Accelerometry and the Capacity–Performance Gap: Case Series Report in Upper-Extremity Motor Impairment Assessment Post-Stroke

This case series investigates whether traditional machine learning (ML) and convolutional neural network (CNN) models trained on wrist-worn accelerometry data collected in a laboratory setting can accurately predict real-world functional hand use in individuals with chronic stroke. Participants (N =...

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Библиографические подробности

Главные авторы:	Estevan M. Nieto, Edaena Lujan, Crystal A. Mendoza, Yazbel Arriaga, Cecilia Fierro, Tan Tran, Lin-Ching Chang, Alvaro N. Gurovich, Peter S. Lum, Shashwati Geed
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2025-06-01
Серии:	Bioengineering
Предметы:	stroke neurorehabilitation accelerometry motor recovery stroke rehabilitation trials motor outcomes
Online-ссылка:	https://www.mdpi.com/2306-5354/12/6/615
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