Adaptive Change-Point Detection for Studying Human Locomotion

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a refined analysis of the successive phases within a gait protocol. Thanks to a training procedure, the algorithm can be applied to a wide range of protocols and handles different levels of granularity. The method is tested on a cohort of 15 healthy subjects performing a complex protocol composed of different activities and shows promising results for the automated and adaptive study of human gait and activity.Clinical relevance– A new approach to study human activity and locomotion in Free-Living Environments FLEs through an adaptive change-point detection which isolates homogeneous phases.