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A Systematic Review of Thermal and Cognitive Stress Indicators: Implications for Use Scenarios on Sensor-Based Stress Detection

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

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Abstract

A systematic literature review aiming to identify the characteristics of physiological signals on two types of stress states - single moderate thermal stress state and moderate thermal stress combined with cognitive stress state – was conducted. Results of the review serve as a backdrop to envision different scenarios on the detection of these stress states in everyday situations, such as in schools, workplaces and residential settings, where the use of interactive technologies is commonplace. Stress detection is one of the most studied areas of affective computing. However, current models developed for stress detection only focus on recognizing whether a person is stressed, but not on identifying stress states. It is essential to differentiate them in order to implement strategies to minimize the source of stress by designing different interactive technologies. Wearables are commonly used to acquire physiological signals, such as heart rate and respiratory rate. Analysis results of these signals can support a user to make a decision for taking actions or to make an automatic system undertake certain strategies to counteract the sources of stress. These technologies can be designed for educational, work or medical environments. Our future work is to validate these use scenarios systematically to enhance the design of the technologies.

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References

  1. Picard, R.W.: Affective Computing. MIT Media Lab., Cambridge (1997)

    Google Scholar 

  2. Cen, L., Wu, F., Yu, Z.L., Hu, F.: A real-time speech emotion recognition system and its application in online learning. In: Emotions, Technology, Design, and Learning, Elsevier Inc., pp. 27–46 (2016)

    Google Scholar 

  3. Jerritta, S., Murugappan, M., Nagarajan, R., Wan, K.: Physiological signals based human emotion recognition: a review. In: Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications, CSPA 2011, pp. 410–415 (2011). https://doi.org/10.1109/CSPA.2011.5759912.

  4. Plass, J.L., Kaplan, U.: Emotional design in digital media for learning. In: Emotions, Technology, Design, and Learning, Elsevier, pp. 131–161 (2016)

    Google Scholar 

  5. Shanmugasundaram, G., Yazhini, S., Hemapratha, E., Nithya, S.: A comprehensive review on stress detection techniques. In: 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019 (2019). https://doi.org/10.1109/ICSCAN.2019.8878795

  6. Blanco, J.A., Vanleer, A.C., Calibo, T.K., Firebaugh, S.L.: Single-trial cognitive stress classification using portable wireless electroencephalography. Sensors 19(3), 1–16 (2019). https://doi.org/10.3390/s19030499

    Article  Google Scholar 

  7. Karthikeyan, P., Murugappan, M., Yaacob, S.: A review on stress inducement stimuli for assessing human stress using physiological signals. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, CSPA, pp. 420–425 (2011). https://doi.org/10.1109/CSPA.2011.5759914

  8. Bong, S.Z., Murugappan, M., Yaacob, S.: Methods and approaches on inferring human emotional stress changes through physiological signals: a review. Int. J. Med. Eng. Inform. 5(2), 152 (2013). https://doi.org/10.1504/IJMEI.2013.053332

    Article  Google Scholar 

  9. Keim, S.M., Guisto, J.A., Sullivan, J.B.: Environmental thermal stress. Ann. Agric. Environ. Med. 9(1), 1–15 (2002)

    Google Scholar 

  10. Klepeis, N.E., et al.: The national human activity pattern survey (NHAPS): a resource for assessing exposure to environmental pollutants. J. Expo. Anal. Environ. Epidemiol. 11(3), 231–252 (2001). https://doi.org/10.1038/sj.jea.7500165

    Article  Google Scholar 

  11. Karmann, C., Schiavon, S., Arens, E.: Percentage of commercial buildings showing at least 80% occupant satisfied with their thermal comfort, April 2018

    Google Scholar 

  12. Wang, D., Song, C., Wang, Y., Xu, Y., Liu, Y., Liu, J.: Experimental investigation of the potential influence of indoor air velocity on students’ learning performance in summer conditions. Energy Build. 219, 110015 (2020). https://doi.org/10.1016/j.enbuild.2020.110015

