## 3 Results
### Keyword Co-Occurrence and Clustering
#### 3.4.2 Knowledge Domain Clustering and Temporal Development  
The keyword cluster analysis in this study reveals a Q value of 0.4938, which exceeds the assessment criterion of 0.3, indicating a well-structured clustering; meanwhile, the S value reaches 0.8611, significantly surpassing the threshold of 0.5, confirming the high confidence level of the clustering results. We employed the Log-Likelihood Ratio (LLR) method for cluster labeling [25], which evaluates statistical significance and conducts hypothesis testing based on rigorous threshold verification, ensuring statistical reliability and optimal confidence levels for the extracted cluster labels [26]. Notably, the silhouette scores of the document co-citation network all exceed 0.7 (Table 5), further validating the reliability of the clustering quality. As shown in Figure 7, the cluster analysis ultimately generated six cluster labels, numbered #0 through #5, with smaller-numbered clusters containing more keywords, reflecting the degree of aggregation of related research topics. In terms of label selection, considering that \"street crossing\" in cluster #0 better represents the core content of this research area than the initially auto-generated \"mobility performance\" (both having identical statistical indicators), we renamed cluster #0 as \"street crossing\" to enhance the representativeness and explanatory power of the label. Similarly, for cluster #2, we selected \"virtual reality\" over \"human computer interaction\" to maintain hierarchical consistency across cluster labels, as \"virtual reality\" provides a more specific and contextually relevant representation of the technological applications within this knowledge domain. Cluster analysis reveals six core knowledge domains in wearable technology for VRU safety (Table 5 and Figure 7). The red \"#0 street crossing\" cluster represents closely related concepts around road crossing safety, with its prominent position highlighting this topic's fundamental role in VRU safety research. The adjacent yellow \"#1 obstacle avoidance\" cluster integrates various sensory substitution technologies for obstacle detection and avoidance for visually impaired individuals and cyclists, demonstrating assistive technology's critical value in enhancing safety autonomy for specific groups. The green \"#2 virtual reality\" cluster focuses on virtual reality applications and immersive technologies for enhancing safety perception among pedestrians, child cyclists, and visually impaired pedestrians. Notably, the light blue \"#3 cyclist safety\" cluster shows the integration of augmented reality with connected vehicle technologies, indicating cycling safety research is rapidly evolving toward intelligent systems. The dark blue \"#4 blind navigation\" cluster focuses on indoor and outdoor barrier-free navigation systems providing precise mobility assistance, highlighting technology's potential to promote independent and safe mobility  
Figure 6: Top 14 keywords with the strongest burst patterns for wearable technology in VRU safety. for visually impaired individuals. The pink \"#5 smart glasses\" cluster combines smart glasses with IoT and AI technologies, representing cutting-edge innovation in wearable technology for vulnerable pedestrian safety. The timeline diagram reveals the evolutionary trajectory of wearable technology research in the VRU safety field (Figure 8). In the #0 street crossing cluster, the early combination of high-frequency core nodes \"wearable technology\" and \"visually impaired\" (2002-2005) established the foundation for road safety research for the visually impaired. With technological development, the emergence of \"servo motor\" and \"warning alerts\" in 2015 reflected a shift towards mechanical assistance and active warning systems, while \"severity\" and \"vehicle impact\" in 2020 indicated that the focus had expanded to accident  
Figure 7: Cluster view of knowledge domains for wearable technology in VRU safety.