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

Siddharth Arora, Vinayak Venkataraman, Sean Donohue, Kevin M. Biglan, Earl R. Dorsey, Max A. Little

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    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.

    Original languageEnglish
    Title of host publication2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    PublisherIEEE
    Pages3641-3644
    Number of pages4
    ISBN (Print)978-1-4799-2892-7
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing - Florence, Italy
    Duration: 4 May 20149 May 2014

    Conference

    Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing
    Abbreviated titleICASSP 2014
    Country/TerritoryItaly
    CityFlorence
    Period4/05/149/05/14

    Bibliographical note

    © 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.

    Keywords

    • Gait
    • Parkinson's disease
    • Postural sway
    • Random forest
    • Smartphones
    • Tri-axial acceleration

    Fingerprint

    Dive into the research topics of 'High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones'. Together they form a unique fingerprint.

    Cite this