Development of Advanced Active Haptic System for Musculokelelton-Exoskeleton Interactions

Shoureshi, R.

doi:10.21236/ada440830

Cited by 1 publication

(1 citation statement)

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“…Surface electrodes on the user's muscle skin acquire SEMG signals that are preamplified to isolate small signals of interest before being amplified, filtered, digitized, and then transferred to a myoelectric controller. [7][8][9][10][11] The knee joint is one of the most complex synovial joints in the human body. Its main functions are to allow locomotive movement and a stable posture.…”

Section: Introductionmentioning

confidence: 99%

Real-time controller for foot-drop correction by using surface electromyography sensor

Mashhadany

Rahim

2013

Proc Inst Mech Eng H

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Foot drop is a disease caused mainly by muscle paralysis, which incapacitates the nerves generating the impulses that control feet in a heel strike. The incapacity may stem from lesions that affect the brain, the spinal cord, or peripheral nerves. The foot becomes dorsiflexed, affecting normal walking. A design and analysis of a controller for such legs is the subject of this article. Surface electromyography electrodes are connected to the skin surface of the human muscle and work on the mechanics of human muscle contraction. The design uses real surface electromyography signals for estimation of the joint angles. Various-speed flexions and extensions of the leg were analyzed. The two phases of the design began with surface electromyography of real human leg electromyography signal, which was subsequently filtered, amplified, and normalized to the maximum amplitude. Parameters extracted from the surface electromyography signal were then used to train an artificial neural network for prediction of the joint angle. The artificial neural network design included various-speed identification of the electromyography signal and estimation of the angles of the knee and ankle joints by a recognition process that depended on the parameters of the real surface electromyography signal measured through real movements. The second phase used artificial neural network estimation of the control signal, for calculation of the electromyography signal to be stimulated for the leg muscle to move the ankle joint. Satisfactory simulation (MATLAB/Simulink version 2012a) and implementation results verified the design feasibility.

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Section: Introductionmentioning

confidence: 99%