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Willingham TB, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:79. [PMID: 38248542 PMCID: PMC10815484 DOI: 10.3390/ijerph21010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024]
Abstract
Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.
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Affiliation(s)
- T. Bradley Willingham
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - Julie Stowell
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
- Department of Physical Therapy, Georgia State University, Atlanta, GA 30302, USA
| | - George Collier
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
| | - Deborah Backus
- Shepherd Center, Virginia C. Crawford Research Institute, Atlanta, GA 30309, USA (D.B.)
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Jayasinghe U, Hwang F, Harwin WS. Inertial measurement data from loose clothing worn on the lower body during everyday activities. Sci Data 2023; 10:709. [PMID: 37848448 PMCID: PMC10582085 DOI: 10.1038/s41597-023-02567-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 09/13/2023] [Indexed: 10/19/2023] Open
Abstract
Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a 'sensor string' and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28-48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5-8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.
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Affiliation(s)
- Udeni Jayasinghe
- Biomedical Engineering Section, University of Reading, RG6 6DH, Reading, UK.
| | - Faustina Hwang
- Biomedical Engineering Section, University of Reading, RG6 6DH, Reading, UK
| | - William S Harwin
- Biomedical Engineering Section, University of Reading, RG6 6DH, Reading, UK
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Jayasinghe U, Janko B, Hwang F, Harwin WS. Classification of static postures with wearable sensors mounted on loose clothing. Sci Rep 2023; 13:131. [PMID: 36599887 DOI: 10.1038/s41598-022-27306-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Inertial Measurement Units (IMUs) are a potential way to monitor the mobility of people outside clinical or laboratory settings at an acceptable cost. To increase accuracy, multiple IMUs can be used. By embedding multiple sensors into everyday clothing, it is possible to simplify having to put on individual sensors, ensuring sensors are correctly located and oriented. This research demonstrates how clothing-mounted IMU readings can be used to identify 4 common postures: standing, sitting, lying down and sitting on the floor. Data were collected from 5 healthy adults, with each providing 1-4 days of data with approximately 5 h each day. Each day, participants performed a fixed set of activities that were video-recorded to provide a ground truth. This is an analysis of accelerometry data from 3 sensors incorporated into right trouser-leg at the waist, thigh and ankle. Data were classified as static/ dynamic activities using a K-nearest neighbour (KNN) algorithm. For static activities, the inclination angles of the three sensors were estimated and used to train a second KNN classifier. For this highly-selected dataset (60000-70000 data points/posture), the static postures were classified with 100% accuracy, illustrating the potential for clothing-mounted sensors to be used in posture classification.
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Affiliation(s)
- Udeni Jayasinghe
- Biomedical Engineering Section, University of Reading, Reading, RG6 6DH, UK. .,University of Colombo School of Computing, Information Systems Engineering, Colombo, Sri Lanka.
| | - Balazs Janko
- RACE, UKAEA, Culham Science Centre, Abingdon, OX14 3DB, UK
| | - Faustina Hwang
- Biomedical Engineering Section, University of Reading, Reading, RG6 6DH, UK
| | - William S Harwin
- Biomedical Engineering Section, University of Reading, Reading, RG6 6DH, UK
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Bello H, Zhou B, Suh S, Sanchez Marin LA, Lukowicz P. Move With the Theremin: Body Posture and Gesture Recognition Using the Theremin in Loose-Garment With Embedded Textile Cables as Antennas. FRONTIERS IN COMPUTER SCIENCE 2022. [DOI: 10.3389/fcomp.2022.915280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
We present a novel intelligent garment design approach for body posture/gesture detection in the form of a loose-fitting blazer prototype, “the MoCaBlazer.” The design is realized by leveraging conductive textile antennas with the capacitive sensing modality, supported by an open-source electronic theremin system (OpenTheremin). The use of soft textile antennas as the sensing element allows flexible garment design and seamless tech-garment integration for the specific structure of different clothes. Our novel approach is evaluated through two experiments involving defined movements (20 arm/torso gestures and eight dance movements). In cross-validation, the classification model yields up to 97.18% average accuracy and 92% f1-score, respectively. We have also explored real-time inference enabled by a radio frequency identification (RFID) synchronization method, yielding an f1-score of 82%. Our approach opens a new paradigm for designing motion-aware smart garments with soft conductive textiles beyond traditional approaches that rely on tight-fitting flexible sensors or rigid motion sensor accessories.
