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PSPHERE: person specific human error estimation

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Abstract

Human error has always been a source of threat for any human–machine interaction system. Incidents like Bhopal gas tragedy, Three Mile Island and Chernobyl are examples of havoc caused due to human errors. With an aim to understand human error, human reliability analysis methods have been introduced. These methods use performance shaping factors (PSFs) to model human behavior. In-depth analyses of PSFs reveal that human factor is one of the important influencing factors affecting human behavior. However, most of the existing approaches depend on fixed or expert opinion values for estimating human error probability thus paying less attention to dynamic nature of human behavior. Every human is different and also their behavior changes along the course of time. Based on this philosophy, the work proposes a Person-Specific Human Error Estimation methodology called P-SPHERE to estimate human error probability. The architectural framework of the proposed method consists of environment, human, task, and organization factors. Using these factors, the framework evaluates human error probability by exploiting the advances of the dynamic human behavior using real values and the existing human reliability analysis methods. Considering the effect of type of task performed, time spent on task, work environment, time of work, work context, and the cognitive aspect of behavior, human error probability is evaluated. A case study has been taken to demonstrate the evaluation process with respect to railway industry. By incorporating human specific factor values, the proposed approach transforms the HEP estimation procedure into a person-specific approach, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex working environment.

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References

  1. Calixto E, Lima GBA, Firmino PRA (2013) Comparing SLIM, SPAR-H and Bayesian network methodologies. Open J Saf Sci Technol 3(02):31

    Article  Google Scholar 

  2. Swain AD, Guttmann HE (1983) Handbook of human-reliability analysis with emphasis on nuclear power plant applications. final report. Tech. rep., Sandia National Labs

  3. Kirwan B (1994) A guide to practical human reliability assessment. Taylor & Francis, London

    Google Scholar 

  4. Bell J, Holroyd J (2009) Review of human reliability assessment methods. Health and Safety Laboratory 78

  5. Hannaman G, Spurgin A, Lukic Y (1984) Human cognitive reliability model for PRA analysis, NUS-4531

  6. Hollnagel E (1998) Cognitive reliability and error analysis method (CREAM). Elsevier, Amsterdam

    Google Scholar 

  7. Williams J (2015) HEART—a proposed method for achieving high reliability in process operation by means of human factors engineering technology. In: Safety and reliability, vol 35. Taylor & Francis, London, pp. 5–25

  8. Parry GW (1995) Suggestions for an improved HRA method for use in probabilistic safety assessment. Reliab Eng Syst Saf 49(1):1

    Article  MathSciNet  Google Scholar 

  9. French S, Bedford T, Pollard SJ, Soane E (2011) Human reliability analysis: a critique and review for managers. Saf Sci 49(6):753

    Article  Google Scholar 

  10. Cooper S, Ramey-Smith A, Wreathall J, Parry G (1996) A technique for human error analysis (atheana). Tech. rep, Nuclear Regulatory Commission

  11. Meyer P, Le Bot P, Pesme H (2007) MERMOS: an extended second generation HRA method. In: 2007 IEEE 8th human factors and power plants and HPRCT 13th annual meeting (IEEE), pp 276–283

  12. Sträter O (2000) Evaluation of human reliability on the basis of operational experience, Gesellschaft f\(\int \) r Anlagenund Reaktorsicherheit (GRS) mbh. GRS-170

  13. Gertman D, Blackman H, Marble J, Byers J, Smith C et al (2005) The SPAR-H human reliability analysis method. US Nuclear Regulatory Commission 230

  14. Kirwan B, Gibson H, Kennedy R, Edmunds J, Cooksley G, Umbers I (2004) Nuclear action reliability assessment (NARA): a data-based HRA tool, In: Probabilistic safety assessment and management. Springer, pp. 1206–1211

  15. Kirwan B, Gibson H, Kennedy R, Edmunds J, Cooksley G, Umbers I (2005) Nuclear action reliability assessment (NARA): a data-based HRA tool. In: Safety and reliability, vol 25. Taylor & Francis, London, pp 38–45

