A context-driven approach to scalable human activity simulation

As demands for human activity recognition technology increase, simulation of human activities for providing datasets and testing purposes is becoming increasingly important. Traditional simulation, however, is based on an event-driven approach, which focuses on single sensor events and models within a single human activity. It requires detailed description and processing of every low-level event that enters into an activity scenario. For many realistic and complex human scenarios, the event-driven approach burdens the simulator users with complicated low-level specifications required to configure and run the simulation. It also increases computational complexity and impedes scalable simulation. Thus, we propose a novel, context-driven approach to simulating human activities in smart spaces. In the proposed approach, vectors of sensors rather than single sensor events drive the simulation quicker from one context to another. Abstracting the space state into contexts highly simplifies the tasks and efforts of the simulation user in setting up and configuring the simulation components for smart space and human activities. We present the context-driven simulation approach and show how it works. Then we present fundamental concepts and algorithms and provide a comparative performance study between the event- and context-driven simulation approaches.