Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring

Abstract

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 R2 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.

Keywords

• Artificial synapse
• Machine learning
• Flexible sensor
• Athletic injury prevention
• Sports monitoring