MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS
Abstract & Details
Research Area
Computer Science and Engineering
Keywords
Stress Detection
Machine Learning
Wearable Sensors
WESAD Dataset
Deep Neural Network
HRV
EDA
RESP
Abstract
Stress significantly impacts both mental and physical health, emphasizing the need for a reliable, real-time, and scalable detection system. Traditional approaches, such as self-reported questionnaires, are often subjective and lack immediacy. This research presents a machine learning-based framework for stress detection using physiological signals—specifically heart rate variability (HRV), electrodermal activity (EDA), and respiration rate (RESP)—captured through wearable sensors. The WESAD dataset is used for extensive data preprocessing and feature extraction, followed by the implementation of three classification models: Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). Model performance is evaluated using leave-one-subject-out cross-validation, with metrics such as accuracy, F1-score, and ROC-AUC. Among the tested models, the DNN achieved the highest accuracy of 91.5% and an F1-score of 0.91. These results highlight the effectiveness of wearable sensor-based machine learning systems for real-time stress detection and continuous health monitoring.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Akash S K | CMR University |
How to Cite
Use the following formats to cite this article in your research.
APA Style
K, Akash S (2025). MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 541-545.
MLA Style
K, Akash S. "MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 541-545.
IEEE Style
Akash S K, "MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 541-545, 2025.
Vancouver Style
K Akash S. MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):541-545.
Harvard Style
K, Akash S (2025) 'MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 541-545.
Chicago Style
K, Akash S. "MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 541-545.
Turabian Style
K, Akash S. "MACHINE LEARNING-BASED STRESS DETECTION USING PHYSIOLOGICAL SIGNALS FROM WEARABLE SENSORS." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 541-545.
Related Research
CYBERSECURITY WITH AI
PDF Unavailable
DESIGN AND IMPLEMENTATION OF A SECURE IMAGE STEGANOGRAPHY SYSTEM USING LSB AND CRYPTOGRAPHY
PDF Unavailable
A NOVEL HYBRID IMAGE STEGANOGRAPHY TECHNIQUE BASED ON LSB AND CRYPTOGRAPHIC SECURITY
PDF Unavailable
BioPrint AI: An Intelligent Deep Learning and Computer Vision Based Blood Group Identification System Using Fingerprint Patterns
PDF Unavailable
AnimalAid AI: A Deep Learning Powered Early Warning System for Detecting Skin Infections and Diseases in Stray Dogs
PDF Unavailable
LiverCare AI: Intelligent Medical Imaging Platform for Liver Tumor Detection and Clinical Guidance
PDF Unavailable