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On the benefits of using Hidden Markov Models to predict emotions

Wu, Yuyuan; Arevalillo-Herráez, Miguel; Katsigiannis, Stamos; Ramzan, Naeem

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Authors

Yuyuan Wu

Miguel Arevalillo-Herráez

Naeem Ramzan



Abstract

The availability of low-cost wireless physiological sensors has allowed the use of emotion recognition technologies in various applications. In this work, we describe a technique to predict emotional states in Russell’s two-dimensional emotion space (valence and arousal), using electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals. For each of the two dimensions, the proposed method uses a classification scheme based on two Hidden Markov Models (HMMs), with the first one trained using positive samples, and the second one using negative samples. The class of new unseen samples is then decided based on which model returns the highest score. The proposed approach was validated on a recently published dataset that contained physiological signals recordings (EEG, ECG, EMG) acquired during a human-horse interaction experiment. The experimental results demonstrate that this approach achieves a better performance than the published baseline methods, achieving an F1-score of 0.940 for valence and 0.783 for arousal, an improvement of more than +0.12 in both cases.

Citation

Wu, Y., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2022). On the benefits of using Hidden Markov Models to predict emotions. . https://doi.org/10.1145/3503252.3531323

Conference Name ACM Conference on User Modeling, Adaptation and Personalization (UMAP)
Conference Location Barcelona
Start Date Jul 4, 2022
End Date Jul 7, 2022
Acceptance Date Apr 11, 2022
Online Publication Date Jul 4, 2022
Publication Date 2022-07
Deposit Date Apr 25, 2022
Publicly Available Date Apr 25, 2022
Pages 164-169
DOI https://doi.org/10.1145/3503252.3531323

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Copyright Statement
© ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, https://doi.org/10.1145/3503252.3531323







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