Littérature scientifique sur le sujet « High density surface electromyography (HD-sEMG) »

Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic “macroscopic” monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91–95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19–27% stronger for HD-sEMG than for “macroscopic” monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.

Superficial skeletal muscle activation is associated with an electric activity. Bidimensional High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique that uses a grid of equally spaced electrodes applied on the skin surface to detect and portray superficial skeletal muscle activation. The goal of the study was to evaluate the feasibility of HD-sEMG to detect electrical activation of skeletal muscle and its application during rehabilitation exercises in horses. To fulfil this aim, activation of the superficial descending pectoral and external abdominal oblique core muscles were measured using HD-sEMG technology during dynamic mobilization exercises to induce lateral bending and flexion/extension tasks of the trunk. Masseter muscle was instrumented during mastication as a control condition. A 64 surface EMG channel wireless system was used with a single 64 electrode grid or a pair of 32 electrode grids. HD-sEMG provided unique information on the muscular activation onset, duration, and offset, along each motor task, and permitting inferences about the motor control strategy actuated by the central nervous system. Signals were further processed to obtain firing frequencies of few motor-neurons. Estimation of electromyographic amplitude and spectral parameters allowed detecting the onset of muscular fatigue during the motor tasks performed. HD-sEMG allows the assessment of muscular activation in horses performing specific motor tasks, supporting its future application in clinical and research settings.

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.

Abstract Diabetic peripheral neuropathy (DPN) is associated with loss of motor units (MUs), which can cause changes in the activation pattern of muscle fibres. This study investigated the pattern of muscle activation using high-density surface electromyography (HD-sEMG) signals from subjects with type 2 diabetes mellitus (T2DM) and DPN. Thirty-five adults participated in the study: 12 healthy subjects (HV), 12 patients with T2DM without DPN (No-DPN) and 11 patients with T2DM with DPN (DPN). HD-sEMG signals were recorded in the tibialis anterior muscle during an isometric contraction of ankle dorsiflexion at 50% of the maximum voluntary isometric contraction (MVIC) during 30-s. The calculated HD-sEMG signals parameters were the normalised root mean square (RMS), normalised median frequency (MDF), coefficient of variation (CoV) and modified entropy (ME). The RMS increased significantly (p = 0.001) with time only for the DPN group, while the MDF decreased significantly (p < 0.01) with time for the three groups. Moreover, the ME was significantly lower (p = 0.005), and CoV was significantly higher (p = 0.003) for the DPN group than the HV group. Using HD-sEMG, we have demonstrated a reduction in the number of MU recruited by individuals with DPN. This study provides proof of concept for the clinical utility of this technique for identifying neuromuscular impairment caused by DPN.

A multi-channel sEMG signal acquisition system based on the analog front-end chip ADS1299 is designed. The whole acquisition system consists of a 2×9 high-density electrode array, ADS1299 multi-channel high-precision A/D conversion chip; A MCU named STM32F103C8, an upper computer, and PC. We carried out electrode array design, The introduction of the function of the ADS1299 chip, and the circuit design of the analog signal acquisition part. The test results show that the acquisition system designed in this paper can ideally collect the sEMG signal of 8 channels on the back of the hand, which proves the effectiveness of this design in extracting weak EMG signals. Therefore, it has reference significance for designing larger-scale sEMG signal acquisition circuits.

In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.

Although the value of high-density surface electromyography (sEMG) has already been proven in fundamental research and for specific diagnostic questions, there is as yet no broad clinical application. This is partly due to limitations of construction principles and application techniques of conventional electrode array systems. We developed a thin, highly flexible, two-dimensional multielectrode sEMG grid, which is manufactured by using flexprint techniques. The material used as electrode carrier (Polyimid, 50 μm thick) allows grids to be cut out in any required shape or size. One universal grid version can therefore be used for many applications, thereby reducing costs. The reusable electrode grid is attached to the skin by using specially prepared double-sided adhesive tape, which allows the selective application of conductive cream only directly below the detection surfaces. To explore the practical possibilities, this technique was applied in single motor unit analysis of the facial musculature. The high mechanical flexibility allowed the electrode grid to follow the skin surface even in areas with very uneven contours, resulting in good electrical connections in the whole recording area. The silverchloride surfaces of the electrodes and their low electrode-to-skin impedances guaranteed high baseline stability and a low signal noise level. The electrode-to-skin attachment proved to withstand saliva and great tensile forces due to mimic contractions. The inexpensive, universally adaptable and minimally obstructive sensor allows the principal advantages of high-density sEMG to be extended to all skeletal muscles accessible from the skin surface and may lay the foundation for more broad clinical application of this noninvasive, two-dimensional sEMG technique.

