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AI Research
arXiv CS.AI·Jun 10·5 min read
Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization
TL;DR
Real-time Human Pose Estimation (HPE) in sports analysis faces challenges in accuracy and efficiency. A lightweight software prototype was developed using the MediaPipe HPE framework to provide real-time insights and AI feedback for athletes.
✦ Why It Matters
Engineers can leverage this prototype to develop accessible motion tracking solutions for sports performance analysis.
Key Takeaways
- Explore MediaPipe for real-time motion tracking applications.
- Consider algorithmic frameworks based on specific deployment needs.
- Integrate additional sensors for enhanced data collection.
- Investigate AR/VR technologies for immersive training experiences.
Full Summary
Human Pose Estimation (HPE) is crucial for analyzing athletic performance but often struggles with real-world application due to accuracy and efficiency issues. This research developed a lightweight software prototype utilizing the MediaPipe HPE framework, which incorporates exercise-specific logic to deliver real-time feedback.
The study compared various algorithmic frameworks, including top-down and bottom-up approaches, assessing metrics like inference latency and mean per-joint position error. Results showed that the prototype could provide reliable insights with low computational demands, making it accessible for non-expert users.
Future research directions include integrating additional sensors and exploring augmented reality (AR) and virtual reality (VR) applications. This work serves as a valuable resource for engineers and researchers aiming to enhance athletic performance through real-time motion tracking.