# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 2 Background of Analysis  
### Navigation Service  
With the advancement of technology, various services are being developed to assist visually impaired individuals in achieving independent mobility. These services provide support through a variety of means, including smart canes and mobile applications. The technologies utilized in navigation for visually impaired individuals can be classified into three main categories, as shown in Table 1. This taxonomical framework encompasses wireless communication technologies (including Wi-Fi, Bluetooth Low Energy beacons, and Ultra-Wideband), sensor-based technologies (such as Global Positioning System, RGB-D cameras, and Inertial Measurement Units), and marker-based approaches (primarily two-dimensional codes like QR codes strategically positioned within environments) [9]. Table 1 presents a structured comparison of these specific technologies, detailing their respective benefits and features alongside their limitations and implementation challenges in a tabular format. The remainder of this section provides a more detailed examination of these technologies. GPS functionality is severely compromised in indoor settings, whereas Wi-Fi-based solutions have emerged as a viable alternative due to the ability to exploit existing access points (APs) and perform localization and proximity detection without requiring line-of-sight (LOS). Techniques such as location and RSS fingerprinting are commonly employed to estimate user positions based on the signal strength of surrounding APs [10, 11]. Wi-Fi-based methods can be implemented with minimal configuration on the client side. However,their accuracy is often limited, particularly in estimating the distance between users and exhibits. Furthermore, RSS fingerprinting techniques require a labor-intensive offline phase to build a signal map, which may be impractical for large-scale indoor environments such as museums. To enhance the accuracy of indoor positioning systems, recent research has applied machine learning (ML) and neural networks to beacon technology [10; 12; 13; 14]. This algorithm employs neural networks to analyze RSS features collected from BLE beacons. However, BLE signals are susceptible to significant noise and attenuation due to building materials, presenting challenges to achieving high positioning accuracy. Filtering methods, including Kalman filters, Bayesian filtering, and fingerprinting techniques, have been proposed to mitigate these issues. These filtering approaches improve accuracy for positioning, proximity detection, and zone classification compared to conventional RSS-based methods. Specifically, they help manage and reduce RSS variability caused by noise, attenuation, and BLE advertising channel fluctuations. Nevertheless, inherent BLE signal characteristics, such as sensitivity to environmental attenuation, remain limiting factors. Moreover, the effectiveness of deep learning models is highly dependent on the availability and quality of training datasets. Without sufficient data, model accuracy is constrained. Training these models also demands substantial computational resources.