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    Imitation and Learning of Human Hand Gesture Tasks of the 3D Printed Robotic Hand by Using Artificial Neural Networks
    (IEEE, 2016) Ergene, Mehmet Celalettin; Durdu, Akif; Cetin, Halil
    In this study social learning and skill acquisition of a robotic hand via teaching and imitation was aimed. The subject of Human-Robot collaboration, which includes the theme of this paper, is a common field of experiments in our age of technology. Many disabilities can be defeated or many other things, which a human being would not be able to do, can be done with the help of this technology. As an example, a robotic hand can be a light of hope of a person who does not have a hand or wants to hold an object remotely over the internet. So that in our paper it is explained how a robotic hand can learn via imitation. In the experiment a robotic hand, which was printed by a 3D printer, was used and controlled wirelessly by a computer that recognizes human hand gesture via image processing algorithms. The communication between the computer and the robot is provided with a Bluetooth module. First of all, the image processing algorithms such as filtering and background subtraction were applied to the frames of the camera and extracted the features. Secondly, the process of teaching and testing of Artificial Neural Networks (ANNs) was made for the recognition of the hand and the gestures. After that, recognized actions were imitated by the robotic-hand hardware. Eventually, the learning of the robot via imitation was achieved with some small errors and the results are given at the end of the paper.
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    Monitoring the Reactions of Athletes with History of Rectus Femoris Proximal Tear Healed with Different Methods to Training Load with Thermography
    (Selçuk Üniversitesi, 2023 Ağustos) Bayrak, Ahmet; Ergene, Mehmet Celalettin; Ceylan, Murat
    Although rectus femoris (RF) injuries are rare, it is an important muscle that should be considered because of its contribution to actions such as shooting and fast running in football. In the literature, there is no consensus on which conservative or surgical methods should be preferred in RF total rupture. Although MRI is the gold standard method in the detection of injury, there is a controversy in the literature for post-injury imaging and follow-up. In addition, there is a lack of diagnostic imaging methods in the literature on how training load affects athletes. In current study, the effect of training load on athletes is evaluated by thermography after treatment of the RF muscle with different methods. This study is worthy of being a case report in terms of providing evidence on how the training load affects the sports lives of athletes who return to sports after surgery or conservative treatment.
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    Robotic Hand Grasping of Objects Classified by Using Support Vector Machine and Bag of Visual Words
    (IEEE, 2017) Ergene, Mehmet Celalettin; Durdu, Akif
    Recent statistics show that more than 10 million people in the world suffer amputation. Most of these people also have depression because of losing their hand, arm and leg movements. With current technology it is possible to give these people hands, arms and legs. Our aim is to give these people a chance to live. In this study we have designed a robotic hand in order to grasp objects. Grid based feature extraction and bag of words method are used to extract features from the images and the classification is made by support vector machine. There are three classes made; cups, pens, and staplers. So we can demonstrate a bureau environment and a handicapped person works in a bureau can grasp daily bureau materials in a real time application. We used a specific computer program toolbox to do software processes and a microprocessor to control the robotic hand. This paper just aims classification and grasping pens, cups, and staplers. However, with some improvements we believe such kind of prostheses can give a future to the handicapped people. We assume this study will be a step to a new and more advanced kind of prostheses than old traditional ones.