Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals

A Laguerre-Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state

With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG-force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre-Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.

Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals

Surface electromyography techniques are widely used in field of motion analysis and ergonomics combining precise muscular activation assessment with low-invasiveness and wearability. The aim of this investigation is to identify the myoelectrical manifestations of fatigue and to compare the effectiveness of sEMG-based quantitative indices for fatigue assessment. The investigated indexes are the ARV and RMS signal amplitudes, the mean frequency, the median frequency, the Dimitrov index, the instantaneous mean frequency and Wavelet distribution-based WIRE51 index. Two different protocols were developed, and the activity of the lateral deltoid and middle trapezius muscles was recorded. The WIRE51 index is found to have the highest sensitivity in the detection of the difference between the repetitions of each exercise for both protocols. Due to the lack of a unified standard for the performance comparison of fatigue indices, a correlation analysis was carried out between the result provided by the indices and the subjective fatigue perception employing the RPE scale.

Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals

This study significantly enhances the accuracy of exercise state identification in wearable devices through improved denoising techniques for sEMG and ECG signals. By adopting an optimized Variational Mode Decomposition (VMD) method, combined with the Improved Sparrow Search Algorithm and Second Generation Wavelet Transform (ISSA-VMD-SWT), and introducing chaos mapping to strengthen the algorithm's initial population, this approach effectively reduces noise while preserving key fatigue-related features. In tests conducted on data from 32 participants, the method achieved accuracy rates of 93.25%, 95.16%, and 93.05% for identifying "Easy," "Transition," and "Tired" exercise states, respectively, showing significant advantages over traditional denoising techniques. These results indicate that the denoising technology developed in this study represents a significant technological advancement for the application of ECG and sEMG fatigue identification technologies in wearable health monitoring devices.

Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.

Analysis and applications of respiratory surface EMG: report of a round table meeting

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of -1.658 × 10-3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: -0.4156 × 10-3) and DispEn (slope: -0.1675 × 10-3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.

Effect of Lactobacillus plantarum PS128 on neuromuscular efficiency after a half-marathon

Introduction: Lactobacillus plantarum PS128 (PS128) could be considered an antioxidant supplement to reduce muscle fatigue and improve exercise capacity recovery after vigorous exercise. Purpose: The purpose of this study is to investigate the effect of PS128 on muscle fatigue and electromyography (EMG) activity after a half-marathon (HM). Methods: The experimental design used a repeated-measures design with a double-blind approach. The participants either took two capsules of PS128 for 4 weeks as the PS128 group (PSG, n = 8) or took two capsules of a placebo for 4 weeks as the placebo group (PLG, n = 8) to ensure counterbalancing. The time points of the maximal voluntary isometric contraction (MVIC) and EMG activity test were set before probiotics were taken (baseline), 48 h before HM (Pre), and immediately at 0 h, 3 h, 24 h, 48 h, 72 h, and 96 h after HM. Results: EMG activity included median power frequency (MDF), integrated EMG (iEMG), and neuromuscular efficiency (peak torque/iEMG). The MVICs of knee extensors, analyzed by using an isokinetic dynamometer, showed a decrease from the Pre to 0 h (p = 0.0001), 3 h (p < 0.0001), 24 h (p < 0.0001), 48 h (p < 0.0001), 72 h (p = 0.0002), and 96 h (p = 0.0408) time points in the PLG. Sidak's multiple comparisons tests showed that the PLG was significantly lower than the PSG at 0 h (p = 0.0173), 3 h (p < 0.0001), 24 h (p < 0.0001), 48 h (p < 0.0001), 72 h (p < 0.0001), and 96 h (p = 0.0004) time points. The MDF of vastus medialis oblique (VMO) in the PLG was significantly decreased 24 h after HM and significantly lower than that in the PSG at all times points after HM. The iEMG of VMO in the PLG was significantly decreased 48 h after HM and significantly lower than that in the PSG at 0 h, 3 h, 24 h, 48 h, and 72 h after HM. Conclusion: The PS128 supplementation may prevent the decrease in MDF, iEMG, and peak torque after vigorous exercise. Recreational runners may consider implementing a probiotic supplementation regimen as a potential strategy to mitigate muscle fatigue following HM.

