## 1 Introduction  
Falls in older adults can have far-reaching consequences, which can lead to either severe injuries or death if they do not obtain medical assistance immediately. Human falls are usually unplanned and involve falling from a higher level, when sitting or standing, to a lower level on the ground. According to [1], falls have been described by the World Health Organization (WHO) as the leading cause of trauma in elderly individuals, which indicates that approximately 30% of individuals aged over 65 years have at least one trauma event annually [2]. A total of 47% of older adults who fall lose their independence and must rely on others for their daily activities [3]. This information shows that there is a need to assist older adults as soon as they fall. In this case, it is essential to have technologies that can detect falls among this population to be able to act as soon as possible. With the rapid development of video monitoring, surveillance, and communication technologies, it is becoming increasingly feasible to detect falls immediately after they occur. There has been increased interest in fall detection, resulting in the development of many technologies that can be used to detect these events [4; 5]. Acceleration and vibration sensors [4; 5] are utilized to identify human motion, sound, and vibration [5; 6; 7]. Most of the proposed methods cannot perform as expected due to several challenges, including noise that affects the acoustic sensors' functions. Additionally, some strategies, such as floor vibration, are only possible if there are sensors on the ground. Another major challenge is that using sensors in large areas can be extremely expensive. These challenges are the reason why some people have embraced smartwatches and smartphones since they have equally effective sensors. Other models used in the detection of falls include those that focus on gathering data from sequences of videos [8]. This strategy includes the use of multiple [9], single, omnidirectional [10], and stereo pair [11] cameras. Since cameras can record detailed information about the subject's mobility, the method is thought to be useful in identifying whether a fall has occurred. In this situation, everyone agrees that the camera's data are more comprehensive and insightful than those of regular sensors. However, vision-based fall detection may save lives and considerably save medical costs for the elderly. Deep learning encompasses a wide range of methods, including hierarchical probabilistic models, artificial neural networks, and various algorithms of supervised and unsupervised feature learning [12]. In comparison to other machine learning techniques utilized in different fields in the past, deep learning approaches exhibit a superior performance [13; 14]. Deep learning is unique since they have multiple layers that allow these systems to obtain data from different abstraction levels. They can comprehend large amounts of complicated data and provide useful details. Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long-Short-Term Memory (LSTM) [15] are widely used deep learning models that outperform state-of-the-art (SOTA) techniques employed in visual processing, audio, neural language, and other sensor-based challenges. Deep learning has demonstrated a considerable leap in computer vision applications, such as object detection, activity recognition [15], semantic segmentation [16], and motion tracking. A video is a series of still images or frames played in rapid succession to provide the impression of uninterrupted motion. CNNs find widespread use in areas such as video analysis, categorization, person identification, and posture estimation. However, incorporating the temporal dimension of videos can be challenging, which can overcome in several ways. One approach is using 3D CNNs, which can capture the movement of objects in videos by applying a 3D filter to a sequence of frames used as input for the convolution process.