Smart Swimming Training: Wearable Body Sensor Networks Empower Technical Evaluation of Competitive Swimming

—The combination of wearable sensors and competitive sports provides quantitative information for scientific training, effectively assisting athletes in improving their athletic performance. This study presents a technical framework for athletic sports assessment in competitive swimming based on body-area sensor networks. In our approach, wearable inertial sensor nodes are placed on specific body parts of the athletes to capture motion data during different competitive swimming strokes. Multiwearable inertial sensor nodes are worn on specific body parts of athletes for real-time monitoring motion data during training sessions. A motion intensity detection-based error-state-Kalman-filter algorithm is proposed for multisensor data fusion. Additionally, through kinematic statistical analysis, the characteristics of joint motion during training are clearly explained. Furthermore, a deep learning network that fuses sensor time series and human skeleton graphs is proposed for different stroke phase segmentation, enabling quantitative measurement of motion phases, and several baseline classifiers are chosen for comparison to validate the robustness of our phase segmentation method. We also investigate the sensor combination selection issue during the phase segmentation process to determine the optimal sensor configuration. Our approach provides a scientific solution for the integration of wearable sensors and competitive sports, contributing to the high-quality development of the next generation of smart sports.