A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.
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...
| Publicado en: | Journal of Medical Systems Vol. 46; no. 11; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159925823&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159925823 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2022 vid: 46 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159925823 159925823 159925823 10.1007/s10916-022-01857-5 159925823 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. aug: au: Zhu, Manli Men, Qianhui Ho, Edmond S. L. Leung, Howard Shum, Hubert P. H. affil: Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK sug: subj: Musculoskeletal Diseases Diagnosis Nervous System Diseases Diagnosis Prediction Models Deep Learning Neural Networks (Computer) Human Machine Learning Motion Analysis Systems Funding Source ROC Curve Random Sample Male Female Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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