Gait Abnormality Detection Using Deep Convolution Network

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Author(s): Saikat Chakraborty (National Institute of Technology, Rourkela, India), Tomoya Suzuki (Tokyo University of Agriculture and Technology, Japan), Abhipsha Das (International Institute of Information Technology, Bhubaneswar, India), Anup Nandy (National Institute of Technology, Rourkela, India)and Gentiane Venture (Tokyo University of Agriculture and Technology, Japan)
Copyright: 2021
Pages: 10
Source title: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
Source Author(s)/Editor(s): Bhushan Patil (Independent Researcher, India)and Manisha Vohra (Independent Researcher, India)
DOI: 10.4018/978-1-7998-3053-5.ch017

Keywords: Data Analysis & Statistics / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.

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Gait Abnormality Detection Using Deep Convolution Network

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