# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 2 Related Works  
### Fall Detection Based on Sensors  
For the elderly, falls are a severe issue, since they can result in life-threatening injuries or even death. The probability of a successful recovery can be considerably increased by prompt intervention following a fall, yet it may be difficult for caregivers to be aware of a fall as soon as it occurs. To address this issue, a wearable device that uses several sensors to detect falls and alert designated caregivers via a mobile app is described. The Internet of Things (IoT) design of this system is built on sensors that measure human acceleration and angular velocity and then transmit to the cloud, where machine learning algorithms analyze the information to determine if a fall has occurred. If a fall is detected, the system sends relevant information to the caregiver, enabling a timely and informed response [4]. The authors of [5] proposed a fall detection method based on a wearable pressure sensor. Firstly, an analysis of the principle and characteristics of the effect of the wearable pressure sensor on fall detection was undertaken based on the physiological structure of the human foot. Secondly, based on the framework of the fall detection system, the settings of pressure sensors and system integration methods, special methods of processing pressure data, and different judgment methods of the fall detection model were adopted. At the same time, in order to investigate the problems of fall detection methods (poor robustness and sparse audio data), the authors of [6] constructed a noisy dataset with background sound and designed a one-dimensional residual neural network model with the original audio waveform as an input of the convolutional network, 1D-Re CNN. Then, a fall detection framework based on heterogeneous sensor data fusion was designed. By analyzing the heterogeneity of acceleration data and audio data, D-S evidence theory was used to integrate acceleration output and audio output at the decision level.  
To further improve the accuracy of fall detection methods, the authors of [7] have proposed a human fall detection method based on multi-sensor fusion, mainly aiming to tackle the problem of single optical sensors or millimeter-wave radar sensor accuracy in human fall detection applications, which are affected by the environment. In this work, CCD optical sensors and millimeter-wave radar sensors were used to simultaneously detect the target, with the obtained multi-sensor data being used for decision-level fusion detection. The authors of [8] proposed a deep classifier for pre-impact fall detection.  
Convolutional Neural Networks (CNNs) and bidirectional gated recurrent units (BiGRUs) with residual connections were combined to detect a fall in the pre-impact phase. In this work, the sensors are used to collect information on posture, and raw sensor data underwent z-score normalization followed by magnitude calculation; this was then fed into the proposed deep learner for fall classification.  
The sensor-based fall detection algorithm has good real-time performance but shows certain problems in adaptability to dynamic scenes and is sensitive to irrelevant external objects. Using cameras or combining sensors with cameras for fall detection can ameliorate these deficiencies.