Biometric recognition and analysis of sports teaching behavior based on wearable devices
Abstract
This study introduces a lightweight, multi-node IMU-based motion capture system optimized for biomechanical analysis, addressing limitations of traditional optical systems and challenges in sensor drift and noise. Multi-node IMU systems offer distinct advantages in biomechanical analysis, such as portability, affordability, and the ability to capture motion data in real-world environments, making them particularly suited for applications in gait analysis, sports performance, and rehabilitation. Enhanced calibration techniques correct biases in accelerometers, gyroscopes, and magnetometers, while an optimized Madgwick algorithm ensures accurate, real-time motion tracking. The system’s scalable design, supported by high-throughput USB 3.0 communication, enables precise capture of human motion. Experimental validation confirms the system’s affordability, robustness, and suitability for biomechanics, offering a practical and effective tool for advancing human movement research.
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