Browsing by Author "Pehlivan, Alp Burak"

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    Dynamic movement primitives for human movement recognition
    (IEEE, 2015) Pehlivan, Alp Burak; Öztop, Erhan; Computer Science; ÖZTOP, Erhan; Pehlivan, Alp Burak
    Dynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation [1] has been also used for recognition tasks [2, 3]. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. This paper presents a comparison of commonly used Hidden Markov Model (HMM) based recognition with DMP based recognition using human generated letter trajectories. As the working principles of these two methods are very different, in addition to the performance, the numbers of adaptable parameters that are used in each method and, process time were compared. The results, indicate that HMM gives better results than DMP, with possible noise robustness advantage in DMPs for human movement.
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    HMM bazlı 3 boyutlu insan hareketi tanıma ile insan ve robotun ortak çalışması
    (IEEE, 2014) Pehlivan, Alp Burak; Öztop, Erhan; Computer Science; ÖZTOP, Erhan; Pehlivan, Alp Burak
    We offer a system for a human-robot cooperation with natural communication in order to make using of robots in human-interactive tasks easier and more effective. This system includes a system with 3D motion capture cameras, and a motion recognition using Hidden Markov Models (HMM). We implemented the solution on a simulation using real data collected for this experiment.
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    Human movement recognition with dynamic movement primitives
    (2015-09) Pehlivan, Alp Burak; Öztop, Erhan; Öztop, Erhan; Sözer, Hasan; Arıca, N.; Department of Computer Science; Pehlivan, Alp Burak
    Dynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation has been also used for recognition tasks. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. We have implemented a movement recognition method based on DMPs with Gaussians centered equally spaced in phase variable and scaled one-nearest-neighbor weight comparison. Furthermore, in thesis, we presented a comparison of commonly used Hidden Markov Model (HMM) based recognition with our implementation of DMP based recognition using human generated letter trajectories. As the working principles of these two methods are very different, in addition to the performance, the numbers of adaptable parameters that are used in each method and, process time were compared. The results indicate that DMP gives better results than HMM in the tests with noiseless data, noisy data and derogated data with given human movement dataset.