GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force
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. 2022 Chandrasen Pandey et al.
Pandey C.; Roy D.S.; Poonia R.C.; Altameem A.; Nayak S.R.; Verma A.; Saudagar A.K.J.
All Open Access; Gold Open Access; Green Open Access
Pandey C., National Institute of Technology, Meghalaya, India; Roy D.S., National Institute of Technology, Meghalaya, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Hosur Road, Karnataka, Bangalore, India; Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Nayak S.R., Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India; Verma A., Department of Computer Science & Engineering, University Centre for Research & Development, Chandigarh University, Punjab, Mohali, 140413, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia