Surface Electromyography: What Limits Its Use in Exercise and Sport Physiology?

Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Decision-Making Time Analysis for Assessing Processing Speed in Athletes during Motor Reaction Tasks

Reaction time (RT) is a widely used measure for testing physical performance in motor tasks. This study focused on assessing the processing speed in athletes. Twenty-five healthy volunteers were assigned to the control (n = 16) or athletes groups (n = 9). They were evaluated during motor reaction tasks based on visual stimuli and three difficulty conditions. Physiological measures were obtained from motion capture and electromyography recordings of several muscles. Two RT phases, decision-making (DMK) and electromechanical delay (EMD), were used to analyze the processing speed. The results show significant RT differences between groups. The athletes were ~30% faster compared to the control group. Despite the fact that all participants were right-handed, RT did not show any differences between hands performances in any group. However, DMK time revealed significant differences between the hands. Controls showed a longer DMK time for the right-hand election, ~20% more than the left, while athletes showed no such disparity. These findings reveal that quantifying the decision-making component of reaction time is crucial to assessing processing speed in sport. This approach could facilitate the monitoring of adaptations in both motor-cognitive and neuromuscular processes. The theoretical implications presented in this study offer perspectives on handedness research.

Electromyographic analysis of the traditional and spin throwing techniques for goalball games related to ball velocity for selected upper extremity muscles

BACKGROUND

Goalball is a popular sport among visually impaired individuals, offering many physical and social benefits. Evaluating performance in Goalball, particularly understanding factors influencing ball velocity during throwing techniques, is essential for optimizing training programs and enhancing player performance. However, there is limited research on muscle activation patterns during Goalball throwing movements, needing further investigation to address this gap. Therefore, this study aims to examine muscle activity in sub-elite visually impaired Goalball players during different throwing techniques and visual conditions, focusing on its relationship with ball velocity.

METHODS

15 sub-elite Goalball players (2 female, 13 males; mean age of 20.46 ± 2.23 years) participated in the study. Muscle activity was evaluated with the Myo armband, while ball velocity was measured using two cameras and analyzed with MATLAB software. Different visual conditions were simulated using an eye band, and the effects of these conditions on muscle activation and ball velocity were examined.

RESULTS

The flexor muscles were found to be more active during the spin throw techniques with the eyes open (p = 0.011). The extensor muscles were found to be more active in the eyes-closed spin throw techniques compared to the eyes-open position (p = 0.031). Ball velocity was found related to the flexor muscles. Interestingly, no significant differences in ball velocity were observed between different throwing techniques or visual conditions (p > 0.05).

CONCLUSIONS

Ball velocity, one of the performance indicators of the athlete, is primarily related to upper extremity flexor muscle strength rather than visual acuity. It has less visual acuity, but an athlete with more upper-extremity flexor muscle strength will have an advantage in Goalball game. The spin throw technique, which is reported to provide a biomechanical advantage for professional players in the literature, did not provide an advantage in terms of ball velocity for the sub elite players evaluated in our study. This knowledge can inform the development of targeted training programs aimed at improving technique and enhancing ball velocity in Goalball players.

A Tutorial on Skeletal Muscle Metabolism and the Role of Blood Lactate: Implications for Speech Production

PURPOSE

The purpose of this tutorial is threefold: (a) present relevant exercise science literature on skeletal muscle metabolism and synthesize the limited available research on metabolism of the adult human speech musculature in an effort to elucidate the role of metabolism in speech production; (b) introduce a well-studied metabolic serum biomarker in exercise science, lactate, and the potential usefulness of investigating this metabolite, through a well-established exercise science methodology, to better understand metabolism of the musculature involved in voice production; and (c) discuss exercise physiology considerations for future voice science research that seeks to investigate blood lactate and metabolism in voice physiology in an ecologically valid manner.

METHOD

This tutorial begins with relevant exercise science literature on the basic cellular processes of muscle contraction that require energy and the metabolic mechanisms that regenerate the energy required for task execution. The tutorial next synthesizes the available research investigating metabolism of the adult human speech musculature. This is followed by the authors proposing a hypothesis of speech metabolism based on the voice science literature and the application of well-studied exercise science principles of muscle physiology. The tutorial concludes with a discussion and the potential usefulness of lactate in investigations to better understand the metabolism of the musculature involved in vocal demand tasks.

CONCLUSION

The role of metabolism during speech (respiratory, laryngeal, and articulatory) is an understudied yet critical aspect of speech physiology that warrants further study to better understand the metabolic systems that are used to meet vocal demands.

Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

Predicting repeat power ability through common field assessments: is repeat power ability a unique physical quality?

Background

The repeat power ability (RPA) assessment is used to test the ability to repeatedly produce maximal ballistic efforts with an external load. The underpinning physical qualities influencing RPA are undetermined. This study aimed to gain further insight into the physical qualities that determine RPA by analysing the association between physical qualities and an assessment of RPA.

Materials and methods

Ten well-trained male field hockey players performed an RPA assessment consisting of 20 repetitions of loaded countermovement jumps (LCMJ20), with a percent decrement score of peak power output calculated. Over a two-week period, each participant performed the YoYo Intermittent Recovery Test 2 (IRT2), a repeated speed ability assessment incorporating a 180° change of direction (RSA180), a 40-meter linear speed test (40 mST), an isometric mid-thigh pull (IMTP), a countermovement jump (CMJ), and a 3-repetition maximum half squat (HS) assessment. Pearson's correlation analysis was used to determine the strength of relationships between each assessment variable and the LCMJ20. The assessment variables with the strongest relationships within each assessment were used in a stepwise multiple linear regression analysis to determine the best predictor model of LCMJ20.

Results

RSA180percent decrement score (RSA180% had a very strong, significant relationship with LCMJ20 (r = 0.736: p < 0.05). HS relative strength (HSrel) was found to have a significant and very strong, negative relationship with LCMJ20 (r =  - 0.728: p < 0.05). Stepwise multiple linear regression analysis showed RSA180 to explain 48.4% of LCMJ20 variance (Adjusted R2 = 0.484) as the only covariate included in the model.

Conclusion

The findings indicate that RSA180 as a repeated high intensity effort (RHIE) task is strongly related to LCMJ20 and is also the best predictor of LCMJ20. This may suggest that RPA can provide practitioners with information on RHIE performance. The variance between assessment methods indicates that RPA may be a distinct physical quality, future research should assess other physical capacities to better understand the factors contributing to RPA.

Masseter and digastric muscle activity evaluation using a novel electromyogram that utilizes elastic sheet electrodes

PURPOSE

To evaluate the reproducibility and reliability of a novel electromyogram (EMG) device with a flexible sheet sensor for measuring muscle activity related to mastication and swallowing.

METHODS

We developed a new EMG device made of elastic sheet electrodes to measure the masseter and digastric muscle activities for evaluating mastication and swallowing. To examine the measurement reproducibility of the new EMG device, masseter muscle activity was analyzed using the intraclass correlation coefficient (ICC). Further, we measured the maximum amplitude, duration, integrated value, and signal-to-noise ratio (SNR) using the new EMG device and conventional EMG devices and evaluated the reliability using ICC and Bland-Altman analysis.

RESULTS

We confirmed high ICC (1,1) and ICC (2,1) scores (0.92 and 0.88, respectively) while measuring the reproducibility of the new EMG device. When compared to the active electrode EMG device, we found a high correlation for the maximum amplitude (0.90), duration (0.99), integrated values (0.90), and SNR (0.75), with no observation of significant fixed errors. Moreover, the regression coefficient was not significant for any of the evaluation items and no proportional error was observed. Compared with the passive electrode EMG device, the maximum amplitude and duration were highly correlated (0.73 and 0.89). In addition, the SNR exhibited a significant fixed error. In contrast, the regression coefficient was not significant for any of the evaluation items and no proportional error was observed.

CONCLUSIONS

Our results suggest that the new EMG device can be used to reliably and reproducibly evaluate muscle activity during mastication and swallowing.

Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo

Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.

A comparison of techniques for verifying the accuracy of precision decomposition-derived relationships between motor unit firing rates and recruitment thresholds from surface EMG signals

Approaches for validating motor unit firing times following surface electromyographic (EMG) signal decomposition with the precision decomposition III (PDIII) algorithm have not been agreed upon. Two approaches have been common: (1) "reconstruct-and-test" and (2) spike-triggered averaging (STA). We sought to compare motor unit results following the application of these approaches. Surface EMG signals were recorded from the vastus lateralis of 13 young males performing trapezoidal, isometric knee extensions at 50% and 80% of maximum voluntary contraction (MVC) force. The PDIII algorithm was used to quantify motor unit firing rates. Motor units were excluded using eight combinations of the reconstruct-and-test approach with accuracy thresholds of 0, 90, 91, and 92% with and without STA. The mean firing rate versus recruitment threshold relationship was minimally affected by STA. At 80% MVC, slopes acquired at the 0% accuracy threshold were significantly greater (i.e., less negative) than when 91% (p = .010) and 92% (p = .030) accuracy thresholds were applied. The application of STA has minimal influence on surface EMG signal decomposition results. Stringent reconstruct-and-test accuracy thresholds influence motor unit-derived relationships at high forces, perhaps explained through the increased presence of large motor unit action potentials. Investigators using the PDIII algorithm can expect negligible changes in motor unit-derived linear regression relationships with the application of secondary validation procedures.

Impact of Verbal Suggestions on Finger Flexor Activation and Strength in Healthy Individuals

BACKGROUND Words uttered by other people can have an enormous influence on how we perceive our surroundings, what we expect, what we experience, and how we behave. This study aimed to evaluate the effect of verbal reinforcement on the placebo effect in the context of finger flexor muscle activation measured with surface electromyography (sEMG) and hand grip strength measured with a hand dynamometer in healthy subjects. MATERIAL AND METHODS Eighty-eight individuals aged 22.64±5.2 years took part in the study. For each person, paper tape was applied (placebo). The participants were randomly assigned to 1 of the 3 groups: positive information group (P) - "the tape increases hand muscle strength", negative information group (N) - "the tape decreases hand muscle strength", and control group (C) - "the effect of the tape on hand muscle strength is unknown." The activation of muscles was assessed using surface electromyography (sEMG) while measuring the strength of wrist and finger flexors with a hand dynamometer. Each participant was examined twice - prior to and immediately after taping and providing verbal reinforcement. RESULTS Only group N manifested a decrease in muscle strength, from 39.7N to 37.6N (P=0.003). Group C displayed an increase in muscle strength from 34.3N to 36.4N (P=0.035). None of the groups demonstrated statistically significant changes in bioelectrical activity of the muscles. At no stage of examination were the differences between the groups significant. CONCLUSIONS Negative verbal information combined with the placebo intervention resulted in a significant decrease in the strength of finger flexors.

Acute Recovery after a Fatigue Protocol Using a Recovery Sports Legging: An Experimental Study

Enhancing recovery is a fundamental component of high-performance sports training since it enables practitioners to potentiate physical performance and minimise the risk of injuries. Using a new sports legging embedded with an intelligent system for electrostimulation, localised heating and compression (completely embodied into the textile structures), we aimed to analyse acute recovery following a fatigue protocol. Surface electromyography- and torque-related variables were recorded on eight recreational athletes. A fatigue protocol conducted in an isokinetic dynamometer allowed us to examine isometric torque and consequent post-exercise acute recovery after using the sports legging. Regarding peak torque, no differences were found between post-fatigue and post-recovery assessments in any variable; however, pre-fatigue registered a 16% greater peak torque when compared with post-fatigue for localised heating and compression recovery methods. Our data are supported by recent meta-analyses indicating that individual recovery methods, such as localised heating, electrostimulation and compression, are not effective to recover from a fatiguing exercise. In fact, none of the recovery methods available through the sports legging tested was effective in acutely recovering the torque values produced isometrically.

Measurement of the lumbrical muscle activity of the hand using electromyography supported by the ultrasound imaging technique with string navigation

Although placing surface electrodes on small muscles by palpation is difficult, ultrasound guidance may enable electrode placement on the small muscles. This study aimed to examine whether ultrasound guidance is helpful for placement of electrodes on a small muscle, such as the hand lumbrical muscle. Twelve dominant hands of 12 healthy right-handed adults were included in this study. The first lumbrical muscle belly of the hands was identified using ultrasound guidance with a string navigation technique for placing surface electrodes. This technique was designed to identify the location of the center of the muscle belly under ultrasound imaging using a string. After the electrodes were placed on the muscle belly using this technique, the surface electromyographic signals of the first lumbrical, first dorsal interosseous, and adductor pollicis muscles were recorded. The activity of the lumbrical muscle could be separately measured of the first dorsal interosseous and adductor pollicis muscles. This technique has the potential to enable surface electromyography of small muscles for which placement of surface electrodes by palpation is challenging.

