Multimodal Emotion Recognition from Wearables using Hybrid Feature Extraction and Ensemble Learning

Multimodal Emotion Recognition from Wearables using Hybrid Feature Extraction and Ensemble Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 January 2025 V1 Latest version Share on Multimodal Emotion Recognition from Wearables using Hybrid Feature Extraction and Ensemble Learning Authors : Muhammed Enes Subasi , Mustafa Karabulut , Ali Serdar Atalay , Saeed Mian Qaisar , and Abdulhamit Subasi [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173675479.91009165/v1 352 views 137 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Emotion is a complex mental experience often accompanied by physiological changes such as rapid heartbeat, altered breathing, sweating, and shifts in facial expressions. Detecting emotions through the physiological signals poses a significant challenge but offers valuable applications, including developing wearable assistive devices and intelligent human-computer interactions. With the proliferation of wearable devices like smartwatches and wristbands, emotion detection in natural environments has gained prominence as a research focus. This study investigates automated emotion detection through physiological signals, specifically electrocardiograms (ECG) and galvanic skin responses (GSR), captured by wirelessly connected wearable devices. By integrating the Time Series Feature Extraction Library (TSFEL) and Recursive Feature Elimination (RFE), the rich set of temporal, statistical, spectral, and fractal features are mined and selected from these non-stationary signals. These features are then analyzed using machine learning classifiers, including “Artificial Neural Networks”, “k-Nearest Neighbor”, “Support Vector Machine”, “Random Forest”, “Adaboost”, and “gradient-boosted decision Trees” (XGBoost), for an automated emotion classification. A benchmark dataset is used to validate the methodology. The devised method secured the average classification accuracy of 79.46% for the case of eight-class problem while using the XGBoost classifier. The findings underscore the potential of intelligently combining the TSFEL, RFE, and ensemble of machine learning classifiers to enable an effective emotion detection via wearable devices. Supplementary Material File (multimodal emotion recognition from wearables using hybrid feature extraction and selection_v7.docx) Download 351.41 KB Information & Authors Information Version history V1 Version 1 13 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords electrocardiogram (ecg) emotion detection galvanic skin conductance (gsr) machine learning recursive feature elimination (rfe) time series feature extraction library (tsfel) Authors Affiliations Muhammed Enes Subasi Izmir Katip Celebi University View all articles by this author Mustafa Karabulut Megalabs View all articles by this author Ali Serdar Atalay BITNET bilişim hizmetleri ltd şti Merkez mah Keçiağılı cad Koru vilları No1A View all articles by this author Saeed Mian Qaisar American University of the Middle East View all articles by this author Abdulhamit Subasi [email protected] University at Albany View all articles by this author Metrics & Citations Metrics Article Usage 352 views 137 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Muhammed Enes Subasi, Mustafa Karabulut, Ali Serdar Atalay, et al. Multimodal Emotion Recognition from Wearables using Hybrid Feature Extraction and Ensemble Learning. Authorea . 13 January 2025. DOI: https://doi.org/10.22541/au.173675479.91009165/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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