Mario Caruso
ABSTRACT
In the recent years there has been a growing interest in the design and implementation of smart homes, and smart buildings in general. The evaluation of approaches in this area typically requires massive datasets of measurements from deployed sensors in real prototypes. While a few datasets obtained by real smart homes are freely available, they are not sufficient for comparing different approaches and techniques in a variety of configurations. In this work, we propose a smart home dataset generation strategy based on a simulated environment populated with virtual autonomous agents, sensors and devices which allow to customize and reproduce a smart space using a series of useful parameters. The simulation is based on declarative process models for modeling habits performed by agents, an action theory for realizing low-level atomic actions, and a 3D virtual execution environment. We show how different configurations generate a variety of sensory logs that can be used as input to a state-of-the-art activity recognition technique in order to evaluate its performance under parametrized scenarios, as well as provide guidelines for actually building real smart homes.
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Index Terms
- Synthesizing daily life logs through gaming and simulation
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