An IoT-based Smart Emotion Recognition Using Internal Body Parameters

An IoT-based Smart Emotion Recognition Using Internal Body Parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article An IoT-based Smart Emotion Recognition Using Internal Body Parameters Tayyaba Rashid, Imran Sarwar Bajwa, Jungsuk Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7165791/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 25 You are reading this latest preprint version Abstract Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in mental health monitoring, human-computer interaction, and stress management. This study focuses on recognizing six emotional states—neutral, happy, sad, fear, anger, and surprise—using internal body parameters such as blood pressure, oxygen saturation, blood glucose, heart rate, and body temperature. Leveraging an Internet of Things (IoT)-enabled framework, real-time data was collected from participants. The collected data underwent preprocessing, including data selection, data cleaning, normalization, and feature extraction, to enhance its quality and reliability. Various machine learning classifiers, including Decision Tree, Random Forest, Gradient Boost, Support Vector Machine, Multi-layer perceptron, and Logistic regression, were employed to classify emotions based on physiological features. Experimental results revealed that the Random Forest model achieved the highest accuracy (96.5%), outperforming other classifiers, followed by Decision Tree (94.2%). The IoT system was tested for real-time performance, achieving robust classification accuracy under varying network conditions. The findings indicate that physiological signals, combined with IoT and machine learning, provide an effective framework for emotion recognition. This research contributes to the development of real-time, non-invasive emotion recognition systems, with promising applications in healthcare, wearable devices, and personalized user experiences. Future work will explore the integration of additional physiological parameters and advanced deep-learning models for enhanced accuracy and scalability, and usage in advanced technology. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Emotion Recognition Internet of Things(IoT) Machine Learning Internal body parameters Smart Emotion Recognition System (SERS) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers invited by journal 31 Jul, 2025 Editor assigned by journal 31 Jul, 2025 Editor invited by journal 31 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 30 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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