The Use of Kinematic Features in Evaluating Upper Limb Motor Function Learning Progress Based on Machine Learning

Evaluating progress throughout a patient's rehabilitation process helps choose effective treatment and formulate personalised and evidence-based rehabilitation interventions. The evaluation process is difficult due to the limitations of current clinical assessments. They lack the ability to reflect sensitive changes continuously throughout the rehabilitation process. Kinematic features have been extracted from individual's movement to address this problem due to their sensitivity and continuity. However, choosing appropriate kinematic features for rehabilitation evaluation has always been challenging. This paper exploits the application of kinematic features to classify movement patterns and movement qualities. 12 kinematic features were firstly extracted from a 7-segment triangle pattern of motion to monitor the learning progress with more numbers of drawing attempts. A statistical analysis was then conducted to compare the selected kinematic features with the clinically validated normalised jerk. Two supervised machine learning models were finally developed to classify movement patterns and movement qualities based on the selected kinematic features. The study was based on data recorded from 14 participants using a single position sensor. 6 kinematic features were able to reflect sensitive changes during the experiment and all kinematic features contributed to the classification tasks. Consistent with the literature, the results indicated that features based on movement velocity were the most beneficial in the classification tasks.

Usefulness of Surface Electromyography Complexity Analyses to Assess the Effects of Warm-Up and Stretching during Maximal and Sub-Maximal Hamstring Contractions: A Cross-Over, Randomized, Single-Blind Trial

This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up procedures that included stretching exercises. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up.

Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field

Muscle fatigue analysis is important in the diagnosis of neuromuscular diseases. Analysis of surface electromyography (sEMG) signals by non-linear probabilistic approach is useful in studying their transitions and thus the neuromuscular system. In this study, a method to visualize sEMG signals using Markov transition field (MTF) under fatigue conditions is proposed. sEMG signals are acquired from 45 healthy participants during biceps curl exercise. They are filtered and divided into ten equal segments. Markov transition matrix is constructed and corresponding MTF image is generated. The average self-transition probability is extracted and compared for both non-fatigue and fatigue segments. It is observed that the extracted feature shows high statistical significance with p value less than 0.001. The increase in average self-transition probability under fatigue condition correlates with the reduction in the degree of signal complexity. Thus, encoding of sEMG signals to images is helpful in analyzing the complexity of the neuromuscular system. Clinical Relevance- This approach may be helpful in analyzing muscle fatigue related with various myoneural conditions.

A Dynamic Submaximal Fatigue Protocol Alters Wrist Biomechanical Properties and Proprioception

Fatigue is a temporary condition that arises as a result of intense and/or prolonged use of muscles and can affect skilled human performance. Therefore, the quantitative analysis of these effects is a topic of crucial interest in both ergonomics and clinical settings. This study introduced a novel protocol, based on robotic techniques, to quantitatively assess the effects of fatigue on the human wrist joint. A wrist manipulandum was used for two concurrent purposes: (1) implementing the fatigue task and (2) assessing the functional changes both before and at four time points after the end of the fatigue task. Fourteen participants completed the experimental protocol, which included the fatigue task and assessment sessions over 2 days. Specifically, the assessments performed are related to the following indicators: (1) isometric forces, (2) biomechanical properties of the wrist, (3) position sense, and (4) stretch reflexes of the muscles involved. The proposed fatigue task was a short-term, submaximal and dynamic wrist flexion/extension task designed with a torque opposing wrist flexion. A novel task termination criterion was employed and based on a percentage decrease in the mean frequency of muscles measured using surface electromyography. The muscle fatigue analysis demonstrated a change in mean frequency for both the wrist flexors and extensors, however, only the isometric flexion force decreased 4 min after the end of the task. At the same time point, wrist position sense was significantly improved and stiffness was the lowest. Viscosity presented different behaviors depending on the direction evaluated. At the end of the experiment (about 12 min after the end of the fatigue task), wrist position sense recovered to pre-fatigue values, while biomechanical properties did not return to their pre-fatigue values. Due to the wide variety of fatigue tasks proposed in the literature, it has been difficult to define a complete framework that presents the dynamic of fatigue-related changes in different components associated with wrist function. This work enables us to discuss the possible causes and the mutual relationship of the changes detected after the same task.

Evaluating the impact of writing surface and configuration on muscle activation level during a handwriting task: An exploratory study

BACKGROUND: Tablets are ubiquitous in workplaces and schools. However, there have been limited studies investigating the effect tablets have on the body during digital writing activities. OBJECTIVE: This study investigated the biomechanical impact of writing interface design (paper, whiteboard, and tablet) and orientation (horizontal, 45°, and vertical) on tablet users. METHODS: Fourteen adults (7 male, 7 female) participated in a study during which they performed simple writing tasks. Surface electromyography (sEMG) sensors were used to measure upper extremity muscle activation. RESULTS: Results indicate that the effects of writing surface type were most pronounced in forearm muscle activation. Specifically, in the extensor carpi radialis (ECR), where muscle activity was lower on the tablet PC surface. The effects of writing configuration were prominent in the shoulder and forearm. The activation of the flexor carpi ulnaris (FCU) and trapezius muscles was significantly lower in the 45° configuration. An exception to the efficacy of this configuration was the anterior deltoid muscle, which exhibited the lowest muscle activity in the horizontal orientation. CONCLUSIONS: Tablet surface and the 45° configuration resulted in the lowest muscle activation levels. Future studies should include longer experiment duration to investigate the effects of continuous writing.

