End-2-End Modeling Of Speech And Gait From Patients With Parkinson'S Disease: Comparison Between High Quality Vs. Smartphone Data

Parkinson's disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Speech and gait signals have been analyzed to detect the presence of the disease and the severity in patients. However, most studies have been performed in controlled conditions using high quality data, which make those studies not suitable for a continuous at-home evaluation of the state of the patients. The developed technology should be evaluated in more realistic scenarios, for instance using smartphone data. We propose the use of state-of-the-art deep learning techniques to evaluate the speech and gait symptoms of patients. The proposed methods are evaluated in two scenarios to cover both high quality and smartphone data. The results indicate that it is possible to classify patients and healthy subjects with accuracies over 92% in both scenarios. The proposed methods are also promising to evaluate the severity of the speech symptoms and the global motor state of the patients.