# New Advances and Challenges of Fall Detection Systems: A Survey
## 1 Introduction  
Falling is one of the major public health problems in the world. An estimated 646,000 fatal falls occur over the world each year, making falling the second leading cause of unintentional injury death [1]. It is a dangerous vital action and not easy to prevent for the elderly, since older people gradually cannot control their action smoothly as they grow older. The rapidly ageing population enhances this threat to public health. A latest report from World Health Organization (WHO) indicates that adults that are older than 65 years of age suffer the greatest number of fatal falls, and death rates caused by falls are highest among adults over the age of 60 years [1].  
Fall detection is an important and significant research direction in the study of falls. The time that the elderly remains lying on the floor after falling is one of the key factors that determines the severity of a fall [2]. Timely detection of falls enables immediate assistance by caregiver and minimizes the negative consequences of falls [3]. A robust fall detection system monitors the fall and provides alerts, which could alleviate the burden of caregivers and resource-strained health care systems.  
Although fall detection is promising, it faces many challenges. A generally accepted definition of fall is as an event which results in a person coming to rest inadvertently on the ground, floor or other lower level [1]. However, it is difficult to distinguish a fall from other actions based on this definition, since there are many similar actions. For example, the most confounding activity to falling is lying [4]. Another issue is that it is not easy to obtain the real data in the daily life, since it is a kind of random,accidental and dangerous action. Therefore, it is a daunting task for researchers to build datasets with real fall data to study and analyze. Furthermore, due to different kinds of sensors used in fall detection, there is a lack of a universal solution to this problem. To obtain a global view, a comprehensive survey should be conducted. Some researchers have done such meaningful work of literature reviews before 2014 [2; 5; 6; 7; 8; 9]. In recent years, the related research increases dramatically with the advent of new sensors and technologies. However, all the reviews were done several years ago; there are few surveys to review the development of fall detection research during recent years.  
The rapid development of smart sensors and the Internet of Things has brought new vitality to fall detection research and produced a series of research results. To review developments in this field after 2014, firstly, we comprehensively searched with related keywords such as \"fall detection\" using Google Scholar, and exacted the twenty most cited paper from 6380 related papers. Then, based on these most influential papers, we reviewed the tendency into three main aspects: revolutions of sensors, changes of fall detection algorithms and performance of fall detection systems. Finally, we summarized issues and challenges emerging in this field and provided some future direction in this field.  
The rest of this paper is organized as follows. Related work is provided in Section 2. Section 3 analyzes the tendency of development of fall detection after 2014. Following the analysis, the challenges, including four points, are outlined in Section 4. Finally, the paper ends with a discussion of the conclusion and future work.