Overview of Techniques and Methods for Stress Recognition

Abstract

Stress has become a significant cause for many diseases in the modern society, such as high blood pressure, atherosclerosis, heart disease, obesity, diabetes, insomnia etc. Moreover, Covid-19 pandemic negatively affects people’s mental health, increasing depression and anxiety. This raised the question of whether automatic stress detection and recognition systems can be developed and used in everyday life. In this review study, we will examine the recent works on stress recognition systems by reviewing the techniques and methods used. Only studies involving human participants were taken into consideration, as no such analysis has been made so far. By providing a comprehensive review of the state-of-the-art, we would like to encourage other researchers to take an active participation in the field of stress research as well as to explore the benefits and opportunities offered by stress recognition systems.

Keywords

• Stress recognition
• stress detection
• physiological
• behavioural
• multimodal
• machine learning

References

1. Rodríguez-Arce, J., Lara-Flores, L., Portillo-Rodríguez, O., & Martínez-Méndez, R. (2020). Towards an anxiety and stress recognition system for academic environments based on physiological features. Computer methods and programs in biomedicine, 190, 105408.
2. Jebelli, H., Hwang, S., & Lee, S. (2018). EEG-based workers' stress recognition at construction sites. Automation in Construction, 93, 315-324.
3. Hong, K., Liu, G., Chen, W., & Hong, S. (2018). Classification of the emotional stress and physical stress using signal magnification and canonical correlation analysis. Pattern Recognition, 77, 140-149.
4. Pourmohammadi, S., & Maleki, A. (2020). Stress detection using ECG and EMG signals: A comprehensive study. Computer methods and programs in biomedicine, 193, 105482.
5. Zhang, B., Morère, Y., Sieler, L., Langlet, C., Bolmont, B., & Bourhis, G. (2017). Reaction time and physiological signals for stress recognition. Biomedical Signal Processing and Control, 38, 100-107.
6. Jebelli, H., Khalili, M. M., & Lee, S. (2018). A continuously updated, computationally efficient stress recognition framework using electroencephalogram (EEG) by applying online multitask learning algorithms (OMTL). IEEE journal of biomedical and health informatics, 23(5), 1928-1939.
7. Moon, J., Lee, J., Cheon, D., Lee, M., & Lee, K. (2019, November). Stress Recognition with State Classification Considering Temporal Variation of Stress Responses. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2852-2859). IEEE.
8. Gaikwad, P., & Paithane, A. N. (2017, July). Novel approach for stress recognition using EEG signal by SVM classifier. In 2017 International Conference on Computing Methodologies and Communication (ICCMC) (pp. 967-971). IEEE.

9. Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., & Sooriyabandara, M. (2016, March). HealthyOffice: Mood recognition at work using smartphones and wearable sensors. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (pp. 1-6). IEEE.
10. Jun, G., & Smitha, K. G. (2016, October). EEG based stress level identification. In 2016 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 003270-003274). IEEE.
11. Hasanbasic, A., Spahic, M., Bosnjic, D., Mesic, V., & Jahic, O. (2019, March). Recognition of stress levels among students with wearable sensors. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-4). IEEE.
12. Jeon, T., Bae, H., Lee, Y., Jang, S., & Lee, S. (2020, January). Stress Recognition using Face Images and Facial Landmarks. In 2020 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-3). IEEE.
13. Montesinos, V., Dell’Agnola, F., Arza, A., Aminifar, A., & Atienza, D. (2019, July). Multi-modal acute stress recognition using off-the-shelf wearable devices. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2196-2201). IEEE.
14. Liew, W. S., Seera, M., Loo, C. K., Lim, E., & Kubota, N. (2015). Classifying stress from heart rate variability using salivary biomarkers as reference. IEEE transactions on neural networks and learning systems, 27(10), 2035-2046.
15. Giannakakis, G., Manousos, D., Chaniotakis, V., & Tsiknakis, M. (2018, March). Evaluation of head pose features for stress detection and classification. In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 406-409). IEEE.
16. Chen, C., Li, C., Tsai, C. W., & Deng, X. (2019, May). Evaluation of mental stress and heart rate variability derived from wrist-based photoplethysmography. In 2019 IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) (pp. 65-68). IEEE.
17. Giannakakis, G., Trivizakis, E., Tsiknakis, M., & Marias, K. (2019, September). A novel multi-kernel 1D convolutional neural network for stress recognition from ECG. In 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) (pp. 1-4). IEEE.
18. Likforman-Sulem, L., Esposito, A., Faundez-Zanuy, M., Clémençon, S., & Cordasco, G. (2017). EMOTHAW: A novel database for emotional state recognition from handwriting and drawing. IEEE Transactions on Human-Machine Systems, 47(2), 273-284.
19. Alić, B., Sejdinović, D., Gurbeta, L., & Badnjevic, A. (2016, June). Classification of stress recognition using artificial neural network. In 2016 5th Mediterranean Conference on Embedded Computing (MECO) (pp. 297-300). IEEE.
20. Liu, Y., Lan, Z., Traspsilawati, F., Sourina, O., Chen, C. H., & Müller-Wittig, W. (2019, October). EEG-based Human Factors Evaluation of Air Traffic Control Operators (ATCOs) for Optimal Training. In 2019 International Conference on Cyberworlds (CW) (pp. 253-260). IEEE.
21. Ayzeren, Y. B., Erbilek, M., & Çelebi, E. (2019). Emotional state prediction from online handwriting and signature biometrics. IEEE Access, 7, 164759-164774.
22. Drosou, A., Giakoumis, D., & Tzovaras, D. (2017, June). Affective state aware biometric recognition. In 2017 International conference on engineering, technology and innovation (ICE/ITMC) (pp. 601-610). IEEE..
23. Liu, Y., & Jiang, C. (2019). Recognition of shooter’s emotions under stress based on affective computing. IEEE Access, 7, 62338-62343.
24. Corichi, E. Z., Carranza, J. M., Garca, C. A. R., & Pineda, L. V. (2017, October). Real-time prediction of altered states in Drone pilots using physiological signals. In 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) (pp. 246-251). IEEE.
25. Khan, A. M., & Lawo, M. (2016, September). Developing a system for recognizing the emotional states using physiological devices. In 2016 12th International Conference on Intelligent Environments (IE) (pp. 48-53). IEEE.
26. Lawanot, W., Inoue, M., Yokemura, T., Mongkolnam, P., & Nukoolkit, C. (2019, January). Daily stress and mood recognition system using deep learning and fuzzy clustering for promoting better well-being. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-6). IEEE.
27. Liao, C. Y., Chen, R. C., & Tai, S. K. (2018, April). Emotion stress detection using EEG signal and deep learning technologies. In 2018 IEEE International Conference on Applied System Invention (ICASI) (pp. 90-93). IEEE.
28. Boccanfuso, L., Wang, Q., Leite, I., Li, B., Torres, C., Chen, L., ... & Shic, F. (2016, August). A thermal emotion classifier for improved human-robot interaction. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 718-723). IEEE.
29. Lee, S. I., Lee, S. H., Plataniotis, K. N., & Ro, Y. M. (2016). Experimental investigation of facial expressions associated with visual discomfort: feasibility study toward an objective measurement of visual discomfort based on facial expression. Journal of Display Technology, 12(12), 1785-1797..
30. Ghosh, S., Goenka, S., Ganguly, N., Mitra, B., & De, P. (2019, September). Representation Learning for Emotion Recognition from Smartphone Keyboard Interactions. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 704-710). IEEE.