## 2 Related Work  
Recent developments in fall-detection systems have increasingly leveraged deep learning architectures to enhance real-time monitoring, particularly for applications in elder care and healthcare surveillance. A number of studies have shown how well detection models work in conjunction with image processing methods to accurately identify fall incidents. For instance, one approach integrated a pre-trained object detection model with optimized camera positioning, reporting an accuracy rate exceeding 93% under controlled lighting conditions, though its performance remained reliant on specific spatial configurations [19]. In a separate study, clustering methods were used to refine anchor boxes in an object detection framework, leading to improved processing speed and detection precision in complex environments. However, challenges such as adaptability across variable settings and diverse populations were not fully addressed [20]. Other works have incorporated motion analysis and posture evaluation into video-based frameworks, detecting and tracking individuals in real time. These systems reported promising results--achieving up to 92% accuracy in daylight--but exhibited limitations under low-light conditions and in scenarios involving multiple subjects, which led to increases in false positives [21]. The use of lightweight detection models optimized for edge computing has also been explored. One such system, trained on a manually annotated dataset of over 3400 images, achieved a precision of 95% and recall of 96%, with an inference speed of under 7 ms. Implemented on compact devices such as Raspberry Pi, it enabled efficient real-time deployment while maintaining high classification performance. The associated study highlighted the superiority of deep learning-based techniques over traditional classifiers like SVM and KNN, and recommended future integration with pose estimation and attention mechanisms for improved robustness [22]. Further advancements were achieved by embedding attention modules such as Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) into object detection networks. Combined with advanced activation functions like Swish, these modifications enhanced feature extraction and mitigated gradient vanishing issues, resulting in accuracy levels of over 97% on large-scale image datasets [10]. This optimized architecture significantly outperformed earlier methods and demonstrated strong generalization, though it relied heavily on labeled training data. In comparative evaluations of newer model variants, compact architectures designed for efficiency (e.g., versions of object detectors with reduced parameter counts) have shown notable improvements in speed and accuracy, especially when trained with diverse datasets including rotated images. The viability of implementing fall-detection systems on hardware with limited resources, like the Raspberry Pi, was further illustrated by hybrid approaches that combined convolutional models with classifiers like Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs) while maintaining sensitivity and accuracy [23; 24; 25]. Recent models have embraced multiscale representation and self-attention mechanisms to enhance temporal and spatial feature extraction. A state-of-the-art model maintained consistent performance across several Intersection-over-Union (IoU) thresholds, achieving 90% mean average accuracy (mAP) and a precision-recall AUC of 0.894. In order to fight class imbalance, this was accomplished by optimizing the model by utilizing unique anchor dimensions and a unique loss function [26]. Beyond healthcare, deep learning-powered fall-detection systems are being explored in industrial safety contexts, underscoring their potential to improve operational efficiency and worker protection [27]. In summary, the integration of deep learning strategies in fall detection--ranging from attention-enhanced networks to lightweight edge-deployable models--has significantly improved system responsiveness, accuracy, and scalability.