Figure: Overview of the PD screening pipeline. A participant can perform finger-tapping task in front of a computer webcam. A hand tracking model is used to locate the key points of the hand. A spatio temporal graph is constructed specifically for the finger-tapping task. Four different feature streams (joint, bone, velocity and acceleration) are generated and fed to the proposed PULSAR model for prediction.
Timely diagnosis of movement disorders like Parkinson’s disease (PD) improves quality of life. However, access to clinical diagnosis is limited in low-income countries. Here, we present PULSAR, a novel method for classifying individuals with or without PD from webcam-recorded videos of the finger-tapping task used in the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PULSAR was trained and evaluated on data from 382 participants, including 183 self-reported PD patients. We used an adaptive graph convolutional neural network to dynamically learn task-specific spatio-temporal edges and enhanced it with a multi-stream convolution model to capture critical features like finger joint locations, tapping velocity, and acceleration of tapping. As video labels are self-reported, some non-PD labels may be undiagnosed cases. To address this, we used Positive Unlabeled (PU) Learning, which outperformed traditional supervised learning. PULSAR achieved 80.95% accuracy on the validation set and 71.29% mean accuracy (2.49% standard deviation) on an independent test set. We hope PULSAR can aid in accessible PD screening and that these techniques may extend to assessing disorders like ataxia and Huntington’s disease.
@misc{alam2024pulsargraphbasedpositive,
title={PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition},
author={Md. Zarif Ul Alam and Md Saiful Islam and Ehsan Hoque and M Saifur Rahman},
year={2024},
eprint={2312.05780},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2312.05780},
}