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Video-based activity recognition for automated motor assessment of Parkinson’s disease
File(s)
679785.pdf (3.71 MB)
Preprint version
OA Location
Author(s)
Sarapata, Grzegorz
Morinan, Gareth
Dushin, Yuriy
Ong, Joshua
Kainz, Bernhard
Type
preprint
Abstract
Over the last decade, video-enabled mobile devices have become almost ubiquitous, while advances in markerless pose estimation allow an individual's body position to be tracked across the frames of a video. Previous work by this and other groups has shown that pose-extracted kinematic features can be used to reliably measure motor impairment in Parkinson's disease (PD). This presents the prospect of developing an asynchronous and scalable, video-based assessment of motor dysfunction. Crucial to this endeavour is the ability to automatically recognise the class of an action being performed, without which manual labelling is required.
Date Issued
2022-11-30
Date Acceptance
2022-11-30
Citation
TechRxiv, 2022
URI
URL
DOI
Journal / Book Title
TechRxiv
Copyright Statement
Copyright © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Description
Preprint version
Identifier
10.36227/techrxiv.21610251.v1