Improvement of Sports Training Efficiency Driven by Artificial Intelligence Practice of Monitoring and Dynamic Adjustment of Training Load through Multimodal Physiological Data Fusion
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Abstract
Against the backdrop of constructing a sports powerhouse and integrating sports science with intelligent technologies, traditional experience-driven training is evolving toward high-precision personalized systems that rely on multimodal sensing and electromagnetic signal acquisition for scientific decision-making. Precise monitoring and dynamic adjustment of training load are essential for improving training efficiency and reducing sports injury risk. However, single physiological indicators suffer from limited information representation, data heterogeneity, and delayed load evaluation. This study proposes an artificial intelligence-driven framework for multimodal physiological data fusion and dynamic training load adjustment based on a technical pipeline comprising data acquisition, fusion processing, load assessment, and adaptive intervention. By integrating heterogeneous physiological information through intelligent sensing interfaces and electromagnetic data transmission mechanisms, the proposed method enhances the reliability and timeliness of athlete monitoring. Experimental results demonstrate that the framework significantly improves training load evaluation accuracy, supports personalized optimization of training strategies, enhances sports performance, accelerates physiological recovery, and reduces injury incidence. The proposed approach provides an effective solution for intelligent sports training while offering valuable insights into electromagnetic sensing-assisted wearable monitoring and adaptive human– machine interaction systems.
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