Gesture recognition enables intuitive human–robot communication, where surface electromyography (sEMG) provides a minimally invasive interface for detecting motor activity. However, the lack of systematic evaluation of processing pipelines repr...
The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis of surface electromyogra...
Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic desig...
This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis i...
Surface electromyography (sEMG), increasingly delivered through wireless wearable systems, is a key non-invasive tool for the objective monitoring of muscle activation during repetitive motor tasks. The clinical and longitudinal usefulness of wearabl...
Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The mai...
Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEM...
Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuatio...
Muscle fatigue impacts performance in sports, rehabilitation, and daily activities, with surface electromyography (sEMG) widely used for monitoring. In this study, we developed a wearable sEMG device and conducted experiments to create a dataset for...
This paper presents the development and cloud deployment of a system for the segmentation of electromyographic (EMG) signals oriented toward muscle fatigue monitoring in the biceps and triceps. A dataset of 30 subjects was used, resulting in 120 EMG...
Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardw...
Energy-based features such as the Teager–Kaiser energy operator (TKEO) and Multi-resolution Teager–Kaiser Energy Operator (MTEO) are widely used in surface electromyography (sEMG) onset analysis, but require multiplication and squaring. T...
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results in...
14 October 2022
This pilot study aimed to explore a method for characterization of the electromyogram frequency spectrum during a sustained exertion task, performed by the upper limb. Methods: Nine participants underwent an isometric localized muscle fatigue protoco...
The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and flexion of the leg upwards for regular and abnormal...
The estimation of the sEMG–force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the...
Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generali...
Background/Objectives: The number of people who practice gym activities is increasing. Most gym activities take place within a building, and the movements are controlled, making them closed-skill activities. This could decrease the processing speed c...
Gesture recognition using surface electromyography (sEMG) serves many applications, from human–machine interfaces to prosthesis control. Many features have been adopted to enhance recognition accuracy. However, studies mostly compare features u...
In patients with respiratory failure, mechanical ventilation aims to balance respiratory muscle loading and gas exchange. The interplay between the ventilator and the respiratory muscles is an increasingly recognized factor in tailoring ventilatory s...
Surface electromyography signal (sEMG) recognition technology requires a large number of samples to ensure the accuracy of the training results. However, sEMG signals generally have the problems of a small amount of data, complicated acquisition proc...
Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to p...
Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and c...
Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behav...
To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults h...
Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of s...
The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exerc...
Background: Overground Robot-Assisted Gait Training (o-RAGT) appears to be a promising stroke rehabilitation in terms of clinical outcomes. The literature on surface ElectroMyoGraphy (sEMG) assessment in o-RAGT is limited. This paper aimed to assess...
Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition...
The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb exte...
Background: Falls among the elderly are a major public health concern, leading to increased disability and mortality. Traditional protective measures are important, but early detection and prevention are equally critical. Surface electromyography (sE...
Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, howeve...
Gesture recognition based on surface electromyography (sEMG) signals typically involves extracting features from the signals and then incorporating recognition models to increase the accuracy of classification. In this paper, drawing on the propertie...
Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study i...
The stomatognathic system represents an important element of human physiology, constituting a part of the digestive, respiratory, and sensory systems. One of the signs of temporomandibular joint disorders (TMD) can be the formation of vibroacoustic a...
sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belo...
3D printing of soft EMG sensing structures enables the creation of personalized sensing structures that can be potentially integrated in prosthetic, assistive and other devices. We developed and characterized flexible carbon-black doped TPU-based sEM...
This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows...
Surface electromyography (sEMG) signals reflect muscle contraction and hence, can provide information regarding a user’s movement intention. High-density sEMG systems have been proposed to measure muscle activity in small areas and to estimate comple...
Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be...
18 January 2021
The aim of the study was to determine the between-trial and between-day reliability of the Glazer protocol and our multi-activity surface electromyography (sEMG) measurement protocol for pelvic floor muscle (PFM) evaluation. The bioelectrical activit...
Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing...
The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract a...
Surface electromyogram (sEMG) signals have been used in human motion intention recognition, which has significant application prospects in the fields of rehabilitation medicine and cognitive science. However, some valuable dynamic information on uppe...
The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) me...
The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventi...
Surface electromyography (sEMG) signals are inevitably affected by noise during acquisition, thereby degrading signal quality and analytical reliability. Most existing denoising methods combine signal decomposition with thresholding, and their perfor...
Gait is a complex motor process that involves the coordination and synchronization of various body parts through continuous interaction with the environment. Monitoring gait is crucial for the early detection of abnormalities, such as toe walking, wh...
10 March 2023
This study evaluated the occlusal relationships in students with bruxism, using the T-Scan III system, and their correlation with the activity of the masticatory muscles assessed through surface electromyography (sEMG). The study group was divided in...
A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classificatio...
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