scholarly journals Effects of Force Load, Muscle Fatigue, and Magnetic Stimulation on Surface Electromyography during Side Arm Lateral Raise Task: A Preliminary Study with Healthy Subjects

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Liu Cao ◽  
Ying Wang ◽  
Dongmei Hao ◽  
Yao Rong ◽  
Lin Yang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Healthy Subjects ◽  
Magnetic Stimulation ◽  
Rest Period ◽  
The Body ◽  
Median Frequency ◽  
Force Load ◽  
Semg Signal ◽  
The Subject

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low-frequency (ELF) magnetic stimulation on surface electromyography (SEMG) signal features during side arm lateral raise task. SEMG signals were recorded from 18 healthy subjects on the anterior deltoid using a BIOSEMI ActiveTwo system during side lateral raise task (with the right arm 90 degrees away from the body) with three different loads on the forearm (0 kg, 1 kg, and 3 kg; their order was randomized between subjects). The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as nonfatigue status and the last 10 s before the subject was exhausted was regarded as fatigue status. The subject was then given a five-minute resting between different loads. Two days later, the same experiment was repeated on every subject, and this time the ELF magnetic stimulation was applied to the subject’s deltoid muscle during the five-minute rest period. Three commonly used SEMG features, root mean square (RMS), median frequency (MDF), and sample entropy (SampEn), were analyzed and compared between different loads, nonfatigue/fatigue status, and ELF stimulation and no stimulation. Variance analysis results showed that the effect of force load on RMS was significant (p<0.001) but not for MDF and SampEn (bothp>0.05). In comparison with nonfatigue status, for all the different force loads with and without ELF stimulation, RMS was significantly larger at fatigue (allp<0.001) and MDF and SampEn were significantly smaller (allp<0.001).

scholarly journals Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1036
Author(s):  
Fuyuan Liao ◽  
Xueyan Zhang ◽  
Chunmei Cao ◽  
Isabella Yu-Ju Hung ◽  
Yanni Chen ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Cupping Therapy ◽  
Nonlinear Method ◽  
Muscle Recovery ◽  
Sham Control ◽  
Semg Signal ◽  
Relative Change ◽  
Semg Signals

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

scholarly journals Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 529 ◽  
Author(s):  
Susanna Rampichini ◽  
Taian Martins Vieira ◽  
Paolo Castiglioni ◽  
Giampiero Merati
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Literature Search ◽  
Complexity Analysis ◽  
Rehabilitation Medicine ◽  
Extensive Literature ◽  
Recurrence Plots ◽  
Semg Signal ◽  
Extensive Literature Search ◽  
The Relationship

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

scholarly journals An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3108 ◽  
Author(s):  
Shing-Hong Liu ◽  
Chuan-Bi Lin ◽  
Ying Chen ◽  
Wenxi Chen ◽  
Tai-Shen Huang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Root Mean Square ◽  
The Body ◽  
Median Frequency ◽  
Mean Square ◽  
Sport Injuries ◽  
Physiological Measurement ◽  
The Real ◽  
Emg Signal

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

scholarly journals An Evaluation Method of Assessing the Low Back Muscle Fatigue in Manual Material Handling

2015 ◽  
pp. 467-475
Author(s):  
A. Komeili ◽  
X. Li ◽  
M. Gul ◽  
J. Lewick ◽  
M. El-Rich
Keyword(s):  
Muscle Fatigue ◽  
Evaluation Method ◽  
Fatigue Behavior ◽  
The Body ◽  
Median Frequency ◽  
Low Back ◽  
Body Parts ◽  
Back Muscle ◽  
Spectral Parameters ◽  
Repetitive Lifting

Workers in certain industries are exposed to high labor-intensive tasks. Low back pain (LBP) is widespread among construction workers (Hildebrandt, 1995) and is extremely prevalent, with every adult having up to an 85% chance of experiencing LBP at least once during his or her lifetime. Back-related complaints are more costly than those from any other body part for Alberta work-related claims. Ergonomic principles in addition to the engineering considerations should be included in the design of work stations to minimize the risk of injury for employees. In this study, we assessed the low back muscle fatigue due to lifting tasks. The objective was to investigate the changes in muscle activity and kinematics of the human body caused by fatigue due to repetitive lifting tasks. Three healthy male volunteers with no recent back complications were asked to complete 3 cycles of lifting task at the Syncrude Centre of the Glenrose Rehabilitation Hospital. Each cycle involved lifting a 15lb window frame for 20 times. Self? adhesive reflective markers were attached on the hands, trunk, and legs to measure displacements and rotation of the body parts while performing the task. Electromyography (EMG) sensors were placed on lower back muscles to record their activity. The muscle fatigue was investigated by studying change in EMG spectral parameters such as RMS, mean frequency (MNF), and median frequency (MDF) as well as physical condition of subjects due to repetitive lifting. The power frequency curve shifted to the low frequency when muscle fatigue occurred. As a result, the slope of the RMS, and MNF indicators were successful to describe the fatigue behavior expected.

