Online and offline anger detection via electromyography analysis

Dilranjan S. Wickramasuriya, Rose T. Faghih

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

    Abstract

    Emotional states involving anger, hostility, anxiety and stress have been associated with an increased risk of cardiovascular disease. Online emotion recognition has achieved little attention in the literature in comparison to offline approaches. We present both online and offline methods to identify anger based on EMG data. In the offline method, the Hilbert-Huang transform is used to extract energy features from different time-frequency blocks. This approach achieves an overall classification accuracy of 87.5%. We also develop a novel online method combining machine learning with the tracking of a single parameter for anger detection. Here, band energy is calculated within time windows, and is continuously adjusted based on classified peak amplitudes. Although this technique has a lower classification accuracy than the offline method, it is quite promising as it is well-suited for wearable monitoring and long-term stress management.

    Original languageEnglish (US)
    Title of host publication2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages52-55
    Number of pages4
    ISBN (Electronic)9781538613924
    DOIs
    StatePublished - Dec 19 2017
    Event2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017 - Bethesda, United States
    Duration: Nov 6 2017Nov 8 2017

    Publication series

    Name2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017
    Volume2017-December

    Conference

    Conference2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017
    Country/TerritoryUnited States
    CityBethesda
    Period11/6/1711/8/17

    ASJC Scopus subject areas

    • Health Informatics
    • Instrumentation
    • Health(social science)
    • Biomedical Engineering

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