Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
subject: 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
description: 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.
creator: Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep
source: Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 21903018; ISBN: 978-981977716-7;
language: English
type: Conference paper
coverage: 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