Real-Time Stress Monitoring Using IoT Wearable Sensors and Machine Learning
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
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.
Probha, Aaron; Thind, Pawanjeet Singh; Gupta, Amarthya Dutta; Prathap, Boppuru Rudra; Kumar, Kukatlapalli Pradeep
Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.331-342
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-97-7717-4_23 https://www.scopus.com/pages/publications/86000473335?origin=resultslist
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