Facial Expression Reading for Describing and Augmenting Interaction

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In this study, we investigated the design of a wearable device that reads positive facial expressions using physiological signals. We can detect EMG signals with high amplitude on areas of low facial mobility on the side of the face, which are correlated to ones obtained from electrodes on traditional surface EMG capturing positions on top of facial muscles on the front of the face. Based on this analysis, we design and implement an ergonomic wearable device with high reliability. Because the signals are recorded distally, the proposed device uses Independent Component Analysis and an Artificial Neural Network to analyze them and achieve a high facial expression recognition rate on the side of the face. The recognized emotional facial expressions through the wearable interface device can be recorded during therapeutic interventions and can be used for a novel method of human robot interaction.