Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network
Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/13/8/194 |
| _version_ | 1827685155723018240 |
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| author | Ibsa K. Jalata Thanh-Dat Truong Jessica L. Allen Han-Seok Seo Khoa Luu |
| author_facet | Ibsa K. Jalata Thanh-Dat Truong Jessica L. Allen Han-Seok Seo Khoa Luu |
| author_sort | Ibsa K. Jalata |
| collection | DOAJ |
| description | Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems. |
| first_indexed | 2024-03-10T08:47:51Z |
| format | Article |
| id | doaj.art-81412f99c26a4b1d99ee2a9e4af7e8ee |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T08:47:51Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-81412f99c26a4b1d99ee2a9e4af7e8ee2023-11-22T07:44:12ZengMDPI AGFuture Internet1999-59032021-07-0113819410.3390/fi13080194Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural NetworkIbsa K. Jalata0Thanh-Dat Truong1Jessica L. Allen2Han-Seok Seo3Khoa Luu4Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USAComputer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USADepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USADepartment of Food Science, University of Arkansas, Fayetteville, AR 72701, USAComputer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USAUsing optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems.https://www.mdpi.com/1999-5903/13/8/194cerebral palsygraph convolutional neural networkdeep learning1D-CNNgait parameters |
| spellingShingle | Ibsa K. Jalata Thanh-Dat Truong Jessica L. Allen Han-Seok Seo Khoa Luu Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network Future Internet cerebral palsy graph convolutional neural network deep learning 1D-CNN gait parameters |
| title | Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network |
| title_full | Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network |
| title_fullStr | Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network |
| title_full_unstemmed | Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network |
| title_short | Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network |
| title_sort | movement analysis for neurological and musculoskeletal disorders using graph convolutional neural network |
| topic | cerebral palsy graph convolutional neural network deep learning 1D-CNN gait parameters |
| url | https://www.mdpi.com/1999-5903/13/8/194 |
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