Simulation Study and At Home Diagnostic Tool for Early Detection of Parkinsons Disease

TL;DR

This paper presents a simulation-based diagnostic tool leveraging facial movement analysis and machine learning models to detect early Parkinson's disease, aiming for a mobile app for at-home testing.

Contribution

It introduces a novel simulation dataset and compares neural network models for early Parkinson's detection using facial expressions.

Findings

CNN outperforms Vision Transformer in accuracy

Model achieves high sensitivity in detecting Parkinson's symptoms

Prototype demonstrates potential for at-home diagnosis

Abstract

Hypomimia is a condition in the early stages of the progression of Parkinsons disease that limits the movement of facial muscles, restricting the accurate depiction of facial expressions. Also known as facial masking, this condition is an early symptom of Parkinson's disease, a neurodegenerative disorder that affects movement. To date, no specific test exists to diagnose the disease. Instead, doctors rely on the patient medical history and symptoms to confirm the onslaught of the disease, delaying treatment. This study aims to develop a diagnostic tool for Parkinsons disease utilizing the Facial Action Coding System, a comprehensive system describing all facially discernible movement. In addition, this project generates image datasets by simulating faces or action unit sets for both Parkinsons patients and non-affected individuals through coding. Accordingly, the model is trained using supervised learning and neural networks. The model efficiency is compared using Convolutional Neural Network and Vision Transformer models and later tested with a test program by passing a Parkinsons disease-affected image as input. The future goal is to develop a self-determining mobile application (PDiagnosis) by utilizing the model to determine the probability of Parkinsons progression.