Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.

Razfar, Najmeh; Kashef, Rasha; Mohammadi, Farah

Razfar, Najmeh; Kashef, Rasha; Mohammadi, Farah.

Afiliação

Razfar N; Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Kashef R; Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Mohammadi F; Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel) ; 23(12)2023 Jun 12.

Article em En | MEDLINE | ID: mdl-37420682

Assuntos

Reabilitação do Acidente Vascular Cerebral; Acidente Vascular Cerebral; Dispositivos Eletrônicos Vestíveis; Humanos; Consenso; Qualidade de Vida; Acidente Vascular Cerebral/diagnóstico; Movimento; Reabilitação do Acidente Vascular Cerebral/métodos

Palavras-chave

Wearable Sensor (Xsens); automated assessment; camera-based system; clustering; consensus clustering; level of severity; stroke; trunk displacement

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Acidente Vascular Cerebral / Reabilitação do Acidente Vascular Cerebral / Dispositivos Eletrônicos Vestíveis Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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