Design of a context-aware model to enhance medication adherence.

Medication adherence is important to patients who suffer from chronic disease. Regular medication activity reduces the cost of caring disease and prohibits the worsening of disease condition. To support patients taking medicine correctly, we developed a medication assistance system which alarms medication situation through multimedia messages and help patients to take a medicine.

Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home.

A Hub-protein based visualization of large protein-protein interaction networks.

According as the protein-protein interaction (PPI) data more increase, we need to optimally visualize them as network, in that describing the relationship among proteins is able to easily analyze biological processes happened in a cell. In this paper, to fast layout large-scale PPI networks, we proposed a method taking hub-proteins into consideration, which have more interactions than any other proteins in a network. In other words, it enforces two core parts of Walshaw's multilevel force-directed placement algorithm (MLFDP) to be modified.