  16. Ekanem NJ, Mosleh A, Shen SH (2016) Phoenix—a model-based human reliability analysis methodology: qualitative analysis procedure. Reliab Eng Syst Saf 145:301

    Article  Google Scholar 

  17. Di Pasquale V, Miranda S, Iannone R, Riemma S (2015) A simulator for human error probability analysis (SHERPA). Reliab Eng Syst Saf 139:17

    Article  Google Scholar 

  18. Aalipour M, Ayele YZ, Barabadi A (2016) Human reliability assessment (HRA) in maintenance of production process: a case study. Int J Syst Assur Eng Manag 7(2):229

    Article  Google Scholar 

  19. Griffith CD, Mahadevan S (2011) Inclusion of fatigue effects in human reliability analysis. Reliab Eng Syst Saf 96(11):1437

    Article  Google Scholar 

  20. Boring RL, Griffith CD, Joe JC (2007) The measure of human error: direct and indirect performance shaping factors. In: 2007 IEEE 8th human factors and power plants and HPRCT 13th annual meeting (IEEE), pp 170–176

  21. USNR Commission et al (1975) Reactor safety study. an assessment of accident risks in us commercial nuclear power plants. executive summary. Tech. rep., United States Nuclear Regulatory Commission

  22. Williams J (2015) HEART—a proposed method for achieving high reliability in process operation by means of human factors engineering technology. In: Safety and reliability, vol 35. Taylor & Francis, London, pp 5–25

  23. Williams J (1986) A proposed method for assessing and reducing human error. In: Proceedings of 9th advances in reliability technology symposium. University of Bradford

  24. Williams J (1988) A data-based method for assessing and reducing human error to improve operational performance. In: Conference record for 1988 IEEE fourth conference on human factors and power plants (IEEE), pp 436–450

  25. Kim I (2001) Human reliability analysis in the man–machine interface design review. Ann Nucl Energy 28(11):1069

    Article  Google Scholar 

  26. Whaley AM, Kelly DL, Boring RL, Galyean WJ (2012) Spar-h step-by-step guidance. Tech. rep, Idaho National Laboratory (INL)

  27. Rasmussen M, Standal MI, Laumann K (2015) Task complexity as a performance shaping factor: a review and recommendations in Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) adaption. Saf Sci 76:228

    Article  Google Scholar 

  28. Griffith CD, Mahadevan S (2015) Human reliability under sleep deprivation: derivation of performance shaping factor multipliers from empirical data. Reliab Eng Syst Saf 144:23

    Article  Google Scholar 

  29. Rangra S, Sallak M, Schön W, Vanderhaegen F (2017) A graphical model based on performance shaping factors for assessing human reliability. IEEE Trans Reliab 66(4):1120

    Article  Google Scholar 

  30. Rasmussen M, Laumann K (2018) The evaluation of fatigue as a performance shaping factor in the Petro-HRA method. Reliab Eng Syst Saf

  31. Kim Y, Park J, Jung W (2017) A quantitative measure of fitness for duty and work processes for human reliability analysis. Reliab Eng Syst Saf 167:595

    Article  Google Scholar 

  32. Kim Y, Park J, Jung W, Jang I, Seong PH (2015) A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls. Reliab Eng Syst Saf 142:378

    Article  Google Scholar 

  33. Swain AD (1987) Accident sequence evaluation program: human reliability analysis procedure. Tech. rep., Sandia National Labs., Albuquerque; Nuclear Regulatory Commission

  34. Paragliola G, Naeem M (2019) Risk management for nuclear medical department using reinforcement learning algorithms. J Reliab Intell Environ 5(2):105

    Article  Google Scholar 

  35. Palumbo F, La Rosa D, Ferro E, Bacciu D, Gallicchio C, Micheli A, Chessa S, Vozzi F, Parodi O (2017) Reliability and human factors in ambient assisted living environments. J Reliab Intell Environ 3(3):139

    Article  Google Scholar 

  36. Coronato A, Cuzzocrea A (2020) An innovative risk assessment methodology for medical information systems. IEEE Trans Knowl Data Eng

  37. Corno F (2018) User expectations in intelligent environments. J Reliab Intell Environ 4(4):189

    Article  Google Scholar 

  38. Liu P, Lyu X, Qiu Y, He J, Tong J, Zhao J, Li Z (2017) Identifying key performance shaping factors in digital main control rooms of nuclear power plants: a risk-based approach. Reliab Eng Syst Saf 167:264

    Article  Google Scholar 

  39. Park J, Jung W, Kim J (2020) Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants. Nucl Eng Technol 52(1):87

    Article  Google Scholar 

  40. Park J, Lee D, Jung W, Kim J (2017) An experimental investigation on relationship between PSFs and operator performances in the digital main control room. Ann Nucl Energy 101:58

    Article  Google Scholar 

  41. Davis TR (1984) The influence of the physical environment in offices. Acad Manag Rev 9(2):271

    Article  Google Scholar 

  42. Yeow JA, Tan KS, Chin TS, Ching ES (2012) A review on ergonomic factors that lead to stress in manufacturing industry

  43. Calabrese C, Mejia B, McInnis CA, France M, Nadler E, Raslear TG (2017) Time of day effects on railroad roadway worker injury risk. J Saf Res 61:53

    Article  Google Scholar 

  44. Hygge S, Knez I (2001) Effects of noise, heat and indoor lighting on cognitive performance and self-reported affect. J Environ Psychol 21(3):291

    Article  Google Scholar 

  45. Pilcher JJ, Nadler E, Busch C (2002) Effects of hot and cold temperature exposure on performance: a meta-analytic review. Ergonomics 45(10):682

    Article  Google Scholar 

  46. Balazova I, Clausen G, Wyon DP (2007) The influence of exposure to multiple indoor environmental parameters on human perception, performance and motivation. In: Proceedings of CLIMA

  47. Lamb S, Kwok KC (2016) A longitudinal investigation of work environment stressors on the performance and wellbeing of office workers. Appl Ergon 52:104

    Article  Google Scholar 

  48. Koussoroplis AM, Pincebourde S, Wacker A (2017) Understanding and predicting physiological performance of organisms in fluctuating and multifactorial environments. Ecol Monogr 87(2):178

    Article  Google Scholar 

  49. Hancock P, Chignell MH (1986) Toward a theory of mental work load: stress and adaptability in human-machine systems. Proc IEEE SMC 1986:378–383

    Google Scholar 

  50. Cain B (2007) A review of the mental workload literature. Tech. rep, Defence Research and Development, Toronto

  51. Casali JG, Wierwille WW (1984) On the measurement of pilot perceptual workload: a comparison of assessment techniques addressing sensitivity and intrusion issues. Ergonomics 27(10):1033

    Article  Google Scholar 

  52. Samima S, Sarma M (2019) EEG-based mental workload estimation. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC)

  53. Kyriakidis M, Majumdar A, Ochieng WY (2018) The human performance railway operational index—a novel approach to assess human performance for railway operations. Reliab Eng Syst Saf 170:226

    Article  Google Scholar 

  54. Naderpour M, Lu J, Zhang G (2016) A safety-critical decision support system evaluation using situation awareness and workload measures. Reliab Eng Syst Saf 150:147

    Article  Google Scholar 

  55. Desai AV, Haque MA (2006) Vigilance monitoring for operator safety: a simulation study on highway driving. J Saf Res 37(2):139

    Article  Google Scholar 

  56. Shen S, Neyens DM (2017) Assessing drivers’ response during automated driver support system failures with non-driving tasks. J Saf Res 61:149

    Article  Google Scholar 

  57. Chen J, Wang RQ, Lin Z, Guo X (2018) Measuring the cognitive loads of construction safety sign designs during selective and sustained attention. Saf Sci 105:9

    Article  Google Scholar 

  58. Reinerman-Jones L, Matthews G, Mercado JE (2016) Detection tasks in nuclear power plant operation: vigilance decrement and physiological workload monitoring. Saf Sci 88:97

    Article  Google Scholar 

  59. Masoudian M, Razavi H (2018) An investigation of the required vigilance for different occupations. Saf Sci

  60. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, Hazen N, Herman J, Katz ES, Kheirandish-Gozal L et al (2015) National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health 1(1):40

    Article  Google Scholar 

  61. Leger D (1994) The cost of sleep-related accidents: a report for the National Commission on Sleep Disorders Research. Sleep 17(1):84

    Article  Google Scholar 

  62. Durmer JS, Dinges DF (2005) Neurocognitive consequences of sleep deprivation. In: Seminars in neurology, vol 25 (Copyright 2005 by Thieme Medical Publishers, Inc., New York), pp 117–129

  63. Park J, Kim J, Jung W (2004) Comparing the complexity of procedural steps with the operators’ performance observed under stressful conditions. Reliab Eng Syst Saf 83(1):79

    Article  Google Scholar 

  64. Yeow JA, Ng PK, Tan KS, Chin TS, Lim WY (2014) Effects of stress, repetition, fatigue and work environment on human error in manufacturing industries. J Appl Sci 14(24):3464

    Article  Google Scholar 

  65. Park J, Jung W (2003) The operators’ non-compliance behavior to conduct emergency operating procedures-comparing with the work experience and the complexity of procedural steps. Reliab Eng Syst Saf 82(2):115

    Article  Google Scholar 

  66. Laumann K, Rasmussen M (2016) Experience and training as performance-shaping factors in human reliability analysis. In: Risk, reliability and safety: innovating theory and practice. Proceedings of the 26th European safety and reliability conference, ESREL

  67. Moore DA, Tenney ER (2012) Time pressure, performance, and productivity. In: Looking back, moving forward: a review of group and team-based research. Emerald Group Publishing Limited, pp 305–326

  68. Perception of time pressure impairs performance. https://www.sciencedaily.com/releases/2009/02/090210162035.htm (2009). Online; Last accessed 25 Sep 2019

  69. Ganguly S (2011) Human error vs. work place management in modern organizations. Int J Res Manag Technol 1(1):13

    Google Scholar 

  70. Baron RM, Kenny DA (1986) The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51(6):1173

    Article  Google Scholar 

  71. James LR, Brett JM (1984) Mediators, moderators, and tests for mediation. J Appl Psychol 69(2):307

    Article  Google Scholar 

  72. Li W, Lee A, Solmon M (2007) The role of perceptions of task difficulty in relation to self-perceptions of ability, intrinsic value, attainment value, and performance. Eur Phys Educ Rev 13(3):301

    Article  Google Scholar 

  73. Rellinger E, Borkowski JG, Turner LA, Hale CA (1995) Perceived task difficulty and intelligence: determinants of strategy use and recall. Intelligence 20(2):125

    Article  Google Scholar 

  74. DeLoache JS, Cassidy DJ, Brown AL (1985) Precursors of mnemonic strategies in very young children’s memory. Child Dev 56:125–137

  75. Belmont JM, Mitchell DW (1987) The general strategies hypothesis as applied to cognitive theory in mental retardation. Intelligence 11(1):91

    Article  Google Scholar 

  76. Robinson P (2001) Task complexity, task difficulty, and task production: exploring interactions in a componential framework. Appl Linguist 22(1):27

    Article  Google Scholar 

  77. Podofillini L, Park J, Dang VN (2013) Measuring the influence of task complexity on human error probability: an empirical evaluation. Nucl Eng Technol 45(2):151

    Article  Google Scholar 

  78. Laumann K, Rasmussen M (2016) Suggested improvements to the definitions of Standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) performance shaping factors, their levels and multipliers and the nominal tasks. Reliab Eng Syst Saf 145:287

    Article  Google Scholar 

  79. Giagloglou E, Mijovic P, Brankovic S, Antoniou P, Macuzic I (2017) Cognitive status and repetitive working tasks of low risk. Saf Sci

  80. Rasmussen M, Laumman K (2014) The suitability of the SPAR-H “Ergonomics/HMI” PSF in a computerized control room in the petroleum industry. In: Proceedings of the 12th probabilistic safety assessment and management conference

  81. Irshad L, Ahmed S, Demirel O, Tumer IY (2019) Coupling digital human modeling with early design stage human error analysis to assess ergonomic vulnerabilities. In: AIAA Scitech 2019 Forum, p 2349

  82. Newman A, Rose PS, Teo ST (2016) The role of participative leadership and trust-based mechanisms in eliciting intern performance: evidence from China. Hum Resour Manag 55(1):53

    Article  Google Scholar 

  83. Mohiuddin ZA (2017) Influence of leadership style on employees performance: evidence from literatures. J Mark Manag 8(1):18

    MathSciNet  Google Scholar 

  84. Iqbal N, Anwar S, Haider N (2015) Effect of leadership style on employee performance. Arab J Bus Manag Rev 5(5):1

    Google Scholar 

  85. Basit A, Sebastian V, Hassan Z (2017) Impact of leadership style on employee performance (a case study on a private organization in Malaysia). Int J Acc Bus Manag 5(2):2289

    Google Scholar 

  86. Toppazzini MA, Wiener KKK (2017) Making workplaces safer: the influence of organisational climate and individual differences on safety behaviour. Heliyon 3(6):e00334

    Article  Google Scholar 

  87. Thao L, Hwang CsJ (2015) Factors affecting employee performance—evidence from Petrovietnam Engineering Consultancy JSC. J Manag Stud 51(17):27

    Google Scholar 

  88. Gilks WR, Richardson S, Spiegelhalter D (1995) Markov chain Monte Carlo in practice. Chapman and Hall/CRC, Boca Raton

    Book  MATH  Google Scholar 

  89. Rausand M, Høyland A (2003) System reliability theory: models, statistical methods, and applications, vol 396. Wiley, New York

    MATH  Google Scholar 

  90. Samima S, Sarma M, Samanta D (2017) Correlation of P300 ERPs with visual stimuli and its application to vigilance detection. In: 2017 39th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 2590–2593. https://doi.org/10.1109/EMBC.2017.8037387

  91. Samima S, Sarma M, Samanta D, Prasad G (2019) Estimation and quantification of vigilance using ERPs and eye blink rate with a fuzzy model-based approach. Cogn Technol Work 21(3):517

    Article  Google Scholar 

  92. Kelly D, Efthymiou M (2019) An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017. J Saf Res 69:155–165

  93. Gonçalves Filho AP, Souza CA, Siqueira ELB, Souza MA, Vasconcelos TP (2019) An analysis of helicopter accident reports in Brazil from a human factors perspective. Reliab Eng Syst Saf 183:39

    Article  Google Scholar 

  94. Baker CC, Seah AK (2004) Maritime accidents and human performance: the statistical trail. In: MarTech conference, Singapore, pp 225–240

  95. Baysari MT, McIntosh AS, Wilson JR (2008) Understanding the human factors contribution to railway accidents and incidents in Australia. Accid Anal Prev 40(5):1750

    Article  Google Scholar 

  96. Chaitezvi L (2014) Factors contributing to accident causation in the grate cooler section at Sino-Zimbabwe Cement company from January 2005 to December 2013

  97. Fullér R, Majlender P (2001) An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets Syst 124(1):53

    Article  MathSciNet  MATH  Google Scholar 

  98. Mackworth NH (1948) The breakdown of vigilance during prolonged visual search. Q J Exp Psychol 1(1):6

    Article  Google Scholar 

  99. Kirchner WK (1958) Age differences in short-term retention of rapidly changing information. J Exp Psychol 55(4):352

    Article  Google Scholar 

  100. Stone J (2021) Cognitive research tools. http://www.cognitivetools.uk/cognition. Online; Last accessed 24 Apr 2021

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  1. Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology Kharagpur, Kharagpur, India

    Shabnam Samima & Monalisa Sarma

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  1. Shabnam Samima
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  2. Monalisa Sarma
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Samima, S., Sarma, M. PSPHERE: person specific human error estimation. J Reliable Intell Environ 8, 133–164 (2022). https://doi.org/10.1007/s40860-021-00146-1

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