Sensor-based activity recognition with dynamically added context

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods

Activity discovering and modelling with labelled and unlabelled data in smart environments

In the past decades, activity recognition had aroused great interest for the community of context-awareness computing and human behaviours monitoring. However, most of the previous works focus on supervised methods in which the data labelling is known to be time-consuming and sometimes error-prone. In addition, due to the randomness and erratic nature of human behaviours in realistic environments, supervised models trained with data from certain subject might not be scaled to others. Further more, unsupervised methods, with little knowledge about the activities to be recognised, might result in poor performance and high clustering overhead. To this end, we propose an activity recognition model with labelled and unlabelled data in smart environments. With small amount of labelled data, we discover activity patterns from unlabelled data based on proposed similarity measurement algorithm. Our system does not require large amount of data to be labelled while the proposed similarity measurement method is effective to discover length-varying, disordered and discontinuous activity patterns in smart environments. Therefore, our methods yield comparable performance with much less labelled data when compared with traditional supervised activity recognition, and achieve higher accuracy with lower clustering overhead compared with unsupervised methods. The experiments based on real datasets from the smart environments demonstrate the effectiveness of our method, being able to discover more than 90% of original activities from the unlabelled data, and the comparative experiments show that our methods are capable of providing a better trade-off, regarding the accuracy, overhead and labelling efforts, between the supervised and unsupervised methods