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

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.

Myoelectric Knee Angle Estimation Algorithms for Control of Active Transfemoral Leg Prostheses

“…Accuracies of 95% and 86% were achieved in the detection of contraction signals from the wrist extensors and flexors, respectively. Lopez et al (2009) proposed two strategies for data fusion based on variance weighted average and decentralized Kalman filter, by means of an arrangement of redundant potentials, that is, by combining the SEMG signals. The muscle contraction amplitude was estimated and transformed to angular reference for the control of the robot joint.…”

“…The muscle contraction amplitude was estimated and transformed to angular reference for the control of the robot joint. The algorithms demonstrated an efficient performance, and the joint never moved beyond its safety range (Lopez et al (2009)). Despite great success in decoding discrete movements such as individual finger flexion or extension, the matter of continuously predicting joint angles using SEMG signals is comparatively underdeveloped (Smith et al (2008)).…”

Robot Manipulator Probabilistic Workspace Applied to Robotic Assistance

“…Furthermore, the knowledge of the most accessed zones of the robot's workspace permits the bounding of the time needed for the robot to reach a given position at its workspace. From its early beginning, the use of robot manipulators within the robotic assistance field was concerned to emulate an orthopaedic arm (Fukuda et al, 2003;Zecca et al, 2002;Lopez et al, 2009). Therefore, the robot manipulator was considered as the final actuator of the assistive system where its main goal was to imitate the movements of an arm.…”

“…Depending on the user/patient capabilities, the robot manipulator was commanded by either electromyographic or electro-encephalic signals (Ferreira et al, 2006a;2006b;. A robotic device controlled by a Muscle-Computer Interface (MCI) can be found in (Artemiadis & Kyriakopoulos, 2006;Lopez et al, 2007;Millan et al, 2004;Ferreira et al, 2006b;Lopez et al, 2009;Ferreira et al, 2008). In these works, the electro-miographic signal is acquired, processed, classified and converted to motion commands.…”

Navy SEAL Prosthetic Hand

“…Poor response of the device is due to myoelectric signals being very faint and needing high levels of amplification and filtering out of invasive electrical noise to be usable. This is coupled with the relatively fragile nature of the electrode connection to the skin and alterations in signal amplitudes based on physiological changes in the muscle leads to the need for complex microcontroller logic and patient customized programming [5]. This controller system is not perfect however, meaning users can become frustrated with the device if it does not behave as expected.…”