Virtual Reality (VR) provides great opportunities for police officers to train decision-making and acting (DMA) in cognitively demanding and stressful situations. This paper presents a summary of findings from a three-year project, including requirements collected from experienced police trainers and industry experts, and quantitative and qualitative results of human factor studies and field trials. Findings include advantages of VR training such as the possibility to safely train high-risk situations in controllable and reproducible training environments, include a variety of avatars that would be difficult to use in real-life training (e.g., vulnerable populations or animals) and handle dangerous equipment (e.g., explosives) but also highlight challenges such as tracking, locomotion and intelligent virtual agents. The importance of strong alignment between training didactics and technical possibilities is highlighted and potential solutions presented. Furthermore training outcomes are transferable to real-world police duties and may apply to other domains that would benefit from simulation-based training.

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.

Background: Mixed reality (MR) simulation training is emerging in paramedical education as a way to practice responding to stress-intensive scenarios like mass casualty incidents in a safe and controlled environment. Current training platforms, however, lack real-time stress and human performance monitoring tools. Objective: The study aims to enhance MR training for medical first responders through real-time evaluation of performance and stress levels, leveraging biosignal monitoring and advanced analytics to allow instructors to tailor feedback and maintain optimal challenge and safety levels. Methods: The study includes a structured, multiphase approach including initial requirement gathering (structured interviews and cocreation workshops), an online design survey, iterative prototype development, and a field trial (including training observations and interviews). Data were collected from 5 end user consortium members across Europe. Quantitative data from checklists were analyzed using frequencies and percentages to understand feature usage and event occurrences. Qualitative data from semistructured interviews and cocreation workshops were transcribed, coded, and subjected to thematic analysis to identify patterns and insights into the usability and effectiveness of the enhanced features in the MR training. Results: The study identified a number of requirements that medical first responders have for an MR training system, including requirements not included in currently available solutions. A total of 80 performance metrics were initially identified and refined to a set of 54 metrics, which were categorized into key performance indicator groups such as scene safety, triage performance, and communication. Requirements for smart wearables to monitor stress levels are provided and highlight the importance of a user-centered design process to provide users with effective tools that fit their needs. Stress visualization preferences are described in the form of a dashboard as well as in virtual environments surrounding the avatar. Using an iterative design process and user feedback, a training system was developed, integrating real-time performance tracking and stress monitoring. The field trial provided insights into the practical use of these features during a real training exercise, showed interaction preferences between trainer and trainees, and highlighted further improvement opportunities. Conclusions: This research enhances MR training for paramedics by integrating real-time performance metrics and stress indicators based on a human-centered design approach that aligns with end user needs, thereby laying the foundation for developing more effective and immersive training solutions for high-stress professions.

This study assesses the integration of mixed reality (MR) technologies in medical first responder (MFR) training, focusing on identifying key factors influencing behavioral intention to use MR systems and practical implications for technology acceptance and enhanced realism through haptic feedback. Through a user-centered design approach, involving co-creation workshops, iterative development, and evaluations in pilot and field trials across six countries, this study evaluated technology acceptance, presence, user experience, and workload among MFRs. Both quantitative measures and qualitative feedback were collected to analyze the determinants of technology acceptance and user engagement. The MED1stMR training system, developed as a result, demonstrates that performance expectancy, effort expectancy, and social presence are significant predictors of behavioral intention to use MR training systems among MFRs. High technology acceptance and positive user experience were reported, with specific emphasis on the educational value of haptic feedback in skill training. Trainer feedback highlighted the importance of real-time performance metrics and openness to AI-driven training assistance for enhancing training outcomes. The study underscores the critical role of realistic patient interaction and the importance of aligning training challenges with users’ skills to create engaging MR training environments for MFRs. Identifying factors influencing behavioral intention offers valuable insights for the development of MR training systems, suggesting a focus on social presence and interactive capabilities to improve realism and educational value. The findings advocate for the integration of adaptive training features and further exploration of AI support in scenario optimization and performance enhancement.