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 p...

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Bibliographic Details
Published in2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 2020 - 2024
Main Authors Jung, Sylvain, Oudre, Laurent, Truong, Charles, Dorveaux, Eric, Gorintin, Louis, Vayatis, Nicolas, Ricard, Damien
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.11.2021
Subjects
Algorithms
Biology
Gait
Healthy Volunteers
Humans
Locomotion
Optimization
Pathology
Protocols
Stability analysis
Training
Visualization
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Summary: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.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9629775