The electromyogram (EMG) as a control signal for functional neuromuscular stimulation. II. Practical demonstration of the EMG signature discrimination system

Hefftner, G.; Járos, G.G.

doi:10.1109/10.1371

Cited by 33 publications

(18 citation statements)

References 2 publications

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“…It is assumed that the power of the volitional EMG is constant in the frames. A proper scaling of the output signal will then result in a residual signal where the power is independent of the filter coefficients and with the same rms-value as the volitional EMG (10) The normal equations (7) for up to six are solved in real time on a ADSP-21020 digital signal processor. A LUdecomposition is performed using Crout's method followed by forward-and backsubstitution [21].…”

Section: B Suppression Of Muscle Responsesmentioning

confidence: 99%

Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG-signal

Sennels

Biering‐Sørensen²,

Andersen

et al. 1997

IEEE Trans. Rehab. Eng.

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Section: B Suppression Of Muscle Responsesmentioning

confidence: 99%

Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG-signal

Sennels

Biering‐Sørensen²,

Andersen

et al. 1997

IEEE Trans. Rehab. Eng.

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“…That is, it shows whether or not the feature in a block 'is similar to that in the reference block, using [a~q:] of the standard AR filter. It should be noted that 6(q-l) is ~culated by j<q), j<q-l), and «», using (8). The concept of the PE index stems from the similarity function which was proposed by Iijima [22] and has greatly contributed to the development of the optical character recognition.…”

Section: B Prediction Error Analysismentioning

confidence: 99%

“…The relationship between forward and backward PE vectors is (8) where K(q) is the q-th Re. According to the geometrical interpretation of the least squares estimation of [a~q)], the optimum forward PE vector, r».…”

Section: B Prediction Error Analysismentioning

confidence: 99%

“…Recently, Capponi et al [6] represented ME signals, detected from the biceps and triceps muscles, with the time courses of AR coefficients during rapid isometric contractions. The benefit of the AR model in ME signal analysis has been confirmed for applications in prosthesis control [4], [7], functional electrical stimulation [8], and clinical diagnosis [9]. These applications notwithstanding, the problems of applying AR model to time-varying ME signals and the time-varying behavior of AR parameters have not been studied in detail.…”

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confidence: 99%

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AR modeling of myoelectric interference signals during a ramp contraction

Kiryu

Luca

Saitoh

1994

IEEE Trans. Biomed. Eng.

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Abstract-We investigated the time-varying behavior of the autoregressive (AR) parameters in a myoelectric (ME) signal detected during a linear force increasing contraction. The AR pa rameters of interest were the reflection coefficients, the AR model spectrum, and the prediction errors. We used well-conditioned ME signals for which the complete time record of the motor units firings was available. In addition, the influence of the recruitment of a new motor unit, the conduction velocity of action potentials, and additive broad-band noise were investigated using simulated ME signals. The simulated ME signals were constructed from a selected group of the available motor unit action potential trains. The results revealed that, as the contraction progressed, the AR parameters displayed a time-varying behavior which coincided with the recruitment of newly recruited motor units whose spectrum of the waveform differed from that of the rest of the ME signal. This property of the AR parameters was obscured by the presence of broad-band noise and low-amplitude motor unit action potentials, both of which are more pronounced during low-level force contractions. I. INlRODUCfION IT HAS BECOME common practice to use the frequency spectrum of the surface myoelectric (ME) signal as a fatigue index for sustained muscle contractions (see reviews by De Luca [1] and Merletti et al. [2], among others). For such analysis, it is important that the ME signal be stationary. This is an important concern because motor units (MU's) may be recruited or derecruited during a contraction due to fluctuations in the force output of the muscle: Such fluctuation may occur even in attempted constant-force isometric contractions.Previous approaches for analyzing the time-varying aspects of the ME signals have used a linear prediction model. Among them, the autoregressive (AR) model has been used to deal with time-varying ME signals because it emphasizes spectral peaks for time records having a small number of samples [3]. This approach was introduced by Graupe and Cline [4] who attempted to use the surface ME signal for controlling prostheses. Subsequently, Sherif et al. [5] studied the behavior of autoregressive integrated moving average (ARIMA) coef ficients of the ME signals from the deltoid muscle during dynamic contractions. Recently, Capponi et al. [6] represented ME signals, detected from the biceps and triceps muscles, with the time courses of AR coefficients during rapid isometric contractions. The benefit of the AR model in ME signal analysis has been confirmed for applications in prosthesis control [4], [7], functional electrical stimulation [8], and clinical diagnosis [9]. These applications notwithstanding, the problems of applying AR model to time-varying ME signals and the time-varying behavior of AR parameters have not been studied in detail. The use of AR modeling for physiological interpretation of the behavior of the ME signal has been limited. In an early report, Inbar and Noujaim [9] described the influence of the statistics of M...

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“…Several advanced methods such as classification [22], [23], autoregressive modeling [24], [25], ceptral-coefficients [26], or artificial neural networks [27]- [29] have been investigated but the traditional methods are the averaged rectified value (ARV) or root mean square (rms) over a moving window of the raw signal. ARV and rms are found to be increasing with increasing muscle contraction [30] but it is still somewhat unclear which one is the better [31], [32].…”

mentioning

confidence: 99%

A pilot study of myoelectrically controlled FES of upper extremity

Thorsen

Spadone

Ferrarin

2001

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Functional electrical stimulation (FES) of upper limbs can be used for the recovery of some hand functions on patients with CNS lesions. This study deals with the control of FES by means of myoelectrical activity detected from voluntarily activated paretic muscles. The specific aim of this paper is to evaluate the accuracy of myoelectrical control in terms of produced force and movement.For this purpose, a specific device called myoelectrical controlled functional electrical stimulator (MeCFES) has been developed and applied to six tetraplegic patients with a spinal cord lesion and one stroke hemiplegic patient.Residual myoelectric signals from the paretic wrist extensor (m. extensor carpi radialis, ECR) have been used to control stimulation of either the wrist extension (i.e., the same muscle) or thumb flexion. A tracking test based on a visual feedback of the produced force or movement compared to a reference target trajectory was used to quantify control accuracy. A comparison was made between the tracking performances of each subject with and without the MeCFES and the learning process for two of the subjects were observed during consecutive sessions.Results showed that the wrist extension was improved in three out of five C5 SCI patients and the thumb flexion was largely increased in one incomplete C3 SCI patient. The hemiplegic patient showed limited thumb control with the MeCFES but indicated the possibility of a carry over effect. It was found that a low residual natural force resulted in a less accurate movement but also with a large increase (up to ten times) of the muscle output. On the contrary, persons with a medium residual force obtained a smaller amplification of muscle force with a higher tracking accuracy.

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The electromyogram (EMG) as a control signal for functional neuromuscular stimulation. II. Practical demonstration of the EMG signature discrimination system

Cited by 33 publications

References 2 publications

Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG-signal

Functional neuromuscular stimulation controlled by surface electromyographic signals produced by volitional activation of the same muscle: adaptive removal of the muscle response from the recorded EMG-signal

AR modeling of myoelectric interference signals during a ramp contraction

A pilot study of myoelectrically controlled FES of upper extremity

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