A comparison of kinematic-based gait event detection methods in a self-paced treadmill application

Kinematic-based algorithms for detecting gait events are efficient and useful in the absence of (reliable) kinetic data. However, the validity of these kinematic-based algorithms for self-paced treadmill walking is unknown, particularly given the influence of walking speed on such data. We quantified offsets in event detection of four foot kinematics-based algorithms (horizontal position, horizontal velocity, vertical velocity, and sagittal resultant velocity) relative to events determined by a threshold in vertical ground reaction force among seven uninjured individuals – and nine with unilateral transtibial amputation – walking on a self-paced treadmill. Across walking speeds from 0.48–1.64 m/s (0.5–31.7% CV), offsets ranged from −7 to +3 frames (≈83.3 ms) in heel strike, and −3 to +5 frames (≈66.6 ms) in toe off. Regardless of method, offsets in heel strike were not influenced (−0.01<r<0.01, all P>0.61) by variability in walking speed. However, offsets in toe-off were positively correlated with variability in walking speed for the horizontal position (r=0.539; P<0.001) and velocity (r=0.463; P<0.001) algorithms, and negatively correlated (r=−0.317; P<0.001) for the vertical velocity algorithm; offsets from the sagittal resultant velocity algorithm, with thresholds adjusted for walking speed, were not strongly associated (r=0.126; P=0.27). Although relatively minimal offsets support the applicability of these algorithms to self-paced walking, for individuals with asymptomatic and pathological gait patterns, sagittal resultant velocity of the foot produces the most consistent event detection over the widest range of (and variability in) walking speeds.