Article: Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video.
Frontiers in bioengineering and biotechnology
2024 Volume 11, Page(s) 1335251
Abstract: Markerless pose estimation based on computer vision provides a simpler and cheaper alternative to human motion capture, with great potential for clinical diagnosis and remote rehabilitation assessment. Currently, the markerless 3D pose estimation is ... ...
Abstract | Markerless pose estimation based on computer vision provides a simpler and cheaper alternative to human motion capture, with great potential for clinical diagnosis and remote rehabilitation assessment. Currently, the markerless 3D pose estimation is mainly based on multi-view technology, while the more promising single-view technology has defects such as low accuracy and reliability, which seriously limits clinical application. This study proposes a high-resolution graph convolutional multilayer perception (HGcnMLP) human 3D pose estimation framework for smartphone monocular videos and estimates 15 healthy adults and 12 patients with musculoskeletal disorders (sarcopenia and osteoarthritis) gait spatiotemporal, knee angle, and center-of-mass (COM) velocity parameters, etc., and compared with the VICON gold standard system. The results show that most of the calculated parameters have excellent reliability (VICON, ICC (2, k): 0.853-0.982; Phone, ICC (2, k): 0.839-0.975) and validity (Pearson r: 0.808-0.978, p |
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Language | English |
Publishing date | 2024-01-09 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2719493-0 |
ISSN | 2296-4185 |
ISSN | 2296-4185 |
DOI | 10.3389/fbioe.2023.1335251 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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