SingStress: Sensor Analysis in Acute Psychological Stimulation

Authors

  • Fan Jin
  • Yuhang Chen
  • Xiaowen Zhang
  • Yifeng Chen
  • Bolun Meng

DOI:

https://doi.org/10.61173/z6m7zd17

Keywords:

acute physiological stress, wearable sensors, correlation, mHealth

Abstract

Acute stress results in significant risks to the cardiovascular and immune systems, so monitor and manage effectively is important. Existing studies frequently utilize sensors such as photoplethysmography (PPG) and electrodermal activity (EDA) to monitor and evaluate stress, however, they often miss to compare the sensitivity of these sensors. In this experiment, we collected PPG and EDA data from 26 participants during resting and public speaking tasks. Participants were also asked to complete a stress-related questionnaire. Pearson’s correlation coefficients were calculated from the analysed data and questionnaire score. The results indicate that for the male group, the correlation with stress for PPG sensors ( ρSH = 0.5796 ) was stronger than for EDA sensors (ρSR = 0.1036 ). Oppositely, for the female group, EDA sensors showed a stronger correlation with stress ( ρSR = 0.6265 ) compared to PPG sensors ( ρSH = 0.2093 ). These findings suggest that PPG sensors are more sensitive for male students, while EDA sensors are more sensitive for female students.

References

[1] Neylon A, Canniffe C, Anand S, Kreatsoulas C, Blake GJ, Sugrue D, McGorrian C. A global perspective on psychosocial risk factors for cardiovascular disease. Prog Cardiovasc Dis. 2013 May-Jun;55(6):574-81. doi: 10.1016/j.pcad.2013.03.009. PMID: 23621967.

[2] Lagraauw HM, Kuiper J, Bot I. Acute and chronic psychological stress as risk factors for cardiovascular disease: Insights gained from epidemiological, clinical and experimental studies. Brain Behav Immun. 2015 Nov;50:18-30. doi: 10.1016/ j.bbi.2015.08.007. Epub 2015 Aug 6. PMID: 26256574.

[3] Lagraauw HM, Kuiper J, Bot I. Acute and chronic psychological stress as risk factors for cardiovascular disease: Insights gained from epidemiological, clinical and experimental studies. Brain Behav Immun. 2015 Nov;50:18-30. doi: 10.1016/ j.bbi.2015.08.007. Epub 2015 Aug 6. PMID: 26256574.

[4] Wirtz PH, von Känel R. Psychological Stress, Inflammation, and Coronary Heart Disease. Curr Cardiol Rep. 2017 Sep 20;19(11):111. doi: 10.1007/s11886-017-0919-x. PMID: 28932967.

[5] Segerstrom,S.C.,Miller,G.E.,2004.Psychological stress and the human immune system:a meta-analytic study of 30 years of inquiry.Psychol.Bull.130,601-630.

[6] Mostofsky E, Penner E A, Mittleman M A. Outbursts of anger as a trigger of acute cardiovascular events: a systematic review and meta-analysis[J]. European heart journal, 2014, 35(21): 1404-1410.

[7] Goyal A, Singh S, Vir D, et al. Automation of stress recognition using subjective or objective measures[J]. Psychological Studies, 2016, 61: 348-364.

[8] García, Esther, et al. “Psychological and physiological profiles in oncology caregivers: a multivariable cross-sectional study.” Transactions on Machine Learning and Artificial Intelligence 5.4 (2017).

[9] Urwyler, Sandrine A., et al. “Copeptin as a stress marker prior and after a written examination–the CoEXAM study.” Stress 18.1 (2015): 134-137.

[10] Arsalan A, Majid M. Human stress classification during public speaking using physiological signals. Comput Biol Med. 2021 Jun;133:104377. doi: 10.1016/j.compbiomed.2021.104377. Epub 2021 Apr 15. PMID: 33866254.

[11] Charlton, Peter H., et al. “Assessing mental stress from the photoplethysmogram: a numerical study.” Physiological measurement 39.5 (2018): 054001.

[12] Panigrahy, Saroj Kumar, Sanjay Kumar Jena, and Ashok Kumar Turuk. “Study and analysis of human stress detection using galvanic skin response (EDA) sensor in wired and wireless environments.” Research Journal of Pharmacy and Technology 10.2 (2017): 545-550.

