Deep Learning-Based Detection of Parkinson’s Disease Using Gait

Parkinson’s disease (PD) affects over a dozen million people worldwide. There is presently no solution for this, much to the dismay of these sufferers. This needs early detection for enhancing the patient’s overall well-being. The goal of our research is to create a prototype tool that can detect PD symptoms and assess their severity rate (dependent on the UPDRS) using gait signals. The projected model beats state-of-the-art approaches accomplishing an accuracy of 98.7% owing to its ability to extract relevant gait characteristics from multiple input signals. We fostered 1D-Convnets that automatically extracts essential deep features for effective gait classification, eliminating the need for human feature extraction.