scholarly journals Development of a Biofeedback Wearable Sensor System to Improve Rehabilitation Following Surgical Repair of Achilles Tendon Tears

10.29007/wh2k ◽  
2019 ◽  
Author(s):  
Sukhi Singh ◽  
Timothy Lee ◽  
Joshua Giles

This paper describes the development, functionality, and initial testing of a wearable sensor system and companion smartphone app intended to support the rehabilitation of Achilles injury patients by providing 1) real time biofeedback, which can help patients adhere to rehabilitation restrictions; 2) exercise support to encourage patients to correctly perform all rehabilitation activities; 3) data summaries to clinicians in order to allow appropriate interventions when necessary. The wearable system is composed of insole pressure sensors, a calf muscle activation sensor, and inertial measurement units, whose data are communicated to the smartphone app via a Bluetooth enabled microcontroller. Initial testing demonstrated the quality of the data recorded by the sensors and the ability of those data to be used to identify functional activities like walking and stairclimbing.

Author(s):  
youwei Zhao ◽  
Ningle Hou ◽  
Yifan Wang ◽  
Chaochao Fu ◽  
Xiaoting Li ◽  
...  

Flexible, wearable self-powered pressure sensors have successfully sparked great interest in a variety of potential applications. However, the fabrication of such a sensor system with ultra-long battery life, ultra-high operational...


2021 ◽  
Vol 12 ◽  
Author(s):  
Karin Keren ◽  
Monica Busse ◽  
Nora E. Fritz ◽  
Lisa M. Muratori ◽  
Eran Gazit ◽  
...  

Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed.Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements.Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements.Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function.Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.


2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.


Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.


2018 ◽  
Vol 56 (3) ◽  
pp. 228-240 ◽  
Author(s):  
Hanne AUSTAD ◽  
Øystein WIGGEN ◽  
Hilde FÆREVIK ◽  
Trine M. SEEBERG

Author(s):  
Wenhao Zhao ◽  
Dongzhi Zhang ◽  
Yan Yang ◽  
Chen Du ◽  
Bao Zhang

The conductive and biocompatible hybrid hydrogel was successfully assembled into an adhesive, flexible wearable sensor for ultra-sensitive human-computer interaction and smart detection, which holds excellent self-healing capability. This conductive, repairable...


2018 ◽  
Vol 2 (2) ◽  
pp. 27 ◽  
Author(s):  
Juan Haladjian ◽  
Johannes Haug ◽  
Stefan Nüske ◽  
Bernd Bruegge

2021 ◽  
Author(s):  
Anna M. Hood ◽  
Hanne Stotesbury ◽  
Jennifer Murphy ◽  
Melanie Kölbel ◽  
April Slee ◽  
...  

BACKGROUND Behavioral mitigation strategies to slow the spread of COVID-19 have resulted in sweeping lifestyle changes, with short and long-term psychological, well-being, and quality of life implications. The Attitudes About COVID-19 and Health (ATTACH) study focuses on understanding attitudes and beliefs whilst considering the impact on mental and physical health and the influence of broader demographic and geographic factors on attitudes, beliefs, and mental health burden. OBJECTIVE In this assessment of our first wave of data collection, we provide baseline cohort descriptives of ATTACH study participants in the United Kingdom (UK), United States of America (USA), and Mexico. Additionally, we assess responses to daily poll questions related to COVID-19 and conduct a cross-sectional analysis of baseline assessments collected in the UK between June 26 and October 31, 2020. METHODS The ATTACH study uses smartphone-app technology and online survey data collection. Participants completed poll questions twice daily related to COVID-19 and a monthly survey assessing mental health, social isolation, physical health, and quality of life. Poll question responses were graphed using 95% Clopper-Pearson (exact) tests with 95% confidence intervals. Pearson correlations, hierarchical linear regression analyses, and generalized linear models assessed relationships, predictors of self-reported outcomes, and group differences, respectively. RESULTS By October 31, 2020, 1405, 80, and 90 participants had consented to participate in the UK, USA, and Mexico, respectively. Descriptive data for the UK daily poll questions indicated that participants were generally following social distancing measures, but worry and negative impacts on families increased as the pandemic progressed. Although participants generally reported feeling that the reasons for current measures had been made clear, there was low trust that the government was doing everything in its power to meet public needs. In the UK, 1282 participants also completed a monthly survey (95% white, 72% female, 21% key or essential workers). Nineteen percent of UK participants reported a pre-existing mental health disorder, 31% reported a pre-existing chronic medical illness, and 35% were over 65. Fifty-seven percent of participants reported being more sedentary since the pandemic began, and 41% reported reduced access to medical care. Those with poorer mental health outcomes lived in more deprived neighborhoods, in larger households (ps < .05), had more pre-existing mental health disorders and medical conditions, and were younger than 65 years (all ps < .001). CONCLUSIONS Communities who have been exposed to additional harm during the COVID-19 pandemic were experiencing worse mental outcomes. Factors including having a medical condition, or living in a deprived neighborhood or larger household were associated with heightened risk. Future longitudinal studies should investigate the link between COVID-19 exposure, mental health, and sociodemographic and residential characteristics.


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