scholarly journals Quantitative Estimation of Forearm Strength and Associated Muscle Fatigue on the Screw Driving Task

2020 ◽  
Author(s):  
Chih-Kun Hsiao ◽  
Yuan-Kun Tu ◽  
Yi-Jung Tsai ◽  
Chun-Yuh Yang ◽  
Chih-Wei Lu
Keyword(s):  
Muscle Fatigue ◽  
Grip Force ◽  
Biceps Brachii ◽  
Quantitative Estimation ◽  
Strength Loss ◽  
Work Related ◽  
Extensor Carpi Ulnaris ◽  
Driving Task ◽  
Screw Driving ◽  
Driving Torque

Abstract Background: Upper extremity musculoskeletal disorders are highly prevalent work-related injuries. The problem is commonly related to tasks that involve forceful exertion and repetitive motion. This study investigated forearm muscular strength and fatigue when performing a screw driving task using the screw driving model.Methods: Ten male and two female adults participated in this study. The pre- and post-fatigue maximum handgrip, driving torque, push force, insertion rate of the screws and corresponding electromyographic responses were measured to assess the muscle strength loss and fatigue of the forearm when driving screws. Results: After screwing, the maximal grip force, maximal driving torque, and maximal push force losses were approximately 32%, 24% and 27%, respectively. The percentage force loss of grip force and driving torque in the brachioradialis and extensor carpi ulnaris was greater than those of the biceps brachii. The percentage of maximum driving torque and push force decreased significantly on the eighth screw compared with the first screw. The insertion rate decreased linearly with the number of inserted screws; however, a significant decrease in the insertion rate of the fourth screw was observed. Conclusion: Muscle fatigue may occur in subjects who are inserting more than four screws. More muscle force loss and a higher risk of fatigue occurred in the brachioradialis and extensor carpi ulnaris. The results of this study can be used to assess the risk of forearm injury and potential for muscle fatigue due to exposure to repetitive driving tasks. Keywords: muscle fatigue, maximum isometric forces, driving torque

Intramuscular and surface electromyogram changes during muscle fatigue

1986 ◽  
Vol 60 (4) ◽  
pp. 1179-1185 ◽  
Author(s):  
T. Moritani ◽  
M. Muro ◽  
A. Nagata
Keyword(s):  
Muscle Fatigue ◽  
Biceps Brachii ◽  
Surface Emg ◽  
Firing Frequency ◽  
Voluntary Contraction ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Surface Electromyogram ◽  
Spike Amplitude ◽  
Frequency Power

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.

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 Analysis of the effects of low-level laser therapy on muscle fatigue of the biceps brachii muscle of healthy individuals and spastic individuals

Medicine ◽  
2019 ◽  
Vol 98 (39) ◽  
pp. e17166 ◽  
Author(s):  
Bruno Procopio da Silva ◽  
Gabriela Aparecida da Silveira Souza ◽  
Alexandre Alves do Nascimento Filho ◽  
Ana Paula Pinto ◽  
Carolina Lobo Guimarães ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Laser Therapy ◽  
Biceps Brachii ◽  
Low Level Laser Therapy ◽  
Biceps Brachii Muscle ◽  
Healthy Individuals ◽  
Low Level ◽  
Low Level Laser ◽  
Level Laser

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.

scholarly journals Assessment of Postural effect on Work Related Musculoskeletal Disorders and Back Muscle Fatigue among the Goldsmiths of India

2017 ◽  
Vol 5 (2) ◽  
pp. 16-22
Author(s):  
Tirthankar Ghosh
Keyword(s):  
Muscle Fatigue ◽  
Musculoskeletal Disorders ◽  
Trunk Muscles ◽  
Questionnaire Study ◽  
Physical Work ◽  
Low Back ◽  
Back Muscle ◽  
Work Related ◽  
Work Related Musculoskeletal Disorders ◽  
Postural Effect

Background: Musculoskeletal disorders (MSD) may be defined as injuries and disorders of the muscles, nerves, tendons, ligaments, joints, cartilage and spinal disc. This may occur due to improper physical work activities or appalling workplace conditions. Awkward or extreme postures are less efficient than posture keep joints near the center of their range of motion.Objective: The objective of the current study was to assessment of postural effect on work related musculoskeletal disorders and back muscle fatigue among the goldsmiths of India.Methods: In this current study, the experiment was performed on 100 male goldsmiths. A detailed questionnaire study on discomfort feeling was done and analysis of body posture by Rapid Upper Limb Assessment was done to evaluate the work stress during their job. Electromyographic activity was collected from ten major trunk muscles sites which consist of right and left muscle.Result: From the analysis, it was revealed that musculoskeletal disorders were the major problem of the goldsmiths. Moreover questionnaire study revealed that most of the workers were affected by pain at Neck (80%), Low back (91%), Wrist (45%), Shoulder (20%). Decreased in RMS and MedF of all the ten major trunk muscles were observed at the end of the every work cycle, which indicating that muscular fatigue was induced by the Gold Smiths tasks.Conclusion: From this study it can be concluded that the goldsmiths are working in awkward and forward bending postures for prolonged period of time with the potential risks of musculoskeletal disorders primarily affecting the low-back and neck region of the body. This can be attributed by the improper design of the workstation.

scholarly journals Mechanical Performance of Metallic Bone Screws Evaluated Using Bone Models

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4836
Author(s):  
Yoshimitsu Okazaki ◽  
Emiko Hayakawa ◽  
Kazumasa Tanahashi ◽  
Jun Mori
Keyword(s):  
Polyurethane Foam ◽  
Mechanical Performance ◽  
Rigid Polyurethane Foam ◽  
Maximum Torque ◽  
Core Diameter ◽  
Bone Screw ◽  
Bone Model ◽  
Pullout Load ◽  
Screw Driving ◽  
Driving Torque

To evaluate mechanical performance properties of various types of cortical bone screw, cancellous bone screw, and locking bolt, we conducted torsional breaking and durability tests, screw driving torque tests into bone models, and screw pullout tests (crosshead speed: 10 mm/min) after driving torque tests. The 2° proof and rupture torques of a screw, which were estimated from torque versus rotational angle curves, increased with increasing core diameter of the screw. The durability limit of metallic screws obtained by four-point bending durability tests increased with increasing core diameter. The compressive, tensile, and shear strengths of the bone models used for the mechanical testing of orthopedic devices increased with increasing density of the bone model. The strength and modulus obtained for solid rigid polyurethane foam (SRPF) and cellular rigid polyurethane foam (CRPF) lay on the same straight line. Among the three strengths, the rate of increase in compressive strength with the increase in density was the highest. The maximum torque obtained by screw driving torque tests for up to 8.3 rotations (3000°) into the bone models tended to increase with increasing core diameter. In particular, the maximum torque increased linearly with increasing effective surface area of the screw, as newly defined in this work. The maximum pullout load increased linearly with increasing number of rotations and mechanical strength of the bone model. Screws with low driving torque and high pullout load were considered to have excellent fixation and are a target for development.

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