Wu, Yuyuan and Arevalillo-Herráez, Miguel and Katsigiannis, Stamos and Ramzan, Naeem (2022) 'On the benefits of using Hidden Markov Models to predict emotions.', ACM Conference on User Modeling, Adaptation and Personalization (UMAP) Barcelona, 04-07 July 2022.
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.
|Item Type:||Conference item (Paper)|
|Full text:||(AM) Accepted Manuscript|
Download PDF (483Kb)
|Publisher Web site:||https://doi.org/10.1145/3503252.3531323|
|Publisher 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|
|Date accepted:||11 April 2022|
|Date deposited:||25 April 2022|
|Date of first online publication:||04 July 2022|
|Date first made open access:||14 November 2022|
Save or Share this output
|Look up in GoogleScholar|