Autors: Nikolova, D. V., Petkova, P. T., Manolova, A. H., Georgieva P.
Title: ECG-Based Human Emotion Recognition Across Multiple Subjects
Keywords: ECG, Affective computing, Human emotion recognition, Machine learning, Artificial Neural Networks, Logistic Regression

Abstract: Electrocardiogram (ECG) based affective computing is a new research field that aims to find correlates between human emotions and the registered ECG signals. Typically, emotion recognition systems are personalized, i.e. the discrimination models are subject-dependent. Building subject-independent models is a harder problem due to the high ECG variability between individuals. In this paper, we study the potential of two machine learning methods (Logistic Regression and Artificial Neural Network) to discriminate human emotional states across multiple subjects. The users were exposed to movies with different emotional content (neutral, fear, disgust) and their ECG activity was registered. Based on extracted features from the ECG recordings, the three emotional states were partially discriminated.

References

    Issue

    Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 283, pp. 25-36, 2019, Germany, Springer, DOI 10.1007/978-3-030-23976-3_3

    Copyright Springer

    Цитирания (Citation/s):
    1. Li, W., Zhang, Z., & Song, A. (2021). Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy. Measurement, 172, 108747. - 2021 - в издания, индексирани в Scopus или Web of Science
    2. Sawalha, S., & Al-Naymat, G. (2021). Internet of things data compression based on successive data grouping. Turkish Journal of Electrical Engineering & Computer Sciences, 29(1), 32-45. - 2021 - в издания, индексирани в Scopus или Web of Science
    3. Xu, H., Tang, J., & Zhang, J. Emotion Recognition Using Multi-core Tensor Learning and Multimodal Physiological Signal. In Human Brain and Artificial Intelligence: Second International Workshop, HBAI 2020, Held in Conjunction with IJCAI-PRICAI 2020, Yokohama, Japan, January 7, 2021, Revised Selected Papers (p. 137). Springer Nature. - 2021 - в издания, индексирани в Scopus или Web of Science
    4. Li, W. (2021). Finding Needles in a Haystack: Recognizing Emotions Just from Your Heart. IEEE Transactions on Affective Computing, (01), 1-1. - 2021 - в издания, индексирани в Scopus или Web of Science

    Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science