Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions

Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.

Facial electromyography during exercise using soft electrode array: A feasibility study

The use of wearable sensors for real-time monitoring of exercise-related measures has been extensively studied in recent years (e.g., performance enhancement, optimizing athlete's training, and preventing injuries). Surface electromyography (sEMG), which measures muscle activity, is a widely researched technology in exercise monitoring. However, due to their cumbersome nature, traditional sEMG electrodes are limited. In particular, facial EMG (fEMG) studies in physical training have been limited, with some scarce evidence suggesting that fEMG may be used to monitor exercise-related measurements. Altogether, sEMG recordings from facial muscles in the context of exercise have been examined relatively inadequately. In this feasibility study, we assessed the ability of a new wearable sEMG technology to measure facial muscle activity during exercise. Six young, healthy, and recreationally active participants (5 females), performed an incremental cycling exercise test until exhaustion, while facial sEMG and vastus lateralis (VL) EMG were measured. Facial sEMG signals from both natural expressions and voluntary smiles were successfully recorded. Stable recordings and high-resolution facial muscle activity mapping were achieved during different exercise intensities until exhaustion. Strong correlations were found between VL and multiple facial muscles' activity during voluntary smiles during exercise, with statistically significant coefficients ranging from 0.80 to 0.95 (p<0.05). This study demonstrates the feasibility of monitoring facial muscle activity during exercise, with potential implications for sports medicine and exercise physiology, particularly in monitoring exercise intensity and fatigue.

Characteristics of Gait Event and Muscle Activation Parameters of the Lower Limb on the Affected Side in Patients With Hemiplegia After Stroke: A Pilot Study

Objectives

To confirm the characteristics of gait events and muscle activity in the lower limbs of the affected and unaffected sides in patients with hemiplegia.

Design

Cross-sectional study.

Setting

Motion analysis laboratory of the Wonkwang University Gwangju Hospital.

Participants

Outpatients, diagnosed with ischemic stroke more than 3 months and less than 9 months before participating in the study (N=29; 11 men, 18 women).

Interventions

Not applicable.

Main Outcome Measures

The gait event parameters and time- and frequency-domain electromyogram (EMG) parameters of the lower limbs of the affected and unaffected sides was determined using BTS motion capture with the Delsys Trigno Avanti EMG wireless system.

Results

The swing time, stance phase, swing phase, single support phase, and median power frequency of the gastrocnemius muscle showed a significant difference between the affected and unaffected sides. Using a logistic regression model, the swing phase, single support phase, and median frequency of the gastrocnemius muscle were selected to classify the affected side.

Conclusion

The single support phase of the affected side is shortened to reduce load bearing, which causes a reduction in the stance phase ratio. Unlike gait-event parameters, EMG data of hemiplegic stroke patients are difficult to generalize. Among them, the logistic regression model with some affected side parameters expected to be set as the severity and improvement baseline of the affected side. Additional data collection and generalization of muscle activity is required to improve the classification model.

Enhanced low-threshold motor unit capacity during endurance tasks in patients with spinal muscular atrophy using pyridostigmine

OBJECTIVE

To investigate the electrophysiological basis of pyridostigmine enhancement of endurance performance documented earlier in patients with spinal muscular atrophy (SMA).

METHODS

We recorded surface electromyography (sEMG) in four upper extremity muscles of 31 patients with SMA types 2 and 3 performing endurance shuttle tests (EST) and maximal voluntary contraction (MVC) measurements during a randomized, double blind, cross-over, phase II trial. Linear mixed effect models (LMM) were used to assess the effect of pyridostigmine on (i) time courses of median frequencies and of root mean square (RMS) amplitudes of sEMG signals and (ii) maximal RMS amplitudes during MVC measurements. These sEMG changes over time indicate levels of peripheral muscle fatigue and recruitment of new motor units, respectively.

RESULTS

In comparison to a placebo, patients with SMA using pyridostigmine had fourfold smaller decreases in frequency and twofold smaller increases in amplitudes of sEMG signals in some muscles, recorded during ESTs (p < 0.05). We found no effect of pyridostigmine on MVC RMS amplitudes.

CONCLUSIONS

sEMG parameters indicate enhanced low-threshold (LT) motor unit (MU) function in upper-extremity muscles of patients with SMA treated with pyridostigmine. This may underlie their improved endurance.

SIGNIFICANCE

Our results suggest that enhancing LT MU function may constitute a therapeutic strategy to reduce fatigability in patients with SMA.

