Variability in surface electromyography of right arm biceps brachii muscles between male adolescent, vicenarian and tricenarian with distinct electrode placement

2012 ◽  
Author(s):  
Nizam Uddin Ahamed ◽  
Kenneth Sundaraj ◽  
R. Badlishah Ahmad ◽  
S.A.M Matiur Rahaman ◽  
Md. Anamul Islam ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Biceps Brachii ◽  
Electrode Placement ◽  
Male Adolescent

Analysis of Muscle Activity Using Surface Electromyography for Muscle Performance in Manual Lifting Task

2014 ◽  
Vol 564 ◽  
pp. 644-649 ◽  
Author(s):  
Halim Isa ◽  
Rawaida ◽  
Seri Rahayu Kamat ◽  
A. Rohana ◽  
Adi Saptari ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Twist Angle ◽  
Erector Spinae ◽  
Manual Lifting ◽  
Experienced Fatigue ◽  
Left And Right ◽  
Lifting Task ◽  
Load Mass

In industries, manual lifting is commonly practiced even though mechanized material handling equipment are provided. Manual lifting is used to transport or move products and goods to a desired place.Improper lifting techniquescontribute to muscle fatigue and low back pain that can lead to work efficiency and low productivity.The objective of this study were to analyze muscle activity in the left and right Erector Spinae, and left and right Biceps Brachii of five female subjects while performing manual lifting taskwithdifferent load mass, lifting height and twist angle.The muscle activitywere measured and analyzed using surface electromyography (sEMG).This study found that the right Biceps Brachii, right and left Erector Spinae experienced fatigue while performingasymmetric lifting (twist angle = 90°) at lifting height of 75 cm and 140 cm with load mass of 5 kg and 10 kg. Meanwhile, the left Biceps Brachii experienced fatigue when the lifting task was set at lifting height of 75 cm, load mass of 5 kg and twist angle of 90°.The load mass and lifting height has a significant influence to Mean Power Frequency (MPF) for left Biceps Brachii, left and right Erector Spinae. This study concluded that reducing the load mass can increase the muscles performance which can extend the transition-to-fatigue stage in the left and right Biceps Brachii and Erector Spinae.

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.

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Information Gain ◽  
Multifractal Spectrum ◽  
Singularity Spectrum ◽  
K Nearest Neighbor ◽  
Dynamic Contraction ◽  
Semg Signals

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

Surface Electromyography-Based Muscle Fatigue Analysis Using Binary and Weighted Visibility Graph Features

2020 ◽  
pp. 2150016
Author(s):  
Navaneethakrishna Makaram ◽  
P. A. Karthick ◽  
Venugopal Gopinath ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Sports Medicine ◽  
Biceps Brachii ◽  
Information Gain ◽  
Graph Transformation ◽  
Visibility Graph ◽  
Invasive Technique ◽  
Learning Machines ◽  
Significant Difference

Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with [Formula: see text] [Formula: see text]. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

A validated approach for collecting fine-wire electromyographic recordings in four canine shoulder muscles during highly dynamic tasks

2015 ◽  
Vol 11 (2) ◽  
pp. 65-74 ◽  
Author(s):  
K.L. Cullen ◽  
J.P. Dickey ◽  
S.H.M. Brown ◽  
S.G. Nykamp ◽  
L.R. Bent ◽  
...  
Keyword(s):  
Data Collection ◽  
Muscle Activation ◽  
Biceps Brachii ◽  
Electrode Placement ◽  
Electromyographic Activity ◽  
Anatomical Landmarks ◽  
Triceps Brachii ◽  
Performance Tasks ◽  
Fine Wire ◽  
Shoulder Muscles

This study investigated the feasibility of obtaining ultrasound-guided intramuscular fine-wire electromyographic (fEMG) recordings from four canine shoulder muscles during highly dynamic activities. Four cadaveric canines were utilised to confirm the appropriate anatomical landmarks and the use of real time ultrasound guidance for electrode placement for four shoulder muscles: Biceps Brachii (BB), Supraspinatus (SP), Infraspinatus (IF), and Triceps Brachii – Long Head (TBLH). Electromyographic activity of the left BB, S P, IF, and TBLH was then recorded in two research dogs while walking and trotting to refine the data collection procedures. Finally, the full experimental protocol was piloted with two client-owned, specially-trained agility dogs, confirming the feasibility of collecting fEMG recordings while performing dynamic, highly-specific agility-related tasks and verifying our EMG amplitude normalisation protocol to enable comparisons across muscles and performance tasks. We present specific guidelines regarding the placement of fEMG electrodes and data collection/normalisation procedures to enable investigations of muscle activation during dynamic activities.

Intra- and Intersession Reliability of Surface Electromyography on Muscles Actuating the Forearm During Maximum Voluntary Contractions

2016 ◽  
Vol 32 (6) ◽  
pp. 558-570 ◽  
Author(s):  
Guillaume Gaudet ◽  
Maxime Raison ◽  
Fabien Dal Maso ◽  
Sofiane Achiche ◽  
Mickael Begon
Keyword(s):  
Surface Electromyography ◽  
Biceps Brachii ◽  
Intraclass Correlation ◽  
Isometric Contractions ◽  
Triceps Brachii ◽  
Pronator Quadratus ◽  
Pronator Teres ◽  
Flexion Extension ◽  
Long Head ◽  
Voluntary Contractions

The aim of this study is to determine the intra- and intersession reliability of nonnormalized surface electromyography (sEMG) on the muscles actuating the forearm during maximum voluntary isometric contractions (MVIC). A subobjective of this study is to determine the intra- and intersession reliability of forearm MVIC force or torque, which is a prerequisite to assess sEMG reliability. Eighteen healthy adults participated at 4 different times: baseline, 1-h post, 6-h post, and 24-h post. They performed 3 MVIC trials of forearm flexion, extension, pronation, and supination. sEMG of the biceps brachii short head, brachialis, brachioradialis, triceps brachii long head, pronator teres, and pronator quadratus were measured. The intraclass correlation coefficient (ICC) on MVIC ranged from 0.36 to 0.99. Reliability was excellent for flexion, extension, and supination MVIC for both intra- and intersession. The ICC on sEMG ranged from 0.58 to 0.99. sEMG reliability was excellent for brachialis, brachioradialis, and pronator quadratus, and good to excellent for triceps brachii, biceps brachii, and pronator teres. This study shows that performing 3 MVICs is sufficient to obtain highly reliable maximal sEMG over 24 h for the main muscles actuating the forearm. These results confirm the potential of sEMG for muscle motor functional monitoring.

Flexor carpi radialis surface electromyography electrode placement for evoked and voluntary measures

2015 ◽  
Vol 52 (5) ◽  
pp. 818-825 ◽  
Author(s):  
Lara A. Green ◽  
Jessica McGuire ◽  
David A. Gabriel
Keyword(s):  
Surface Electromyography ◽  
Electrode Placement ◽  
Flexor Carpi Radialis
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