scholarly journals Poster 82: Rate of Change in Surface Electromyography Median Frequency to Measure Muscle Fatigue

2008 ◽  
Vol 89 (11) ◽  
pp. e46
Author(s):  
Niles M. Roberts ◽  
Caryn Easterling ◽  
Kyu J. Kim ◽  
Jacqueline Wertsch ◽  
Kevin T. White
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Rate Of Change ◽  
Median Frequency

scholarly journals Muscle Extremely Low Frequency Magnetic Stimulation Eliminates the Effect of Fatigue on EEG-EMG Coherence during the Lateral Raise Task: A Pilot Quantitative Investigation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qian Qiu ◽  
Liu Cao ◽  
Dongmei Hao ◽  
Lin Yang ◽  
Rajshree Hillstrom ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Magnetic Stimulation ◽  
Low Frequency ◽  
Deltoid Muscle ◽  
Force Load ◽  
Extremely Low Frequency ◽  
Univariate Anova ◽  
Male Subjects ◽  
Coherence Area ◽  
Resting Period

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low frequency (ELF) magnetic stimulation on electroencephalography- (EEG-) electromyography (EMG) coherence during right arm lateral raise task. Eighteen healthy male subjects were recruited. EEG and EMG signals were simultaneously recorded from each subject while three different loads (0, 1, and 3kg) were added on the forearm. ELF magnetic stimulation was applied to the subject’s deltoid muscle between tasks during the resting period. Univariate ANOVA showed that all EEG-EMG coherence areas of C3, C4, CP5, and CP6 were not significantly affected by the force load (all p>0.05) and that muscle fatigue led to statistically significant reductions on the coherence area of gamma band in C3 (p=0.014) and CP5 (p=0.019). More interestingly, these statistically significant reductions disappeared with the application of muscle ELF magnetic stimulation, indicating its potential application to eliminate the effect of fatigue.

scholarly journals Surface Electromyography Spectral Parameters for the Study of Muscle Fatigue in Swimming

2021 ◽  
Vol 3 ◽  
Author(s):  
Luca Puce ◽  
Ilaria Pallecchi ◽  
Lucio Marinelli ◽  
Laura Mori ◽  
Marco Bove ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Maximum Speed ◽  
Median Frequency ◽  
Front Crawl ◽  
Spectral Parameters ◽  
Dry Land ◽  
Kinematic Data ◽  
Mechanical Fatigue ◽  
Multi Stage

The purpose of this study was to assess validity, stability and sensitivity, of 4 spectral parameters–median frequency (Fmed), mean frequency (Fmean), Dimitrov index (DI), and mean instant frequency (Fmi)–in measuring localized muscle fatigue in swimming and to investigate their correlation with the variations of kinematic data and mechanical fatigue. Electrophysiological measures of muscle fatigue were obtained in real-time during a 100 m front crawl test at maximum speed in 15 experienced swimmers, using surface electromyography in six muscles employed in front crawl, while kinematic data of swimming was measured from video analysis. Mechanical fatigue was measured as the difference between muscle strength prior to and immediately after the 100 m front crawl in a dry-land multi-stage isometric contraction test. Statistically significant fatigue (p &lt; 0.0001) was found for all spectral parameters in all muscles. Fmed and Fmean varied between 10 and 25%, DI between 50 and 150%, and Fmi between 5 and 10%. Strong correlation (Pearson r ≥ 0.5) with mechanical fatigue was found for all spectral parameters except for Fmi and it was strongest for Fmed and Fmean. From our study, it turns out that Fmed and Fmean are more valid and stable parameters to measure fatigue in swimming, while DI is more sensitive.

scholarly journals Low-profile elastic exosuit reduces back muscle fatigue

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Erik P. Lamers ◽  
Juliana C. Soltys ◽  
Keaton L. Scherpereel ◽  
Aaron J. Yang ◽  
Karl E. Zelik
Keyword(s):  
Muscle Fatigue ◽  
Muscle Activity ◽  
Healthy Subjects ◽  
Median Frequency ◽  
Back Muscle ◽  
Low Profile ◽  
Extensor Muscles ◽  
Lumbar Muscle ◽  
Lumbar Extension ◽  
Withdrawal Design

Abstract We investigated the extent to which an un-motorized, low-profile, elastic exosuit reduced the rate of fatigue for six lumbar extensor muscles during leaning. Six healthy subjects participated in an A-B-A (withdrawal design) study protocol, which involved leaning at 45º for up to 90 s without exosuit assistance (A1), then with assistance (B), then again without assistance (A2). The exosuit provided approximately 12–16 Nm of lumbar extension torque. We measured lumbar muscle activity (via surface electromyography) and assessed fatigue rate via median frequency slope. We found that five of the six subjects showed consistent reductions in fatigue rate (ranging from 26% to 87%) for a subset of lumbar muscles (ranging from one to all six lumbar muscles measured). These findings objectively demonstrate the ability of a low-profile elastic exosuit to reduce back muscle fatigue during leaning, which may improve endurance for various occupations.

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