Electromyography Signals Research Articles

Asymmetric Wettability Hydrogel Surfaces for Enduring Electromyographic Monitoring.

Hydrogel electrode interfaces have shown tremendous promise in the acquisition of surface electromyography (EMG) signals. However, the perspiration or moisture environments will trigger the deadhesion between hydrogel electrodes and human skin. Despite the hydrophobic/hydrophilic surfaces can perform the anti-moisture or adhesion respectively, it remains a challenge to integrally form a Janus hydrogel with homogeneous mechanical elasticity and electronic performance. Herein, a surface induction strategy is proposed to approach the hydrophobic/hydrophilic hydrogel surfaces. The hydrophobic interaction between surfactants and molds regulates the distribution of hydrophobic/hydrophilic monomers on the surface. The hydrophobic molds induce a hydrophilic hydrogel surface, while the hydrophilic molds induce a hydrophobic surface. It presents a new phenomenon of reversal wettability inducing and optional hydrogel surfaces. The integral Janus hydrogel can be easily obtained by the hydrophilic molds. Balance of adhesion, elasticity, and conductivity endows the hydrogel electrode patch with durable conformal adhesion and high-fidelity EMG signals even in the sweaty epidermis due to the asymmetric wettability surfaces. This hydrogel performs the quantitative description of muscle strength and accurate fatigue assessment. It offers a reliable candidate for future practical applications in continuous digital healthcare and intelligent human-machine interaction, even the Metaverse.

Barefoot vs shod walking and jogging on the electromyographic activity of the medial and lateral gastrocnemius

Gastrocnemius weakness is associated with Achilles tendinopathies and muscle strains, with the medial gastrocnemius (MG) more commonly injured than the lateral gastrocnemius (LG). Walking and jogging are common in daily activities and sports, and biomechanical differences between shod and barefoot exercise may influence MG and LG activation. Understanding these activation patterns could help optimize training programs for injury prevention and/or rehabilitation. The aim was to compare MG and LG electromyographic activity during walking and jogging, both shod and barefoot. Twenty-nine participants (25.28 ± 4.53 years, 171.31 ± 0.76 cm, 72.68 ± 6.36 kg) completed a warm-up followed by 1 min of walking (80–99 steps/min) and jogging (130–150 steps/min) in both conditions (barefoot and shod, random order). Electromyographic signals were recorded using wearable devices (mDurance Solutions S.L., Granada, Spain; 1024 Hz sampling rate). We measured the root-mean-square (RMS) amplitudes for an entire stride cycle and digitally filtered the signals. For analysis, we normalized electromyographic values to the average peak values obtained during two sprints. We analyzed differences with a repeated-measures analysis of variance. Significant effects of condition (barefoot-shod) and gastrocnemius (MG-LG) were observed (all p ≤ 0.023, ƞp2 = 0.17–0.39), with higher MG activation compared to LG in the barefoot conditions (p = 0.004–0.027, d = 0.72–0.83), and nonsignificant differences between muscles in the shod conditions (p > 0.05). Shod exercise compared to barefoot resulted in lower MG activation (p = 0.001–0.003, d = 0.62–0.63) and non-significant differences in LG activation. These results indicate that barefoot walking and jogging increase MG activation compared to shod conditions, with no differences in LG activation. Additionally, footwear reduces differences between MG and LG.

Electromyographic analysis of bilateral upper trapezius muscles at different levels of work-pace among sewing machine operators

Workers are driven to work faster in the industrial work environment to meet high productivity targets. An increased work pace leads to increased muscle activation. However, the effect of work pace on bilateral upper trapezius muscles during sewing machine operation in an industrial work environment has not been thoroughly investigated in experimental studies. Therefore, this research aims to conduct an experimental study to analyze the bilateral upper trapezius muscle activity of industrial sewing machine operators at different levels of work pace. Thirty subjects (15 males, and 15 females) continuously performed the sewing operation for two hours in an industrial work environment. Experiments were conducted for two levels of work pace i.e. low pace (100% of standard cycle time) and high pace (120% of standard cycle time). Electromyographic signals were recorded from the bilateral upper trapezius muscles. The EMG amplitude (RMS) among the muscles was computed. A statistically significant (p < 0.05) increase in muscle activity was observed with an increased work pace. In this study, right upper trapezius muscle activity increased by 30.4% during high work pace tasks compared to low pace, while the left upper trapezius showed a 24.12% increase. The right upper trapezius showed a mean difference of 0.696 (%MVC), and the left upper trapezius showed 0.399 (%MVC), both indicating greater activity during high-pace tasks. The increase in muscle activity with time indicated the presence of muscle fatigue among sewing machine operators. Furthermore, higher muscular activity was observed among females than males. This research highlights the critical need to balance productivity goals with the health and safety of workers, reducing the risk of muscle fatigue and associated work-related musculoskeletal disorders.

