Overview of Biosignal Analysis Methods for the Assessment of Stress

I. Ladakis, I. Chouvarda


Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation   of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems.  Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community.


Doi: 10.28991/esj-2021-01267

Full Text: PDF


Stress; Biosignals; Analysis; Stress Detection; Stress Assessment.


Selye, Hans. “The Stress of Life”, McGraw-Hill Book Company, United States (1956).

Cannon, W. “Bodily Changes in Pain, Hunger, Fear and Rage. Ed.” Appleton & Company, (1915).

Suzuki, Shin-ichi, and Daisuke Ito. “Psychological Stress.” Encyclopedia of Behavioral Medicine (2013): 1561–1561. doi:10.1007/978-1-4419-1005-9_421.

Salomon K. “Mental Stress. In: Gellman M.D., Turner J.R. (eds) Encyclopedia of Behavioral Medicine. Springer, New York, NY, (2013).

Elliot, Glenn R., and Carl Eisdorfer. "Stress and human health: An analysis and implications of research. A study by the Institute of Medicine." National Academy of Sciences 79 (1982): 11-24.

Segerstrom, Suzanne C., and Gregory E. Miller. “Psychological Stress and the Human Immune System: A Meta-Analytic Study of 30 Years of Inquiry.” Psychological Bulletin 130, no. 4 (2004): 601–630. doi:10.1037/0033-2909.130.4.601.

Elzeiny, Sami, and Marwa Qaraqe. "Blueprint to workplace stress detection approaches." In 2018 International Conference on Computer and Applications (ICCA), IEEE (2018): 407-412. doi:10.1109/COMAPP.2018.8460293.

Schneiderman, Neil, Gail Ironson, and Scott D. Siegel. "Stress and health: psychological, behavioral, and biological determinants." Annual review of clinical psychology 1 (2005). doi:10.1146/annurev.clinpsy.1.102803.144141.

The Workplace and Health. 2016. The workplace and health. The Robert Wood Johnson Foundation. Available online: http://www.rwjf.org/en/library/research/2016/07/the-workplace-and-health.html (accessed on 26 December 2020).

Hassard, Juliet, Kevin RH Teoh, Gintare Visockaite, Philip Dewe, and Tom Cox. "The cost of work-related stress to society: A systematic review." Journal of occupational health psychology 23, no. 1 (2018): 1-17. doi:10.1037/ocp0000069.

Zawadzki, Matthew J., Stacey B. Scott, David M. Almeida, Stephanie T. Lanza, David E. Conroy, Martin J. Sliwinski, Jinhyuk Kim, et al. “Understanding Stress Reports in Daily Life: a Coordinated Analysis of Factors Associated with the Frequency of Reporting Stress.” Journal of Behavioral Medicine 42, no. 3 (January 1, 2019): 545–560. doi:10.1007/s10865-018-00008-x.

Ganster, Daniel C., and Christopher C. Rosen. “Work Stress and Employee Health.” Journal of Management 39, no. 5 (February 19, 2013): 1085–1122. doi:10.1177/0149206313475815.

Gladwell, Valerie, and D K Brown. “Green Exercise in the Workplace.” Green Exercise: Linking Nature, Health and Well-Being, (2016): 139–49.

Klatt, Maryanna, Chris Norre, Brenda Reader, Laura Yodice, and Susan White. "Mindfulness in motion: A mindfulness-based intervention to reduce stress and enhance quality of sleep in Scandinavian employees." Mindfulness 8, no. 2 (2017): 481-488. doi:10.1007/s12671-016-0621-x.

Yaribeygi, Habib, Yunes Panahi, Hedayat Sahraei, Thomas P. Johnston, and Amirhossein Sahebkar. "The impact of stress on body function: A review." EXCLI journal 16 (2017): 1057-1072. doi:10.17179/excli2017-480.

Sioni, Riccardo, and Luca Chittaro. "Stress detection using physiological sensors." Computer 48, no. 10 (2015): 26-33. doi:10.1109/MC.2015.316.

Kyriakou, Kalliopi, Bernd Resch, Günther Sagl, Andreas Petutschnig, Christian Werner, David Niederseer, Michael Liedlgruber, Frank H. Wilhelm, Tess Osborne, and Jessica Pykett. "Detecting moments of stress from measurements of wearable physiological sensors." Sensors 19, no. 17 (2019): 3805. doi:10.3390/s19173805.

