Analysis and Biophysics of Surface EMG for Physiotherapists and Kinesiologists: Toward a Common Language With Rehabilitation Engineers

Children and adolescents with all forms of shoulder instability demonstrate differences in their movement and muscle activity patterns when compared to age- and sex-matched controls

HYPOTHESIS AND BACKGROUND

Shoulder instability (SI) is a complex impairment, and identifying biomarkers that differentiate subgroups is challenging. Children and adolescents with SI (irrespective of etiology) have differences in their movement and muscle activity profiles compared to age- and sex-matched controls (2-tailed). There are limited fundamental movement and muscle activity data for identifying different mechanisms for SI in children and adolescents that can inform subgrouping and treatment allocation.

METHODS

Young people between 8 and 18 years were recruited into 2 groups of SI and age- and sex-matched controls (CG). All forms of SI were included, and young people with coexisting neurologic pathologies or deficits were excluded. Participants attended a single session and carried out 4 unweighted and 3 weighted tasks in which their movements and muscle activity was measured using 3-dimensional (3D) movement analysis and surface electromyography (sEMG). Statistical parametric mapping was used to identify between-group differences.

RESULTS

Data were collected for 30 young people (15 SI [6 male, 9 female] and 15 CG [8 male, 7 female]). The mean (standard deviation) age of the participants was 13.6 years (3.0). The SI group demonstrated consistently more protracted and elevated sternoclavicular joint positions during all movements. Normalized muscle activity in latissimus dorsi was lower in the SI group and had the most statistically significant differences across all movements. Where differences were identified, the SI group also had increased normalized activity of their middle trapezius, posterior deltoid, and biceps muscles but decreased activity of their latissimus dorsi, triceps and anterior deltoid muscles compared with the CG group. No statistically significant differences were found for the pectoralis major across any movements. Weighted tasks produced fewer differences in muscle activity patterns compared with unweighted tasks.

DISCUSSION AND CONCLUSION

Young people with SI may adapt their movements to minimize glenohumeral joint instability. This was demonstrated by reduced variability in acromioclavicular and sternoclavicular joint angles, adoption of different movement strategies across the same joints, and increased activity of the scapular stabilizing muscles, despite achieving similar arm positions to the CG. Young people with SI demonstrated consistent differences in their muscle activity and movement patterns. Consistently observed differences at the shoulder girdle included increased sternoclavicular protraction and elevation accompanied by increased normalized activity of the posterior scapula-stabilizing muscles. Existing methods of measurement may be used to inform clinical decision making; however, further work is needed to evaluate the prognostic and clinical utility of derived 3D and sEMG data for informing decision making within SI.

Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications

INTRODUCTION

Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research.

AREAS COVERED

This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors.

EXPERT OPINION

The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management

BACKGROUND

While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering-Sorensen test) and abnormal muscle activation patterns (i.e., the flexion-extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment.

METHODS

Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering-Sorensen and flexion-extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5.

RESULTS

NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion-extension movements (p < 0.05) that were similar to CSS (p > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60-0.68; RMS: 0.53-0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation (p > 0.05).

CONCLUSIONS

This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests.

Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies

Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.

Technological interventions in functional capacity evaluations: An insight into current applications

BACKGROUND

Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results.

OBJECTIVE

This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments.

METHODS

Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews.

RESULTS

Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5-6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility.

CONCLUSION

The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.

Validity and reliability study of a novel surface electromyography sensor using a well-consolidated electromyography system in individuals with cervical spinal cord injury

STUDY DESIGN

Non-interventional, cross-sectional pilot study.

OBJECTIVES

To establish the validity and reliability of the BioStamp nPoint biosensor (Medidata Solutions, New York, NY, USA [formerly MC10, Inc.]) for measuring electromyography in individuals with cervical spinal cord injury (SCI) by comparing the surface electromyography (sEMG) metrics with the Trigno wireless electromyography system (Delsys, Natick, MA, USA).

SETTING

Participants were recruited from the Shirley Ryan AbilityLab registry.