Pelvic floor muscle electrical coupling in chronic pelvic pain: Insights into pathophysiology and botulinum toxin treatment effects

This study aimed to assess the electrical coupling between both pelvic floor muscle (PFM) sides (two-sided coupling) and within individual PFM sides (one-sided coupling) in chronic pelvic pain (CPP) before and after botulinum neurotoxin type A (BoNT/A) treatment. Surface electromyographic (sEMG) signals were recorded from the left and right PFM of 24 patients (P) with CPP before and after being treated with BoNT/A (Weeks 0,8,12,24). Recordings were also made in 24 healthy women (H). PFM two-sided and one-sided coupling was evaluated during contractions by the cross-correlation (CC) and the imaginary part of coherency (iCOH) of their sEMG signals. Significant differences between their values were assessed comparing P(0) vs. P(8,12,24) and H vs. P(0,8,12,24). This study showed that PFM two-sided coupling is similar across groups before treatment, while PFM one-sided coupling on the patients’ most painful side is deranged before and also after BoNT/A treatment: amplitude coupling is lower (<CC) and phase difference is greater (>iCOH) than healthy women’s. This could be justified by altered neuromotor control strategies developed as an adaptation to muscle pain, structural and electrical changes in PFM, and alterations in their innervation pattern, which may influence the onset, perpetuation, or recurrence of CPP after treatment.

Hybrid microstructure-based stretchable biosensors for multi-physiological signal sensing

Wearable biosensors provide continuous, real-time physiological monitoring of biochemical markers in biofluids such as sweat, tears, saliva, and interstitial fluid. However, achieving high stretchability and stable biochemical signal monitoring remains challenging. Here, we propose a hybrid microstructure (HMS) strategy to fabricate highly stretchable multifunctional biosensors capable of detecting sweat electrolyte concentrations, pH levels, and surface electromyography (EMG) signals. By integrating a HMS, stable conductivity under large strains is ensured. Stretching tests up to 5000 cycles demonstrated the electrodes’ stretchable stability and reliability. The high-performance electrodes were used for EMG monitoring on human skin. Additionally, active materials were coated onto the stretchable electrodes to create multifunctional sweat sensors capable of monitoring pH as well as calcium, sodium, and potassium ions (Ca2+, Na+, K+). The electrodes reliably maintained their functionality under 60% strain, providing new insights into the fabrication of stable, highly stretchable biosensors.

Changes in high-frequency neural inputs to muscles during movement cancellation

Objective.Cortical beta (13-30 Hz) and gamma (30-60 Hz) oscillations are prominent in the motor cortex and are known to be transmitted to the muscles despite their limited direct impact on force modulation. However, we currently lack fundamental knowledge about the saliency of these oscillations at spinal level. Here, we developed an experimental approach to examine the modulations in high-frequency inputs to motoneurons under different motor states while maintaining a stable force, thus constraining behaviour.Approach.Specifically, we acquired brain and muscle activity during a 'GO'/'NO-GO' task. In this experiment, the effector muscle for the task (tibialis anterior) was kept tonically active during the trials, while participants (N= 12) reacted to sequences of auditory stimuli by either keeping the contraction unaltered ('NO-GO' trials), or by quickly performing a ballistic contraction ('GO' trials). Motor unit (MU) firing activity was extracted from high-density surface and intramuscular electromyographic signals, and the changes in its spectral contents in the 'NO-GO' trials were analysed.Main results.We observed an increase in beta and low-gamma (30-45 Hz) activity after the 'NO-GO' cue in the MU population activity. These results were in line with the brain activity changes measured with electroencephalography. These increases in power occur without relevant alterations in force, as behaviour was restricted to a stable force contraction.Significance.We show that modulations in motor cortical beta and gamma rhythms are also present in muscles when subjects cancel a prepared ballistic action while holding a stable contraction in a 'GO'/'NO-GO' task. This occurs while force levels produced by the task effector muscle remain largely unaltered. Our results suggest that muscle recordings are informative also about motor states that are not force-control signals. This opens up new potential use cases of peripheral neural interfaces.

