Modern buildings are large and complex, and as more time is spent inside them, the risk of indoor disasters such as fires and gas leaks increases. In the event of such a disaster, the success of the rescue operation depends on the ability of the rescue team to navigate and respond to the complex environment. To address this challenge, we designed a mixed reality (MR)-based system simulating indoor disaster rescue. This system uses augmented indoor maps and MR technology to help rescue teams quickly, and effectively respond to unexpected environmental variables and carry out rescue activities. To test the effectiveness of the system, we created a virtual disaster scenario and evaluated the rescue and escape performance within a shortened “golden time” of 2 min, as opposed to 5 min, given the virtual characteristics of the simulation. The results showed that the system is particularly effective at increasing the speed of rescue activities. Additionally, our results indicated the potential for further improvement through additional research. By applying this system for disaster rescue simulations and training, the safety of firefighters and rescuers can be improved by reducing the risk of injury during rescue operations.

Traditionally, paraplegic patients have relied on a wheelchair to travel. However, new developments in walking assistance technology have led to promising exoskeleton-type wearable robots that can help paraplegic patients walk. Operation of this new robotic device requires that patients have appropriate training to ensure safe and optimal use. Here, we propose an algorithm that can optimize rehabilitation outcomes by comparing posture data generated during the rehabilitation of a paraplegic patient wearing a body-tracking sensor with reference posture data. The proposed algorithm guarantees a certain level of accuracy when comparing rehabilitation and reference posture data. It can also correct for timescale differences between reference and rehabilitation data to ensure a high level of accuracy. Compared with other algorithms that perform similar functions, this algorithm can accommodate different postures, including those associated with walking, and has the advantage of being able to derive the desired results by setting usability features in an intuitive way.

Algorithms that automatically search for routes have various applications, such as games, navigation, and robots, and are used to create optimal paths in the space of real and virtual worlds. This paper introduces an indoor navigation algorithm allowing rescuers to create a topology map of floor plans, given that the amount of data provided by conventional mapping constructs is limited. Traditional pathfinding algorithms include the A* algorithm, which has demonstrated excellent performance; however, its applicability is limited in the case of high-resolution images, as the number of nodes increases significantly. Here, we use the corner node algorithm (CNA) to improve pathfinding performance by preprocessing the data to create a topology map for the pathfinding algorithm. The algorithm can reduce the number of nodes required for the pathfinding process by generating topology maps in advance through two preprocessing processes: node creation and node connection. This effectively reduces the number of nodes to be processed, as well as the error rate when creating more accurate routes. Furthermore, a generated topology map can be reused by the algorithm unless its structure changes, which enhances efficiency. For the user, this means that the sought-after mapping information of the floor plan is quickly made available for navigation. With this algorithm, we created an augmented reality navigation solution to display virtual objects for rescuers, to demonstrate the enhanced performance of our approach.

— Large and complex buildings are increasing, and many objects exist in these buildings. Objects generate events through interaction with each other. There is an augmented reality among the technologies for recognizing the surrounding environment, and through augmented reality, data generated in the surrounding environment can be grasped to understand the situation. Among the events that occur indoors are indoor disasters, which can cause damage to people and property. Therefore, it is necessary to collect various indoor sensor data to identify indoor disaster events. At this time, the collected indoor sensor data should confirm and understand information. In the case of an indoor sensor, there are signal data for determining a location or a measurement sensor for understanding an indoor environment. Instant feedback may be possible if such indoor sensor data can be checked through a human visual action rather than devices. Accordingly, if augmented reality technology is used, information can be obtained more easily, and data readability becomes convenient. First of all, the augmented reality-based indoor sensor data monitoring system framework was designed by applying coordinate data of inertial sensors to identify a human’s indoor location so that sensor data can be monitored, and a prototype system was implemented through this. This system can be used as a system to monitor data from firefighters or control centers in the event of an indoor disaster.