited by 3 | Viewed by 2989

Abstract

Background: Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be [...] Read more.

Background: Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e., self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. Methods: To conduct such an investigation, a wide range of continuous non-invasive biomedical sensing technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. Results: With the proposed methodology described and exemplified in this paper, we were able to collect relevant physiological data for the assessment of neurocardiovascular and neuromuscular functioning. We were also able to integrate signals from a variety of instruments in a synchronized fashion and visualize the interactions between different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. Conclusions: The proposed methodology can offer an overview of the functioning of the neurocardiovascular and neuromuscular systems in a combined NCVI/NMFI setup and is capable of conducting comparative studies with signals from multiple participants at any given time in the assessment. This could help clinicians and researchers generate and test hypotheses based on the multimodal inspection of raw data in long COVID and other cohorts. Full article

► Show Figures

Figure 1

2021

Jump to: 2023, 2022

21 pages, 1059 KiB

Open AccessArticle

Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults

by En-Fan Chou, Michelle Khine, Thurmon Lockhart and Rahul Soangra

Sensors 2021, 21(18), 6286; https://doi.org/10.3390/s21186286 - 19 Sep 2021

Cited by 9 | Viewed by 4755

Abstract

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data [...] Read more.

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann–Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60–100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers. Full article

► Show Figures

Figure 1

21 pages, 5357 KiB

Open AccessArticle

Generation of Gait Events with a FSR Based Cane Handle

by Andrés Trujillo-León, Arturo de Guzmán-Manzano, Ramiro Velázquez and Fernando Vidal-Verdú

Sensors 2021, 21(16), 5632; https://doi.org/10.3390/s21165632 - 21 Aug 2021

Cited by 3 | Viewed by 2989

Abstract

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited [...] Read more.

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems. Full article

► Show Figures

Figure 1

9 pages, 739 KiB

Open AccessCommunication

A Case–Control Study of the Effects of Chronic Low Back Pain in Spatiotemporal Gait Parameters

by Aurora Castro-Méndez, Inmaculada Requelo-Rodríguez, Manuel Pabón-Carrasco, María Luisa González-Elena, José Antonio Ponce-Blandón and Inmaculada Concepción Palomo-Toucedo

Sensors 2021, 21(15), 5247; https://doi.org/10.3390/s21155247 - 3 Aug 2021

Cited by 6 | Viewed by 3369

Abstract

Chronic low back pain and biomechanical walking imbalances are closely related. It is relevant to identify if there are alterations in spatiotemporal gait patterns in subjects with CLBP (cases) versus healthy subjects (controls) to plan training interventions of motor control gait patterns, and [...] Read more.

Chronic low back pain and biomechanical walking imbalances are closely related. It is relevant to identify if there are alterations in spatiotemporal gait patterns in subjects with CLBP (cases) versus healthy subjects (controls) to plan training interventions of motor control gait patterns, and thus allowing normal physical activity of the individual. This study is intended to identify if spatiotemporal alterations occur in the gait cycle in CLBP subjects (cases) compared with a control group (healthy patients) analyzed with an OptoGait LED sensors gait program. Method: A total of n = 147 participants: n = 75 cases (CLBP) and n = 72 healthy controls subjects were studied with OptoGait gait program. Results: Significant differences were found between the two groups and both feet in foot stride, for the differences of the total stride and contact, for gait cadence and total stride length of the gait cycle (p < 0.05). Conclusions: CLBP may alter some normal gait patterns measured by OptoGait; this finding presents imbalances in gait cycle as an underlying factor. The gait is part of daily life of any individual and it is an important physical activity in relation to the maintenance of an optimal state of health. In addition, future studies are deemed necessary. Full article

► Show Figures

Figure 1

16 pages, 903 KiB

Open AccessSystematic Review

Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

by Nicky Baker, Claire Gough and Susan J. Gordon

Sensors 2021, 21(15), 5167; https://doi.org/10.3390/s21155167 - 30 Jul 2021

Cited by 25 | Viewed by 4397

Abstract

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic [...] Read more.

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls. Full article

► Show Figures

Figure 1

16 pages, 2343 KiB

Open AccessArticle

Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study

by Eun Bin Kim, Songee Kim and Onseok Lee

Sensors 2021, 21(8), 2790; https://doi.org/10.3390/s21082790 - 15 Apr 2021

Cited by 1 | Viewed by 2729

Abstract

With aging, cerebrovascular diseases can occur more often. Stroke cases involve hemiplegia, which causes difficulties in performing activities of daily living. Existing rehabilitation treatments are based on the subjective evaluation of the therapist as the need for non-contact care arises; it is necessary [...] Read more.

With aging, cerebrovascular diseases can occur more often. Stroke cases involve hemiplegia, which causes difficulties in performing activities of daily living. Existing rehabilitation treatments are based on the subjective evaluation of the therapist as the need for non-contact care arises; it is necessary to develop a system that can self-rehabilitate and offer objective analysis. Therefore, we developed rehabilitation tools that enable self-rehabilitation exercises in a virtual space based on haptics. Thirty adults without neurological damage were trained five times in a virtual environment, and the time, number of collisions, and coordinates were digitized and stored in real time. An analysis of variance (ANOVA) of the time and distance similarity changes revealed that as the number of rounds increased, no changes or increases occurred (p ≥ 0.05), and the collisions and paths were stable as the training progressed (p < 0.05). ANOVA showed a high correlation (0.90) with a decrease in the number of crashes and time required. It was meaningful to users when performing rehabilitation training more than four times and significantly impacted the analysis. This study analyzed the upper limb and cognitive rehabilitation of able-boded people in three-dimensional space in a virtual environment; the performance difficulty could be controlled through variations in rehabilitation models. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensors for Gait, Posture, and Health Monitoring

Share This Topical Collection

Editor

Topical Collection Information

Related Special Issue

Published Papers (17 papers)

2023

Jump to: 2022, 2021

2022

Jump to: 2023, 2021

2021

Jump to: 2023, 2022

Further Information

Guidelines

MDPI Initiatives

Follow MDPI