Durability Design and Construction Enhancement of Textile Electrode Shape on Analysis of Its Surface Electromyography Signal

Durability Design and Construction Enhancement of Textile Electrode Shape on Analysis of Its Surface Electromyography Signal

An electron-ion dual-conductive electrode enables wearable monitoring of electromyography signals

An electron-ion dual-conductive electrode enables wearable monitoring of electromyography signals

Evaluasi Model Deep Learning pada Pola Dataset Biomedis

This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.

Legendre Polynomial Fitting-Based Permutation Entropy Offers New Insights into the Influence of Fatigue on Surface Electromyography (sEMG) Signal Complexity.

In a recently published work, we introduced local Legendre polynomial fitting-based permutation entropy (LPPE) as a new complexity measure for quantifying disorder or randomness in time series. LPPE benefits from the ordinal pattern (OP) concept and incorporates a natural, aliasing-free multiscaling effect by design. The current work extends our previous study by investigating LPPE's capability to assess fatigue levels using both synthetic and real surface electromyography (sEMG) signals. Real sEMG signals were recorded during biceps brachii fatiguing exercise maintained at 70% of maximal voluntary contraction (MVC) until exhaustion and were divided into four consecutive temporal segments reflecting sequential stages of exhaustion. As fatigue levels rise, LPPE values can increase or decrease significantly depending on the selection of embedding dimensions. Our analysis reveals two key insights. First, using LPPE with limited embedding dimensions shows consistency with the literature. Specifically, fatigue induces a decrease in sEMG complexity measures. This observation is supported by a comparison with the existing multiscale permutation entropy (MPE) variant, that is, the refined composite downsampling (rcDPE). Second, given a fixed OP length, higher embedding dimensions increase LPPE's sensitivity to low-frequency components, which are notably present under fatigue conditions. Consequently, specific higher embedding dimensions appear to enhance the discrimination of fatigue levels. Thus, LPPE, as the only MPE variant that allows a practical exploration of higher embedding dimensions, offers a new perspective on fatigue's impact on sEMG complexity, complementing existing MPE approaches.

Effects of the Acoustic-Visual Indoor Environment on Relieving Mental Stress Based on Facial Electromyography and Micro-Expression Recognition

People working and studying indoors for a long time can easily experience mental fatigue and stress. Virtual natural elements introduced into indoor environments can stimulate the human visual and auditory senses, thus relieving psychological stress. In this study, stress induction was achieved through noise playback, and the recovery effects on psychological stress of three set indoor environments, visual, auditory, and audio-visual, were investigated through changes in subjects’ facial expressions, electromyographic (EMG) signals, and subjective questionnaires. The experiment found that after stress induction through noise, the participants’ stress levels changed significantly. At this time, the subject scored low on the questionnaire, with electromyography readings higher than usual, and micro-expression recognition indicated negative emotions. After the restoration effects under the three working conditions of visual, auditory, and audio-visual combination, the average EMG values during the recovery period decreased from the baseline period (10 min after the subject acclimated to the environment), respectively. The results indicate that all three restoration conditions have the effect of relieving psychological stress, with the stress recovery effects of auditory and audio-visual conditions being superior to visual conditions. This study is of great significance for creating comfortable indoor environments and minimizing psychological pressure on indoor office workers.

Investigating Skeletal Muscle Pump Function Using Thigh EMG, Thigh NIRS, And Stroke Volume Data In The FRAILMatics Physiological Cohort