[13] Saeed, Sanay Muhammad Umar, et al. “EEG based classification of long-term stress using psychological labeling.” Sensors 20.7 (2020): 1886.

[14] Herborn, Katherine A., et al. “Skin temperature reveals the intensity of acute stress.” Physiology & behavior 152 (2015): 225-230.

[15] Carroll, Douglas, et al. “Blood pressure reactions to acute mental stress and future blood pressure status: data from the 12- year follow-up of the West of Scotland Study.” Psychosomatic medicine 73.9 (2011): 737-742.

[16] Hakimi, Naser, et al. “Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy.” Computers in biology and medicine 121 (2020): 103810.

[17] Cho, Hyun-Myung, et al. “Ambulatory and laboratory stress detection based on raw electrocardiogram signals using a convolutional neural network.” Sensors 19.20 (2019): 4408.

[18] Tanev, George, et al. “Classification of acute stress using linear and non-linear heart rate variability analysis derived from sternal ECG.” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014.

[19] Karthikeyan, P., M. Murugappan, and Sazali Yaacob. “EMG signal based human stress level classification using wavelet packet transform.” Trends in Intelligent Robotics, Automation, Dean&Francis and Manufacturing: First International Conference, IRAM 2012, Kuala Lumpur, Malaysia, November 28-30, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

[20] Arsalan, Aamir, et al. “Classification of perceived human stress using physiological signals.” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019.

[21] Kothgassner, Oswald D., et al. “Salivary cortisol and cardiovascular reactivity to a public speaking task in a virtual and real-life environment.” Computers in human behavior 62 (2016): 124-135.

[22] Giraud, Tom, et al. “Multimodal expressions of stress during a public speaking task: Collection, annotation and global analyses.” 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, 2013.

[23] Ryoo DW, Kim YS, Lee JW. Wearable Systems for Service based on Physiological Signals. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2437-40. doi: 10.1109/IEMBS.2005.1616961. PMID: 17282730.

[24] Patil VK, Pawar VR, Randive S, Bankar RR, Yende D, Patil AK. From face detection to emotion recognition on the framework of Raspberry pi and galvanic skin response sensor for visual and physiological biosignals. Journal of Electrical Systems and Inf Technol. 2023;10(1):24. doi: 10.1186/s43067- 023-00085-2. Epub 2023 Apr 18. PMCID: PMC10110488.

[25] Seeed studio, Grove - EDA sensor, www.seeedstudio.com/ Grove-EDA-sensor-p-1614.html

[26] Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2018;4(4):195-202. doi: 10.15406/ ijbsbe.2018.04.00125. Epub 2018 Aug 6. PMID: 30906922; PMCID: PMC6426305.

[27] Warttig SL, Forshaw MJ, South J, White AK. New, normative, English-sample data for the Short Form Perceived Stress Scale (PSS-4). Journal of Health Psychology. 2013;18(12):1617-1628. doi:10.1177/1359105313508346)

[28] Bujang MA, Omar ED, Baharum NA. A Review on Sample Size Determination for Cronbach’s Alpha Test: A Simple Guide for Researchers. Malays J Med Sci. 2018 Nov;25(6):85-99. doi: 10.21315/mjms2018.25.6.9. Epub 2018 Dec 28. PMID: 30914882; PMCID: PMC6422571.

[29] Costa MA, Gonçalves FG, Ferreira-Garcia R, de Moraes F, Guedes de Nonohay R, Manfro GG. Heart rate variability as a predictor of improvement in emotional interference in Generalized Anxiety Disorder. J Psychiatr Res. 2021 Aug;140:22-29. doi: 10.1016/j.jpsychires.2021.05.059. Epub 2021 May 27. PMID: 34087752.

[30] P. Madhan Mohan et al., “Stress measurement from wearable photoplethysmographic sensor using heart rate variability data”, International Conference on Communication and Signal Processing, 2016.

[31] G. Shanmugasundaram, S. Yazhini, E. Hemapratha and S. Nithya, “A Comprehensive Review on Stress Detection Techniques,” 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-6, doi: 10.1109/ ICSCAN.2019.8878795.

[32] Montero-López, Eva, et al. “A virtual reality approach to the Trier Social Stress Test: Contrasting two distinct protocols.” Behavior research methods 48 (2016): 223-232.

[33] Szell, Michael, and Stefan Thurner. “How women organize social networks different from men.” Scientific reports 3.1 (2013): 1214.

Downloads

Published

2024-10-29