Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors
Name
Patel-2009-Monitoring Motor Flu.pdf
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Author(s)
Patel, Shyamal
Lorincz, Konrad
Hughes, Richard
Huggins, Nancy
Growdon, John H.
Standaert, David
Akay, Metin
Dy, Jennifer G.
Welsh, Matt
Bonato, Paolo
Date Issued
November 2009
Journal
IEEE Transactions on Information Technology in Biomedicine
Publisher
Institute of Electrical and Electronics Engineers
Citation
Patel, S. et al. “Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors.” Information Technology in Biomedicine, IEEE Transactions on 13.6 (2009): 864-873. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.
Subjects
wearable sensors
support vector machines (SVMs)
Parkinson's disease
Body sensor networks
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1109/titb.2009.2033471
