Computer Vision and Abnormal Patient Gait: A Comparison of Methods

Jasmin Hundal,Benson A Babu

doi:10.2174/1874061802006010029

Jasmin Hundal, Benson A Babu

Open Access

https://doi.org/10.2174/1874061802006010029

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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.

Highlights

Abnormalities with patient gait fall and associated complications have high morbidity and mortality [1]
The findings show the efficiency of combining gait and Gait Energy Image (GEI) approach for individual recognition, and the competitiveness of its performance [58, 59]
Search Criteria The systematic review aimed at reviewing published papers, as well as academic journals, in a step-by-step manner

Summary

Introduction

Abnormalities with patient gait fall and associated complications have high morbidity and mortality [1]. Preventable complications include hip fractures, medical deconditioning, myocardial infarction, and pulmonary emboli. These complications are devastating in the elderly population [2]. Computer vision assesses fall risk, provides physicians with an opportunity to outline an early treatment plan, limiting any morbidity and mortality. Computer vision is used to assess gait in disorders like dementia, depression, intellectual disability, musculoskeletal disorders, and stroke [4 - 7]. These conditions are managed in the fields of neurology, physical medicine rehabilitation, rheumatology, and orthopaedics [8]. Computer vision assesses postural abnormalities; its parameters’ provide strength and an endurance plan for patients during their treatment course [9]

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Conclusion