Predicting Parkinson's Disease Progression with Smartphone Data

Pramod Anantharam; Krishnaprasad Thirunarayan; Vahid Taslimi; Amit P. Sheth

Predicting Parkinson's Disease Progression with Smartphone Data

Pramod Anantharam, Krishnaprasad Thirunarayan, Vahid Taslimi, Amit P. Sheth

Wright State University

Research output: Working paper › Preprint

Abstract

Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.

Original language	American English
State	Published - Mar 1 2013

Keywords

Activity Monitoring
Health and Well-Being
Sensor Data Analytics
Smart Phones

Disciplines

Bioinformatics
Communication
Communication Technology and New Media
Computer Sciences
Databases and Information Systems
Life Sciences
OS and Networks
Physical Sciences and Mathematics
Science and Technology Studies
Social and Behavioral Sciences

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Predicting Parkinsons Disease Progression with Smartphone DataFinal published version, 555 KBLicense: Unspecified

Cite this

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AU - Sheth, Amit P.

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N2 - Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.

AB - Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.

KW - Activity Monitoring

KW - Health and Well-Being

KW - Sensor Data Analytics

KW - Smart Phones

UR - https://corescholar.libraries.wright.edu/knoesis/569

M3 - Preprint

BT - Predicting Parkinson's Disease Progression with Smartphone Data

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Predicting Parkinson's Disease Progression with Smartphone Data

Abstract

Keywords

Disciplines

Access to Document

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