Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning

Xu Sun, Hisashi Kashima, Naonori Ueda

Abstract: Personalized activity recognition usually has the problem of highly biased activity patterns among different tasks/persons. Traditional methods face problems on dealing with those conflicted activity patterns. We try to effectively model the activity patterns among different persons via casting this personalized activity recognition problem as a multitask learning issue. We propose a novel online multitask learning method for large-scale personalized activity recognition. In contrast with existing work of multitask learning that assumes fixed task relationships, our method can automatically discover task relationships from real-world data. Convergence analysis shows reasonable convergence properties of the proposed method. Experiments on two different activity data sets demonstrate that the proposed method significantly outperforms existing methods in activity recognition.