Human-Fire Classification Method Based on Wireless Sensing Technology

High-rise fires pose significant risks due to their complex structures and high population density, making timely and effective detection methods essential. Compared to traditional sensor- or camera-based detection methods, wireless sensing-based human-fire classification (HFC) methods offer significant cost-effectiveness advantages. While Channel State Information (CSI) provides more detailed and accurate channel data compared to Received Signal Strength (RSS), its acquisition and processing are more challenging. On the other hand, RSS-based methods are easier to implement and more cost-effective. Therefore, selecting appropriate methods for specific scenarios is crucial to achieving a balance between performance and cost-effectiveness. To this end, this article proposes an innovative method called Wi-HFC, which leverages deep learning to evaluate the performance of RSS and CSI in various HFC tasks. Specifically, a dataset of RSS and CSI data from five classification tasks in real fire scenarios was collected and evaluated using a custom-designed convolutional neural network-based deep learning model. Experimental results indicate that RSS is a cost-effective choice for short distances or simple environments, whereas CSI demonstrates significant advantages in scenarios requiring higher accuracy and involving greater environmental complexity. Furthermore, the developed dataset is publicly available at https://github.com/T-bjq/Wi-HFC-dataset, providing resources for further research.