Inter- and Intra-Individual Differences in EMG and MMG during Maximal, Bilateral, Dynamic Leg Extensions

Anders, John Paul V.; Smith, Cory M.; Keller, Joshua L.; Hill, Ethan C.; Housh, Terry J.; Schmidt, Richard J.; Johnson, Glen O.

doi:10.3390/sports7070175

Cited by 23 publications

(36 citation statements)

References 59 publications

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“…Specifically, Anders et al[ 7 ] characterized bilateral fatigue by decreased muscle fiber action potential conduction velocity (EMG MPF) and motor unit recruitment (MMG AMP), as well as no change in muscle excitation (EMG AMP) and motor unit firing rate (MMG MPF). Thus, the findings of previous studies[ 4 - 6 , 11 ] suggested BL and UL muscle actions exhibit distinct patterns of neuromuscular fatigue.…”

Section: Introductionmentioning

confidence: 71%

Performance Fatigability And Neuromuscular Patterns Of Responses For Bilateral Versus Unilateral Leg Extensions In Men.

Anders

Keller

Smith

et al. 2020

Medicine &Amp; Science in Sports &Amp; Exercise

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Objectives: This study examined performance fatigability and the patterns of neuromuscular responses for electromyographic (EMG) and mechanomyographic (MMG) amplitude (AMP) and mean power frequency (MPF) during bilateral (BL) and unilateral (UL) maximal, isokinetic leg extensions. Methods: Peak torque for each repetition and EMG and MMG signals from the non-dominant vastus lateralis were recorded in 11 men during 50 BL and UL maximal, concentric, isokinetic leg extensions at 60 o · -1 that were performed on separate days. Separate repeated measures ANOVAs were performed to examine the normalized isokinetic torque and neuromuscular parameters. Results: Normalized isokinetic peak torque demonstrated a significant Conditions by Repetition interaction ( p <0.001, η 2 p = 0.594). There were no interactions, but significant main effects for Repetition with increases in EMG AMP ( p <0.001; η 2 p =0.255) and decreases in EMG MPF ( p <0.001; η 2 p =0.650), MMG AMP ( p <0.001; η 2 p =0.402), and MMG MPF ( p <0.001; η 2 p =0.796). In addition, EMG MPF and MMG AMP demonstrated main effects for Condition ( p =0.031; η 2 p =0.387 and p =0.002; η 2 p =0.64, respectively) with the BL exhibiting greater values than UL leg extensions for both parameters. Conclusions: The current findings indicated greater performance fatigability during UL versus BL leg extensions, but similar patterns of neuromuscular responses consistent with the Muscle Wisdom Hypothesis.

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Section: Introductionmentioning

confidence: 71%

Performance Fatigability And Neuromuscular Patterns Of Responses For Bilateral Versus Unilateral Leg Extensions In Men.

Anders

Keller

Smith

et al. 2020

Medicine &Amp; Science in Sports &Amp; Exercise

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“…Hence, we place sEMG sensors on the four muscles. The patterns of lower limb responses during dynamic muscle actions are with no differences [16]. To collect massive amounts of training data, the sEMG sensors were placed on both limbs in the data in the data collection process.…”

Section: ) Experimental Protocolmentioning

confidence: 99%

A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Lin

Ruan

2020

IEEE Access

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Recently, surface electromyography (sEMG) has been used to detect running-related works. sEMG provides a non-invasive and real-time method that allows quantification of muscle energy. However, noises in sEMG signals are a serious issue to be considered as these will interrupt the analysis of muscular activity. Hence, this work aims at distinguishing between sEMG valid signals and noises during running exercise by taking advantage of the combination of 3D-CNN and LSTM, which we called 3D-LCNN. Furthermore, according to the possible cases that happen in the sEMG data-collection procedure, we proposed two data-augmentation approaches to expend our sEMG dataset, which are the simulation of the surface electrodes displacement on the skin and the muscle fatigue. Experiment results show that the classification accuracy of the proposed 3D-LCNN can achieve 90.52%. Additionally, this work provides excellent serviceoriented architecture (SOA). The recognition process can be done after the subject placed the sEMG sensors and performed a trial. Therefore, the process can help clinicians or therapists to distinguish between sEMG valid signals and noises more efficiently.

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“…Indeed, MMG signal represents the mechanical manifestation of muscle activity [ 7 ] and further indicates the neurophysiology reflected by the mechanical counterpart to the electrical activity of unfused active motor units [ 5 ]. Attempts made in the assessment of crosstalk [ 8 ], quantification [ 9 ], and applications in assistive technology [ 10 ] support MMG signal as an alternative to EMG signal for the screening of muscle function [ 11 ], in terms of fatigue [ 12 ], muscle force [ 13 ], and its derivative (torque) [ 14 ] as well as for prosthesis control [ 15 ] and the detection of myopathies [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

2021

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This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.

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Inter- and Intra-Individual Differences in EMG and MMG during Maximal, Bilateral, Dynamic Leg Extensions

Cited by 23 publications

References 59 publications

Performance Fatigability And Neuromuscular Patterns Of Responses For Bilateral Versus Unilateral Leg Extensions In Men.

Performance Fatigability And Neuromuscular Patterns Of Responses For Bilateral Versus Unilateral Leg Extensions In Men.

A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Assessment of muscle activity using electrical stimulation and mechanomyography: a systematic review

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