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Arousal-Valence Classification from Peripheral Physiological Signals Using Long Short-Term Memory Networks.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study presents a novel method for emotion recognition using only wearable peripheral physiological signals. Our approach achieves high accuracy, enabling real-time emotional state monitoring for everyday applications.

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    Area of Science:

    • Affective Computing
    • Human-Computer Interaction
    • Wearable Technology

    Background:

    • Automated emotion recognition is crucial for emotionally intelligent machines.
    • Current models heavily rely on images, audio, video, or brain signals, limiting real-world application.
    • Peripheral physiological signals from wearables offer a less intrusive method for continuous emotion monitoring.

    Purpose of the Study:

    • To develop and evaluate an emotion classification method using solely peripheral physiological signals from wearable devices.
    • To enable real-time emotion recognition suitable for daily life settings.
    • To assess the performance of a Long Short-Term Memory neural network for this task.

    Main Methods:

    • Collected peripheral physiological data from 20 participants during a naturalistic debate.
    • Utilized a Long Short-Term Memory (LSTM) neural network for emotion classification.
    • Investigated the impact of different annotation schemes on classification performance.

    Main Results:

    • Achieved classification accuracy of over 93% for binary emotion levels.
    • Demonstrated classification accuracy exceeding 89% for arousal-valence quadrants.
    • Validated the effectiveness of using peripheral physiological signals for emotion recognition.

    Conclusions:

    • Peripheral physiological signals, when analyzed with LSTM networks, provide a highly accurate method for emotion recognition.
    • This research supports the integration of wearable devices for continuous, real-world emotional state monitoring.
    • The findings pave the way for enhanced affective computing applications using accessible wearable technology.