# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 5 Application of AR to PHM
### AR Human-Computer Interaction  
In the forthcoming discussion, we aim to encapsulate the global advancements in AR technology, focusing on its application to human-computer interaction and diagnostic and repair strategies. Augmented reality (AR) technology has garnered widespread attention in recent years due to its ability to overlay digital information onto the physical world, creating an immersive and interactive environment. This technology is promising to revolutionize fault diagnosis and maintenance processes across various industries. In visual tracking, [PERSON] et al. proposed VisMerge, which combines a thermal imaging head-mounted display (HMD) and algorithms that can temporally and spectrally merge video streams from different optical bands into the same field of view [29]. [PERSON] et al. used an IntelRealSense (RS) camera to overcome or enhance the problem of developing augmented reality scenarios that can be used to integrate naturalhuman activities with virtual worlds or real-time computer graphics [30]. [PERSON] et al. presented a deep learning neural network-based task assistant model using YOLOv5s that could successfully inspect parts in real-time video streams with high accuracy, thus helping train professionals to learn to handle new devices using augmented reality [31]. [PERSON] describes the development of a WAS system with maintenance and assembly instructions, discusses the problem of fast markerless augmented reality (AR) instruction generation based on a monocular camera, and implements a learning-based module for fast inspection of equipment panels, facilitating the deployment of the system [32]. [PERSON] proposed a module for the visual recognition of aircraft mechanical parts, which was included in the design of the Alenia Maki M346 in an intelligent maintenance prototype system [33]. [PERSON] et al. combined visual saliency algorithms with edge analysis to identify potentially significant image regions and geometric constraints for placing labels. [PERSON] et al. proposed an improved image augmented reality approach by acting on two axes in the augmented reality process. First, a machine learning step is incorporated into the detection part. Second, the augmented image is aligned using a statistical appearance model and a dense image descriptor covariance matrix [34]. [PERSON] et al. used RGB-D cameras to optimize the computationally time-consuming part of the traditional visual SLAM method in order to solve the problems of poor real-time 3D scene modeling and the low robustness of loopback detection due to limited hardware resources and the insufficient computational power of the head-mounted device in the application of equipment maintenance using augmented reality [35]. Based on pupil center vision tracking technology using corneal reflection, [PERSON] constructed two sets of vision tracking hardware frameworks, namely a stationary-head pedestal type and a helmet type with free motion of the head, and developed a software system to support them. In terms of gesture recognition, [PERSON] uses a new gesture recognition method to recognize the whole hand of a participant by using gesture image attribute features to preprocess the input gesture to calculate the Fourier description subset of the gesture boundary point sequence and obtain the gesture feature vector by calculating the Euclidean distance and the similarity between the input gesture and a sample gesture in the database for recognition of four types of gestures in the system according to the recognition results. Real-time drawing of auxiliary pictures and realization of virtual overlay functionality within systems have been achieved using augmented reality registration techniques. [PERSON] et al. proposed a real-time collaborative system for remote assistance, in which the remote assistants use gesture-based mobile augmented reality. This method can be used to help remote workers perform manual tasks [36]. It is worth mentioning that [PERSON] et al. proposed a PHM system that combines deep learning and AR.