Key result
An ensemble learning approach using ECG signals improved emotion classification accuracy by 10.77% compared to the best performing single biosensor-based model in the literature.
Why the study?
Most machine learning models for biosensor-based human emotion recognition lack the ability to achieve high classification accuracy using a limited number of biosensors.
Effect estimate: 10.77% accuracy gain
An ensemble learning approach using ECG signals significantly improves the classification accuracy of human emotion recognition compared to existing single biosensor models.
May advance ECG-based emotion monitoring in cardiac care; leaves open prospective clinical validation.
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.
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Dissanayake et al. (2019) studied Human emotion recognition. Ensemble learning approach using ECG signals vs. Single biosensor based models in the literature was evaluated on Emotion classification accuracy (10.77% accuracy gain). An ensemble learning approach using ECG signals improved emotion classification accuracy by 10.77% compared to the best performing single biosensor-based model in the literature.
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