Biomimetic Decision Making in a Multisensor Assisted Living Environment

Various sensors were found adequate to monitor human behavior in natural habitat. Besides metrological factors they were adopted to specific conditions of unobtrusive acquisition in human (e.g. an elder or child). An initial approach focused on use of a single sensor to detect a particular event evolved to behavioral studies based on complex recordings in multisensor environments. In such environment sensors based on different physical principles play complementary role and the resultant detection outperforms any of single component-based if combines the detail information correctly. We follow the rules of neural modulation in human sensory system to propose a biomimetic decision making in multisensor assisted living environment. In our approach each sensor contributes to the final detection accordingly to its presumed reliability in particular human behavior. Moreover, the system learns human habits, predicts most probable future actions from the history and anticipates accordingly the importance of particular sensors.