Emotion Recognition for hHman-Machine Communication

Choubeila Maaoui · Alain Pruski · F. Abdat

40 citations0/0 files storedIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Face and Expression Recognition

Abstract

The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition. Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.

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References

1997Affective Computing 2001Emotion recognition in human-computer interaction 2001Toward machine emotional intelligence: analysis of affective physiological state 2005From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification 1995Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1 2004Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals 2004Emotion recognition from physiological signals using wireless sensors for presence technologies 2002Digital processing of affective signals

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2022Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals 2013Individual Emotion Classification between Happiness and Sadness by Analyzing Photoplethysmography and Skin Temperature

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