Skip to main content

A Realistic Training System for Maternal and Infant Health Care Based on MR Virtual Technology

  • Conference paper
  • First Online:
Frontier Computing on Industrial Applications Volume 2 (FC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1132))

Included in the following conference series:

  • 94 Accesses

Abstract

Maternal and infant nursing students must master sufficient knowledge and skills and have good professional competencies, especially before graduation, the assessment should be based on the requirements of maternal and infant health nursing professional competencies to assess the competencies that nursing students should have. The aim of this paper is to investigate the design and implementation of a realistic training system for maternal and child health care based on MR virtual technology. A complete imitation training system is constructed under the context-aware mechanism of maternal and infant health care, the workflow of the system is illustrated, and the design strategy of the maternal and infant health care imitation training system is proposed from the environmental context and task context. In the design strategy, a more detailed design guideline is proposed for the mother-infant health care imitation training system based on the multi-camera collaborative tracking technology of MR glasses. The experimental results show that the design solution is feasible.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Hammady, R., Ma, M., Al-Kalha, Z., Strathearn, C.: A framework for constructing and evaluating the role of MR as a holographic virtual guide in museums. Virtual Real. 25(4), 895–918 (2021)

    Article  Google Scholar 

  2. Havenith, H.-B., Cerfontaine, P., Mreyen, A.-S.: How virtual reality can help visualise and assess geohazards. Int. J. Digit. Earth 12(2), 173–189 (2019)

    Article  Google Scholar 

  3. Dieker, L.A., et al.: Using virtual rehearsal in a simulator to impact the performance of science teachers. Int. J. Gaming Comput. Mediat. Simul. 11(4), 1–20 (2019)

    Article  MathSciNet  Google Scholar 

  4. Alea, N., Bluck, S., Mroz, E.L., Edwards, Z.: The social function of autobiographical stories in the personal and virtual world: an initial investigation. Top. Cogn. Sci. 11(4), 794–810 (2019)

    Article  Google Scholar 

  5. Cirelli, L.K., Jurewicz, Z.B., Trehub, S.E.: Effects of maternal singing style on mother-infant arousal and behavior. J. Cogn. Neurosci. 32(7), 1213–1220 (2020)

    Article  Google Scholar 

  6. Ahmad, I., Asghar, Z., Kumar, T., Li, G., Manzoor, A., Mikhaylov, K.: Syed attique shah, marko höyhtyä, jarmo reponen, jyrki huusko, erkki harjula: emerging technologies for next generation remote health care and assisted living. IEEE Access 10, 56094–56132 (2022)

    Article  Google Scholar 

  7. Saleem, J.J., Wilck, N.R., Murphy, J.J., Herout, J.: Veteran and staff experience from a pilot program of health care system-distributed wearable devices and data sharing. Appl. Clin. Inform. 13(3), 532–540 (2022)

    Article  Google Scholar 

  8. Gillies, A., Smith, P.: Can AI systems meet the ethical requirements of professional decision-making in health care? AI Ethics 2(1), 41–47 (2022)

    Article  Google Scholar 

  9. Maibaum, A., Bischof, A., Hergesell, J., Lipp, B.: A critique of robotics in health care. AI Soc. 37(2), 467–477 (2022)

    Article  Google Scholar 

  10. Almagrabi, A.O., Ali, R., Alghazzawi, D., AlBarakati, A., Khurshaid, T.: A reinforcement learning-based framework for crowdsourcing in massive health care internet of things. Big Data 10(2), 161–170 (2022). https://doi.org/10.1089/big.2021.0058

    Article  Google Scholar 

  11. Kurazume, R., et al.: Development of AR training systems for humanitude dementia care. Adv. Robot. 36(7), 344–358 (2022)

    Article  Google Scholar 

  12. Fuchs, R., et al.: A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data. Int. J. Comput. Assist. Radiol. Surg. 17(9), 1619–1631 (2022)

    Article  Google Scholar 

  13. Tuena, C., Riva, G.: Active Navigation training: an Innovative embodied-Based training system for spatial navigation in aging. Cyberpsychology Behav. Soc. Netw. 25(1), 77–78 (2022)

    Article  Google Scholar 

  14. Lattuada, M., Gianniti, E., Ardagna, D., Zhang, L.: Performance prediction of deep learning applications training in GPU as a service systems. Clust. Comput. 25(2), 1279–1302 (2022)

    Article  Google Scholar 

  15. Lavric, T., Bricard, E., Preda, M., Zaharia, T.B.: A low-cost AR training system for manual assembly operations. Comput. Sci. Inf. Syst. 19(2), 1047–1073 (2022)

    Article  Google Scholar 

  16. Fujs, D., Vrhovec, S., Vavpotic, D.: Towards personalized user training for secure use of information systems. Int. Arab J. Inf. Technol. 19(3), 307–313 (2022)

    Google Scholar 

  17. Manabe, T., Omura, K.: Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E105.A(5), 778–786 (2022). https://doi.org/10.1587/transfun.2021WBP0007

    Article  Google Scholar 

  18. Suda, H., Kotani, G., Saito, D.: INmfCA algorithm for training of nonparallel voice conversion systems based on non-negative matrix factorization. IEICE Trans. Inf. Syst. E105.D(6), 1196–1210 (2022). https://doi.org/10.1587/transinf.2021EDP7234

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Feng Sun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, F. (2024). A Realistic Training System for Maternal and Infant Health Care Based on MR Virtual Technology. In: Hung, J.C., Yen, N., Chang, JW. (eds) Frontier Computing on Industrial Applications Volume 2. FC 2023. Lecture Notes in Electrical Engineering, vol 1132. Springer, Singapore. https://doi.org/10.1007/978-981-99-9538-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9538-7_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9537-0

  • Online ISBN: 978-981-99-9538-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics