Resource-Efficient Stress Forecasting: Validating Lightweight Machine Learning Models for Wearable Implementation

Resource-Efficient Stress Forecasting: Validating Lightweight Machine Learning Models for Wearable Implementation | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 2 December 2025 V1 Latest version Share on Resource-Efficient Stress Forecasting: Validating Lightweight Machine Learning Models for Wearable Implementation Author : Sandhya Patel 0009-0003-2263-4573 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176463688.83698999/v1 124 views 91 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The generalization of wearable-based stress forecasting models to unseen individuals is a well-established barrier to real-world deployment. While deep temporal architectures like LSTMs are often presumed superior for physiological time-series, this assumption remains poorly tested against robust, featurebased baselines in subject-independent settings. This research provides a rigorous evaluation using the public WESAD dataset, comprising electrocardiography (ECG), electrodermal activity (EDA), and other peripheral signals. We compare an LSTM network against a strong Logistic Regression baseline trained on windowed statistical features, employing a gold-standard Leave-One-Subject-Out (LOSO) cross-validation protocol. Our results reveal that the feature-based Logistic Regression (74.67% accuracy) outperforms the end-to-end LSTM (73.33%). Crucially, the LSTM demonstrates no statistical advantage (p = 0.859), indicating its complex sequential modeling fails to overcome the challenge of inter-subject physiological variability. This work establishes a critical and surprisingly competitive performance benchmark, arguing that the field's focus must shift from increasing architectural complexity towards robust feature engineering and explicit personalization techniques to achieve meaningful generalization. Supplementary Material File (patel_resource_efficient_stress_forecasting.pdf) Download 861.23 KB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords logistic regression lstm subject-independent forecasting wearable stress detection wesad Authors Affiliations Sandhya Patel 0009-0003-2263-4573 [email protected] Dept. of Mechanical and Automation Engineering, Indira Gandhi Delhi Technical University for Women New Delhi View all articles by this author Metrics & Citations Metrics Article Usage 124 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sandhya Patel. Resource-Efficient Stress Forecasting: Validating Lightweight Machine Learning Models for Wearable Implementation. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463688.83698999/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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