Robotic hand grasping of objects classified by using support vector machine and bag of visual words

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.