Surface electromyography (sEMG) array based gesture recognition, which is widely-used, could provide natural surfaces for human-computer interaction. Currently, most existing gesture recognition methods with sEMG array only work with the fixed and pre-defined electrodes configuration. However, changes in the number of electrodes (i.e., increment or decrement) is common in real scenarios due to the variability of physiological electrodes. In this paper, we study this challenging problem and propose a random forest based ensemble learning method, namely feature incremental and decremental ensemble learning (FIDE). FIDE is able to support continuous changes in the number of electrodes by dynamically maintaining the matrix sketches of every sEMG electrode and spatial structure of sEMG array. To evaluate the performance of FIDE, we conduct extensive experiments on three benchmark datasets, including NinaPro, CSL-hdemg, and CapgMyo. Experimental results demonstrate that FIDE outperforms other state-of-the-art methods and has the potential to adapt to the evolution of electrodes in the changing environments. Moreover, based on FIDE, we implement a multi clients/server collaboration system, namely McS, to support feature adaption in real-world environment. By collecting sEMG using two clients (smartphone and personal computer) and adaptively recognizing gestures in the cloud server, FIDE significantly improves the gesture recognition accuracy in electrode increment and decrement circumstances.

Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.

Avec le vieillissement de la population, préserver la fonction musculaire est important pour éviter la perte de mobilité et d'autonomie. De nos jours, la prévention de la maladie musculaire, la sarcopénie, est une préoccupation majeure et des facteurs de risque importants tels que l'âge avancé ainsi que des facteurs modifiables, notamment une faible activité physique et une alimentation déséquilibrée ont été identifiés. Compte tenu de la croissance des populations plus âgées et de la diminution de l'activité physique, qui touche également les jeunes citoyens, la sensibilisation à la qualité musculaire peut être cruciale pour promouvoir un vieillissement en bonne santé dans nos sociétés. Les besoins en évaluations fonctionnelles musculaires ont été exprimés par les chercheurs et les cliniciens. Le groupe de travail européen sur la sarcopénie chez les personnes âgées (EWGSOP) recommande de définir la sarcopénie comme la présence à la fois d'une faible masse musculaire et d'une faible fonction musculaire (force et performance physique). Pour cela, nous avons développé une méthode d’évaluation du vieillissement musculaire, en utilisant une technologie ambulatoire et non invasive, appelée technologie d'électromyographie de surface haute densité (HD-sEMG), à travers un projet de recherche clinique sur cinq catégories d'âge (25 à 74 ans), actifs et sédentaires. Nous avons réalisé une étude comparative avec une analyse complète et multimodale du rectus femoris (RF), muscle impliqué dans les mouvements de la vie quotidienne, pour dévoiler le potentiel prometteur de la technique HD sEMG, par rapport aux techniques cliniques classiques, l’objectif étant de détecter les changements précoces de la qualité de la fonction musculaire impactée par le vieillissement et le niveau d'activité physique. La partie clinique de ce projet de thèse a été financée par une subvention européenne, EIT Health. En analysant principalement la dynamique de contraction musculaire et l'intensité du rectus femoris, nos résultats ont montré que la technique HD-sEMG, était capable de discriminer entre les cinq catégories d'âge de sujets sains physiquement actifs. Plus intéressant encore, les scores HD-sEMG proposés discriminaient entre les participants actifs et sédentaires, de la même catégorie d'âge (45-54 ans), contrairement aux paramètres cliniques et aux autres techniques couramment utilisées (absortiométrie biphotonique par rayons X, DXA et échographie). De plus, ces scores pour les participants sédentaires de cette catégorie d'âge étaient significativement plus proches de ceux des participants actifs des catégories d'âge supérieures (55-64 ans et 65-74 ans). Cela suggère fortement qu'un mode de vie sédentaire semble accélérer le processus de vieillissement musculaire au niveau anatomique et fonctionnel, et ce processus accéléré subtil peut être détecté par la technique HD-sEMG. Ces résultats préliminaires prometteurs pourraient contribuer au développement d’un outil intéressant aux cliniciens pour améliorer à la fois la précision et la sensibilité de l'évaluation musculaire utile pour les programmes de prévention et de réadaptation afin d'éviter ou de retarder la sarcopénie, problème de santé publique actuel alerté par l'Organisation Mondiale de la Santé (OMS) et promouvoir un vieillissement en bonne santé
With the aging of the population, preserving muscle function is important to prevent loss of mobility and autonomy. Nowadays, the prevention of the muscle disease, sarcopenia, is a major concern and important risk factors such as older age as well as modifiable factors including low physical activity and unhealthy diet have been identified. Considering the growth of older populations and the decreased physical activity, which also includes young citizens, muscle quality awareness can be crucial in promoting a healthy aging process in our societies. Muscle functional assessments needs were expressed by researchers and clinicians, The European Working Group on Sarcopenia in Older People (EWGSOP) recommends defining sarcopenia as the presence of both low muscle mass and low muscle function (strength, and physical performance). For this purpose, we have developed a method for muscle aging evaluation, using an ambulatory and non-invasive technology, called high-density surface electromyography (HDsEMG), through a clinical research project on five age categories (25 to 74 yrs.). We performed a comparative study with a complete and multimodal analysis of the rectus femoris, muscle involved in daily life motions, in order to reveal the promising potential of the HD-sEMG technique, compared to conventional clinical techniques, to detect early changes in the quality of muscle function impacted by aging and physical activity level. The clinical part of this thesis project was funded by a European grant, EITH Health. By analyzing both muscle contraction dynamics and intensity of the rectus femoris, our results showed that the HD-sEMG technique, was able to discriminate between the five age categories of healthy physically active subjects. More interestingly, the proposed HD-sEMG scores discriminated between active and sedentary participants, from the same age category(45-54 yrs.), in contrary to clinical parameters and others usual techniques (dual-energy x-ray absorptiometry, DXA and ultrasonography). In addition, these scores for sedentary participants from this age category were significantly closer to those of active participants from higher age categories (55-64 yrs. and 65-74 yrs.). This strongly suggests that sedentary lifestyle seems to accelerate the muscle aging process at both anatomical and functional level, and this subtle accelerated process can be detected by the HD-sEMG technique. These promising preliminary results can contribute to the development of an interesting tool for clinicians to improve both accuracy and sensitivity of functional muscle evaluation useful for prevention and rehabilitation to avoid the effects of unhealthy lifestyle that can potentially lead to sarcopenia. This can support also the actual public health concern alerted by Word Health Organization (WHO) regarding aging and sarcopenia, to promote healthy aging

