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Gesture imitation and recognition using Kinect sensor and extreme learning machines


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dc.contributor.author Yavşan, Emrehan
dc.contributor.author Uçar, Ayşegül
dc.date.accessioned 2016-10-18T11:38:11Z
dc.date.available 2016-10-18T11:38:11Z
dc.date.issued 2016-12-01
dc.identifier.citation Yavşan, E. ve Uçar, A. (2016). Gesture imitation and recognition using Kinect sensor and extreme learning machines. Measurement, 94(2016), 852-861. tr_TR
dc.identifier.uri http://hdl.handle.net/11508/8895
dc.description.abstract 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. tr_TR
dc.language.iso İngilizce tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Fırat Üniversitesi Kütüphanesi::TEKNOLOJİ tr_TR
dc.subject.ddc Human action recognition tr_TR
dc.subject.ddc NAO humanoid robot tr_TR
dc.subject.ddc Xbox 360 Kinect tr_TR
dc.subject.ddc Extreme learning machines tr_TR
dc.title Gesture imitation and recognition using Kinect sensor and extreme learning machines tr_TR
dc.type Makale - Bilimsel Dergi Makalesi - Çok Yazarlı tr_TR
dc.contributor.YOKID TR32854 tr_TR
dc.contributor.YOKID TR24225 tr_TR
dc.relation.journal Measurement tr_TR
dc.identifier.volume 94 tr_TR
dc.identifier.issue 2016 tr_TR
dc.identifier.pages 852;861
dc.identifier.doi http://dx.doi.org/10.1016/j.measurement.2016.09.026
dc.published.type Uluslararası tr_TR


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University of Fırat
23119
Elazığ-Merkez
TURKEY