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OBJECTIVES: To evaluate the effect of urethral catheterization on the accuracy of EMG uroflowmetry in children with non-neurogenic voiding disorders during pressure-flow (PF) studies compared to the non-invasive EMG uroflowmetry test. METHODS: A retrospective study of children undergoing a urodynamic evaluation at our institution between 8/2018 and 7/2022 was employed. Urination curves and pelvic floor muscle activity were compared between PF studies and non-invasive EMG uroflowmetry test. The non-invasive test was selected as the standard benchmark. RESULTS: 104 children were tested, with 34 children (33%) being able to urinate only in a non-invasive EMG uroflowmetry. The percentage of boys unable to urinate with a catheter was significantly higher than girls (54% vs. 13%, p-value < 0.001). In 70 children, a normal bell-shaped urination curve was found in 13 compared to 33 children in the PF studies and non-invasive uroflowmetry, respectively. PF studies demonstrated a specificity of 39% (95% CI 23-57) and a positive predictive value (PPV) of 61% (95% CI 53-67) in finding non-bell-shaped curves. Relaxation of pelvic muscles was found in 21 (30%) as opposed to 39 (55%) of children in invasive and non-invasive EMG uroflowmetry, respectively (p-value = 0.5). CONCLUSION: The accuracy of PF studies in children, primarily in boys, compared to the non-invasive uroflowmetry, was poor. This may pose potential errors in diagnosis and subsequent treatment. We recommend completing a non-invasive EMG uroflowmetry in cases where the child refused to urinate, or pathology was found, requiring a modification in treatment.

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The diagnosis of amyotrophic lateral sclerosis (ALS) is based on evidence of upper and lower motor neuron degeneration in the bulbar, cervical, thoracic, and lumbar regions in a patient with progressive motor weakness, in the absence of differential diagnosis. Despite these well-defined criteria, ALS can be difficult to diagnose, given the wide variety of clinical phenotypes. Indeed, the central or peripheral location of the disease varies with a spectrum ranging from predominantly central to exclusively peripheral, symptoms can be extensive or limited to the limbs, bulbar area or respiratory muscles, and the duration of the disease may range from a few months to several decades. In the absence of a specific test, the diagnostic strategy relies on clinical, electrophysiological, biological and radiological investigations to confirm the disease and exclude ALS mimics. The main challenge is to establish a diagnosis based on robust clinical and paraclinical evidence without delaying treatment initiation by increasing the number of additional tests. This approach requires a thorough knowledge of the phenotypes of ALS and its main differential diagnoses.

The diagnosis of amyotrophic lateral sclerosis (ALS) is based on progressive degeneration of upper and lower motor neurons.ALS can be difficult to diagnose due to the wide range of clinical phenotypes (central/peripheral location, symptom distribution, disease duration).A thorough diagnostic strategy including clinical, electrophysiological, biological and radiological investigations is essential to confirm ALS and exclude differential diagnoses.

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The basic function of the tongue in pronouncing diadochokinesis and other syllables is not fully understood. This study investigates the influence of sound pressure levels and syllables on tongue pressure and muscle activity in 19 healthy adults (mean age: 28.2 years; range: 22-33 years). Tongue pressure and activity of the posterior tongue were measured using electromyography (EMG) when the velar stops /ka/, /ko/, /ga/, and /go/ were pronounced at 70, 60, 50, and 40 dB. Spearman's rank correlation revealed a significant, yet weak, positive association between tongue pressure and EMG activity (ρ = 0.14, p < 0.05). Mixed-effects model analysis showed that tongue pressure and EMG activity significantly increased at 70 dB compared to other sound pressure levels. While syllables did not significantly affect tongue pressure, the syllable /ko/ significantly increased EMG activity (coefficient = 0.048, p = 0.013). Although no significant differences in tongue pressure were observed for the velar stops /ka/, /ko/, /ga/, and /go/, it is suggested that articulation is achieved by altering the activity of both extrinsic and intrinsic tongue muscles. These findings highlight the importance of considering both tongue pressure and muscle activity when examining the physiological factors contributing to sound pressure levels during speech.

