AR GearX: A Review

This comprehensive review investigates the potential of augmented and virtual reality (AR and VR) technologies, collectively known as Extended Reality (XR), particularly focusing on user profiling and historical context experiences. The study delves into the efficiency of profiling users based on behavioural data in XR environments, employing machine learning techniques to infer individual attributes like age and gender. Utilizing a general framework tested across various actions and mental loads in AR and VR scenarios, the study achieves high accuracy in user identification and attribute inference, with VR profiling outperforming AR, largely due to the presence of eye sensors. Additionally, the research proposes an augmented reality system to enhance understanding and interaction with traditional court paintings, providing historical context and immersive experiences. Furthermore, the paper evaluates and compares AR frameworks, ARKit and AR-Core, highlighting their performance across different computing settings. Lastly, the study introduces a novel method for constructing plant datasets using augmented reality techniques, aiming to address challenges in plant growth state detection. Overall, the research contributes to advancing user profiling in XR environments, enhancing historical interpretation through AR, and improving learning efficacy in educational settings with AR-based instructional tools.