EMG Signals Research Articles

A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor.

Parkinsonian tremor (PT) and Essential tremor (ET) exist as upper limb tremors in clinical practice. Notably, their types of trembling share similar presentations and overlapping frequencies. To enhance objectivity and efficiency in the diagnosis of these two diseases, there is a pressing need for more objective tremor classification procedures. This study proposes a novel multimodal fusion network based on a cross-attention mechanism (MFCA-Net) to automate the classification of upper limb tremors between PTs and ETs. To this end, 140 patients with PTs and ETs were recruited, and acceleration and surface electromyography (sEMG) signals were collected from the forearm during tremor episodes. To comprehensively capture the global and local features of input signals, a multiscale convolution in MFCA-Net was designed. Furthermore, the cross-attention mechanism was applied to fuse the features of the two input signals. The results demonstrate that the final classification accuracy exceeded 97.18% when MFCA-Net was used. Compared with the single acceleration signal and single sEMG signal inputs, the recognition accuracies increased by 18.91% and 10.04%, respectively. Therefore, the proposed MFCA-Net in this study serves as an objective and potential tool for assisting clinicians in the diagnosis of PT and ET patients.

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals.

A new ICT system coupling electromyography and coma recovery scale-revised to support the diagnostic process in disorders of consciousness

The Coma Recovery Scale-revised (CRS-r) is the gold standard for the behavioral assessment of patients with Disorders of Consciousness (DoCs). However, the misdiagnosis rate is around 40%. For this reason, recent guidelines suggested enhancing the assessment with neurophysiological measures: among these, surface electromyography (sEMG) represents a convenient bedside solution. This work presents the use of the STRIVEfc system, a wearable device that allows CRS-r administration while recording four sEMG signals. STRIVEfc was employed in 40 sessions on 33 DoCs patients and the sEMG was analyzed to look for voluntary and consistent over threshold (OT) muscular activities. Their duration, amplitude, and number were retained and compared between patients in Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), revealing more numerous and significantly longer OTs in the latter group. Lastly, the EMG information was exploited to enrich the behavioral assessment by building the instrumented CRS-r score (ICRS-r). In 9/16 UWS sessions, the ICRS-r score suggested a higher-level functioning, not translated into a behavioral response, compatible with MCS diagnosis. Overall, the use of STRIVEfc allows to reveal hidden muscular patterns not detectable by the clinician, thus improving the characterization of DoCs patient’s functional capabilities and supporting the diagnostic process.

Surface electromyography character of upper limb muscle after open reduction combine with ulnar osteotomy in children with neglected Monteggia fracture

ObjectiveThis study aims to investigate the surface electromyography (sEMG) characteristics of upper limb muscles in children with neglected Monteggia fracture after open reduction of radial head dislocation and ulna osteotomy and bone grafting internal fixation, and to understand the recovery of muscle activity in children after operation, provide reference for clinical rehabilitation.MethodsA retrospective analysis was conducted on sixteen children with neglected Monteggia fracture who underwent ulna osteotomy at our hospital from January 2021 to August 2022. The biceps brachii, triceps brachii, flexor carpi radialis, and extensor carpi ulnaris muscle activities were recorded during grip strength tests, flexion and extension of elbow joint while holding a 1 kg dumbbell, as well as gripping tasks. The root mean square (RMS) values of sEMG signals, co-contraction ratio, and elbow joint function scores were compared between pre- and post-operation periods as well as between the affected side and unaffected side.ResultsThe preoperative maximum grip strength, as well as the average RMS values of flexor carpi radialis and average RMS value of extensor carpi ulnaris on the affected side were significantly lower. After surgery, both the maximum RMS value of biceps brachii and maximum and average RMS value of extensor carpi ulnaris on the affected side remained lower. Prior to surgery, when performing elbow flexion and extension tests while holding a 1 kg dumbbell, both mean RMS values of biceps brachii and flexor carpi radialis on the affected side were smaller. However, after surgery, there was an increase in mean RMS values of biceps brachii on the affected side. Furthermore, postoperative elbow function scores were significantly higher than preoperative scores.ConclusionOpen reduction of radial head dislocation combined with ulna osteotomy and bone grafting can achieve good functional activities in the treatment of neglected Monteggia fractures in children. The EMG activity of the extensor carnosus ulnalis muscle on the affected side related to grip strength was low, and the desired effect was not achieved within the expected time.

