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    Gesture imitation and recognition using Kinect sensor and extreme learning machines
    (Elsevier Sci Ltd, 2016) Yavsan, Emrehan; Ucar, Aysegul
    This study presents a framework that recognizes and imitates human upper-body motions in real time. The framework consists of two parts. In the first part, a transformation algorithm is applied to 3D human motion data captured by a Kinect. The data are then converted into the robot's joint angles by the algorithm. The human upper-body motions are successfully imitated by the NAO humanoid robot in real time. In the second part, the human action recognition algorithm is implemented for upper-body gestures. A human action dataset is also created for the upper-body movements. Each action is performed 10 times by twenty-four users. The collected joint angles are divided into six action classes. Extreme Learning Machines (ELMs) are used to classify the human actions. Additionally, the Feed-Forward Neural Networks (FNNs) and K-Nearest Neighbor (K-NN) classifiers are used for comparison. According to the comparative results, ELMs produce a good human action recognition performance. (C) 2016 Elsevier Ltd. All rights reserved.
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    Teaching Human Gestures to Humanoid Robots by Using Kinect Sensor
    (IEEE, 2015) Yavsan, Emrehan; Ucar, Aysegul
    In this study, a novel algorithm is developed to recognize human actions and reproduce human actions on a humanoid robot. The study consists of two parts. In the first part, the real time human imitation system is realized. The three dimensional skeleton joint positions obtained from Xbox 360 Kinect. These positions are transformed to joint angles of robot arms via a transformation algorithm and these angles are transferred to NAO robot. The human upper body movements are finally successfully imitated by NAO robot in real time. In the second part, the algorithm is generated for the recognition of human actions. Extreme Learning Machines (ELMs) and the Feed Forward Neural Networks (FNNs) with back propagation algorithm are used to classify actions. According to the comparative results, ELMs produce a better recognition performance.