Autors: Nikolova, D. V., Petkova, P. T., Manolova, A. H., Georgieva P.
Title: ECG-based Emotion Recognition: Overview of Methods and Applications
Keywords: ECG-based Emotion Recognition

Abstract: This paper presents an overview of recent methods for recognition of human emotions based on Electrocardiogram (ECG) signals and related applications. The major challenges in emotion modeling (affective computing) from ECG data are finding representations that are invariant to inter- and intra-subject differences, as well as the inherent noise associated with the ECG data recordings. The most common invariant features (in frequency and time domain) extracted from the raw ECG signals are outlined. The reviewed studies reveal the great potential of ECG to decode basic human emotional states such as joy, sadness, anger, fear in combination with other physiological signals and facial expression. Major application areas cover patient monitoring, marketing, car driving.

References

    Issue

    ANNA'18; Advances in Neural Networks and Applications, pp. 1-5, 2018, Bulgaria, IEEE, ISBN 978-3-8007-4756-6

    Copyright IEEE

    Цитирания (Citation/s):
    1. Dzedzickis, A., Kaklauskas, A., & Bucinskas, V. (2020). Human emotion recognition: Review of sensors and methods. Sensors, 20(3), 592. - 2020 - в издания, индексирани в Scopus или Web of Science
    2. Davila-Montero, S., Dana-Le, J. A., Bente, G., Hall, A. T., & Mason, A. J. (2021). Review and Challenges of Technologies for Real-time Human Behavior Monitoring. IEEE Transactions on Biomedical Circuits and Systems. - 2021 - в издания, индексирани в Scopus или Web of Science
    3. Han, L., Lu, L., Dong, H., Xie, S., Yu, G., Shen, T., ... & Pei, X. (2021, April). Feature Extraction Method of EEG Signal Based on Synchroextracting Transform. In International Conference on Multimedia Technology and Enhanced Learning (pp. 462-468). Springer, Cham. - 2021 - в издания, индексирани в Scopus или Web of Science
    4. Park, S., Lee, S. W., & Whang, M. (2021). The Analysis of Emotion Authenticity Based on Facial Micromovements. Sensors, 21(13), 4616. - 2021 - в издания, индексирани в Scopus или Web of Science
    5. Hasnul, M. A., Alelyani, S., & Mohana, M. (2021). Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review. Sensors, 21(15), 5015. - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Davila-Montero, S., Dana-Le, J. A., Bente, G., Hall, A. T., & Mason, A. J. (2021). Review and Challenges of Technologies for Real-time Human Behavior Monitoring. IEEE Transactions on Biomedical Circuits and Systems. - 2021 - в издания, индексирани в Scopus или Web of Science
    7. Hamza, S., & Ayed, Y. B. (2022). Toward improving person identification using the ElectroCardioGram (ECG) signal based on non-fiducial features. Multimedia Tools and Applications, 81(13), 18543-18561. - 2022 - в издания, индексирани в Scopus или Web of Science
    8. Ohkura, M., Laohakangvalvit, T., Sripian, P., Sugaya, M., Chiba, H., & Berque, D. (2022). Affective evaluation of virtual kawaii robotic gadgets using biological signals in a remote collaboration of American and Japanese students. In International Conference on Human-Computer Interaction (pp. 478-488). Springer, Cham. - 2022 - в издания, индексирани в Scopus или Web of Science
    9. Wierciński, T., Rock, M., Zwierzycki, R., Zawadzka, T., & Zawadzki, M. (2022). Emotion Recognition from Physiological Channels Using Graph Neural Network. Sensors, 22(8), 2980. - 2022 - в издания, индексирани в Scopus или Web of Science
    10. Hamza, S., & Ayed, Y. B. (2022). Toward improving person identification using the ElectroCardioGram (ECG) signal based on non-fiducial features. Multimedia Tools and Applications, 81(13), 18543-18561. - 2022 - в издания, индексирани в Scopus или Web of Science
    11. Li, R., Yuizono, T., & Li, X. (2022). Affective computing of multi-type urban public spaces to analyze emotional quality using ensemble learning-based classification of multi-sensor data. PloS one, 17(6), e0269176. - 2022 - в издания, индексирани в Scopus или Web of Science
    12. Kaklauskas, A., Abraham, A., Ubarte, I., Kliukas, R., Luksaite, V., Binkyte-Veliene, A., ... & Kaklauskiene, L. (2022). A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. Sensors, 22(20), 7824. - 2022 - в издания, индексирани в Scopus или Web of Science
    13. Khan, C. M. T., Ab Aziz, N. A., Raja, J. E., Nawawi, S. W. B., & Rani, P. (2022). Evaluation of Machine Learning Algorithms for Emotions Recognition using Electrocardiogram. Emerging Science Journal, 7(1), 147-161. - 2022 - в издания, индексирани в Scopus или Web of Science
