Towards Low - Cost Mems Imu Gait Analysis: Improvements Using Calibration and State Estimation

Inexpensive, unobtrusive 3D motion tracking of human gait is of increasing interest for the medical and entertainment industries. Of particular interest are rehabilitative applications. For instance, being able to measure foot travel, e.g. stride length or foot clearance, would be very useful. Approaches using low-cost MEMS inertial measurement units have often been limited by requiring expensive calibration procedures and by the sensor’s inherent noise and bias drift. The authors apply two techniques to improve IMU based gait tracking: a novel calibration routine and a zero-velocity bias update algorithm. The application of these aids reduces error by an average of 99.55% over six trails. Results show a 5.96% tracking accuracy in the progressive direction, which corresponds to errors on the centimeter scale.

[1]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[2]  Peter H. Veltink,et al.  Measuring orientation of human body segments using miniature gyroscopes and accelerometers , 2005, Medical and Biological Engineering and Computing.

[3]  Maja J. Mataric,et al.  Motion capture from inertial sensing for untethered humanoid teleoperation , 2004, 4th IEEE/RAS International Conference on Humanoid Robots, 2004..

[4]  Zhaoying Zhou,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. , 2004, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  Kevin Huang,et al.  Development of a real-time three-dimensional spinal motion measurement system for clinical practice , 2006, Medical and Biological Engineering and Computing.

[6]  Jing Yang,et al.  A novel hand gesture input device based on inertial sensing technique , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[7]  Rong Zhu,et al.  A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Jung-Keun Lee,et al.  A Fast Quaternion-Based Orientation Optimizer via Virtual Rotation for Human Motion Tracking , 2009, IEEE Transactions on Biomedical Engineering.

[9]  Jung-Keun Lee,et al.  Minimum-Order Kalman Filter With Vector Selector for Accurate Estimation of Human Body Orientation , 2009, IEEE Transactions on Robotics.

[10]  Michael Harrington,et al.  Miniature six-DOF inertial system for tracking HMDs , 1998, Defense, Security, and Sensing.

[11]  Eric Foxlin,et al.  Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter , 1996, Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium.

[12]  Eric Allen Johnson Investigating inertial measurement for human-scale motion tracking , 2011 .

[13]  Huosheng Hu,et al.  Human motion tracking for rehabilitation - A survey , 2008, Biomed. Signal Process. Control..

[14]  Yu-Liang Hsu,et al.  An Inertial-Measurement-Unit-Based Pen With a Trajectory Reconstruction Algorithm and Its Applications , 2010, IEEE Transactions on Industrial Electronics.

[15]  Mark A. Minor,et al.  A state estimator for rejecting noise and tracking bias in inertial sensors , 2008, 2008 IEEE International Conference on Robotics and Automation.

[16]  W. H. Baird An introduction to inertial navigation , 2009 .

[17]  Joseph A. Paradiso,et al.  Gait Analysis Using a Shoe-Integrated Wireless Sensor System , 2008, IEEE Transactions on Information Technology in Biomedicine.