Emergency situation monitoring service using context motion tracking of chronic disease patients

Nowadays, great attention is paid to studies on fusion health and medical care combined of IT and BT for chronic disease patients due to westernization of dietary life, increase in stress, decrease in physical activities, and others. In reality, full recovery of chronic disease is difficult to achieve as its cause is diverse and complex. Therefore, the necessity for continuous management is proposed rather than approach to treat the disease. It is urgent to come up with the countermeasure since the lengthening of life expectancy in the aging society brings about the increase in chronic diseases and such increased medical expense becomes a big burden in socioeconomic activities. Companies are promoting test-projects in association with health management together with nationwide health management business for chronic diseases. In this study, we proposed the emergency situation monitoring service using context motion tracking for chronic disease patients. Proposed service diagnoses current status of patient based on contextual information collected and it provides information necessary for chronic disease management by analyzing life habits. Bio status recognition can provide proper service through the extraction of contextual data relevant to chronic disease patients. The context motion tracking provides emergency situation monitoring service accordingly with alert and symptom level in case of detecting symptoms through measured results and analysis. Semantic inference engine for context awareness conducts active and intelligent analysis on health condition and life patterns. Since it can properly correspond to extraordinary circumstances, it provides necessary service environment for emergency situation or symptoms. The cameras, speakers, and sensors are installed accordingly with the structure of indoor living space of user and the contextual information is transmitted from them. Considering the user convenience, motion history image is used for the motion recognition and continuous tracking from video. The system detects the patterns of expertise based on life pattern and psychological state through life log based motion detection and provides the service accordingly. It provides health related information and emergency situation monitoring service to user at anytime and anywhere and it is easy to use with simple handling. As a result, this system has the advantage of being able to detect emergency situations realistically and intuitively.