A two-stream convolutional network for musculoskeletal and neurological disorders prediction

Zhu, M., Men, Q., Ho, E. S.L., Leung, H. and Shum, H. P.H. (2022) A two-stream convolutional network for musculoskeletal and neurological disorders prediction. Journal of Medical Systems, 46(11), 76. (doi: 10.1007/s10916-022-01857-5) (PMID:36201114) (PMCID:PMC9537228)

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Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

Item Type:Articles
Additional Information:This work was supported in part by the Royal Society (Ref: IESnR1n191147).
Glasgow Author(s) Enlighten ID:Ho, Dr Edmond S. L
Authors: Zhu, M., Men, Q., Ho, E. S.L., Leung, H., and Shum, H. P.H.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal of Medical Systems
ISSN (Online):1573-689X
Published Online:06 October 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of Medical Systems 46(11): 76
Publisher Policy:Reproduced under a Creative Commons License
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