Sharif University of Technology Scientia Iranica 1026-3098 27 3 2020 06 01 Recognizing involuntary actions from 3D skeleton data using body states 1424 1436 20446 10.24200/sci.2018.20446 EN M. Mokari Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran H. Mohammadzade Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran B. Ghojogh Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran Journal Article 2017 06 03 Human action recognition has been one of the most active fields of research in computer vision over the last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method for action recognition which uses<br />temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition (recall) rate of 88.64% for eight different actions and up to 96.18% for classifying the class of all fall actions versus normal actions. https://scientiairanica.sharif.edu/article_20446_ea35f6f03838aa88195865faf3aa793b.pdf