The comparison of electromyographic pattern classifications with active and passive electrodes

Chiou, Ying Han; Luh, Jer-Junn; Chen, Shih Ching; Lai, Jin-Shin; Kuo, Te Son

doi:10.1016/j.medengphy.2004.04.001

Cited by 15 publications

(12 citation statements)

References 20 publications

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“…This biomimetic paradigm necessitates both an interface for the extraction of neuromotor signatures from within the residuum [1][2][3][4][5] and a high-fidelity grip force control for proportional grasping in near real time [2,[6][7][8][9][10][11][12]. The goal of this study was to critically evaluate force myography (FMG) as a representation of dynamic grip force for proportional control.…”

Section: Introductionmentioning

confidence: 99%

Pressure signature of forearm as predictor of grip force

Wininger

Kim

Craelius

2008

JRRD

113

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Abstract-We studied the relationship between grip force and external forearm pressure in nondisabled subjects using force myography (FMG). FMG uses a sensorized cuff surrounding the forearm to register the distributed mechanical force, detecting pressure on the sensors generated by the volumetric changes of the underlying musculo-tendinous complex. Each of nine nondisabled subjects donned the FMG cuff and applied grip forces to a cylindrical dynamometer; grip forces ranged from 0% to 100% of the subjects' maximum voluntary contraction. The cuff was positioned with seven force sensors located on both the anterior and posterior surfaces of the proximal forearm, but no attempt was made to match sensor placement with particular muscles or sites. Grip prediction was simply encoded as the rectified sum of the FMG sensor outputs. During grip and release cycles, the FMG waveforms of each subject correlated closely with his or her force waveforms (r > 0.89). FMG also correlated highly with the timing of grip onset and release (intraclass correlation coefficient (ICC(A,2)) = 0.99) and time to peak (ICC(A,2) = 0.91), with negligible lag. These results demonstrate that when applied to the forearm, FMG represents a grip force signature that may be useful for near-real-time proportional control of upper-limb prosthetic devices.

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Section: Introductionmentioning

confidence: 99%

Pressure signature of forearm as predictor of grip force

Wininger

Kim

Craelius

2008

JRRD

113

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“…Some works have reported that the AR-derived cepstrum feature has better performance than the unprocessed AR feature (Kang et al (1995) ; Chiou et al (2004)). Even though the cepstral coefficients are derived directly from the AR coefficients, they do not contain exactly the same information, because the recursive operation changes the distribution of the features nonlinearly (Kang et al (1995)).…”

Section: Feature Extractionmentioning

confidence: 99%

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

Delis¹,

Carvalho²,

Rocha³

et al. 2011

Self Organizing Maps - Applications and Novel Algorithm Design

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“…Figure 3 illustrates the block diagram of control concepts in this study. A non-invasive FES system [14,15] was employed to validate the patient-driven loop control strategy. This system with a digital signal processor as its kernel showed greater versatility and computing capability.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Residual EMG signals were used to control the stimuli to excite the paralysed muscles and to trigger the electrical stimulation sequences via four-channel EMG recording amplifiers and ADCs in the electrical stimulator, which had the potential to modify stimulus parameters for avoiding the problems of muscle fatigue or force decrement during long-term electrical stimuli. Furthermore, the FES system's interfaces were written by LabVIEW 7.1 (National Instrument) [14,15].…”

Section: Experimental Evaluationmentioning

confidence: 99%