ReviewFrontiers in physiology2025
Commercial vision sensors and AI-based pose estimation frameworks for markerless motion analysis in sports and exercises: a mini review.
Review in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Article
- Developmental Postural Modeling: A Historical and Conceptual Narrative Review of Posture Assessment.Journal of clinical medicine · 2026Review
- Golden Ratio and Cybernetic Aesthetics for Hidden Patterns in Repetitive Human Movements: Golden-faux-sinus as a Two-Dimensional Optimization Model.Biological cybernetics · 2026Article
- Kinematic Characteristics of the Racket in Table Tennis During Backhand Flick Followed by Backhand Fast Block and Forehand Fast Block Combinations.Sensors (Basel, Switzerland) · 2026Article
- Evaluating a Multi-Camera Markerless System for Capturing Basketball-Specific Movements: An Exploration Using 25 Hz Video Streams.Sensors (Basel, Switzerland) · 2026Article
- Evaluation of Smartphone Camera Positioning on Artificial Intelligence Pose Estimation Accuracy for Exercise Detection: Observational Study.JMIR mHealth and uHealth · 2026Observational
- Clinical Validation of an On-Device AI-Driven Real-Time Human Pose Estimation and Exercise Prescription Program; Prospective Single-Arm Quasi-Experimental Study.Healthcare (Basel, Switzerland) · 2026Article
- Mechanical Efficiency and Injury Risk in Leg Kicks Across Combat Sports: A Narrative Review of Stance, Hip Rotation, and Striking Surface Effects.Healthcare (Basel, Switzerland) · 2026Review
- Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Evaluating the accuracy of table tennis 3D markerless motion capture based on two video cameras: a pilot study.Frontiers in bioengineering and biotechnology · 2026Article
- Development and implementation of a MediaPipe-based AI teaching-learning model in school physical education for health promotion.Frontiers in public health · 2026Article
- OpenCap vs. LESS: Sport-Specific Profiling of Dynamic Knee Valgus.Journal of clinical medicine · 2025Article
- Editorial: Assessment and monitoring of human movement.Frontiers in physiology · 2025Article
- "Though helpful, still hesitant": a TAM-based qualitative study on older adults' ambivalent acceptance and model extensions in AI fitness coaches.Frontiers in psychology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.