Skip to main content

An Augmented Reality-Based Solution for Monitoring Patients Vitals in Surgical Procedures

  • Conference paper
  • First Online:
Augmented Reality, Virtual Reality, and Computer Graphics (AVR 2021)

Abstract

In this work, an augmented reality (AR) system is proposed to monitor in real time the patient’s vital parameters during surgical procedures. This system is characterised metrologically in terms of transmission error rates and latency. These specifications are relevant for ensuring real-time response. The proposed system automatically collects data from the equipment in the operating room (OR), and displays them in AR. The system was designed, implemented and validated through experimental tests carried out using a set of Epson Moverio BT-350 AR glasses to monitor the output of a respiratory ventilator and a patient monitor in the OR.

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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. Alesanco, A., García, J.: Clinical assessment of wireless ECG transmission in real-time cardiac telemonitoring. IEEE Trans. Inf Technol. Biomed. 14(5), 1144–1152 (2010)

    Article  Google Scholar 

  2. Alharthi, A.S., Yunas, S.U., Ozanyan, K.B.: Deep learning for monitoring of human gait: a review. IEEE Sens. J. 19(21), 9575–9591 (2019). https://doi.org/10.1109/JSEN.2019.2928777

    Article  Google Scholar 

  3. Alotaibi, B.: Utilizing blockchain to overcome cyber security concerns in the internet of things: a review. IEEE Sens. J. 19(23), 10953–10971 (2019)

    Article  Google Scholar 

  4. Angrisani, L., Grazioso, S., Di Gironimo, G., Panariello, D., Tedesco, A.: On the use of soft continuum robots for remote measurement tasks in constrained environments: a brief overview of applications. In: 2019 IEEE International Symposium on Measurements and Networking, M&N 2019 (2019). https://doi.org/10.1109/IWMN.2019.8805050

  5. Angrisani, L., Arpaia, P., Esposito, A., Moccaldi, N.: A wearable brain-computer interface instrument for augmented reality-based inspection in industry 4.0. IEEE Trans. Instrum. Meas. 69, 1530–1539 (2019)

    Article  Google Scholar 

  6. Arpaia, P., Dallet, D., Erra, E., Tedesco, A.: Reliability measurements of an augmented reality-based 4.0 system for supporting workmen in handmade assembly. In: 24th IMEKO TC4 International Symposium and 22nd International Workshop on ADC and DAC Modelling and Testing, pp. 190–195 (2020)

    Google Scholar 

  7. Arpaia, P., De Benedetto, E., Duraccio, L.: Design, implementation, and metrological characterization of a wearable, integrated AR-BCI hands-free system for health 4.0 monitoring. Measurement 177, 109280 (2021). https://doi.org/10.1016/j.measurement.2021.109280

  8. Arpaia, P., De Benedetto, E., Dodaro, C.A., Duraccio, L., Servillo, G.: Metrology-based design of a wearable augmented reality system for monitoring patient’s vitals in real time. IEEE Sens. J. 21(9), 11176–11183 (2021). https://doi.org/10.1109/JSEN.2021.3059636

    Article  Google Scholar 

  9. Bernasconi, R., Meroni, D., Aliverti, A., Magagnin, L.: Fabrication of a bioimpedance sensor via inkjet printing and selective metallization. IEEE Sens. J. 20(23), 14024–14031 (2020)

    Article  Google Scholar 

  10. Bloomfield, R.A., Teeter, M.G., McIsaac, K.A.: A convolutional neural network approach to classifying activities using knee instrumented wearable sensors. IEEE Sens. J. 20, 14975–14983 (2020)

    Article  Google Scholar 

  11. Cepisca, C., Adochiei, F.C., Potlog, S., Banica, C.K., Seritan, G.C.: Platform for bio-monitoring of vital parameters in critical infrastructures operation. In: 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. E-7. IEEE (2015)

    Google Scholar 

  12. Chang, J.Y.C., Tsui, L.Y., Yeung, K.S.K., Yip, S.W.Y., Leung, G.K.K.: Surgical vision: Google glass and surgery. Surg. Innov. 23(4), 422–426 (2016)

    Article  Google Scholar 

  13. Condino, S., et al.: Hybrid simulation and planning platform for cryosurgery with Microsoft Hololens. Sensors 21(13) (2021). https://doi.org/10.3390/s21134450

  14. Corchia, L., et al.: Fully-textile, wearable chipless tags for identification and tracking applications. Sensors 20(2) (2020). https://doi.org/10.3390/s20020429

  15. Corchia, L., Monti, G., De Benedetto, E., Tarricone, L.: Low-cost chipless sensor tags for wearable user interfaces. IEEE Sens. J. 19(21), 10046–10053 (2019). https://doi.org/10.1109/JSEN.2019.2927823

    Article  Google Scholar 

  16. Cutolo, F., Fida, B., Cattari, N., Ferrari, V.: Software framework for customized augmented reality headsets in medicine. IEEE Access 8, 706–720 (2020). https://doi.org/10.1109/ACCESS.2019.2962122

