Segmentation and Evaluation of Continuous Rehabilitation Exercises
To provide an accurate and objective feedback on rehabilitation training movements and to improve the motivation of rehabilitation patients in rehabilitation training, a motion evaluation method capable of processing continuous human rehabilitation training movement data is proposed. First, a motion...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2023-05-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-5-533.shtml |
| _version_ | 1797811069493706752 |
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| author | HU Mingxuan, QIAO Jun, ZHANG Zhinan |
| author_facet | HU Mingxuan, QIAO Jun, ZHANG Zhinan |
| author_sort | HU Mingxuan, QIAO Jun, ZHANG Zhinan |
| collection | DOAJ |
| description | To provide an accurate and objective feedback on rehabilitation training movements and to improve the motivation of rehabilitation patients in rehabilitation training, a motion evaluation method capable of processing continuous human rehabilitation training movement data is proposed. First, a motion segmentation method based on the Gaussian mixture model (GMM) is developed to extract single motion repetition from continuous repetitive motion sequences of the same motion. Then, based on relevant a priori knowledge, a multi-feature fusion motion evaluation method combining significant motion feature dynamic time warping (DTW) distance evluation and Gaussian mixture model likelihood evaluation is proposed to perform motion evaluation in both the overall motion feature and local joint information of rehabilitation exercises. The results show that the motion segmentation method can segment the motion data of continuous repetitive motions well, and the correct rate of segmented motions on the dataset reaches more than 95%. The multi-feature fusion motion evaluation method effectively improves the differentiation of motion evaluation between healthy samples and rehabilitation patient samples, so that the motion scores of healthy samples are mainly distributed in the range of 0.93—0.94 on a scale of 0—1, while the motion scores of patient samples are mainly distributed in the range of 0.81—0.89. |
| first_indexed | 2024-03-13T07:18:11Z |
| format | Article |
| id | doaj.art-8386372dbfab4fd4a1a66d173ef60e89 |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-03-13T07:18:11Z |
| publishDate | 2023-05-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-8386372dbfab4fd4a1a66d173ef60e892023-06-05T02:26:10ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672023-05-0157553354410.16183/j.cnki.jsjtu.2021.458Segmentation and Evaluation of Continuous Rehabilitation ExercisesHU Mingxuan, QIAO Jun, ZHANG Zhinan01. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Department of Rehabilitation, Shanghai Changning Mental Health Center, Shanghai 200335, ChinaTo provide an accurate and objective feedback on rehabilitation training movements and to improve the motivation of rehabilitation patients in rehabilitation training, a motion evaluation method capable of processing continuous human rehabilitation training movement data is proposed. First, a motion segmentation method based on the Gaussian mixture model (GMM) is developed to extract single motion repetition from continuous repetitive motion sequences of the same motion. Then, based on relevant a priori knowledge, a multi-feature fusion motion evaluation method combining significant motion feature dynamic time warping (DTW) distance evluation and Gaussian mixture model likelihood evaluation is proposed to perform motion evaluation in both the overall motion feature and local joint information of rehabilitation exercises. The results show that the motion segmentation method can segment the motion data of continuous repetitive motions well, and the correct rate of segmented motions on the dataset reaches more than 95%. The multi-feature fusion motion evaluation method effectively improves the differentiation of motion evaluation between healthy samples and rehabilitation patient samples, so that the motion scores of healthy samples are mainly distributed in the range of 0.93—0.94 on a scale of 0—1, while the motion scores of patient samples are mainly distributed in the range of 0.81—0.89.https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-5-533.shtmlmotion evaluationmotion segmentationgaussian mixture model (gmm)dynamic time warping (dtw) |
| spellingShingle | HU Mingxuan, QIAO Jun, ZHANG Zhinan Segmentation and Evaluation of Continuous Rehabilitation Exercises Shanghai Jiaotong Daxue xuebao motion evaluation motion segmentation gaussian mixture model (gmm) dynamic time warping (dtw) |
| title | Segmentation and Evaluation of Continuous Rehabilitation Exercises |
| title_full | Segmentation and Evaluation of Continuous Rehabilitation Exercises |
| title_fullStr | Segmentation and Evaluation of Continuous Rehabilitation Exercises |
| title_full_unstemmed | Segmentation and Evaluation of Continuous Rehabilitation Exercises |
| title_short | Segmentation and Evaluation of Continuous Rehabilitation Exercises |
| title_sort | segmentation and evaluation of continuous rehabilitation exercises |
| topic | motion evaluation motion segmentation gaussian mixture model (gmm) dynamic time warping (dtw) |
| url | https://xuebao.sjtu.edu.cn/article/2023/1006-2467/1006-2467-57-5-533.shtml |
| work_keys_str_mv | AT humingxuanqiaojunzhangzhinan segmentationandevaluationofcontinuousrehabilitationexercises |