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Deep multimodal fusion for subject-independent stress detection

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.

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