L'identification rapide ou en temps réel de l'activation spatio-temporelle des unités motrices (UM) qui représentent les unités fonctionnelles du système neuromusculaire est fondamentale dans les applications de contrôle des prothèses et en réhabilitation fonctionnelle. Cependant, cette procédure demande un temps de calcul énorme. Par conséquent, le travail de cette thèse a été consacré à fournir un algorithme permettant l'identification en temps réel des stratégies d'activation spatiale et temporelle des UMs en appliquant des méthodes inverses sur les signaux HD-sEMG (électromyogramme de surface à haute densité) à partir d'une grille placée sur le Biceps Brachial (BB). À cette fin, nous proposons une approche innovante, qui implique l'utilisation de la méthode inverse classique de minimisation de norme et une interpolation de courbe en 3D, à savoir l'approche est nommée CFB-MNE. Cette méthode, fondée sur l'identification inverse (estimation de la norme minimale) couplée à un dictionnaire des potentiels d'action des unités motrices simulées (MUAP) d'un modèle récent et testée sur des simulations, a permis la localisation en temps réel des unités motrices individuelles simulées. Une analyse de robustesse (modifications anatomiques, physiologiques et instrumentales) a ensuite été effectuée pour vérifier l'efficacité de l'algorithme proposé. Enfin, l'algorithme proposé a été testé sur des UMs avec des schémas de recrutement réalistes donnant des résultats prometteurs et encourageants en identification spatiale et temporelle sur trois scenarios. Pour conclure, en perspectives, les résultats prometteurs obtenus suggèrent l'utilisation de l'apprentissage automatique et de l'intelligence artificielle (IA) pour améliorer encore les performances de l'algorithme proposé
Fast or real-time identification of the spatiotemporal activation of Motor Units (MUs), functional units of the neuromuscular system, is fundamental in applications as prosthetic control and rehabilitation guidance but often dictates expensive computational times. Therefore, the thesis work was devoted to providing an algorithm that enables the real-time identification of MU spatial and temporal activation strategies by applying inverse methods on HD-sEMG (high-density surface electromyogram) signals from a grid placed over the Biceps Brachii (BB). For this purpose, we propose an innovative approach, that involves the use of the classical minimum norm inverse method and a 3D fitting curve interpolation, namely CFB-MNE approach. This method, based on inverse identification (minimum norm estimation) coupled to simulated motor unit action potential (MUAP) dictionary from a recent model and tested on simulations, allowed the real time localization of simulated individual motor units. A robustness analysis (anatomical, physiological, and instrumental modifications) was then performed to verify the efficiency of the proposed algorithm. Finally, the proposed algorithm was tested on MUs with realistic recruitment patterns giving promising results in both spatial and temporal identification. To conclude, a door to future perspectives was opened, according to the obtained promising results, suggesting the use of machine learning and artificial intelligence (AI) to further boost the performance of the proposed algorithm

The relationship between sEMG signals and muscle force, and associated joint torque, is an object of study for clinical applications such as rehabilitation robotics and commercial applications as wearable motion control devices. The information type and quality obtained by sEMG can impact the classification and prediction accuracy of ankle joint torque. In this thesis project, HD-sEMG based data was collected together with ankle joint torque measurements from 5 subjects during MVIC of plantarflexors and dorsiflexors. Machine learning approaches ideally suited for nonlinear regression tasks, such as MLP and LSTM, have been implemented and evaluated to best predict joint torque profiles given extracted features from sEMG data. An evaluation of machine learning performances using HD-sEMG data over bipolar sEMG data has been conducted in intra-session, inter-subjective and intra-subjective study cases.