High-rise fire escape training for the public poses a significant challenge, owing to the prohibitive cost and logistical intricacies involved in deploying high-fidelity simulations. While virtual reality (VR) technologies have demonstrated potential in safety education, the number of interaction modalities they offer is mostly limited to 3 to 5 types, and they heavily rely on controllers that abstract actions into button presses, limiting immersion and skill transfer. In this paper, we propose the MIFE system, which designs 8 distinct interaction modalities along with a real-time dynamic fire spread simulation to further improve immersion, allowing trainees to perceive the fire scene and efficiently master key escape skills through vision, auditory, olfactory, etc. Moreover, we have designed a controller-free and real-time full-body motion capture (MoCap) module, achieving precise mapping between the trainees’ full-body movements and the virtual avatar. Additionally, MIFE leverages a knowledge graph to provide tailored guidance, adapting dynamically to trainees with varying levels of fire escape proficiency. The results of the user study demonstrate that MIFE significantly outperforms self-study and controller-based training systems, particularly, improving by 3.08/10 and 2.38/10 in the performance score of the evaluation stage, respectively. This implies its practical utility and potential for broader adoption in high-rise fire emergency training.

A high-quality traumatic brain injury (TBI) dataset is essential to intelligent assisted diagnosis. In this work, we present a new labeled public TBI dataset containing head CT scans and various examination variables. The dataset was collected from 545 patients with moderate to severe TBI and CT annotations were produced. We provide example applications of segmentation, multilabel classification, and in-hospital mortality prediction models.