# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 2 Related Works
### Fall Detection Based on WiFi Signals  
Early WiFi-based wireless perception studies mainly focused on a received signal strength indicator (RSSSI), which is a coarse-grained signal that cannot be applied to fall detection tasks. With the open-source release of the CSI tool, researchers began to extract CSI based on specific network cards and apply it to fall detection research [17]. [PERSON], [PERSON] et al. proposed a fall detection method based on CSI, WiFall [18], which applied a moving average to reduce data noise. The work in [19] describes a low-cost, accurate, and non-invasive wireless fall detection system using a commercial off-the-shelf 802.11n Wireless Local Area Network (WLAN) interface card. The system uses the phase difference in two receiving antennae to detect human falls. In [20], Principal Component Analysis (PCA) was applied to CSI amplitude to extract the main part of the data stream, short-time Fourier transform extracted data features in the frequency domain, and some frequency components were simultaneously extracted to avoid sparsity in the feature vector. It was used as a semaphore for fall detection. Recently, machine learning and its combination with other methods to process CSI signals and achieve fall detection have been attractive topics [21; 22; 23; 24; 25; 26; 27]. [PERSON], [PERSON] et al. introduced a smart home passive device-free system based on a commodity WiFi framework named FDS [21], which collected WiFi signals in real time and transmitted them to a data analysis platform, adopting the Discrete Wavelet Transform (DWT) method to eliminate the influence of random data noise. Finally, an recurrent neural network (RNN) model was used to classify human motion and automatically identify the fall state. Additionally, a novel deep learning-based fall detection technique was proposed in [22]. First, different WiFi CSI collection tools were implemented and their potential for fall detection was evaluated. Then, to develop a high-precision fall detection technique, a comprehensive dataset consisting of more than 700 CSI samples was constructed, including different fall types and other daily activities, performed in four different indoor environments. Finally, a deep learning-based classifier with an image classification algorithm was developed. In [23], a new method for intelligent real-time fall detection using a fine-grained Wi-Fi signal CSI was proposed. The proposed deep CNN model, FallCNN, was trained on a Wi-Fi CSI dataset that collected seven everyday human activities in an indoor environment. FallCNN used time series data enhancement and it benefited from raw Wi-Fi CSI data and output binary classifications for fall detection, improving its performance and generalization ability. In this study, to improve the fall detection accuracy and make the detection model more lightweight, a dual model of elderly fall detection was designed, as shown in Figure 1.  
1. When the monitoring target is visible, the camera can capture an image of the target. Then, an improved YOLOv5s model is adopted to process the image data and detect the target posture. The following improvements to the YOLOv5s model are made: * Firstly, adaptive picture scaling technology (Letterbox) is introduced from YOLOv5s to process the images and improve the detection effect. * Secondly, we add the SE and CA attention mechanisms to the YOLOv5s model Backbone network, and change their activation functions to make the model pays more attention to the detected target and improve the model's understanding of the input data's spatial structure. * Finally, we introduce the BiFPN from the YOLOv5s model Neck network to fuse the information of feature maps of different scales and strengthen the feature information. 2. When the monitoring target is invisible, the camera cannot capture an image of the target. At this time, an improved LSTM model is adopted to process the collected CSI data and output the target posture.