Filtering the surface EMG signal: Movement artifact and baseline noise contamination

Luca, Carlo J. De; Gilmore, L. Donald; Kuznetsov, M.; Roy, Serge H.

doi:10.1016/j.jbiomech.2010.01.027

Cited by 967 publications

(620 citation statements)

References 11 publications

Supporting

Mentioning

556

Contrasting

Unclassified

Order By: Relevance

“…35 Next, we aligned the time intervals for R EMG during the APAs and CPAs using the onset of electrical activity of the adductor muscle (t), which we called the focal muscle and was the principal muscle used for the kicking task in this experiment. Adductor onset corresponded to the time at which the signal exceeded the average threshold plus 2 standard deviations of its baseline, lasting for at least 25 milliseconds.…”

Section: Data Processingmentioning

confidence: 99%

Changes in Postural Control After a Ball-Kicking Balance Exercise in Individuals With Chronic Ankle Instability

Conceição

Araújo

Santos

et al. 2016

Journal of Athletic Training

View full text Add to dashboard Cite

Context:Rehabilitation programs for patients with chronic ankle instability (CAI) generally involve balance-perturbation training (BPT). Anticipatory postural adjustments (APAs) and compensatory postural adjustments (CPAs) are the primary strategies used to maintain equilibrium during body perturbations. Little is known, however, about how APAs and CPAs are modified to promote better postural control for individuals with CAI after BPT.Objective: To investigate the effect of BPT that involves kicking a ball on postural-control strategies in individuals with CAI.Design: Randomized controlled clinical trial. Setting: Laboratory. Patients or Other Participants: We randomly assigned 44 volunteers with CAI to either a training group (TG; 11 women, 11 men; age ¼ 24 6 4 years, height ¼ 173.0 6 9.8 cm, mass ¼ 72.64 6 11.98 kg) or control group (CG; 11 women, 11 men; age ¼ 22 6 3 years, height ¼ 171.0 6 9.7 cm, mass ¼ 70.00 6 11.03 kg).Intervention(s): The TG performed a single 30-minute training session that involved kicking a ball while standing on 1 foot. The CG received no intervention.Main Outcome Measure(s): The primary outcome was the sum of the integrated electromyographic activity ( PR EMG) of the lower extremity muscles in the supporting limb that were calculated during typical intervals for APAs and CPAs. A secondary outcome was center-of-pressure displacement during similar intervals.Results: In the TG after training, the PR EMG decreased in both dorsal and ventral muscles during compensatory adjustment (ie, the time interval that followed lower limb movement). During this interval, muscle activity ( PR EMG) was less in the TG than in the CG. Consequently, center-of-pressure displacement increased during the task after training.Conclusions: A single session of ball-kicking BPT promoted changes in postural-control strategies in individuals with CAI. These results should stimulate new and more comprehensive studies to investigate the effect of this and other BPT techniques on postural control in patients with CAI.Key Words: recurrent sprains, reactive responses, balance training Key PointsAfter balance-perturbation training, postural sway increased during the ball-kicking activity and decreased during a static task in individuals with chronic ankle instability. Ventral and dorsal muscle activity decreased just before kicking the ball, and tibialis anterior and peroneus longus activity increased after the kick in the balance-perturbation-training group. Researchers should conduct more comprehensive studies to determine if balance-perturbation training improves postural control and simultaneously augments ankle stability in individuals with chronic ankle instability.

show abstract

Section: Data Processingmentioning

confidence: 99%

Changes in Postural Control After a Ball-Kicking Balance Exercise in Individuals With Chronic Ankle Instability

Conceição

Araújo

Santos

et al. 2016

Journal of Athletic Training

View full text Add to dashboard Cite

show abstract

“…The baseline is the combination of the two noise sources originated in the electronics of the amplification system (thermal noise) and at the skin-electrode interface (electrochemical noise), respectively [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

Bartolomeo¹,

Zecca

Sessa³

et al. 2011

AIP Conference Proceedings

View full text Add to dashboard Cite

“…With these manual annotations, the tool should test different threshold values and select the value that enables to correctly determine the activation timings. A future development should also be the inclusion of accelerometry data as a mean to minimize the influence of movement artifacts from the EMG data ( Luca et al, 2010). In the post-processing circumstances, a single signal must be processed at a time, and we intend to expand this processing to all signals simultaneously to allow the comparison of all the processing results at the same time.…”

Section: Discussionmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

Cited by 967 publications

References 11 publications

Changes in Postural Control After a Ball-Kicking Balance Exercise in Individuals With Chronic Ankle Instability

Changes in Postural Control After a Ball-Kicking Balance Exercise in Individuals With Chronic Ankle Instability

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

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

Contact Info

Product

Resources

About