Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language

Liang, X., Kapetanios, D.E., Woll, B. and Angelopoulou, A. 2019. Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language. in: Machine Learning and Knowledge Extraction. CD-MAKE 2019 Springer.

Chapter titleReal Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language
AuthorsLiang, X., Kapetanios, D.E., Woll, B. and Angelopoulou, A.
Abstract

Real time hand movement trajectory tracking based on machine learning approaches may assist the early identification of dementia in ageing Deaf individuals who are users of British Sign Language (BSL), since there are few clinicians with appropriate communication skills, and a shortage of sign language interpreters. Unlike other computer vision systems used in dementia stage assessment such as RGB-D video with the aid of depth camera, activities of daily living (ADL) monitored by information and communication technologies (ICT) facilities, or X-Ray, computed tomography (CT), and magnetic resonance imaging (MRI) images fed to machine learning algorithms, the system developed here focuses on analysing the sign language space envelope(sign trajectories/depth/speed) and facial expression of deaf individuals, using normal 2D videos. In this work, we are interested in providing a more accurate segmentation of objects of interest in relation to the background, so that accurate real-time hand trajectories (path of the trajectory and speed) can be achieved. The paper presents and evaluates two types of hand movement trajectory models. In the first model, the hand sign trajectory is tracked by implementing skin colour segmentation. In the second model, the hand sign trajectory is tracked using Part Afinity Fields based on the OpenPose Skeleton Model [1, 2]. Comparisons of results between the two different models demonstrate that the second model provides enhanced improvements in terms of tracking accuracy and robustness of tracking. The pattern differences in facial and trajectory motion data achieved from the presented models will be beneficial not only for screening of deaf individuals for dementia, but also for assessment of other acquired neurological impairments associated with motor changes, for example, stroke and Parkinsons disease.

KeywordsSegmentation, Hand Tracking, OpenPose, British Sign Language, Dementia, Time-series Data Analytics, Artificial Neural Networks, Machine Learning
Book titleMachine Learning and Knowledge Extraction. CD-MAKE 2019
Year2019
PublisherSpringer
Publication dates
Published23 Aug 2019
ISSN0302-9743
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-29726-8_24
FunderDunhill Medical Trust
Web address (URL)https://doi.org/10.1007/978-3-030-29726-8_24
EventCross Domain Conference for Machine Learning and Knowledge Extraction
JournalLecture Notes in Computer Science
Journal citation11713, pp. 377-394
Book editorHolzinger A.
Kieseberg P.
Tjoa A.
Weippl E.
Accepted author manuscript

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