Gram-Schmidt M-Wave Canceller for the EMG Controlled FES

Yeom, Hojoon; Park, Young-Cheol; Yoon, Hyoung-Ro

doi:10.1093/ietisy/e88-d.9.2213

Cited by 12 publications

(21 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few of these reports validate the success in extracting the volitional EMG. To accomplish that, the hybrid EMG signal was emulated by adding up together its volitional and induced components [7,16]. …”

Section: Discussionmentioning

confidence: 99%

“…In these latter studies muscle was under static contraction and the EMG signals were represented mathematically (i.e., by an explicit mathematical equation); thus the extraction process necessitated complex processing procedures such as filtration with adaptive filters [16], or Gram-Schmidt prediction error filters [7]. …”

Section: Discussionmentioning

confidence: 99%

“…Yoem et al [7] used three evaluation criteria: (a) visual inspection of the power spectra of the signals; (b) comparison of the signals' RMS values; and (c) 'false-positive' parameter that calculated the number of times in which the extracted signal peak amplitude is higher than the maximum value of the pure original EMG ingredient. The obtained parameters' values, which indicated: good visual matching, RMS value close to 1, and small 'false-positive' value, enabled the authors to conclude that a 6 th order Gram-Schmidt prediction error filter successfully preserves the original volitional EMG signal.…”

Section: Discussionmentioning

confidence: 99%

“…Software solutions were used for both stimulus artifact and M-wave elimination [6-8,12,20-23] and were achieved by a variety of signal processing filters, including the comb-filter [6,12], wavelets [22,23], adaptive filters [16], or Gram-Schmidt filters [7]. …”

Section: Introductionmentioning

confidence: 99%

“…The few reports that did so, based their evaluation on steady muscle contraction analysis, and involved mathematical procedures that are difficult to implement [7,16]. …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

Langzam

Isakov

Mizrahi

2006

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background: Hybrid muscle activation is a modality used for muscle force enhancement, in which muscle contraction is generated from two different excitation sources: volitional and external, by means of electrical stimulation (ES). Under hybrid activation, the overall EMG signal is the combination of the volitional and ES-induced components. In this study, we developed a computational scheme to extract the volitional EMG envelope from the overall dynamic EMG signal, to serve as an input signal for control purposes, and for evaluation of muscle forces.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The few reports that did so, based their evaluation on steady muscle contraction analysis, and involved mathematical procedures that are difficult to implement [7,16]. …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

Langzam

Isakov

Mizrahi

2006

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control

Yeom

Chang

2010

Journal of Neuroscience Methods

View full text Add to dashboard Cite

Our objectives were to develop and test a new system for the potential for stable, real-time cancellation of residual stimulation artefacts (RSA) using surface electrode autogenic electromyography-controlled functional electrical stimulator (aEMGcFES). This type of closed-loop FES could be used to provide more natural, continuous control of lower extremity paretic muscles. We built upon work that has been done in the field of FES with one major technological innovation, an adaptive Gram-Schmidt filtering algorithm, which allowed us to digitally cancel RSA in real-time. This filtering algorithm resulted in a stable real-time estimation of the volitional intent of the stimulated muscle, which then acted as the direct signal for continuously controlling homonymous muscle stimulation. As a first step toward clinical application, we tested the viability of our aEMGcFES system to continuously control ankle dorsiflexion in a healthy subject. Our results indicate positively that an aEMGcFES device with adaptive filtering can respond proportionally to voluntary EMG and activate forceful movements to assist dorsiflexion during controlled isometric activation at the ankle. We also verified that normal ankle joint range of movement could be maintained while using the aEMGcFES system. We suggest that real-time cancellation of both primary and RSA is possible with surface electrode aEMGcFES in healthy subjects and shows promising potential for future clinical application to gait pathologies such as drop foot related to hemiparetic stroke.

show abstract

A hybrid method for real-time stimulation artefact removal during functional electrical stimulation with time-variant parameters

Bi¹,

Zhou²,

Xie³

et al. 2021

J. Neural Eng.

View full text Add to dashboard Cite

Objective. In this study, a hybrid method combining hardware and software architecture is proposed to remove stimulation artefacts (SAs) and extract the volitional surface electromyography (sEMG) in real time during functional electrical stimulations (FES) with time-variant parameters. Approach. First, an sEMG detection front-end (DFE) combining fast recovery, detector and stimulator isolation and blanking is developed and is capable of preventing DFE saturation with a blanking time of 7.6 ms. The fragment between the present stimulus and previous stimulus is set as an SA fragment. Second, an SA database is established to provide six high-similarity templates with the current SA fragment. The SA fragment will be de-artefacted by a 6th-order Gram–Schmidt (GS) algorithm, a template-subtracting method, using the provided templates, and this database-based GS algorithm is called DBGS. The provided templates are previously collected SA fragments with the same or a similar evoking FES intensity to that of the current SA fragment, and the lengths of the templates are longer than that of the current SA fragment. After denoising, the sEMG will be extracted, and the current SA fragment will be added to the SA database. The prototype system based on DBGS was tested on eight able-bodied volunteers and three individuals with stroke to verify its capacity for stimulation removal and sEMG extraction. Results. The average stimulus artefact attenuation factor, SA index and correlation coefficient between clean sEMG and extracted sEMG for 6th-order DBGS were 12.77 ± 0.85 dB, 1.82 ± 0.37 dB and 0.84 ± 0.33 dB, respectively, which were significantly higher than those for empirical mode decomposition combined with notch filters, pulse-triggered GS algorithm, 1st-order and 3rd-order DBGS. The sEMG-torque correlation coefficients were 0.78 ± 0.05 and 0.48 ± 0.11 for able-bodied volunteers and individuals with stroke, respectively. Significance. The proposed hybrid method can extract sEMG during dynamic FES in real time.

show abstract

Gram-Schmidt M-Wave Canceller for the EMG Controlled FES

Cited by 12 publications

References 0 publications

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control

A hybrid method for real-time stimulation artefact removal during functional electrical stimulation with time-variant parameters

Contact Info

Product

Resources

About