## 2 Related Work
### Fall Detection Based on Video Datasets  
The authors of [23] presented ARFDNet, an efficient activity recognition and fall-detection system. The authors proposed extracting skeleton features from raw RGB videos using a pose-estimation network. These coordinates were then preprocessed and fed into specially designed CNNs followed by GRUs to capture the spatiotemporal dynamics. The GRU outputs were passed to fully connected layers for classification. The proposed model was evaluated on the ADLF and UP fall-detection datasets. The authors achieved an accuracy of 89.05% (prepolling) and 89.64% (post-polling) on the ADLF dataset and 96.7%on the UP fall-detection dataset. These results demonstrated the effectiveness of ARFDNet in accurately recognizing activities and detecting falls. The authors of [24] proposed a fall-detection approach that leverages human body geometry extracted from video frames. They used pose-estimation techniques to identify the facial image's head centroid and the body's center hip, calculating the angle and distance between them. These measurements were employed to construct distinctive image features. The authors trained a two-class Support Vector Machine (SVM) classifier and a temporal convolution network (TCN) on these new feature images, and an LSTM network was also trained on sequences of calculated angles and distances to classify fall and non-fall activities. The experiments were conducted on the Le2i FD and URFD datasets, including a cross-dataset evaluation. The results demonstrated the efficiency and effectiveness of the developed approach in accurately detecting falls. The authors of [25] proposed a methodology that utilized the PoseNet TensorFlow Lite CNN model for the real-time detection of human key points. They employed LSTM to analyze the extracted key points, which enabled the handling of extended data sequences. The system accurately identified human movement by merging two comprehensive fall-detection datasets for training. The model extracted the positions of 17 joint points in the human body and monitored changes in these positions. Integrating these joint point locations into the LSTM model improved training and inference times. Furthermore, this approach mitigated the impact of light sources and shadows on the model's performance. The authors of [26] developed a vision-based fall-detection system focusing on skeletal kinematic features. Their proposed feature descriptor analyzes body geometry and dynamic patterns to distinguish falls from non-fall activities effectively. Using AlphaPose to identify key points on the human skeleton, they calculated additional metrics from segmented body parts, enhancing the spatial representation of movements. Their novel feature descriptor preserves spatiotemporal dynamics, making it highly effective for machine learning models like decision trees and gradient boosting. Evaluated on the UP fall dataset, their method showed an excellent performance, marking a significant advancement in vision-based fall detection. The authors of [27] introduced a robust, automated vision-based system employing 3D multistream CNNs with an image-fusion technique for fall detection. Their system processes live surveillance footage, identifies fall events in real time, and issues instant alerts. This model's effectiveness was validated on the Le2i dataset, achieving high metrics across accuracy, sensitivity, specificity, and precision, demonstrating its potential to significantly reduce healthcare and productivity loss costs by preventing fall injury complications. The authors of [28] proposed a dynamic fall-detection system using a graph-based spatial-temporal convolution and attention network (GSTCAN). This system enhances the robustness and accuracy of fall detection by integrating spatial and temporal contextual relationships among body joints, combined with an attention model to focus on the most informative features.