ArticleFrontiers in bioengineering and biotechnology2023
Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video.
Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Gait assessment using a 2D video-based pose estimation app in comparison to a markerless motion capture system in subjects with osteoarthritis of the knee - a pilot study.Frontiers in bioengineering and biotechnology · 2026Article
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Gait assessment using a 2D video-based motion analysis app in healthy subjects and subjects with lower limb amputation - A pilot study.PloS one · 2025Article
- Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion.Diagnostics (Basel, Switzerland) · 2024Article
- [Orthopedics and trauma surgery in the digital age].Orthopadie (Heidelberg, Germany) · 2024Review
- A novel multi-level 3D pose estimation framework for gait detection of Parkinson's disease using monocular video.Frontiers in bioengineering and biotechnology · 2024Article
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9 authors.
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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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