Emotional Valence Tracking and Classification via State-Space Analysis of Facial Electromyography

Taruna Yadav, Md Moin Uddin Atique, Hamid Fekri Azgomi, Joseph T. Francis, Rose T. Faghih

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

    Abstract

    Tracking the emotional valence state of an individual can serve as an important marker of personal health and well-being. Through automatic detection of emotional valence, timely intervention can be provided in the events of long periods of negative valence, such as anxiety, particularly for people prone to cardiovascular diseases. Our goal here is to use facial electromyogram (EMG) signal to estimate one's hidden self-labelled emotional valence (EV) state during presentation of emotion eliciting music videos via a state-space approach. We present a novel technique to extract binary and continuous features from EMG signals. We then present a state-space model of valence in which the observation process includes both the continuous and binary extracted features. We use these features simultaneously to estimate the model parameters and unobserved EV state via an expectation maximization algorithm. Using experimental data, we illustrate that the estimated EV State of the subject matches the music video stimuli through different trials. Using three different classifiers: support vector machine, linear discriminant analysis, and k-nearest neighbors, a maximum classification accuracy of 89% was achieved for valence prediction based on the estimated emotional valance state. The results illustrate our system's ability to track valence for personal well-being monitoring.

    Original languageEnglish (US)
    Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
    EditorsMichael B. Matthews
    PublisherIEEE Computer Society
    Pages2116-2120
    Number of pages5
    ISBN (Electronic)9781728143002
    DOIs
    StatePublished - Nov 2019
    Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
    Duration: Nov 3 2019Nov 6 2019

    Publication series

    NameConference Record - Asilomar Conference on Signals, Systems and Computers
    Volume2019-November
    ISSN (Print)1058-6393

    Conference

    Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
    Country/TerritoryUnited States
    CityPacific Grove
    Period11/3/1911/6/19

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Networks and Communications

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