    Article  Google Scholar 

  13. Li, D., Wang, X., Menassa, C.C., Kamat, V.R.: Understanding the impact of building thermal environments on occupants’ comfort and mental workload demand through human physiological sensing. In: Start-Up Creation, pp. 291–341 (2020)

    Google Scholar 

  14. Lan, L., Wargocki, P., Lian, Z.: Quantitative measurement of productivity loss due to thermal discomfort. Energy Build. 43(5), 1057–1062 (2011). https://doi.org/10.1016/j.enbuild.2010.09.001

    Article  Google Scholar 

  15. Silva, L.B.D., de Souza, E.L., de Oliveira, P.A.A., Andrade, B.J.M.: Implications of indoor air temperature variation on the health and performance of Brazilian students. Indoor Built Environ., pp. 1–12 (2019). https://doi.org/10.1177/1420326X19878228

  16. Rousselle, J.G., Blascovich, J., Kelsey, R.M.: Cardiorespiratory response under combined psychological and exercise stress. Int. J. Psychophysiol. 20(1), 49–58 (1995). https://doi.org/10.1016/0167-8760(95)00026-O

    Article  Google Scholar 

  17. Myrtek, M., Spital, S.: Psychophysiological response patterns to single, double and triple stressors. Soc. Psychophysiol. Res. 23, 663–671 (1986)

    Google Scholar 

  18. Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., Tsiknakis, M.: Review on psychological stress detection using biosignals. IEEE Trans. Affect. Comput. 1–22 (2019) https://doi.org/10.1109/TAFFC.2019.2927337

  19. Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6(7), e1000097 (2009). https://doi.org/10.1371/journal.pmed.1000097

    Article  Google Scholar 

  20. Zhang, Z., Zhang, Y., Khan, A.: Thermal comfort of people from two types of air-conditioned buildings - evidences from chamber experiments. Build. Environ. 162, 106287 (2019). https://doi.org/10.1016/j.buildenv.2019.106287

  21. Zhang, F., de Dear, R., Hancock, P.: Effects of moderate thermal environments on cognitive performance: a multidisciplinary review. Appl. Energy 236, 760–777 (2019). https://doi.org/10.1016/j.apenergy.2018.12.005

  22. Zheng, G., Li, K., Bu, W., Wang, Y.: Fuzzy comprehensive evaluation of human physiological state in indoor high temperature environments. Build. Environ. 150, 108–118 (2019). https://doi.org/10.1016/j.buildenv.2018.12.063

  23. Stotz, A., et al.: Effect of a brief heat exposure on blood pressure and physical performance of older women living in the community—a pilot-study. Int. J. Environ. Res. Public Health 11(12), 12623–12631 (2014). https://doi.org/10.3390/ijerph111212623

    Article  Google Scholar 

  24. Luo, M., Zhou, X., Zhu, Y., Sundell, J.: Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate. Energy Build. 118, 152–159 (2016). https://doi.org/10.1016/j.enbuild.2016.02.041

    Article  Google Scholar 

  25. Luo, M., Ji, W., Cao, B., Ouyang, Q., Zhu, Y.: Indoor climate and thermal physiological adaptation: evidences from migrants with different cold indoor exposures. Build. Environ. 98, 30–38 (2016). https://doi.org/10.1016/j.buildenv.2015.12.015

    Article  Google Scholar 

  26. Choi, J.H., Loftness, V., Lee, D.W.: Investigation of the possibility of the use of heart rate as a human factor for thermal sensation models. Build. Environ. 50, 165–175 (2012). https://doi.org/10.1016/j.buildenv.2011.10.009

    Article  Google Scholar 

  27. Zhu, H., Wang, H., Liu, Z., Li, D., Kou, G., Li, C.: Experimental study on the human thermal comfort based on the heart rate variability (HRV) analysis under different environments. Sci. Total Environ. 616–617, 1124–1133 (2018). https://doi.org/10.1016/j.scitotenv.2017.10.208

