Intelligent Gait Analysis and Evaluation System Based on Cane Robot
Gait analysis and evaluation are vital for disease diagnosis and rehabilitation. Current gait analysis technologies require wearable devices or high-resolution vision systems within a limited usage space. To facilitate gait analysis and quantitative walking-ability evaluation in daily environments w...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9919168/ |
| _version_ | 1827927067047493632 |
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| author | Qingyang Yan Jian Huang Dongrui Wu Zhaohui Yang Yujie Wang Yasuhisa Hasegawa Toshio Fukuda |
| author_facet | Qingyang Yan Jian Huang Dongrui Wu Zhaohui Yang Yujie Wang Yasuhisa Hasegawa Toshio Fukuda |
| author_sort | Qingyang Yan |
| collection | DOAJ |
| description | Gait analysis and evaluation are vital for disease diagnosis and rehabilitation. Current gait analysis technologies require wearable devices or high-resolution vision systems within a limited usage space. To facilitate gait analysis and quantitative walking-ability evaluation in daily environments without using wearable devices, a mobile gait analysis and evaluation system is proposed based on a cane robot. Two laser range finders (LRFs) are mounted to obtain the leg motion data. An effective high-dimensional Takagi-Sugeno-Kang (HTSK) fuzzy system, which is suitable for high-dimensional data by solving the saturation problem caused by softmax function in defuzzification, is proposed to recognize the walking states using only the motion data acquired from LRFs. The gait spatial-temporal parameters are then extracted based on the gait cycle segmented by different walking states. Besides, a quantitative walking-ability evaluation index is proposed in terms of the conventional Tinetti scale. The plantar pressure sensing system records the walking states to label training data sets. Experiments were conducted with seven healthy subjects and four patients. Compared with five classical classification algorithms, the proposed method achieves the average accuracy rate of 96.57%, which is improved more than 10%, compared with conventional Takagi-Sugeno-Kang (TSK) fuzzy system. Compared with the gait parameters extracted by the motion capture system OptiTrack, the average errors of step length and gait cycle are only 0.02 m and 1.23 s, respectively. The comparison between the evaluation results of the robot system and the scores given by the physician also validates that the proposed method can effectively evaluate the walking ability. |
| first_indexed | 2024-03-13T05:46:55Z |
| format | Article |
| id | doaj.art-64881c326eea468f8436dc4c16f68ad7 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:55Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-64881c326eea468f8436dc4c16f68ad72023-06-13T20:08:34ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302916292610.1109/TNSRE.2022.32138239919168Intelligent Gait Analysis and Evaluation System Based on Cane RobotQingyang Yan0https://orcid.org/0000-0002-1317-8129Jian Huang1https://orcid.org/0000-0002-6267-8824Dongrui Wu2https://orcid.org/0000-0002-7153-9703Zhaohui Yang3https://orcid.org/0000-0002-8929-4505Yujie Wang4Yasuhisa Hasegawa5https://orcid.org/0000-0001-9917-098XToshio Fukuda6Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaKey Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Rehabilitation Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Rehabilitation Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Micro-Nano Systems Engineering, Nagoya University, Nagoya, JapanInstitutes of Innovation for Future Society, Nagoya University, Nagoya, JapanGait analysis and evaluation are vital for disease diagnosis and rehabilitation. Current gait analysis technologies require wearable devices or high-resolution vision systems within a limited usage space. To facilitate gait analysis and quantitative walking-ability evaluation in daily environments without using wearable devices, a mobile gait analysis and evaluation system is proposed based on a cane robot. Two laser range finders (LRFs) are mounted to obtain the leg motion data. An effective high-dimensional Takagi-Sugeno-Kang (HTSK) fuzzy system, which is suitable for high-dimensional data by solving the saturation problem caused by softmax function in defuzzification, is proposed to recognize the walking states using only the motion data acquired from LRFs. The gait spatial-temporal parameters are then extracted based on the gait cycle segmented by different walking states. Besides, a quantitative walking-ability evaluation index is proposed in terms of the conventional Tinetti scale. The plantar pressure sensing system records the walking states to label training data sets. Experiments were conducted with seven healthy subjects and four patients. Compared with five classical classification algorithms, the proposed method achieves the average accuracy rate of 96.57%, which is improved more than 10%, compared with conventional Takagi-Sugeno-Kang (TSK) fuzzy system. Compared with the gait parameters extracted by the motion capture system OptiTrack, the average errors of step length and gait cycle are only 0.02 m and 1.23 s, respectively. The comparison between the evaluation results of the robot system and the scores given by the physician also validates that the proposed method can effectively evaluate the walking ability.https://ieeexplore.ieee.org/document/9919168/Cane robotgait analysiswalking-ability evaluationmachine learning |
| spellingShingle | Qingyang Yan Jian Huang Dongrui Wu Zhaohui Yang Yujie Wang Yasuhisa Hasegawa Toshio Fukuda Intelligent Gait Analysis and Evaluation System Based on Cane Robot IEEE Transactions on Neural Systems and Rehabilitation Engineering Cane robot gait analysis walking-ability evaluation machine learning |
| title | Intelligent Gait Analysis and Evaluation System Based on Cane Robot |
| title_full | Intelligent Gait Analysis and Evaluation System Based on Cane Robot |
| title_fullStr | Intelligent Gait Analysis and Evaluation System Based on Cane Robot |
| title_full_unstemmed | Intelligent Gait Analysis and Evaluation System Based on Cane Robot |
| title_short | Intelligent Gait Analysis and Evaluation System Based on Cane Robot |
| title_sort | intelligent gait analysis and evaluation system based on cane robot |
| topic | Cane robot gait analysis walking-ability evaluation machine learning |
| url | https://ieeexplore.ieee.org/document/9919168/ |
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