METHODS

Individuals aged 18-70 years with cervical SCI were evaluated with the two biosensors to capture activity on upper-extremity muscles during two study sessions conducted over 2 days (day 1-consent alone; day 2-two data collections in same session). Time and frequency metrics were captured, and signal-to-noise ratio was determined for each muscle group. Test-retest reliability was determined using Pearson's correlation. Validation of the BioStamp nPoint system was based on Bland-Altmann analysis.

RESULTS

Among the 11 participants, 30.8% had subacute cervical injury at C5-C6; 53.8% were injured within 1 year of the study. Results from the test-retest reliability assessment revealed that most Pearson's correlations between the two sensory measurements were strong (≥0.50). The Bland-Altman analysis found values of the signal-to-noise ratio, frequency, and peak amplitude were within the level of agreement. Signal-to-noise ratios ranged from 7.06 to 22.1.

CONCLUSIONS

In most instances, the performance of the BioStamp nPoint sensors was moderately to strongly correlated with that of the Trigno sensors in all muscle groups tested. The BioStamp nPoint system is a valid and reliable approach to assess sEMG measures in individuals with cervical SCI.

SPONSORSHIP

The present study was supported by AbbVie Inc.

A sensorimotor enhanced neuromusculoskeletal model for simulating postural control of upright standing

The human's upright standing is a complex control process that is not yet fully understood. Postural control models can provide insights into the body's internal control processes of balance behavior. Using physiologically plausible models can also help explaining pathophysiological motion behavior. In this paper, we introduce a neuromusculoskeletal postural control model using sensor feedback consisting of somatosensory, vestibular and visual information. The sagittal plane model was restricted to effectively six degrees of freedom and consisted of nine muscles per leg. Physiologically plausible neural delays were considered for balance control. We applied forward dynamic simulations and a single shooting approach to generate healthy reactive balance behavior during quiet and perturbed upright standing. Control parameters were optimized to minimize muscle effort. We showed that our model is capable of fulfilling the applied tasks successfully. We observed joint angles and ranges of motion in physiologically plausible ranges and comparable to experimental data. This model represents the starting point for subsequent simulations of pathophysiological postural control behavior.

Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals' quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5-15 Hz range to identify significant differences. Upon analysis of the 5-15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering.

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.

Metrology in sEMG and movement analysis: the need for training new figures in clinical rehabilitation

A new educational curriculum for the next generation of physical and occupational therapists is urgent in order to manage the recent fast advances in sensors, measurement technologies and related instrumentation. This is required by the growing role of STEM in rehabilitation, kinesiology, and sport sciences. Surface EMG technology is used in this work as a representative example of similar problems present in movement analysis, exoskeletons, and many other fields. A review of the most relevant articles and international projects in the field of interfacing physical therapy with measurement technology for quantitative assessment of outcome is presented. It is concluded that a new generation of educators is needed as well as a Ph.D. and/or a clinical doctorate degree in physical therapy, still lacking in many countries. It is urgent to consider knowledge translation since it will take many years before any recommended change in teaching will be accepted and show some effect. A call for a "white paper" on rehabilitation metrology is highly auspicable.

Electromyographic biofeedback therapy for improving limb function after stroke: A systematic review and meta-analysis

BACKGROUND

Upper and lower limb impairment is common after stroke. Electromyographic biofeedback therapy is a non-invasive treatment, and its effectiveness in functional rehabilitation of the limb after stroke still remains uncertain.

OBJECTIVE

The objective of this study was to evaluate whether electromyographic biofeedback can improve upper and lower limb dysfunction in stroke patients.

METHODS

PubMed, Embase, Cochrane Library, and Physiotherapy Evidence Database (PEDro) were searched from inception to 1st May 2022. Inclusion criteria were randomized controlled clinical trials of electromyographic biofeedback therapy interventions reporting changes in upper and lower limb function in post-stroke patients. Data were extracted by two independent reviewers and pooled in random-effects models using Review manager (RevMan) software.

RESULTS

Our analyses included 10 studies enrolling a total of 303 participants. Electromyographic biofeedback therapy can effectively improve limb function after stroke (standardized mean difference [SMD], 0.44; 95% confidence interval [CI], 0.12-0.77; P = 0.008) and in subgroup analyses, the effect sizes of short-term effect (SMD, 0.33; 95% CI, 0.02-0.64; P = 0.04) was significant, but the long-term was not (SMD, 0.61; 95% CI, -0.11-1.33; P = 0.10). In addition, Electromyographic biofeedback therapy can improve the active range of motion of shoulder (SMD, 1.49; 95% CI, 2.22; P<0.0001) and wrist joints (SMD, 0.77; 95% CI, 0.13-1.42; P = 0.02) after stroke.

