# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 5 Results
### Evaluation Metrics  
To evaluate the performance of the fall detection system, extensive tests were carried out. The confusion matrix provides an overview of the algorithm's ability to correctly distinguish between fall events and normal activities. The confusion matrix for this scenario, based on our collected data from simulated scenarios, is presented in Figure 6.  
The components of the confusion matrix are explained below:  
* _True Positives (TPs)_: A real fall occurred and was correctly detected by the system (103 instances);
* _False Positives (FPs)_: The system falsely detected a fall when none occurred (3 instances);
* _True Negatives (TNs)_: No fall occurred, and the system correctly ignored it (92 instances);
* _False Negatives (FNs)_: A fall occurred but was not detected by the system (2 instances).  
Based on these counters, the following metrics were computed for each processed frame:  
* _Accuracy_: The overall correctness of the model's predictions across all classes;
* _Precision_: The proportion of detected falls that were actual falls;
* _Recall (Sensitivity)_: The ability of the system to detect actual falls;
* _F1-Score_: Harmonic mean of precision and recall, useful when the dataset is imbalanced;
* _Specificity_: The proportion of actual negatives that are correctly identified by the model.  
These metrics, obtained from the confusion matrix and summarized in Table 8, offer a comprehensive insight into the algorithm's effectiveness in identifying fall events and differentiating them from non-fall activities.  
The fall detection model demonstrates strong and well-balanced performance across key evaluation metrics, making it suitable for practical deployment. It achieves a high recall of 98.1%, indicating that the system successfully identifies the vast majority of actual fall events, which is essential for these types of applications. The overall accuracy of 97.5% reflects the model's ability to correctly classify both fall and non-fall instances across the dataset. A precision of 97.17% further confirms that most fall predictions made by the system are accurate, minimizing false alarms. Complementing these metrics, the F1 score of 97.65% highlights the model's effective balance between precision and recall, ensuring both reliable detection and alert efficiency. Additionally, the model achieves a specificity of  
Figure 6: Confusion matrix based on data collected from simulated scenarios conducted by the development team.  
96.84%, meaning it correctly identifies non-fall events in the majority of cases, reducing the likelihood of unnecessary interventions. Together, these results underscore the system's robustness and its potential for real-world use in intelligent fall detection scenarios.  
In addition to the evaluation on our internally collected dataset of 200 simulated sequences (Table 8), we also tested the algorithm on another external dataset, the Le2i dataset, to further assess its robustness and to enable comparison with prior studies (Table 9). The observed differences in performance metrics between the two tables reflect the variability of scenarios, participant behaviors, and environmental conditions in the two datasets.