ItemConference Paper

Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning

Title
Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
Creator
Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep
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.
Source
Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
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
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21903018; ISBN: 978-981977716-7;
Format
online
Language
English
Type
Conference paper

Citation

Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep, “Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25651.

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