JACIII Vol.23 No.3 pp. 519-527
doi: 10.20965/jaciii.2019.p0519


Human Posture Recognition for Estimation of Human Body Condition

Wei Quan*, Jinseok Woo*, Yuichiro Toda**, and Naoyuki Kubota*

*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

November 30, 2018
December 25, 2018
May 20, 2019
human posture recognition, growing neural gas, particle swarm optimization, human-robot interaction

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.

Cite this article as:
W. Quan, J. Woo, Y. Toda, and N. Kubota, “Human Posture Recognition for Estimation of Human Body Condition,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 519-527, 2019.
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