Recognizing context-aware activities of daily living using RGBD sensor

In this paper, we propose a Bayesian conditional probability with latent-structure model for context-aware activities of daily living (ADL) recognition. The proposed ADL recognition system takes RGBD sensor (Microsoft Kinect) as the input device. In ADL recognition, the object interacted with human is a sort of important context as well as human action. To better understand the activity, we model the interacted object and the human action together. As far as we known, many related works failed to take into account the relation between the context information and human action features, instead, most of them only consider the human action features, causing ambiguity in classifying the activities with similar human actions. In this paper, the context information and human action features are taken into consideration, concurrently, so that the performance of recognition can be greatly improved from previous works as has been demonstrated in our experimental results.