Design, Development and Validation of a Novel Mechanical Pain Inducer and the EMG Signal Analysis for the Induced Mechanical Pain

Design, Development and Validation of a Novel Mechanical Pain Inducer and the EMG Signal Analysis for the Induced Mechanical Pain

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG

BackgroundPhysical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.MethodssEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.ResultsStatistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.ConclusionThis study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Enhancing EMG signal classification using convolution neural network optimized with fractional order bat algorithm

Enhancing EMG signal classification using convolution neural network optimized with fractional order bat algorithm

Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches

Surface Electromyography (sEMG) has become an essential tool in various fields, including prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent years, the application of machine learning and deep learning techniques to sEMG signal classification has gained significant interest. This survey provides a detailed exploration of feature extraction methods for sEMG classification, from traditional handcrafted features to learned features. ObjectivesThis review aims to provide a comprehensive overview of feature extraction techniques for sEMG signal classification, focusing on both handcrafted and learned features. It seeks to advance research by offering a deeper understanding of fundamental concepts in sEMG signal analysis, along with comparisons and summaries of state-of-the-art approaches. Materials and MethodsThe survey covers various feature extraction techniques used for sEMG classification, including signal acquisition, preprocessing, and the application of conventional machine learning and deep learning classifiers. It offers taxonomies, definitions, and performance comparisons, equipping researchers with a broad understanding of current methodologies. ResultsHandcrafted features combined with traditional machine learning classifiers have demonstrated strong performance, especially with smaller datasets. However, deep learning techniques have shown superior results in many applications, despite challenges related to data availability and model interpretability. The survey highlights key findings regarding the performance of both approaches. ConclusionThis study bridges the gap between traditional and learned feature extraction techniques for sEMG signal classification. It provides a valuable resource for researchers and practitioners, offering insights that can guide future advancements. Key areas for future research include addressing data scarcity in deep learning and improving model interpretability for clinical applications.

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning From sEMG.

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF achieved an improved average prediction accuracy of 96.60%, outperforming seven benchmark models. Even with an extended 500ms prediction horizon, the accuracy only marginally decreased to 93.22%. The averaged stable prediction times for detecting next upcoming transitions spanned from 31.47 to 371.58 ms across the 100-500 ms time advances. Although the prediction accuracy of the trained Deep-STF initially dropped to 71.12% when tested on four new terrains, it achieved a satisfactory accuracy of 92.51% after fine-tuning with just 5 trials and further improved to 96.27% with 15 calibration trials. These results demonstrate the remarkable prediction ability and adaptability of Deep-STF, showing great potential for integration with walking-assistive devices and leading to smoother, more intuitive user interactions.

Comparison of SEMG-Based Hand Gesture Classifiers

Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption of non-invasive technologies, such as surface electromyography (sEMG), becomes paramount. This study systematically assesses the isolated influence of classification algorithms within hand gesture recognition (HGR) systems using sEMG data and dynamic time warping (DTW) based features. This approach effectively handles temporal variations in sEMG signals by leveraging DTW, ensuring input features are invariant to gesture speed. Six supervised learning classifiers were evaluated: the multilayer perceptron, support vector machine, logistic regression, linear discriminant analysis, k-nearest neighbors, and decision tree. Cross-validation was employed to fine-tune the segmentation hyperparameters, significantly improving results. To ensure reproducibility, the source code has been made available, the proposed system design has been detailed, and the evaluation protocols have been described. Our findings indicate that logistic regression outperformed other classifiers in this setup, achieving 95.2% accuracy in classifying six hand movements from ten healthy individuals, representing a 1.6% improvement over the best previously reported performance using the same publicly available dataset. Future research will assess the proposed HGR system’s generalization capability on larger datasets suitable for training more complex classifiers, including deep learning models.

Characteristics of Human Postactivation Effect of Skeletal Muscles Using Spectral and Non-Linear Parameters of the Surface Electromyogram

Postactivation effect (PAE, postactivation phenomenon) is a specific type of involuntary muscle tone (tonic automatism) which is generated in the “tonogenic” structures of the brain, presumably without the “sensory copy” and “motor command” mechanisms. In this regard, the electromyogram (EMG) signal of PAE may have a simpler temporal signal structure compared to PAE-inducing voluntary activity. The purpose of this work is to characterize the temporal structure and complexity of surface EMG (sEMG) of the human deltoid and biceps brachii muscles using fractal (D) and correlation dimensions (Dc). It was found that in deltoid muscles the value of D was 1.78–1.81 both during PAE and voluntary effort (p 0.05). Dc (approximately 4.0–4.2) also did not differ between PAE and voluntary effort, although the average frequency of the sEMG spectrum during PAE was 15–16 Hz (p 0.05) higher compared to voluntary effort. In biceps brachii muscles, the D value was 1.8 during PAE and 1.62 during voluntary effort (p 0.05). Dc values did not differ between PAE and voluntary contraction (4–4.8). Thus, despite the supposed difference in the central organization of PAE and voluntary effort, the temporal structure of their sEMG did not differ, indicating that isometric voluntary effort and involuntary tone in the form of PAE share a common principle of sEMG signal generation. At the same time, the differences in the frequency of the sEMG spectrum indicate that the organization of sEMG signal during PAE is specific on the level of the motoneuron pool.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Comparison Between InterCriteria and Correlation Analyses over sEMG Data from Arm Movements in the Horizontal Plane