Goumopoulos, C., and E. Menti. "Stress Detection in Seniors Using Biosensors and Psychometric Tests." Procedia Computer Science 152 (2019): 18-27. doi:10.1016/j.procs.2019.05.022.

Borthakur, Debanjan. "Cardiorespiratory Optimized Guided-Breathing for Post-Stress Recovery in a Group Setting." PhD diss., McMaster University, (2020).

Bakker, Jorn, Mykola Pechenizkiy, and Natalia Sidorova. "What's your current stress level? Detection of stress patterns from GSR sensor data." In 2011 IEEE 11th international conference on data mining workshops, IEEE, (2011): 573-580. doi:10.1109/ICDMW.2011.178.

Zhang, Bo, Yann Morère, Loïc Sieler, Cécile Langlet, Benoît Bolmont, and Guy Bourhis. “Stress Recognition from Heterogeneous Data.” Journal of Image and Graphics 4, no. 2 (2016): 116–121. doi:10.18178/joig.4.2.116-121.

Yu, Bin, Mathias Funk, Jun Hu, Qi Wang, and Loe Feijs. “Biofeedback for Everyday Stress Management: A Systematic Review.” Frontiers in ICT 5 (September 7, 2018). doi:10.3389/fict.2018.00023.

Giannakakis, Giorgos, Dimitris Grigoriadis, Katerina Giannakaki, Olympia Simantiraki, Alexandros Roniotis, and Manolis Tsiknakis. "Review on psychological stress detection using biosignals." IEEE Transactions on Affective Computing (2019): 1-22. doi:10.1109/TAFFC.2019.2927337.

Huysmans, Dorien, Elena Smets, Walter De Raedt, Chris Van Hoof, Katleen Bogaerts, Ilse Van Diest, and Denis Helic. "Unsupervised learning for mental stress detection-exploration of self-organizing maps." Proc. of Biosignals 2018 4 (2018): 26-35. doi:10.5220/0006541100260035.

Westerink, Joyce HDM, Egon L. Van Den Broek, Marleen H. Schut, Jan Van Herk, and Kees Tuinenbreijer. "Computing emotion awareness through galvanic skin response and facial electromyography." In Probing experience, Springer, Dordrecht, (2008): 149-162. doi:10.1007/978-1-4020-6593-4_14.

Hui, Terence KL, and R. Simon Sherratt. "Coverage of emotion recognition for common wearable biosensors." Biosensors 8, no. 2 (2018): 30. doi:10.3390/bios8020030.

Cheetham, Marcus, Cátia Cepeda, Hugo Gamboa, James Gilbert, Haim Azhari, and Ali Hesham. "Automated Detection of Mind Wandering: A Mobile Application." BIOSIGNALS 2016 (2016): 198-205. doi:10.5220/0005702401980205.

Al-shargie F. M., T. B. Tang, N. Badruddin, and M. Kiguchi. “Mental Stress Quantification Using EEG Signals.” International Conference for Innovation in Biomedical Engineering and Life Sciences (December 18, 2015): 15–19. doi:10.1007/978-981-10-0266-3_4.

Hosseini, Seyyed Abed, and Mohammad Bagher Naghibi-Sistani. "Classification of emotional stress using brain activity." Applied Biomedical Engineering 7 (2011): 32-41. doi:10.5772/18294.

Jena, Sunil. “Examination Stress and Its Effect on EEG.” International Journal of Medical Science and Public Health 4, no. 11 (2015): 1493. doi:10.5455/ijmsph.2015.23042015308.

Hou, Xiyuan, Yisi Liu, Olga Sourina, and Wolfgang Mueller-Wittig. “CogniMeter: EEG-Based Emotion, Mental Workload and Stress Visual Monitoring.” 2015 International Conference on Cyberworlds (CW) (October 2015): 153-160. doi:10.1109/cw.2015.58.

Subhani, Ahmad Rauf, Likun Xia, and Aamir Saeed Malik. "EEG signals to measure mental stress." In 2nd International Conference on Behavioral, Cognitive and Psychological Sciences, BCPS, Maldives (2011): 84-88.

