Examining Everyday Speech and Motor Symptoms of Parkinson's Disease for Diagnosis and Progression Tracking

Abstract

Statistical methods to correlate multiple variables has long been applied in many fields of research. This paper applies such techniques to Unified Parkinson's Disease Rating Scale (UPDRS) data to examine relationships between speech and movement variables. This data analysis uses select speech and motor variables to explore Parkinson's Disease (PD) symptom correlations. The analysis is a prerequisite study of speech and movement symptoms prior to collecting data from everyday living in PD patients using HCI systems for movement and AI methods for analyzing speech and language. This data analysis is a first level examination of the current gold standards for measuring speech and movement in PD patients.