A proposed set of general purpose features extracted from physiological biosignals achieved better emotion classification accuracy in virtual reality compared to traditional domain-specific features.
A proposed set of general-purpose features extracted from physiological biosignals improves emotion classification accuracy in virtual reality environments compared to traditional features.
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.
Hinkle et al. (Sat,) conducted a other in Emotion recognition. General purpose features from physiological biosignals vs. Traditional domain-specific features was evaluated on Emotion classification accuracy. A proposed set of general purpose features extracted from physiological biosignals achieved better emotion classification accuracy in virtual reality compared to traditional domain-specific features.