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Emotion assessment using Machine Learning and low-cost wearable devices.

R Laureanti, M Bilucaglia, M Zito

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study shows that affordable devices like the MUSE headband and Shimmer GSR+ can detect emotional states. Machine learning analysis of physiological signals accurately predicts emotional responses to visual stimuli.

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    Area of Science:

    • Affective Computing
    • Bioelectrical Measurement
    • Machine Learning

    Background:

    • Advancements in bioelectrical measurement technology have led to accessible, non-invasive devices for recording physiological states.
    • Brain-Computer Interfaces (BCI) and Affective Computing aim for greater real-world impact, necessitating reliable physiological state assessment tools.

    Purpose of the Study:

    • To evaluate the efficacy of the MUSE headband and Shimmer GSR+ device in assessing human emotional states.
    • To determine if physiological data from these devices can predict emotional responses (valence and arousal) during stimuli exposure.

    Main Methods:

    • Fifty-four subjects were exposed to 24 images from the International Affective Picture System (IAPS) database.
    • Emotional values (valence and arousal) were assessed using the Self-Assessment Manikin (SAM) scale.
    • Fifty-two scalar features were extracted from MUSE and Shimmer GSR+ signals, utilized to train six binary classifiers via a Machine Learning approach.

    Main Results:

    • Machine learning classifiers achieved accuracies ranging from 53.6% to 69.9% in predicting emotional valence and arousal.
    • The results demonstrate that the MUSE headband and Shimmer GSR+ provide valuable information regarding users' emotional states.

    Conclusions:

    • The MUSE headband and Shimmer GSR+ are viable, low-cost tools for non-invasive emotional state assessment.
    • Physiological data captured by these devices, when analyzed with Machine Learning, can effectively inform about emotional responses.