Human-Computer Interaction (HCI) interfaces need unambiguous instructions in the form of mouse clicks or keyboard taps from the user and thus gets complex. To simplify this monotonous task, a real-time hand gesture recognition method using computer vision, image, and video processing techniques has been proposed. Controlling infections has turned out to be the major concern of the healthcare environment. Several input devices such as keyboards, mouse, touch screens can be considered as a breeding ground for various micro pathogens and bacteria. Direct use of hands as an input device is an innovative method for providing natural HCI ensuring minimal physical contact with the devices i.e., less transmission of bacteria and thus can prevent cross infections. Convolutional Neural Network (CNN) has been used for object detection and classification. CNN architecture for 3d object recognition has been proposed which consists of two models: 1) A detector, a CNN architecture for detection of gestures; and 2) A classifier, a CNN for classification of the detected gestures. By using dynamic hand gesture recognition to interact with the system, the interactions can be increased with the help of multidimensional use of hand gestures as compared to other input methods. The dynamic hand gesture recognition method focuses to replace the mouse for interaction with the virtual objects. This work centralises the efforts of implementing a method that employs computer vision algorithms and gesture recognition techniques for developing a low-cost interface device for interacting with objects in the virtual environment such as screens using hand gestures.
Dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks
