Conference Paper2024Cited by 5

Performance Analysis of Ensemble and DNN Models for Decoding Mental Stress Utilizing ECG-Based Wearable Data Fusion

Noor Masrur, Nabarun Halder, Sami Rashid, Ashraful Islam, Tarem Ahmed

IEEE 12th International Black Sea Conference on Communications and Networking

IEEE, pp. 276-279

  • Good Health and Well-being — Relevance: 92%
DOI: 10.1109/blackseacom61746.2024.10646297URL

Abstract

The growing number of stress-related illnesses highlights the critical need for accurate and efficient stress detection techniques. Stress detection using wearable devices has gained significant attention due to its potential for promoting well- being. With the increased use of smartwatches, there is a scope to utilize this wearable for stress detection and management. Nowadays, the advancement of machine learning and deep learning techniques has significantly simplified the tasks of detection and classification. This study conducts a performance analysis of Ensemble and Deep Neural Network (DNN) models for real time stress detection leveraging a fusion of Electrocardiogram (ECG) data, a well-known indicator of stress-induced physiological changes, sourced from two different datasets of smartwatch data. The models undergo training and testing using four different combinations of both datasets, and the optimum results are subsequently compared. The study demonstrated an accuracy of 94.66%, coupled with a Matthews Correlation Coefficient (MCC) score of 86.01 %, when utilizing a single dataset. Conversely, employing a data-level fusion approach with two datasets yielded an accuracy of 91.93 % and an MCC score of 79.3%. The results highlight a promising method for stress detection using real-time data obtained from smartwatches.

Keywords

  • deep neural network
  • mental stress detection
  • electrocardiogram
  • smartwatches
  • Ensemble learning
  • wearable devices
  • real-time stress monitoring
  • data fusion
  • physiological signals

CCDS Authors

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Sami Rashid
Sami Rashid

Sami Rashid

  • Intern, CCDS

Research Interests

Machine Learning

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

References

  1. 1.Omer Sagi, Lior Rokach. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 8(4)[10.1002/widm.1249]
  2. 2.Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, Kristof Van Laerhoven. (2018). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. , 400–408[10.1145/3242969.3242985]
  3. 3.Oona Rainio, Jarmo Teuho, Riku Klén. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1), 6086[10.1038/s41598-024-56706-x]
  4. 4.Jacopo Tosi, Fabrizio Taffoni, Marco Santacatterina, Roberto Sannino, Domenico Formica. (2017). Performance Evaluation of Bluetooth Low Energy: A Systematic Review. Sensors, 17(12), 2898[10.3390/s17122898]
  5. 5.Fatma M. Talaat, Rana Mohamed El-Balka. (2023). Stress monitoring using wearable sensors: IoT techniques in medical field. Neural Computing and Applications, 35(25), 18571–18584[10.1007/s00521-023-08681-z]
  6. 6.Pedro Elkind Velmovitsky, Paulo Alencar, Scott T. Leatherdale, Donald Cowan, Plinio Pelegrini Morita. (2022). Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study. Frontiers in Digital Health, 4, 1058826[10.3389/fdgth.2022.1058826]
  7. 7.Sara Alnasser, Dalal Alkalthem, Sara Alenazi, Muneera Alsowinea, Narin Alanazi, Ahmed Al Fagih. (2023). The Reliability of the Apple Watch’s Electrocardiogram. Cureus, 15(12), e49786[10.7759/cureus.49786]
  8. 8.Areej Mustafa, Maitha Alahmed, Aysha Alhammadi, Bassel Soudan. (2020). Stress Detector System Using IoT and Artificial Intelligence. 2020 Advances in Science and Engineering Technology International Conferences (ASET), 1–6[10.1109/aset48392.2020.9118345]

Cited By

  1. 1.Mengting Huang, Haidong Yang, Ningtao Sun, Guangshun Chen, Dalin Li, Tianyuan Zhu, Yanchun Liang, Gang Lin. (2024). Study of a Hybrid CNN-SVM Model for Stress Detection with Automated Heart Rate Variability Feature Extraction Method. , 316–319[10.1109/ichih63459.2024.11064910]
  2. 2.Maxine He, Abdulrahman Alkurdi, Jean L. Clore, Richard B. Sowers, Elizabeth T. Hsiao-Wecksler, Manuel E. Hernandez. (2025). Scoping Review of ML Approaches in Anxiety Detection from In-Lab to In-the-Wild. Applied Sciences, 15(18), 10099[10.3390/app151810099]
  3. 3.Aarti Sathyanarayana, Ohida Binte Amin, Jennie An, Jukka Pekka Onnela. (2026). Examining the Use of Consumer Wearable Devices and Digital Tools for Stress Measurement in College Students: Scoping Review of Methods. JMIR mhealth and uhealth, 14, e64144[10.2196/64144]
  4. 4.Milan Milivojević, Ana Gavrovska. (2025). Cognitive Cardiac Assessment Using Low-Cost Electrocardiogram Acquisition System. Electronics, 14(12), 2468[10.3390/electronics14122468]
  5. 5.Fateh Bahadur Kunwar, Rakesh KumarYadav, Hitendra Singh. (2025). Novel Hybrid Neural Network Architectures for Stress Detection. Smart innovation, systems and technologies, 327–336[10.1007/978-981-96-5589-2_29]
← All publications