Gesture recognition for virtual reality applications using data gloves and neural networks

Explores the use of hand gestures as a means of human-computer interactions for virtual reality applications. For the application, specific hand gestures, such as “fist”, “index finger” and “victory sign”, have been defined. Most existing approaches use various camera-based recognition systems, which are rather costly and very sensitive to environmental changes. In contrast, this paper explores a data glove as the input device, which provides 18 measurement values for the angles of different finger joints. The paper compares the performance of different neural network models, such as backpropagation and radial-basis functions, which are used by the recognition system to recognize the actual gesture. Some network models achieve a recognition rate (training as well a generalization) of up to 100% over a number of test subjects. Due to its good performance, this recognition system is the first step towards virtual reality applications in which program execution is controlled by a sign language