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Content Provider | IEEE Xplore Digital Library |
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Author | Parnandi, A. Wade, E. Matarić, M. |
Copyright Year | 2010 |
Description | Author affiliation: University of Southern California, Los Angeles, 90089, USA (Matarić, M.) || Department of Electrical Engineering, University of Southern California, Los Angeles, 90089, USA (Parnandi, A.) || Department of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, 90089, USA (Wade, E.) |
Abstract | We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach. |
Starting Page | 86 |
Ending Page | 89 |
File Size | 1449735 |
Page Count | 4 |
File Format | |
ISBN | 9781424441235 |
ISSN | 1557170X |
DOI | 10.1109/IEMBS.2010.5626156 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-08-31 |
Publisher Place | Argentina |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Extremities Estimation Robots Sensors Feature extraction Accelerometers Cutoff frequency |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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