Using Computer Vision Tools To Analyse Human Motion to Study and Manage Parkinson's Disease

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The project explores how to build computer vision tools to analyse motion disorder symptoms, in the context of studying and managing Parkinson's Disease (PD). Parkinson's' disease is progressive neurodegenerative disease, with an increasing global prevalence. Mobility dysfunctions, like gait or turning abnormalities, are the main symptoms of Parkinson's disease and they also tightly connect with the quality of life. Currently, the gold-standard of clinical diagnosis and evaluation for PD requires face-to-face assessment in the clinic. People living with PD, when being observed by a clinician in the hospital in the short time window, behaves differently from a home-setting and therefore the assessment is not able to fully evaluate the everyday motor disorders. Therefore, a frequent and objective measurement of PD will benefit both the study and management of Parkinson's disease. The novelty of the project is applying state-of-the-art deep learning computer vision models to analyse special, valuable, expert-annotated dataset on people with PD, like the dataset collected using Sphere technology. In a domestic-setting, potentially more in-depth, more personalized information is collected, and with the advancement in computer vision technology, more insightful knowledge could be learned from these data. Specifically, the project involves training effective machine learning and deep learning models to estimate human pose in the customized videos datasets, and to perform downstream analysis on disease progression, and to deriving other statistical insights. Clinicians' expert analysis could be used as ground truth annotations to train the model to perform quantitive and qualitative analysis as well.