Wearable bioelectronics fabricated in situ on skins

In recent years, wearable bioelectronics has rapidly expanded for diagnosing, monitoring, and treating various pathological conditions from the skin surface. Although the devices are typically prefabricated as soft patches for general usage, there is a growing need for devices that are customized in situ to provide accurate data and precise treatment. In this perspective, the state-of-the-art in situ fabricated wearable bioelectronics are summarized, focusing primarily on Drawn-on-Skin (DoS) bioelectronics and other in situ fabrication methods. The advantages and limitations of these technologies are evaluated and potential future directions are suggested for the widespread adoption of these technologies in everyday life.

Convergent Validity between Electromyographic Muscle Activity, Ultrasound Muscle Thickness and Dynamometric Force Measurement for Assessing Muscle

Muscle fatigue is defined as a reversible decline in performance after intensive use, which largely recovers after a resting period. Surface electromyography (EMG), ultrasound imaging (US) and dynamometry are used to assess muscle activity, muscle morphology and isometric force capacity. This study aimed to assess the convergent validity between these three methods for assessing muscle fatigue during a manual prehension maximal voluntary isometric contraction (MVIC). A diagnostic accuracy study was conducted, enrolling 50 healthy participants for the measurement of simultaneous changes in muscle thickness, muscle activity and isometric force using EMG, US and a hand dynamometer, respectively, during a 15 s MVIC. An adjustment line and its variance (R²) were calculated. Muscle activity and thickness were comparable between genders (p > 0.05). However, men exhibited lower force holding capacity (p < 0.05). No side-to-side or dominance differences were found for any variable. Significant correlations were found for the EMG slope with US (r = 0.359; p < 0.01) and dynamometry (r = 0.305; p < 0.01) slopes and between dynamometry and US slopes (r = 0.227; p < 0.05). The sample of this study was characterized by comparable muscle activity and muscle thickness change between genders. In addition, fatigue slopes were not associated with demography or anthropometry. Our findings showed fair convergent associations between these methods, providing synergistic muscle fatigue information.

Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays

Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire-and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions.

Our experimental understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to the observation of small samples of active MNs obtained from EMG decomposition. EMG decomposition therefore provides an important but incomplete description of the role of individual MNs in the firing activity of the complete MN pool, which limits our understanding of the neural strategies of the whole MN pool and of how the firing activity of each MN contributes to the neural drive to muscle. Here, we combine decomposed high-density EMG (HDEMG) data and a physiologically realistic model of MN population to predict the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. In brief, an experimental estimation of the synaptic current is input to a cohort of MN models, which are calibrated using the available decomposed HDEMG data, and predict the MN spike trains fired by the entire MN population. This novel approach is experimentally validated and applied to muscle force prediction from neuromuscular modelling.

A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2-10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.

Predictors of Step Length from Surface Electromyography and Body Impedance Analysis Parameters

Step length is a critical hallmark of health status. However, few studies have investigated the modifiable factors that may affect step length. An exploratory, cross-sectional study was performed to evaluate the surface electromyography (sEMG) and body impedance analysis (BIA) parameters, combined with individual demographic data, to predict the individual step length using the GAITRite® system. Healthy participants aged 40−80 years were prospectively recruited, and three models were built to predict individual step length. The first model was the best-fit model (R2 = 0.244, p < 0.001); the root mean square (RMS) values at maximal knee flexion and height were included as significant variables. The second model used all candidate variables, except sEMG variables, and revealed that age, height, and body fat mass (BFM) were significant variables for predicting the average step length (R2 = 0.198, p < 0.001). The third model, which was used to predict step length without sEMG and BIA, showed that only age and height remained significant (R2 = 0.158, p < 0.001). This study revealed that the RMS value at maximal strength knee flexion, height, age, and BFM are important predictors for individual step length, and possibly suggesting that strengthening knee flexor function and reducing BFM may help improve step length.

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

How to Work with Electromyography Decomposition in Practical Classes of Exercise Physiology and Biomechanics

Concepts about motor unit recruitment are important learning contents in exercise physiology and biomechanics classes that are usually taught theoretically. In the last few years, great advances have occurred in the decomposition of surface electromyography, allowing the learning of theoretical contents in an experimental way. In this tutorial paper, we have described the decomposition of surface electromyography methodological aspects and examples to teach motor unit recruitment concepts in exercise physiology and biomechanics practical lessons. This work has the aim to facilitate physiology and biomechanics academics to introduce this technique in practical classes.

