A Human Motion Capture System Based on Inertial Sensing and a Complementary Filter

A human motion capture system is becoming one of the most useful tools in rehabilitation application because it can record and reconstruct a patient’s motion accurately for motion analysis. In this paper, a human motion capture system is proposed based on inertial sensing. A microprocessor is implemented on-board to obtain raw sensing data from the inertial measurement unit (IMU), and transmit the raw data to the central processing unit. To reject noise in the accelerometer, drift in the gyroscope, and magnetic distortion in the magnetometer, a time-varying complementary filter (TVCF) is implemented in the central processing unit to provide accurate attitude estimation. A forward kinematic model of the human arm is developed to create an animation for patients and physical therapists. Performance of the hardware and filtering algorithm is verified by experimental results.