ParkinsonAI: An AI-Powered Solution for Early Parkinson’s Disease Monitoring and Tracking

Existing AI systems already have the capabilities to transform the way diseases are diagnosed and treated. ParkinsonAI is no exception—we propose a solution to the cumbersome and inaccessible methods currently used to diagnose Parkinson’s Disease. These diagnostic methods either rely on specialized medical equipment or require trained professionals to operate, making prompt diagnosis at home impossible. Since Parkinson’s disease cannot be cured, treatments stem solely from its management. As such, the early diagnosis of Parkinson’s disease is crucial as it allows for the initiation of treatment plans at an earlier stage, which can significantly improve the overall quality of life for patients. This study introduces ParkinsonAI, an AI-powered system designed for the early identification of potential Parkinson’s Disease (PD) related motor dysfunctions. ParkinsonAI leverages Google’s Mediapipe framework to analyze hand motion through 21 key points, capturing intricate skeletal movements. This data from the hand is then fed into our trained Recurrent Neural Network (RNN) to report the severity of the condition. To train the AI model, 62 user datasets were collected, with 19 used for model validation. The final confusion matrix revealed an 84.2% accuracy, though it potentially may be influenced by limited data for specific severity levels. Future research will focus on refining and expanding ParkinsonAI’s capabilities to enhance its accuracy, sensitivity, and clinical utility.