InterCriteria analysis (ICrA) and two kinds of correlation analyses, Pearson (PCA) and Spearman (SCA), were applied to surface electromyography (sEMG) signals obtained from human arm movements in the horizontal plane. Ten healthy participants performed ten movements, eight of which were cyclic. Each cyclic movement (CM) consisted of flexion and extension phases with equal duration (10 s, 6 s, 2 s, and 1 s) and two 5 s rest poses between them. The CMs were performed with and without an added load of 0.5 kg on the wrists of the participants. The sEMG signals from six different muscles or separate muscle heads (m. deltoideus pars clavicularis, m. deltoideus pars spinata, m. brachialis, m. anconeus, m. biceps brachii, and m. triceps brachii long head) were recorded and used to compare the results of the ICrA, PCA, and SCA. All three methods found identical consonance pairs for the flexion and extension CM phases. Additionally, PCA detected two more consonance pairs in the extension phases. In this investigation, ICrA, PCA, and SCA were proven to be reliable tools when applied separately or in combination for sEMG data. These three methods are appropriate for researching arm movements in the horizontal plane and experimental protocol revision.

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

The Development and Reliability of a Surface Electromyography-Based Index for Quantifying Knee Muscle Coactivation During the Lower Quarter Y-Balance Test

This study aimed to develop and evaluate the reliability of a surface electromyography-based (sEMG) index to quantify knee muscle coactivation in healthy recreational athletes during the Lower Quarter Y-Balance Test (YBT-LQ). A prospective observational repeated measures design was used to assess both intra- and inter-rater reliability. Forty males completed three trials, with two raters independently conducting assessments. sEMG signals from the vastus medialis, vastus lateralis, biceps femoris, and semitendinosus muscles were collected to calculate four coactivation indices (CoAIs) for different muscle pairs. The reliability evaluation of these CoAIs was based on intraclass correlation coefficients. The results revealed that the reliability of the CoAIs varied depending on the muscle pair, direction, and limb examined. The highest intra- and inter-rater reliability was noted for the left lower limb in the posterolateral direction. The lowest reliability was found for the right lower limb in the anterior direction. Statistical analyses indicated significant differences in specific CoAIs across different YBT-LQ directions. These findings highlight the potential of sEMG-based CoAIs for assessing knee muscle activity during the YBT-LQ in healthy male recreational athletes. However, choosing CoAIs is critical for reliable clinical and research assessments of knee stability and neuromuscular control.

The probability density function of the surface electromyogram and its dependence on contraction force in the vastus lateralis

IntroductionThe probability density function (PDF) of the surface electromyogram (sEMG) depends on contraction force. This dependence, however, has so far been investigated by having the subject generate force at a few fixed percentages of MVC. Here, we examined how the shape of the sEMG PDF changes with contraction force when this force was gradually increased from zero.MethodsVoluntary surface EMG signals were recorded from the vastus lateralis of healthy subjects as force was increased in a continuous manner vs. in a step-wise fashion. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal.Results(1) In 84% of the subjects, as contraction force increased from 0 to 10% MVC, the sEMG PDF shape oscillated back and forth between the semi-degenerate and the Gaussian distribution.(2) The PDF–force relation varied greatly among subjects for forces between 0 and ~ 10% MVC, but this variability was largely reduced for forces above 10% MVC.(3) The pooled analysis showed that, as contraction force gradually increased, the sEMG PDF evolved rapidly from the semi-degenerate towards the Laplacian distribution from 0 to 5% MVC, and then more slowly from the Laplacian towards the Gaussian distribution for higher forces.ConclusionsThe study demonstrated that the dependence of the sEMG PDF shape on contraction force can only be reliably assessed by gradually increasing force from zero, and not by performing a few constant-force contractions. The study also showed that the PDF–force relation differed greatly among individuals for contraction forces below 10% MVC, but this variability was largely reduced when force increased above 10% MVC.