    Article  Google Scholar 

  28. Yao, Y., Lian, Z., Liu, W., Jiang, C., Liu, Y., Lu, H.: Heart rate variation and electroencephalograph - the potential physiological factors for thermal comfort study. Indoor Air 19(2), 93–101 (2009). https://doi.org/10.1111/j.1600-0668.2008.00565.x

    Article  Google Scholar 

  29. Yao, Y., Lian, Z., Liu, W., Shen, Q.: Experimental study on physiological responses and thermal comfort under various ambient temperatures. Physiol. Behav. 93(1–2), 310–321 (2008). https://doi.org/10.1016/j.physbeh.2007.09.012

    Article  Google Scholar 

  30. Liu, W., Lian, Z., Liu, Y.: Heart rate variability at different thermal comfort levels. Eur. J. Appl. Physiol. 103(3), 361–366 (2008). https://doi.org/10.1007/s00421-008-0718-6

    Article  Google Scholar 

  31. Shin, H.: Ambient temperature effect on pulse rate variability as an alternative to heart rate variability in young adult. J. Clin. Monit. Comput. 30(6), 939–948 (2015). https://doi.org/10.1007/s10877-015-9798-0

    Article  Google Scholar 

  32. Guan, H., Hu, S., Lu, M., He, M., Mao, Z., Liu, G.: People’s subjective and physiological responses to the combined thermal-acoustic environments. Build. Environ. 172, 106709 (2020). https://doi.org/10.1016/j.buildenv.2020.106709

    Article  Google Scholar 

  33. Kim, J., Hong, T., Kong, M., Jeong, K.: Building occupants’ psycho-physiological response to indoor climate and CO2 concentration changes in office buildings. Build. Environ. 169, 106596 (2020). https://doi.org/10.1016/j.buildenv.2019.106596

  34. Hashiguchi, N., Tochihara, Y., Ohnaka, T., Tsuchida, C., Otsuki, T.: Physiological and subjective responses in the elderly when using floor heating and air conditioning systems. J. Physiol. Anthropol. Appl. Human Sci. 23(6), 205–213 (2004). https://doi.org/10.2114/jpa.23.205

    Article  Google Scholar 

  35. Yasuoka, A., Kubo, H., Tsuzuki, K., Isoda, N.: Gender differences in thermal comfort and responses to skin cooling by air conditioners in the Japanese summer. J. Human-Environment Syst. 18(1), 011–020 (2015). https://doi.org/10.1618/jhes.18.011

    Article  Google Scholar 

  36. Liu, Y., Wang, L., Liu, J., Di, Y.: A study of human skin and surface temperatures in stable and unstable thermal environments. J. Therm. Biol. 38(7), 440–448 (2013). https://doi.org/10.1016/j.jtherbio.2013.06.006

    Article  Google Scholar 

  37. Son, Y.J., Chun, C.: Research on electroencephalogram to measure thermal pleasure in thermal alliesthesia in temperature step-change environment. Indoor Air 28(6), 916–923 (2018). https://doi.org/10.1111/ina.12491

    Article  Google Scholar 

  38. Choi, Y., Kim, M., Chun, C.: Effect of temperature on attention ability based on electroencephalogram measurements. Build. Environ. 147, 299–304 (2019). https://doi.org/10.1016/j.buildenv.2018.10.020

    Article  Google Scholar 

  39. Wu, Q., Liu, J., Zhang, L., Zhang, J., Jiang, L.: Study on thermal sensation and thermal comfort in environment with moderate temperature ramps. Build. Environ. 171, 106640(2020). https://doi.org/10.1016/j.buildenv.2019.106640

  40. Liu, W., Zhong, W., Wargocki, P.: Performance, acute health symptoms and physiological responses during exposure to high air temperature and carbon dioxide concentration. Build. Environ. 114, 96–105 (2017). https://doi.org/10.1016/j.buildenv.2016.12.020

