Spatio-Temporal Attention and Magnification for Classification of Parkinson’s Disease from Videos Collected via the Internet - Ali, Mohammad Rafayet; Hernandez, Javier; Dorsey, E. Ray; Hoque, Ehsan; McDuff, Daniel (International Conference on Automatic Face and Gesture Recognition, 2020)
We present an automated framework for detecting Parkinson’s disease (PD) from videos collected through a scalable online platform. We analyzed 1380 videos of age-matched participants performing four standard motor tasks from the MDS-UPDRS. Our proposed framework leverages multiple deep neural networks to temporally and spatially segment the videos as well as magnify relevant motions. Frequency domain representations of the resulting data are then classified using supervised learning. Overall, the proposed framework achieves an accuracy of 82.5% when discriminating between those with PD and those without, and 61.8% when discriminating between those with PD with treatment, with PD without treatment, and those without PD. These results increased up to 91.8% and 73.5%, respectively, when combining the predictions of multiple models. To understand the contributions of each part of our framework we perform systematic ablation studies. We also compare between motion features based on pixel, phase-based and deep learning-based representations. This work demonstrates the possibility of identifying PD cues in challenging real-life settings with inexpensive webcams.