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

Masaaki Hioki, Haruhisa Kawasaki
2012 ISRN Rehabilitation  
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
more » ... ubjects. 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.
doi:10.5402/2012/604314 fatcat:55y2xj6wvncqblp6aumijxm3ai