Neurodegenerative Disease Prediction Based on Gait Analysis Signals Acquired with Force-Sensitive Resistors

Abstract

Neurodegenerative diseases (NDD) affect the lives of thousands of people around the world. One of the consequences of such diseases is the change in gait pattern, which can be measured using force sensitive resistors. This project uses signals from the Gait Dynamics in Neuro-Degenerative Disease database to extract features for classification of NDD. Manually labeled features from the database are used for comparison with previous studies. Time series signals are also used, where algorithms for signal reliability, feature extraction, and feature selection are implemented. Multiple feature sets are used for classification with different machine learning algorithms, achieving accuracy exceding 82%.