Surface electromyography: A pilot study in canine spinal muscles

In veterinary practice, rehabilitation modalities are often used to help in the recovery of animals affected by InterVertebral Disc Disease (IVDD), a condition frequently observed in chondrodystrophic dog breeds and can lead to Spinal Cord Injury (SCI), resulting in pain, motor impairments and neurological deficits, but there is a lack of objective assessment tools for patient evolution. In this work, an innovative approach using surface ElectroMyoGraphy (sEMG) is proposed to be applied in the field of veterinary medicine rehabilitation. The observed results are thought to be a direct result of nerve compression, leading to unusual patterns of muscle activation; this phenomenon can be attributed to muscle denervation, where the loss of Motor Units (MU) is the primary cause. This is thought to be responsible for the decrease in recorded sEMG amplitude and the increase in frequency observed in the pathological group.•This study involved rigorous animal preparation and signal acquisition protocols, involving multiple exercises and sub-movements, which were subsequently analysed.•RMSA is most used metric to analyse amplitude in sEMG signals, as it results in a more representative measurement of the signal variability than the Mean amplitude or the Standard Deviation.

Effect of fatigue on intermuscular EMG-EMG coupling during bench press exercise at 60% 1RM workload in males.

To explore the neuromuscular control mechanism and quantifying the fatigue response during bench press exercise is important references to prescribe an appropriate exercise program. However, current literature struggles to provide a concrete conclusion on the changes of intermuscular EMG-EMG coupling between synergistic and antagonist muscles during the exercise. Thus, the current study was designed to reveal fatigue-related changes of intermuscular EMG-EMG coupling during bench press exercise. Thirty-one healthy male participants performed a bench press exercise on the Smith machine at 60% One Repetition Maximum (1RM) workload to exhaustion, while surface electromyographic signals (sEMG) were collected from triceps brachii (TB), biceps brachii (BB), anterior deltoid (AD), posterior deltoid (PD), and pectoralis major (PM). Surface EMG signals were divided into the first half and second half of the bench press exercise. Phase synchronization index (PSI) was calculated between sEMG of synergistic muscle pairs AD-TB, AD-PM and antagonist muscle pairs BB-TB, AD-PD. EMG power of TB, AD, PD, PM muscles in alpha (8-12 Hz) frequency band and EMG power of each muscle in beta (15-35 Hz), and gamma (35-60 Hz) frequency bands were all increased during the second half of contraction compared with the first half of contraction. PSI of gamma frequency band was significantly decreased in BB-TB muscle pair while EMG-EMG coupling of AD-TB in gamma frequency band was significantly increased during the second half of contraction compared to the first half of contraction. The results indicated a decrease of interconnection between synchronized cortical neurons and the motoneuron pool of BB and TB, and an increase of interconnection between AD-TB muscles during fatiguing bench press exercise at 60% 1RM workload. The changes of intermuscular coupling may be related to the supraspinal modulations to compensate for the decrease of muscle force as well as a result of unbalanced changes of agonist and antagonist muscle contractility.

Comparative analysis of myoelectric activity and mandibular movement in healthy and nonpainful articular temporomandibular disorder subjects.

Temporomandibular disorders (TMDs) are one of the leading causes of craniofacial pain, and a high incidence of TMDs in young adults has been reported. Previous studies have used surface electromyography (sEMG) and mandibular kinematic analysis for TMDs diagnosis. This study aimed to provide normal reference values of sEMG and mandibular kinematics in Han Chinese adults, compare the sex differences and assess their diagnosis value on pain-free articular TMDs. This observational study recruited healthy young adults with individual normal occlusion and nonpainful articular TMDs patients with disc displacement. The sEMG signals of the anterior temporalis (TA), masseter (MM), and sternocleidomastoid and digastric were recorded in the mandibular postural positions (MPP) and during maximal voluntary clenching (MVC) with K7 electromyograph. Mandibular kinematics, including maximum opening and opening/closing velocities, were assessed by K7 kinesiograph. The sEMG reference values ranged from 3.0 to 4.3 µV in the MPP (n = 90) in healthy subjects. The sEMG values during MVC, maximum opening, and opening velocity were significantly higher in males than in females. Also, these indices showed good diagnostic efficiency for pain-free articular TMDs (n = 26). Reference values and sex differences in sEMG and mandibular kinematics in healthy subjects were determined. Compared with them, articular TMDs patients showed significantly lower myoelectric activity during maximal biting and restricted mouth opening range and velocity, which may assist in TMDs diagnosis. By analyzing sEMG of masticatory muscle and mandibular kinematics data from healthy Han Chinese young adults and TMDs patients with disc displacement, this study evaluated the sex difference and their diagnosis value on nonpainful TMDs with disc displacement. NCT06372769.