Our research article, Efficient wavelet-based artifact removal for electrodermal activity in real-world applications, has been published in Biomedical Signal Processing and Control. In this research we have proposed a computationally efficient method for denoising electrodermal activity (EDA) signals in real-time. We have compared the proposed method to three state-of-the-art methods for EDA signal filtering, including one from MIT Media Lab. In addition, we also tested the proposed method for the online filtering of EDA signals collected while twelve volunteers conducted tasks designed to elicit various stress states. The results confirm that the proposed method outperforms existing approaches and it has a lower computational cost.
Since many of the possible EDA monitoring applications (including virtually in any ICT system, such as tactile gaming consoles, robots during human-robot interaction, neuro-marketing, etc.) involve users interaction in real-world situations, the results of the present research mean a valuable contribution in this direction to recognize emotions and engagement level from EDA signals in real time.
This research work was supported by the Industrial Doctorate program (Ref. ID.: 2014-DI-022) of AGAUR, Govt. of Catalonia, and was partially funded by the La Caixa via project AutonoMe (Ref. ID.: AD16-00877). We gratefully acknowledge the cooperation of Dr. Manida Swangnetr from North Carolina State University, USA and Dr. Alberto Greco from University of Pisa, Italy in discussion of their work on related research.