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Background: 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., Methods: 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., Results: 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., Significance: 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., Competing Interests: Declaration of Competing Interest RSM owns stock in MC10, Inc. This conflict is being managed by the University of Vermont., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v 0 and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v 0 , respectively. This study aims to automate sprint assessment by estimating v 0 and τ using machine learning and accelerometer data. To this end, photocells recorded 10-m split times of 28 subjects for three 40-m sprints while wearing an accelerometer around the waist. Features extracted from the accelerometer data were used to train a classifier to identify the sprint start and regression models to estimate the sprint model parameters. Estimates of v 0 , τ, and 30-m sprint time (t 30 ) were compared between the proposed method and a photocell method using root mean square error and Bland-Altman analysis. The root mean square error of the sprint start estimate was .22 seconds and ranged from .52 to .93 m/s for v 0 , .14 to .17 seconds for τ, and .23 to .34 seconds for t 30 . Model-derived sprint performance metrics from most regression models were significantly (P < .01) correlated with t 30 . Comparison of the proposed method and a physics-based method suggests pursuit of a combined approach because their strengths appear to complement each other.