Gait speed estimation using Kalman Filtering on inertial measurement unit data.

Gait speed measurement is vital for diagnosis of motor disorder and monitoring the progress of patient rehabilitation. This study presents an algorithm for moderate distance gait speed measurement from data acquired with inertial motion sensors comprised of a tri-axial accelerometer and a tri-axial gyroscope. Gait speed was measured in four different speed levels set by a treadmill: 0.5, 1, 2, and 3 miles/hour. The calculated speed was tuned by implementing Kalman Filter. The performance of the proposed algorithm was evaluated by calculating the mean square error between estimated speed and the actual treadmill speed. The preliminary results obtained from various treadmill speeds suggest that proposed algorithm estimated speed in a reasonable accuracy. The average error rate was 0.23 m/h which is nearly similar to other studies in this area. Algorithm performance evaluation for various speeds implied that the best performance was exhibited when the speed was set at 1 mile/hour. Moreover, the use of Kalman Filter helped to fine-tune the estimated speed by removing uncertainty and eventually provided a better approximation of the speed measured from the inertial measurement unit.