Key result
A supervised learning approach using electrocardiogram and respiration signal features achieved an overall classification accuracy of 78.3% for facial expression-based emotion recognition.
Why the study?
Self-reported emotion scores introduce labeling inconsistencies due to inter-subject variability, whereas facial expressions may provide more consistent labels for automated emotion recognition from physiological signals.
A supervised learning approach using EKG and respiration signals with facial expression-based labels achieved 78.3% accuracy in classifying emotional responses.
Demonstrates feasibility of ECG-respiration emotion classification; leaves open clinical validation and utility in cardiovascular settings.
Automated emotion recognition from physiological signals is an ongoing research area. Many studies rely on self-reported emotion scores from subjects to generate classification labels. This can introduce labeling inconsistencies due to inter-subject variability. Facial expressions provide a more consistent means of generating labels. We generate labels by selecting locations at which subjects either displayed a visibly averse/negative reaction or laughed in video recordings. We next use a supervised learning approach for classifying these emotional responses based on electrocardiogram (EKG) and respiration signal features in an experiment where different movie/video clips were utilized to elicit feelings of joy, disgust, amusement, etc. As features, we extract wavelet coefficient patches from EKG RR-interval time series and respiration waveform parameters. We use principal component analysis for dimensionality reduction and support vector machines for classification. We achieved an overall classification accuracy of 78.3%.
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Wickramasuriya et al. (2019) studied Emotion recognition. Supervised learning using EKG and respiration signal features was evaluated on Overall classification accuracy. A supervised learning approach using electrocardiogram and respiration signal features achieved an overall classification accuracy of 78.3% for facial expression-based emotion recognition.
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