Not all Forms of Muscle Hypertonia Worsen With Fatigue: A Pilot Study in Para Swimmers

In hypertonic muscles of patients with upper motor neuron syndrome (UMNS), investigation with surface electromyography (EMG) with the muscle in a shortened position and during passive muscle stretch allows to identify two patterns underlying hypertonia: spasticity and spastic dystonia. We recently observed in Para swimmers that the effect of fatigue on hypertonia can be different from subject to subject. Our goal was, therefore, to understand whether this divergent behavior may depend on the specific EMG pattern underlying hypertonia. We investigated eight UMNS Para swimmers (five men, mean age 23.25 ± 3.28 years), affected by cerebral palsy, who presented muscle hypertonia of knee flexors and extensors. Muscle tone was rated using the Modified Ashworth Scale (MAS). EMG patterns were investigated in rectus femoris (RF) and biceps femoris (BF) before and after two fatiguing motor tasks of increasing intensity. Before the fatiguing tasks, two subjects (#2 and 7) had spasticity and one subject (#5) had spastic dystonia in both RF and BF. Two subjects (#3 and 4) showed spasticity in RF and spastic dystonia in BF, whereas one subject (#1) had spasticity in RF and no EMG activity in BF. The remaining two subjects (#6 and 8) had spastic dystonia in RF and no EMG activity in BF. In all the 16 examined muscles, these EMG patterns persisted after the fatiguing tasks. Spastic dystonia increased (p &lt; 0.05), while spasticity did not change (p &gt; 0.05). MAS scores increased only in the muscles affected by spastic dystonia. Among the phenomena possibly underlying hypertonia, only spastic dystonia is fatigue-dependent. Technical staff and medical classifiers should be aware of this specificity, because, in athletes with spastic dystonia, intense and prolonged motor activity could negatively affect competitive performance, creating a situation of unfairness among Para athletes belonging to the same sports class.

Stacking classifier to improve the classification of shoulder motion in transhumeral amputees

In recent years surface electromyography signals-based machine learning models are rapidly establishing. The efficacy of prosthetic arm growth for transhumeral amputees is aided by efficient classifiers. The paper aims to propose a stacking classifier-based classification system for sEMG shoulder movements. It presents the possibility of various shoulder motions classification of transhumeral amputees. To improve the system performance, adaptive threshold method and wavelet transformation have been applied for features extraction. Six different classifiers Support Vector Machines (SVM), Tree, Random Forest (RF), K-Nearest Neighbour (KNN), AdaBoost and Naïve Bayes (NB) are designed to extract the sEMG data classification accuracy. With cross-validation, the accuracy of RF, Tree and Ada Boost is 97%, 92% and 92% respectively. Stacking classifiers provides an accuracy as 99.4% after combining the best predicted multiple classifiers.

Retentive capacity of power output and linear versus non-linear mapping of power loss in the isotonic muscular endurance test

The limit of dynamic endurance during repetitive contractions has been referred to as the point of muscle fatigue, which can be measured by mechanical and electrophysiological parameters combined with subjective estimates of load tolerance for revealing the human real-world capacity required to work continuously. In this study, an isotonic muscular endurance (IME) testing protocol under a psychophysiological fatigue criterion was developed for measuring the retentive capacity of the power output of lower limb muscles. Additionally, to guide the development of electrophysiological evaluation methods, linear and non-linear techniques for creating surface electromyography (sEMG) models were compared in terms of their ability to estimate muscle fatigue. Forty healthy college-aged males performed three trials of an isometric peak torque test and one trial of an IME test for the plantar flexors and knee and hip extensors. Meanwhile, sEMG activity was recorded from the medial gastrocnemius, lateral gastrocnemius, vastus medialis, rectus femoris, vastus lateralis, gluteus maximus, and biceps femoris of the right leg muscles. Linear techniques (amplitude-based parameters, spectral parameters, and instantaneous frequency parameters) and non-linear techniques (a multi-layer perception neural network) were used to predict the time-dependent power output during dynamic contractions. Two mechanical manifestations of muscle fatigue were observed in the IME tests, including power output reduction between the beginning and end of the test and time-dependent progressive power loss. Compared with linear mapping (linear regression) alone or a combination of sEMG variables, non-linear mapping of power loss during dynamic contractions showed significantly higher signal-to-noise ratios and correlation coefficients between the actual and estimated power output. Muscular endurance required in real-world activities can be measured by considering the amount of work produced or the activity duration via the recommended IME testing protocol under a psychophysiological termination criterion. Non-linear mapping techniques provide more powerful mapping of power loss compared with linear mapping in the IME testing protocol.

Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals

BACKGROUND AND OBJECTIVE

Human factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and electromyography (EMG) signals in miners working under the extreme conditions of a plateau environment. To provide insights into models for fatigue classification and recognition based on machine learning, multi-modal feature information fusion and miner fatigue identification based on ECG and EMG signals as physiological indicators were studied.

METHODS

Fifty-five miners were randomly selected as field test subjects, and characteristic signals were extracted from 110 groups of ECG and EMG signals as the basic signals for fatigue analysis. We conducted principal component analysis (PCA) and grey relational analysis (GRA) on the measurement indicators. Support vector machine (SVM), random forest (RF) and extreme gradient boosting (XG-Boost) machine learning models were used for fatigue classification based on multi-modal information fusion. The area under the receiver operating characteristic (ROC) curve and the confusion matrix were used to evaluate the performance of the recognition models.

RESULTS

The ECG and EMG signals showed obvious changes with fatigue. The results of fatigue model identification showed that PCA feature fusion was superior to GRA feature fusion for all three machine learning approaches, and XG-Boost achieved the best performance, with a recognition accuracy of 89.47%, a sensitivity and specificity of 100%, and an AUC of 0.90. The SVM model also showed good recognition performance (89.47% accuracy, AUC=0.89). The worst performance was that of the RF model, with a recognition accuracy of only 78.95%.

