Movement Recognition Algorithm Empowered Wearable Devices on Internet of Things Platform for Sports Training
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Abstract
This study presents an Internet-of-Things-enabled movement recognition framework (MRA-IoT) for real-time monitoring and assessment of athletic performance. The system employs a wearable sensor array to acquire multi-joint kinematic signals, which are processed through high-precision floating-point computation, gradient analysis, and vector operations to detect subtle biomechanical deviations. A multi-point calibration and validation procedure ensures accurate mapping between sensor measurements and body motion, forming a closed-loop feedback system for performance optimization. Experimental validation on 3,000 athlete recordings demonstrates a recall of 97.5%, precision of 95.3%, and overall accuracy of 98.2%, confirming the framework’s capability to provide timely, data-driven feedback. By interpreting the wearable sensor network as a **multi-node signal acquisition and propagation system**, the study provides an engineering-oriented perspective on high-resolution motion monitoring, anomaly detection, and real-time feedback, offering insights applicable to intelligent human-motion analysis and cyberphysical performance evaluation systems.
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