The Movement Estimation & Assessment Laboratory has been working towards creating an Artificial Intelligence tool that can pull objective information from frontal plane videos of subjects performing tasks such as opening and closing the palm, finger tapping, speaking, etc. The purpose of this research is to have a baseline for Parkinsonian symptoms that may be too small for physicians rating with the UPDRS-3 scale to notice.    

My specific project is focusing on analyzing a class of sequential tasks that expose bradykinesia. These tasks include finger tapping, hand movement, leg agility, and toe tapping. Processing the data and interpreting the data is a collective lab effort. Recognizing significant differences, through statistical formulas, between the RBD and PD population shows that one population can decline into another. As the undergrad on this project I was tasked with data entry, data interpretation, and paper/poster writing. "VisionMD: An Open-Source Tool for Video-Based Analysis of Motor Function in Movement Disorders" has been submitted for publication in npj Digital Medicine. There is also a public website with the paper and tutorials on the tool (visionmd.ai). Poster presentations on the tool and using it to compare velocity movement features of healthy control, idiopathic rem sleep behavior disorder (iRBD), and Parkinson's Disease (PD) were completed at 2024 HHP AI days, 2024 UF AI Days, and 2024 Fall Undergraduate Research Symposium.