CONCLUSIONS

This study shows that the physiological indexes of ECG and EMG exhibit obvious, regular changes with fatigue and that it is feasible to use SVM, RF and XG-Boost models for miner fatigue identification. The PCA fusion technique can improve the identification accuracy more than the GRA method. XG-Boost classification yields the best accuracy and robustness. This study can serve as a reference for clinical research on the identification of human fatigue at high altitudes and for the clinical study of acute mountain sickness and human acclimatization to high altitudes.

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

Continuous, integrated sensors for predicting fatigue during non-repetitive work: demonstration of technique in the operating room

Surface electromyography (sEMG) can monitor muscle activity and potentially predict fatigue in the workplace. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles where sEMG may be influenced by the dynamically changing posture demands. This study proposes a multi-modal approach integrating sEMG with motion sensors and demonstrates the approach in the live surgical work environment. Seventy-two exposures from twelve participants were collected, including self-reported musculoskeletal discomfort, sEMG, and postures. Posture sensors were used to identify time windows where the surgeon was static and in non-demanding positions, and mean power frequencies (MPF) were then calculated during those time windows. In 57 out of 72 exposures (80%), participants experienced an increase in musculoskeletal discomfort. Integrated (multi-modality) measurements showed better performance than single-modality (sEMG) measurements in detecting decreases in MPF, a predictor of fatigue. Based on self-reported musculoskeletal discomfort, sensor-based thresholds for identifying fatigue are proposed for the trapezius and deltoid muscle groups. Practitioner summary Work-related fatigue is one of the intermediate risk factors to musculoskeletal disorders. This article presents an objective integrated approach to identify musculoskeletal fatigue using wearable sensors. The presented approach could be implemented by ergonomists to identify musculoskeletal fatigue more accurately and in a variety of workplaces. Abbreviations: sEMG: surface electromyography; IMU: inertia measurement unit; MPF: mean power frequency; ACGIH: American Conference of Governmental Industrial Hygienists; SAGES: Society of American Gastrointestinal and Endoscopic Surgeons; LD: left deltoid; LT: left trapezius; RD: right deltoid; RT: right trapezius.

Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas

Fatigue-induced human error is a leading cause of accidents. The purpose of this exploratory study in China was to perform field tests to measure fatigue psychophysiological parameters, such as electrocardiography (ECG), electromyography (EMG), pulse, blood pressure, reaction time and vital capacity (VC), in miners in high-altitude and cold areas and to perform multi-feature information fusion and fatigue identification. Forty-five miners were randomly selected as subjects for a field test, and feature signals were extracted from 90 psychophysiological features as basic signals for fatigue analysis. Fatigue sensitivity indices were obtained by Pearson correlation analysis, t-test and receiver operating characteristic (ROC) curve performance evaluation. The ECG time-domain, ECG frequency-domain, EMG, VC, systolic blood pressure (SBP), and pulse were significantly different after miner fatigue. The support vector machine (SVM) and random forest (RF) techniques were used to classify and identify fatigue by information fusion and factor combination. The optimal fatigue classification factors were ECG-FD (CV Accuracy = 85.0%) and EMG (CV Accuracy = 90.0%). The optimal combination of factors was ECG-TD + ECG-FD + EMG (CV accuracy = 80.0%). Furthermore, SVM machine learning had a good recognition effect. This study shows that SVM and RF can effectively identify miner fatigue based on fatigue-related factor combinations. ECG-FD and EMG are the best indicators of fatigue, and the best performance and robustness are obtained with three-factor combination classification. This study on miner fatigue identification provides a reference for research on clinical medicine and the identification of human fatigue under high-altitude, cold and low-oxygen conditions.

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

The purpose of this study was to assess validity, stability and sensitivity, of 4 spectral parameters–median frequency (F_med), mean frequency (F_mean), Dimitrov index (DI), and mean instant frequency (F_mi)–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 < 0.0001) was found for all spectral parameters in all muscles. F_med and F_mean varied between 10 and 25%, DI between 50 and 150%, and F_mi between 5 and 10%. Strong correlation (Pearson r ≥ 0.5) with mechanical fatigue was found for all spectral parameters except for F_mi and it was strongest for F_med and F_mean. From our study, it turns out that F_med and F_mean are more valid and stable parameters to measure fatigue in swimming, while DI is more sensitive.

Motor unit reserve capacity in spinal muscular atrophy during fatiguing endurance performance

OBJECTIVE

To investigate the availability of any motor unit reserve capacity during fatiguing endurance testing in patients with spinal muscular atrophy (SMA).

METHODS

We recorded surface electromyography (sEMG) of various muscles of upper- and lower extremities of 70 patients with SMA types 2-4 and 19 healthy controls performing endurance shuttle tests (ESTs) of arm and legs. We quantitatively evaluated the development of fatigability and motor unit recruitment using time courses of median frequencies and amplitudes of sEMG signals. Linear mixed effect statistical models were used to evaluate group differences in median frequency and normalized amplitude at onset and its time course.

