###### Abstract  
Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients' home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user's behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness. activities of daily living; smart home; edge computing; ambient assisted living; IoT +
Footnote †: journal: Medical  
A typical smart home system uses a centralized cloud computing method that transmits data measured at end devices to a cloud. Cloud computing has the advantage that organizations can avoid the capital expenditure of creating a data center because cloud computing consumes computing resources over the Internet from a large service provider [3]. In contrast, edge computing--a new paradigm compared to cloud computing--enables technologies that allow computation to be performed at the network edge, so that computing happens near data sources [4]. Massive raw data from end devices can be reduced at the network edge and sent to the cloud for a shorter response time [5]. The device network proposed in this study adopts the decentralized topology of edge computing, which simplifies the implementation of unit space-based indoor localization services. Each indoor unit space in the proposed system has one edge network, from which generated data can be recognized along with specific location information. We determined the user's forgetfulness, the adequacy of activities, and physical moving ability by collecting the user's activity and location data from each indoor unit space. We also found that a smart home solution based on edge computing is suitable for identifying a user's behavioral features, frequency, and tendency based on the user's indoor location information. A typical existing house is challenging to upgrade to a smart home that measures the user's ADL because of the need to replace existing home appliances with new smart devices and subscribe to mobile service providers' IoT services. Transforming an ordinary home into a smart home environment at minimal cost requires that the system enables the seamless installation of devices, autonomous collaboration, and a scalable network. The ADL measurement system needs to collect ADL data and recognize the user, location, and whether activities were finished successfully or abnormally.