# 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
These metrics, derived from the confusion matrix, provide a detailed understanding of the algorithm's performance in detecting falls and distinguishing them from non-falls. To further contextualize these results, it is important to compare our system with other existing fall detection solutions. By analyzing the performance metrics--such as accuracy, precision, recall, specificity, and F1 score--of similar systems, the strengths and potential areas for improvement in our approach were highlighted (Table 4). We utilized the Le2i fall dataset for our evaluations, which comprises a total of 130 video sequences. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Formula** & **Value** \\ \hline Accuracy & \(\frac{TP+TN}{TP+TN+FP+FN}\) (\(\times\)100\%) & 95.7\% \\ \hline Precision (P) & \(\frac{TP}{TP+FP}\) (\(\times\)100\%) & 94.7\% \\ \hline Recall (R) & \(\frac{TP}{TP+FN}\) (\(\times\)100\%) & 96.4\% \\ \hline Specificity & \(\frac{TN}{TN+FP}\) (\(\times\)100\%) & 95\% \\ \hline F1 score & \(\frac{2\times P+R}{P+R}\) (\(\times\)100\%) & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key metrics used for system evaluation. dataset consists of video sequences from four environments: Home, Coffee room, Office, and Lecture room. Each video is formatted at a resolution of 320 \(\times\) 240 and 25 FPS. For each video, a corresponding ground truth file is provided. These files contain the frame numbers marking the start and end of a fall, along with the bounding box details: height, width, and the coordinates of the center for each frame. No augmentation methods were applied to the dataset. The dataset includes 99 sequences that depict falls and 31 that showcase various normal activities. The falls in the dataset are categorized into several types (forward falls, backward falls, lateral falls, falls from various heights, and falls with different impact levels). The normal activities included in the dataset are walking, sitting down or standing up from a chair, bending over to pick up objects, changing shoes, and performing stretches or exercises. The dataset presents challenges related to variable lighting conditions and occlusions. Our proposed model outperforms other similar methods in terms of accuracy, specificity, precision, and F1 score, while also achieving above-average recall. These outcomes highlight the efficacy of our approach to detecting human falls and suggest its potential applicability in real-world scenarios. Our approach has been tested in laboratory conditions. Analyzing the Performance of Using the YOLO Algorithm Based on Pose Estimation in the Context of Elderly Care  
In the context of elderly care, the implementation of the YOLO algorithm provides a number of key advantages for detecting falls and improving the overall safety and quality of life:  
* _Real-time monitoring_: The YOLO algorithm enabled the rapid and accurate detection of falls in real time, facilitating immediate intervention and reducing the risk of complications for the elderly. Specifically, the average pose estimation time was 361.4 milliseconds, the average fall detection time was 0.06162 milliseconds, and the average alarm transmission time was 0.0336 milliseconds. * _Improved safety_: By proactively identifying hazards, the algorithm helped prevent accidents, thereby increasing the safety of residents. * _Resource Optimization_: Reducing the need for constant human supervision has allowed caregivers to focus on more critical and personalized tasks, improving the quality of care. * _Respect for privacy_: The algorithm provided selective monitoring, ensuring the privacy and dignity of the elderly. * _Adaptability and customization_: The YOLO algorithm's flexibility and pose estimation allowed the system to be customized to recognize specific scenarios and objects, thus improving its effectiveness and applicability in various elderly care contexts.