RESULTS

Normalized sEMG amplitudes at onset of upper body ESTs were significantly higher in patients compared to controls, yet submaximal when related to maximal voluntary contractions, and showed an inverse correlation to SMA phenotype. sEMG median frequencies decreased and amplitudes increased in various muscles during execution of ESTs in patients and controls.

CONCLUSIONS

Decreasing median frequencies and increasing amplitudes reveal motor unit reserve capacity in individual SMA patients during ESTs at submaximal performance intensities.

SIGNIFICANCE

Preserving, if not expanding motor unit reserve capacity may present a potential therapeutic target in clinical care to reduce fatigability in individual patients with SMA.

Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (rrm=-0.86) and ratings of perceived exertion (RPE) (rrm=0.87), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.

EFFECT OF AQUATIC EXERCISE AFTER ECCENTRIC CONTRACTION-INDUCED MUSCLE INJURY

ABSTRACT Introduction: Muscle microlesions produced by eccentric contractions (EC) cause changes in strength, endurance, power and neuromuscular activity parameters for an extended period of time. Objectives: To investigate the effect of aquatic exercise after EC-induced muscle injury on strength, endurance, power and neuromuscular activity parameters. Methods: A cross-sectional experimental study with six subjects (age 25 ± 4 years, weight 77 ± 4kg and height of 162 ± 2 cm) with EC-induced muscle injury followed up during a recovery period (48h, 72h and 96h) without intervention (Group 1A) and involving aquatic exercises (Group 1B). Dynamic and isometric strength, muscular endurance, and vertical/horizontal power tests as well as vastus lateralis neuromuscular activity measurements were performed before, immediately after, and during the recovery period. Results: Our results indicate that the intervention in Group 1B, when compared to Group 1A, accelerated the recovery of dynamic (p <0.01) and isometric (p <0.03) strength at 48h and 72h, increased vertical power at 48h (p <0.05) and horizontal power at 48h and 72h (p <0.05), and reduced neuromuscular activity (p <0.05) at 48h and 72h after EC. Conclusions: According to our findings, performing aquatic exercises during the recovery period improves muscle efficiency and accelerates strength, power and neuromuscular activity recovery. Level of evidence l; Randomized clinical trial.

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.

Age-specific differences in the time-frequency representation of surface electromyographic data recorded during a submaximal cyclic back extension exercise: a promising biomarker to detect early signs of sarcopenia

PURPOSE

Motivated by the goal of developing new methods to detect early signs of sarcopenia, we investigated if surface electromyographic (SEMG) data recorded during the performance of cyclic, submaximal back extensions are marked by age-specific differences in their time and frequency characteristics. Furthermore, day-to-day retest reliability of the EMG measures was examined.

METHODS

A total of 86 healthy volunteers used a back dynamometer to perform a series of three maximal voluntary contractions (MVC) consisting of isometric back extensions, followed by an isometric back extension at 80% MVC, and finally 25 slow cyclic back extensions at 50% MVC. SEMG data was recorded bilaterally at L1, L2, and L5 from the iliocostalis lumborum, longissimus, and multifidus muscles, respectively. Tests were repeated two days and six weeks later. A linear mixed-effects model with fixed effects "age, sex, test number" and the random effect "person" was performed to investigate age-specific differences in both the initial value and the time-course (as defined by the slope of the regression line) of the root mean square (RMS-SEMG) values and instantaneous median frequency (IMDF-SEMG) values calculated separately for the shortening and lengthening phases of the exercise cycles. Generalizability Theory was used to examine reliability of the EMG measures.

RESULTS

Back extensor strength was comparable in younger and older adults. The initial value of RMS-SEMG and IMDF-SEMG as well as the RMS-SEMG time-course did not significantly differ between the two age groups. Conversely, the IMDF-SEMG time-course showed more rapid changes in younger than in older individuals. Absolute and relative reliability of the SEMG time-frequency representations were comparable in older and younger individuals with good to excellent relative reliability but variable absolute reliability levels.

CONCLUSIONS

The IMDF-SEMG time-course derived from submaximal, cyclic back extension exercises performed at moderate effort showed significant differences in younger vs. older adults even though back extension strength was found to be comparable in the two age groups. We conclude that the SEMG method proposed in this study has great potential to be used as a biomarker to detect early signs of sarcopenic back muscle function.

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82 ± 0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.

Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study

Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.

A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.

Fatigue Detection during Sit-To-Stand Test Based on Surface Electromyography and Acceleration: A Case Study

The latest studies of the 30-second sit-to-stand (30-STS) test aim to describe it by employing kinematic variables, muscular activity, or fatigue through electromyography (EMG) instead of a number of repetitions. The aim of the present study was to develop a detection system based on acceleration measured using a smartphone to analyze fatigue during the 30-STS test with surface electromyography as the criterion. This case study was carried out on one woman, who performed eight trials. EMG data from the lower limbs and trunk muscles, as well as trunk acceleration were recorded. Both signals from eight trials were preprocessed, being averaged and temporarily aligned. The EMG signal was processed, calculating the spectral centroid (SC) by Discrete Fourier Transform, while the acceleration signal was processed by Discrete Wavelet Transform to calculate its energy percentage. Regarding EMG, fatigue in the vastus medialis of the quadriceps appeared as a decrease in SC, with a descending slope of 12% at second 12, indicating fatigue. However, acceleration analysis showed an increase in the percentage of relative energy, acting like fatigue firing at second 19. This assessed fatigue according to two variables of a different nature. The results will help clinicians to obtain information about fatigue using an accessible and inexpensive device, i.e., as a smartphone.

