Key result
System using ECG, GSR, EMG, and PANAS identifies arousal and valence for emotion recognition without AI.
The study presents a system for emotion recognition based on physiological signals and questionnaires without the use of artificial intelligence.
Offers non-AI emotion recognition from physiological signals; leaves open validation and clinical utility.
This paper presents a system that allows for the identification of two values: arousal and valence, which represent the degree of stimulation in a subject, using Russell’s model of affect as a reference. To identify emotions, a step-by-step structure is used, which, based on statistical data from physiological signal metrics, generates the representative arousal value (direct correlation); from the PANAS questionnaire, the system generates the valence value (inverse correlation), as a first approximation to the techniques of emotion recognition without the use of artificial intelligence. The system gathers information concerning arousal activity from a subject using the following metrics: beats per minute (BPM), heart rate variability (HRV), the number of galvanic skin response (GSR) peaks in the skin conductance response (SCR) and forearm contraction time, using three physiological signals (Electrocardiogram - ECG, Galvanic Skin Response - GSR, Electromyography - EMG).
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Callejas-Cuervo et al. (2020) studied Emotion recognition. Emotion recognition system using physiological signals (ECG, GSR, EMG) and PANAS questionnaire was evaluated on Identification of arousal and valence values. A system using physiological signals (ECG, GSR, EMG) and the PANAS questionnaire was developed to identify arousal and valence values for emotion recognition without artificial intelligence.
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