RiNeo MR: A mixed reality simulator for newborn life support training

PLoS One. 2023 Dec 21;18(12):e0294914. doi: 10.1371/journal.pone.0294914. eCollection 2023.

Abstract

Neonatal resuscitation is an uncommon, albeit critical task that is more likely to succeed if performed properly and promptly. In this context, simulation is an appropriate way for training and assessing the abilities of all medical staff involved in delivery room care. Recent studies have shown that learning is enhanced if the simulation experience is realistic and engaging. Hence, Virtual Reality can be beneficial for newborn resuscitation training. However, the difficulty of providing realistic haptic interaction limits its use. To overcome this constraint, we have designed RiNeo MR, a simulator for newborn life support training, combining a sensorized manikin to monitor in real time resuscitation skills, with a Virtual Reality application. The system includes a Virtual Reality headset, Leap Motion to track the user's hands, sensorized bag valve mask, and manikin to monitor head and mask positioning, ventilation, and chest compression. RiNeo MR can be used in two modalities: 2D to let the trainee practice resuscitation manoeuvres on the physical manikin, while receiving real time feedback; 3D that allows the user to be immersed in a virtual environment and practice in an hospital-like setting. In the 3D mode, virtual and real manikins are overlapped and communicate in real time. Tests on 16 subjects (11 controls without medical expertise and 5 paediatric residents) demonstrated that the simulator is well tolerated in terms of discomfort. Moreover, the simulator is high rated for user experience and system usability, suggesting that RiNeo MR can be a promising tool to improve newborn life support training. RiNeo MR is a proof of concept of a mixed-reality newborn life support simulator that can be a promising tool to spread newborn resuscitation high-quality training among healthcare providers involved in perinatal medicine.

MeSH terms

  • Augmented Reality*
  • Child
  • Clinical Competence
  • Computer Simulation
  • Humans
  • Infant, Newborn
  • Learning
  • Resuscitation
  • Simulation Training*
  • User-Computer Interface
  • Virtual Reality*

Grants and funding

This work was partially supported by the European Union, PON Ricerca e Innovazione 2014-2020 D35F210 0230 0 0 07 (SR) and PON Ricerca e Innovazione 2014-2020 D31B210 0815 0 0 01 (MCo). This work was carried out within Italian Ministry of Health within the PNRR Complementary National Plan Ecosistema Innovativo della Salute, D33C220 0198 0 0 01 (MCa). Funded by the European Union - NextGenerationEU. However, the views and opinions expressed are hose of the authors alone and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them (MCa).