A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Activity recognition: an evolutionary ensembles approach
Proceedings of the 2011 international workshop on Situation activity & goal awareness
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
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Reliable recognition of activities from cluttered sensory data is challenging and important for a smart home to enable various activity-aware applications. In addition, understanding a user's preferences and then providing corresponding services is substantial in a smart home environment. Traditionally, activity recognition and preference learning were dealt with separately. In this work, we aim to develop a hybrid system which is the first trial to model the relationship between an activity model and a preference model so that the resultant hybrid model enables a preference model to assist in recovering performance of activity recognition in a dynamic environment. More specifically, on-going activity which a user performs in this work is regarded as high level contexts to assist in building a user's preference model. Based on the learned preference model, the smart home system provides more appropriate services to a user so that the hybrid system can better interact with the user and, more importantly, gain his/her feedback. The feedback is used to detect if there is any change in human behavior or sensor deployment such that the system can adjust the preference model and the activity model in response to the change. Finally, the experimental results confirm the effectiveness of the proposed approach.