Sports injury prediction based on sensor information fusion and neural network

A sensor information fusion method for sports injury prediction is proposed in this paper. The hole effect is eliminated by employing the accumulation of multi-frame differences. On this basis, accurate motion regions are determined by fusion sensors to monitor motion in different scenes. Non-stationary signals of monitoring results are analysed by wavelet analysis method to obtain motion injury characteristics. Machine learning algorithms can be trained on this sensor data to develop predictive models for sports injuries. Sensor information fusion and wavelet radial basis function neural network are combined to obtain the wavelet eigenvector of all sensors. A radial basis function neural network will output a value when the data sent to it matches a certain risk level to achieve sports injury prediction. The results reveal that the proposed model performs well in prediction accuracy and running time, which can provide real-time feedback to athletes and coaches.