Learning skeleton information for human action analysis using Kinect

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Human action recognition plays an important role in modern intelligent systems, such as human–computer interaction (HCI), sport analysis, and somatosensory game. Compared with conventional 2-D based human action analysis, using Kinect sensor can obtain depth information of human action, which is significant for human action recognition. In this paper, we propose a joint angle sequence model for recognizing human actions, where depth images are acquired by using Kinect sensor. We design an improved DTW method to improve the matching accuracy. Comprehensive experiments show the effectiveness and robustness of our proposed method.