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Mukaino M, Ogasawara T, Matsuura H, Aoshima Y, Suzuki T, Furuzawa S, Yamaguchi M, Nakashima H, Saitoh E, Tsukada S, Otaka Y. Validity of trunk acceleration measurement with a chest-worn monitor for assessment of physical activity intensity. BMC Sports Sci Med Rehabil 2022; 14:104. [PMID: 35689292 PMCID: PMC9185863 DOI: 10.1186/s13102-022-00492-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 05/27/2022] [Indexed: 11/30/2022]
Abstract
Background Recent advancements in wearable technology have enabled easy measurement of daily activities, potentially applicable in rehabilitation practice for various purposes such as maintaining and increasing patients’ activity levels. In this study, we aimed to examine the validity of trunk acceleration measurement using a chest monitor embedded in a smart clothing system (‘hitoe’ system), an emerging wearable system, in assessing the physical activity in an experimental setting with healthy subjects (Study 1) and in a clinical setting with post-stroke patients (Study 2). Methods Study 1 involved the participation of 14 healthy individuals. The trunk acceleration, heart rate (HR), and oxygen consumption were simultaneously measured during treadmill testing with a Bruce protocol. Trunk acceleration and HR were measured using the "hitoe" system, a smart clothing system with embedded chest sensors. Expiratory gas analysis was performed to measure oxygen consumption. Three parameters, moving average (MA), moving standard deviation (MSD), and moving root mean square (RMS), were calculated from the norm of the trunk acceleration. The relationships between these accelerometer-based parameters and oxygen consumption-based physical activity intensity measured with the percent VO2 reserve (%VO2R) were examined. In Study 2, 48 h of simultaneous measurement of trunk acceleration and heart rate-based physical activity intensity in terms of percent heart rate reserve (%HRR) was conducted with the "hitoe" system in 136 post-stroke patients. Results The values of MA, MSD, RMS, and %VO2R were significantly different between levels 1, 2, 3, and 4 in the Bruce protocol (P < 0.01). The average coefficients of determination for individual regression for %VO2R versus MA, %VO2R versus MSD, and %VO2R versus RMS were 0.89 ± 0.05, 0.96 ± 0.03, and 0.91 ± 0.05, respectively. Among the parameters examined, MSD showed the best correlation with %VO2R, indicating high validity of the parameter for assessing physical activity intensity. The 48-h measurement of MSD and %HRR in post-stroke patients showed significant within-individual correlation (P < 0.05) in 131 out of 136 patients (correlation coefficient: 0.60 ± 0.16). Conclusions The results support the validity of the MSD calculated from the trunk acceleration measured with a smart clothing system in assessing the physical activity intensity. Trial registration: UMIN000034967. Registered 21 November 2018 (retrospectively registered). Supplementary Information The online version contains supplementary material available at 10.1186/s13102-022-00492-4.
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Affiliation(s)
- Masahiko Mukaino
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan.
| | - Takayuki Ogasawara
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Kanagawa, Japan
| | - Hirotaka Matsuura
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan.,Department of Rehabilitation Medicine, Nippon Medical School Chiba Hokuso Hospital, Inzai, Chiba, Japan
| | - Yasushi Aoshima
- Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Takuya Suzuki
- Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Shotaro Furuzawa
- Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Masumi Yamaguchi
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Kanagawa, Japan
| | - Hiroshi Nakashima
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Kanagawa, Japan
| | - Eiichi Saitoh
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan
| | - Shingo Tsukada
- NTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Kanagawa, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan
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6
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Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts. ELECTRONICS 2022. [DOI: 10.3390/electronics11020226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.
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Sevil M, Rashid M, Maloney Z, Hajizadeh I, Samadi S, Askari MR, Hobbs N, Brandt R, Park M, Quinn L, Cinar A. Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems. IEEE SENSORS JOURNAL 2020; 20:12859-12870. [PMID: 33100923 PMCID: PMC7584145 DOI: 10.1109/jsen.2020.3000772] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.