CONCLUSION

In this meta-analysis, electromyographic biofeedback therapy intervention can improve upper and lower limb function in patients with stroke. Short-term (less than one month) improvement after electromyographic biofeedback therapy was supported, while evidence for long-term (more than one month) benefits was lacking. Range of motion in the glenohumeral and wrist joints were improved. Stronger evidence for individualized parameters, such as optimal treatment parameters and intervention period, is needed in the future.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/display_record.php?recordID=267596], identifier [CRD42022354363].

Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics

By observing the actions taken by operators, it is possible to determine the risk level of a work task. One method for achieving this is the recognition of human activity using biosignals and inertial measurements provided to a machine learning algorithm performing such recognition. The aim of this research is to propose a method to automatically recognize physical exertion and reduce noise as much as possible towards the automation of the Job Strain Index (JSI) assessment by using a motion capture wearable device (MindRove armband) and training a quadratic support vector machine (QSVM) model, which is responsible for predicting the exertion depending on the patterns identified. The highest accuracy of the QSVM model was 95.7%, which was achieved by filtering the data, removing outliers and offsets, and performing zero calibration; in addition, EMG signals were normalized. It was determined that, given the job strain index's purpose, physical exertion detection is crucial to computing its intensity in future work.

Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention

Surface electromyography (sEMG) can provide multiplexed information about muscle performance. If current sEMG electrodes are stretchable, arrayed, and able to be used multiple times, they would offer adequate high-quality data for continuous monitoring. The lack of these properties delays the widespread use of sEMG in clinics and in everyday life. Here, we address these constraints by design of an adhesive dry electrode using tannic acid, polyvinyl alcohol, and PEDOT:PSS (TPP). The TPP electrode offers superior stretchability (~200%) and adhesiveness (0.58 N/cm) compared to current electrodes, ensuring stable and long-term contact with the skin for recording (>20 dB; >5 days). In addition, we developed a metal-polymer electrode array patch (MEAP) comprising liquid metal (LM) circuits and TPP electrodes. The MEAP demonstrated better conformability than commercial arrays, resulting in higher signal-to-noise ratio and more stable recordings during muscle movements. Manufactured using scalable screen-printing, these MEAPs feature a completely stretchable material and array architecture, enabling real-time monitoring of muscle stress, fatigue, and tendon displacement. Their potential to reduce muscle and tendon injuries and enhance performance in daily exercise and professional sports holds great promise.

Effects of metacarpophalangeal joint position and finger joint movement speed on lumbrical muscle activity

OBJECTIVES

The effect of metacarpophalangeal joint position and finger joint movement speed on lumbrical muscle activity remains unproven and was examined in this study.

MATERIAL AND METHODS

Twenty-four healthy adults performed flexion-extension movements of the index finger in different metacarpophalangeal joint positions (extension or flexion) and movement speeds (60, 120, 240, and 360 beats per minute). The activities of the first lumbrical, first dorsal interosseous, and extensor digitorum muscles were evaluated using surface electromyography, and compared with those during finger joint extension.

RESULTS

The metacarpophalangeal joint positions affected only lumbrical muscle activity, which was greater during extension. Further, finger movement speed affected the lumbrical and extensor digitorum muscle activities, which increased with increasing movement speeds.

CONCLUSION

The present study suggests that position and movement speed can influence the lumbrical muscle activity during metacarpophalangeal joint extension. These findings may help expound lumbrical function and develop suitable strategies for inducing lumbrical muscle activity.

