IoT-Enabled Exoskeletons for Firefighters Using Reinforcement Learning for Adaptive Support in Emergency Situations
D. Anitha, Shanmugam Kolangiammal, K. Lalitha, K Vijaya Naga Valli, Enthrakandi Narasimhan Ganesh, M. Muthulekshmi · 2024
A potential approach to improve firefighters' performance in difficult emergencies has been the combination of exoskeletons with Internet of Things (IoT) technologies in recent years. To provide adaptive assistance to firefighters during crucial missions, this research presents a new framework that uses reinforcement learning (RL) algorithms combined with IoT exoskeletons. The proposed system uses interconnected sensors built into the exoskeletons to track various environmental and physiological variables, including core temperature, heart rate, and ambient light intensity. Firefighters' health and the danger level of the environment are evaluated using these data points in real time. Autonomously adjusting their support systems to offer appropriate help based on the dynamic situation, the exoskeletons use RL techniques. The system learns to predict the actions of firefighters, adapt the amount of help in real-time, and maximize energy efficiency to keep running for longer due to feedback and iterative learning processes. Firefighters and incident command centers can communicate seamlessly with the proposed framework, allowing real-time situational awareness and decision assistance. Firefighters' ability to be safe and successful during emergency operations is greatly enhanced by exoskeletons. Smart solutions can protect first responders from harm and make them more resilient in dangerous situations.