Kalas, Mamta S., and B.F. Momin. “Stress Detection and Reduction Using EEG Signals.” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (March 2016): 471-475. doi:10.1109/iceeot.2016.7755604.

Jebelli, Houtan, Sungjoo Hwang, and Sang Hyun Lee. 2018. “EEG-Based Workers’ Stress Recognition at Construction Sites.” Automation in Construction 93 (May): 315–24. https://doi.org/10.1016/j.autcon.2018.05.027.

Arsalan, Aamir, Muhammad Majid, Amna Rauf Butt, and Syed Muhammad Anwar. "Classification of perceived mental stress using a commercially available EEG headband." IEEE journal of biomedical and health informatics 23, no. 6 (2019): 2257-2264. doi:10.1109/JBHI.2019.2926407.

Sun, Feng-Tso, Cynthia Kuo, Heng-Tze Cheng, Senaka Buthpitiya, Patricia Collins, and Martin Griss. “Activity-Aware Mental Stress Detection Using Physiological Sensors.” Mobile Computing, Applications, and Services (2012): 282–301. doi:10.1007/978-3-642-29336-8_16.

Shaffer, Fred, and J. P. Ginsberg. “An Overview of Heart Rate Variability Metrics and Norms.” Frontiers in Public Health 5 (September 28, 2017): 1-17. doi:10.3389/fpubh.2017.00258.

Kleiger, Robert E., Phyllis K. Stein, and J. Thomas Bigger. “Heart Rate Variability: Measurement and Clinical Utility.” Annals of Noninvasive Electrocardiology 10, no. 1 (January 2005): 88–101. doi:10.1111/j.1542-474x.2005.10101.x.

Fernandes de Godoy, Moacir. “Nonlinear Analysis of Heart Rate Variability: A Comprehensive Review.” Journal of Cardiology and Therapy 3, no. 3 (2016): 528–533. doi:10.17554/j.issn.2309-6861.2016.03.101-4.

Järvelin-Pasanen, Susanna, Sanna Sinikallio, and Mika P. Tarvainen. “Heart Rate Variability and Occupational Stress—systematic Review.” Industrial Health 56, no. 6 (2018): 500–511. doi:10.2486/indhealth.2017-0190.

Kim, Hye-Geum, Eun-Jin Cheon, Dai-Seg Bai, Young Hwan Lee, and Bon-Hoon Koo. “Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature.” Psychiatry Investigation 15, no. 3 (March 25, 2018): 235–245. doi:10.30773/pi.2017.08.17.

Rodríguez-Liñares, Leandro, X. Vila, A. Mendez, M. Lado, and D. Olivieri. "RHRV: An R-based software package for heart rate variability analysis of ECG recordings." In 3rd Iberian conference in systems and information technologies (CISTI 2008): 565-574.

Boucsein, Wolfram, Don C. Fowles, Sverre Grimnes, Gershon Ben-Shakhar, Walton T. Roth, Michael E. Dawson, and Diane L. Filion. “Publication Recommendations for Electrodermal Measurements.” Psychophysiology 49, no. 8 (June 8, 2012): 1017–1034. doi:10.1111/j.1469-8986.2012.01384.x.

Posada-Quintero, Hugo F. "Electrodermal Activity: What it can Contribute to the Assessment of the Autonomic Nervous System." Doctoral Dissertations, University of Connecticut, United States, (2016).

Lee, Heera, and Andrea Kleinsmith. “Public Speaking Anxiety in a Real Classroom.” Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (May 2, 2019). doi:10.1145/3290607.3312875.

Aqajari, S. Amir Hossein, E. Kasaeyan Naeini, M. Asgari Mehrabadi, S. Labbaf, A. M. Rahmani, and Nikil Dutt. "GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data." arXiv preprint arXiv:2005.01834 (2020).

Braithwaite, Jason J., Derrick G. Watson, Robert Jones, and Mickey Rowe. "A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments." Psychophysiology 49, no. 1 (2013): 1017-1034.

Greco, Alberto, Gaetano Valenza, Antonio Lanata, Enzo Scilingo, and Luca Citi. “CvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing.” IEEE Transactions on Biomedical Engineering 63 (4) (2016): 797–804. doi:10.1109/tbme.2015.2474131.

Benedek, Mathias, and Christian Kaernbach. “A Continuous Measure of Phasic Electrodermal Activity.” Journal of Neuroscience Methods 190, no. 1 (June 2010): 80–91. doi:10.1016/j.jneumeth.2010.04.028.