Abstract Background The pioneering female physiologist Frances Hellebrandt first highlighted the significance of the skeletal muscle pump in circulation in the 1930s. However, the mechanisms underlying the muscle pump's function remain elusive, particularly in older individuals. It is thought that during muscle contraction, increased intramuscular pressure facilitates the expulsion of blood from the muscle's veins, aiding venous return. This contraction-induced pressure gradient assists in pushing blood towards the heart, increasing preload, and subsequently stroke volume (SV). In this study we investigated muscle pump activity using continuous measurements of thigh muscle activity, thigh total haemoglobin concentration (Hb), and cardiac SV. Methods 22 individuals (aged 70.8±5.3 years, 77.3% female) recruited from a Falls and Syncope Unit (St James’s Hospital, Dublin) underwent non-invasive measurements of Hb (NIRS PortaLite, Artinis Medical Systems, The Netherlands), electromyography (EMG) (Shimmer3, Research Ltd, Ireland), and SV (Finapres Nova, Finapres Medical Systems, The Netherlands) during a 10-second thigh squeeze test. Pre-processing involved data quality checks, alignment, and resampling. 60-second regions of interest were extracted, normalised, and processed using MATLAB (R2023a, TheMathWorks, Inc., Natick, MA, USA). Cross-correlation and Granger causality analyses were conducted to explore relationships between signals. Results Thigh Hb and EMG signals lagged 8.4 and 8.5 seconds behind SV, respectively. Granger Causality analysis revealed a significant causal relationship from both Hb and EMG to SV in 68% of participants at measured lags. Conclusion The observed ~8.5-second lag between thigh measurements and SV implies a plausible temporal relationship between muscle activation and venous return, further supported by significant causal relationships found in a substantial portion of participants using Granger causality analysis. These findings underscore the importance of continued research in this area, not only for advancing our understanding of physiological mechanisms but also for informing potential clinical applications aimed at optimising cardiovascular health and function.

Self-Healing and Shear-Stiffening Electrodes for Wearable Biopotential Sensing and Gesture Recognition.

The achievement of flexible skin electrodes for dynamic monitoring of biopotential is one of the challenging issues in flexible electronics due to the interference of large acceleration and heavy sweat that influence the stability of skin-electrode interfaces. This work presents materials and techniques to achieve self-healing and shear-stiffening electrodes and an associated flexible system that can be used for multichannel biopotential measurement on the skin. The electrode that is based on a composite of silver (Ag) flakes, Ag nanowires, and polyborosiloxane offers an electrical conductivity of 9.71 × 104 S/m and a rheological characteristic that ensures stable and fully conformal contact with skin and easy removal under different shear rates. The electrode can maintain its conductivity even after being stretched by more than 60% and becomes self-healed after mechanical damage. The combination of the electrodes with a screen-printed multichannel flexible sensor allows stable monitoring of both static and dynamic electromyography signals, leading to the acquisition of high-quality multilead biopotential signals that can be readily extracted to yield gesture recognition results with over 97.42% accuracy. The conductive self-healing materials and flexible sensors may be utilized in various daily biopotential sensing applications, allowing highly stable dynamic measurement to facilitate artificial intelligence-enabled health condition diagnosis and human-computer interface.

A high-entropy electrode material for electrobiochemical and eletrophysiological signals detection

Electrobiochemical and eletrophysiological signals indicate human physiology, health, and activity performance. It is still a big challenge to detect these biosignals due to the lack of sensing material. Here, we report a high-entropy material for electrobiochemical and eletrophysiological signals sensing. The high-entropy material consists of the conventional transition metallic elements of Fe, Co, Ni, Cu, and Zn. This high-entropy biosignal sensing material is able to detect the concentration of the biomarker of glucose and electrophysiological signals of Electromyography (EMG), Electroencephalography (EEG), Electrocardiography (ECG). The limitation of the glucose detection is 3.3 μM with the sensitivity of 2861 μA mM−1 cm−2. The HEA electrode also presents good linearship to the glucose concentration in PBS. The cell assay and skin assessment results also demonstrate the excellent biocompatibility of this HEA electrode, indicating good feasibility to the real biosamples. The contact impedance of this HEA electrode is lower than the conventional dry electrode, which leads to the good capability in electrophysiological signals detection. This report not only extends the application of high-entropy material in the field of biomedical engineering but also provides a new pathway for electrobiochemical and eletrophysiological signals detection.

Electromyographic and behavioral analysis of horses submitted to medial patellar desmotomy.

