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Article

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Department of Computer Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka
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Sensors 2019, 19(20), 4495; https://doi.org/10.3390/s19204495
Received: 14 August 2019 / Revised: 27 September 2019 / Accepted: 5 October 2019 / Published: 16 October 2019
(This article belongs to the Section Biomedical Sensors)
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. View Full-Text
Keywords: bio-signal processing; wearable computing; ensemble learning; electrocardiogram; machine learning bio-signal processing; wearable computing; ensemble learning; electrocardiogram; machine learning
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MDPI and ACS Style

Dissanayake, T.; Rajapaksha, Y.; Ragel, R.; Nawinne, I. An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition. Sensors 2019, 19, 4495. https://doi.org/10.3390/s19204495

AMA Style

Dissanayake T, Rajapaksha Y, Ragel R, Nawinne I. An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition. Sensors. 2019; 19(20):4495. https://doi.org/10.3390/s19204495

Chicago/Turabian Style

Dissanayake, Theekshana, Yasitha Rajapaksha, Roshan Ragel, and Isuru Nawinne. 2019. "An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition" Sensors 19, no. 20: 4495. https://doi.org/10.3390/s19204495

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