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 Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep Title A name given to the resource Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning 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 Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7717-4_23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000473335?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000473335?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 Probha A., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thind P.S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Gupta A.D., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource This research explores the potential of Internet of Things (IoT)-enabled wearable sensors in conjunction with machine learning techniques for real-time, non-invasive stress monitoring in women, using physiological data indicative of stress states. An IoT Wearables Dataset for Womens Safety: Stress Detection and Analysis was sourced from IEEE Data port and was used to train four supervised machine learning models, namely, random forest, support vector machines (SVM), gradient boosting, and logistic regression to classify individuals into categories of stressed and unstressed based on a predefined threshold of 0.35. The random forest algorithm attained the highest accuracy of 88.5% in categorizing stress, demonstrating reliable capabilities in identifying stress indicators from wearable sensor data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Accuracy; Internet of Things (IoT); Logistic regression; Machine learning; Mental health; Personalized stress detection; Physiological data; Random forest; Safety; Stress detection; Support vector machines (SVM); Wearable sensors; Womens health Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981977716-7; 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