Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Preema, P.Y.; Chandra, J.; Angel, C. Steffi Title A name given to the resource Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1294 LNNS;pp.197-209 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3253-4_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3253-4_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010645489?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010645489?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Preema P.Y., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India; Chandra J., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India; Angel C.S., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India Description An account of the resource Real-time emotion identification is an innovation in the field of humancomputer interaction, which is an essential and challenging task. The existing studies methods for identifying emotions include face, audio, and physiological signals. The study aims to develop a model for emotion classification to identify and interpret human emotions through skin temperature, respiration, and plethysmography. The study also includes analyzing and interpreting emotional states through ensemble models. The classification is based on the frequency domain signal components extracted using the Fast Fourier Transform (FFT), such as amplitude and frequency, to identify emotional states. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and ensemble methods to analyze the signals. The comparative classification rate of unimodal results with ensemble shows that it is the highest at 85.99%, achieved for sad emotions by XGBoost. Fusing modules like respiration, skin temperature, and plethysmography maintains the accuracy level for all four emotions. The unimodal temperature has the highest accuracy of 86.1% for calm, whereas the fusion model has maintained accuracy for all the emotional states. The feature amplitude is the most promising feature for the classification method, which attains an average of 83.2% for XGBoost. The applications enhance user experiences and contribute valuable help in psychology, mental health care, and HumanComputer Interaction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource DEAP dataset; Emotion recognition; Frequency domain analysis; Peripheral signals; Signal processing Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981963252-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online