Gait event detection using a thigh-worn accelerometer - Gurchiek, Reed D.; Garabed, Cole P.; McGinnis, Ryan S. (Gait & posture, 2020)
•Validation of a gait event detection algorithm using a thigh-worn accelerometer.•Strong correlations between estimated and measured spatiotemporal gait variables.•Low mean absolute error in gait event detection (28–39 ms).•Source code for the algorithm has been made available. Gait event detection is critical for remote gait analysis. Algorithms using a thigh-worn accelerometer for estimating spatiotemporal gait variables have demonstrated clinical utility in monitoring the gait of patients with gait and balance impairment. However, one may obtain accurate estimates of spatiotemporal variables, but with biased estimates of foot contact and foot off events. Some biomechanical analyses depend on accurate gait phase segmentation, but previous studies using a thigh-worn accelerometer have not quantified the error in estimating foot contact and foot off events. Gait events and spatiotemporal gait variables were estimated using a thigh-worn accelerometer from 32 healthy subjects across a range of walking speeds (0.56–1.78 m/s). Ground truth estimates were obtained using vertical ground reaction forces measured using a pressure treadmill. Estimation performance was quantified using absolute error, root mean square error, and correlation analysis. Across all strides (N = 3,898), the absolute error in estimating foot contact, foot off, stride time, stance time, and swing time was similar to other accelerometer-based techniques (39 ± 28 ms, 28 ± 28 ms, 11 ± 14 ms, 46 ± 31 ms, and 45 ± 30 ms, respectively). The correlation between reference measurements and estimates of bout-average stride time, stance time, and swing time were 1.00, 0.92, and 0.80, respectively. The (5th, 95th) percentiles of the foot contact and foot off estimation errors were (-91 ms, 51 ms) and (-70 ms, 60 ms), the largest of which amounts to about three samples using the 31.25 Hz sampling frequency used in this study. Use of the proposed algorithm for estimating spatiotemporal gait variables is supported by the strong correlations with reference measurements. The gait event estimation error distributions provide bounds on the estimated gait events for enforcing gait phase-dependent task constraints for biomechanical analysis.