    Article  Google Scholar 

  41. Lan, L., Xia, L., Hejjo, R., Wyon, D.P., Wargocki, P.: Perceived air quality and cognitive performance decrease at moderately raised indoor temperatures even when clothed for comfort. Indoor Air, 1–19(2020). https://doi.org/10.1111/ina.12685

  42. Lan, L., Wargocki, P., Wyon, D.P., Lian, Z.: Effects of thermal discomfort in an office on perceived air quality, SBS symptoms, physiological responses, and human performance. Indoor Air 21(5), 376–390 (2011). https://doi.org/10.1111/j.1600-0668.2011.00714.x

    Article  Google Scholar 

  43. Fan, X., Liu, W., Wargocki, P.: Physiological and psychological reactions of sub-tropically acclimatized subjects exposed to different indoor temperatures at a relative humidity of 70%. Indoor Air 29(2), 215–230 (2019). https://doi.org/10.1111/ina.12523

    Article  Google Scholar 

  44. Siqueira, J.C.F., Da Silva, L.B., Coutinho, A.S., Rodrigues, R.M.: Analysis of air temperature changes on blood pressure and heart rate and performance of undergraduate students. Work 57(1), 43–54 (2017). https://doi.org/10.3233/WOR-172533

    Article  Google Scholar 

  45. Mäkinen, T.M., et al.: Effect of repeated exposures to cold on cognitive performance in humans. Physiol. Behav. 87(1), 166–176 (2006). https://doi.org/10.1016/j.physbeh.2005.09.015

    Article  Google Scholar 

  46. Abbasi, A.M., Motamedzade, M., Aliabadi, M., Golmohammadi, R., Tapak, L.: The impact of indoor air temperature on the executive functions of human brain and the physiological responses of body. Heal. Promot. Perspect. 9(1), 55–64 (2019). https://doi.org/10.15171/hpp.2019.07

    Article  Google Scholar 

  47. Barbic, F., et al.: Effects of different classroom temperatures on cardiac autonomic control and cognitive performances in undergraduate students. IPEM (2019). https://doi.org/10.1088/1361-6579/ab1816

    Article  Google Scholar 

  48. Lan, L., Lian, Z., Pan, L.: The effects of air temperature on office workers’ well-being, workload and productivity-evaluated with subjective ratings. Appl. Ergon. 42(1), 29–36 (2010). https://doi.org/10.1016/j.apergo.2010.04.003

    Article  Google Scholar 

  49. Kim, J., Kong, M., Hong, T., Jeong, K., Lee, M.: Physiological response of building occupants based on their activity and the indoor environmental quality condition changes. Build. Environ. 145(September), 96–103 (2018). https://doi.org/10.1016/j.buildenv.2018.09.018

    Article  Google Scholar 

  50. Tham, K.W., Willem, H.C.: Room air temperature affects occupants’ physiology, perceptions and mental alertness. Build. Environ. 45(1), 40–44 (2010). https://doi.org/10.1016/j.buildenv.2009.04.002

    Article  Google Scholar 

  51. Wang, X., Li, D., Menassa, C.C., Kamat, V.R.: Investigating the effect of indoor thermal environment on occupants’ mental workload and task performance using electroencephalogram. Build. Environ. 158(March), 120–132 (2019). https://doi.org/10.1016/j.buildenv.2019.05.012

    Article  Google Scholar 

  52. Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C., Berger, A.C., Cohen, R.J.: Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science, 80 (1981). https://doi.org/10.1126/science.6166045

  53. Jaffe, R.S., Fung, D.L., Behrman, K.H.: Optimal frequency ranges for extracting information on autonomic activity from the heart rate spectrogram. J. Auton. Nerv. Syst. 46(1–2), 37–46 (1993). https://doi.org/10.1016/0165-1838(94)90142-2

