# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 3 Material and Methods
### Experiment Environment  
We conducted experimental evaluations of our system at the Wumin Art Center. The system architecture integrates BLE beacons, a mobile device, and a server for indoor positioning. Figure 4 depicts the spatial configuration of the installed BLE beacons. Raspberry Pi 4B devices, functioning as BLE beacons, were strategically mounted on the gallery ceiling. Table 2 provides detailed information regarding the system specifications, including both the hardware and software components utilized in our experimental setup. To preserve the aesthetic appearance as much as possible, the Raspberry Pi devices were installed on the ceiling and enclosed in white cases matching the ceiling color. According to the museum curator, visitors did not noticeably pay attention to the Raspberry Pi installations. The exhibition space was partitioned into three discrete zones, with RSSI data collected exclusively from beacons within the user's current zone to enhance positional accuracy. The circles in the figure represent data collection points used for training the ML model,  
Figure 3: Navigation for decent service. with red circles specifically denoting corner positions. Real-time location estimation was performed through the application of the trained ML model to current RSSI measurements. For validation purposes, accuracy assessment data were collected at identical locations as the training dataset. The implementation of Raspberry Pi boards as BLE beacon receivers can present a cost-effective solution, including requisite peripherals such as power supplies, BLE dongles, enclosures, and networking components [32]. However, a comprehensive cost-benefit analysis evaluating total hardware investment and long-term maintenance feasibility is recommended prior to large-scale deployment in library or gallery environments. While Raspberry Pi units necessitate continuous power supply and network connectivity, public exhibition spaces such as galleries and museums can leverage pre-existing electrical infrastructure originally established for lighting installations, thereby minimizing or eliminating the requirement for additional wiring installations to support IoT hardware. As demonstrated in [33], increasing the density of Raspberry Pi devices typically exhibits correlation with enhanced location estimation accuracy, though the marginal improvement diminishes significantly beyond a threshold of approximately five to six units. Empirical evidence from this study indicates optimal performance when Raspberry Pi devices are positioned at the corners and center of a defined space, effectively establishing a bounding box that ensures triangulation coverage by a minimum of three beacons for each target location. Research presented in [34] demonstrates that the implementation of just three BLE beacons reduced positional error from 9.09 m to 2.94 m, while additional beacons contributed only marginally to performance enhancement. This finding substantiates \begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Component** & **Details** \\ \hline \multirow{4}{*}{Hardware} & BLE beacons & Raspberry Pi 4B unit \\ \cline{2-3}  & Mobile device & Galaxy S21 smartphone \\ \cline{2-3}  & GPU (training) & NVIDIA GeForce RTX 3070 GPU \\ \hline \multirow{6}{*}{Software} & YOLOv5 & YOLOv5m architecture \\  & & - Epochs: 128 \\ \cline{1-1}  & & - Batch size: 4 \\ \cline{1-1}  & & - Image resolution: 416 \(\times\) 416 pixels \\ \cline{1-1}  & & - Dataset: CrowdHuman dataset \\ \cline{1-1} \cline{2-3}  & Python & Version: 3.9.17 \\ \cline{1-1} \cline{2-3}  & Library & TFLite, TensorFlow \\ \hline \hline \end{tabular}
\end{table} 
Table 2: System implementation specifications: hardware and software components. Figure 4: Beacon deployment location at the art gallery. the conclusion that increasing device quantity does not necessarily yield proportional improvements in system performance, underscoring the importance of strategic beacon placement to optimize system efficiency while minimizing hardware requirements.