Active-learning assisted self-reconfigurable activity recognition in a dynamic environment
It is desirable to know a resident's on-going activities before a robot or a smart system can provide attentive services to meet real human needs. This work addresses the problem of learning and recognizing human daily activities in a dynamic environment. Most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. However, the activity models become outdated when human behaviors or device deployment have changed. It is a tedious and error-prone job to recollect data for retraining the activity models. In such a case, it is important to adapt the learnt activity models to the changes without much human supervision. In this work, we present a self-reconfigurable approach for activity recognition which reconfigures previously learnt activity models and infers multiple activities under a dynamic environment meanwhile pursuing minimal human efforts in relabeling training data by utilizing active-learning assistance.