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OBJECTIVE: There is little proof to determine the features of the muscles' motor unit potentials (MUPs) in children with poor posture. Current evaluation could be of value for future studies as a reference. The purpose was to detect the impact of rounded back posture on the characteristics of the MUPs and fascicle length of the shoulder retractors in children. METHODS: Participants in this study were 60 children (boys and girls), their ages were from 7 to 10 years old. Children were allocated into healthy children group (A) and rounded back posture group (B). MUPs and fascicle length of middle trapezius were assessed by electromyography and ultrasonography respectively. RESULTS: When compared to the normal group, the rounded back group's right and left middle trapezius MUPs count and amplitude significantly increased. As regards to the middle trapezius MUPs duration between the two groups, there was no significant difference. Also, the rounded back posture group exhibited significantly lower fascicle length in middle trapezius of both sides than the normal group. CONCLUSION: Forward shoulder posture is accompanied by atypical middle trapezius MUPs characteristics and also lowered fascicle length. Thus, children with forward-leaning posture could increase the likelihood of developing any of the many shoulder disorders.

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Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

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INTRODUCTION: Reliable, noninvasive early diagnostics of neuromuscular function in Bell's palsy, which causes facial paralysis and reduced quality of life, remain to be established. Here, we aimed to evaluate the utility of the motor unit number index (MUNIX) for the quantitative electrophysiological assessment of early-stage Bell's palsy, its correlation with clinical assessments, changes following treatment, and association with clinical prognosis. METHODS: MUNIX measures were recorded from the bilateral zygomaticus, orbicularis oculi, and orbicularis oris muscles of 10 healthy individuals and 64 patients with Bell's palsy. The patients were assessed by two specialist neurologists using the House-Brackmann and Sunnybrook Facial Grading Systems. Repeat assessments were performed on 20 patients with Bell's palsy who received treatment. Additionally, the 64 patients were reassessed using clinical scales after a 1-month interval. RESULTS: The MUNIX values of the main affected muscles on the affected side were lower than those on the healthy side in patients with Bell's palsy (p < .05). The MUNIX measurements significantly correlated with the clinical facial nerve palsy scale scores (p < .05). Significant improvements were observed in the MUNIX values on repeat testing following treatment (p < .05). The baseline motor unit size index (the compound muscle action potential amplitude divided by MUNIX) was positively associated with improved clinical presentation after 1 month (p < .05). CONCLUSION: MUNIX can be used as an electrophysiological biomarker for the quantitative assessment of facial nerve palsy and treatment response, and as a prognostic biomarker, in patients with early Bell's palsy, and is recommended as a complement to conventional neurophysiological examinations.

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Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

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Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human-machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.

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Elite athletes in speed roller skates perceive skating to be a more demanding exercise for the groin when compared to other cyclic disciplines, increasing their risk of injury. The objective of this study was to monitor the kinematic and electromyographic parameters of roller speed skaters, linearly, on a treadmill, and to compare different skating speeds, one at 20 km/h and one at 32 km/h, at a 1° inclination. The acquisition was carried out by placing an inertial sensor at the level of the first sacral vertebra, and eight surface electromyographic probes on both lower limbs. The kinematic and electromyographic analysis on the treadmill showed that a higher speed requires more muscle activation, in terms of maximum and average values and co-activation, as it not only increases the intrinsic muscle demand in the district, but also the athlete's ability to coordinate the skating technique. The present study allows us to indicate not only how individual muscle districts are activated during skating on a surface different from the road, but also how different speeds affect the overall district load distributions concerning effective force, which is essential for the physiotherapist and kinesiologist for preventive and conditional purposes, while also considering possible variations in the skating technique in linear advancement.

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Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

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Visual information affects static postural control, but how it affects dynamic postural control still needs to be fully understood. This study investigated the effect of proprioception weighting, influenced by the presence or absence of visual information, on dynamic posture control during voluntary trunk movements. We recorded trunk movement angle and angular velocity, center of pressure (COP), electromyographic, and electroencephalography signals from 35 healthy young adults performing a standing trunk flexion-extension task under two conditions (Vision and No-Vision). A random forest analysis identified the 10 most important variables for classifying the conditions, followed by a Wilcoxon signed-rank test. The results showed lower maximum forward COP displacement and trunk flexion angle, and faster maximum flexion angular velocity in the No-Vision condition. Additionally, the alpha/beta ratio of the POz during the switch phase was higher in the No-Vision condition. These findings suggest that visual deprivation affects cognitive- and sensory-integration-related brain regions during movement phases, indicating that sensory re-weighting due to visual deprivation impacts motor control. The effects of visual deprivation on motor control may be used for evaluation and therapeutic interventions in the future.

