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Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System

Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, curr...

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Autores principales: Verlekar, Tanmay T., Soares, Luís D., Correia, Paulo L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165287/
https://www.ncbi.nlm.nih.gov/pubmed/30134527
http://dx.doi.org/10.3390/s18092743
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author Verlekar, Tanmay T.
Soares, Luís D.
Correia, Paulo L.
author_facet Verlekar, Tanmay T.
Soares, Luís D.
Correia, Paulo L.
author_sort Verlekar, Tanmay T.
collection PubMed
description Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.
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spelling pubmed-61652872018-10-10 Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System Verlekar, Tanmay T. Soares, Luís D. Correia, Paulo L. Sensors (Basel) Article Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%. MDPI 2018-08-21 /pmc/articles/PMC6165287/ /pubmed/30134527 http://dx.doi.org/10.3390/s18092743 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Verlekar, Tanmay T.
Soares, Luís D.
Correia, Paulo L.
Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title_full Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title_fullStr Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title_full_unstemmed Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title_short Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
title_sort automatic classification of gait impairments using a markerless 2d video-based system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165287/
https://www.ncbi.nlm.nih.gov/pubmed/30134527
http://dx.doi.org/10.3390/s18092743
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