Derivation of simple muscle fatigue index for biceps muscle based on surface electromyography temporal characteristics

2014 ◽  
Author(s):  
Mohd Z. Che Hassan ◽  
Puspa I. Khalid ◽  
Nurul A. Kamaruddin ◽  
Nurul A. Ishak ◽  
Mokhtar Harun
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Biceps Muscle ◽  
Fatigue Index ◽  
Temporal Characteristics

scholarly journals Investigation of different stimulation patterns with doublet pulses to reduce muscle fatigue

2019 ◽  
Vol 6 ◽  
pp. 205566831982580 ◽  
Author(s):  
Ruslinda Ruslee ◽  
Jennifer Miller ◽  
Henrik Gollee
Keyword(s):  
Electrical Stimulation ◽  
Muscle Fatigue ◽  
Functional Electrical Stimulation ◽  
Muscle Activation ◽  
Motor Units ◽  
Pulse Interval ◽  
Time Interval ◽  
Fatigue Index ◽  
Significant Difference ◽  
Cord Injury

Introduction: Functional electrical stimulation is a common technique used in the rehabilitation of individuals with a spinal cord injury to produce functional movement of paralysed muscles. However, it is often associated with rapid muscle fatigue which limits its applications. Methods: The objective of this study is to investigate the effects on the onset of fatigue of different multi-electrode patterns of stimulation via multiple pairs of electrodes using doublet pulses: Synchronous stimulation is compared to asynchronous stimulation patterns which are activated sequentially (AsynS) or randomly (AsynR), mimicking voluntary muscle activation by targeting different motor units. We investigated these three different approaches by applying stimulation to the gastrocnemius muscle repeatedly for 10 min (300 ms stimulation followed by 700 ms of no-stimulation) with 40 Hz effective frequency for all protocols and doublet pulses with an inter-pulse-interval of 6 ms. Eleven able-bodied volunteers (28 ± 3 years old) participated in this study. Ultrasound videos were recorded during stimulation to allow evaluation of changes in muscle morphology. The main fatigue indicators we focused on were the normalised fatigue index, fatigue time interval and pre-post twitch–tetanus ratio. Results: The results demonstrate that asynchronous stimulation with doublet pulses gives a higher normalised fatigue index (0.80 ± 0.08 and 0.87 ± 0.08) for AsynS and AsynR, respectively, than synchronous stimulation (0.62 ± 0.06). Furthermore, a longer fatigue time interval for AsynS (302.2 ± 230.9 s) and AsynR (384.4 ± 279.0 s) compared to synchronous stimulation (68.0 ± 30.5 s) indicates that fatigue occurs later during asynchronous stimulation; however, this was only found to be statistically significant for one of two methods used to calculate the group mean. Although no significant difference was found in pre-post twitch–tetanus ratio, there was a trend towards these effects. Conclusion: In this study, we proposed an asynchronous stimulation pattern for the application of functional electrical stimulation and investigated its suitability for reducing muscle fatigue compared to previous methods. The results show that asynchronous multi-electrode stimulation patterns with doublet pulses may improve fatigue resistance in functional electrical stimulation applications in some conditions.

The Use of Surface Electromyography in Biomechanics

1997 ◽  
Vol 13 (2) ◽  
pp. 135-163 ◽  
Author(s):  
Carlo J. De Luca
Keyword(s):  
Surface Electromyography ◽  
International Agreement ◽  
Fatigue Index ◽  
Detection Analysis ◽  
Emg Signal ◽  
Measured Force ◽  
Technical Considerations

This lecture explores the various uses of surface electromyography in the field of biomechanics. Three groups of applications are considered: those involving the activation timing of muscles, the force/EMG signal relationship, and the use of the EMG signal as a fatigue index. Technical considerations for recording the EMG signal with maximal fidelity are reviewed, and a compendium of all known factors that affect the information contained in the EMG signal is presented. Questions are posed to guide the practitioner in the proper use of surface electromyography. Sixteen recommendations are made regarding the proper detection, analysis, and interpretation of the EMG signal and measured force. Sixteen outstanding problems that present the greatest challenges to the advancement of surface electromyography are put forward for consideration. Finally, a plea is made for arriving at an international agreement on procedures commonly used in electromyography and biomechanics.

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.

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.

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