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. 1. Background It is hoped that biological signals may be used as a new type of human-machine interface. The electroencephalogram (EEG) and surface electromyogram (sEMG) have been used in many studies. The EEG provides a great variety of information on humans and can be used as a brain-machine interface (BMI); applications currently being studied include interface systems for computers [1] and robot hands [2]. While the EEG examines internal human information (i.e., emotion, thinking), the sEMG is used to study human motion [3–6] because it is generated while voluntary muscle movements. It is highly useful as a machine interface, especially for prosthetics and robotic devices that assist rehabilitation. The human hand has many joints and performs important functions; therefore, injury to or loss of fingers is a serious problem. Prosthetic hands [7–12] and robotic devices that assist rehabilitation [13–26] are being developed in many institutes, and some prosthetic hands are available in the world market [7–9]. Robotic devices that assist rehabilitation are helpful in achieving an early recovery for injured patients. Devices that target the upper limb (shoulder or elbow, without hand) are currently being researched [13–19], while other work focuses on hand and finger function [20–26]. While research on prosthetic hardware has achieved a certain level, the next step must focus on how to control these devices. One solution is to use sEMG, which in fact has already been applied to controlling a device that assists rehabilitation [19, 27] and to estimating the degree of recovery [28]. In research that targets estimating hand state from sEMG, many
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