An EMG-controlled omnidirectional pointing device using a HMM-based neural network

This paper proposes a new EMG-controlled pointing device using a novel statistical neural network. This device can be used as an interface tool for wearable computers since it does not restrict the operator to being in front of computer devices such as a keyboard or a mouse. The distinctive feature of this device is that we adopt a statistical neural network, which includes a continuous density hidden Markov model, to model the relationship between EMG signals and directions of a pointer movement. The operator can move a pointer in any direction throughout 360 degrees. We also introduced a physical model, such as a mass in a viscous space, into our system to realize a smooth pointer movement corresponding to the operator's force sense. In the experiments, omnidirectional pointer control is achieved using the proposed method and the applicability of our method is confirmed.