NDLI: Correlations between statistical models of robotically collected kinematics and clinical measures of upper extremity function

Content Provider	IEEE Xplore Digital Library
Author	Rohafza, M. Fluet, G.G. Qinyin Qiu Adamovich, S.
Copyright Year	2012
Description	Author affiliation: Univ. of Med. & Dentistry of New Jersey, Newark, NJ, USA (Fluet, G.G.) \|\| New Jersey Inst. of Technol., Newark, NJ, USA (Rohafza, M.; Qinyin Qiu; Adamovich, S.)
Abstract	One of the obstacles in the development of rehabilitation robotics has been inadequacy in the measurement of treatment effects due to interventions. A measurement tool that will efficiently produce a large reliable sample of measurements collected during a single session that can also produce a rich set of data which reflects a subject's ability to perform meaningful functional activities has not been developed. This paper presents three linear regression models generated from seven kinematic measures collected during the performance of virtually simulated rehabilitation activities that were integrated with haptic robots by 19 persons with upper extremity hemiparesis due to chronic stroke. One of these models demonstrated a statistically significant correlation with the subjects' scores on the Jebsen Test of Hand Function (JTHF), a battery of six standardized upper extremity functional activities. The second and third models demonstrated a statistically significant correlation with the subjects' change scores on the JTHF.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	4120
Ending Page	4123
File Size	671513
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kinematics Robots Predictive models Correlation Thumb Training
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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