期刊
MEASUREMENT
卷 161, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2020.107896
关键词
Automated system; Motor function assessment; Stroke; Kinect V2; Feed-forward neural network
资金
- Cooperation Program of Fujian Key Laboratory of Rehabilitation Technology and Fujian Provincial Rehabilitation Industrial Institution [2015Y200165]
- KAIST College of Engineering Global Initiative Convergence Research Program
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). (C) 2020 Elsevier Ltd. All rights reserved.
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