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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.

Surface ElectroMyoGraphy (EMG) signals from the forearm used in prosthetic hand and finger control systems require precise anatomy data of finger muscles that are small and located deep within the forearm. The main problem of this method is that the signal quality depends on the placement of EMG sensor, which can significantly affects the accuracy and precision to estimate joint angles or forces. Moreover, in case of amputees, the location of finger muscles is unknown and needed to be identified manually for EMG recording. As a result, most modern prosthetic hands utilize limited number of muscles with pattern recognition to control finger according to pre-defined grip which is unable to mimic natural finger motion.To address such issue, we used array EMG sensors to obtain EMG signals from all possible positions on the forearm and applied regression method to produce natural finger motion. The signals were analyzed using independent component analysis (ICA) to find the best-fitted independent component (IC) that match the anatomical data taken after the experiment. Next, from the IC and EMG signals, finger angles were estimated using linear regression model (LRM). Each finger was assigned EMG and IC component for flexion and extension muscles, to assess the possibility of controlling each finger angle separately. We compared the joint angles of each finger between calculated from IC and EMG by correlation coefficients (CC) for all fingers. The average CC values were higher than 0.7, demonstrating the strength of the linear relationship. The different between IC and EMG methods suggests that the IC method can reduce noise and increase the signal to noise ratio. The performance of ICA method show higher CC value at around 0.2 $\\pm$ 0.10. In order to confirm the performance of ICA method, we also tested mathematical musculoskeletal model (MSM). The result from this study showed that not only array EMG sensors with ICA significantly improve the quality of signal detected from forearm but also reduce problems of conventional EMG sensors and consequently improve the performance of regression method to imitate natural finger motion.

To evaluate the compatibility and correlation between noninvasive surface respiratory electromyography and invasive transesophageal diaphragmatic electromyography measurements as facilitating indicators of neural respiratory drive (NRD) evaluation during treadmill exercise. Transesophageal diaphragmatic electromyogram activity (EMGdi,es) and surface inspiratory electromyogram (EMG) activity, including surface diaphragmatic EMG activity (EMGdi,sur), surface parasternal intercostal muscle EMG activity (EMGpara), and surface sternocleidomastoid EMG activity (EMGsc), were detected simultaneously during increasing exercise capacity in 20 stable patients with COPD. EMGdi,es, EMGdi,sur, EMGpara, and EMGsc were quantified using the root mean square (RMS) and were represented as RMSdi,es, RMSdi,sur, RMSpara, and RMSsc, respectively. There was a significant association between EMGdi,es and EMGdi,sur ( =0.966, <0.01), EMGpara ( =0.967, <0.01), and EMGsc ( =0.956, <0.01) in the COPD patients during exercise. Bland-Altman plots showed that the lowest mean bias value was between EMGdi,es and EMGpara compared with the bias values between EMGdi,es and the other two EMG parameters. In comparing the estimation of EMGdi,es, we observed the lowest bias values (-1%) and the lowest limits of agreement values (-10% to -12%). Intraclass correlation coefficient (ICC) between EMGdi,es and EMGdi,sur was 0.978 ( <0.01), between EMGdi,es and EMGpara was 0.980 ( <0.01), and between EMGdi,es and EMGsc was 0.868 ( <0.01). RMSdi,sur, RMSpara, and RMSsc could provide useful physiological markers of NRD in COPD. RMSpara shows the best compatibility and correlation with transesophageal diaphragmatic electromyography during treadmill exercise in stable patients with COPD.

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims to provide researchers with the information to select the most appropriate EMG technique for their investigations. Graphical abstract

High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.

NOABSTRACTThe aim of the study was to investigate the repeatability of the calibration protocol performed by the Teethan® device (Teethan S.p.A. Italy) for surface electromyography (sEMG) when biting on cotton rolls to eliminate the effect of occlusal contacts.Thirty-three fully dentate subjects aged 13-43 years (mean 21.33 years) of which 15 men and 18 women were enrolled in the study All subjects had a good state of dental and periodontal health without temporomandibular disorders (TMDs). Each examined participant performed two distinct maximal voluntary clenches on cotton rolls: the first one for calibration of the device and the second one for recording of the signals. The results were presented as the indices: Percentage overlapping coefficient (POC), Barycenter (BAR), Torsion (TORS), Impact (IMPACT), Global index and Asymmetry (ASIM).All the examined indices showed good repeatability during the test, compared with the normal values, determined by the manufacturer of the device. Some of the recorded indices were insignificantly lower than the norm (POC TA, POC MM, BAR and TORS). Global index and IMPACT were within the reference values and showed highest repeatability in this study.Instrument calibration is an important prerequisite for exact testing. The indices, recorded by this device are rather repeatable, with Global index and Impact found to be the most repeatable ones. The values of the measured indices show that the process of calibration is highly reliable.

The global aging trend is becoming increasingly severe, and the demand for life assistance and medical rehabilitation for frail and disabled elderly people is growing. As the best solution for assisting limb movement, guiding limb rehabilitation, and enhancing limb strength, exoskeleton robots are becoming the focus of attention from all walks of life. This paper reviews the progress of research on upper limb exoskeleton robots, sEMG technology, and intention recognition technology. It analyzes the literature using keyword clustering analysis and comprehensively discusses the application of sEMG technology, deep learning methods, and machine learning methods in the process of human movement intention recognition by exoskeleton robots. It is proposed that the focus of current research is to find algorithms with strong adaptability and high classification accuracy. Finally, traditional machine learning and deep learning algorithms are discussed, and future research directions are proposed, such as using a deep learning algorithm based on multi-information fusion to fuse EEG signals, electromyographic signals, and basic reference signals. A model with stronger generalization ability is obtained after training, thereby improving the accuracy of human movement intention recognition based on sEMG technology, which provides important support for the realization of human–machine fusion-embodied intelligence of exoskeleton robots.

Few studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for monitoring motor rehabilitation progress, this study used sEMG signals from a subset of movements of the Arm Motor Ability Test (AMAT) as inputs into a movement classification algorithm. The importance of a particular frequency domain feature, that is the ratio of the mean absolute values between sub-bands, was discovered in this work. An average classification accuracy of 75.5% was achieved using the proposed approach with a maximum accuracy of 100%. The performance of the proposed method was compared with results obtained using three other classification algorithms: support vector machine (SVM), k-Nearest Neighbors (k-NN), and probabilistic neural network (PNN) in terms of sEMG movement classification. The study demonstrated the capability of using upper limb sEMG signals to identify and distinguish between functional movements used in standard upper limb motor assessments for stroke patients. The classification algorithm used in the proposed method, EPNN, outperformed SVM, k-NN, and PNN.

Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN’s performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN’s reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN’s adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.

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.