Diego Guarin, Ph.D., an assistant professor of applied physiology and kinesiology in UF’s College of Health and Human Performance, created the software to address the potential risk of inconsistency and subjectivity in traditional clinical assessments. 

“Over the years, we have shown through our research that video analysis of patients performing finger-tapping and other movements provides valuable information about how the disease is progressing and responding to medications or deep brain stimulation,” Guarin said. 

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. 

“By studying these videos, we could detect even the smallest alterations in hand movements that are characteristic of Parkinson’s disease but might be difficult for clinicians to visually identify,” said Guarin, who is affiliated with the Norman Fixel Institute for Neurological Diseases at UF Health. “The beauty of this technology is that a patient can record themselves performing the test, and the software analyzes it and informs the clinician how the patient is moving so the clinician can make decisions.”