NextGen Training for Medical First Responders: Advancing Mass-Casualty Incident Preparedness through Mixed Reality Technology

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Multimodal Technologies and Interaction Pub Date : 2023-12-01 DOI:10.3390/mti7120113
Olivia Zechner, Daniel García Guirao, Helmut Schrom-Feiertag, Georg Regal, Jakob Carl Uhl, Lina Gyllencreutz, David Sjöberg, Manfred Tscheligi
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Abstract

Mixed reality (MR) technology has the potential to enhance the disaster preparedness of medical first responders in mass-casualty incidents through new training methods. In this manuscript, we present an MR training solution based on requirements collected from experienced medical first responders and technical experts, regular end-user feedback received through the iterative design process used to develop a prototype and feedback from two initial field trials. We discuss key features essential for an effective MR training system, including flexible scenario design, added realism through patient simulator manikins and objective performance assessment. Current technological challenges such as the responsiveness of avatars and the complexity of smart scenario control are also addressed, along with the future potential for integrating artificial intelligence. Furthermore, an advanced analytics and statistics tool that incorporates complex data integration, machine learning for data analysis and visualization techniques for performance evaluation is presented.
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医疗急救人员的 NextGen 培训:通过混合现实技术推进大规模伤亡事件的准备工作
混合现实(MR)技术有潜力通过新的培训方法,加强大规模伤亡事件中医疗急救人员的备灾能力。在本文中,我们根据从经验丰富的医疗急救人员和技术专家那里收集的需求、通过用于开发原型的迭代设计过程收到的定期最终用户反馈以及两次初始现场试验的反馈,提出了MR培训解决方案。我们讨论了有效磁共振训练系统的关键特征,包括灵活的场景设计,通过患者模拟器模型增加的真实感和客观的绩效评估。目前的技术挑战,如虚拟角色的响应能力和智能场景控制的复杂性,以及未来集成人工智能的潜力也得到了解决。此外,介绍了一种先进的分析和统计工具,该工具结合了复杂的数据集成、用于数据分析的机器学习和用于性能评估的可视化技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multimodal Technologies and Interaction
Multimodal Technologies and Interaction Computer Science-Computer Science Applications
CiteScore
4.90
自引率
8.00%
发文量
94
审稿时长
4 weeks
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