Exploring Data- and Knowledge-Driven Methods for Adaptive Activity Learning with Dynamically Available Contexts

Various aspects of human activity recognition have been researched so far and a variety of methods have been used to address them. Most of this research assumed that the data sources used for the recognition task are static. In real environments, however, sensors can be added or can fail and be replaced by different types of sensors. It is therefore important to create an activity recognition model that is able to leverage dynamically available sensors. To approach this problem, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. In our previous work, we proposed sensor and activity context models to address sensor heterogeneity and also a learning-to-rank method for activity learning and its adaptation based on the proposed context models. However, most of the existing solutions, including our previous work, require labelled data for training. To tackle this problem and further improve the recognition accuracy, in this paper, we propose a knowledge-based method for activity recognition and activity model adaptation with dynamically available contexts in an unsupervised manner. We also propose a semi-supervised data selection method for activity model adaptation, so the activity model can be adapted without labelled data. We use comprehensive datasets to demonstrate effectiveness of the proposed methods, and show their advantage over the conventional machine learning algorithms in terms of recognition accuracy.