# Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly
## 5 Deep-cARe System Application
### Scenario Design  
1. MonitoringThe elderly can experience sudden injuries caused by accidental falls or tripping owing to the deterioration of their physical abilities. Occasionally, emergencies that may threaten their life can occur. This problem is more serious for those who live alone and spend considerable time indoors. There is a growing need to develop a monitoring system that can take the appropriate action in the event of an emergency inside a residence. Therefore, we developed a monitoring system that can identify such emergencies using a pan-tilt system and an RGB-depth camera. This system continuously tracks the user location using a camera after a 3D geometric environment around the user is reconstructed. The user location is essentially denoted by 3D-space coordinates using a 3D camera, which are used to augment information through the projection when the contents approach a registered plane (Figure 13). Furthermore, the system can recognize a fall accident or an emergency by identifying abnormal situations through the deep-learning pose estimation module. The situation of elderly people living alone often worsens because they cannot request medical support in case of an emergency. Therefore, the proposed system performs continuous monitoring of the location and state of the elderly while rotating the pan-tilt system and provides information to linked users so that they can prepare for emergencies. Accordingly, elderly users can receive proper support. 2. Smart IoT intercom The elderly who often experience problems associated with long-term memory exhibit reduced ability to recognize faces [32]. In particular, they have difficulty identifying people quickly. The proposed system can resolve this problem. We propose an Internet of Things (IoT) application that can identify users in shown Figure 14. The scenario is as follows. Assuming that a visitor rings the doorbell, the elderly receives an information of the visitor. A visitor identification can be performed using the deep-learning face recognition module. Therefore, if the person on the screen is registered, his/her name is provided to the user. If the person is an unregistered previous visitor, \"undefined\" is displayed. However, if the person is a new visitor, \"unknown\" is displayed. In addition to classifying visitors and providing the relevant information, as shown in data management in the Figure 15, if someone is classified as \"unknown,\" his/her input images are saved as an \"undefined\" class. For the \"undefined\" class, the face can be registered by saving the label information of the user. The system was designed to open or close the door by using a MUI (Figure 14c) or SUI (Figure 14-front wall). Therefore, for the elderly with discomfort in movement, physical support is provided by allowing remote control ability. Furthermore, long-term memory support is provided, and the elderly are alerted to unregistered visitors so that illicit activities may be prevented. Figure 13: Monitoring scenario. (**a**) User in 3D map environment. (**b**) User in real environment. 3. Daily Alarm The elderly's also experiences short-term and long-term memory problems, which result in forgetting important schedules or the state of the house. In addition to memory decline, the elderly also suffers from chronic diseases such as hypertension and diabetes. However, elderly patients can easily forget a dose or the time for taking a medicine. Therefore, their medication adherence (i.e., properly taking medicine according to the medical professional's instructions) is poor. Medication notification can be provided through the proposed platform with no support devices, as in the case of smartphones and smart medicine.