NDLI: Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease

Content Provider	IEEE Xplore Digital Library
Author	Patel, Shyamal Hughes, Richard Huggins, Nancy Standaert, David Growdon, John Dy, Jennifer Bonato, Paolo
Copyright Year	2008
Description	Author affiliation: Department of Neurology, Harvard Medical School, Massachusetts General Hospital. Boston, 02114 USA (Huggins, Nancy; Growdon, John) \|\| Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston MA 02114 USA (Patel, Shyamal; Hughes, Richard; Bonato, Paolo)
Abstract	This paper is focused on the analysis of data obtained from wearable sensors in patients with Parkinson's Disease. We implemented Support Vector Machines (SVM's) to predict clinical scores of the severity of Parkinsonian symptoms and motor complications. We determined the optimal window length to extract features from the sensor data. Furthermore, we performed tests to determine optimal parameters for the SVM's. Finally, we analyzed how well individual tasks performed by patients captured the severity of various symptoms and motor complications.
Starting Page	3686
Ending Page	3689
File Size	215504
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650009
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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