Using Wearable Sensors to Predict the Severity of Symptoms and Motor Complications in Late Stage Parkinson's Disease

被引：14

作者：

Patel, Shyamal ^{[1
]}

Hughes, Richard ^{[1
]}

Huggins, Nancy ^{[2
]}

Standaert, David

Growdon, John ^{[2
]}

Dy, Jennifer

Bonato, Paolo ^{[1
]}

机构：

[1] Harvard Univ, Sch Med, Dept Phys Med & Rehabil, Spaulding Rehabil Hosp, Boston, MA 02114 USA

[2] Massachusetts Gen Hosp, Dept Neurol Harvard Med Sch, Boston, MA 02114 USA

来源：

2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8 | 2008年

基金：

美国国家卫生研究院;

关键词：

D O I：

10.1109/IEMBS.2008.4650009

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：3686 / +

页数：2

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