The passive stay apparatus works by blocking flexion of the knee and tarsus joints in the horse, preventing muscle fatigue. During ambulation, if this mechanism fails to release, the animal will present upward fixation of the patella, which in severe cases, can only be treated by medial patellar desmotomy (MPD). The objective of this study was to investigate the possible electromyographic and behavioral changes, after the impairment of the passive stay apparatus, in horses who have undergone MPD surgery. Five horses presenting dorsal fixation of the patella underwent electromyographic measurements at preoperatively and immediately postoperatively, 24h, seven, 14 and 21 days after surgery. The samples were collected on a computerized surface electromyograph designed in accordance with the standards of international society of electrophysiology and kinesiology (ISEK). To capture the signals, electrodes positioned on the tensor fasciae lata, vastus lateralis, biceps femoris and semitendinosus muscles were used, bilaterally. These muscles were chosen due to their participation in the passive stay apparatus. The electromyographic signals were collected and processed using a software calibrated to collect data with a sampling frequency of 1000Hz per channel. The collections took place over 60min periods, and the results were obtained from the root means square (RMS) analysis. It was noted that the preoperative average of rest in tripedal support was 62%. On the other hand, change in weight bearing member and rest in three limbs in the postoperative period did not occur. This inability to maintain tripedal support in the postoperative period prevents the horse from resting while standing. Furthermore, there was a gradual increase in the need for post-surgical muscle recruitment, resulting in long periods of decubitus due to muscle fatigue, which is less than ideal for such large animals. Therefore, it was concluded that the animals submitted to medial patellar desmotomy, in this study, presented an inability to maintain tripedal support, in addition to a greater need for recruitment of muscle fibers, evidenced by the gradual increase in the electromyographic tracing and total RMS value of the muscles evaluated.

Force estimation for human–robot interaction using electromyogram signals from varied arm postures

In this paper, a system for force estimation based on surface electromyography signals measured from the eight channels of the Myo armband is presented. We evaluated nineteen regression models to continuously estimate force in three scenario cases to cover the natural movement in two degrees of freedom planer rehabilitation mobile robots. The best estimation model that could overcome the challenge in a variety of different scenarios was determined. Based on the experimental results, the Gaussian process regression model performed best, giving a root mean square error in the overall range of 1.18–1.77 N. Additionally, the results showed that the exponential algorithm outperformed other solutions, significantly reducing the force estimation error.

Data-Driven Stroke Classification Utilizing Electromyographic Muscle Features and Machine Learning Techniques

Background: Predicting a stroke in advance or through early detection of subtle prodromal symptoms is crucial for determining the prognosis of the remaining life. Electromyography (EMG) has the advantage of easy and quick collection of biological data in clinical settings; however, its application in data processing and utilization is somewhat limited. Thus, this study aims to verify how simple signal processing and feature extraction utilize EMG in machine learning (ML)-based prediction models. Methods: EMG data were collected from the legs of 120 healthy individuals and 120 stroke patients during gait. Four statistical features were extracted from 16 EMG signals and trained on seven ML-based models. The accuracy of the validation and test datasets was also examined. Results: The model with the best performance was Random Forest. Among the 16 EMG signals, the average and maximum values of the muscle activities involved in knee extension (i.e., vastus medialis and rectus femoris) contributed significantly to the predictions. Conclusion: The results of this study confirmed that the simple processing and feature extraction of EMG signals effectively contributed to the accuracy of ML-based models. Routine use of EMG data collected in clinical environments is expected to provide benefits in terms of stroke prevention and rehabilitation.

A Novel 5-Chloro-N-Phenyl-1 H-Indole-2-carboxamide Derivative as a Glycogen Phosphorylase Inhibitor: Evaluating the Long-Term Drug Effects on Muscle Function for the First Time.

Compound 1 was previously identified by our team as a glycogen phosphorylase (GP) inhibitor with glucose-lowering activity and demonstrated to have protective effects against myocardial and cerebral ischemia. However, its impact on muscle function has not been clarified. This study is the first to evaluate the long-term effects of GP inhibitors on muscle function and metabolism. After a 28-day administration of Compound 1, we performed assays to assess muscle function and biochemical parameters in rats. We observed reductions in peak holding force, duration, tetanic contraction force, single-contraction force, and electromyographic signals under 20 s and 10 min contraction stimuli. The metabolic analysis showed no significant effects on muscle glycogen, ATP, lactic acid, and uric acid levels at low doses. In contrast, medium to high doses resulted in increased glycogen, decreased ATP, and reduced lactic acid (only at high doses), without affecting uric acid. These findings suggest that Compound 1 may adversely affect muscle function in rats, potentially due to the glycogen inhibition effects of GP inhibitors. This study provides crucial safety data and insights into the long-term effects of GP inhibitors on rat muscles, which will guide future developments and applications.

A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals

A clinical decision support system for diagnosis and severity quantification of lumbosacral radiculopathy using intramuscular electromyography signals

Individuals With Chronic Migraine Present Changes in The Electromyographic Signal Oh The Head and Neck Flexors

Individuals With Chronic Migraine Present Changes in The Electromyographic Signal Oh The Head and Neck Flexors