After decades of development, sports biomechanics analysis technology has become a crucial tool for competitive sports training and public health management. Traditional laboratory equipment and surface electromyography (sEMG) signals have provided scientific bases for optimizing athletes' movement patterns and studying the mechanisms of sports injuries. However, such equipment faces bottlenecks such as high costs, complex operations, and limited environments, leading grassroots sports teams and fitness institutions to rely predominantly on subjective observations or simple measurement tools. In recent years, breakthroughs in wearable sensor technology, through the integration of accelerometers, gyroscopes, and magnetometer modules, have significantly reduced equipment costs and support continuous monitoring in real-world scenarios such as sports fields and gyms. This paper aims to systematically review the innovative pathways of wearable technology in sports biomechanics analysis, construct a multidimensional evaluation framework for sports performance and injury prevention, and explore the challenges of technological transformation and the directions for standardization development. Method: 1) Analyzed global wearable sensor studies; 2) Tested multi-sensor devices on athletes in real-world settings; 3) Applied ML for biomechanical pattern decoding. 1) Triboelectric nanogenerators (TENGs) and flexible electrodes enable precise biomechanical capture in dynamic environments; 2) Kinetics-kinematics-physiology framework resolves single-parameter limitations; 3) ML-driven systems achieve adaptive load control and preclinical injury detection, shifting training from experiential to predictive paradigms. (1) Specialized Evaluation: Establish biomechanics databases and verification systems for specialized sports such as track and field and ball games to unify wearable sensor data with laboratory standards. (2) Cross-domain Collaboration: Collaborate with research institutions, manufacturers, and data platforms to formulate standards for device compatibility, open-source algorithms, and data privacy, promoting technological transformation. (3) Multidisciplinary Integration: Integrate sports physiology, flexible electronics, and AI technology to construct a closed-loop system of "dynamic perception - intelligent analysis - autonomous regulation." For example, smart running shoes can dynamically adjust midsole cushioning performance based on real-time pressure distribution data, optimizing marathon runners' running economy. (4) Precision Training: Utilize wearable systems to quantify technical details, load intensity, and fatigue thresholds, enhancing athletes' technical efficiency. (5) Injury Early Warning: Develop predictive models based on biomechanical characteristics, shifting injury prevention from "empirical intervention" to "intelligent prediction."
Yang et al. (Thu,) studied this question.
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