Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs
Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and...
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| Format: | Artykuł |
| Język: | English |
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MDPI AG
2024-03-01
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| Seria: | Sensors |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/1424-8220/24/6/1935 |
| _version_ | 1827304977616338944 |
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| author | Yu-Jung Huang Chao-Shu Chang Yu-Chi Wu Chin-Chuan Han Yuan-Yang Cheng Hsian-Min Chen |
| author_facet | Yu-Jung Huang Chao-Shu Chang Yu-Chi Wu Chin-Chuan Han Yuan-Yang Cheng Hsian-Min Chen |
| author_sort | Yu-Jung Huang |
| collection | DOAJ |
| description | Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system’s adaptability shows promise for applications in medical rehabilitation and sports. |
| first_indexed | 2024-04-24T17:49:24Z |
| format | Article |
| id | doaj.art-5ebab5fb9a19480c8dc52a3cbe5b1412 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:49:24Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ebab5fb9a19480c8dc52a3cbe5b14122024-03-27T14:04:11ZengMDPI AGSensors1424-82202024-03-01246193510.3390/s24061935Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower LimbsYu-Jung Huang0Chao-Shu Chang1Yu-Chi Wu2Chin-Chuan Han3Yuan-Yang Cheng4Hsian-Min Chen5Department of Electrical Engineering, National United University, Miaoli 36003, TaiwanDepartment of Information Management, National United University, Miaoli 36003, TaiwanDepartment of Electrical Engineering, National United University, Miaoli 36003, TaiwanDepartment of Computer Science and Information Engineering, National United University, Miaoli 36003, TaiwanDepartment of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taichung City 40705, TaiwanCenter for Quantitative Imaging in Medicine (CQUIM), Department of Medical Research, Taichung Veterans General Hospital, Taichung City 40705, TaiwanLower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system’s adaptability shows promise for applications in medical rehabilitation and sports.https://www.mdpi.com/1424-8220/24/6/1935rehabilitationfitness activity recognitionwearable devicesradio frequencyinertial measurement unitmachine learning |
| spellingShingle | Yu-Jung Huang Chao-Shu Chang Yu-Chi Wu Chin-Chuan Han Yuan-Yang Cheng Hsian-Min Chen Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs Sensors rehabilitation fitness activity recognition wearable devices radio frequency inertial measurement unit machine learning |
| title | Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs |
| title_full | Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs |
| title_fullStr | Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs |
| title_full_unstemmed | Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs |
| title_short | Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs |
| title_sort | development of wearable devices for collecting digital rehabilitation fitness data from lower limbs |
| topic | rehabilitation fitness activity recognition wearable devices radio frequency inertial measurement unit machine learning |
| url | https://www.mdpi.com/1424-8220/24/6/1935 |
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