Background. The surface electromyogram (sEMG) is strongly related to human motion and is useful as a human interface in robotics and rehabilitation. The purpose of this study was to establish a new system for estimating finger joint angles using few sEMG channels. Methods. To deal with a dynamic system, the proposed method adopts time delay factors and a feedback stream into a neural network (NN) with 6 system parameters. The 2 target motion patterns were each tested with 5 subjects. 1000 combinations of system parameter sets were tested. Results. A system with only 4 channels can estimate angles with 7.1-11.8% root mean square (RMS) error, which is approximately the same level of accuracy achieved by other systems using 15 channels. Conclusions. The use of so few channels is a great advantage in an sEMG system because it provides a convenient interface system. This advantage is conferred by the proposed NN system.

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This paper presents a new type of finger rehabilitation system using a multifingered haptic interface that is controlled by the patient though a surface electromyogram. We have developed the multifingered haptic interface robot: HIRO III that can give 3-directional forces to 5 fingertips. This robot can also be used as a rehabilitation device that can provide various fingertip exercises and measure various types of information. The sEMG works together with the HIRO III to consider the patient's intent. The proposed system is intended for patients having paralysis in the hand and fingers, and the motions will be provided as biofeedback to the fingertips with the device. In contrast to completely passive rehabilitation, the proposed system can provide active rehabilitation using sEMG. The experiment involved finger opening and closing with this system by ten able-bodied subjects. The results show that almost all subjects felt appropriate motion support from the device.

The properties of a-GeNx:H films have been studied including the effects of oxygen and carbon impurities. The contamination of a-GeNx:H with oxygen and carbon is expressed as a-GeNx(OyCz):H, where x=N/Ge, y=O/Ge and z=C/Ge. The characteristics of a-GeNx(OyCz):H are summarized as follows: the sample is transparent at the center of a film, and the color of a film varies to light yellow and to brown at the outer edge depending on the magnetic field of the magnetron sputtering. The compositional ratios of the film vary from x=0.34 to 0.28, y=0.15 to 0.25 and z=0.05 to 0.02 at the center and at outer edge of a film, respectively. Optical gap energy EO5, obtained by the photon energy at optical absorption coefficient of 5×103 cm−1, are 2.9 eV at the center and 1.7 eV at the outer edge of a film. Eo5 increases with the nitrogen content x in a-GeNx(OyCz):H but is independent of the content of oxygen and carbon.

This paper reports a new technique for estimating continuous finger joint angles from surface electromyogram (sEMG). Using an artificial neural network including a feedback stream (recurrent structure) and a time-delay factor for input, continuous angles scaled to range between 0 and 1 are able to estimated with network from feature vectors. Feature vectors extracted from sEMG are scaled to range between 0 and 10. Target hand motions are free state, fist with five fingers, grip with four fingers except thumb, and thumb flexion only. In this paper, two types of estimation networks are compared. The type 1 network is an older system that cannot be used to train the dynamics of estimation system. The type 2 network is a newer system that can train the dynamics with a recurrent structure by a feedback stream and time-delay factor for input. A comparing of the two types networks show that estimations of finger joint angles with type 2 are better than those with type 1. In particular, the results from type 2 are better than those from type 1 at the transition from one motion to another motion.