A Conceptual Framework for Personalization of Indoor Comfort Parameters Based on Office Workers’ Preferences

  • Saeed MirzaeifarEmail author
  • Pedram Daee
  • Vishal Singh
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


Prevention of building-related illnesses and improving indoor air quality has become an emerging research area not only because of the comfort of workers in an office or the quality of the perceived air, but also because it can provide financial benefits to both employees and employers through a potential reduction in prolonged sick leaves. Therefore, building facility managers attempt to achieve the most comfortable and healthy environment conditions for the office workers. However, the parameters associated with achieved comfort vary from person to person as workers` preferences, as well physiological characteristics, are heterogeneous. In the ideal case, the indoor health parameters should be personalized based on individuals` feedback. This paper presents a computational framework for personalization of environmental parameters based on limited office workers’ feedback. We propose that by using current state of the art machine learning methods it is possible to learn the preference model of individuals, by employing both the limited feedback and the relevant literature on health-related symptoms. The framework is explained and discussed in a potential example scenario. Evaluation based on real data is left as a future work.


Personalization Building-related symptoms Indoor health parameters Building information modelling Office building Worker preference 


  1. 1.
    Brasche, S., Bischof, W.: Daily time spent indoors in German homes–baseline data for the assessment of indoor exposure of German occupants. Int. J. Hyg. Environ. Health 208(4), 247–253 (2005). Scholar
  2. 2.
    Leech, J.A., Nelson, W.C., Burnett, R.T., Aaron, S., Raizenne, M.E.: It’s about time: a comparison of Canadian and American time–activity patterns. J. Expo. Anal. & Environ. Epidemiol. 12(6), 427 (2002). Scholar
  3. 3.
    World Health Organization: Indoor air pollutants exposure and health effects report on a WHO meeting, Nordlingen, EURO Reports and Studies, 78 (1982)Google Scholar
  4. 4.
    Sterling, E., Sterling, T., McIntyre, D.: New health hazards in sealed buildings. Am. Inst. Archit. J. (United States) 72(4) (1983)Google Scholar
  5. 5.
    Reijula, K., Sisäilmaongelmat, H.T.: Altistumisen arviointi ja potilaan tutkiminen [indoor air problems: Assessment of exposure and examination of the patient]. Finn. Med. J. 53(36), 4215–4230 (1998)Google Scholar
  6. 6.
    Ghazanfari, E.: Applying product design and digital construction methodologies to conceptualize modular and distributed healthcare facilities. Master thesis, Aalto University, Finland (2016)Google Scholar
  7. 7.
    Gillespie I.: Modular Office (2002)Google Scholar
  8. 8.
    Jansz, J.: Theories and Knowledge About Sick Building Syndrome. In: Abdul-Wahab, S. (ed.) Sick Building Syndrome. Springer, Berlin (2011). Scholar
  9. 9.
    Hellgren, U.: Indoor air problems in Finnish hospitals-from the occupational health perspective (2012)Google Scholar
  10. 10.
    Norbäck, D.: An update on sick building syndrome. Curr. Opin. Allergy Clin. Immunol. 9(1), 55–59 (2009). Scholar
  11. 11.
    Amin, N.D.M., Akasah, Z.A., Razzaly, W.: Architectural evaluation of thermal comfort: sick building syndrome symptoms in engineering education laboratories. Procedia - Soc. Behav. Sci. 204, 19–28 (2015). Scholar
  12. 12.
    Reinikainen, L.M., Jaakkola, J.: Significance of humidity and temperature on skin and upper airway symptoms. Indoor Air 13(4), 344–352 (2003). Scholar
  13. 13.
    Schiavon, S., Yang, B., Donner, Y., Chang, V., Nazaroff, W.W.: Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons. Indoor Air 27(3), 690–702 (2017). Scholar
  14. 14.
    Shan, X., Zhou, J., Chang, V.W., Yang, E.: Comparing mixing and displacement ventilation in tutorial rooms: students’ thermal comfort, sick building syndromes, and short-term performance. Build. Env. 102, 128–137 (2016). Scholar
  15. 15.
