Effects of Load on Myoelectric Signals: The ARIMA Representation

Sherif, Mostafa Hashem; Gregor, Robert J.; Lyman, John

doi:10.1109/tbme.1981.324724

Cited by 34 publications

(8 citation statements)

References 7 publications

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“…This factorization is only an approximation since Kaiser and Petersen [21] and Sherif et a. [22] have presented evidence that the shape of the EMG spectrum does vary with force. However, Sherif et al [22], modeling the medial deltoid muscle with a (1, 1, 1) autoregressive integrated moving average model (see Box and Jenkins [23] )' show that the variation with load of the shape of the EMG spectrum during the "mobilization" and "buildup" phases of the contraction is negligible and that the variation during the "activation" phase is small-for instance, the autoregression coefficient moves from the 0.55-0.65 range to the 0.60-0.73 range when the load is increased from 0 to 1.2 kg (see Table I of Sherif et at [22] ).…”

Section: B Previous Approaches To Emg Signal Processingmentioning

confidence: 99%

“…The [1], [22] In his work on EMG prosthesis control, Graupe In the on-line operation mode, Graupe determines which limb function model gives the best fit to the current data. This is done by calculating the sample second-order statistics for the one-step-ahead prediction errors using the mth limb function model on data windows of specified length, say N1.…”

Section: B Previous Approaches To Emg Signal Processingmentioning

confidence: 99%

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Upper Extremity Limb Function Discrimination Using EMG Signal Analysis

Doerschuk

Gustafon

Willsky

1983

IEEE Trans. Biomed. Eng.

153

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Section: B Previous Approaches To Emg Signal Processingmentioning

confidence: 99%

Section: B Previous Approaches To Emg Signal Processingmentioning

confidence: 99%

Upper Extremity Limb Function Discrimination Using EMG Signal Analysis

Doerschuk

Gustafon

Willsky

1983

IEEE Trans. Biomed. Eng.

153

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“…AR parameters employed in the subsequent detailed analysis were RC's and MEM power spectrum. The MEM power spectra were obtained in every nonoverlapping interval of 0.25 s (or 512 samples) during a positive ramp contraction, using (5). The AR model of the order 20 was used for the MEM power spectrum because a detailed structure of the power spectrum was required.…”

Section: B Rc's and Mem Power Spectra Of Both Original And Simulatedmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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“…It is absolutely essential to define parameters that reflect characteristics of certain movements in order to classify movements. Thus, there have been many studies on defining such parameters over the last two decades [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A study on a sensing system for artificial arm's control

Yoo

Ahn

2006

2006 SICE-ICASE International Joint Conference

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In this paper, a sensing system for artificial arm's control is studied. The sensing system is consisted of 3 main parts. First part is a sensor system. It can measure how flexed the muscle is. So we use flex sensor. To get flexion signal, first sensor is attached the biceps brachii muscle. And other sensor is attached the triceps brachii muscle or other muscle in the left arm. The two signals that output from the sensor are passed to the amplification that has gain of 50 -100 times and Low Pass Filter of cut off frequency of 200[Hz]. And then arm's movement is classified 4 motionsflexion and extension, pronation and supination by the sensing system. Finally, to verify the validity of the proposed sensing system we will be experiment to healthy people in their late twenties to middle thirties with different muscular movements in their arms were selected as participants.

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Effects of Load on Myoelectric Signals: The ARIMA Representation

Cited by 34 publications

References 7 publications

Upper Extremity Limb Function Discrimination Using EMG Signal Analysis

Upper Extremity Limb Function Discrimination Using EMG Signal Analysis

AR modeling of myoelectric interference signals during a ramp contraction

A study on a sensing system for artificial arm's control

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