Denoising of surface EMG with a modified Wiener filtering approach

Aschero, G.; Gizdulich, P.

doi:10.1016/j.jelekin.2009.02.003

Cited by 31 publications

(16 citation statements)

References 66 publications

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“…For example, a Wiener filtering based approach was used to remove noise in surface EMG signal, composed by non-stationary sharp spectral line of variable intensity and frequency [18]. The approach required simultaneous acquisition of the environmental noise via a separate channel.…”

Section: Discussionmentioning

confidence: 99%

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

Liu

Ying

Zhou

2014

Medical Engineering & Physics

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Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation.

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Section: Discussionmentioning

confidence: 99%

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

Liu

Ying

Zhou

2014

Medical Engineering & Physics

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“…1 a)). Different methodologies have been established to that end, as Empirical Mode Decomposition [14], Wiener filtering [30] or Digital Butterworth filtering [31]. The approach presented in this work uses the Singular Spectrum Analysis (SSA) to filter sEMG signals.…”

Section: Semg Signal Filtering Using Singular Spectrum Analysismentioning

confidence: 99%

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

Romero

Sánchez

Gragera

et al. 2016

J Braz. Soc. Mech. Sci. Eng.

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The estimation of muscular forces is useful in several areas such as Biomedical or Rehabilitation Engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of Inverse Dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using Singular Spectrum Analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.

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“…EMG signals are non-stationary characteristics which always depends on the subject [45]. Several parameters can have an impact on the EMG signals such as muscle fatigue [2] and force [30].…”

Section: Introductionmentioning

confidence: 99%

Design Of Smart Robot For Wrist Rehabilitation

Aabdallah

Bouteraa

Rekik

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Generally, the rehabilitation process needs a physical interactions between patients and therapists. Based on the principles governing such human-human interactions (HHI), the design of rehabilitation robots received several attempts in order to abstract the HHI in human-robot interaction (HRI). To achieve this goal, the rehabilitation robot should be smart and provides a useful and comprehensive platform to track the patient status. In this paper, a biofeedback-based high fidelity smart robot for wrist rehabilitation is designed. This robot is intended for repetitive exercises without

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Denoising of surface EMG with a modified Wiener filtering approach

Cited by 31 publications

References 66 publications

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

Design Of Smart Robot For Wrist Rehabilitation

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