Publication Type : Conference Proceedings
Publisher : IEEE
Source : In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 105-109. IEEE, 2021
Url : https://ieeexplore.ieee.org/document/9377132
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2021
Abstract : This paper explores the influence of convolutional layer in deep multimodal fusion (intermediate fusion) for the detection of subject-independent stress using physiological signals including Electrocardiogram (ECG) and Electrodermal Activity (EDA). We compare performance of deep multimodal fusion models based on Convolutional Neural Network (CNN) by combining modals at different levels of the network. On two benchmark datasets ASCERTAIN and CLAS, the proposed approach is validated and the results shown to be higher for intermediate fusion on convolutional layer.
Cite this Research Publication : Radhika, K., and V. Ramana Murthy Oruganti. "Deep multimodal fusion for subject-independent stress detection." In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 105-109. IEEE, 2021.