This study focuses on finding better ways to assess and manage Parkinson's disease. The study aims to validate two machine learning algorithms, developed respectively by PragmaClin Research Inc., and researchers at the Tyndall Institute in collaboration with the Centre for Gerontology and Rehabilitation at University College Cork (UCC). These algorithms are both designed to analyse data collected from video recordings and wearable devices to provide a rating of PD symptom severity.

We are testing two tools: PRIMS and WESAA. PRIMS is a system that uses cameras to assess Parkinson's disease symptoms without needing a doctor's input. WESAA, on the other hand, is a wrist-worn device that tracks hand movements, heart rate, and blood pressure. The study aims to see if these tools—PRIMS and WESAA—work well for evaluating people with Parkinson's disease.

The validation of machine learning algorithms and wearable monitoring devices could lead to more accurate and reliable assessment tools for Parkinson’s in the future.