A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification

Graupe, Daniel; Magnussen, Jonas; Beex, A.A.

doi:10.1109/tac.1978.1101783

Cited by 143 publications

(44 citation statements)

References 3 publications

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“…Those who have an amputation must produce unique contraction patterns . This class of control strategies, though, recognizes these unique contraction patterns by use of mathematical modeling of the raw MES (7)(8)(9)(10)(11)14).…”

Section: Control Classesmentioning

confidence: 99%

Operator performance in myoelectric control of a multifunction prosthesis stimulator

Daley

Scott²,

Parker³

et al. 1990

JRRD

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-In this paper, a microcomputer-based system is described which facilitates the evaluation of different myoelectric control strategies . The system is based on an IBM-PC/AT microcomputer which generates and develops tracking targets, processes and displays the operator's response, and computes tracking performance . The dynamics of the prosthetic elbow, hand, and forearm are simulated in software which drives a stick figure in response to the operator's input . Performance is measured in terms of integral absolute error in targetresponse match . Control strategies to simulate different systems are easily modified through software and are evaluated under identical conditions . As a test of this evaluation system and to obtain some comparative data on control strategies, four strategies which are either in use or proposed for use are evaluated . The test procedure is divided into a control training session and a control evaluation session . Data collected from groups of three normally-limbed subjects per strategy over a period of five control evaluation sessions are presented . The results of this evaluation are discussed and improvements in the evaluation system suggested.

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Section: Control Classesmentioning

confidence: 99%

Operator performance in myoelectric control of a multifunction prosthesis stimulator

Daley

Scott²,

Parker³

et al. 1990

JRRD

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show abstract

“…Hence, it is possible to achieve more intuitive human-machine interface using EMG signals than conventional interfaces such as joysticks, data gloves, motion captures. Various interfaces using EMG signals have been proposed to control robot hands (Graupe et al;Jacobson et al;Yoshikawa et al, 2009;Ibe at al.). Some methods for hand motion identification have been reported since the 1990s based on soft-computing approaches, e. g. artificial neural networks (Fukuda et al; Hudgins et al), fuzzy logic (Karlik & Tokhi; Chan et al), support vector machine (Yoshikawa et al, 2007;Oskoei & Huosheng), and so on (Chen et al; Huang et al).…”

Section: Introductionmentioning

confidence: 99%

Hand Sign Classification Employing Myoelectric Signals of Forearm

Tsujimura¹,

Yamamoto²,

Izumi³

2012

Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges

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“…In the second step, the muscular contraction levels are estimated, and the joints selected in the first step are controlled using the impedance control. Although many studies on discrimination of motion from EMG patterns [7], [10], [11] and impedance control of a single-joint prosthetic arm using EMG signals [6], [8], [9] have been reported so far, no previous method has realized multi-joint motion control based on EMG pattern discrimination and impedance control using internal and external forces arising from agonist and antagonist muscles. In this chapter, the method proposed by Tsuji et al [17] is used for the first step.…”

Section: Control Strategymentioning

confidence: 99%