Teaching Pepper Robot to Recognize Emotions of Traumatic Brain Injured Patients Using Deep Neural Networks

Abstract

Social signal extraction from the facial analysis is a popular research area in human-robot interaction. However, recognition of emotional signals from Traumatic Brain Injured (TBI) patients with the help of robots and non-intrusive sensors is yet to be explored. Existing robots have limited abilities to automatically identify human emotions and respond accordingly. Their interaction with TBI patients could be even more challenging and complex due to unique, unusual and diverse ways of expressing their emotions. To tackle the disparity in a TBI patient's Facial Expressions (FEs), a specialized deep-trained model for automatic detection of TBI patients' emotions and FE (TBI-FER model) is designed, for robot-assisted rehabilitation activities. In addition, the Pepper robot's built-in model for FE is investigated on TBI patients as well as on healthy people. Variance in their emotional expressions is determined by comparative studies. It is observed that the customized trained system is highly essential for the deployment of Pepper robot as a Socially Assistive Robot (SAR).