Skip to main content

Mixed Reality Simulation for Medical Training: How It Affects Learners' Cognitive State

  • Conference paper
  • First Online:
Advances in Simulation and Digital Human Modeling (AHFE 2021)

Abstract

A mixed reality (MR) system, by providing visual, auditory, and haptic feedback to the learner, can offer a high level of immersion and realism, especially in the healthcare context. In medical training through MR simulations, it is particularly important to avoid mental overload, discomfort, fatigue, and stress, to guarantee productive learning. The present work proposes a systematic assessment of stress, cognitive load, and performance (through subjective and objective measures) of students during an MR simulation for the rachicentesis procedure. A specific application has been developed to enhance the sense of realism, by showing, over the skill trainer, a digital patient that responds with auditory and visual feedback, based on the learner’s interaction. A sample of 18 students has been enrolled in the pilot study. Preliminary results suggest the effectiveness of the proposed MR application using Hololens: high performances are achieved, and the cognitive conditions are well balanced.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Zhu, E., Hadadgar, A., Masiello, I., Zary, N.: Augmented reality in healthcare education: an integrative review. Peer J. 2, e469 (2014)

    Google Scholar 

  2. Gerup, J., Soerensen, C.B., Dieckmann, P.: Augmented reality and mixed reality for healthcare education beyond surgery: an integrative review. Int. J. Med. Educ. 11, 1–18 (2020)

    Article  Google Scholar 

  3. Kobayashi, L., Zhang, X.C., Collins, S.A., Karim, N., Merck, D.L.: Exploratory application of augmented reality/mixed reality devices for acute care procedure training. Educ. Adv. 158–164 (2017)

    Google Scholar 

  4. Rochlen, L.R., Levine, R., Tait, A.R.: First-person point-of-view-augmented reality for central line insertion training: a usability and feasibility study. Simul. Healthcare 12, 57–62 (2017)

    Article  Google Scholar 

  5. Margarido Mendes, H.C., Costa, C.I.A.B., da Silva, N.A., Leite, F.P., Esteves, A., Lopes, D.S.: PIÑATA: pinpoint insertion of intravenous needles via augmented reality training assistance. Comput. Med. Imaging Graph. 82, 101731 (2020)

    Google Scholar 

  6. Sherstyuk, A., Vincent, D., Berg, B., Treskunov, A.: Mixed reality manikins for medical education. In: Furht B. (eds) Handbook of Augmented Reality. Springer, New York, NY (2011). https://doi.org/10.1007/978-1-4614-0064-6_23

  7. Dias, R.D., Ngo-Howard, M.C., Boskovski, M.T., Zenati, M.A., Yule, S.J.: Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload. Br. J. Surg. 105, 491–501 (2018)

    Article  Google Scholar 

  8. Goldberg, M.B., et al.: Optimizing performance through stress training - an educational strategy for surgical residents. Am. J. Surg. 216, 618–623 (2018)

    Article  Google Scholar 

  9. Atalay, K.D., Can, G.F., Erdem, S.R., Muderrisoglu, I.H.: Assessment of mental workload and academic motivation in medical students. J. Pak. Med. Assoc. 66(5), 574-578 (2016)

    Google Scholar 

  10. Akçayır, M., Akçayır, G.: Advantages and challenges associated with augmented reality for education: a systematic review of the literature. Educ. Res. Rev. 20, 1–11 (2017)

    Article  Google Scholar 

  11. Fraser, K., Ma, I., Teteris, E., Baxter, H., Wright, B., McLaughlin, K.: Emotion, cognitive load and learning outcomes during simulation training. Med. Educ. 46, 1055–1062 (2012)

    Article  Google Scholar 

  12. Scafà, M., Serrani, E.B., Papetti, A., Brunzini, A., Germani, M.: Assessment of students’ cognitive conditions in medical simulation training: a review study. In: Cassenti D. (eds) Advances in Human Factors and Simulation. AHFE 2019. Advances in Intelligent Systems and Computing, vol. 958, pp. 224–233 (2019)

    Google Scholar 

  13. Naismith, L.M., Cavalcanti, R.B.: Validity of cognitive load measures in simulation-based training: a systematic review. Acad. Med. 90, S24–S35 (2015)

    Article  Google Scholar 

  14. Munzer, B.W., Khan, M.M., Shipman, B., Mahajan, P.: Augmented reality in emergency medicine: a scoping review. J. Med. Internet Res. 21(4), e12368 (2019)

    Google Scholar 

  15. Linde, A.S., Geoffrey, T.M.: Applications of future technologies to detect skill decay and improve procedural performance. Mil. Med. 184, 72–77 (2019)

    Article  Google Scholar 

  16. Brunzini, A., Papetti, A., Serrani, E.B., Scafà, M., Germani, M.: How to improve medical simulation training: a new methodology based on ergonomic evaluation. In: Karwowski, W., Ahram, T., Nazir, S. (eds.) AHFE 2019. AISC, vol. 963, pp. 145–155. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20135-7_14

    Chapter  Google Scholar 

  17. Paul, G., Scataglini, S.: Open-source software to create a kinematic model in digital human modeling. In: DHM and Posturography, pp. 201–213 (2019)

    Google Scholar 

  18. Scataglini, S., Danckaers, F., Haelterman, R., Huysmans, T., Sijbers, J.: Moving statistical body shape models using blender. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 822, pp. 28–38. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96077-7_4

    Chapter  Google Scholar 

  19. Spielberger, C.D., Gorsuch, R.L.: State-trait anxiety inventory for adults: Sampler set: manual, test, scoring key. Mind Garden, Redwood City, California (1983)

    Google Scholar 

  20. Sugarindra, M., Suryoputro, M.R., Permana, A.I.: Mental workload measurement in operator control room using NASA-TLX. In: IOP Conference Series: Materials Science and Engineering, vol. 277, no. 1, p. 012022 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Agnese Brunzini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brunzini, A., Papetti, A., Germani, M., Barbadoro, P., Messi, D., Adrario, E. (2021). Mixed Reality Simulation for Medical Training: How It Affects Learners' Cognitive State. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_41

Download citation

Publish with us

Policies and ethics