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BACKGROUND: Reaching and grasping (R&G) in rats is commonly used as an outcome measure to investigate the effectiveness of rehabilitation or treatment strategies to recover forelimb function post spinal cord injury. Kinematic analysis has been limited to the wrist and digit movements. Kinematic profiles of the more proximal body segments that play an equally crucial role in successfully executing the task remain unexplored. Additionally, understanding of different forelimb muscle activity, their interactions, and their correlation with the kinematics of R&G movement is scarce. NEW METHOD: In this work, novel methodologies to comprehensively assess and quantify the 3D kinematics of the proximal and distal forelimb joints along with associated muscle activity during R&G movements in adult rats are developed and discussed. RESULTS: Our data show that different phases of R&G identified using the novel kinematic and EMG-based approach correlate with the well-established descriptors of R&G stages derived from the Whishaw scoring system. Additionally, the developed methodology allows describing the temporal activity of individual muscles and associated mechanical and physiological properties during different phases of the motor task. COMPARISON WITH EXISTING METHOD(S): R&G phases and their sub-components are identified and quantified using the developed kinematic and EMG-based approach. Importantly, the identified R&G phases closely match the well-established qualitative descriptors of the R&G task proposed by Whishaw and colleagues. CONCLUSIONS: The present work provides an in-depth objective analysis of kinematics and EMG activity of R&G behavior, paving the way to a standardized approach to assessing this critical rodent motor function in future studies.

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The current trends in the development of methods for non-invasive prediction of premature birth based on the electromyogram of the uterus, i.e., electrohysterogram (EHG), suggest an ever-increasing use of large number of features, complex models, and deep learning approaches. These "black-box" approaches rarely provide insights into the underlying physiological mechanisms and are not easily explainable, which may prevent their use in clinical practice. Alternatively, simple methods using meaningful features, preferably using a single feature (biomarker), are highly desirable for assessing the danger of premature birth. To identify suitable biomarker candidates, we performed feature selection using the stabilized sequential-forward feature-selection method employing learning and validation sets, and using multiple standard classifiers and multiple sets of the most widely used features derived from EHG signals. The most promising single feature to classify between premature EHG records and EHG records of all other term delivery modes evaluated on the test sets appears to be Peak Amplitude of the normalized power spectrum (PA) of the EHG signal in the low frequency band (0.125-0.575 Hz) which closely matches the known Fast Wave Low (FWL) frequency band. For classification of EHG records of the publicly available TPEHG DB, TPEHGT DS, and ICEHG DS databases, using the Partition-Synthesis evaluation technique, the proposed single feature, PA, achieved Classification Accuracy (CA) of 76.5% (AUC of 0.81). In combination with the second most promising feature, Median Frequency (MF) of the power spectrum in the frequency band above 1.0 Hz, which relates to the maternal resting heart rate, CA increased to 78.0% (AUC of 0.86). The developed method in this study for the prediction of premature birth outperforms single-feature and many multi-feature methods based on the EHG, and existing non-invasive chemical and molecular biomarkers. The developed method is fully automatic, simple, and the two proposed features are explainable.

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Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.

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Background: The aim of the study was to assess the inter-rater and intra-rater agreement of measurements performed with the Luna EMG (electromyography) multifunctional robot, a tool for evaluation of upper limb proprioception in individuals with stroke. Methods: The study was conducted in a group of patients with chronic stroke. A total of 126 patients participated in the study, including 78 women and 48 men, on average aged nearly 60 years (mean = 59.9). Proprioception measurements were performed using the Luna EMG diagnostic and rehabilitation robot to assess the left and right upper limbs. The examinations were conducted by two raters, twice, two weeks apart. The results were compared between the raters and the examinations. Results: High consistency of the measurements performed for the right and the left hand was reflected by the interclass correlation coefficients (0.996-0.998 and 0.994-0.999, respectively) and by Pearson's linear correlation which was very high (r = 1.00) in all the cases for the right and the left hand in both the inter-rater and intra-rater agreement analyses. Conclusions: Measurements performed by the Luna EMG diagnostic and rehabilitation robot demonstrate high inter-rater and intra-rater agreement in the assessment of upper limb proprioception in patients with chronic stroke. The findings show that Luna EMG is a reliable tool enabling effective evaluation of upper limb proprioception post-stroke.

