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International Conference on Applied Human Factors and Ergonomics

AHFE 2020: Advances in Usability, User Experience, Wearable and Assistive Technology pp 367–373Cite as

An Application of Bayesian Regression in Ergonomics

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1217)

Abstract

Statistics plays an important part in almost all disciplines, including applied ergonomics and human factors. Majority of applied ergonomics literature uses the classical or frequentist statistical methods, and the applications of Bayesian statistical methods in applied ergonomics has been quite limited. This is possibly due to two reasons: (i) the discipline of applied ergonomics is relatively new, dating back to WWI, and (ii) computationally intensive nature of Bayesian solutions. In other disciplines, Bayesian statistical methods have become quite popular. The purpose of this article is to introduce Bayesian regression modeling to the research area of applied ergonomics, via a dataset from ergonomics research.

Keywords

  • Ordinal regression
  • Proportional odds assumption
  • Weekly informative prior
  • Prior distribution
  • Posterior distribution
  • HPD credible sets
  • Markov chain monte carlo simulation

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References

  1. Bertone, C.M.: Preface to the special section on soviet and european human factors. Hum. Factors 11(1), 65–68 (1969)

    CrossRef  Google Scholar 

  2. Cumming, G., Corkindale, K.: Human factors in the United Kingdom. Hum. Factors 11(1), 75–80 (1969)

    CrossRef  Google Scholar 

  3. Fowler, R.D.: An overview of human factor in Europe. Hum. Factors 11(1), 91–94 (1969)

    CrossRef  Google Scholar 

  4. Faraway, J., Reed, M.P.: Statistics for digital human motion modeling in ergonomics. Technometrics 49(3), 277–290 (2007). https://doi.org/10.1198/004017007000000281

    CrossRef  MathSciNet  Google Scholar 

  5. Atkinson, G.: A comparison of statistical methods for assessing measurement repeatability in ergonomics research. In: Atkinson, G., Reilly, T. (ed.) Sport, Leisure and Ergonomics, pp. 218–222. Taylor & Francis (2013)

    Google Scholar 

  6. Straker, L., Abbott, R.A., Heiden, M., Mathiassen, S.E., Toomingas, A.: Sit-stand desks in call centres: Associations of use and ergonomics awareness with sedentary behavior. Appl. Ergon. 44, 517–522 (2013)

    CrossRef  Google Scholar 

  7. Yayan Harry Yadi, L., Kurniawidjaja, M., Susilowati, I.H.: Ergonomics intervention study of the RULA/REBA method in chemical industries for MSDs’ risk assessment. In: International Conference of Occupational Health and Safety (ICOHS-2017), KnE Life Sciences, pp. 181–189 (2018). https://doi.org/10.18502/kls.v4i5.2551

  8. Wahab, N.A., Rusli, R., Shariff1, A.M., Buang, A.: Assessment of human factor performance using bayesian inference and inherent safety. Advanced Materials Research, vol. 845, pp 658–662. Trans Tech Publications, Switzerland (2014). https://www.scientific.net ©. Accessed 04 Dec 2013

  9. Dansie, C., Sesek, R., Bloswick, D.S.: Ergonomic risk assessment based on a Bayesian-optimised expert system. Int. J. Hum. Factors Model. Simul. 4(1), 23–96 (2013)

    CrossRef  Google Scholar 

  10. Alizadeh, S.S., Mortazavi, S.B., Sepehri, M.M.: Assessment of accident severity in the construction industry using the Bayesian theorem. Int. J. Occup. Saf. Ergon. 21(4), 551–557 (2015). https://doi.org/10.1080/10803548.2015.1095546

    CrossRef  Google Scholar 

  11. Huang, S.-M.: Effects of font size and font style of traditional Chinese characters on readability on smartphones. Int. J. Ind. Ergon. 69, 66–72 (2019)

    CrossRef  Google Scholar 

  12. Chang, M., Dalpatadu, R.J., Phanord, D., Singh, A.K.: Breast Cancer Prediction Using Bayesian Logistic Regression. Open Acc. Biostat. Bioinform. 2(3), OABB.000537 (2018). https://doi.org/10.31031/oabb.2018.02.000537

  13. Casella, G.: Statistical Design, pp. 96–97. Springer Science & Business Media, New York (2008)

    CrossRef  Google Scholar 

  14. Harrell, F.E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis, pp. 334–335. Springer Science & Business Media, Ney York (2013)

    Google Scholar 

  15. Osborne, J.W.: Best Practices in Logistic Regression, pp. 407–410. SAGE Publications, Thousand Oaks (2014)

    Google Scholar 

  16. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2018). https://www.R-project.org/

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Author information

Authors and Affiliations

  1. William F. Harrah College of Hospitality, University of Nevada, Las Vegas, USA

    Ashok K. Singh

  2. Department of Mathematical Sciences, University of Nevada, Las Vegas, USA

    Rohan J. Dalpatadu

Authors
  1. Ashok K. Singh
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  2. Rohan J. Dalpatadu
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Corresponding author

Correspondence to Ashok K. Singh .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Dr. Tareq Ahram

  2. Catholic University of Pernambuco, Boa Vista, Recife, Brazil

    Christianne Falcão

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Singh, A.K., Dalpatadu, R.J. (2020). An Application of Bayesian Regression in Ergonomics. In: Ahram, T., Falcão, C. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-51828-8_47

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  • DOI: https://doi.org/10.1007/978-3-030-51828-8_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51827-1

  • Online ISBN: 978-3-030-51828-8

  • eBook Packages: EngineeringEngineering (R0)

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