A fast and reliable technique for muscle activity detection from surface EMG signals

Merlo, Andrea; Farina, Dario; Merletti, Roberto

doi:10.1109/tbme.2003.808829

Cited by 241 publications

(177 citation statements)

References 21 publications

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“…In all of these algorithms, the process outputs a nonnegative value (similar to a full-wave rectification), then the resulting output is compared with a threshold. Similarly, [50] proposes a matched filter on the Wavelet Transform of the signal to detect muscle activation. However, all of these techniques are used to recognize patterns in the signal, which requires the specific signal waveform a priori and have unsatisfactory performance in stochastic environments.…”

Section: Prior Art and Comparisonsmentioning

confidence: 99%

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Ergeneci¹,

Gokcesu

Ertan³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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Abstract-Wearable technology has gained increasing popularity in the applications of healthcare, sports science and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition and fatigue tracking. To this end, we introduce an embedded, 8-channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multi-channel data acquisition unit. For the first stage, we propose a low cost, dry and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multi-channel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used, commercially available product and our data acquisition system achieves 4.583 dB SNR gain with 98.9784% accuracy in the detection of the contractions.

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Section: Prior Art and Comparisonsmentioning

confidence: 99%

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Ergeneci¹,

Gokcesu

Ertan³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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“…Muscle forces were calculated using EMGs and Hill-type excitation-and contraction-dynamic models according to the guidelines proposed by Zajac [33]. The raw EMG signal was band-pass filtered (zero-pole-gain design, eighth order, Butterworth filter) with cut-off frequencies of 10 and 400 Hz to minimize noise owing to motion artefacts and the EMG amplifier [34]. The filtered EMG signal was rectified and low-pass filtered (zero-pole-gain design, second order, Butterworth filter) with a cut-off frequency of 6 Hz [33] and a 22 ms electromechanical delay, representing the muscle time response to stimuli, applied to synchronize the processed signal with the muscle response [35].…”

Section: Physiologically Plausible Muscle Forcesmentioning

confidence: 99%

Stochastic modelling of muscle recruitment during activity

et al. 2015

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One contribution of 11 to a theme issue 'Multiscale modelling in biomechanics: theoretical, computational and translational challenges'. Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo (MCMC) methods, whereas EMG-driven muscle forces were calculated using EMG-driven modelling. We calculated the differences between the spectrum and EMG-driven muscle force for 1-15 input EMGs, and we identified the muscle strategy that best matched the recorded EMG pattern. The best-fit strategy, static optimization solution and EMG-driven force data were compared using correlation analysis. Possible and plausible muscle forces were defined as within physiological boundaries and within EMG boundaries. Possible muscle and joint forces were calculated by constraining the muscle forces between zero and the peak muscle force. Plausible muscle forces were constrained within six selected EMG boundaries. The spectrum to EMG-driven force difference increased from 40 to 108 N for 1-15 EMG inputs. The best-fit muscle strategy better described the EMG-driven pattern (R 2 ¼ 0.94; RMSE ¼ 19 N) than the static optimization solution (R 2 ¼ 0.38; RMSE ¼ 61 N). Possible forces for 27 of 34 muscles varied between zero and the peak muscle force, inducing a peak hip force of 11.3 body-weights. Plausible muscle forces closely matched the selected EMG patterns; no effect of the EMG constraint was observed on the remaining muscle force ranges. The model can be used to study alternative muscle recruitment strategies in both physiological and pathophysiological neuromotor conditions.

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“…Generally, visual inspection is considered to provide highly accurate event detection, because all details of the signal can be assessed by the clinical specialists. Besides the accuracy in the detection, the speed of the algorithm can be an important consideration for specific applications (Merlo et al, 2003). However, for long acquisitions of repetitive movements the visual inspection can be time consuming, becoming an impracticable method for the biosignal analysis.…”

Section: Introductionmentioning

confidence: 99%

A New Tool for the Automatic Detection of Muscular Voluntary Contractions in the Analysis of Electromyographic Signals

Pimentel¹,

Gomes²,

Olstad

et al. 2015

Interact. Comput.

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This file was dowloaded from the institutional repository Brage NIH -brage.bibsys.no/nih Pimentel, A., Gomes, R., Olstad, B. H., Gamboa, H. (2015 (3) to analyze muscular fatigue through analysis of the frequency spectrum of the signal. We have developed an EMG tool that was incorporated in an existing web-based biosignal acquisition and processing framework. This tool can be used on a post-processing environment and provides not only frequency and time parameters, but also an automatic detection of starting and ending times for muscular voluntary contractions using a threshold-based algorithm with the inclusion of the Teager-Kaiser energy operator. The algorithm for the muscular voluntary contraction detection can also be reported after a real-time acquisition, in order to discard possible outliers and simultaneously compare activation times in different muscles. This tool covers all known applications and allows a careful and detailed analysis of the EMG signal for both clinicians and researchers. The detection algorithm works without user interference and is also user-independent. It manages to detect muscular activations in an interactive process. The user simply has to select the signal's time interval as input, and the outcomes are provided afterwards. RESEARCH HIGHLIGHTS• An interactive analysis tool for electromyographic (EMG) signals has been developed.• The tool gives the user control over signal processing algorithms, enabling human-computer interaction and provides visual information from the signals and processing results.• This tool differs from the existing ones due to the inclusion of an automatic detection algorithm for the muscular voluntary contraction-threshold-based method with the inclusion of the Teager-Keaser energy operator.• It allows a careful and detailed analysis of EMG signals both for clinicians and researchers.

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A fast and reliable technique for muscle activity detection from surface EMG signals

Cited by 241 publications

References 21 publications

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Stochastic modelling of muscle recruitment during activity

A New Tool for the Automatic Detection of Muscular Voluntary Contractions in the Analysis of Electromyographic Signals

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