    Article  Google Scholar 

  54. Perlis, M.L., Merica, H., Smith, M.T., Giles, D.E.: Beta EEG activity and insomnia. Sleep Med. Rev. 5(5), 365–376 (2001). https://doi.org/10.1053/smrv.2001.0151

    Article  Google Scholar 

  55. Hall, J.E., Hall, M.E.: Guyton and Hall Textbook of Medical Physiology, 14th ed. (2020)

    Google Scholar 

  56. Lean, Y., Shan, F.: Brief review on physiological and biochemical evaluations of human mental workload. Hum. Factors Ergon. Manuf. Serv. Ind. 22(3), 177–187 (2012). https://doi.org/10.1002/hfm.20269

    Article  Google Scholar 

  57. Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999). https://doi.org/10.1016/S0165-0173(98)00056-3

    Article  Google Scholar 

  58. Wright Jr, K.P., Hull, C.A., Czeisler, C.A., Kenneth, P., Hull, J.T.: Relationship between alertness, performance, and body temperature in humans. vol. 0354, pp. 1370–1377(2002). https://doi.org/10.1152/ajpregu.00205.2002.

  59. Elzeiny, S., Qaraqe, M.: Machine learning approaches to automatic stress detection: a review. In: Proceedings IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, vol. 2018 pp. 1–6 (2019). https://doi.org/10.1109/AICCSA.2018.8612825

  60. Dubey, H., Constant, N., Mankodiya, K.: RESPIRE: a spectral kurtosis-based method to extract respiration rate from wearable PPG signals. In: Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017, 2017, pp. 84–89 (2017). https://doi.org/10.1109/CHASE.2017.64

  61. Sanjog, J., Patel, T., Karmakar, S.: Indoor physical work environment: an ergonomics perspective. Int. J. Sci. Eng. Technol. Res. 2(3), 2278–7798 (2013)

    Google Scholar 

  62. Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Programs Biomed. 161, 1–13 (Jul. 2018). https://doi.org/10.1016/j.cmpb.2018.04.005

    Article  Google Scholar 

  63. Vannieuwenborg, F., Verbrugge, S., Colle, D.: Choosing IoT-connectivity’ A guiding methodology based on functional characteristics and economic considerations. Trans. Emerg. Telecommun. Technol. (2018). https://doi.org/10.1002/ett.3308

    Article  Google Scholar 

  64. Stahl, B.C., Wright, D.: Ethics and privacy in AI and big data: implementing responsible research and innovation. IEEE Secur. Priv. 16(3), 26–33 (May 2018). https://doi.org/10.1109/MSP.2018.2701164

    Article  Google Scholar 

  65. Li, X., Zhang, T.: An exploration on artificial intelligence application: From security, privacy and ethic perspective. In: 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, pp. 416–420 (2017). https://doi.org/10.1109/ICCCBDA.2017.7951949

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Authors and Affiliations

  1. Universidad EAFIT, Cra 49, #7 sur 50, Medellín, Colombia

    Susana Carrizosa-Botero, Elizabeth Rendón-Vélez & Tatiana A. Roldán-Rojo

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  1. Susana Carrizosa-Botero
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  2. Elizabeth Rendón-Vélez
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  3. Tatiana A. Roldán-Rojo
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Editor information

Editors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Carmelo Ardito

  2. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Rosa Lanzilotti

  3. Computer Science Department, University of Pisa, Pisa, Italy

    Alessio Malizia

  4. Department of Computer Science, University of York, York, UK

    Helen Petrie

  5. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  6. Computer Science Department, University of Bari Aldo Moro, Bari, Italy

    Giuseppe Desolda

  7. Microsoft Research, Redmond, WA, USA

    Kori Inkpen

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Carrizosa-Botero, S., Rendón-Vélez, E., Roldán-Rojo, T. (2021). A Systematic Review of Thermal and Cognitive Stress Indicators: Implications for Use Scenarios on Sensor-Based Stress Detection. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12932. Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-85623-6_7

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