A video-processing technique developed at the University of Florida that uses artificial intelligence will help neurologists better track the progression of Parkinson’s disease in patients, ultimately enhancing their care and quality of life.
The system, developed by Diego Guarin, Ph.D., an assistant professor of applied physiology and kinesiology in the UF College of Health and Human Performance, applies machine learning to analyze video recordings of patients performing the finger-tapping test, a standard test for Parkinson’s disease that involves quickly tapping the thumb and index finger 10 times.