Improving Parkinson Detection using Dynamic Features from Evoked Expressions in Video

Abstract

Hypomimia, also known as "facial masking", is a common symptom of Parkinson’s Disease (PD). PD is a neurological disorder characterized by non-motor and motor impairments. Hypomimia is the reduction of facial expressiveness, including the emotion expressions. In this work, we explore the use of static and dynamic features for the analysis of evoked facial gestures in PD patients. The main contributions of this work are: (1)We propose a multimodal PD detection system based on both static and dynamic features obtained from evoked face gestures; (2) we propose a novel set of 17 dynamic features to characterize the facial expressiveness and demonstrate that facial dynamics features can be used to improve PD detection; and (3) we analyze different evoked facial expressions and its performance for PD detection. Different expressions activate different Action Units (AUs) and we analyze to what extent each of these AUs contribute to PD detection. The results show that the use of static features generated by pre-trained deep architectures yield up to 77.36% of accuracy for PD detection and the combination with dynamic features improves PD detection by up to 13.46% (from 75.00% to 88.46%). Our experiments also suggest differences in the performance of evoked face gestures in this PD detection task.