AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
This addresses the need for more objective and efficient symptom quantification in Parkinson's Disease patients, though it appears incremental as it applies existing computer vision techniques to a new medical application.
The authors tackled the problem of subjective and inefficient manual assessment of Parkinson's Disease motor symptoms using the MDS-UPDRS scale by developing a computer-vision-based approach that extracts motion features from videos via an app, enabling quick and easy analysis.
Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP.