When an indoor disaster occurs, the disaster site can become very difficult to escape from due to the scenario or building. Most people evacuate when a disaster situation occurs, but there are also disaster victims who cannot evacuate and are isolated. Isolated disaster victims often cannot move quickly because they do not have all the necessary information about the disaster, and secondary damage can occur. Rescue workers must rescue disaster victims quickly, before secondary damage occurs, but it is not always easy to locate isolated victims within a disaster site. In addition, rescue operators can also suffer from secondary damage because they are exposed to disaster situations. We present a HHD technique that can detect isolated victims in indoor disasters relatively quickly, especially when covered by fire smoke, by merging one-stage detectors YOLO and RetinaNet. HHD is a technique with a high human detection rate compared to other techniques while using a 1-stage detector method that combines YOLO and RetinaNet. Therefore, the HHD of this paper can be beneficial in future indoor disaster situations.

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.

The development of wearable robotic technology has accelerated recently due to active research in industrial technology. Wearable robots are used to help patients with lower-body palsy to walk. This paper reports a generic wearable-robot monitoring system (GWRMS) that supports resource monitoring and status prediction. The GWRMS offers universal support for operating systems that comply with the Portable Operating System Interface (POSIX) application programming interface (API), reducing limitations that arise due to the choice of operating system and processor. This system improves optimisation of a program operating in a robot-embedded system by detecting overhead in the software process of the wearable robot. In addition, it operates using TCP-socket-based monitoring on an on-demand basis only.

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.

Biometrics is the measurement and statistical analysis of biological data of personal physiological or behavioral characteristics to recognize an individual. Unlike keys and passwords, this is almost impossible to forge. Therefore, biometrics has been used in various areas for security and safety. In this paper, we propose a safety management system for a passenger ship using biometrics for efficient passenger management and ship safety. We designed a passenger management system using a face recognition system. In the proposed system, the passenger's facial information is registered automatically during check-in. This makes the checkin procedure more rapid and comfortable. Additionally, we supplemented the passenger management system with hand-gesture recognition. The passenger enrollment combined face recognition and hand-gesture recognition. The passenger's registered data is used as a key to access the internal facilities of the ship and recorded history. Crews can determine a passenger's whereabouts. Therefore, in an emergency situation, the ship safety system associated with the passenger management system enables the crew to act efficiently for rescue operations.