Object-usage-based human activity recognition systems require activity data for learning. Acquiring such data from the real world is expensive and time consuming. To overcome such difficulties, the exploitation of web activity data is gaining popularity. However, due to a lack of much real-world information in such data, existing activity models are not suitable for web data. In this paper, we propose a hidden Markov model-(HMM-) based activity model specially designed to use web activity data for activity recognition. It utilizes a sequence of object-usage information for activity recognition. We also propose a web activity data mining algorithm for this model. It is extremely fast and efficient in comparison with the existing algorithms. We perform three experiments to validate the proposed model. We show that the model can be effectively utilized by an activity recognition system.
Sarkar proposed a hybrid activity recognition method based on web activity data REF .
Hidden Markov Mined Activity Model for Human Activity Recognition
International Journal of Distributed Sensor Networks
Computer Science