# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Vision-Based Systems** & **Wearable Technologies** \\ \hline \multirow{4}{*}{Accuracy} & Detects falls in the context of the surroundings; false positives, illumination, and occlusion problems. & Direct body data (posture, motion) could contain errors or false positives depending on device placement. \\ \hline \multirow{4}{*}{Privacy} & Less intrusive; only records health and mobility information. & Less intrusive; only records health and mobility information. \\ \hline \multirow{4}{*}{Cost} & Expensive initial setup; more economical in shared environments (e.g., care facilities). & Individual users can afford it, but long-term maintenance raises expenses. \\ \hline \multirow{4}{*}{Environmental Awareness} & Provides environmental context, detecting falls and hazards like obstacles or slippery floors. & Lacks awareness of surroundings (e.g., slippery floors or obstacles). \\ \cline{2-3}  & Costly for individual homes, but more scalable in communal or institutional age care settings. & Scalable for individual use; higher costs for large populations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between wearable technologies and vision-based systems for fall detection of elderly. and complexity of implementation. YOLO is known for its speed. It processes images in a single forward pass through the network (having the capability to perform people detection and pose estimation simultaneously), making it extremely fast and suitable for real-time applications [15]. SSD strikes a balance between speed and accuracy. It uses a convolutional neural network to predict bounding boxes and classes directly from feature maps at multiple scales. RetinaNet is known for its accuracy due to the Focal Loss function, which improves detection performance for hard-to-detect objects, such as small objects or imbalanced datasets [16]. To better understand the differences between these architectures and determine the most appropriate choice for real-time drop detection, we present a comparison table below (Table 2). The sets of values for YOLO, SSD, and RetinaNet are presented based on references [12; 13; 14; 15; 16] and are obtained by using various types of hardware. YOLO stands out as the most suitable architecture for a fall detection system due to its exceptional speed, which enables near-immediate detection and reaction to critical events. The simplicity of implementation, the ability to perform well in low-light conditions, and the low resource requirements make YOLO an optimal solution for real-time monitoring of the elderly. While SSD and RetinaNet offer advantages in other areas, YOLO provides the best balance for a fast and efficient fall detection system [17]. The integration of the YOLO algorithm into elderly care systems offers numerous benefits, significantly enhancing the quality of care provided to seniors [18]. One of its primary advantages is its real-time monitoring capability. YOLO, a cutting-edge object detection model, redefines object detection as a single regression problem by mapping image pixels directly to bounding box coordinates and class probabilities [19]. Unlike traditional methods that apply classifiers across multiple image locations and scales, YOLO uses a single neural network for the entire image. It divides the image into regions and predicts bounding boxes and probabilities for each, allowing for extremely fast processing. This capability makes real-time applications, such as fall detection, not only feasible but also highly effective in dynamic environments [20; 21; 22; 23; 24; 25]. The model's precision minimizes false positives and negatives, ensuring caregivers are alerted only for genuine emergencies, thereby improving response times and care quality. One of the most important benefits of the YOLO algorithm is its _capability of real-time object detection_, which is essential for elderly care, where the immediate detection of falls can trigger prompt responses, potentially preventing severe injuries and complications.