Impact of Safety Signage Placement on Evacuation Behavior in Virtual Fire Scenarios Based on EDA Data
Year: 2025
Type: article
Abstract: Evacuation efficiency during fire emergencies directly influences casualty rates. Traditional fire drills suffer from limited scenario realism, elevated safety risks, and single-modal data acquisition, making it difficult to objectively quantify participants’ physiological and behavioral responses under authentic fire conditions. To address these limitations, this paper employs a high-immersion, controllable virtual reality (VR) hotel environment to simulate fire evacuation while concurrently recording electrodermal activity (EDA). Five features are extracted from the EDA signal: PhasicData, PhasicDriver, Skin Conductance (SC), TonicData, and TonicDriver. Three variables are evaluated, signage height (1m, 0.5m, and 0m), spacing (5m and 10m), and presence of active fire, using a hybrid classification model that integrates an im-proved convolutional neural network (CNN), a Transformer-based sequence encoder, and a multi-layer spiking neural network. The CNN autonomously captures multi-scale local features to heighten sensitivity to stress-related signal changes, the Trans-former encoder models long-range temporal dependencies, and the spiking neural network simulates neural dynamics to accurately reconstruct transient stress spikes. Experimental results yield average classification accuracies of 93.36%, 97.71%, and 95.99% across the three scenarios, demonstrating the neuro-engineering value of combining VR-based simulations with EDA monitoring to optimize safety-sign placement.
Source: IEEE Access
Authors: Wenqi Song, Xu Feng, Yu Song
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4413213130