The Open Artificial Intelligence Journal




(Discontinued)

ISSN: ― Volume ,
REVIEW ARTICLE

Computer Vision and Abnormal Patient Gait: A Comparison of Methods



Jasmin Hundal1, Benson A. Babu2, *
1 Department of Internal Medicine, University of Connecticut, Hartford, CT, USA
2 Department of Internal Medicine, Hospital Medicine, Plainview Medicine Centre, Northwell Health, Plainview, NY, USA

Abstract

Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential-prediction-framework-based approaches, convolutional pose machines, gait energy image, 2-Directional 2-dimensional principles component analysis ((2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI), SVM, ANN, K-Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.

Keywords: Gait analysis, Computer vision, Machine learning, Gait energy image, Gait abnormalities, Low-cost sensors.


Article Information


Identifiers and Pagination:

Year: 2020
Volume: 6
First Page: 29
Last Page: 34
Publisher Id: TOAIJ-6-29
DOI: 10.2174/1874061802006010029

Article History:

Received Date: 16/03/2020
Revision Received Date: 29/07/2020
Acceptance Date: 31/07/2020
Electronic publication date: 20/10/2020
Collection year: 2020

© 2020 Hundal and Babu.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Internal Medicine, Hospital Medicine, Plainview Medicine Centre, Northwell Health, Plainview, NY, USA; Tel: 516-491-3713; E-mail: bensonbabumd@gmail.com



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