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.

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This paper proposes a novel phoneme classification method using facial electromyography (EMG) signals. This method makes use of differential EMG signals between muscles for phoneme classification, which enables a speech synthesizer to be constructed using fewer electrodes. The EMG signal is derived as a differential between monopolar electrodes attached to two different muscles, unlike conventional methods in which the EMG signal is derived as a differential between bipolar electrodes attached to the same muscle. Frequency-based feature patterns are then extracted using a filter bank, and the phonemes are classified using a probabilistic neural network, called a reduced-dimensional log-linearized Gaussian mixture network (RD-LLGMN). Since RD-LLGMN merges feature extraction and pattern classification processes into a single network structure, a lower-dimensional feature set that is consistent with classification purposes can be extracted; consequently, classification performance can be improved. Experimental results indicate that the proposed method with a fewer number of electrodes can achieve a considerably high classification accuracy.

SUMMARYThis research paper proposes an omnidirectional pointing device that uses EMG signals. Since this device does not keep the user in front of the mouse or keyboard, there are expectations for its use as an input device for a wearable computer, and other equipment. A Recurrent LogLinearized Gaussian Mixture Network is introduced in order to make use of the time series characteristics of EMG signals. This neural net is configured based on the hidden Markov model, which is a dynamic probability model, and infinitely many movement directions of a pointer can be expressed as combinations of probabilities of movement in preset reference directions. As a result of a verification experiment, it was confirmed that the pointer could be controlled with good precision. The effectiveness of the proposed method was confirmed by comparative experimentation with conventional methods.