## 2 Related Works
### Vision-Based Sensors
The pre-trained models from ImageNet were used for each stream, which was trained separately using Optical flow, Visual Rhythm, and pose estimation features. An SVM was utilized in the classification, and the URFD and Le2i FDD datasets were used. The datasets were split into 65% training, 15% validation, and 20% testing. Another study Ref. [62] used 3D CNN to develop a fall detection system. Initially, the input to the 3D CNN was the frames' sequences to extract 3D features, which were then converted into 2D ones using a reshape layer. Multi-scale features were generated using a spatial pyramid and fed into the movement tube regression layer, tube anchors generation layer, and SoftMax classification layer. Tube anchors that matched the ground truth were found using a matching and hard negative mining layer, and falls were detected using SoftMax and the movement tube regression layer. A large-scale spatial-temporal (LSST) dataset was generated, along with the Le2i FDD [63], and data augmentation was achieved by altering the spatial, brightness, and temporal properties. From the 3D-CNN model, 16 frame sequences were used to produce 3D features, followed by tube anchor generation and SoftMax classification layers, respectively. A recent study [64] aimed to improve the accuracy of human fall detection by developing a 3D CNN- and LSTM-based system that utilizes multiple cameras. The researchers found that using multiple cameras was more effective to capture videos than a single camera. The system also incorporates an LSTM-based mechanism and 3D CNN to identify activities within the video footage. To address the limitations of auxiliary equipment-based systems and vision-based systems, the system uses deep learning and activity features to create a more accurate human fall detection system [65]. The system successfully performs scene analysis using the Faster Region-based Convolutional Neural Network (R-CNN) deep learning method, which can differentiate between human beings and furniture, such as a sofa. In another approach, researchers [66] proposed a deep learning method that uses either single or multiple camera setups. They used the improved visual background extractor (IViBe), which is an enhanced version of the visual background extractor (ViBe) algorithm, to implement background subtraction and obtain a silhouette of the human. These data generate a history image as well as a dynamic image, which were then used as input into a three-streams of CNN in corresponding sequences. Although this strategy was computationally heavy, it showed balanced sensitivity and specificity compared to other systems. Another fall detection system proposed by researchers [67] uses low-resolution thermal sensors and recurrent neural networks (RNNs). Sensors were placed in pairs, with the first at 10 cm and the second at 1 m above the ground. The structure involved using a 1-dimensional deep learning convolution layer with a Rectified Linear Unit (ReLU) activation layer, a max pooling layer, RNN layers, and a dense layer. The output of the model indicates a fall if it is 0, and no fall if it is 1. Due to the challenge of using a supervised classification algorithm to assess the possibility of a person falling off an object, fall detection is framed using anomaly detection as a semi-supervised learning-based strategy [68]. One of the approaches used that can understand temporal and spatial characteristics is the deep spatiotemporal convolutional autoencoder. Researchers have conducted studies and tests on depth and thermal cameras with non-invasive sensing modalities. Although the use of autoencoders is advantageous when handling falls as an abnormality, the model's main drawback is its boosted computational ability during the construction phase. Optimal metrics can be obtained from pictures taken by depth cameras, making this model one of the most reliable fall detection systems. In their seminal work, [PERSON] et al. [69] introduced a novel dataset that included various categories of activities, including falls.