Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System
Ankle sprains, the leading injuries in the emergency department that affect people worldwide, often leading to chronic ankle instability (CAI) characterized by recurring pain and weakness. However, challenges are presented in accurately identifying CAI-related abnormal gait patterns and assessing re...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10975823/ |
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| author | Zhonghe Guo Yanzhang Li Yuchen Wang Haoxuan Liu Rui Guo Jingzhong Ma Xiaoming Wu Dong Jiang Tianling Ren |
| author_facet | Zhonghe Guo Yanzhang Li Yuchen Wang Haoxuan Liu Rui Guo Jingzhong Ma Xiaoming Wu Dong Jiang Tianling Ren |
| author_sort | Zhonghe Guo |
| collection | DOAJ |
| description | Ankle sprains, the leading injuries in the emergency department that affect people worldwide, often leading to chronic ankle instability (CAI) characterized by recurring pain and weakness. However, challenges are presented in accurately identifying CAI-related abnormal gait patterns and assessing rehabilitation effects. Traditional plantar pressure systems lack portability and can only be used in limited specific actions, while a few early proposed portable systems have demonstrated insufficient accuracy. Besides, no previous studies have yet focused on assessing rehabilitation effects, which is crucial to providing the treatment selection and rehabilitation evaluation of CAI. Considering this, we propose a novel approach to improve the diagnostic process for CAI. A Shoe-Integrated Sensor System (SISS) which can accurately capture gait data during various activities was implemented. We collected and processed level walking data from 80 CAI patients diagnosed by professional experts and 42 healthy individuals using the system, including feature extraction and filtering algorithms. An artificial intelligence diagnosis was applied to the data, achieving a classification accuracy of 93.39% and an area under the curve (AUC) of 0.959, satisfying the clinical requirements for accuracy. Furthermore, a novel methodology was proposed to assess the level of patient rehabilitation. The validation results of rehabilitation status prediction demonstrated highly consistent results with doctors’ diagnoses. Due to the significant impact of gait data in assisting the diagnosis of various neurological and musculoskeletal diseases that result in gait abnormalities, the proposed system can also be extended and utilized in other similar medical fields for diagnosing and real-time monitoring, promoting the development of smart healthcare. |
| format | Article |
| id | doaj-art-27d23b54eef3463bbad23d6483a592c9 |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-27d23b54eef3463bbad23d6483a592c92025-08-20T02:34:36ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331978198510.1109/TNSRE.2025.356392410975823Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor SystemZhonghe Guo0https://orcid.org/0009-0007-1129-7857Yanzhang Li1Yuchen Wang2Haoxuan Liu3https://orcid.org/0009-0001-9553-3816Rui Guo4Jingzhong Ma5Xiaoming Wu6https://orcid.org/0000-0002-6411-1717Dong Jiang7https://orcid.org/0000-0003-4380-7683Tianling Ren8https://orcid.org/0000-0002-7330-0544Beijing National Research Center for Information Science and Technology, School of Integrated Circuits, Tsinghua University, Beijing, ChinaDepartment of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine, Peking University, Beijing, ChinaDepartment of Automation, Tsinghua University, Beijing, ChinaDepartment of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine, Peking University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, School of Integrated Circuits, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, School of Integrated Circuits, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, School of Integrated Circuits, Tsinghua University, Beijing, ChinaDepartment of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine, Peking University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, School of Integrated Circuits, Tsinghua University, Beijing, ChinaAnkle sprains, the leading injuries in the emergency department that affect people worldwide, often leading to chronic ankle instability (CAI) characterized by recurring pain and weakness. However, challenges are presented in accurately identifying CAI-related abnormal gait patterns and assessing rehabilitation effects. Traditional plantar pressure systems lack portability and can only be used in limited specific actions, while a few early proposed portable systems have demonstrated insufficient accuracy. Besides, no previous studies have yet focused on assessing rehabilitation effects, which is crucial to providing the treatment selection and rehabilitation evaluation of CAI. Considering this, we propose a novel approach to improve the diagnostic process for CAI. A Shoe-Integrated Sensor System (SISS) which can accurately capture gait data during various activities was implemented. We collected and processed level walking data from 80 CAI patients diagnosed by professional experts and 42 healthy individuals using the system, including feature extraction and filtering algorithms. An artificial intelligence diagnosis was applied to the data, achieving a classification accuracy of 93.39% and an area under the curve (AUC) of 0.959, satisfying the clinical requirements for accuracy. Furthermore, a novel methodology was proposed to assess the level of patient rehabilitation. The validation results of rehabilitation status prediction demonstrated highly consistent results with doctors’ diagnoses. Due to the significant impact of gait data in assisting the diagnosis of various neurological and musculoskeletal diseases that result in gait abnormalities, the proposed system can also be extended and utilized in other similar medical fields for diagnosing and real-time monitoring, promoting the development of smart healthcare.https://ieeexplore.ieee.org/document/10975823/Chronic ankle instabilitygait recognizemachine learningshoe-integrated sensor system |
| spellingShingle | Zhonghe Guo Yanzhang Li Yuchen Wang Haoxuan Liu Rui Guo Jingzhong Ma Xiaoming Wu Dong Jiang Tianling Ren Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System IEEE Transactions on Neural Systems and Rehabilitation Engineering Chronic ankle instability gait recognize machine learning shoe-integrated sensor system |
| title | Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System |
| title_full | Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System |
| title_fullStr | Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System |
| title_full_unstemmed | Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System |
| title_short | Intelligent Diagnosis and Predictive Rehabilitation Assessment of Chronic Ankle Instability Using Shoe-Integrated Sensor System |
| title_sort | intelligent diagnosis and predictive rehabilitation assessment of chronic ankle instability using shoe integrated sensor system |
| topic | Chronic ankle instability gait recognize machine learning shoe-integrated sensor system |
| url | https://ieeexplore.ieee.org/document/10975823/ |
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