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 intelli...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | PPAR Research |
| Online Access: | http://dx.doi.org/10.1155/2022/9355015 |
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| _version_ | 1832551092983955456 |
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| author | Chandrasen Pandey Diptendu Sinha Roy Ramesh Chandra Poonia Ayman Altameem Soumya Ranjan Nayak Amit Verma Abdul Khader Jilani Saudagar |
| author_facet | Chandrasen Pandey Diptendu Sinha Roy Ramesh Chandra Poonia Ayman Altameem Soumya Ranjan Nayak Amit Verma Abdul Khader Jilani Saudagar |
| author_sort | Chandrasen Pandey |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0f9fa9ae327741e4a753b410ed9158ea |
| institution | Kabale University |
| issn | 1687-4765 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | PPAR Research |
| spelling | doaj-art-0f9fa9ae327741e4a753b410ed9158ea2025-02-03T06:04:53ZengWileyPPAR Research1687-47652022-01-01202210.1155/2022/9355015GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction ForceChandrasen Pandey0Diptendu Sinha Roy1Ramesh Chandra Poonia2Ayman Altameem3Soumya Ranjan Nayak4Amit Verma5Abdul Khader Jilani Saudagar6National Institute of TechnologyNational Institute of TechnologyDepartment of Computer ScienceDepartment of Computer Science and EngineeringAmity School of Engineering and TechnologyDepartment of Computer Science & Engineering and University Centre for Research & DevelopmentInformation Systems DepartmentWalking (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.http://dx.doi.org/10.1155/2022/9355015 |
| spellingShingle | Chandrasen Pandey Diptendu Sinha Roy Ramesh Chandra Poonia Ayman Altameem Soumya Ranjan Nayak Amit Verma Abdul Khader Jilani Saudagar GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force PPAR Research |
| title | GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force |
| title_full | GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force |
| title_fullStr | GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force |
| title_full_unstemmed | GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force |
| title_short | GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force |
| title_sort | gaitrec net a deep neural network for gait disorder detection using ground reaction force |
| url | http://dx.doi.org/10.1155/2022/9355015 |
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