Application Of Surface Electromyography Research Articles

Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression

In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R2 of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer’s motion intentions in human–robot collaboration control.

Application of Surface Electromyography in Exercise Fatigue: A Review.

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.

Automatic decomposition of pediatric high density surface EMG: A pilot study

This pilot study presents a novel application of high density surface electromyography (EMG) decomposition in pediatric patients, with a view toward promoting its potential clinical application for examination of pediatric neuromuscular diseases. Automatic progressive FastICA peel-off (APFP) framework was used to decompose high density surface EMG signals recorded from the first dorsal interosseous and abductor pollicis brevis muscles of children with spinal muscular atrophy. The performance of the decomposition with reduced EMG recording channels was also investigated. In total 131 motor units were extracted from 15 trials of 64-channel (8 ​× ​8) surface EMG. The decomposition yield reduced to 102 motor units when 36-channel (6 ​× ​6) was used, among which 84 motor units were common ones with an average matching rate of (95.68 ​± ​5.26) %. The decomposition yield further reduced to 65 motor units when only 16-channel (4 ​× ​4) was used, among which 57 were common ones with an average matching rate of (95.99 ​± ​4.56) %. These results indicate that using the APFP framework single motor unit activity can be reliably and automatically extracted from pediatric surface EMG signals recorded by an electrode array, which can facilitate potential clinical application of surface EMG as an alternative or supplement to invasive needle EMG for examination of neuromuscular diseases in children.

Multi-Joint Angles Estimation of Forearm Motion Using a Regression Model.

To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from 10 participants was 0.87–0.92, pronation/supination motion was 0.72–0.95, and hand grip/open motion was 0.75–0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90–0.97, pronation/supination was 0.84–0.96, hand grip/open was 0.85–0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future.

Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science

The application of surface electromyography (sEMG) in neurology is sometimes limited by a scientific background in the use of sEMG. Students frequently use sEMG only when developing their graduate studies. To reduce these barriers, we promoted a free Winter School on sEMG to Latin American students. The school was a 3-day event with theoretical classes and computer programming in Matlab. Lectures were delivered in Portuguese and Spanish to 50 participants. All lectures were recorded and made available on YouTube®. After the School, participants completed a written exam to receive a certificate. The written exam revealed the average effectiveness of 71 ± 20% in the comprehension of topics addressed during the school. Participants rated the School as “excellent” and considered the event as having changed their thoughts about the use of sEMG. Limited mathematical skills or background were the main barriers identified to follow the lectures and to make use of sEMG. We conclude that the Winter School had a positive impact on participant's formation, especially by showing them the importance of continuous involvement with the concepts related to sEMG to become proficient in its use. From the participant's point of view, the activity was excellent and the follow up of the school on YouTube® suggests that combining face-to-face activities followed by the online availability of lectures is a valid strategy to reinforce the learning process and to reduce barriers in the use of sEMG. Whether similar results would be achieved for a paid registration event in an economically developing region, still requires further investigation.

Surface electromyographic evaluation of the neuromuscular activation of the inspiratory muscles during progressively increased inspiratory flow under inspiratory-resistive loading.

This study aimed to evaluate neuromuscular activation in the scalene and sternocleidomastoid muscles using surface electromyography (EMG) during progressively increased inspiratory flow, produced by increasing the respiratory rate under inspiratory-resistive loading using a mask ventilator. Moreover, we attempted to identify the EMG inflection point (EMGIP) on the graph, at which the root mean square (RMS) of the EMG signal values of the inspiratory muscles against the inspiratory flow velocity acceleration abruptly increases, similarly to the EMG anaerobic threshold (EMGAT) reported during incremental-resistive loading in other skeletal muscles. We measured neuromuscular activation of healthy male subjects and found that the inspiratory flow velocity increased by approximately 1.6-fold. We successfully observed an increase in RMS that corresponded to inspiratory flow acceleration with ρ ≥ 0.7 (Spearman's rank correlation) in 17 of 27 subjects who completed the experimental protocol. To identify EMGIP, we analyzed the fitting to either a straight or non-straight line related to the increasing inspiratory flow and RMS using piecewise linear spline functions. As a result, EMGIP was identified in the scalene and sternocleidomastoid muscles of 17 subjects. We believe that the identification of EMGIP in this study infers the existence of EMGAT in inspiratory muscles. Application of surface EMG, followed by identification of EMGIP, for evaluating the neuromuscular activation of respiratory muscles may be allowed to estimate the signs of the respiratory failure, including labored respiration, objectively and non-invasively accompanied using accessory muscles in clinical respiratory care.