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.

Using infrared imaging for assessment of muscular activity in the forearm of surgeons in the performance of laparoscopic tasks

Surgeons working in laparoscopic surgery are subjected to hard working conditions because of the poor ergonomic characteristics of the workplace. The improvement in the working conditions requires the use of reliable techniques for the assessment of muscular activity. In this article infrared imaging is used and compared with electromyography for the evaluation of muscle activity in the performance of laparoscopic surgical tasks. Electromyography has been widely used for the evaluation of the electrical activity produced by the muscles in the performance of surgery. On the contrary, infrared imaging is an innovative technique that has not been sufficiently explored. An experimental evaluation was carried out using a thermography camera and recording the infrared images from volunteers in different tests. Pearson's correlation was obtained between the electromyography and thermographic measurements in two stages: Endurance Stage (best value: ρ = 0.8401 with p < 0.01) and Surgical Task (best value: ρ = 0.8309 with p < 0.01). The article demonstrates that infrared imaging is a valuable technique for the evaluation of muscle activity in laparoscopic surgery, and it can be compared with electromyography. The main advantages of infrared imaging are that it allows remote measurement and provides activity information in the whole area of interest. However, drawbacks such as delayed response of the infrared imaging due to thermal conductivity of the skin should be considered. Electromyography only provides information in the location of the electrodes, but it is a real-time response. For these reasons, the techniques complement each other.

Changes in blood lactate and muscle activation in elite rock climbers during a 15-m speed climb

PURPOSE

The purpose of this study was to investigate the changes in blood lactate concentration (BL) and muscle activity patterns during a 15-m speed climbing competition that consisted of ten consecutive climbing actions on a standardized artificial wall in trained rock climbers.

METHODS

Twelve trained rock climbers participated in this study. Surface electromyography (sEMG) and video signals were synchronized and recorded during climbing. The blood lactate was also tested 3 min after completing the climb.

RESULTS

The average climbing time was 8.1 ± 2.1 s for the 15-m speed climb across all subjects, accompanied by a BL of 7.6 ± 1.9 mmol/L. The climbing speed and power firstly increased and then slightly decreased relative to peak value during the 15-m speed climbing. The results showed there was a positive correlation between the BL and the climbing time, r = 0.59, P = 0.043. The sEMG showed the flexor digitorum superficialis (FDS) electric activity was the highest, followed by the biceps brachii (BB) and latissimus dorsi. The instantaneous median frequency of sEMG of FDS and BB significantly decreased during the 15-m speed climbing. All the participants showed the higher sEMG RMS (%) in the terminal phase than that in the initial phase, especially with a greater increase in the left upper limbs. However, the lower limbs muscles presented no significant changes in the sEMG amplitude during climbing.

CONCLUSIONS

The FDS and BB play an important role in completing the 15-m speed climbing. The median frequency of arm EMG decreased more than that of legs, suggesting more fatigue. The blood lactate concentration increases in the current study suggest that a certain amount of glycolysis supplies energy in completing 15-m speed rock climbing. Based on the current data, it is suggested that muscular endurance of FDS and BB muscles in upper limbs should be improved for our climbers in this study.

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Graphical abstract ᅟ.

Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation

BACKGROUND

Surface electromyographic (EMG) recordings collected during the performance of functional evaluations allow clinicians to assess aberrant patterns of muscle activity associated with musculoskeletal disorders. This assessment is typically achieved via visual inspection of the surface EMG data. This approach is time-consuming and leads to accurate results only when the assessment is carried out by an EMG expert.

METHODS

A set of algorithms was developed to automatically evaluate aberrant patterns of muscle activity. EMG recordings collected during the performance of functional evaluations in 62 subjects (22 to 61 years old) were used to develop and characterize the algorithms. Clinical scores were generated via visual inspection by an EMG expert using an ordinal scale capturing the severity of aberrant patterns of muscle activity. The algorithms were used in a case study (i.e. the evaluation of a subject with persistent back pain following instrumented lumbar fusion who underwent lumbar hardware removal) to assess the clinical suitability of the proposed technique.

RESULTS

The EMG-based algorithms produced accurate estimates of the clinical scores. Results were primarily obtained using a linear regression approach. However, when the results were not satisfactory, a regression implementation of a Random Forest was utilized, and the results compared with those obtained using a linear regression approach. The root-mean-square error of the clinical score estimates produced by the algorithms was a small fraction of the ordinal scale used to rate the severity of the aberrant patterns of muscle activity. Regression coefficients and associated 95% confidence intervals showed that the EMG-based estimates fit well the clinical scores generated by the EMG expert. When applied to the clinical case study, the algorithms appeared to capture the characteristics of the muscle activity patterns associated with persistent back pain following instrumented lumbar fusion.

CONCLUSIONS

The proposed approach relies on EMG-based measures to generate accurate estimates of the severity of aberrant patterns of muscle activity. The results obtained in the case study suggest that the proposed technique is suitable to derive clinically-relevant information from EMG data collected during functional evaluations.

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods.

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms

BACKGROUND AND OBJECTIVE

Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals.

METHODS

In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naïve Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification.

RESULTS

The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD- polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA.

CONCLUSIONS

The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD- polynomial kernel based SVM could be used to detect the dynamic muscle fatigue conditions.

A systematic method for isolating, tracking and discriminating time-frequency components of bat echolocation calls

Echolocating bats can rapidly modify frequency modulation (FM) curvatures of their calls when facing challenging echolocation tasks. Frequency parameters, such as start/end/peak frequency, have often been extracted from the time-frequency domain to study the call variation. Even though this kind of signal investigation method reveals important findings, these approaches to analyze bat echolocation calls use bulk parameters, which hide subtleties in the call structure that may be important to the bat. In some cases, calls can have the same start and end frequencies but have different FM curvatures, and subsequently may influence the sensory task performance. In the present study, the authors demonstrate an algorithm using a combination of digital filters, power limited time-frequency information, derivative dynamic time warping, and agglomerative hierarchical clustering to extract and categorize the time-frequency components (TFCs) of 21 calls from Brazilian free-tailed bat (Tadarida brasiliensis) to quantitatively compare FM curvatures. The detailed curvature analysis shows an alternative perspective to look into the TFCs and hence serves as the preliminary step to understand the adaptive call design of bats.

Alterations in Spectral Attributes of Surface Electromyograms after Utilization of a Foot Drop Stimulator during Post-Stroke Gait

BACKGROUND

A foot drop stimulator (FDS) is a rehabilitation intervention that stimulates the common peroneal nerve to facilitate ankle dorsiflexion at the appropriate time during post-stroke hemiplegic gait. Time-frequency analysis (TFA) of non-stationary surface electromyograms (EMG) and spectral variables such as instantaneous mean frequency (IMNF) can provide valuable information on the long-term effects of FDS intervention in terms of changes in the motor unit (MU) recruitment during gait, secondary to improved dorsiflexion.

OBJECTIVE

The aim of this study was to apply a wavelet-based TFA approach to assess the changes in neuromuscular activation of the tibialis anterior (TA), soleus (SOL), and gastrocnemius (GA) muscles after utilization of an FDS during gait post-stroke.

METHODS

Surface EMG were collected bilaterally from the TA, SOL, and GA muscles from six participants (142.9 ± 103.3 months post-stroke) while walking without the FDS at baseline and 6 months post-FDS utilization. Continuous wavelet transform was performed to get the averaged time-frequency distribution of band pass filtered (20-300 Hz) EMGs during multiple walking trials. IMNFs were computed during normalized gait and were averaged during the stance and swing phases. Percent changes in the energies associated with each frequency band of 25 Hz between 25 and 300 Hz were computed and compared between visits.

RESULTS

Averaged time-frequency representations of the affected TA, SOL, and GA EMG show altered spectral attributes post-FDS utilization during normalized gait. The mean IMNF values for the affected TA were significantly lower than the unaffected TA at baseline (p = 0.026) and follow-up (p = 0.038) during normalized stance. The mean IMNF values significantly increased (p = 0.017) for the affected GA at follow-up during normalized swing. The frequency band of 250-275 Hz significantly increased in the energies post-FDS utilization for all muscles.

CONCLUSION

The application of wavelet-based TFA of EMG and outcome measures (IMNF, energy) extracted from the time-frequency distributions suggest alterations in MU recruitment strategies after the use of FDS in individuals with chronic stroke. This further establishes the efficacy of FDS as a rehabilitation intervention that may promote motor recovery in addition to treating the secondary complications of foot drop due to post-stroke hemiplegia.

Electromyographic and Kinematic Analysis of the Flexion-Rotation Trunk Test

García-Vaquero, MP, Ruiz-Pérez, I, Barbado, D, and Vera-Garcia, FJ. Electromyographic and kinematic analysis of the flexion-rotation trunk test. J Strength Cond Res 34(12): 3386-3394, 2020-Although most trunk endurance field protocols are performed in the sagittal or frontal planes, the flexion-rotation trunk (FRT) test combines trunk flexion with rotation, which may be relevant to rotation-related sports. The aim of this study was to describe the trunk and hip muscle activation and fatigue and the range of hip flexion of this test. Twenty-seven physically active males and females performed the FRT test after a period of practice. Electromyographic (EMG) signals were bilaterally collected from the rectus abdominis (RA), internal oblique (IO), and rectus femoris (RF), and hip flexion amplitude was measured using a biaxial electrogoniometer. Because the fast Fourier transform algorithm requires stationary EMG signals, subjects performed a 6-second isometric trunk flexion-rotation repetition just before and just after the test execution (preexecution and postexecution repetitions, respectively). Rectus abdominis showed the highest mean activation levels (approximately 30% maximal voluntary isometric contractions [MVC]) in the preexecution repetition, followed by IO (approximately 20% MVC). Also, the mean power frequency (MPF) significantly decreased from the pre-execution to the postexecution repetition for RA and IO, which shows abdominal muscle fatigue. Although each trunk flexion-rotation repetition involved an average 8-14° hip flexion, the RF activation was lower than 10% MVC, and no significant MPF reduction (i.e., no muscle fatigue) was observed for this muscle. In addition, significant negative correlations were found between the FRT test scores and the normalized EMG amplitudes of RF. Based on these results, the FRT test seems a valid field protocol to assess abdominal muscle endurance in trunk flexion-rotation exertions.

