# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 1 Introduction
The speed of YOLO (see Table 2) ensures that caregivers are alerted without delay, enabling swift intervention. Beyond speed, YOLO is renowned for its _accuracy_, minimizing false positives and negatives in object detection. This precision is important in fall detection systems, ensuring caregivers are alerted only for genuine emergencies, reducing false alarms, and improving care quality [25]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **YOLO** & **SSD** & **RetinaNet** \\ \hline \multirow{2}{*}{Used hardware} & NVIDIA GeForce GTX 1080 Ti GPU, 32 GB of RAM, Intel Core i7 processor & \\ \hline Speed (frames per second FPS) & \(\sim\)40–84 FPS & \(\sim\)20–46 FPS & \(\sim\)5–17 FPS \\ \hline \multirow{2}{*}{Accuracy} & Good, struggles with small objects & Good, better for various sizes & High, especially for small objects \\ \hline Complexity & Simple to implement & Moderate complexity & More complex \\ \hline \multirow{2}{*}{Use Case} & Real-time applications & Real-time applications & Best for accuracy, not real-time \\ \hline \multirow{2}{*}{Special Features} & Unified model, fast processing & \multirow{2}{*}{Multi-scale detection} & Focal loss for class imbalance \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of three of the most popular architectures used for object detection. Another significant advantage of YOLO is _its versatility in handling multiple objects within a single frame_. Elderly care environments are often dynamic, involving multiple individuals and objects. YOLO's ability to differentiate between objects and people ensures focused monitoring, even in complex and cluttered settings. This robustness makes YOLO-based systems reliable and adaptable to diverse environments. YOLOv7, a more advanced version, incorporates architectural improvements like efficient convolutional neural networks (CNNs), multi-scale detection for varying object sizes, and robust training techniques such as data augmentation. These upgrades enhance its speed, accuracy, and ability to generalize across diverse scenarios [26; 27; 28; 29; 30]. When combined with pose estimation, YOLOv7 enables a detailed analysis of human posture and movement, which is critical for distinguishing between routine actions and falls, even in challenging conditions such as poor lighting or unusual camera angles [31; 32; 33]. CUDA Python further optimizes YOLOv7 by leveraging Nvidia GPUs, significantly accelerating the computational demands of deep learning, particularly for real-time applications [34; 35]. This is essential for tasks requiring high-speed and reliable processing, such as fall detection in elderly care, where immediate responses are paramount. The hybrid YOLOv7-W6-Pose model enhances these capabilities by integrating advanced object detection with detailed pose estimation. YOLOv7-W6, a wider version, balances speed and accuracy, making it ideal for real-time scenarios. When paired with pose estimation, it identifies key points like joints and limbs, enabling nuanced interpretations of body movements and postures [36; 37]. This combination not only detects falls but also captures the context and dynamics leading to them, offering a comprehensive monitoring solution. The implementation of YOLOv7-W6-Pose involves several steps:  
1. _Image Preprocessing_: Input images are resized while maintaining aspect ratios through a letterbox function to avoid distortions. 2. _Forward Pass_: Images are processed through the network, which predicts bounding boxes, class probabilities, and key point locations for each grid cell. 3. _Output Generation_: The model outputs bounding boxes encapsulating detected objects and maps of key body points such as the head, neck, shoulders, elbows, hips, and ankles. 4. _Redundant Detection Elimination_: Non-Maximum Suppression (NMS) eliminates redundant detections, retaining the most confident predictions for improved accuracy. 5.