Analysis of Running Parameters Using IMU and Multi-modal Network
Objectives:Real-time measurement of running speed and stride length is of great significance in avoiding injury and improving exercise efficiency.Methods:We propose a method using inertial measure-ment unit(IMU)to detect these two indicators.First,3 IMU are placed on the foot,calf and thigh of the 10 runners which we recruited,and 5 137 data of gait cycles are collected.Second,principal component analysis is used to analyze the data,and Pearson correlation coefficient is used to discuss the relationship be-tween the detection indicators of running and the sensor position and physical parameters.Then a multi-modal attention-long short-term memory(M-Att-LSTM)is proposed for feature extraction,two long short-term memory(LSTM)modules with attention mechanism are used to extract features of acceleration and angle,and regression fitting is carried out.Results:The experiment result shows that M-Att-LSTM has errors of 0.058 m/s in speed and 0.023 m in stride,the standard deviation is 0.013 m/s and 0.022 m,respectively.Both indicators are better than pure Att-LSTM.Conclusions:The studies show that multi-modal network can improve network processing capabilities,compared with relevant researches in recent years,our study has obvious advantages in error control.
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