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EMG filling curve characterizes the EMG filling process and EMG probability density function (PDF) shape change for the entire force range of a muscle. We aim to understand the relation between the physiological and recording variables, and the resulting EMG filling curves. We thereby present an analytical and simulation study to explain how the filling curve patterns relate to specific changes in the motor unit potential (MUP) waveforms and motor unit (MU) firing rates, the two main factors affecting the EMG PDF, but also to recording conditions in terms of noise level. We compare the analytical results with simulated cases verifying a perfect agreement with the analytical model. Finally, we present a set of real EMG filling curves with distinct patterns to explain the information about MUP amplitudes, MU firing rates, and noise level that these patterns provide in the light of the analytical study. Our findings reflect that the filling factor increases when firing rate increases or when newly recruited motor unit have potentials of smaller or equal amplitude than the former ones. On the other hand, the filling factor decreases when newly recruited potentials are larger in amplitude than the previous potentials. Filling curves are shown to be consistent under changes of the MUP waveform, and stretched under MUP amplitude scaling. Our findings also show how additive noise affects the filling curve and can even impede to obtain reliable information from the EMG PDF statistics.

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Rapid mapping is a transcranial magnetic stimulation (TMS) mapping method which can significantly reduce data collection time compared to traditional approaches. However, its validity and reliability has only been established for upper-limb muscles during resting-state activity. Here, we determined the validity and reliability of rapid mapping for non-upper limb muscles that require active contraction during TMS: the masseter and quadriceps muscles. Eleven healthy participants attended two sessions, spaced two hours apart, each involving rapid and 'traditional' mapping of the masseter muscle and three quadriceps muscles (rectus femoris, vastus medialis, vastus lateralis). Map parameters included map volume, map area and centre of gravity (CoG) in the medial-lateral and anterior-posterior directions. Low to moderate measurement errors (%SEMeas = 10-32) were observed across muscles. Relative reliability varied from good-to-excellent (ICC = 0.63-0.99) for map volume, poor-to-excellent (ICC = 0.11-0.86) for map area, and fair-to-excellent for CoG (ICC = 0.25-0.8) across muscles. There was Bayesian evidence of equivalence (BF's > 3) in most map outcomes between rapid and traditional maps across all muscles, supporting the validity of the rapid mapping method. Overall, rapid TMS mapping produced similar estimates of map parameters to the traditional method, however the reliability results were mixed. As mapping of non-upper limb muscles is relatively challenging, rapid mapping is a promising substitute for traditional mapping, however further work is required to refine this method.

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Objective. The decline in the performance of electromyography (EMG)-based silent speech recognition is widely attributed to disparities in speech patterns, articulation habits, and individual physiology among speakers. Feature alignment by learning a discriminative network that resolves domain offsets across speakers is an effective method to address this problem. The prevailing adversarial network with a branching discriminator specializing in domain discrimination renders insufficiently direct contribution to categorical predictions of the classifier.Approach. To this end, we propose a simplified discrepancy-based adversarial network with a streamlined end-to-end structure for EMG-based cross-subject silent speech recognition. Highly aligned features across subjects are obtained by introducing a Nuclear-norm Wasserstein discrepancy metric on the back end of the classification network, which could be utilized for both classification and domain discrimination. Given the low-level and implicitly noisy nature of myoelectric signals, we devise a cascaded adaptive rectification network as the front-end feature extraction network, adaptively reshaping the intermediate feature map with automatically learnable channel-wise thresholds. The resulting features effectively filter out domain-specific information between subjects while retaining domain-invariant features critical for cross-subject recognition.Main results. A series of sentence-level classification experiments with 100 Chinese sentences demonstrate the efficacy of our method, achieving an average accuracy of 89.46% tested on 40 new subjects by training with data from 60 subjects. Especially, our method achieves a remarkable 10.07% improvement compared to the state-of-the-art model when tested on 10 new subjects with 20 subjects employed for training, surpassing its result even with three times training subjects.Significance. Our study demonstrates an improved classification performance of the proposed adversarial architecture using cross-subject myoelectric signals, providing a promising prospect for EMG-based speech interactive application.

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This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.

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Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism.

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