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training.

Effects of various walking intensities on leg muscle fatigue and plantar pressure distributions

Background

Physical activity may benefit health and reduce risk for chronic complications in normal and people with diabetes and peripheral vascular diseases. However, it is unclear whether leg muscle fatigue after weight-bearing physical activities, such as brisk walking, may increase risk for plantar tissue injury. In the literature, there is no evidence on the effect of muscle fatigue on plantar pressure after various walking intensities. The objectives of this study were to investigate the effects of various walking intensities on leg muscle fatigue and plantar pressure patterns.

Methods

A 3 × 2 factorial design, including 3 walking speeds (1.8 (slow and normal walking), 3.6 (brisk walking), and 5.4 (slow running) mph) and 2 walking durations (10 and 20 min) for a total of 6 walking intensities, was tested in 12 healthy participants in 3 consecutive weeks. The median frequency and complexity of electromyographic (EMG) signals of tibialis anterior (TA) and gastrocnemius medialis (GM) were used to quantify muscle fatigue. Fourier transform was used to compute the median frequency and multiscale entropy was used to calculate complexity of EMG signals. Peak plantar pressure (PPP) values at the 4 plantar regions (big toe, first metatarsal head, second metatarsal head, and heel) were calculated.

Results

Two-way ANOVA showed that the walking speed (at 1.8, 3.6, 5.4 mph) significantly affected leg muscle fatigue, and the duration factor (at 10 and 20 min) did not. The one-way ANOVA showed that there were four significant pairwise differences of the median frequency of TA, including walking speed of 1.8 and 3.6 mph (185.7 ± 6.1 vs. 164.9 ± 3.0 Hz, P = 0.006) and 1.8 and 5.4 mph (185.7 ± 6.1 vs. 164.5 ± 5.5 Hz, P = 0.006) for the 10-min duration; and walking speed of 1.8 and 3.6 mph (180.0 ± 5.9 vs. 163.1 ± 4.4 Hz, P = 0.024) and 1.8 and 5.4 mph (180.0 ± 5.9 vs. 162.8 ± 4.9 Hz, P = 0.023) for the 20-min duration. The complexity of TA showed a similar trend with the median frequency of TA. The median frequency of TA has a significant negative correlation with PPP on the big toe ( r = -0.954, P = 0.003) and the first metatarsal head ( r = -0.896, P = 0.016).

Conclusions

This study demonstrated that brisk walking and slow running speeds (3.6 and 5.4 mph) cause an increase in muscle fatigue of TA compared to slow walking speed (1.8 mph); and the increased muscle fatigue is significantly related to a higher PPP.

Effects of Various Physical Interventions on Reducing Neuromuscular Fatigue Assessed by Electromyography: A Systematic Review and Meta-Analysis

Introduction: Various interventions have been applied to improve recovery from muscle fatigue based on evidence from subjective outcomes, such as perceived fatigue and soreness, which may partly contribute to conflicting results of reducing muscle fatigue. There is a need to assess the effectiveness of various intervention on reducing neuromuscular fatigue assessed by a quantitative outcome, such as electromyography (EMG). The objective of this review and meta-analysis was to evaluate the effectiveness of different interventions and intervention timing for reducing fatigue rates during exercise. Methods: The literature was searched from the earliest record to March 2021. Eighteen studies with a total of 87 data points involving 281 participants and seven types of interventions [i.e., active recovery (AR), compression, cooling, electrical stimulation (ES), light-emitting diode therapy (LEDT), massage, and stretching] were included in this meta-analysis. Results: The results showed that compression (SMD = 0.28; 95% CI = -0.00 to 0.56; p = 0.05; I ² = 58%) and LEDT (SMD = 0.49; 95% CI = 0.11 to 0.88; p = 0.01; I ² = 52%) have a significant recovery effect on reducing muscle fatigue. Additionally, compression, AR, and cooling have a significant effect on reducing muscle fatigue when conducted during exercise, whereas a non-effective trend when applied after exercise. Discussion: This meta-analysis suggests that compression and LEDT have a significant effect on reducing muscle fatigue. The results also suggest that there is a significant effect or an effective trend on reducing muscle fatigue when compression, AR, cooling, and ES are applied during exercise, but not after exercise.

Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection-A Simulation Study

This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, "cleaned" EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.

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

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 (E_ms) algorithm. When exercise was performed immediately or 24 h after sham control, E_ms of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, E_ms 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, E_ms 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 E_ms 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.

Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

Cupping therapy has been popular in elite athletes in recent years. However, the effect of cupping therapy on reducing muscle fatigue has not been investigated. The purpose of this study was to investigate the immediate and delayed effects of cupping therapy on reducing biceps brachii fatigue during biceps curls. Twelve healthy untrained participants were recruited for this repeated-measures study. Cupping therapy (–300 mmHg pressure for 5 min) and sham control (no negative pressure for 5 min) were applied after biceps fatigue induced by performing repeated biceps curls at 75% of the 10 repetitions of maximum of the non-dominant hand. Surface electromyography (EMG) with spectral analyses [mean frequency (MNF), median frequency (MDF), and spectral moments ratio (SMR)] were used to assess muscle fatigue during the fatigue task. EMG signals during the first 10 repetitions and the last 10 repetitions of biceps curls were used to assess neuromuscular fatigue. There were significant decreases in MNF and MDF and a significant increase in SMR immediately and 24 h after the sham control (no intervention). When comparing the MNF, MDF, and SMR after cupping therapy to the sham control, there was no significant immediate effect on reducing muscle fatigue. However, there was a significant delayed effect on improving recovery following fatigue for the cupping therapy compared to the sham control (MNF changes: sham 0.87 ± 0.02 vs. cupping 0.91 ± 0.02, p < 0.05; MDF changes sham: 0.85 ± 0.03 vs. cupping: 0.91 ± 0.02, p < 0.05; SMR changes: sham 1.89 ± 0.15 vs. cupping 1.58 ± 0.13, p < 0.05). The findings of this study demonstrate that there is a time effect of cupping therapy for reducing muscle fatigue. Cupping therapy is effective on reducing biceps brachii muscle fatigue after 24 h.

Which subject-related variables contribute to movement variability during a simulated repetitive and standardised occupational task? Recurrence quantification analysis of surface electromyographic signals

Movement variability is a component of human movement. This study applied recurrence quantification analysis (RQA) on electromyographic signals to determine the effects of two types of variables on movement variability during a short, simulated repetitive and standardised occupational clip-fitting task. The electrical activity of six muscles in the dominant upper limb was recorded in 21 participants. Variables related to the task performance (insertion force and movements performed when fitting clips) affected RQA measures: recurrence rate (RR), percentage of determinism (DET) and diagonal line length entropy (ENT). Variables related to participant's characteristics (sex, age, and BMI) affected only DET and ENT. A constrasting variability was observed such as a high-DET value combined with a high-ENT value and inversely. Variables affected mainly the recurrences organisation of the more distal muscles. Even if movement variability is complex, it should be considered by ergonomists and work place designers to better understanding of operators' movements. Practitioner summary: It is essential to consider the complexity of operators' movement variability to understand their activities. Based on intrinsic movement variability knowledge, ergonomists and work place designers will be able to modulate the movement variability by acting on workstation designs and occupational organisation with the aim of preserving operators' health. Abbreviations: RR: recurrence rate; DET: percentage of determinism; ENT: diagonal line length entropy; BMI: body mass index; FDS: flexor digitorum superficialis; EXT: extensor digitorum communis; BIC: biceps brachii; TRI: triceps brachii; DEL: deltoideus anterior; TRA: trapezius pars descendens; F: female; M: male; S: supinated; P: pronated; CM: continuous movement; DM: discontinuous movement.

Human motor function estimation based on EMG signal fractal dimension standard deviation

