Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

Ibsa K. Jalata, Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo and Khoa Luu
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Ibsa K. Jalata: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Thanh-Dat Truong: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Jessica L. Allen: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
Han-Seok Seo: Department of Food Science, University of Arkansas, Fayetteville, AR 72701, USA
Khoa Luu: Computer Vision and Image Understanding Lab, Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA

Future Internet, 2021, vol. 13, issue 8, 1-14

Abstract: 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.

Keywords: cerebral palsy; graph convolutional neural network; deep learning; 1D-CNN; gait parameters (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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