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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Autores principales:	Kazuki HIRANAI, Akihiko SEO
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2021
Materias:	anomaly detection feature extraction sparse structure learning interjoint coordination repetitive task human motion analysis working posture Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/a440296d837249d081ba89c8cbb2ca3a
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spelling	oai:doaj.org-article:a440296d837249d081ba89c8cbb2ca3a2021-11-29T06:04:29ZEvaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning2187-974510.1299/mej.20-00500https://doaj.org/article/a440296d837249d081ba89c8cbb2ca3a2021-02-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/8/1/8_20-00500/_pdf/-char/enhttps://doaj.org/toc/2187-9745To 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.Kazuki HIRANAIAkihiko SEOThe Japan Society of Mechanical Engineersarticleanomaly detectionfeature extractionsparse structure learninginterjoint coordinationrepetitive taskhuman motion analysisworking postureMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 8, Iss 1, Pp 20-00500-20-00500 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anomaly detection feature extraction sparse structure learning interjoint coordination repetitive task human motion analysis working posture Mechanical engineering and machinery TJ1-1570
spellingShingle	anomaly detection feature extraction sparse structure learning interjoint coordination repetitive task human motion analysis working posture Mechanical engineering and machinery TJ1-1570 Kazuki HIRANAI Akihiko SEO Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning
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.
format	article
author	Kazuki HIRANAI Akihiko SEO
author_facet	Kazuki HIRANAI Akihiko SEO
author_sort	Kazuki HIRANAI
title	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_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_sort	evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning
publisher	The Japan Society of Mechanical Engineers
publishDate	2021
url	https://doaj.org/article/a440296d837249d081ba89c8cbb2ca3a
work_keys_str_mv	AT kazukihiranai evaluationoftimevaryingworkingposturebasedoninterjointcoordinationfeaturesextractedfromsparsestructurelearning AT akihikoseo evaluationoftimevaryingworkingposturebasedoninterjointcoordinationfeaturesextractedfromsparsestructurelearning
_version_	1718407603755679744

Evaluation of time-varying working posture based on interjoint coordination features extracted from sparse structure learning

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