Firefighter Skill Advancement through IoT-Enabled Virtual Reality and CNN-Based Training

Abstract

To maintain the safety and efficacy of firefighters in various circumstances, modern firefighting necessitates constantly improving skills and training techniques. Utilizing the Internet of Things (IoT), virtual reality (VR), and convolutional neural networks (CNN), this paper details a novel method for training firefighters. The proposed system collects real-time data on ambient variables, equipment state, and firefighter biometrics via integrating IoT sensors into firefighting equipment and training settings. Using this information, it can develop lifelike VR training simulations of difficult and potentially dangerous scenarios. To make the training settings more realistic and malleable, CNN-based algorithms are used to assess the data. The capacity to simulate a wide variety of firefighting situations, customize training difficulty depending on individual and team performance, and provide instant feedback and performance metrics to trainees are all major benefits of this method. The method also allows teachers to check in and evaluate their learners remotely, improving instruction quality. An IoT-enabled VR and CNN-based training technique has shown promising preliminary results in pilot trials, suggesting it might greatly enhance firefighter competence, situational awareness, and decision-making ability. Because of this, it has the potential to completely alter the way firefighters are informed and prepared for the ever-changing dangers users may encounter on the job.