# Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly
## 1 Introduction  
Human lifespan has increased owing to recent medical developments. It is expected that approximately 20% of the global population will be 60 years of age or older by 2050 [1]. Furthermore, the number of elderly single-person/couple households is sharply increasing with changing family structures, such as nuclear families, owing to urbanization in modern society. In the past, the elderlywere usually attended to by their families; however, they are currently forced to lead independent lives [2]. There has been increasing interest in social welfare and medical support owing to the increasing aging rate. The social government is increasing social welfare and medical benefits to support elderly self-sustenance; however, most elderly people attend to various problems in their daily life on them own. Moreover, various diseases as well as physical and mental deterioration can occur with aging [3]. Muscle weakness is a representative example of physical deterioration. Muscle strength of people in their 60's decreases to approximately one half of that in their 20s, resulting in significant discomfort in everyday life. In the case of presbyopia, which is another example, the eyes of 60-year-old people can only accept approximately one third of the light that they could in their 20 s, accordingly bright places are preferred owing to the reduced pupil diameter. Moreover, presbyopia commonly results in focus loss of nearby objects. Consequently, elderly people experience difficulty in reading information presented in small text or on smartphones. Furthermore, sensory functions begin to deteriorate as people approach their 50s. As the ability to regulate and control body temperature declines with age, the elderly become vulnerable and sensitive to low and high external temperatures [3]. Despite the discomfort in everyday life, the number of elderly people who choose to live at home rather than in dedicated facilities is increasing [4]. However, safety-related accidents occur most often at home. In particular, 25.1% of the elderly experienced falls during the one year. Elderly people living alone experience considerable anxiety associated with falling accidents [2]. Information and communication technologies can be used to assist in the prevention of accidents and to mitigate the social, physical, and mental problems that the elderly generally experience. Several health management systems and elderly support functions are being developed by fusing various sensor and network technologies. An example is smart homes for the elderly and the technologies, tools, and systems associated with active assisted living [5]. In these homes, vital signals, behavioral information, and other contextual information can be collected through motion, radar, pressure, and vision sensors. The data collected can be analyzed to monitor the health and behavior of the elderly. In particular, vision and video sensors are most commonly used for elderly care. Vision cameras are installed inside the walls and ceilings of a house to monitor the locations and behaviors of the residents [6]. Several studies on the prevention and recognition of falls, including rehabilitation training after fall accidents, have been conducted using joint information received through RGB-depth cameras [7]. This study proposes healthcare support functions for the elderly in situations that can cause discomfort in daily life. Specifically, we employ an augmented reality (AR) system and deep-learning technology based on vision cameras and projection. This paper is organized as follows. Section 2 presents related work on support systems for the elderly using AR and deep-learning technologies, which are classified based on the display type. Moreover, trends in elderly care systems are analyzed, and the distinctiveness of the proposed system is described. Section 3.1 introduces the hardware components used in the proposed system.