# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Fall Detection Technologies: A Classification Based on AI Paradigms
#### 2.1.2 Computer Vision-Based Systems in Fall Detection  
Computer vision has become a central pillar in fall detection research due to its ability to non-invasively monitor human posture and behavior. Over time, researchers have developed various methods, ranging from traditional image processing techniques to state-of-the-art deep learning architectures. 1. Traditional Vision Systems  
Early computer vision systems used handcrafted image processing techniques, including background subtraction, silhouette tracking, and optical flow analysis. These systems attempted to detect abnormal posture or sudden motion patterns using RGB or depth video. While innovative for their time, these methods struggled in cluttered environments and were prone to false positives from non-fall activities (e.g., sitting abruptly, bending, or crouching) [33]. 2. Pose Estimation with Rule-Based Logic Systems  
The introduction of depth sensors, such as Microsoft Kinect [34], and pose estimation algorithms, like OpenPose, HRNet, and MediaPipe, has enabled fall detection systems to use skeletal keypoints (e.g., head, hips, knees) to infer postural instability or horizontal collapse [35]. These systems typically apply rule-based logic or threshold-based conditions to skeletal angles [36] or keypoint velocities to identify potential falls [37]. These approaches offer several advantages, including increased interpretability, improved performance in multi-person environments, and enhanced privacy due to depth-based imaging. However, rule-based logic can be susceptible to errors in dynamic or cluttered environments. Moreover, real-time inference is often constrained by the computational limitations of edge devices. Additionally, such systems generally require extensive camera coverage to achieve consistent detection accuracy across different areas. 1. Convolutional Neural Network (CNN)-Based SystemsCNN-based models have enhanced visual recognition tasks in fall detection by automatically learning spatial features from image sequences. Architectures such as VGG, ResNet, and 3D-CNNs have been used to classify activity types or detect fall events within video segments [38; 39; 40]. Compared to classical methods, these models demonstrate greater robustness; however, they often rely on frame accumulation or sliding window techniques, which can introduce latency, an important limitation in time-critical applications such as fall detection [41]. (d) YOLO-Based Fall Detection Systems  
YOLO stands out as a real-time, highly efficient framework for object detection and pose-aware activity recognition. YOLO networks are designed to process entire images in a single forward pass, identifying objects, humans, and actions in real time [42]. For fall detection, pose estimation integrated with YOLO (e.g., YOLO + AlphaPose, YOLO-Pose, or YOLOv8-seg) amplifies its benefits, allowing systems to detect human bodies and analyze posture changes frame by frame [43]. These systems offer high processing speed, established accuracy in recent versions, and the ability to integrate on the edge device. However, they can be sensitive to occlusions, rely primarily on frame-by-frame analysis, and require specialized datasets for training [44]. To contextualize the strengths and limitations of different fall detection paradigms, Table 2 presents a comparative summary of their core characteristics and capabilities. As summarized in Table 2, YOLO-based systems clearly outperform traditional and CNN-based approaches in terms of real-time performance, posture sensitivity, and suitability for edge deployment. Furthermore, the integration of pose estimation into the YOLO framework enhances its ability to differentiate between intentional and accidental movements while maintaining efficiency and scalability. Based on these advantages, the proposed system adopts a YOLOv11-based architecture that integrates with a voice-based AI dialogue module.