Research

Robust EMG sensing system based on data fusion for myoelectric control of a robotic arm

Natalia M López^1,2 , Fernando di Sciascio² , Carlos M Soria² and Max E Valentinuzzi^1,3

¹  Gabinete de Tecnología Médica, Facultad Ingeniería, Universidad Nacional de San Juan, San Juan, Argentina

²  Instituto de Automática, Facultad Ingeniería, Universidad Nacional de San Juan, San Juan, Argentina

³  Instituto Superior de Investigaciones Biológicas, CONICET, Tucumán, Argentina

author email corresponding author email

BioMedical Engineering OnLine 2009, 8:5doi:10.1186/1475-925X-8-5

Published:	25 February 2009

Abstract

Background

Myoelectric control of a robotic manipulator may be disturbed by failures due to disconnected electrodes, interface impedance changes caused by movements, problems in the recording channel and other various noise sources. To correct these problems, this paper presents two fusing techniques, Variance Weighted Average (VWA) and Decentralized Kalman Filter (DKF), both based on the myoelectric signal variance as selecting criterion.

Methods

Tested in five volunteers, a redundant arrangement was obtained with two pairs of electrodes for each recording channel. The myoelectric signals were electronically amplified, filtered and digitalized, while the processing, fusion algorithms and control were implemented in a personal computer under MATLAB^® environment and in a Digital Signal Processor (DSP). The experiments used an industrial robotic manipulator BOSCH SR-800, type SCARA, with four degrees of freedom; however, only the first joint was used to move the end effector to a desired position, the latter obtained as proportional to the EMG amplitude.

Results

Several trials, including disconnecting and reconnecting one electrode and disturbing the signal with synthetic noise, were performed to test the fusion techniques. The results given by VWA and DKF were transformed into joint coordinates and used as command signals to the robotic arm. Even though the resultant signal was not exact, the failure was ignored and the joint reference signal never exceeded the workspace limits.

Conclusion

The fault robustness and safety characteristics of a myoelectric controlled manipulator system were substantially improved. The proposed scheme prevents potential risks for the operator, the equipment and the environment. Both algorithms showed efficient behavior. This outline could be applied to myoelectric control of prosthesis, or assistive manipulators to better assure the system functionality when electrode faults or noisy environment are present.