## 2 Related Works
### Vision-Based Sensors
This system can detect falls by considering posture classification provided by a 2D CNN, depth information, and background elimination on RGB-D.  
Several studies have employed machine learning techniques to detect human falls. For instance, in [50], a vision-based approach for detecting falls was discussed, which relied on the temporal gradualness norm. Similarly, the system in [51] employed a DeeperCut model to generate features by extracting the human skeleton and decomposing it into five parts, including two legs, two arms, and one head. The five parts generated 14 data points, which were used as input features for a deep classifier. In [52], a fall detection system based on computer vision was proposed, which was unique from previous systems as it incorporated non-vision styles, such as the use of accelerometers. The primary goal of this study was to create a depth-camera-based vision system with improved speed, precision, and reliability, without relying on RGB pictures or requiring excessive processing complexity. An SVM was used to identify falls, and in order to limit false positives, it was trained using data from free fall body models. The system was highly effective, as it did not produce any false positives, and the number of falls not detected was significantly low. A multiple-stage fall detection system using computer vision was demonstrated in the research study described in [53]. The stages include extracting frames from a video, locating humans in the frames, extracting features PCANet, and classifying the fall events usingan SVM classifier. On the other hand, in [54], background subtraction was first achieved with a Gaussian distribution. Using Gaussian distributions, each pixel was modeled so that variations in illumination could be handled as well as static objects placed or removed. In order to identify the foreground objects, highlighted pixels were used, and the image frame was then smoothed with post-processing to eliminate lonely pixels and fill holes in the background. Several researchers have attempted to address the challenge of detecting falls. In one study [55], a fall detection method was proposed that relied on several scene characteristics. The author emphasized the importance of improving accuracy while minimizing incorrect classifications. Another study [56] proposed an image-based fall detection method that could be useful in nursing homes, focusing on accidents that occur when sitting on a chair or standing. The first part of this procedure entails utilizing an object detector to find the person in the picture and then determining where they are sitting in relation to the chair and the rest of the room. A program takes into account the vertical and horizontal distances between the chair and the occupant to calculate the likelihood of a fall. However, this approach only utilizes one image and does not take into account other critical factors, such as speed, body dynamics, or movement. In [57], a dataset was created by utilizing four cameras with various angles and settings, which was then used to train a model. The proposed model involves using a median filter to eliminate the background and then applying morphological techniques to segment the area. Fall detection is achieved by combining the segmented information with the velocity of the head's movement, the center of gravity's speed of movement, and the person's proportion to the image's size. These results are fed into an SVM classifier to determine the risk of falls. The SVM model serves as the foundation for this study [58] and is used to achieve an optimal separation among the classes. However, there is a limitation in the generalization of new samples due to the insufficient feature description in the trained dataset [59]. By using a median filter, [PERSON] et al. [60] were able to improve the technique by eliminating the background. Recently, researchers have been focusing on using deep learning for human fall detection. In [61], a fall detection system based on a three-streamed 3D CNN network was proposed.