For improving e-health services, we propose a context-aware framework to monitor the activities of daily living of dependent persons. We define a strategy for generating long-term realistic scenarios and a framework containing an adaptive monitoring algorithm based on three approaches for optimizing resource usage. The used approaches provide a deep knowledge about the person's context by considering: the person's profile, the activities and the relationships between activities. We evaluate the performances of our framework and show its adaptability and significant reduction in network, energy and processing usage over a traditional monitoring implementation.
In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25 to 89 depending on the evaluation criteria used. Recently, how to support and care elderly people is serious problem. Modern approach is trying to support to keep health (body and mental) seniors living alone, but it is hard to keep a health by oneself. Our aim is monitoring a user's life for providing suitable services to keep his her health. To providing suitable service, life log is very useful information. However, traditional monitoring system is only focusing elementary motion, walking, sleeping, and so on. We deal with recognizing advanced motions by using transportation data from an area to other areas in a house. These advanced motion logs support not only elderly but also care stuff or family who plans supports. In daily life, our motion is very variety. Therefore we focused on Basic Activities of Daily Living (ADL) which categories normally doing in daily life such as feeding, bathing, homemaking, and so on. In this research, we pick up 11 motion from ADL for recognition.  In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime without sacrificing system original performances (sensing coverage and sensing reliability). In this paper, we propose a node-scheduling scheme, which can reduce system overall energy consumption, therefore increasing system lifetime, by identifying redundant nodes in respect of sensing coverage and then assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty one. Our scheme aims to completely preserve original sensing coverage theoretically. Practically, sensing coverage degradation caused by location error, packet loss and node failure is very limited, not more than 1 as shown by our experimental results. In addition, the experimental results illustrate that certain redundancy is still guaranteed after node-scheduling, which we believe can provide enough sensing reliability in many applications. We implement the proposed scheme in NS-2 as an extension of the LEACH protocol and compare its energy consumption with the original LEACH. Simulation results exhibit noticeably longer system lifetime after introducing our scheme than before. Copyright © 2003 John Wiley & Sons, Ltd. 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.