Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

David C. Wong; Samuel D. Relton; Hui Fang; Rami Qhawaji; Christopher D. Graham; Jane Alty; Stefan Williams

doi:10.1109/CBMS.2019.00017

Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

David C. Wong, Samuel D. Relton, Hui Fang, Rami Qhawaji, Christopher D. Graham, Jane Alty, Stefan Williams

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

18 Citations (Scopus)

20 Downloads (Pure)

Abstract

Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.

Original language	English
Title of host publication	2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)
Place of Publication	Piscataway, NJ
Pages	32-37
Number of pages	6
ISBN (Electronic)	9781728122861
DOIs	https://doi.org/10.1109/CBMS.2019.00017
Publication status	Published - 5 Aug 2019
Event	32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain Duration: 5 Jun 2019 → 7 Jun 2019

Conference

Conference	32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
Country/Territory	Spain
City	Cordoba
Period	5/06/19 → 7/06/19

Keywords

bradykinesia
classification
computer vision
diagnosis
Parkinson's
support vector machine
video

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10.1109/CBMS.2019.00017

Wong-etal-CBMS2019-Supervised-classification-of-bradykinesia-for-Parkinsons-disease-diagnosisAccepted author manuscript, 666 KBLicence: Strath_1

Cite this

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title = "Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos",

abstract = "Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.",

keywords = "bradykinesia, classification, computer vision, diagnosis, Parkinson's, support vector machine, video",

author = "Wong, {David C.} and Relton, {Samuel D.} and Hui Fang and Rami Qhawaji and Graham, {Christopher D.} and Jane Alty and Stefan Williams",

note = "{\textcopyright} 2019 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.; 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 ; Conference date: 05-06-2019 Through 07-06-2019",

year = "2019",

month = aug,

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doi = "10.1109/CBMS.2019.00017",

language = "English",

pages = "32--37",

booktitle = "2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)",

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Wong, DC, Relton, SD, Fang, H, Qhawaji, R, Graham, CD, Alty, J & Williams, S 2019, Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos. in 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)., 8787472, Piscataway, NJ, pp. 32-37, 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019, Cordoba, Spain, 5/06/19. https://doi.org/10.1109/CBMS.2019.00017

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T1 - Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

AU - Wong, David C.

AU - Relton, Samuel D.

AU - Fang, Hui

AU - Qhawaji, Rami

AU - Graham, Christopher D.

AU - Alty, Jane

AU - Williams, Stefan

N1 - © 2019 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.

PY - 2019/8/5

Y1 - 2019/8/5

N2 - Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.

AB - Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.

KW - bradykinesia

KW - classification

KW - computer vision

KW - diagnosis

KW - Parkinson's

KW - support vector machine

KW - video

UR - https://repository.lboro.ac.uk/articles/conference_contribution/Supervised_classification_of_bradykinesia_for_Parkinson_s_disease_diagnosis_from_smartphone_videos/9401510

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DO - 10.1109/CBMS.2019.00017

M3 - Conference contribution book

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BT - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

CY - Piscataway, NJ

T2 - 32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019

Y2 - 5 June 2019 through 7 June 2019

ER -

Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

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