# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 5 Results
### Impact of Voice Interaction on System Performance  
To evaluate the contribution of the voice assistant module, we conducted study using the same dataset of 200 annotated video sequences described in Section 3.2. Two configurations were tested:  
* _YOLOv11 without voice interaction_: The system automatically sent alerts upon detecting a fall based on pose estimation alone.
* _YOLOv11 with voice assistant_: After a detected fall, the system engaged in voice interaction to confirm the user's condition before triggering an alert.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Formula** & **Value** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+TN}\times 100\) & 97.5\% \\ \hline Precision (P) & \(\frac{TP}{TP+FP}\times 100\) & 97.17\% \\ \hline Recall (R) & \(\frac{TP}{TP+FN}\times 100\) & 98.1\% \\ \hline Specificity & \(\frac{TN}{TN+FP}\times 100\) & 96.84\% \\ \hline F1 score & \(\frac{2\times P\times R}{P+R}\) & 97.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Key metrics used for system evaluation on data collected from simulated scenarios conducted by the development team.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** \\ \hline [PERSON] et al. [54] & 79.30\% & 84.30\% & 73.07\% & 79.40\% & 78.28\% \\ \hline [PERSON] et al. [55] & 90.90\% & 94.56\% & 81.08\% & 90.84\% & 87.30\% \\ \hline [PERSON] et al. [56] & 86.21\% & 89.1\% & 93.18\% & 64.29\% & 91.1\% \\ \hline [PERSON] et al. [6] & 96.15\% & 97\% & 97.98\% & 90.32\% & 97.48\% \\ \hline Our new method & 96.92\% & 97.94\% & 97.94\% & 93.94\% & 97.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: A comparison of our new proposed method with other studies, with all results obtained on the same Le2i dataset.  
Table 10 summarizes the results.  
Its human-in-the-loop design explains the significant reduction in false positives observed with the addition of the voice interaction module (Table 10). While the vision-based detector sometimes misclassifies abrupt but harmless activities (e.g., sitting down abruptly or bending) as falls, the voice assistant prompts the user to confirm their condition. If the user responds that they are fine, the alert is suppressed, effectively filtering out ambiguous cases that would otherwise trigger false alarms.  
The contribution of the voice assistant was quantified by comparing system configurations with and without voice interaction, as shown in Table 10. Integrating the voice assistant reduced the number of false positives from 12 to 3, corresponding to approximately a 75% reduction, while maintaining the same detection accuracy and recall. These results demonstrate that even a simple, rule-based voice confirmation mechanism can substantially improve reliability in fall detection scenarios.  
We further analyzed the misclassifications observed during testing. False positives were primarily associated with non-fall activities involving abrupt postures, such as quickly sitting down, bending to pick up objects, or crawling. These movements sometimes mimic a lying position or rapid transition, triggering false alarms. Infrequent false negatives typically resulted when furniture partially obscured falls or when the fall resulted in a posture that was not fully horizontal (e.g., curled), thus making it harder to distinguish from normal activities.