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

This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time se...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 23; číslo 8; s. 4000
Hlavní autoři: Jung, Sylvain, de l’Escalopier, Nicolas, Oudre, Laurent, Truong, Charles, Dorveaux, Eric, Gorintin, Louis, Ricard, Damien
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 14.04.2023
MDPI
Témata:
Algorithms
change point detection
Data mining
Falls
Feedback
free-living
Gait
Gait Analysis
graphical feedback
Health care
Human Activity Recognition
Human acts
Human behavior
Humans
IMU
Life Sciences
Locomotion
Longitudinal studies
Machine Learning
Pathology
Pipe lines
Signal processing
Visualization (Computers)
Wearable Electronic Devices
wearable sensor
France
IMU
change point detection
wearable sensor
Human Activity Recognition
free-living
graphical feedback
C
Ricard
E
Jung
L
L. Truong
S. de l'Escalopier
E. Gorintin
D. A Machine free-living wearable sensor IMU graphical feedback change point detection Human Activity Recognition
N
S
L. Ricard
C. Dorveaux
de l'Escalopier
Oudre
Gorintin
Truong
D. A Machine free-living
N. Oudre
Dorveaux
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí:This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23084000