Wearable robots must adjust the assist mode/intensity according to human motion during the motion assistance process. By decoding the surface electromyography (sEMG) signal, the standard deviation of the fractal dimension is used as a characteristic index of muscle contraction-relaxation ability, and explore the feasibility of using the standard deviation of the fractal dimension to estimate the human motor function and thus provide a basis for decision-making for the flexible control of wearable robots. First, the sEMG signals of several subjects with different motor functions were collected and their time-domain and frequency-domain features were extracted. The experimental results for one hour of walking showed that the time-domain and frequency-domain feature values increased with muscle fatigue. The trend has little to do with the inherent motor function of the human body; Second, due to the strong nonlinearity, time-varying, and strong complexity of the sEMG signal, the fractal dimension nonlinear method is used to characterize the complexity of the EMG signal that is closely related to muscle function. Besides, theoretical and experimental studies have been conducted to clarify the feasibility of the complexity of fractal dimension representation and to provide theoretical support for the further use of the standard deviation of fractal dimension to estimate human motor function. The experimental results of continuous walking for one hour show that, macroscopically, the fractal dimension of each muscle of the individual subject does not change significantly with walking time, which shows that the fractal dimension has nothing to do with exercise time and muscle fatigue; On the microscopic level, the value of the fractal dimension changes when the subject’s muscles contract and relax. Subjects with strong motor function have smaller fractal dimensions when their muscles contract than subjects with weaker motor function, and the opposite happens when their muscles relax, and it can be seen that there is a positive correlation between the difference in the fractal dimension during muscle contraction and relaxation and the muscle contraction-relaxation ability and the human body’s inherent motor function. The test results verify the feasibility of using the standard deviation of fractal dimension to estimate the intrinsic motor function of the human body.

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

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.

New joint analysis of electromyography spectrum and amplitude-based methods towards real-time muscular fatigue evaluation during a simulated surgical procedure: A pilot analysis on the statistical significance

The Joint Analysis of Spectral and Amplitude (JASA) method offers a valuable tool for the indication of four muscle conditions in occupational field studies. It has both advantages of an easy computation and an ergonomic display of the results. However, several limitations are pointed out such as the lack of information on the change of the muscle condition all along the completion of task. This paper proposes two evolutions of the JASA method to quantify muscular fatigue in real time for comfort analysis. The evolutionary method focuses on increased time windows; and the sequential method analyses the muscle conditions on constant sliding time lapses. A pilot study is conducted for a minimally-invasive laparoscopic procedure. The outcome results obtained with the original, the evolutionary and the sequential methods are compared. Both new methods provide a complementary analysis of the muscular fatigue over a task and the statistical significance of the condition.

A simple and accessible inkjet platform for ultra-short concept-to-prototype sEMG electrodes production

Inkjet-printing is a well-known technology that has been recently revalued for the production of flexible sensors and biosensors, thank to the use of engineered nanostructured inks. In a previous work, we developed a general-purpose biosensors printing platform that made use of a simple and low-cost consumer printer and allowed to produce customized flexible electrodes with an ultra-short concept-to-prototype time, without requiring any sintering step. In this study we show the preliminary results about the use of such a newly easily-accessible, low-cost inkjet-based platform to produce flexible and fully customizable electrodes for reliable surface electromyographic (sEMG) recordings.

An Optimized Selection Method of Channel Numbers and Electrode Layouts for Hand Motion Recognition

The channel numbers and electrode layouts are usually determined empirically that would reduce robustness when acquiring surface electromyography (EMG) signals for prosthetic hand systems. It is necessary to study how they can be exploited effectively for a more accurate extraction. In response to the problem, an experiment is designed that establishes the relationship between sEMG signals and forearm muscles based on signal-to-noise ratio (SNR). The SNR of sEMG signals in different sampling channels can be calculated and compared, and then the potential contribution of each channel during different hand motions will be evaluated comprehensively. The active muscle regions can be obtained from the established relationship that is a useful reference for feature extraction. Finally, the relations between the computational cost, channel numbers and electrode layouts are explored. The findings of this paper support the idea that the accuracy of pattern recognition will not be affected when reducing the redundant electrodes.

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.

Muscle fatigue assessment during robot-mediated movements

BACKGROUND

Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.

METHODS

40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects' effort throughout the task, the energy consumption was estimated.

RESULTS

From the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.

CONCLUSION

The robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.

The Use of the Anchor System Reduces Postural Sway During Upright Standing Irrespective of Plantar Flexors Muscle Fatigue in Young and Older Adults

Our purpose was to verify the effects of the use of the anchors on postural control after the fatigue of the plantar flexor muscles in young and older adults. They stood barefoot, with their eyes closed in four conditions combining the use of the anchors and the fatigue. When using the anchors, participants held one cable in each hand and kept the cable taut without removing the loads (125 g) from the ground. The fatigue protocol consisted of performing a single series of bilateral plantar flexion movements. The fatigue protocol increased postural sway in both groups. Both groups reduced postural sway with the anchors, but this effect was independent of fatigue. We conclude that the anchors contributed to the reduction of postural sway in young and older adults, but they were unable to compensate for the disturbing effect in postural control created by fatigue of the plantar flexor muscles.