Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications.
Occupational therapists evaluate various aspects of a client's occupational performance. Among these, postural control is one of the fundamental skills that need assessment. Recently, several methods have been proposed to estimate postural control abilities using deep-learning-based approaches. Such...
| Publicado en: | Occupational Therapy International Vol. 2022; pp. 1 - 10 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/30/2022
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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=160634229&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160634229 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09667903 GPF jtl: Occupational Therapy International issn: 09667903 maglogo: Y pubinfo: dt: 11/30/2022 vid: 2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 160634229 160634229 160634229 10.1155/2022/6952999 160634229 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications. aug: au: Ienaga, Naoto Takahata, Shuhei Terayama, Kei Enomoto, Daiki Ishihara, Hiroyuki Noda, Haruka Hagihara, Hiromichi affil: Faculty of Engineering, Information and Systems, University of Tsukuba, Japan sug: subj: Balance, Postural Evaluation Deep Learning Physical Performance Human Male Female Quantitative Studies Comparative Studies Conceptual Framework Occupational Therapy Practice Child, Preschool Child Occupational Therapists Funding Source Child, Preschool: 2-5 years Child: 6-12 years Male Female ab: Occupational therapists evaluate various aspects of a client's occupational performance. Among these, postural control is one of the fundamental skills that need assessment. Recently, several methods have been proposed to estimate postural control abilities using deep-learning-based approaches. Such techniques allow for the potential to provide automated, precise, fine-grained quantitative indices simply by evaluating videos of a client engaging in a postural control task. However, the clinical applicability of these assessment tools requires further investigation. In the current study, we compared three deep-learning-based pose estimators to assess their clinical applicability in terms of accuracy of pose estimations and processing speed. In addition, we verified which of the proposed quantitative indices for postural controls best reflected the clinical evaluations of occupational therapists. A framework using deep-learning techniques broadens the possibility of quantifying clients' postural control in a more fine-grained way compared with conventional coarse indices, which can lead to improved occupational therapy practice. 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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