A Multi-modal Tool Suite for Parkinson’s Disease Evaluation and Grading

Abstract

The traditional diagnosis of Parkinson’s disease (PD) aims to assess several clinical manifestations, and it is commonly based on medical observations. However, an overall evaluation is extremely difficult due to the large variety of symptoms that affect PD patients. Furthermore, the traditional PD assessment is based on visual subjective observation of different motor tasks. For this reasons, an automatic system could be able to automatically assess and rate the PD and objectively evaluate the performed motor tasks. Such system could then support medical specialists in the assessment and rating of PD patients in a real clinical scenario. In this work, we developed multi-modal tool suite able to extract and process meaningful features from different motor tasks by means of two main experimental set-ups. In detail, we acquired and evaluated the motor performance acquired during the finger tapping, the foot tapping and the hand writing exercises. Several sets of features have been extracted from the acquired signals and used to both successfully classify a subject as PD patient or healthy subject, and rate the disease among PD patients.