Measurement of Muscle Stiffness in Children with Spastic Cerebral Palsy

Editor: We read with great interest the article in the June 2012 issue of Radiology by Kwon and colleagues (1), who used sonoelastography to demonstrate a difference in stiffness of the medial gastrocnemius muscle between healthy children and those with spastic cerebral palsy. The authors provided a new insight about biomechanical assessment of spasticity in addition to the neurophysiologic aspect. Measurement of muscle stiffness may be more complicated than measurements of the tendons or the plantar fascia (2) because more factors must be controlled. We note that the authors performed sonoelastography while subjects were in a prone position with feet hanging from the edge, but no information was provided regarding the angle of the ankle when performing the measurement. Stiffness of the biarticular gastrocnemius muscle may be increased when the ankle is in dorsiflexion and decreased when the ankle is in plantar flexion. Furthermore, although the authors discontinued scanning whenever reflexive or voluntary contraction of the lower limbs muscle was apparently visualized, application of surface electromyography can provide the solid evidence of muscle relaxation. Spasticity was not defined clearly in the article (1). The definition of spasticity has been widened to “disordered sensorimotor control, resulting from an upper motor neuron lesion, presenting as intermittent or sustained involuntary activations of muscles” (3). Because the modified Ashworth scale (MAS) simply measures muscle resistance to passive movement, which has been described as a grading of muscle stiffness (4), it is reasonable that the authors found significant correlations between the MAS scores and sonoelastographic parameters. However, the MAS is unable to help differentiate between the neural and peripheral contributions. The stiffer gastrocnemius muscle in these children with cerebral palsy may indicate muscle atrophy or fibrosis but has nothing to do with spasticity.

Surface electromyography in the assessment of jaw elevator muscles

Surface electromyography (EMG) allows the non-invasive investigation of the bioelectrical phenomena of muscular contraction. The clinical application of surface EMG recordings has been long debated. This paper reviews the main limitations and the current applications of the surface EMG in the investigation of jaw elevator muscles. Methodological factors associated with the recording of the surface EMG may reduce the reliability and sensitivity of this technique and may have been the cause of controversial results reported in different studies. Despite these problems, several clinical applications of surface EMG in jaw muscles are promising. Moreover, technological advances in signal detection and processing have improved the quality of the information extracted from the surface EMG and furthered our understanding of the anatomy and physiology of the stomatognathic apparatus.

Application of Surface Electromyography in Assessing Muscle Recruitment Patterns in a Six-Minute Continuous Rowing Effort

Specific sequences of muscle coordination exist in movements of every sport. In particular, sports involving repetitive movement patterns such as rowing may rely more heavily on coordinated muscle contraction sequencing in order to produce optimal performance. The aim of this study was to monitor the fatigue patterns of the major muscles engaged during the rowing stroke in rowers of varying abilities during a 6-minute continuous maximal rowing effort on a Concept II rowing ergometer. Sixteen male rowers were categorized into 5 groups based on years of training and their average pace of the 6-minute continuous maximal rowing effort. Continuous surface electromyography signals, recorded from brachioradialis, biceps brachii, middeltoid, rectus abdominis, erector spinae, rectus femoris, biceps femoris, and medial gastrocnemius, were used to investigate the influence of local muscle fatigue on optimal muscle coordination sequences during the rowing exercise. Rowers who performed better on the ergometer test and had more rowing experience tended to portray muscle recruitment patterning, which alternately emphasized different major muscle groups in a form of sharing of workload. This sharing allowed mean peak frequency restitution to take place in some muscles, while others took on more of the workload. The muscles of rowers with less experience and lower levels of performance did not appear to exhibit this same phenomenon known as biodynamic compensation. If coaches have a clearer picture of the fatigue patterns and recruitment strategies occurring in their athletes during a maximal effort row, strength training program adaptations could be made to compensate for weaker areas, which may assist rowers in attaining and sustaining more optimal patterns and strategies throughout the exercise effort.

The application of surface electromyography in the assessment of ergonomic risk factors associated with manual lifting tasks.

