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. Particularly, in order to robustly infer ADLs, we present component ADL, which is decided with conjunction of human motion together, not just only contacted object identification. It is an important component in inferring ADL. In special, component ADL decision firstly refines misclassified initial activities, which improves the accuracy of recognizing ADL. Preliminary experiments results for proposed system provides overall recognition rate of over 97% over 8 component ADLs, which can be effectively applicable to recognize the final ADLs.

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. To enable the system copes with various situations related to a medication service, we designed a medication context model and implemented a state based context aware application. We also applied our system to patients and saw a little improvement in medication adherence.