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Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model

Authors :
Jong-Wook Kim
Jin-Young Choi
Eun-Ju Ha
Jae-Ho Choi
Source :
Applied Sciences, Vol 13, Iss 4, p 2700 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Seniors who live alone at home are at risk of falling and injuring themselves and, thus, may need a mobile robot that monitors and recognizes their poses automatically. Even though deep learning methods are actively evolving in this area, they have limitations in estimating poses that are absent or rare in training datasets. For a lightweight approach, an off-the-shelf 2D pose estimation method, a more sophisticated humanoid model, and a fast optimization method are combined to estimate joint angles for 3D pose estimation. As a novel idea, the depth ambiguity problem of 3D pose estimation is solved by adding a loss function deviation of the center of mass from the center of the supporting feet and penalty functions concerning appropriate joint angle rotation range. To verify the proposed pose estimation method, six daily poses were estimated with a mean joint coordinate difference of 0.097 m and an average angle difference per joint of 10.017 degrees. In addition, to confirm practicality, videos of exercise activities and a scene of a person falling were filmed, and the joint angle trajectories were produced as the 3D estimation results. The optimized execution time per frame was measured at 0.033 s on a single-board computer (SBC) without GPU, showing the feasibility of the proposed method as a real-time system.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.2380e8743b574f048a11d8240205e3a9
Document Type :
article
Full Text :
https://doi.org/10.3390/app13042700