The purpose of this study was to evaluate the ergonomic risk factors associated with manual lifting tasks using surface electromyography (EMG). 13 volunteers lifted loads of 6 and 13 kg at two speeds and at two horizontal distances in 3 different postures and three boxes of different sizes, from floor to knuckle height, performing 72 lifting tasks. For each lift, the surface electromyography signals from the erector spinae muscles, bilaterally at T10 and L3, was recorded. The ergonomic risk factors associated with manual lifting tasks were evaluated by comparing the average amplitude of EMG signals from the erector spinae muscles. The EMG average amplitude for lifting the load of 13 kg was 14.3% greater than that for lifting the load of 6 kg (t=-10.93, P<0.01). The EMG average amplitude at the site of L3 was 10.3% greater than that at the site of T10 (t=-7.98, P<0.01). The EMG average amplitude when performing "fast" lift was 5.9% greater than the "slow" lift (t=-4.63, P<0.01). The posture of lifting affected the EMG average amplitude. It was lowest with semi-squat posture and greatest with squat posture (F=27.76, P<0.01). The result of multiple stepwise regression analysis showed that the loads of lifting, the size of box, horizontal distance, posture of lifting, the site of the spine subjected to force, lifting speed were the factors affecting the EMG average amplitude. The most significant factor was the loads of lifting, followed by the site of the spine subjected to force and the lifting speed in terms of risk. The ergonomic risk factors associated with manual lifting tasks includes the loads, posture, lifting speed, horizontal distance, the site of the spine subjected to force etc. The results of signal amplitude of EMG from the erector spinae muscles showed that semi-squat posture is the best posture for lifting tasks.

Introduction to the Special Issue on Surface Electromyography (SEMG)

During the past couple of years surface electromyography (SEMG), with a few exceptions, appears to have been in the doldrums. When Frank Andrasik, Editor of Applied Psychophysiology and Biofeedback, asked me as President of the Surface Electromyographic Society of North America (SESNA) to be the Guest Editor for a special issue of this journal I saw it as an opportunity to review the field and examine its current status. I must admit to being very pleasantly surprised as to what I found. SEMG is alive and thriving throughout North America. The number of different applications from a number of various disciplines was truly amazing. This shaped the nature of the special issue that follows. It became immediately apparent that there was much too much material for the available space. Thus, I decided to limit this issue to (a) the scientific basis of SEMG in the hope that this issue will serve as a resource manual and (b) to applications of SEMG that are unique. Fields involving neuromuscular retraining and incontinence training were thus excluded with a recommendation to Frank that another special issue be written concerning these topics. Initially the members of the Board of Directors of SESNA were invited to submit articles. When this was completed, I invited various individuals to submit articles. This latter process was based upon their recognized status in the field of SEMG and uniqueness of the applications. Space limitations precluded inviting all eminently qualified people. The invitations to submit are solely my responsibility. This issue is divided into two sections. The first section starts with an overview by Jeffrey Cram of the history of SEMG. The second article in this section, written by Gabe Sella, focuses on medical problems that affect the SEMG signal. Richard Sherman covers the basis of SEMG instrumentation, while my colleagues and I, Stuart Donaldson, Mary Donaldson, and Leslie Snelling, address SEMG assessment procedures. The second section covers some rather unique applications of SEMG. Jeffrey Bolek demonstrates how his research group uses SEMG to guide gait training in a pediatric population. Randy Neblett, Robert Gatchel, and Tom Mayer have contributed two papers, one discussing the theory and rationale for incorporating SEMG with stretching and the other providing a clinical guide and illustrative cases. The final two articles focus on the use of SEMG in ergonomics, with Robert Clasby, D. J. Derro, Leslie Snelling, and Stuart Donaldson demonstrating the use in

Methodologies for evaluating electromyographic field data in ergonomics

Surface electromyography (SEMG) is an important tool for work load assessment in ergonomics. Several different approaches using amplitude as well as frequency parameters give fruitful information depending on question at issue in the laboratory as well as in field studies. One basic factor determining the choice of analysis method is whether the SEMG is interpreted as an indicator of forces/torques or pure muscular activation. Two methods for occupational SEMG data reduction representing two different approaches to SEMG applications in ergonomics, Exposure Variation Analysis (EVA), and Joint Analysis of EMG Spectrum and Amplitude (JASA), applied on the same SEMG recording from three muscles during urology surgeon work, have been compared. The EVA method categorised the three muscle recordings as too static with no EMG gaps while the JASA method identifies fatigue in two of the three recordings. The practical relevance of these findings is discussed.

Application of Surface Electromyography in the Assessment of Low Back Pain: A Literature Review

Low back pain is a common and leading cause of disability and results in huge costs associated with lost productivity and treatment. The cause of low back pain is often difficult to ascertain and optimum treatment strategies have not yet been defined. The importance of low back musculature fatigability in the aetiology of low back disorder has been highlighted over the last 15 years. A number of studies have investigated paraspinal muscle activity using surface electromyography. There is great variation in these studies regarding methodology, procedure, type of muscle contraction, sample size employed, and the duration, degree and source of the patients' pain. However, based on this review (1985–99), there appears to be a convincing body of evidence to suggest that surface electromyography is a reliable and valid tool for differentiating low back pain patients from normal subjects and for monitoring rehabilitation programmes.

Application of Surface Electromyography in the Assessment of Low Back Pain: A Literature Review

Application of Surface Electromyography in the Assessment of Low Back Pain: A Literature Review