## 2 Related Works
### Vision-Based Sensors  
Machine vision-based systems, unlike wearable systems, do not require users to wear anything, making them less intrusive and potentially more efficient. These systems have been applied in various fields, including human fall detection [37; 39; 40]. [PERSON] et al. [34] proposed both active and passive ambient fall detection systems and evaluated their feasibility based on factors such as power connectivity, affordability, obtrusiveness, installation environment, and complexity of installation. In [41], an ambient-assisted living system that utilized a 3D range camera to detect human falls via time-of-flight technology was presented. The camera's field of view was not impacted by shadows or lighting, and the system segmented elderly silhouettes from that data. An extensive dataset of falls experienced by people while leading their normal lives was analyzed to confirm the system's efficacy. Range-Doppler radars, also known as frequency-modulated continuous wave radars, have been used with deep learning algorithms to detect falls in elevation [42]. Timefrequency and range features were combined to improve the output of the system. The radar signal offers advantageous properties, such as providing unimpeded lighting and sensing without invading users' privacy, which contributes to the system's widespread adoption [43]. To identify the Doppler effect, one has to observe frequency shifts in radar readings brought on by human motion (through radar backscatter). Human falls at an assisted living facility may be detected, categorized, and located by radar. Nevertheless, radar has a number of drawbacks that need to be addressed before it can be widely used, including a high false alarm rate, a limited radar range of vision, and the obscuring of falls by big fixed objects. In their research, the authors in [44] put forth a system for identifying falls by monitoring the spatiotemporal context of a 3D depth image taken with a Microsoft Kinect sensor. Assuming that only one elderly person was present, they utilized a Single Gaussian Model (SGM) to determine the person's head position and the coefficients for the floor plane. The system was equipped with an algorithm that followed the target's movements in relation to the floor. The team recorded real-life video sequences with a Kinect sensor in a simulated environment for their experiments. In another proposal [45], a low-cost vision-based system was introduced, which could identify falls causing wounds and was insensitive to changes in illumination and occlusion. In the fall detection system proposed by [46], RGB and in-depth data were extracted using a Microsoft Kinect camera. The system was capable of successfully capturing each frame from the camera, and it also included an event for ground segmentation achieved by dynamically subtracting the background. Similarly, [PERSON] and [PERSON] [47] used a Microsoft Kinect camera to detect falls in a household environment through a two-step process. The first step involved tracking the individual of interest for a period of time to obtain time-series features. The succeeding step utilized a decision tree ensemble to predict potential falls based on the sequential data collected. In their study, the authors of [48] utilized a Microsoft Kinect sensor to identify older adults by capturing 3D depth images. To achieve target and background processing, a median filter was applied. By subtracting frames in the background from the picture, a method was used to view the individual's silhouette as they moved through the images. The creation of a disparity map using depth images required vertical and horizontal projection histogram data. In [49], the authors discussed the process of identifying falls using a 2D CNN. The system's uniqueness lies in its ability to recognize activities from photos captured with a Microsoft Kinect camera. The researchers demonstrated that indoor conditions could be utilized to create a new dataset.