An automated system for motor function assessment in stroke patients using motion sensing technology: A pilot study

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This study aims to develop and evaluate an automated system for upper-limb motor function assessment of stroke patients. The proposed system contains one motion tracking subsystem (to measure the kinematic data of participants through one Kinect V2) and one motor function assessment subsystem (to realize the automated assessment based on a feed-forward neural network (FFNN)-based assessment model). For validation, 16 stroke patients and 10 healthy subjects were recruited to perform 4 WMFT-FAS tasks, and 5 evaluation metrics were used. The experimental results showed that the proposed system could present satisfactory performance (accuracy: 0.87-0.96, F1-score: 0.83-0.93, specificity: 0.94-0.98, sensitivity: 0.87-0.95, and AUC: 0.93-1.00), and the FFNN-based assessment model could also present promising comprehensive performance (top two in all tasks in terms of accuracy and F1-score).
Publisher
ELSEVIER SCI LTD
Issue Date
2020-09
Language
English
Article Type
Article
Citation

MEASUREMENT, v.161

ISSN
0263-2241
DOI
10.1016/j.measurement.2020.107896
URI
http://hdl.handle.net/10203/274694
Appears in Collection
IE-Journal Papers(저널논문)
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