Design of an integrated firefighting suit with hazardous gas monitoring and early warning applying a time series model

Abstract

Fire accident environments expose firefighters to life-threatening hazardous gases such as CO, HCN, and HCl, which can cause asphyxiation, organ damage, or even fatalities. Despite advancements in protective gear, conventional firefighting suits primarily offer passive protection, lacking real-time hazard forecasting. This reactive paradigm often results in delayed warnings against dynamic gas threats. This study proposes an innovative hardware-software integrated firefighting suit designed for proactive safety. The system combines wearable multi-gas sensors, edge computing, and a time series prediction model to forecast gas concentrations with 96.25 % accuracy. By analyzing historical data trends, the suit dynamically classifies hazard levels using a human vulnerability probit model, enabling proactive risk mitigation. Experimental results from simulated fire scenarios demonstrate superior performance in predicting concentrations of gases like H2S and CO. The integration of predictive algorithms with real-time monitoring shifts safety management from passive response to proactive decision-making, enhancing firefighter survivability and operational efficiency. This advancement lays the foundation for next-generation intelligent firefighting equipment. This study is expected to provide a basis for the design of a kind of active protective firefighting suit.