Facial Expression-Based Emotion Classification using Electrocardiogram and Respiration Signals

Dilranjan S. Wickramasuriya, Mikayla K. Tessmer, Rose T. Faghih

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Automated emotion recognition from physiological signals is an ongoing research area. Many studies rely on self-reported emotion scores from subjects to generate classification labels. This can introduce labeling inconsistencies due to inter-subject variability. Facial expressions provide a more consistent means of generating labels. We generate labels by selecting locations at which subjects either displayed a visibly averse/negative reaction or laughed in video recordings. We next use a supervised learning approach for classifying these emotional responses based on electrocardiogram (EKG) and respiration signal features in an experiment where different movie/video clips were utilized to elicit feelings of joy, disgust, amusement, etc. As features, we extract wavelet coefficient patches from EKG RR-interval time series and respiration waveform parameters. We use principal component analysis for dimensionality reduction and support vector machines for classification. We achieved an overall classification accuracy of 78.3%.

    Original languageEnglish (US)
    Title of host publication2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages9-12
    Number of pages4
    ISBN (Electronic)9781728138121
    DOIs
    StatePublished - Nov 2019
    Event2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019 - Bethesda, United States
    Duration: Nov 20 2019Nov 22 2019

    Publication series

    Name2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019

    Conference

    Conference2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
    Country/TerritoryUnited States
    CityBethesda
    Period11/20/1911/22/19

    Keywords

    • continuous wavelet transform
    • emotion recognition
    • respiration
    • RR-intervals

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Biomedical Engineering
    • Health Informatics
    • Instrumentation
    • Health(social science)

    Fingerprint

    Dive into the research topics of 'Facial Expression-Based Emotion Classification using Electrocardiogram and Respiration Signals'. Together they form a unique fingerprint.

    Cite this