MINI REVIEW article
Front. Physiol.
Sec. Exercise Physiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1649330
This article is part of the Research TopicAssessment and Monitoring of Human Movement Volume IIView all articles
Commercial Vision Sensors and AI-based Pose Estimation Frameworks for Markerless Motion Analysis in Sports and Exercises: A Mini Review
Provisionally accepted
- 1Universita degli Studi di Roma Tor Vergata Dipartimento di Ingegneria Industriale, Rome, Italy
- 2Università telematica San Raffaele, Rome, Italy
- 3Universita degli Studi di Roma Tor Vergata, Rome, Italy
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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.
Keywords: Markerless Motion Capture, Vision sensors, Pose estimation, Human movement analysis, Sports biomechanics
Received: 18 Jun 2025; Accepted: 25 Jul 2025.
Copyright: © 2025 Edriss, Romagnoli, Caprioli, Bonaiuto, Padua and Annino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cristian Romagnoli, Università telematica San Raffaele, Rome, Italy
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