Estimating Neural Control from Concentric vs. Eccentric Surface Electromyographic Representations during Fatiguing, Cyclic Submaximal Back Extension Exercises

Purpose: To investigate the differences in neural control of back muscles activated during the eccentric vs. the concentric portions of a cyclic, submaximal, fatiguing trunk extension exercise via the analysis of amplitude and time-frequency parameters derived from surface electromyographic (SEMG) data.

Methods: Using back dynamometers, 87 healthy volunteers performed three maximum voluntary isometric trunk extensions (MVC's), an isometric trunk extension at 80% MVC, and 25 cyclic, dynamic trunk extensions at 50% MVC. Dynamic testing was performed with the trunk angular displacement ranging from 0° to 40° and the trunk angular velocity set at 20°/s. SEMG data was recorded bilaterally from the iliocostalis lumborum at L1, the longissimus dorsi at L2, and the multifidus muscles at L5. The initial value and slope of the root mean square (RMS-SEMG) and the instantaneous median frequency (IMDF-SEMG) estimates derived from the SEMG recorded during each exercise cycle were used to investigate the differences in MU control marking the eccentric vs. the concentric portions of the exercise.

Results: During the concentric portions of the exercise, the initial RMS-SEMG values were almost twice those observed during the eccentric portions of the exercise. The RMS-SEMG values generally increased during the concentric portions of the exercise while they mostly remained unchanged during the eccentric portions of the exercise with significant differences between contraction types. Neither the initial IMDF-SEMG values nor the time-course of the IMDF-SEMG values significantly differed between the eccentric and the concentric portions of the exercise.

Conclusions: The comparison of the investigated SEMG parameters revealed distinct neural control strategies during the eccentric vs. the concentric portions of the cyclic exercise. We explain these differences by relying upon the principles of orderly recruitment and common drive governing motor unit behavior.

Fadiga dos músculos extensores do punho diminui a força de preensão palmar

RESUMO O objetivo deste estudo foi avaliar os efeitos de um protocolo de fadiga dos músculos extensores de punho na força de preensão e da pinça lateral através da dinamometria e eletromiografia de superfície (EMG). Foram selecionados 40 indivíduos do sexo masculino, divididos em dois grupos: preensão ou pinça lateral. O protocolo de fadiga foi baseado no teste de 1 Repetição Máxima (1-RM), seguido da realização do movimento de extensão de punho repetidas vezes com carga de 75% da 1-RM. Os voluntários realizaram as tarefas de preensão ou pinça lateral associadas à dinamometria. A EMG foi realizada para ambos os grupos, analisando o comportamento, segundo o protocolo, pela frequência mediana (FM) do extensor radial do carpo (ERC), do extensor ulnar do carpo (EUC) e do flexor superficial dos dedos (FD). A dinamometria de preensão ou pinça lateral e a EMG foram realizadas antes e após o protocolo de fadiga para ambos os grupos. O protocolo de fadiga foi eficaz na diminuição da força de preensão palmar (43,5±3,85 kgf inicial e 36,50±5,1 kgf final) e da pinça lateral (10,26±1,01 kgf inicial e 8,54±0,86 kgf final), bem como na diminuição da FM, sugerindo uma condição de fadiga do EUC no grupo preensão. Os achados do presente estudo possibilitam relacionar a fadiga dos extensores de punho à diminuição de força em atividades funcionais, como a preensão, o que pode implicar em disfunções musculoesqueléticas do membro superior.

A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients' impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.

Analysis of concentric and eccentric contractions in biceps brachii muscles using surface electromyography signals and multifractal analysis

Muscle contractions can be categorized into isometric, isotonic (concentric and eccentric) and isokinetic contractions. The eccentric contractions are very effective for promoting muscle hypertrophy and produce larger forces when compared to the concentric or isometric contractions. Surface electromyography signals are widely used for analyzing muscle activities. These signals are nonstationary, nonlinear and exhibit self-similar multifractal behavior. The research on surface electromyography signals using multifractal analysis is not well established for concentric and eccentric contractions. In this study, an attempt has been made to analyze the concentric and eccentric contractions associated with biceps brachii muscles using surface electromyography signals and multifractal detrended moving average algorithm. Surface electromyography signals were recorded from 20 healthy individuals while performing a single curl exercise. The preprocessed signals were divided into concentric and eccentric cycles and in turn divided into phases based on range of motion: lower (0°–90°) and upper (>90°). The segments of surface electromyography signal were subjected to multifractal detrended moving average algorithm, and multifractal features such as strength of multifractality, peak exponent value, maximum exponent and exponent index were extracted in addition to conventional linear features such as root mean square and median frequency. The results show that surface electromyography signals exhibit multifractal behavior in both concentric and eccentric cycles. The mean strength of multifractality increased by 15% in eccentric contraction compared to concentric contraction. The lowest and highest exponent index values are observed in the upper concentric and lower eccentric contractions, respectively. The multifractal features are observed to be helpful in differentiating surface electromyography signals along the range of motion as compared to root mean square and median frequency. It appears that these multifractal features extracted from the concentric and eccentric contractions can be useful in the assessment of surface electromyography signals in sports medicine and training and also in rehabilitation programs.

