Active-learning assisted self-reconfigurable activity recognition in a dynamic environment.
Journal
2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12-17, 2009
Pages
813-818
Date Issued
2009
Author(s)
Abstract
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 However, the activity models become outdated when human behaviors or device deployment have changed.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. © 2009 IEEE.
Type
conference paper