Non-Linear Template-Based Approach for the Study of Locomotion

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2020 Apr 3

PMID 32235667

Citations 9

Authors

Tristan Dot

Flavien Quijoux

Laurent Oudre

Alienor Vienne-Jumeau

Albane Moreau

Pierre-Paul Vidal

Damien Ricard

Affiliations

Soon will be listed here.

Abstract

The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.

Citing Articles

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

Voisard C, De LEscalopier N, Ricard D, Oudre L J Neuroeng Rehabil. 2024; 21(1):104.

PMID: 38890696 PMC: 11184826. DOI: 10.1186/s12984-024-01405-x.

A method for selecting the optimal warping path of dynamic time warping in gait analysis.

Lee H, Lee J, Kim K J Exerc Rehabil. 2024; 20(1):42-48.

PMID: 38433858 PMC: 10902693. DOI: 10.12965/jer.2346580.290.

A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.

Jung S, De LEscalopier N, Oudre L, Truong C, Dorveaux E, Gorintin L Sensors (Basel). 2023; 23(8).

PMID: 37112339 PMC: 10145775. DOI: 10.3390/s23084000.

Normalization and possibility of classification analysis using the optimal warping paths of dynamic time warping in gait analysis.

Lee H J Exerc Rehabil. 2023; 19(1):85-91.

PMID: 36910677 PMC: 9993011. DOI: 10.12965/jer.2244590.295.

A topological data analysis-based method for gait signals with an application to the study of multiple sclerosis.

Bois A, Tervil B, Moreau A, Vienne-Jumeau A, Ricard D, Oudre L PLoS One. 2022; 17(5):e0268475.

PMID: 35560328 PMC: 9106173. DOI: 10.1371/journal.pone.0268475.

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