Real-Time Motion Tracking System Using AI for Athletic Performance Analysis
A new paper proposes a modular, lightweight software prototype using the MediaPipe framework to deliver real-time human pose estimation and AI-driven feedback for sports performance analysis. The work surveys existing approaches—from marker-based motion capture to modern markerless deep learning methods—and benchmarks algorithmic frameworks on metrics such as inference latency, frame rate, and joint position error. The system aims to make advanced biomechanical feedback accessible to non-expert users with minimal computational resources.
Submitted to arXiv ahead of the IC2E3-2026 conference, this paper addresses the challenge of deploying Human Pose Estimation (HPE) in real-world sports environments. The authors survey the evolution from traditional marker-based motion capture to contemporary markerless deep learning approaches, comparing top-down, bottom-up, and one-stage algorithmic frameworks on practical deployment metrics. As their primary contribution, they present a lightweight prototype built on the MediaPipe HPE framework, incorporating exercise-specific logic to generate real-time performance insights and feedback without requiring expert users or high-end hardware. The paper evaluates the prototype's performance and reliability, reporting metrics including inference latency, frame rate, mean per-joint position error, and temporal jitter. The authors also outline future research directions, including sensor fusion and integration with augmented and virtual reality environments, positioning the work as both a technical survey and an implementation blueprint for researchers, engineers, and sports scientists.
What's missing
The paper does not report validation against ground-truth biomechanical data from established motion capture gold standards, leaving the absolute accuracy of the prototype's joint estimations unverified. The study's own scope is limited to a prototype demonstration; generalizability across diverse sports, body types, lighting conditions, and occlusion scenarios is not systematically evaluated. As a preprint, the work has not yet undergone formal peer review.
What different sources said
- arXiv cs.AICenter
Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization
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