Commercial vision sensors and AI-based pose estimation frameworks for markerless motion analysis in sports and exercises: a mini review
Kinematic and biomechanical analysis in monitoring human movement to assess athletes’ or patients’ motor control behaviors. Traditional motion capture systems provide high accuracy but are expensive and complex for the public. Recent advancements in markerless systems using videos captured with comm...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1649330/full |
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| Summary: | Kinematic and biomechanical analysis in monitoring human movement to assess athletes’ or patients’ motor control behaviors. Traditional motion capture systems provide high accuracy but are expensive and complex for the public. Recent advancements in markerless systems using videos captured with commercial RGB, depth, and infrared cameras, such as Microsoft Kinect, StereoLabs ZED Camera, and Intel RealSense, enable the acquisition of high-quality videos for 2D and 3D kinematic analyses. On the other hand, open-source frameworks like OpenPose, MediaPipe, AlphaPose, and DensePose are the new generation of 2D or 3D mesh-based markerless motion tools that utilize standard cameras in motion analysis through real-time and offline pose estimation models in sports, clinical, and gaming applications. The review examined studies that focused on the validity and reliability of these technologies compared to gold-standard systems, specifically in sports and exercise applications. Additionally, it discusses the optimal setup and perspectives for achieving accurate results in these studies. The findings suggest that 2D systems offer economic and straightforward solutions, but they still face limitations in capturing out-of-plane movements and environmental factors. Merging vision sensors with built-in artificial intelligence and machine learning software to create 2D-to-3D pose estimation is highlighted as a promising method to address these challenges, supporting the broader adoption of markerless motion analysis in future kinematic and biomechanical research. |
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| ISSN: | 1664-042X |