GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.

Autor: Pandey C; National Institute of Technology, Meghalaya, India., Roy DS; National Institute of Technology, Meghalaya, India., Poonia RC; Department of Computer Science, CHRIST (Deemed to be University), Hosur Road, Bangalore, Karnataka, India., Altameem A; Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh 11533, Saudi Arabia., Nayak SR; Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India., Verma A; Department of Computer Science & Engineering and University Centre for Research & Development, Chandigarh University, Mohali, 140413 Punjab, India., Saudagar AKJ; Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Jazyk: angličtina
Zdroj: PPAR research [PPAR Res] 2022 Aug 22; Vol. 2022, pp. 9355015. Date of Electronic Publication: 2022 Aug 22 (Print Publication: 2022).
DOI: 10.1155/2022/9355015
Abstrakt: Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability. In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait. This article presents an artificial intelligence-based comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force. The used dataset comprised GRF measurements from different patients. The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. A deep learning-based architecture GaitRec-Net is proposed for this classification. The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach. Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification. As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 Chandrasen Pandey et al.)
Databáze: MEDLINE
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