References
Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18– 37. https://doi.org/10.1109/T-AFFC.2010.1 Chen, L., & Wang, H. (2024). Towards robust quadrant-based emotion recognition: Challenges and opportunities in multimodal fusion. IEEE Transactions on Affective Computing, 15(1), 112-125. D’Mello, S. K., & Kory, J. (2021). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys , 54(10s), 1-35. Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2012). DEAP: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18-31. (Included as foundational dataset). Li, Y., Wang, L., & Zheng, W. (2023). A multimodal variational autoencoder with residual learning for dimensional emotion recognition. IEEE Transactions on Affective Computing, 14(3), 2058-2071. Markets and Markets. (2024). Affective computing market by technology (touch-based, touchless), component (hardware, software), end-user (IT & telecom, BFSI, healthcare, retail & e-commerce), and region—global forecast to 2028 (Report SE 3967). https://www.marketsandmarkets.com/Market-Reports/affective-computing- market-130730698.html Miranda-Correa, J. A., Abadi, M. K., Sebe, N., & Patras, I. (2021). VREED: A dataset for affect, personality and mood research on individuals and groups. IEEE Transactions on Affective Computing, 12(2), 479-493. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714 Schmidt, P., Reiss, A., Duerichen, R., & Van Laerhoven, K. (2023). A comparative analysis of labeling paradigms in multimodal affect datasets. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(1), 1-24. Schneider, T., Schuller, B., & Huber, M. (2024). Improving quadrant classification in dimensional emotion recognition using ordinal loss functions. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , 1-5. Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2012). A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 3(1), 42-55. (Included as foundational dataset). Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022). Multimodal deep learning for biomedical data fusion: a review. Briefings in Bioinformatics, 23(2), bbab569. Tabbaa, L., Searle, R., Ang, C.S., Bafti, S.B., Hossain, M.M., Intarasirisawat, J., and Glancy, M. 2021. : Virtual Reality Emotion Recognition Dataset using EyeTracking Physiological Measures. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 269.904, doi: https://doi.org/10.1145/3495002. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., ... & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83-84, 19-52. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83–84, 19– 52. https://doi.org/10.1016/j.inffus.2022.03.009 Yang, D., Huang, S., Kuang, H., & Zhang, L. (2024). Transformer-based asynchronous multimodal learning for continuous emotion prediction. IEEE Transactions on Multimedia, 26, 1234-1247. Zhang, T., Lian, C., Xu, B., Su, Y., & Zeng, Z. (2021). Cardiac signals classification via optional multimodal multiscale receptive fields CNN-enhanced transformer. Knowledge-Based Systems, 229, 107345. Zong, Y., Zheng, W., Cui, Z., & Huang, X. (2022). Multimodal attention for emotion recognition in the wild. IEEE Transactions on Affective Computing, 13(2), 880-891.
