A robust algorithm for gait cycle segmentation

Abstract

In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating the missing peaks. The variance of segmented cycles' lengths is computed as a criterion for evaluating the performance of segmentation. The proposed algorithm is tested on gait signals of patients diagnosed with Parkinson's disease collected from three databases. The segmentation results on three types of gait signals demonstrate the capability of the proposed algorithm to segment gait cycles accurately, and have achieved better performance than the original peak detection methods.