A Dynamic Monitoring System for Wearable Devices in Human Motion Based on Augmented Reality
This study investigates a dynamic monitoring system for wearable devices in human motion based on augmented reality.Taking basketball training as an example,a wearable device dynamic monitoring system is proposed,and a gait trajectory prediction model based on LSTM and attention mechanism is designed.Real time online analysis is conducted on the angles of athletes'hip,knee,and ankle joints,and the gait trajectory prediction results of athletes are obtained.Firstly,the traditional LSTM network struc-ture was studied and analyzed,and a gait trajectory prediction model based on LSTM and attention mechanism was constructed.Sub-sequently,the overall framework of the dynamic monitoring system was designed briefly,and finally,the prediction model was experi-mentally tested.The test results show that the RMSE and MAE values of the LSTM and attention mechanism prediction model de-signed in this article are significantly lower than those of the traditional LSTM prediction model,which is effective and can accurately predict the angles of athletes'hip and ankle joints;The trajectory prediction results provided by the gait trajectory prediction model based on LSTM and attention mechanism,both overall and in detail,are better aligned with actual data,almost overlapping with real gait trajectories,indicating that the performance of the model is better and more suitable for the design of wearable device dynamic monitoring systems in this paper.
万方数据
维普