Effects of short-term fatigue on biomechanical and physiological aspects of double poling in high-level cross-country skiers

The study aim was to evaluate biomechanical and physiological alterations in double poling technique (DP) after a short-term fatiguing exercise. Eight high-level skiers performed a sub-maximal DP trial (20kmh(-1), 1°) before (PRE) and after (POST) a DP test to exhaustion while roller skiing on a treadmill. An integrated analysis of DP technique during PRE and POST included measurement of pole, joint, and centre of mass (COM) kinematics, poling forces, cycle timing, and metabolic parameters. Muscle fatigue in three upper-body muscles was assessed by calculating the Dimitrov' fatigue index (FInms5) of specific electromyographic segments. FInms5 tended to increase in the latissimus dorsi and teres major muscles (P=0.023 and P=0.030, respectively) across consecutive DP cycles, as did blood lactate concentration (P=0.001) and rating of perceived exertion (P=0.005). The changes indicated a state of fatigue during POST and coincided with the reduction in poling force exertion capacity (P=0.020). Pole, joint and COM kinematics did not differ between PRE and POST (P>0.050), whereas recovery phase and cycle times were shorter at POST (P<0.001 and P=0.001, respectively). Short-term fatigue led to a reduction in poling force exertion capacity and cycle time in high-level skiers, without altering body and pole kinematics.

Age and gender related neuromuscular pattern during trunk flexion-extension in chronic low back pain patients

BACKGROUND

The root mean square surface electromyographic activity of lumbar extensor muscles during dynamic trunk flexion and extension from standing has repeatedly been recommended to objectively assess muscle function in chronic low back pain patients. However, literature addressing older patients is sparse. This cross sectional study sought to examine differences in neuromuscular activation between age groups (>60 versus 40-60 versus <40 years) and sexes during a standardized trunk flexion-extension task.

METHODS

A total of 216 patients (62 older, 84 middle-aged, 70 younger) performed maximum trunk extensions followed by trunk flexion extension testing thereby holding static positions at standing, half, and full trunk flexion. The lumbar extensor muscle activity and 3d-accelerometric signals intended to monitor hip and trunk position angles were recorded from the L5 (multifidus) and T4 (semispinalis thoracis) levels. Permutation ANOVA with bootstrapped confidence intervals were performed to examine for age and gender related differences. Ridge-regressions investigated the impact of physical-functional and psychological variables to the half flexion relaxation ratio (i.e. muscle activity at the half divided by that in maximum flexion position).

RESULTS

Maximum back extension torque was slightly but significantly higher in youngest compared to oldest patients if male and females were pooled. Normalized RMS-SEMG revealed highest lumbar extensor muscle activity at standing in the oldest and the female groups. Patients over 60 years showed lowest activity changes from standing to half (increments) and from half to the maximum flexion position (decrements) leading to a significantly lower half flexion relaxation ratio compared to the youngest patients. These oldest patients demonstrated the highest hip and lowest lumbothoracic changes of position angles. Females had higher regional hip and gross trunk ranges of movement compared to males. Lumbothoracic flexion and the muscle activity at standing had a significant impact on the half flexion relaxation ratio.

CONCLUSIONS

The neuromuscular activation pattern and the kinematics in this trunk flexion-extension task involving static half flexion position changed according to age and sex. The test has a good potential to discriminate between impaired and unimpaired neuromuscular regulation of back extensors in cLBP patients, thereby allowing the design of more individualized exercise programs.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Muscular Activity and Fatigue in Lower-Limb and Trunk Muscles during Different Sit-To-Stand Tests

Sit-to-stand (STS) tests measure the ability to get up from a chair, reproducing an important component of daily living activity. As this functional task is essential for human independence, STS performance has been studied in the past decades using several methods, including electromyography. The aim of this study was to measure muscular activity and fatigue during different repetitions and speeds of STS tasks using surface electromyography in lower-limb and trunk muscles. This cross-sectional study recruited 30 healthy young adults. Average muscle activation, percentage of maximum voluntary contraction, muscle involvement in motion and fatigue were measured using surface electrodes placed on the medial gastrocnemius (MG), biceps femoris (BF), vastus medialis of the quadriceps (QM), the abdominal rectus (AR), erector spinae (ES), rectus femoris (RF), soleus (SO) and the tibialis anterior (TA). Five-repetition STS, 10-repetition STS and 30-second STS variants were performed. MG, BF, QM, ES and RF muscles showed differences in muscle activation, while QM, AR and ES muscles showed significant differences in MVC percentage. Also, significant differences in fatigue were found in QM muscle between different STS tests. There was no statistically significant fatigue in the BF, MG and SO muscles of the leg although there appeared to be a trend of increasing fatigue. These results could be useful in describing the functional movements of the STS test used in rehabilitation programs, notwithstanding that they were measured in healthy young subjects.