## 3 Our Proposed Method  
In recent years, deep learning has gained significant attention as a powerful tool in various applications of image and video processing, including the challenging task of human fall detection. Figure 1 shows a block diagram of a human fall detection system that uses deep learning. Various sensors, including wearable smartwatches, body sensors, and mounted IP cameras, can be utilized to gather data about the patient [29]. The obtained data are conveyed to a local server for pre-processing, which may involve filtering out redundant information, consolidating frames, and eliminating noise. Subsequently, the pre-processed data are input into a deep learning model, such as CNN. The system's output could be standing, falling, fallen, or other activities [82]. In our proposed work, we aim to build upon our previous research [82] to develop an advanced automated vision-based method for detecting human falls using multi-streams of 3D CNNs. The complex event of human falls occurs in live or video scenes and typically involves transitions from standing (or sitting) to falling to a resting position. A video-level CNN model was trained to capture appearance and motion information from surveillance recordings, allowing for the collection of a wide variety of elements from people's everyday lives. Standard 2D convolutional networks focus only on feature extraction at the frame or picture level, excluding any consideration of dynamics over many frames. In contrast, 3D CNNs are proficient in extracting features from both spatial and temporal dimensions, making them well-suited for video data processing. Furthermore, multi-stream CNNs have emerged as a new trend for handling video data, and by leveraging the capabilities of 3D CNNs, the aim is to capture both spatial and temporal features from video scenes, which is essential for robust and accurate human fall detection. Figure 1: Example of a general block diagram of a human fall detection system. Building upon the aforementioned discussion, our proposed work created an advanced method for detecting human falls utilizing multi-stream 3D CNNs. This was accomplished by leveraging the 4S-3 DCNN architecture [82], which incorporated a 4-stream 3D CNN architecture capable of analyzing videos to capture both the spatial and temporal features of human fall actions. Unlike our previous method [82], this work employed the 4S-3 DCNN model within a comprehensive fall video scenario. There were other different improvements. The current work incorporated human segmentation as a preliminary processing measure aimed at improving the accuracy of the classification process, thereby resulting in more precise fall detection. In addition to human segmentation, the method also employed image fusion on the segmented images utilizing a sequence of 32 frames instead of 16 frames. This expansion allowed for the capture of a broader scope of temporal data, further enhancing the accuracy of the method. The proposed method, as shown in Figure 2, begins by capturing 32 consecutive frames from an input video, which corresponds to roughly one second of live video at a frame rate of 30 frames per second (fps), representing a fall action. These frames are then converted into grayscale images. Next, a fine-tuned deep learning model designed for human semantic segmentation [83] is utilized to segment human appearance within the frames. An image fusion technique is then applied to the segmented images to generate four RGB pre-processed images. Details of the pre-processing procedure, which involves human segmentation and image fusion, are provided in Sections 3.1 and 3.2, respectively. These pre-processed images are then fed into the previously developed 4S-3 DCNN model that uses a 4-branch architecture, wherein each branch concentrates on a single image's worth of feature-learning data, gathered in a distinct order via space and time. This model classifies the 32 frames into one of the four classes that are trained on, including either Standing, Falling, Fallen, or Others.