title: A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks.
abstract: To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset. Considering the FMA scale is time-consuming and complicated, seven training exercises were designed to replace the upper limb related 33 items in FMA scale. 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital. Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data, the coefficient of determination can reach as high as 0.917. It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation.
authors.affiliation: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China. Electronic address: yul@sibet.ac.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Yu
authors.affiliation: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China.
authors.forename: Daxi
authors.initials: D
authors.lastname: Xiong
authors.affiliation: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China.
authors.forename: Liquan
authors.initials: L
authors.lastname: Guo
authors.affiliation: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China.
authors.forename: Jiping
authors.initials: J
authors.lastname: Wang
mesh_terms: D000054:Acceleration; D000368:Aged; D000465:Algorithms; D003020:Clothing; D004867:Equipment Design; D015444:Exercise; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009068:Movement; D020127:Recovery of Function; D015203:Reproducibility of Results; D018570:Risk Assessment; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000000981:diagnostic imaging* / Q000503:physiopathology; D034941:Upper Extremity / Q000000981:diagnostic imaging / Q000473:pathology
keywords: Fugl-Meyer; Non-clinical settings; Quantitative assessment; Stroke; Upper limb motor function; Wearable sensor networks