extending event-driven experiments for human activity for an assistive environment
Many different aspects go into the generation of the data and methods needed to recognize human activity within an ambient assistive living environment. The Heracleia @Home apartment has been configured to include both wireless sensor networks as well as wired sensors of multiple types to capture information about subjects in the living space. The responses of these sensors are then analyzed to create key episodic events that occur at each time and place. Once these sensors are equipped to recognize a set of events, the data can then be processed by applying a Hidden Markov Model approach and by an adaptation of the Baum-Welch algorithm to identify different human activities within the assistive living environment. An application has been created to help manage and track the different sensors placed within the environment.
human activity assistive environments wireless sensor networks episodic events baum-welch modification events of interest database