Human Posture Recognition for Estimation of Human Body Condition

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment...

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Published inJournal of advanced computational intelligence and intelligent informatics Vol. 23; no. 3; pp. 519 - 527
Main Authors Quan, Wei, Woo, Jinseok, Toda, Yuichiro, Kubota, Naoyuki
Format Journal Article
LanguageEnglish
Published 20.05.2019
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ISSN1343-0130
1883-8014
DOI10.20965/jaciii.2019.p0519

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Abstract Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.
AbstractList Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.
Author Quan, Wei
Toda, Yuichiro
Woo, Jinseok
Kubota, Naoyuki
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CorporateAuthor Graduate School of Systems Design, Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0055, Japan
Graduate School of Natural Science and Technology, Okayama University 3-1-1 Tsushima-Naka, Kita, Okayama, Okayama 700-8530, Japan
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