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Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010275","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T17:34:59Z","timestamp":1659029699000},"page":"e1010275","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":49,"title":["Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4680-3071","authenticated-orcid":true,"given":"Rafiul","family":"Amin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5117-2628","authenticated-orcid":true,"given":"Rose T.","family":"Faghih","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,7,28]]},"reference":[{"key":"pcbi.1010275.ref001","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4614-1126-0","volume-title":"Electrodermal activity","author":"W Boucsein","year":"2012"},{"key":"pcbi.1010275.ref002","doi-asserted-by":"crossref","unstructured":"Wickramasuriya DS, Qi C, Faghih RT. 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