Dynamic gesture recognition using machine learning techniques and factors affecting its accuracy

Kinect, a motion sensing input device for gaming consoles has been successfully utilized for video games, and rehabilitation of paralyzed patients. We use this device to make learning a fun activity for children. Children learn to draw shapes by moving their hands in front of the Kinect device. We automatically recognize and classify their dynamic hand gestures into predefined shapes, namely; rectangles, triangles, and circles. To decrease over fitting and the cost of generating sample shapes a novel feature engineering approach is also proposed that increases the performance by more than 11%. We used three different machine learning algorithms and successfully classified the shapes with an accuracy of more than 97%.