Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

Abstract EN

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.