High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones


The aim of this study is to accurately distinguish Parkinson's disease (PD) participants from healthy controls using self-administered tests of gait and postural sway. Using consumer-grade smartphones with in-built accelerometers, we objectively measure and quantify key movement severity symptoms of Parkinson's disease. Specifically, we record tri-axial accelerations, and extract a range of different features based on the time and frequency-domain properties of the acceleration time series. The features quantify key characteristics of the acceleration time series, and enhance the underlying differences in the gait and postural sway accelerations between PD participants and controls. Using a random forest classifier, we demonstrate an average sensitivity of 98.5% and average specificity of 97.5% in discriminating PD participants from controls.

Publication DOI: https://doi.org/10.1109/ICASSP.2014.6854280
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing
Event Type: Other
Event Dates: 2014-05-04 - 2014-05-09
Uncontrolled Keywords: Gait,Parkinson's disease,Postural sway,Random forest,Smartphones,Tri-axial acceleration,Signal Processing,Software,Electrical and Electronic Engineering
ISBN: 978-1-4799-2892-7
Last Modified: 14 May 2024 07:32
Date Deposited: 13 May 2015 15:00
Full Text Link: http://ieeexplo ... rnumber=6854280
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2014
Authors: Arora, Siddharth
Venkataraman, Vinayak
Donohue, Sean
Biglan, Kevin M.
Dorsey, Earl R.
Little, Max A. (ORCID Profile 0000-0002-1507-3822)



Version: Draft Version

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