    Article  Google Scholar 

  17. Grazioso, S., Tedesco, A., Selvaggio, M., Debei, S., Chiodini, S.: Towards the development of a cyber-physical measurement system (CPMS): case study of a bioinspired soft growing robot for remote measurement and monitoring applications. ACTA IMEKO 10(2), 103–109 (2021). http://dx.doi.org/10.21014/acta_imeko.v10i2.1123

  18. Grazioso, S., et al.: Design of a soft growing robot as a practical example of cyber-physical measurement systems. In: IEEE Metrology for Industry 4.0 and IoT Proceedings. IEEE (2021). https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488477

  19. He, C., Liu, Y., Wang, Y.: Sensor-fusion based augmented-reality surgical navigation system. In: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 1–5 (May 2016)

    Google Scholar 

  20. McDuff, D., Hurter, C., Gonzalez-Franco, M.: Pulse and vital sign measurement in mixed reality using a Hololens. In: Proceedings of the 23rd ACM Symposium on Virtual Reality Software and Technology, pp. 1–9 (2017)

    Google Scholar 

  21. Meyer, J., Schlebusch, T., Fuhl, W., Kasneci, E.: A novel camera-free eye tracking sensor for augmented reality based on laser scanning. IEEE Sens. J. 20, 15204–15212 (2020)

    Article  Google Scholar 

  22. Muhammed, T., Mehmood, R., Albeshri, A., Katib, I.: UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6, 32258–32285 (2018)

    Article  Google Scholar 

  23. Ormerod, D., Ross, B., Naluai-Cecchini, A.: Use of an augmented reality display of patient monitoring data to enhance anesthesiologists’ response to abnormal clinical events. Stud. Health Technol. Inform. 94, 248–250 (2003). https://doi.org/10.3233/978-1-60750-938-7-248

    Article  Google Scholar 

  24. Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., Liotta, A.: An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Trans. Ind. Inform. 15(1), 481–489 (2019)

    Google Scholar 

  25. Rauschnabel, P.A., Ro, Y.K.: Augmented reality smart glasses: an investigation of technology acceptance drivers. Int. J. Technol. Mark. 11(2), 123–148 (2016)

    Article  Google Scholar 

  26. Sanderson, P.M., et al.: Advanced auditory displays and head-mounted displays: advantages and disadvantages for monitoring by the distracted anesthesiologist. Anesth. Analg. 106(6), 1787–1797 (2008)

    Article  MathSciNet  Google Scholar 

  27. Schiavoni, R., et al.: Feasibility of a wearable reflectometric system for sensing skin hydration. Sensors 20(10), 2833 (2020). https://doi.org/10.3390/s20102833

    Article  Google Scholar 

  28. Spanò, E., Di Pascoli, S., Iannaccone, G.: Low-power wearable ECG monitoring system for multiple-patient remote monitoring. IEEE Sens. J. 16(13), 5452–5462 (2016)

    Article  Google Scholar 

  29. Teague, C.N., et al.: A wearable, multimodal sensing system to monitor knee joint health. IEEE Sens. J. 20(18), 10323–10334 (2020). https://doi.org/10.1109/JSEN.2020.2994552

    Article  Google Scholar 

  30. Viglialoro, R., Condino, S., Turini, G., Carbone, M., Ferrari, V., Gesi, M.: Augmented reality, mixed reality, and hybrid approach in healthcare simulation: a systematic review. Appl. Sci. (Switz.) 11(5), 1–20 (2021). https://doi.org/10.3390/app11052338

    Article  Google Scholar 

  31. Wannenburg, J., Malekian, R., Hancke, G.P.: Wireless capacitive-based ECG sensing for feature extraction and mobile health monitoring. IEEE Sens. J. 18(14), 6023–6032 (2018)

    Article  Google Scholar 

  32. Wehde, M.: Healthcare 4.0. IEEE Eng. Manage. Rev. 47(3), 24–28 (2019). https://doi.org/10.1109/EMR.2019.2930702

  33. Zhang, B., Hong, X., Liu, Y.: Multi-task deep transfer learning method for guided wave-based integrated health monitoring using piezoelectric transducers. IEEE Sens. J. 20(23), 14391–14400 (2020)

    Article  Google Scholar 

Download references

Acknowledgment

This work was carried out as part of the “ICT for Health” project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative ‘Departments of Excellence’ (Italian Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Egidio De Benedetto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arpaia, P., Crauso, F., De Benedetto, E., Duraccio, L., Improta, G. (2021). An Augmented Reality-Based Solution for Monitoring Patients Vitals in Surgical Procedures. In: De Paolis, L.T., Arpaia, P., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2021. Lecture Notes in Computer Science(), vol 12980. Springer, Cham. https://doi.org/10.1007/978-3-030-87595-4_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87595-4_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87594-7

  • Online ISBN: 978-3-030-87595-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics