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 Measurement Unit (IMU) to detect these two indicators． 3 IMU are placed on the foot, calf and thigh of the 10 runners which we recruited, more than 5 137 data of gait cycles were collected． Then, principal component analysis was used to analyze the data, and Pearson correlation coefficient is used to discuss the relationship between 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, then 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．