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Affiliation(s)
- Mert Sevil
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Mudassir Rashid
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Zacharie Maloney
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Iman Hajizadeh
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Sediqeh Samadi
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Mohammad Reza Askari
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Nicole Hobbs
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Rachel Brandt
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Minsun Park
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Laurie Quinn
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
| | - Ali Cinar
- Mert Sevil, Rachel Brandt, Nicole Hobbs and Zacharie Maloney are with the Department of Biomedical Engineering (BME); Mudassir Rashid, Mohammad Reza Askari, Iman Hajizadeh and Sedigeh Samadi are with the Department of Chemical and Biological Engineering (ChBE); Ali Cinar is with the Departments of ChBE and BME, Illinois Institute of Technology, Chicago, IL 60616; Minsun Park and Laurie Quinn are with the College of Nursing, University of Illinois at Chicago, IL, 60616
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Lind CM, Diaz-Olivares JA, Lindecrantz K, Eklund J. A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6010. [PMID: 33113922 PMCID: PMC7660182 DOI: 10.3390/s20216010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/15/2020] [Accepted: 10/21/2020] [Indexed: 01/14/2023]
Abstract
Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.
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Affiliation(s)
- Carl Mikael Lind
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden; (J.A.D.-O.); (K.L.); (J.E.)
- Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 4, 11365 Stockholm, Sweden
| | - Jose Antonio Diaz-Olivares
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden; (J.A.D.-O.); (K.L.); (J.E.)
- Department of Biosystems, Biosystems Technology Cluster Campus Geel, KU Leuven, Kleinhoefstraat 4, 2440 Geel, Belgium
| | - Kaj Lindecrantz
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden; (J.A.D.-O.); (K.L.); (J.E.)
- Science Park Borås, University of Borås, SE-501 90 Borås, Sweden
| | - Jörgen Eklund
- Division of Ergonomics, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, 14157 Huddinge, Sweden; (J.A.D.-O.); (K.L.); (J.E.)
- Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 4, 11365 Stockholm, Sweden
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Liu R, Li J, Li M, Zhang Q, Shi G, Li Y, Hou C, Wang H. MXene-Coated Air-Permeable Pressure-Sensing Fabric for Smart Wear. ACS APPLIED MATERIALS & INTERFACES 2020; 12:46446-46454. [PMID: 32954717 DOI: 10.1021/acsami.0c11715] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Considering the fast development of wearable electronics and soft robotics, pressure sensors with high sensitivity, durability, and washability are of great importance. However, the surface modification of fabrics with high-sensitivity active materials requires that issues associated with poor interface adhesion and stability are resolved. In this study, we explored the key factors for firmly bonding MXene to fabric substrates to fabricate wearable and washable pressure sensing fabric. The interactions between MXene and various fabrics were elucidated by investigating the adsorption and binding capacities. The natural rough surface of cotton fibers also promoted the firm adsorption of MXene. As a result, MXene was difficult to detach, even with mechanical washing and ultrasonic treatment. Further, the abundant functional groups on the MXene surface were conducive to interfacial interactions with cotton fibers. An increase in the amount of fluorine-containing functional groups also improved the hydrophobicity of the fabric surface. The good force-sensitive resistance of MXene-coated cotton allowed this pressure-sensing fabric to function as a flexible pressure sensor, which showed a high gauge factor (7.67 kPa-1), a rapid response and relaxation speed (<35 ms), excellent stability (>2000 cycles), and good washing durability. Further, the as-fabricated flexible pressure sensor was demonstrated as a wearable human-machine interface that supported multitouch interactions and exhibited a rapid response. Thus, this work provides a new approach for developing next-generation high-sensitivity wearable pressure sensors.
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Affiliation(s)
- Rui Liu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China
| | - Jianmin Li
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China
| | - Meng Li
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China
| | - Qinghong Zhang
- Engineering Research Center of Advanced Glasses Manufacturing Technology, Ministry of Education, Donghua University, Shanghai 201620, P. R. China
| | - Guoying Shi
- College of Chemistry, Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, P. R. China
| | - Yaogang Li
- Engineering Research Center of Advanced Glasses Manufacturing Technology, Ministry of Education, Donghua University, Shanghai 201620, P. R. China
| | - Chengyi Hou
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, P. R. China
| | - Hongzhi Wang
- Engineering Research Center of Advanced Glasses Manufacturing Technology, Ministry of Education, Donghua University, Shanghai 201620, P. R. China
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Drăgulinescu A, Drăgulinescu AM, Zincă G, Bucur D, Feieș V, Neagu DM. Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4316. [PMID: 32748872 PMCID: PMC7435916 DOI: 10.3390/s20154316] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/23/2020] [Accepted: 07/28/2020] [Indexed: 12/25/2022]
Abstract
The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.
