Gait Analysis Based on Magnetometer and Inertial Sensors Data Fusion

Gait analysis mainly contains gait phase detection and step counting, providing effective standards for health status and setting appropriate limits for the amount of physical activity. A method based on magnetometer and inertial sensors data fusion is presented for gait phase detection and step counting conveniently and accurately. For gait phase detection, the transient state (turning points) can be detected using the amplitude of magnetic field generated by a permanent attached to the ankle. We can calculate accurate gait phase with gait event in a gait cycle, of which adaptive threshold is estimated based on turning points. For step counting, we can pick out preselected peaks by detecting the peak of the magnetic field intensity amplitude, processing inertial sensor data corresponding to continuous peaks of magnetic field intensity to realize adaptive step counting. The comparative experiment results show that for gait phase detection, average relative errors of foot-flat, push-off, swing, and foot-flat phase are 11.07%, 9.95%, 13.04%, and 10.92%, respectively, based on the proposed method to detect four phases; for step counting, the average accuracy of the presented method is 96.04% in walking without interference and 94.46% in walking with interference. Overall, our proposed method has an improvement in the anti-interference ability and accuracy.