# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 4 Results  
### Analysis of Performance of the System in the Detection of Falls  
The evaluation of the results can be better understood using a confusion matrix, which provides a comprehensive visualization of the performance of a classification algorithm. The confusion matrix is a fundamental tool in evaluating the performance of classification algorithms, providing a detailed breakdown of predictions versus actual outcomes across different classes. In the context of fall detection or any binary classification task, the confusion matrix helps quantify the model's accuracy, precision, recall (sensitivity), specificity, and other performance metrics. To evaluate the performance of the fall detection system, extensive tests were performed using a confusion matrix that provides an overview of the algorithm's ability to correctly identify both falls and normal activities. The confusion matrix for this scenario is represented in Figure 5:  
Each component of the confusion matrix is explained below:  
* _True Positives (TP)_: True positives are instances where the model correctly predicts the positive class. In fall detection, this would be when the system correctly identifies a fall event. In this case, there were 54 instances where falls were correctly identified by the model. Figure 5: Confusion matrix. * _True Negatives (TN)_: True negatives are instances where the model correctly predicts the negative class. In fall detection, this would be when the system correctly identifies a non-fall activity. This represents 58 instances where the model correctly identified activities as non-falls. * _False Positives (FP)_: False positives occur when the model predicts the positive class incorrectly. In fall detection, this would be when the system incorrectly identifies a fall when no fall has occurred. False positives are also known as Type I errors. In this scenario, there were three false alarms. * _False Negatives (FN)_: False negatives occur when the model predicts the negative class incorrectly. In fall detection, this would be when the system fails to identify a fall when one has actually occurred. False negatives are also known as Type II errors. Here, two fall events were missed by the model. Using these values, key metrics can be calculated to assess the performance of the algorithm, as illustrated in Table 3:  
Interpretation and Application  
* _High Recall_: Recall, also known as sensitivity or the true-positive rate, measures the proportion of actual positives that are correctly identified by the model. This exhibits a high recall (96.4%), indicating that it effectively detects a large majority of actual fall events. * _Good Accuracy_: Accuracy measures the overall correctness of the model's predictions across all classes. The accuracy (95.7%) means that the model is correct in its predictions for many of the instances in the dataset, demonstrating its capability to make reliable assessments in the context of fall detection. * _Good F1 Score_: The F1 score combines both precision and recall into a single metric, providing a balanced assessment of the model's performance in detecting falls. An F1 score of 95.5% indicates that the fall detection model achieves a robust balance between precision and recall, making it suitable for practical deployment in scenarios where accurate detection and minimal false alarms are important. A higher F1 score indicates better overall performance in terms of both precision and recall. * _Good Precision_: Precision measures the proportion of true-positive predictions among all positive predictions made by the model. The precision (94.7%) indicates that when the model predicts a fall, it is correct most of the time. * _Good Specificity_: Specificity measures the proportion of actual negatives that are correctly identified by the model. The specificity (95%) shows that the model correctly identifies non-fall events in a majority of cases.