Physiological Measurement for Emotion Recognition in Virtual Reality
PubDate: October 2019
Teams: Texas State University
Writers: Lee Hinkle; Kamrad Khoshhal; Vangelis Metsis
In this work, various non-invasive sensors are used to collect physiological data during subject interaction with virtual reality environments. The collected data are used to recognize the subjects’ emotional response to stimuli. The shortcomings and challenges faced during the data collection and labeling process are discussed, and solutions are proposed. A machine learning approach is adopted for emotion classification. Our experiments show that feature extraction is a crucial step in the classification process. A collection of general purpose features that can be extracted from a variety of physiological biosignals is proposed. Our experimental results show that the proposed feature set achieves better emotion classification accuracy compared to traditional domain-specific features used in previous studies.