A Feature-Based Knowledge Transfer Framework for Cross-Environment Activity Recognition Toward Smart Home Applications

Building contextual models for new "smart" environments is not considered cost effective if data for model training must be collected from scratch. It is more practical to transfer as much learned knowledge as possible from an existing environment to the new target environment in order to reduce the data collection effort. In order to reuse learned knowledge from an original environment, this study proposed a feature-based knowledge transfer framework. The framework makes use of transfer learning, which relaxes the constraint requiring model training and testing datasets to be highly similar in distribution. Experimental results show that this framework can successfully help extract and transfer knowledge between two different smart-home environments. Models trained via the proposed framework can even outperform nontransfer-learning models by up to 8% in accuracy. Finally, the flexibility of the proposed framework enables used as a test bed for evaluating different methods and models in order to improve the service quality of human-centric context-aware applications.