Adaptive mixture-of-experts models for data glove interface with multiple users

Hand gestures have great potential to act as a computer interface in the entertainment environment. However, there are two major problems when implementing the hand gesture-based interface for multiple users, the complexity problem and the personalization problem. In order to solve these problems and implement multi-user data glove interface successfully, we propose an adaptive mixture-of-experts model for data-glove based hand gesture recognition models which can solve both the problems.The proposed model consists of the mixture-of-experts used to recognize the gestures of an individual user, and a teacher network trained with the gesture data from multiple users. The mixture-of-experts model is trained with an expectation–maximization (EM) algorithm and an on-line learning rule. The model parameters are adjusted based on the feedback received from the real-time recognition of the teacher network.The model is applied to a musical performance game with the data glove (5DT Inc.) as a practical example. Comparison experiments using several representative classifiers showed both outstanding performance and adaptability of the proposed method. Usability assessment completed by the users while playing the musical performance game revealed the usefulness of the data glove interface system with the proposed method.