A Graph Representation of Subject's Time-State Space

Surveillance systems are currently the most developed branch of assisted living applications providing the disabled or elderly people with unprecedented security in their independent life. This paper presents a design of a telemedical surveillance system, where graph theory is used to describe subjects’ states. Patient’s states expressed by sets of medically-derived parameters and his or her daily activity (a behavioral pattern) are represented by attributed probabilistic graphs with indexed and labeled nodes. This representation provides high flexibility in a state and transient description as well as a reliable measure of behavior divergence, which is a basis for automatic alerting. The system is designed for the subject’s apartment and supports a localization-dependent definition of his or her usual and unusual behavior. The apartment topology is also represented in the form of a graph determining subject’s pathways and states. This approach has been found very flexible in all aspects of personalization, appropriate to work with the behavioral presumption set or with the auto-adaptive artificial intelligence recognition engine. Also the patient’s state, thanks to the semantic description may be easily extended or refined if necessary by adding new, complementary data capture methods.