A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle

Wang, W.; Stefano, A. De; Allen, Robert

doi:10.1016/j.compbiomed.2005.04.002

Cited by 37 publications

(23 citation statements)

References 19 publications

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“…They were uniformly distributed in MU territories [24,27]. The diameters of MUs were distributed according to the Poisson law in the range of 2 to 8 mm, with an average of 6 mm [25,27].…”

Section: Simulation Of the Muap And Muap Trainmentioning

confidence: 99%

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Classification of the Systems Used in Surface Electromyographic Signal Detection according to the Degree of Isotropy

Messaoudi

Bekka

Belkacem

2018

ABE

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Surface electromyographic (EMG) signals are known to be strongly in uenced by anatomical, physiological and detection system parameters. Among the detection system parameters, we are interested in the effect of muscle ber inclination on the electrode arrangement. The purpose of this study was to determine the best and the worst orientation of the electrodes arranged in nine detection systems relative to the muscleber direction and also to classify the investigated systems according to their degree of isotropy. The study was based on simulated surface EMG (sEMG) signals generated in a cylindrical multilayer volume conductor. The orientation of electrodes with respect to the ber direction was de ned by the ber inclination angle (FIA). For each detection system, the mean power (MP) of the simulated signals was computed at different FIAs and used as a basis for evaluating the effect of muscle ber inclination. We showed that for the FIA range of 0-180 , approximately isotropic systems had three positions to record sEMG signals under good conditions (MP was maximum). However, longitudinal and transversal highly anisotropic systems had two and one positions, respectively, at which sEMG signals were detected under good conditions. We showed also that the degree of isotropy of the nine detection systems investigated was less affected by the increase in muscle and fat thicknesses. However, with an increase in inter-electrode distance (IED), the degree of isotropy of approximately isotropic systems decreased while the degree of isotropy of highly anisotropic systems increased.

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“…They were uniformly distributed in MU territories [24,27]. The diameters of MUs were distributed according to the Poisson law in the range of 2 to 8 mm, with an average of 6 mm [25,27].…”

Section: Simulation Of the Muap And Muap Trainmentioning

confidence: 99%

“…The ring rates (FRs) of MUs ranged between 8 imp/s and 42 imp/s. They were distributed on the activated MUs according to the Poisson law with an average of 12 imp/s [25]. According to the size principle rule, smaller motor units will be recruited at lower muscular voluntary contractions (MVCs) than larger ones.…”

Section: Simulation Of the Muap And Muap Trainmentioning

confidence: 99%

Classification of the Systems Used in Surface Electromyographic Signal Detection according to the Degree of Isotropy

Messaoudi

Bekka

Belkacem

2018

ABE

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“…Subjects were asked to walk along a long flat road normally at three different gait cadences, during which we focused our attention on the right leg. In this case, the time length of one entire gait cycle was defined as ranging from the first moment the right heel to the ground to the next moment the homolateral heel onto the ground [4].…”

Section: A Subjects and Proceduresmentioning

confidence: 99%

Gait cadence detection based on surface electromyography (sEMG) of lower limb muscles

Sun

Zhou

Jiang

et al. 2014

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

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Surface electromyography (sEMG) signals could represent the contractions of muscle activity which contains approximately accurate information about joint movements and motor torque. In this study, a new gait cadence detection method was presented by using the sEMG signals of lower limb muscles. To detect the gait cadence, sEMG signals were collected from Tibialis Anterior (TA) and Gastronomies Lateral (GL) of lower limb muscles when subjects were walking at different gait cadences. By evaluating the different rhythms of the sEMG activities, different gait cadences of the subject could be detected. In our experiment, subjects were asked to walk at three different cadences, and the averaged estimated error from sEMG signals was 6.8%. Additionally, the different activities of TA and GL muscles in single gait cycle could also be found from the sEMG signals, which could provide an advanced method for human gait analysis. These results showed that the sEMG signals of lower limb muscles varied in a predictable way during walking tasks, which could be detected as input commands to control humanoid robot, exoskeleton assistive systems or other humanmachine systems.

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“…Intracellular action potential (IAP) was adopted as the starting point of this sEMG model. IAP is mathemati- MU size (D MU ) [19] 12 mm Muscle fiber lengths L 0.5f (right+left) [17] 70 mm, mean=0, SD=1, Range=±2 mm Fiber-endplate position [18] Mean=0, Range=±3 mm Fiber-tendon distance [17] Range=±6 mm Fiber position [16] Range ±1 MU radius MU position [16] Range ±1 muscle radius cally formulated as [21] 3 ( ) 768 90,…”

Section: Intracellular Action Potential Simulationmentioning

confidence: 99%

“…The BB muscle is commonly described as a cylindrical volume conductor. It has a multiple-layer structure and an anisotropic media characteristic [16]. To develop a simulated model of the BB sEMG signal, the BB muscle structure and its parameters must be known (Figure 1).…”

Section: Physiological Structure Of Bbmentioning

confidence: 99%

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Yan

2012

Sci. China Life Sci.

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Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates (FRs) increase as exertion increases. A hypothesis was proposed that the Gaussianity test (S g ) and linearity test (S ℓ ) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial (active MUs) and temporal (three FRs) information to test the hypothesis. Higher-order statistics (HOS) from the bi-frequency domain were used to perform S g and S ℓ . Multivariate covariance analysis and a correlation test were employed to determine the nMUs-Sg relationship and the nMUs-S ℓ relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the Sg and S ℓ values. The nMUs negatively correlated to both the Sg and S ℓ values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control. simulation surface electromyography model, higher-order statistics, motor unit, firing rate Citation:Zhao Y, Li D X. A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography. Sci China Life Sci, 2012, 55: 958 -967,

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A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle

Cited by 37 publications

References 19 publications

Classification of the Systems Used in Surface Electromyographic Signal Detection according to the Degree of Isotropy

Classification of the Systems Used in Surface Electromyographic Signal Detection according to the Degree of Isotropy

Gait cadence detection based on surface electromyography (sEMG) of lower limb muscles

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

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