Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning
To evaluate tasks that have low physical workloads or small variations in the working postures, a method that can be used to analyze slight differences in postures and movements based on human motion characteristics is required. The interjoint coordination, which produces multijoint movements by org...
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| Format: | Article |
| Language: | English |
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The Japan Society of Mechanical Engineers
2021-02-01
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| Series: | Mechanical Engineering Journal |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/mej/8/1/8_20-00500/_pdf/-char/en |
| _version_ | 1818427471872131072 |
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| author | Kazuki HIRANAI Akihiko SEO |
| author_facet | Kazuki HIRANAI Akihiko SEO |
| author_sort | Kazuki HIRANAI |
| collection | DOAJ |
| description | To evaluate tasks that have low physical workloads or small variations in the working postures, a method that can be used to analyze slight differences in postures and movements based on human motion characteristics is required. The interjoint coordination, which produces multijoint movements by organizing the redundant degrees of freedom into fewer major covarying relationships, is one of the primary characteristics of human motion. This work proposes a novel evaluation method for interjoint coordination using the graphical lasso and clarifies its efficacy for evaluations of interjoint coordination in complete tasks as well as time-varying interjoint coordination and working postures. In a subject experiment, eleven male participants performed lightweight material-handling tasks under different working conditions and paces, and an electromagnetic motion-tracking system was used to measure their working postures. The principal interjoint coordination for measured joint angles was extracted using the graphical lasso tool as a sparse precision matrix. Further, the contribution of each joint angle to the changes in the working postures in the time series was calculated by correlation anomaly using the graphical lasso. The estimated sparse precision matrix using the graphical lasso suggested that the principal interjoint coordination reflecting the differences in movement strategies by task type can be extracted in comparison with the covariance matrix. The time-varying correlation anomaly score suggested that the joint angle that contributes to differences between interjoint coordination at the onset and termination of tasks could be determined according to the final score. Therefore, our study shows the efficacy of the graphical lasso for evaluation of interjoint coordination and working postures from the time series for repetitive lightweight material-handling tasks. |
| first_indexed | 2024-12-14T14:46:16Z |
| format | Article |
| id | doaj.art-a440296d837249d081ba89c8cbb2ca3a |
| institution | Directory Open Access Journal |
| issn | 2187-9745 |
| language | English |
| last_indexed | 2024-12-14T14:46:16Z |
| publishDate | 2021-02-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Mechanical Engineering Journal |
| spelling | doaj.art-a440296d837249d081ba89c8cbb2ca3a2022-12-21T22:57:16ZengThe Japan Society of Mechanical EngineersMechanical Engineering Journal2187-97452021-02-018120-0050020-0050010.1299/mej.20-00500mejEvaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learningKazuki HIRANAI0Akihiko SEO1Graduate School of Systems Design, Tokyo Metropolitan UniversityFaculty of Systems Design, Tokyo Metropolitan UniversityTo evaluate tasks that have low physical workloads or small variations in the working postures, a method that can be used to analyze slight differences in postures and movements based on human motion characteristics is required. The interjoint coordination, which produces multijoint movements by organizing the redundant degrees of freedom into fewer major covarying relationships, is one of the primary characteristics of human motion. This work proposes a novel evaluation method for interjoint coordination using the graphical lasso and clarifies its efficacy for evaluations of interjoint coordination in complete tasks as well as time-varying interjoint coordination and working postures. In a subject experiment, eleven male participants performed lightweight material-handling tasks under different working conditions and paces, and an electromagnetic motion-tracking system was used to measure their working postures. The principal interjoint coordination for measured joint angles was extracted using the graphical lasso tool as a sparse precision matrix. Further, the contribution of each joint angle to the changes in the working postures in the time series was calculated by correlation anomaly using the graphical lasso. The estimated sparse precision matrix using the graphical lasso suggested that the principal interjoint coordination reflecting the differences in movement strategies by task type can be extracted in comparison with the covariance matrix. The time-varying correlation anomaly score suggested that the joint angle that contributes to differences between interjoint coordination at the onset and termination of tasks could be determined according to the final score. Therefore, our study shows the efficacy of the graphical lasso for evaluation of interjoint coordination and working postures from the time series for repetitive lightweight material-handling tasks.https://www.jstage.jst.go.jp/article/mej/8/1/8_20-00500/_pdf/-char/enanomaly detectionfeature extractionsparse structure learninginterjoint coordinationrepetitive taskhuman motion analysisworking posture |
| spellingShingle | Kazuki HIRANAI Akihiko SEO Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning Mechanical Engineering Journal anomaly detection feature extraction sparse structure learning interjoint coordination repetitive task human motion analysis working posture |
| title | Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning |
| title_full | Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning |
| title_fullStr | Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning |
| title_full_unstemmed | Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning |
| title_short | Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning |
| title_sort | evaluation of time varying working posture based on interjoint coordination features extracted from sparse structure learning |
| topic | anomaly detection feature extraction sparse structure learning interjoint coordination repetitive task human motion analysis working posture |
| url | https://www.jstage.jst.go.jp/article/mej/8/1/8_20-00500/_pdf/-char/en |
| work_keys_str_mv | AT kazukihiranai evaluationoftimevaryingworkingposturebasedoninterjointcoordinationfeaturesextractedfromsparsestructurelearning AT akihikoseo evaluationoftimevaryingworkingposturebasedoninterjointcoordinationfeaturesextractedfromsparsestructurelearning |