Benedek, Mathias, and Christian Kaernbach. “Decomposition of Skin Conductance Data by Means of Nonnegative Deconvolution.” Psychophysiology 47 (4) (March 2010): 647-658. doi:10.1111/j.1469-8986.2009.00972.x.

Nicolò, Andrea, Carlo Massaroni, and Louis Passfield. “Respiratory Frequency During Exercise: The Neglected Physiological Measure.” Frontiers in Physiology 8 (December 11, 2017): 1-8. doi:10.3389/fphys.2017.00922.

Han, Lu, Qiang Zhang, Xianxiang Chen, Qingyuan Zhan, Ting Yang, and Zhan Zhao. “Detecting Work-Related Stress with a Wearable Device.” Computers in Industry 90 (September 2017): 42–49. doi:10.1016/j.compind.2017.05.004.

Massaroni, Carlo, Andrea Nicolo, Massimo Sacchetti, and Emiliano Schena. “Contactless Methods for Measuring Respiratory Rate: A Review.” IEEE Sensors Journal (2020): 1–1. doi:10.1109/jsen.2020.3023486.

Hernando, Alberto, Jesus Lazaro, Eduardo Gil, Adriana Arza, Jorge Mario Garzon, Raul Lopez-Anton, Concepcion de la Camara, Pablo Laguna, Jordi Aguilo, and Raquel Bailon. “Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment.” IEEE Journal of Biomedical and Health Informatics 20, no. 4 (July 2016): 1016–1025. doi:10.1109/jbhi.2016.2553578.

Harianto, Januar, Nicholas Carey, and Maria Byrne. “respR—An R Package for the Manipulation and Analysis of Respirometry Data.” Edited by Samantha Price. Methods in Ecology and Evolution 10, no. 6 (February 20, 2019): 912–920. doi:10.1111/2041-210x.13162.

Elsayed, Nelly, Zaghloul Saad, and Magdy Bayoumi. “Brain Computer Interface: EEG Signal Preprocessing Issues and Solutions.” International Journal of Computer Applications 169, no. 3 (July 17, 2017): 12–16. doi:10.5120/ijca2017914621.

Bigdely-Shamlo, Nima, Tim Mullen, Christian Kothe, Kyung-Min Su, and Kay A. Robbins. “The PREP Pipeline: Standardized Preprocessing for Large-Scale EEG Analysis.” Frontiers in Neuroinformatics 9 (June 18, 2015): 1-19. doi:10.3389/fninf.2015.00016.

Pedroni, Andreas, Amirreza Bahreini, and Nicolas Langer. “Automagic: Standardized Preprocessing of Big EEG Data.” NeuroImage 200 (October 2019): 460–473. doi:10.1016/j.neuroimage.2019.06.046.

Saitis, Charalampos, and Kyriaki Kalimeri. “Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals.” IEEE Transactions on Affective Computing 12, no. 1 (January 1, 2021): 203–214. doi:10.1109/taffc.2018.2866865.

Bao, Forrest Sheng, Xin Liu, and Christina Zhang. "PyEEG: an open source python module for EEG/MEG feature extraction." Computational intelligence and neuroscience 2011 (2011). doi:.1155/2011/406391.

Helwig, Author Nathaniel E, and Maintainer Nathaniel E Helwig. (2018). “Package ‘Eegkit’”. Available online: https://cran.r-project.org/web/packages/eegkit/eegkit.pdf (accessed on January 2021).

A.R., Sohara Banu, and Nagaveni V. “Bio-Signal Analysis for StressDetection Using Machine Learning Methods: A Review.” 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (October 9, 2020): 452-458. doi:10.1109/icstcee49637.2020.9277314.

Shatte, Adrian B. R., Delyse M. Hutchinson, and Samantha J. Teague. “Machine Learning in Mental Health: a Scoping Review of Methods and Applications.” Psychological Medicine 49, no. 09 (February 12, 2019): 1426–1448. doi:10.1017/s0033291719000151.

Gradl, Stefan, Markus Wirth, Robert Richer, Nicolas Rohleder, and Bjoern M. Eskofier. “An Overview of the Feasibility of Permanent, Real-Time, Unobtrusive Stress Measurement with Current Wearables.” Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (May 20, 2019). doi:10.1145/3329189.3329233.

