Multi-Touch technology provides a successful gesture based Human Computer Interface. The contact and gesture recognition algorithms of this interface are based on full hand function and, therefore, are not accessible to many people with physical disability. In this paper, we design a set of command-like gestures for users with limited range and function in their digits and wrist. Trajectory and angle features are extracted from these gestures and passed to a recurrent neural network for recognition. Experiments are performed to test the feasibility of gesture recognition system and determine
Multi-touch gestures have become popular on a wide range of touchscreen devices, but the programming of these gestures remains an art. It is time-consuming and error prone for a developer to handle the complicated touch state transitions that result from multiple fingers and their simultaneous movements. In this paper, we present Gesture Coder, which by learning from a few examples given by the developer automatically generates code that recognizes multi-touch gestures, tracks their state changes and invokes corresponding application actions. Developers can easily test the generated code in