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

doi:10.1109/ICASSP.2014.6854280

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

Duke University
Johns Hopkins University
University of Rochester
University of Oxford
Massachusetts Institute of Technology

Research output: Chapter in Book/Published conference output › Conference publication

65 Citations (Scopus)

229 Downloads (Pure)

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 language	English
Title of host publication	2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	3641-3644
Number of pages	4
ISBN (Print)	978-1-4799-2892-7
DOIs	https://doi.org/10.1109/ICASSP.2014.6854280
Publication status	Published - 2014
Event	2014 IEEE International Conference on Acoustics, Speech, and Signal Processing - Florence, Italy Duration: 4 May 2014 → 9 May 2014

Conference

Conference	2014 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2014
Country/Territory	Italy
City	Florence
Period	4/05/14 → 9/05/14

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

Access to Document

10.1109/ICASSP.2014.6854280

High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones
© 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.
Submitted manuscript, 467 KB

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6854280

Cite this

@inproceedings{ee53f089e3cd4ef19d7d64f8adfe4b0d,

title = "High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones",

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.",

keywords = "Gait, Parkinson's disease, Postural sway, Random forest, Smartphones, Tri-axial acceleration",

author = "Siddharth Arora and Vinayak Venkataraman and Sean Donohue and Biglan, \{Kevin M.\} and Dorsey, \{Earl R.\} and Little, \{Max A.\}",

note = "{\textcopyright} 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.; 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",

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doi = "10.1109/ICASSP.2014.6854280",

language = "English",

isbn = "978-1-4799-2892-7",

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booktitle = "2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

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address = "United States",

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Arora, S, Venkataraman, V, Donohue, S, Biglan, KM, Dorsey, ER & Little, MA 2014, High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 3641-3644, 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, Florence, Italy, 4/05/14. https://doi.org/10.1109/ICASSP.2014.6854280

High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. / Arora, Siddharth; Venkataraman, Vinayak; Donohue, Sean et al.
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. p. 3641-3644.

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Biglan, Kevin M.

AU - Dorsey, Earl R.

AU - Little, Max A.

N1 - © 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.

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N2 - 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.

AB - 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.

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

Abstract

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Bibliographical note

Keywords

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