Elzeiny, Sami, and Marwa Qaraqe. “Machine Learning Approaches to Automatic Stress Detection: A Review.” 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (October 2018). doi:10.1109/aiccsa.2018.8612825.

Rizwan, Md Fahim, Rayed Farhad, Farhan Mashuk, Fakhrul Islam, and Mohammad Hasan Imam. “Design of a Biosignal Based Stress Detection System Using Machine Learning Techniques.” 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST) (January 2019): 364-368. doi:10.1109/icrest.2019.8644259.

Padmaja, B., V. V. Rama Prasad, and K. V. N. Sunitha. “Machine Learning Approach for Stress Detection Using Wireless Physical Activity Tracker.” International Journal of Machine Learning and Computing 8, no. 1 (February 2018): 33–38. doi:10.18178/ijmlc.2018.8.1.659.

Pandey, Purnendu Shekhar. “Machine Learning and IoT for Prediction and Detection of Stress.” 2017 17th International Conference on Computational Science and Its Applications (ICCSA) (July 2017). doi:10.1109/iccsa.2017.8000018.

Munla, Nermine, Mohamad Khalil, Ahmad Shahin, and Azzam Mourad. “Driver Stress Level Detection Using HRV Analysis.” 2015 International Conference on Advances in Biomedical Engineering (ICABME) (September 2015): 61-64. doi:10.1109/icabme.2015.7323251.

Elgendi, Mohamed, and Carlo Menon. “Machine Learning Ranks ECG as an Optimal Wearable Biosignal for Assessing Driving Stress.” IEEE Access 8 (2020): 34362–34374. doi:10.1109/access.2020.2974933.

Smets, Elena, Pierluigi Casale, Ulf Großekathöfer, Bishal Lamichhane, Walter De Raedt, Katleen Bogaerts, Ilse Van Diest, and Chris Van Hoof. “Comparison of Machine Learning Techniques for Psychophysiological Stress Detection.” Pervasive Computing Paradigms for Mental Health (2016): 13–22. doi:10.1007/978-3-319-32270-4_2.

Nakashima, Yoshiki, Jonghwa Kim, Simon Flutura, Andreas Seiderer, and Elisabeth André. “Stress Recognition in Daily Work.” Pervasive Computing Paradigms for Mental Health (2016): 23–33. doi:10.1007/978-3-319-32270-4_3.

Sriramprakash, S., Vadana D Prasanna, and O.V. Ramana Murthy. “Stress Detection in Working People.” Procedia Computer Science 115 (2017): 359–366. doi:10.1016/j.procs.2017.09.090.

Cho, Dongrae, Jinsil Ham, Jooyoung Oh, Jeanho Park, Sayup Kim, Nak-Kyu Lee, and Boreom Lee. “Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.” Sensors 17, no. 10 (October 24, 2017): 2435. doi:10.3390/s17102435.

Minguillon, Jesus, Eduardo Perez, Miguel Lopez-Gordo, Francisco Pelayo, and Maria Sanchez-Carrion. “Portable System for Real-Time Detection of Stress Level.” Sensors 18, no. 8 (August 1, 2018): 2504. doi:10.3390/s18082504.

Lima, Rodrigo, Daniel Osório, and Hugo Gamboa. “Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome.” Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (2019): 42-51. doi:10.5220/0007355200420051.

Radhamani, Rakhi, Nijin Nizar, Dhanush Kumar, Gayathri Suresh Pillai, Lakshmi Swapna Prasad, Sreehari Sudheer Jitha, Midhun Krishna Vannathi Kuniyil, et al. “Computational Analysis of Cortical EEG Biosignals and Neural Dynamics Underlying an Integrated Mind-Body Relaxation Technique.” Procedia Computer Science 171 (2020): 341–349. doi:10.1016/j.procs.2020.04.035.

Siirtola, Pekka. “Continuous Stress Detection Using the Sensors of Commercial Smartwatch.” Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (September 9, 2019): 1198-1201. doi:10.1145/3341162.3344831.

Full Text: PDF

DOI: 10.28991/esj-2021-01267


  • There are currently no refbacks.

Copyright (c) 2021 Ioannis Ladakis, Ioanna Chouvarda