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

[+] Author Affiliations
Kan Kanjanapas, Yizhou Wang, Wenlong Zhang, Lauren Whittingham, Masayoshi Tomizuka

University of California, Berkeley, Berkeley, CA

Paper No. DSCC2013-3852, pp. V003T40A004; 9 pages
doi:10.1115/DSCC2013-3852
From:
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5614-7
  • Copyright © 2013 by ASME

abstract

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.

Copyright © 2013 by ASME
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