Context-Adaptive Heterogeneous Models for Human Activity Recognition

Driven by a wide range of real-world applications, significant efforts have recently been made to explore the data-based human activity recognition model that utilizes the information collected by the AP adaptor.

The data is generally collected from the daily environment, which contains a lot of environment-specific factors hampering the recognition of human activities.

On the other hand, the data is distributed at collection devices with different computing capabilities and battery life.

These two challenges motivate the research to design context-adaptive heterogeneous models that are theoretically and empirically effective for human activity recognition in multiple scenes and can generalize to a new context with minimal effort. The sustainable energy requirement introduces another constraint on the scalability of models to be lightweight.

The proposed models will be verified using open datasets such as widar3.0 and the measured data.