Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson’s Severity Assessment

Amirhossein Dadashzadeh, Alan Whone, Alan Whone, Michal Rolinski, Michal Rolinski, Majid Mirmehdi

Abstract

Evaluating neurological disorders such as Parkinsons disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we present an end-to-end deep learning framework to measure PD severity in two important components, hand movement and gait, of the Unified Parkinsons Disease Rating Scale (UPDRS). Our method leverages on an Inflated 3D CNN trained by a temporal segment framework to learn spatial and long temporal structure in video data. We also deploy a temporal attention mechanism to boost the performance of our model. Further, motion boundaries are explored as an extra input modality to assist in obfuscating the effects of camera motion for better movement assessment. We ablate the effects of different data modalities on the accuracy of the proposed network and compare with other popular architectures. We evaluate our proposed method on a dataset of 25 PD patients, obtaining 72.3% and 77.1% top-1 accuracy on hand movement and gait tasks respectively.

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Paper Citation


in Harvard Style

Dadashzadeh A., Whone A., Rolinski M. and Mirmehdi M. (2021). Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson’s Severity Assessment.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 89-97. DOI: 10.5220/0010309200890097


in Bibtex Style

@conference{icpram21,
author={Amirhossein Dadashzadeh and Alan Whone and Michal Rolinski and Majid Mirmehdi},
title={Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson’s Severity Assessment},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={89-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010309200890097},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson’s Severity Assessment
SN - 978-989-758-486-2
AU - Dadashzadeh A.
AU - Whone A.
AU - Rolinski M.
AU - Mirmehdi M.
PY - 2021
SP - 89
EP - 97
DO - 10.5220/0010309200890097