    14. Chang, R. I., Tsai, C. Y., & Chung, P. (2022). Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers. Sensors, 23(1), 430. - 2022 - в издания, индексирани в Scopus или Web of Science
    15. Yu, S. N., Wang, S. W., & Chang, Y. P. (2022). Improving Distinguishability of Photoplethysmography in Emotion Recognition Using Deep Convolutional Generative Adversarial Networks. IEEE Access, 10, 119630-119640. - 2022 - в издания, индексирани в Scopus или Web of Science
    16. Salman, A., & Busso, C. (2022, November). Privacy Preserving Personalization for Video Facial Expression Recognition Using Federated Learning. In Proceedings of the 2022 International Conference on Multimodal Interaction (pp. 495-503). - 2022 - в издания, индексирани в Scopus или Web of Science
    17. Huang, J., Peng, Y., & Hu, L. (2024). A multilayer stacking method base on RFE-SHAP feature selection strategy for recognition of driver’s mental load and emotional state. Expert Systems with Applications, 238, 121729. - 2023 - в издания, индексирани в Scopus или Web of Science
    18. Singh, N., & Kapoor, R. (2023). Multi-modal Expression Detection (MED): A cutting-edge review of current trends, challenges and solutions. Engineering Applications of Artificial Intelligence, 125, 106661. - 2023 - в издания, индексирани в Scopus или Web of Science
    19. Aly, L., Godinho, L., Bota, P., Bernardes, G., & da Silva, H. P. (2024). Acting Emotions: a comprehensive dataset of elicited emotions. Scientific Data, 11(1), 147. - 2023 - в издания, индексирани в Scopus или Web of Science
    20. Abdelaal, Y., & Al-Thani, D. (2023). Accessibility first: detecting frustration in web browsing for visually impaired and sighted smartphone users. Universal Access in the Information Society, 1-17. - 2023 - в издания, индексирани в Scopus или Web of Science
    21. Zhou, D., Cheng, Y., Wen, L., Luo, H., & Liu, Y. (2023). Drivers’ Comprehensive Emotion Recognition Based on HAM. Sensors, 23(19), 8293. - 2023 - в издания, индексирани в Scopus или Web of Science
    22. Shichkina, Y., Bureneva, O., Salaurov, E., & Syrtsova, E. (2023). Assessment of a Person’s Emotional State Based on His or Her Posture Parameters. Sensors, 23(12), 5591. - 2023 - в издания, индексирани в Scopus или Web of Science
    23. Rossi, S., Rossi, A., & Sangiovanni, S. (2023, September). Towards the Evaluation of the Role of Embodiment in Emotions Elicitation. In 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) (pp. 1-8). IEEE. - 2023 - в издания, индексирани в Scopus или Web of Science
    24. Govarthan, P. K., Kumar, P. S., Ganapathy, N., & Ronickom, J. F. A. (2023, October). Investigating Windowing Techniques in Emotion Classification with ECG and Machine Learning. In 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) (pp. 348-353). IEEE. - 2023 - в издания, индексирани в Scopus или Web of Science
    25. Chandra Murty, P. S., Anuradha, C., Appala Naidu, P., Balaswamy, C., Nagalingam, R., Jagatheesaperumal, S. K., & Ponnusamy, M. (2023). An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14. - 2023 - в издания, индексирани в Scopus или Web of Science
    26. Paul, A., Das, N., Pal, S., & Mitra, M. (2023, March). Automated Classification of Happy and Sad Emotional States from PPG Signal Using Time Domain Analysis. In International Conference on Data Science and Communication (pp. 133-143). Singapore: Springer Nature Singapore. - 2023 - в издания, индексирани в Scopus или Web of Science
    27. Roldan-Castellanos, F. A., Pérez Olguín, I. J. C., Méndez-González, L. C., & Vidal-Portilla, L. R. (2023). Emotional Diagnosis for Employees Within the Framework of Industry 4.0: A Case Study in Ciudad Juarez. In Innovation and Competitiveness in Industry 4.0 Based on Intelligent Systems (pp. 243-273). Cham: Springer International Publishing. - 2023 - в издания, индексирани в Scopus или Web of Science
    28. Rossi, D., Billeci, L., Bonfiglio, L., Aliboni, S., Posteraro, F., & Bortone, I. (2023, December). Combining biosignals to assess and monitor VR-assisted rehabilitation of children with Cerebral Palsy: a machine learning approach. In 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology (pp. 139-140). IEEE. - 2023 - в издания, индексирани в Scopus или Web of Science
    29. Han, H. A. N., Xunhua, H. U. A. N. G., Huihui, C. H. A. N. G., Haoyi, F. A. N., Peng, C. H. E. N., & Jijia, C. H. E. N. A review of self-supervised learning methods in the field of ECG. Journal of Frontiers of Computer Science & Technology, 1. - 2023 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus