Skip to main content

Influence of Complexity and Noise on Mental Workload During a Manual Assembly Task

  • Conference paper
  • First Online:
Human Mental Workload: Models and Applications (H-WORKLOAD 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1318))

Abstract

Mass customization implies an increase of product variants, complexity, and information processing of operators. Generally it is supposed that this leads to an increase of mental workload. Using a real-work-like laboratory setting, subjects should complete tasks of increasing complexity while mental workload is obtained using various parameters (subjective, performance related, and physiological). Additionally subjects are confronted with two levels of industrial noise which will increase mental workload on top of complexity. Results indicate that there is a significant influence of complexity and the interaction of complexity and noise on mental workload. Further physiological reaction patterns (electrocardiographic and eye tracking data) to process parts with higher informational load are investigated and concurrent patterns for pupillary response, fixation duration, and heart rate variability can be shown.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sivadasan, S., Efstathiou, J., Calinescu, A., Huatuco, L.H.: Advances on measuring the operational complexity of supplier–customer systems. Eur. J. Oper. Res. 171, 208–226 (2006). https://doi.org/10.1016/j.ejor.2004.08.032

    Article  MathSciNet  MATH  Google Scholar 

  2. MacDuffie, J.P., Sethuraman, K., Fisher, M.L.: Product variety and manufacturing performance: evidence from the international automotive assembly plant study. Manag. Sci. 42, 350–369 (1996)

    Article  Google Scholar 

  3. Frizelle, G., Woodcock, E.: Measuring complexity as an aid to developing operational strategy. Int. J. Oper. Prod. Manag. 15, 26–39 (1995). https://doi.org/10.1108/01443579510083640

    Article  Google Scholar 

  4. Zhu, X., Hu, S.J., Koren, Y., Marin, S.P.: Modeling of manufacturing complexity in mixed-model assembly lines. J. Manuf. Sci. Eng. 130, 051013 (2008). https://doi.org/10.1115/1.2953076

    Article  Google Scholar 

  5. Wickens, C.D., Hollands, J.G., Banbury, S., Parasuraman, R.: Engineering Psychology and Human Performance. Pearson, Boston (2013)

    Google Scholar 

  6. Hick, W.E.: On the rate of gain of information. Q. J. Exp. Psychol. 4, 11–26 (1952). https://doi.org/10.1080/17470215208416600

    Article  Google Scholar 

  7. Hancock, P.A., Warm, J.S.: A dynamic model of stress and sustained attention. J. Hum. Perform. Extreme Environ. 7 (1989). https://doi.org/10.7771/2327-2937.1024

  8. Wickens, C.D.: Multiple resources and mental workload. Hum. Factors: J. Hum. Factors Ergon. Soc. 50, 449–455 (2008). https://doi.org/10.1518/001872008X288394

    Article  Google Scholar 

  9. Recarte, M.A., Pérez, E., Conchillo, A., Nunes, L.M.: Mental workload and visual impairment: differences between pupil, blink, and subjective rating. Span. J. Psychol. 11, 374–385 (2008)

    Article  Google Scholar 

  10. Chen, F., et al.: Robust Multimodal Cognitive Load Measurement. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31700-7

  11. Miller, G.A.: The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81–97 (1956)

    Article  Google Scholar 

  12. Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Trans. Syst. Man Cybern. SMC-13, 257–266 (1983). https://doi.org/10.1109/TSMC.1983.6313160

  13. Kahneman, D.: Attention and Effort. Prentice-Hall, Englewood Cliffs (1973)

    Google Scholar 

  14. Kahneman, D.: Thinking, Fast and Slow. Penguin Books, London (2012)

    Google Scholar 

  15. Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors: J. Hum. Factors Ergon. Soc. 37, 32–64 (1995). https://doi.org/10.1518/001872095779049543

    Article  Google Scholar 

  16. Thomas, H.R.: Schedule acceleration, work flow, and labor productivity. J. Constr. Eng. Manag. 126, 261–267 (2000). https://doi.org/10.1061/(ASCE)0733-9364(2000)126:4(261)

    Article  Google Scholar 

  17. Longo, L.: Experienced mental workload, perception of usability, their interaction and impact on task performance. PLoS One 13, e0199661 (2018). https://doi.org/10.1371/journal.pone.0199661

    Article  Google Scholar 

  18. Young, M.S., Brookhuis, K.A., Wickens, C.D., Hancock, P.A.: State of science: mental workload in ergonomics. Ergonomics 58, 1–17 (2015). https://doi.org/10.1080/00140139.2014.956151

    Article  Google Scholar 

  19. Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3, 159–177 (2002). https://doi.org/10.1080/14639220210123806

    Article  Google Scholar 

  20. Hollnagel, E.: Cognitive ergonomics: it’s all in the mind. Ergonomics 40, 1170–1182 (1997). https://doi.org/10.1080/001401397187685

    Article  Google Scholar 

  21. Bornewasser, M., Bläsing, D., Hinrichsen, S.: Informatorische Assistenzsysteme in der manuellen Montage: Ein nützliches Werkzeug zur Reduktion mentaler Beanspruchung? Zeitschrift für Arbeitswissenschaft 72, 264–275 (2018). https://doi.org/10.1007/s41449-018-0123-x

  22. Mattsson, S., Fast-Berglund, Å.: How to support intuition in complex assembly? Proc. CIRP 50, 624–628 (2016). https://doi.org/10.1016/j.procir.2016.05.014

    Article  Google Scholar 

  23. Parasuraman, R., Rizzo, M. (eds.): Neuroergonomics: the Brain at Work. Oxford University Press, New York (2008)

    Google Scholar 

  24. Parasuraman, R., Christensen, J., Grafton, S.: Neuroergonomics: the brain in action and at work. NeuroImage. 59, 1–3 (2012). https://doi.org/10.1016/j.neuroimage.2011.08.011

    Article  Google Scholar 

  25. Loeb, M.: Noise and Human Efficiency. Wiley, Chichester (1986)

    Google Scholar 

  26. Szalma, J.L., Hancock, P.A.: Noise effects on human performance: a meta-analytic synthesis. Psychol. Bull. 137, 682–707 (2011). https://doi.org/10.1037/a0023987

    Article  Google Scholar 

  27. Poulton, E.C.: Masking, beneficial arousal and adaptation level: a reply to Hartley. Br. J. Psychol. 72, 109–116 (1981). https://doi.org/10.1111/j.2044-8295.1981.tb02168.x

    Article  Google Scholar 

  28. Baddeley, A.D., Hitch, G.: Working memory. In: Psychology of Learning and Motivation, pp. 47–89. Elsevier (1974). https://doi.org/10.1016/S0079-7421(08)60452-1

  29. Broadbent, D.E.: The current state of noise research: reply to Poulton. Psychol. Bull. 85, 1052–1067 (1978). https://doi.org/10.1037/0033-2909.85.5.1052

    Article  Google Scholar 

  30. Casali, J., Robinson, G.: Noise in industry: auditory effects, measurement, regulations, and management. In: Karwowski, W., Marras, W. (eds.) Occupational Ergonomics, pp. 16-1–16-32. CRC Press (2003). https://doi.org/10.1201/9780203010457.pt2

  31. Hart, S.G.: NASA-task load index (NASA-TLX); 20 years later. Proc. Hum. Factors Ergon. Soc. Ann. Meet. 50, 904–908 (2006). https://doi.org/10.1177/154193120605000909

    Article  Google Scholar 

  32. Ramsay, D.S., Woods, S.C.: Clarifying the roles of homeostasis and allostasis in physiological regulation. Psychol. Rev. 121, 225–247 (2014). https://doi.org/10.1037/a0035942

    Article  Google Scholar 

  33. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5 (2017). https://doi.org/10.3389/fpubh.2017.00258

  34. Thayer, J.F., Åhs, F., Fredrikson, M., Sollers, J.J., Wager, T.D.: A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci. Biobehav. Rev. 36, 747–756 (2012). https://doi.org/10.1016/j.neubiorev.2011.11.009

    Article  Google Scholar 

  35. Castaldo, R., Montesinos, L., Wan, T.S., Serban, A., Massaro, S., Pecchia, L.: Heart rate variability analysis and performance during a repeated mental workload task. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds.) EMBEC/NBC -2017. IP, vol. 65, pp. 69–72. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5122-7_18

    Chapter  Google Scholar 

  36. Sammito, S., Thielmann, B., Seibt, R., Klussmann, A., Weippert, M., Böckelmann, I.: Guideline for the application of heart rate and heart rate variability in occupational medicine and occupational science. ASU Int. 2015 (2015). https://doi.org/10.17147/ASUI.2015-06-09-03

  37. Cinaz, B., La Marca, R., Arnrich, B., Tröster, G.: Towards continuous monitoring of mental workload (2012). https://doi.org/10.5167/UZH-66801

  38. McCraty, R., Shaffer, F.: Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Adv. Health Med. 4, 46–61 (2015). https://doi.org/10.7453/gahmj.2014.073

    Article  Google Scholar 

  39. Vollmer, M.: A robust, simple and reliable measure of heart rate variability using relative RR intervals. In: 2015 Computing in Cardiology Conference (CinC), pp. 609–612. IEEE, Nice (2015). https://doi.org/10.1109/CIC.2015.7410984

  40. Iqbal, S.T., Zheng, X.S., Bailey, B.P.: Task-evoked pupillary response to mental workload in human-computer interaction. In: Extended abstracts of the 2004 conference on Human factors and computing systems - CHI 2004, p. 1477. ACM Press, Vienna (2004). https://doi.org/10.1145/985921.986094

  41. Mathôt, S.: Pupillometry: psychology, physiology, and function. J. Cogn. 1 (2018). https://doi.org/10.5334/joc.18

  42. Kosch, T., Hassib, M., Buschek, D., Schmidt, A.: Look into my eyes: using pupil dilation to estimate mental workload for task complexity adaptation. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018, pp. 1–6. ACM Press, Montreal (2018). https://doi.org/10.1145/3170427.3188643

  43. Laeng, B., Sirois, S., Gredebäck, G.: Pupillometry: a window to the preconscious? Perspect. Psychol. Sci. 7, 18–27 (2012). https://doi.org/10.1177/1745691611427305

    Article  Google Scholar 

  44. Marquart, G., de Winter, J.: Workload assessment for mental arithmetic tasks using the task-evoked pupillary response. PeerJ Comput. Sci. 1, e16 (2015). https://doi.org/10.7717/peerj-cs.16

    Article  Google Scholar 

  45. Marandi, R.Z., Madeleine, P., Omland, Ø., Vuillerme, N., Samani, A.: Eye movement characteristics reflected fatigue development in both young and elderly individuals. Sci. Rep. 8 (2018). https://doi.org/10.1038/s41598-018-31577-1

  46. Marquart, G., Cabrall, C., de Winter, J.: Review of eye-related measures of drivers’ mental workload. Proc. Manuf. 3, 2854–2861 (2015). https://doi.org/10.1016/j.promfg.2015.07.783

    Article  Google Scholar 

  47. Luke, S.G., Darowski, E.S., Gale, S.D.: Predicting eye-movement characteristics across multiple tasks from working memory and executive control. Memory Cogn. 46(5), 826–839 (2018). https://doi.org/10.3758/s13421-018-0798-4

    Article  Google Scholar 

  48. Underwood, G., Crundall, D., Chapman, P.: Driving simulator validation with hazard perception. Transp. Res. Part F: Traffic Psychol. Behav. 14, 435–446 (2011). https://doi.org/10.1016/j.trf.2011.04.008

    Article  Google Scholar 

  49. Di Nocera, F., Camilli, M., Terenzi, M.: Using the distribution of eye fixations to assess pilots’ mental workload. Proc. Hum. Factors Ergon. Soc. Ann. Meet. 50, 63–65 (2006). https://doi.org/10.1177/154193120605000114

    Article  Google Scholar 

  50. May, J.G., Kennedy, R.S., Williams, M.C., Dunlap, W.P., Brannan, J.R.: Eye movement indices of mental workload. Acta Physiol. (Oxf) 75, 75–89 (1990). https://doi.org/10.1016/0001-6918(90)90067-P

    Article  Google Scholar 

  51. Brookings, J.B., Wilson, G.F., Swain, C.R.: Psychophysiological responses to changes in workload during simulated air traffic control. Biol. Psychol. 42, 361–377 (1996). https://doi.org/10.1016/0301-0511(95)05167-8

    Article  Google Scholar 

  52. Chen, S., Epps, J., Ruiz, N., Chen, F.: Eye activity as a measure of human mental effort in HCI. In: Proceedings of the 15th International Conference on Intelligent User Interfaces - IUI 2011, p. 315. ACM Press, Palo Alto (2011). https://doi.org/10.1145/1943403.1943454

  53. Yang, Y., McDonald, M., Zheng, P.: Can drivers’ eye movements be used to monitor their performance? A case study. IET Intell. Transp. Syst. 6, 444–452 (2012). https://doi.org/10.1049/iet-its.2012.0008

    Article  Google Scholar 

  54. Manuel, V., et al.: AdELE: a framework for adaptive e-learning through eye tracking. In: Proceedings of IKNOW 2004. pp. 609–616 (2004)

    Google Scholar 

  55. He, X., Wang, L., Gao, X., Chen, Y.: The eye activity measurement of mental workload based on basic flight task. In: IEEE 10th International Conference on Industrial Informatics, pp. 502–507. IEEE, Beijing (2012). https://doi.org/10.1109/INDIN.2012.6301203

  56. Zu, T., Hutson, J., Loschky, L.C., Rebello, N.S.: Use of eye-tracking technology to investigate cognitive load theory. In: 2017 Physics Education Research Conference Proceedings, pp. 472–475. American Association of Physics Teachers, Cincinnati (2018). https://doi.org/10.1119/perc.2017.pr.113

  57. Di Stasi, L.L., et al.: Saccadic peak velocity sensitivity to variations in mental workload. Aviat. Space Environ. Med. 81, 413–417 (2010). https://doi.org/10.3357/ASEM.2579.2010

    Article  Google Scholar 

  58. Fraser, S.A., Dupuy, O., Pouliot, P., Lesage, F., Bherer, L.: Comparable cerebral oxygenation patterns in younger and older adults during dual-task walking with increasing load. Front. Aging Neurosci. 08 (2016). https://doi.org/10.3389/fnagi.2016.00240

  59. Vollmer, M.: HRVTool - an open-source matlab toolbox for analyzing heart rate variability. Presented at the 2019 Computing in Cardiology Conference, 30 December 2019 (2019). https://doi.org/10.22489/CinC.2019.032

  60. DiDomenico, A., Nussbaum, M.A.: Effects of different physical workload parameters on mental workload and performance. Int. J. Ind. Ergon. 41, 255–260 (2011). https://doi.org/10.1016/j.ergon.2011.01.008

    Article  Google Scholar 

  61. Macken, W., Tremblay, S., Alford, D., Jones, D.: Attentional selectivity in short-term memory: similarity of process, not similarity of content, determines disruption. Int. J. Psychol. 34, 322–327 (1999). https://doi.org/10.1080/002075999399639

    Article  Google Scholar 

  62. Sammito, S., Böckelmann, I.: Factors influencing heart rate variability. Int. Cardiovasc. Forum J. 6, (2016). https://doi.org/10.17987/icfj.v6i0.242

  63. Hancock, P.A.: Whither workload? Mapping a path for its future development. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 3–17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61061-0_1

    Chapter  Google Scholar 

  64. Wickens, C.D.: Mental workload: assessment, prediction and consequences. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 18–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61061-0_2

    Chapter  Google Scholar 

  65. Hancock, P.A.: Neuroergonomics: where the cortex hits the concrete. Front. Hum. Neurosci. 13 (2019). https://doi.org/10.3389/fnhum.2019.00115

  66. Lobo, J.L., Ser, J.D., De Simone, F., Presta, R., Collina, S., Moravek, Z.: Cognitive workload classification using eye-tracking and EEG data. In: Proceedings of the International Conference on Human-Computer Interaction in Aerospace - HCI-Aero 2016, pp. 1–8. ACM Press, Paris (2016). https://doi.org/10.1145/2950112.2964585

  67. Tops, M., Boksem, M.A.S.: Absorbed in the task: personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity. Cogn. Affect. Behav. Neurosci. 10, 441–453 (2010). https://doi.org/10.3758/CABN.10.4.441

    Article  Google Scholar 

  68. Wascher, E., Getzmann, S., Karthaus, M.: Driver state examination—Treading new paths. Accid. Anal. Prev. 91, 157–165 (2016). https://doi.org/10.1016/j.aap.2016.02.029

    Article  Google Scholar 

  69. Li, L., Liu, Z., Zhu, H., Zhu, L., Huang, Y.: Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions. Ergonomics 62, 406–419 (2019). https://doi.org/10.1080/00140139.2018.1535093

    Article  Google Scholar 

  70. Wascher, E., et al.: Evaluating mental load during realistic driving simulations by means of round the ear electrodes. Front. Neurosci. 13 (2019). https://doi.org/10.3389/fnins.2019.00940

  71. Hoover, A., Singh, A., Fishel-Brown, S., Muth, E.: Real-time detection of workload changes using heart rate variability. Biomed. Sig. Process. Control 7, 333–341 (2012). https://doi.org/10.1016/j.bspc.2011.07.004

    Article  Google Scholar 

Download references

Funding

The author acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the project Montexas4.0 (FKZ 02L15A261).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dominic Bläsing .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bläsing, D., Bornewasser, M. (2020). Influence of Complexity and Noise on Mental Workload During a Manual Assembly Task. In: Longo, L., Leva, M.C. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2020. Communications in Computer and Information Science, vol 1318. Springer, Cham. https://doi.org/10.1007/978-3-030-62302-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62302-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62301-2

  • Online ISBN: 978-3-030-62302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics