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Gait Abnormality Detection Using Deep Convolution Network

Gait Abnormality Detection Using Deep Convolution Network

Saikat Chakraborty, Tomoya Suzuki, Abhipsha Das, Anup Nandy, Gentiane Venture
Copyright: © 2021 |Pages: 10
ISBN13: 9781799830535|ISBN10: 1799830535|EISBN13: 9781799830542
DOI: 10.4018/978-1-7998-3053-5.ch017
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MLA

Chakraborty, Saikat, et al. "Gait Abnormality Detection Using Deep Convolution Network." Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, edited by Bhushan Patil and Manisha Vohra, IGI Global, 2021, pp. 363-372. https://doi.org/10.4018/978-1-7998-3053-5.ch017

APA

Chakraborty, S., Suzuki, T., Das, A., Nandy, A., & Venture, G. (2021). Gait Abnormality Detection Using Deep Convolution Network. In B. Patil & M. Vohra (Eds.), Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 363-372). IGI Global. https://doi.org/10.4018/978-1-7998-3053-5.ch017

Chicago

Chakraborty, Saikat, et al. "Gait Abnormality Detection Using Deep Convolution Network." In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics, edited by Bhushan Patil and Manisha Vohra, 363-372. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3053-5.ch017

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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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