Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring
XiaoLan Gai
Abstract
This research proposes a intelligent system of prevention of athletic injuries and monitoring of health with flexible sensors, artificial synapses, and machine learning. The primary goal is to achieve real-time monitoring of athletes' health and injury prevention through the collection and analysis of sport data. The system achieves a 92.1% accuracy rate in the detection of improper motion patterns and prediction of injury risks, much higher than traditional methods. As for health monitoring, the system achieves an R 2 of 0.96 and an RMSE of 5.41, proving to be valid and effective. The major contributions lie in the integration of artificial synapses and flexible sensors, feature-level fusion technology, and a blend of SVM and LSTM networks. This innovative solution bridges the existing work and presents an extremely valuable athletic injury prevention tool as well as a health monitoring platform.