摘要
Activity recognition is a core aspect of ubiquitous computing applications. In order to deploy activity recognition systems in the real world, we need simple sensing systems with lightweight computational modules to accurately analyze sensed data. In this paper, we propose a simple method to recognize human activities using simple object information involved in activities. We apply activity theory for representing complex human activities and propose a penalized naive Bayes classifier for performing activity recognition. Our results show that our method reduces computation up to an order of magnitude in both learning and inference without penalizing accuracy, when compared to hidden Markov models and conditional random fields.
Activity recognition is a core aspect of ubiquitous computing applications. In order to deploy activity recognition systems in the real world, we need simple sensing systems with lightweight computational modules to accurately analyze sensed data. In this paper, we propose a simple method to recognize human activities using simple object information involved in activities. We apply activity theory for representing complex human activities and propose a penalized naive Bayes classifier for performing activity recognition. Our results show that our method reduces computation up to an order of magnitude in both learning and inference without penalizing accuracy, when compared to hidden Markov models and conditional random fields.
基金
supported by the Korea Research Foundation under Grant No. KRF-2008-357-D00221
