A Novel Algorithm for Automatic Estimation of Gait Parameters Using Lower-Body Kinematics

Abstract

Accurate estimation of gait parameters from the limb kinematics data remains a challenge for researchers. Several deterministic algorithms have been proposed in the literature for this purpose but their applicability is limited by their accuracy, especially for gait-impaired subjects. Gait parameter estimation relies on the accurate detection of key gait events of initial contact (IC) and terminal contact (TC) for gait segmentation. This study aimed to evaluate literature algorithms for IC and TC prediction for the amputee population and to introduce a novel algorithm to improve gait parameter estimation. A marker data set of 12 amputee subjects during treadmill walking was used. Seven algorithms, including one novel algorithm, detecting the landmarks (maxima, minima, and zero-crossings) in the foot, shank, and thigh velocity data were implemented and their IC and TC estimations were compared against the force platform data. IC was best detected by foot and shank velocity methods with median bias in the range of -3 to -27ms. However, the literature algorithms produced inferior accuracy for the TC event with consistent early prediction. The newly-proposed thigh velocity-based algorithm produced the best result for the TC prediction -14 to 19ms median bias. By combining the IC prediction from foot velocity data and the TC prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters with a median bias of within 5% of gait cycle time. Statistical analysis revealed the robustness of the algorithm across most walking speeds.