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Skeleton-Based Abnormal Gait Detection
Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134451/ https://www.ncbi.nlm.nih.gov/pubmed/27792181 http://dx.doi.org/10.3390/s16111792 |
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author | Nguyen, Trong-Nguyen Huynh, Huu-Hung Meunier, Jean |
author_facet | Nguyen, Trong-Nguyen Huynh, Huu-Hung Meunier, Jean |
author_sort | Nguyen, Trong-Nguyen |
collection | PubMed |
description | Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%. |
format | Online Article Text |
id | pubmed-5134451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-51344512017-01-03 Skeleton-Based Abnormal Gait Detection Nguyen, Trong-Nguyen Huynh, Huu-Hung Meunier, Jean Sensors (Basel) Article Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%. MDPI 2016-10-26 /pmc/articles/PMC5134451/ /pubmed/27792181 http://dx.doi.org/10.3390/s16111792 Text en © 2016 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 Nguyen, Trong-Nguyen Huynh, Huu-Hung Meunier, Jean Skeleton-Based Abnormal Gait Detection |
title | Skeleton-Based Abnormal Gait Detection |
title_full | Skeleton-Based Abnormal Gait Detection |
title_fullStr | Skeleton-Based Abnormal Gait Detection |
title_full_unstemmed | Skeleton-Based Abnormal Gait Detection |
title_short | Skeleton-Based Abnormal Gait Detection |
title_sort | skeleton-based abnormal gait detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134451/ https://www.ncbi.nlm.nih.gov/pubmed/27792181 http://dx.doi.org/10.3390/s16111792 |
work_keys_str_mv | AT nguyentrongnguyen skeletonbasedabnormalgaitdetection AT huynhhuuhung skeletonbasedabnormalgaitdetection AT meunierjean skeletonbasedabnormalgaitdetection |