    Wolkoff, P., Nojgaard, J.K., Franck, C., Skov, P.: The modern office environment desiccates the eyes? Indoor Air 16(4), 258–265 (2006). Scholar
  16. 16.
    Godish, T.: Sick Buildings: Definition, Diagnosis and Mitigation. CRC Press, Boca Raton (1994)Google Scholar
  17. 17.
    Pitarma, R., Marques, G., Ferreira, B.R.: Monitoring indoor air quality for enhanced occupational health. J. Med. Syst. 41(2), 23 (2017). Scholar
  18. 18.
    Lu, C., Lin, J., Chen, Y., Chen, Y.: Building-related symptoms among office employees associated with indoor carbon dioxide and total volatile organic compounds. Int. J. Environ. Res. Public Health 12(6), 5833–5845 (2015). Scholar
  19. 19.
    Dales, R., Raizenne, M.: Residential exposure to volatile organic compounds and asthma. J. Asthma 41(3), 259–270 (2004). Scholar
  20. 20.
    Venn, A.J., Cooper, M., Antoniak, M., Laughlin, C., Britton, J., Lewis, S.A.: Effects of volatile organic compounds, damp, and other env’l exposures in the home on wheezing illness in children. Thorax 58(11), 955–960 (2003). Scholar
  21. 21.
    Lim, F., Hashim, Z., Said, S.M., Than, L.T., Hashim, J.H., Norbäck, D.: Sick building syndrome (SBS) among office workers in a Malaysian university—Associations with atopy, fractional exhaled nitric oxide (FeNO) and the office environment. Sci. Total Environ. 536, 353–361 (2015). Scholar
  22. 22.
    Mi, Y., Norback, D., Tao, J., Mi, Y., Ferm, M.: Current asthma and respiratory symptoms among pupils in Shanghai, China: influence of building ventilation, nitrogen dioxide, ozone, and formaldehyde in classrooms. Indoor Air 16(6), 454–464 (2006). Scholar
  23. 23.
    Joshi, S.M.: The sick building syndrome. Indian J. Occup. Environ. Med. 12(2), 61 (2008). Scholar
  24. 24.
    Skov, P., Valbjorn, O.: The “sick” building syndrome in the office environment: the Danish town hall study. Env. Int. 13(4–5), 339–349 (1987). Scholar
  25. 25.
    Teeuw, K.B., Vandenbroucke-Grauls, C.M., Verhoef, J.: Airborne gram-negative bacteria and endotoxin in sick building syndrome: a study in Dutch governmental office buildings. Arch. Intern. Med. 154(20), 2339–2345 (1994). Scholar
  26. 26.
    Marmot, A.F., Eley, J., Stafford, M., Stansfeld, S.A., Warwick, E., Marmot, M.G.: Building health: an epidemiological study of “sick building syndrome” in the whitehall II study. Occup. Environ. Med. 63(4), 283–289 (2006). Scholar
  27. 27.
    Nordström, K., Norbäck, D., Akselsson, R.: Influence of indoor air quality and personal factors on the sick building syndrome (SBS) in Swedish geriatric hospitals. Occup. Environ. Med. 52(3), 170–176 (1995). Scholar
  28. 28.
    Grandjean, E., Hünting, W.: Ergonomics of posture-review of various problems of standing and sitting posture. Appl. Ergon. 8(3), 135–140 (1977). Scholar
  29. 29.
    Leivseth, G., Drerup, B.: Spinal shrinkage during work in a sitting posture compared to work in a standing posture. Clin. Biomech. 12(7–8), 409–418 (1997). Scholar
  30. 30.
    Johnstone, I.M., Titterington D.M.: Statistical Challenges of High-Dimensional Data (2009).
  31. 31.
    O’Hagan, A., et al.: Uncertain Judgements: Eliciting Experts’ Probabilities. Wiley, Hoboken (2006)CrossRefGoogle Scholar
  32. 32.
    Daee, P., Peltola, T., Soare, M., Kaski, S.: Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. Mach. Learn. 106(9–10), 1599–1620 (2017). Scholar
  33. 33.
    Afrabandpey, H., Peltola, T., Kaski, S.: Interactive prior elicitation of feature similarities for small sample size prediction. In: Proceedings of 25th Conference on User Modeling, Adaptation and Personalization, pp. 265–269 (2017).
  34. 34.
    Sundin, I., et al.: Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation (2017). arXiv Preprint arXiv:1705.03290
  35. 35.
    Daee, P., Peltola, T., Vehtari, A., Kaski, S.: User modelling for avoiding overfitting in interactive knowledge elicitation for prediction. In: Proceedings of the 23rd ACM International Conference on Intelligent User Interfaces, pp. 305–310 (2018).
  36. 36.
    Mirzaeifar, S., Dave, B., Singh, V.: Development of systematic construction logistics using ‘intelligent products’ (2017).

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Aalto UniversityEspooFinland

Personalised recommendations