## 2 Related Works
### Vision-Based Sensors
The researchers also developed an algorithm that combines data from two different channels based on previous studies [70], using the RCNN [71] model. The first channel is an inception of Inflated 3D ConvNet (I3D) that processes RGB images, while the second channel utilizes flow inputs that carry optimized smooth flow information, which is processed by optical flow data [72]. The researchers emphasized the need to use keyframe marks to highlight the dataset's novelty and the importance of combining multiple channels to improve fall detection accuracy. Overall, the method proposed in [69] is considered innovative and effective in detecting falls. However, some factors can affect the system's performance, such as the quality of the sample, which can be uneven and limit the dataset. While the dataset contains digital recreations of images obtained from movies, it can also include real photos captured by security cameras. However, the use of specific filters to process such images can lead to a loss of naturalness in the situation. In contrast, [PERSON] et al. [73] presented a new fall detection strategy that focuses on determining the actual duration of a fall by detecting the four phases of standing, falling, fallen, and not moving. The authors used dynamic images, which provide more details, including appearance and temporal features. To achieve this, they trained a Deep ConvNet on various fall sequences using a single image based on the frames of the training video. This approach was only possible after merging the frames into a single image, and a sliding window approach was used to detect the four fall phases accurately. Other researchers focused on exploring the use of optical flow and multi-stream fall detection methods, such as [74; 75; 76; 77]. For example, in [74], a generic CNN model was trained on optical flow images and fed to a fully connected neural network for fall classification. Similarly, [PERSON] et al. [76] used three independent CNNs, each with optical flow, RGB, and pose inputs, ensembled for improved fall detection accuracy. [PERSON] et al. [77] proposed a system that used optical flow, saliency map, and RGB as inputs for three independent VGG-16 networks. Each stream was classified and weighed using Dual SVM, with pre-training on ImageNet and fine-tuning on UCF101. In [78], a CNN model with fewer parameters was used to create an optical-flow-based fall detection model that could be implemented on low-computing devices, such as mobile robots. An enhanced dynamic optical flow-based fall detection system was demonstrated in [79], which used URFD, MCFD, and Le2i FDDbenchmark datasets. The pre-processing phase involved converting the input video into RGB images, calculating optical flow, rank pooling, and dynamic flow to create a dynamic image. Further feature extraction and classification were performed using a modified pre-trained VGG-16 model, with the addition of a variation light to make fall detection more challenging. In [80], a 3D-CNN vector-based fall detection method was described based on a fall motion mixture model from several sequential frames, using a pre-trained ResNet-101 based multi-label dataset. [PERSON] and [PERSON] [81] proposed a Siamese neural network with two symmetrical convolutional networks for fall detection using single-shot classification. Stacked RGB and optical flow features were used with a conventional 2D convolutional filter and depth-wise convolutional filter to generate a distance value in [0, 1], indicating the similarity between the input video sequence and the ADL video sequence. This literature review reveals that deep learning has emerged as a prominent approach in fall detection systems. However, there is a lack of clarity on how multi-stream CNNs can be utilized to distinguish between different phases of falls. There is a clear need for an effective fusion network that can efficiently combine the features extracted from various phases.