# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 6 Discussion and Conclusions
### Limitations  
Table 1 highlights the improvements of YOLOv11 over YOLOv7-W6-Pose in terms of architecture, inference speed, and detection accuracy. However, we acknowledge that this work lacked a formal ablation study isolating the impact of YOLOv11 on fall detection performance. We plan to include such a systematic analysis in future research to further substantiate the benefits of the YOLOv11 upgrade.  
The experimental room was furnished and cluttered to approximate a home environment, but it does not fully replicate the diversity of real homes, which may vary in layout, lighting conditions, and furniture density. The room lacked natural lighting variations and more complex occlusion scenarios found in actual residences. These challenges can affect detection reliability, as visual accuracy remains sensitive to environmental factors such  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configuration** & **TP** & **FP** & **TN** & **FN** & **F1 Score** & **Notes** \\ \hline YOLOv11 only & 103 & 12 & 83 & 2 & 93.64\% & Higher false positives on ambiguous movements \\ \hline YOLOv11 + Voice Assistant & 103 & 3 & 92 & 2 & 97.94\% & Voice confirmation reduced false alarms by \(\sim\)75\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison between system configurations with and without voice confirmation.  
as low lighting, motion blur, and partial occlusions, particularly when falls occur behind obstacles or outside the cameras' field of view.  
Another key limitation is the composition of the testing dataset, which predominantly includes healthy adult participants. As a result, the model may not accurately represent or respond to the movement characteristics of elderly individuals or users with assistive devices, such as canes or walkers. This restricts its generalizability to its intended target group.  
Furthermore, the effectiveness of the voice assistant component is limited in noisy or multi-speaker environments, where background noise and overlapping speech can impair accurate intent recognition. Additionally, the current system does not support multilingual input or dialectal variations, which may reduce usability in culturally and linguistically diverse settings. The voice interaction relies on rule-based keyword matching, which, although lightweight and efficient, can restrict the system's ability to interpret spontaneous or non-standard expressions that may occur during real-life emergencies. Moreover, this study does not include a direct performance comparison between the YOLOv11-Pose model with and without the integrated voice assistant. Although preliminary observations suggest verbal confirmation reduces false positives, future work should formally assess the voice module's precise impact using ablation studies because it remains unquantified.  
While the voice assistant proved effective in reducing false positives, it was not benchmarked against other speech recognition solutions or evaluated under varying noise levels, multilingual conditions, or with users having speech impairments. Furthermore, its current implementation relies on standard Python libraries without specialized optimization. These aspects will be explored in future work to enhance robustness and generalizability.  
These limitations can affect both detection reliability and the user experience.