Embedded Emotion Recognition System Based on Electrocardiogram Attributes

Investigated research proved the relevance of the analysis of physiological signals to detect human emotional states. This paper presents an embedded emotion recognition system based on electrocardiogram attributes. We applied the Support Vector Machine (SVM) with a subject independent classificatio...

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Bibliographic Details
Published in2018 41st International Conference on Telecommunications and Signal Processing (TSP) pp. 1 - 4
Main Authors Ben Henia, Wiem Mimoun, Lachiri, Zied
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Arousal-Valence space
electrocardiogram
Electrocardiography
Emotion recognition
Feature extraction
Physiology
Raspberry Pi
support vector machine
Support vector machines
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DOI10.1109/TSP.2018.8441234

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Summary:Investigated research proved the relevance of the analysis of physiological signals to detect human emotional states. This paper presents an embedded emotion recognition system based on electrocardiogram attributes. We applied the Support Vector Machine (SVM) with a subject independent classification and we implemented the whole proposed system on the Raspberry Pi 3 model B. This System can be easily mounted on robots for an affective Human-Machine interactivity. Thus, we explored the multimodal MAHNOB-HCI database for the two-class problem discrimination in the Arousal-Valence space. After using the ten cross-validations and testing several SVM' kernels, the average classification rates were 57.01% and 54.07% for arousal and valence, respectively.
DOI:10.1109/TSP.2018.8441234