A Novel Methodology for the Synchronous Collection and Multimodal Visualization of Continuous Neurocardiovascular and Neuromuscular Physiological Data in Adults with Long COVID

Background: Reports suggest that adults with post-COVID-19 syndrome or long COVID may be affected by orthostatic intolerance syndromes, with autonomic nervous system dysfunction as a possible causal factor of neurocardiovascular instability (NCVI). Long COVID can also manifest as prolonged fatigue, which may be linked to neuromuscular function impairment (NMFI). The current clinical assessment for NCVI monitors neurocardiovascular performance upon the application of orthostatic stressors such as an active (i.e., self-induced) stand or a passive (tilt table) standing test. Lower limb muscle contractions may be important in orthostatic recovery via the skeletal muscle pump. In this study, adults with long COVID were assessed with a protocol that, in addition to the standard NCVI tests, incorporated simultaneous lower limb muscle monitoring for NMFI assessment. Methods: To conduct such an investigation, a wide range of continuous non-invasive biomedical sensing technologies were employed, including digital artery photoplethysmography for the extraction of cardiovascular signals, near-infrared spectroscopy for the extraction of regional tissue oxygenation in brain and muscle, and electromyography for assessment of timed muscle contractions in the lower limbs. Results: With the proposed methodology described and exemplified in this paper, we were able to collect relevant physiological data for the assessment of neurocardiovascular and neuromuscular functioning. We were also able to integrate signals from a variety of instruments in a synchronized fashion and visualize the interactions between different physiological signals during the combined NCVI/NMFI assessment. Multiple counts of evidence were collected, which can capture the dynamics between skeletal muscle contractions and neurocardiovascular responses. Conclusions: The proposed methodology can offer an overview of the functioning of the neurocardiovascular and neuromuscular systems in a combined NCVI/NMFI setup and is capable of conducting comparative studies with signals from multiple participants at any given time in the assessment. This could help clinicians and researchers generate and test hypotheses based on the multimodal inspection of raw data in long COVID and other cohorts.

Deep Learning-Based Myoelectric Potential Estimation Method for Wheelchair Operation

Wheelchair sports are recognized as an international sport, and research and support are being promoted to increase the competitiveness of wheelchair sports. For example, an electromyogram can observe muscle activity. However, it is generally used under controlled conditions due to the complexity of preparing the measurement equipment and the movement restrictions imposed by cables and measurement equipment. It is difficult to perform measurements in actual competition environments. Therefore, in this study, we developed a method to estimate myoelectric potential that can be used in competitive environments and does not limit physical movement. We developed a deep learning model that outputs surface myoelectric potentials by inputting camera images of wheelchair movements and the measured values of inertial sensors installed on wheelchairs. For seven subjects, we estimated the myoelectric potential during chair work, which is important in wheelchair sports. As a result of creating an in-subject model and comparing the estimated myoelectric potential with the myoelectric potential measured by an electromyogram, we confirmed a correlation (correlation coefficient 0.5 or greater at a significance level of 0.1%). Since this method can estimate the myoelectric potential without limiting the movement of the body, it is considered that it can be applied to the performance evaluation of wheelchair sports.

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Evidence of resistance training-induced neural adaptation in older adults

The deleterious effects of aging on force production are observable from the age of 40 upwards, depending on the measure. Neural mechanisms contributing to maximum force production and rate of force development have been suggested as descending drive from supraspinal centers, spinal motoneuron excitability, and corticospinal inhibition of descending drive; all of which influence motor unit recruitment and/or firing rate. Resistance-trained Master athletes offer a good source of information regarding the inevitable effects of aging despite the countermeasure of systematic resistance-training. However, most evidence of neural adaptation is derived from longitudinal intervention studies in previously untrained (i.e. resistance-training naïve) older adults. There is good evidence for the effect of resistance-training on the end-point of neural activation, i.e. motor unit behavior, but little to no data on the generation of descending drive from e.g. transcranial magnetic stimulation or cortical imaging studies in older adults. This, along with tracking master athletes over several years, would provide valuable information and could be the focus of future research.