Human Motion Capture Based on MEMS Sensor

Abstract

In order to realize the monitoring of human joint rehabilitation, a human motion capture and recognition system is constructed by using micro electro mechanical system (MEMS) sensor nodes. A two-stage extended Kalman filter algorithm is proposed for multi-sensor data fusion. The error matrix between the coordinate system of sensor node and the coordinate system of body was calculated by using the stationary posture calibration. The root mean squared error (RMSE) of the computed joint angle time series is less than 0.5°. The feature of joint angle time series was extracted and the support vector machine (SVM) classification model based on particle swarm optimization (PSO) was established. The experimental results show that the SVM algorithm optimized by PSO has better recognition effect than BP neural network. The average recognition rate can reach more than 97%. The human motion capture system designed in this paper can effectively realize human motion capture, recognition and joint rehabilitation monitoring.