# New Advances and Challenges of Fall Detection Systems: A Survey
## 3 Bird's Eye View of Fall Detection Research from 2014
### Evolution of Sensors  
One of the significant progresses in fall detection system is the use of advanced sensors. In recent years, sensor technology has developed remarkably, which can perceive different kinds of information from the environment more precisely and conveniently than ever before. Researchers have always attempted to adopt new devices to help them detect a fall. According to types of sensor, [PERSON] et al. [6] divided fall detection algorithms into three main categories: wearable device based, ambience device based and vision based. [PERSON] et al. [2] roughly categorized them into two types: context-aware systems and wearable devices. After analyzing the situation of sensors used in recent years, we have divided them into three main categories: vision-based, accelerometer-based, and Radio Frequency (RF) sensor-based. The first category refers to the visual sensors including the normal camera and the depth camera, such as Kinect. The Kinect sensor features an \"RGB camera, depth sensor and multi-array microphone running proprietary software\", which provides full-body 3D motion capture, facial recognition and voice recognition. In contrast to RGB camera, it can get 3D image and video according to infrared projector and a special microchip. It can distinguish the foreground from the background directly, while a normal camera cannot. In our work, camera refers to the normal RGB camera or webcam. The second category includes specialized accelerometer devices and accelerometers built-in the smart phone. The last one covers Wi-Fi and Radar. We collected types of sensors used in fall detection system from most cited related papers before 2014 (the most cited paper's data obtained from [2]) and compared data from the most cited related papers after 2014. As shown in Figure 1, we compare the trends of used sensors before 2014 and after 2014. Since some studies have used both Kinect and accelerometer, we plot the figure and regard it as a standalone sub-category in Figure 1b to present it more precisely. According to Figure 1a,b, we conclude four main changes from the period before 2014 to the period after 2014. First, the numbers of fall detection systems using camera declined a lot after 2014, which was second place before 2014. Recently, sensors associated with vision-based technology have been developing rapidly. Previous vision-based algorithms always adopted the camera to detect a fall. The advantages of this technology are low cost and easy installation of the camera in home environment. Due to the camera's natural weakness of merely capturing 2D image, it is difficult to distinguish the foreground from the background, and it is sensitive to light variation. Therefore, fall detection based on camera lacks high robustness. Due to camera's inherent weakness, that is, privacy preserving, it has been used less in the indoor environment recently. [PERSON] and coworkers' work [24] is  
Figure 1: Comparison of types of sensor used in fall detection system: (**a**) the proportion of different types of sensor used in fall detection systems before 2014; and (**b**) the proportion of different types of sensor used in fall detection systems from 2014. Note that “Kinect and accelerometer” represents systems using both. the only one of twenty most cited the papers published after 2014 that employed the camera to detect a fall. Second, statistical results indicate that Kinect replaced the camera and became the most popular type of sensor used in fall detection systems after 2014. It (including Kinect, as well as Kinect and the accelerometer) accounts for almost half of the total systems. The emergence of the depth camera, such as Kinect, overcomes some weakness of traditional cameras. It can provide full-body 3D motion captured by an infrared projector, a camera and a special microchip. Since Kinect is less affected by lighting conditions, [PERSON] et al. [11] used this feature to propose a real-time fall detection system. [PERSON] et al.