Utilizing Neural Networks to Predict Freezing of Gait in Parkinson's Patients

Abstract

With the appropriate mathematical models, data from wearable devices can be used to help Parkinson's patients live safer and more independent lives. Inspired by this idea, the purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. A class of neural networks known as layered recurrent networks (LRNs) was applied to an open-source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent variables in this experiment -- the subject being tested, neural network architecture, and down sampling of the majority classes -- were each varied and compared against the performance of the neural network in predicting impending FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied between patients.