# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
###### Abstract  
This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target's posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.  
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Control and Prevention, falls have become the leading cause of injury and death among people over 65 years old in China.  
Therefore, the health and safety of the elderly has become an important issue of concern. Many electronic products related to elderly health monitoring have been promoted, such as smart bracelets [1; 2] and intelligent monitoring systems [3]. At the same time, to improve the accuracy of fall detection algorithms, many related studies have been conducted in recent years. These have mainly used sensors, cameras, and WiFi signals to realize fall detection.  
The main principle of sensor-based fall detection algorithms is collecting data through one or more sensors (accelerometer, gyroscope, pressure sensors, etc.) that are placed on the human body or within the detection environment. These data are then processed by intelligent algorithms and the corresponding target's posture is revealed.  
Fall detection based on machine vision or video uses a camera to capture movement images of the human body and analyzes the human posture and movement in these images through computer vision algorithms to determine whether a fall has occurred.  
The principle of fall detection algorithms based on WiFi signals mainly relies on the analysis and processing of CSI. During the propagation of WiFi signals, different propagation paths will be generated due to phenomena such as reflection, diffraction, and scattering when encountering objects. The signal strength and phase information along these paths is transmitted to the receiver, forming the CSI data. Through the analysis and processing of these CSI data, features related to human activities can be extracted to realize fall detection.