# Design of a Mixed Reality System for Simulating Indoor Disaster Rescue
## 2 Related Work
### Disaster Safety  
Disasters include events such as earthquakes and fires, which often cause damage that is sufficiently severe and widespread to exceed management capabilities. These events often result in injury, property damage, and even loss of life, and thus require an immediate and effective response (especially as they tend to happen unexpectedly). There have been numerous studies on safety in disaster situations. In recent years, the huge amount of data generated during disasters, i.e., \"big data,\" has led to the development of new safety technologies aimed at improving disaster responses. For example, blockchain technology can be used to verify and store data (i.e., improve disaster data management) to identify the cause of a disaster [6; 7]. [PERSON] et al. conducted a study that analyzed and classified the characteristics of \"cold wave\" disasters using daily report data [8]. [PERSON] et al. evaluated the safety of facilities during natural disasters by accumulating big data [9]. [PERSON] and colleagues used a system that collects big data from Internet of Things (IoT) sensors for disaster detection and prompt evacuation [10]. Big data collection, analysis, and visualization are all important for disaster management. [PERSON] et al. aimed to make disaster-related big data more understandable [11]. As research on disaster-related big data has increased, so too has the use of artificial intelligence in disaster research. [PERSON] and colleagues applied artificial intelligence to convert voice data into text for automatic disaster classification [12]. [PERSON] et al. used recurrent neural network models and contextual data from speech to analyze disasters [13]. [PERSON] et al. applied a word embedding model to evaluate disasters by analyzing text data from multiple news articles and social media posts [14]. [PERSON] et al. analyzed disaster occurrences using sensor data and machine learning models [15]. [PERSON] and colleagues used an artificial intelligence model to identify the optimal evacuation route during a disaster, based on user location information obtained from sensors [16]. [PERSON] et al. achieved earthquake detection using seismic vibration data in conjunction with convolutional neural network-long short-term memory models [17]. [PERSON] and colleagues conducted a study that used convolutional neural network models to predict fires through smoke image analysis [18]. [PERSON] et al. applied a convolutional neural network model, ResNet50, to determine whether a person was wearing a mask by analyzing images captured by a camera [19]. [PERSON] et al. used closed-circuit television (CCTV) footage to detect fires in advance during fire disasters [20]. [PERSON] et al. developed a fire detection system based on deep learning models to classify wildfire images as \"fire\" or \"normal\" by training the model on wildfire footage data [21]. [PERSON] et al. used the YOLOv3 model to classify dangerous areas based on image data collected via CCTV [22]. [PERSON] et al. developed a deep learning model to detect underground disasters [23]. Thus, artificial intelligence plays a key role in disaster management and mitigation efforts. In a disaster situation, it is crucial to understand the current status of the surrounding environment. Therefore, many disaster management systems collect and analyze various forms of environmental data, and then use that data for disaster preparation training. A growing number of studies are using IoT for disaster management systems. IoT technology enables the collection of data from various sensors in real time, for a more effective and prompt disaster response. [PERSON] and colleagues developed a disaster safety monitoring system that uses IoT sensors to collect data on, and monitor, environmental variables such as temperature, rainfall, and vibration under normal conditions, as a way to prepare for potential disasters [24]. [PERSON] et al. applied IoT sensors to detect potential disasters by monitoring electrical energy, which can cause fires [25]. [PERSON] et al.