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Affiliation(s)
- Andrei Drăgulinescu
- Electronics Technology and Reliability Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Ana-Maria Drăgulinescu
- Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Gabriela Zincă
- Automation and Industrial Informatics Department, Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Doina Bucur
- Mechatronics Department, Faculty of Mechanical Engineering and Mechatronics, Biomedical Engineering and Biotechnology Department, Faculty of Medical Engineering, University Politehnica of Bucharest, 061071 Bucharest, Romania;
| | - Valentin Feieș
- Electronics Technology and Reliability Department, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania;
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Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People. SENSORS 2020; 20:s20113207. [PMID: 32516995 PMCID: PMC7309155 DOI: 10.3390/s20113207] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 05/26/2020] [Accepted: 06/03/2020] [Indexed: 12/13/2022]
Abstract
Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.
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12
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Davoudi M, Shokouhyan SM, Abedi M, Meftahi N, Rahimi A, Rashedi E, Hoviattalab M, Narimani R, Parnianpour M, Khalaf K. A Practical Sensor-Based Methodology for the Quantitative Assessment and Classification of Chronic Non Specific Low Back Patients (NSLBP) in Clinical Settings. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2902. [PMID: 32443827 PMCID: PMC7287918 DOI: 10.3390/s20102902] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 11/26/2022]
Abstract
The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0° in the sagittal plane, as well as 15° and 30° lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each patient. An ANOVA mixed model was conducted on the maximum and average angular velocity, linear acceleration and maximum jerk, respectively. The effect of the three-way interaction of Subgroup by direction by PLM on the mean trunk acceleration was significant. Subgrouping by STarT had no main effect on the kinematic indices in the sagittal plane, although significant effects were observed in the asymmetric directions. A significant difference was also identified during pre-rotation in the transverse plane, where the velocity and acceleration decreased while the jerk increased with increasing asymmetry. The acceleration during trunk flexion was significantly higher than that during extension, in contrast to the velocity, which was higher in extension. A Linear Discriminant Analysis, utilized for classification purposes, demonstrated that 51% of the total performance classifying the three STarT subgroups (65% for high risk) occurred at a position of 15° of rotation to the right during extension. Greater discrimination (67%) was obtained in the classification of the high risk vs. low-medium risk. This study provided a smart "sensor-based" practical methodology for quantitatively assessing and classifying NSLBP patients in clinical settings. The outcomes may also be utilized by leveraging cost-effective inertial sensors, already available in today's smartphones, as objective tools for various health applications towards personalized precision medicine.
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Affiliation(s)
- Mehrdad Davoudi
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Seyyed Mohammadreza Shokouhyan
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Mohsen Abedi
- Physiotherapy Research Center, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran 1616913111, Iran;
| | - Narges Meftahi
- Physical Therapy Department, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran;
- Rehabilitation Sciences Research Center, Shiraz University of Medical Sciences, Shiraz 7194733669, Iran
| | - Atefeh Rahimi
- Department of Physical Therapy, University of Social Welfare and Rehabilitation Sciences, Tehran 1985713871, Iran;
| | - Ehsan Rashedi
- Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA;
| | - Maryam Hoviattalab
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Roya Narimani
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Mohamad Parnianpour
- Department of Mechanical Engineering, Sharif University of Technology, Tehran 1136511155, Iran; (M.D.); (S.M.S.); (M.H.); (R.N.); (M.P.)
| | - Kinda Khalaf
- Department of Biomedical Engineering and Health Engineering Innovation Center, Khalifa University of Science and Technology, P.O. Box 127788 Abu Dhabi, UAE
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Toward Using Wearables to Remotely Monitor Cognitive Frailty in Community-Living Older Adults: An Observational Study. SENSORS 2020; 20:s20082218. [PMID: 32295301 PMCID: PMC7218861 DOI: 10.3390/s20082218] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/02/2020] [Accepted: 04/07/2020] [Indexed: 12/19/2022]
Abstract
Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog- individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog- (p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.
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Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions. SENSORS 2020; 20:s20010314. [PMID: 31935910 PMCID: PMC6982902 DOI: 10.3390/s20010314] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/30/2019] [Accepted: 01/04/2020] [Indexed: 01/25/2023]
Abstract
The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.
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