Single activity sensor-based ensemble analysis for health monitoring of solitary elderly people

Public health monitoring for solitary elderly people must be implemented in a particularly economic way because of their low-income status. The aim of this paper is to propose a model-based method combined with conventional reasoning methods such as multiple regression and the boosting strategy. The role of model-based reasoning is to generate secondary situational information from activity data gathered at home. Current health condition information is then provided as part of an activity-based smart management system for health monitoring. Only one activity sensor per house is considered. In this paper, we also discuss how the Korean government has actually applied this method in smart-care services for more than ten thousand solitary elderly people. The experiments are conducted based on the u-care system, which is composed of an activity sensor connected to a remote server using a wireless sensor network. The remote server includes multi-agents which analyze and diagnose current health conditions.