Surface-Electromyography-Based Co-Contraction Index for Monitoring Upper Limb Improvements in Post-Stroke Rehabilitation: A Pilot Randomized Controlled Trial Secondary Analysis

Persons post-stroke experience excessive muscle co-contraction, and consequently the arm functions are compromised during the activities of daily living. Therefore, identifying instrumental outcome measures able to detect the motor strategy adopted after a stroke is a primary clinical goal. Accordingly, this study aims at verifying whether the surface electromyography (sEMG)-based co-contraction index (CCI) could be a new clinically feasible approach for assessing and monitoring patients' motor performance. Thirty-four persons post-stroke underwent clinical assessment and upper extremity kinematic analysis, including sEMG recordings. The participants were randomized into two treatment groups (robot and usual care groups). Ten healthy subjects provided a normative reference (NR). Frost's CCI was used to quantify the muscle co-contraction of three different agonist/antagonist muscle pairs during an object-placing task. Persons post-stroke showed excessive muscle co-contraction (mean (95% CI): anterior/posterior deltoid CCI: 0.38 (0.34-0.41) p = 0.03; triceps/biceps CCI: 0.46 (0.41-0.50) p = 0.01) compared to NR (anterior/posterior deltoid CCI: 0.29 (0.21-0.36); triceps/biceps CCI: 0.34 (0.30-0.39)). After robot therapy, persons post-stroke exhibited a greater improvement (i.e., reduced CCI) in proximal motor control (anterior/posterior deltoid change score of CCI: -0.02 (-0.07-0.02) p = 0.05) compared to usual care therapy (0.04 (0.00-0.09)). Finally, the findings of the present study indicate that the sEMG-based CCI could be a valuable tool in clinical practice.

A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.

[New perspectives in orthopedics : Developments through neurotechnology and metaverse]

The combination of neurotechnology and metaverse holds high potentials for orthopedics, as it offers a broad spectrum of possibilities to overcome the limits of traditional medical care. The vision of a medical metaverse providing the infrastructure as a link for innovative technologies opens up new opportunities for therapy, medical collaborations and practical, personalized training for aspiring physicians. However, risks and challenges, such as security and privacy, health-related issues, acceptance by patients and doctors, as well as technical hurdles and access to the technologies, remain. Hence, future research and development is paramount. Nonetheless, due to technological progress, the exploration of new research areas, and the improved availability of the technologies paired with cost reduction, the prospects for neurotechnology and metaverse in orthopedics are promising.

Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram

The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800 Hz in electroencephalogram data recorded from elderly subjects (>49 years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150 Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200 Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.

Compensatory muscle activation and spinal curve changes in response to fatigue among adolescent male athletes

BACKGROUND

The prone plank test has been often used to assess the strength and endurance of trunk muscles. We aimed to develop a new measurement protocol to objectively monitor the changes in spinal curves and muscle activity simultaneously.

METHODS

Eleven adolescent male basketball athletes (13-17 years) performed a one-minute plank test. Spinal curvatures (thoracic kyphosis (TK) and lumbar lordosis (LL)) were determined at each time point by optical tracking of markers placed on the spinous processes of 10 vertebrae. Eleven muscles were measured by surface electromyography to determine muscle fatigue via changes in median frequency.

RESULTS

TK significantly increased (p = 0.003) from the first to the last 10 s of the plank test; changes in LL were mixed within the group. Only the rectus abdominis showed consistent and significant fatigue (p < 0.001). The increased spinal curves significantly correlated with the fatigue of biceps femoris (TK: r = -0.75, p = 0.012; LL: r = -0.71, p = 0.019) indicating a compensatory muscle activation and spinal curve changes in response to fatigue.

CONCLUSION

Our protocol may support future researches that aim to objectively evaluate the prone plank test and which posture-related muscles need strengthening for the individual.

Prediction of biomedical signals using deep learning techniques

The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals.

Preservation of functional descending input to paralyzed upper extremity muscles in motor complete cervical spinal cord injury

OBJECTIVE

Spinal cord injury (SCI) is classified as complete or incomplete depending on the extent of sensorimotor preservation below the injury level. However, individuals with complete SCIs can voluntarily activate paralyzed lower limb muscles alone or by engaging non-paralyzed muscles during neurophysiological assessments, indicating presence of residual pathways across the injury. However, similar phenomena have not been explored for the upper extremity (UE) muscles following cervical SCIs.

METHODS

Eighteen individuals with motor complete cervical SCI (AIS A or B) and five age-matched non-injured (NI) individuals performed various UE events against manual resistance during functional neurophysiological assessment (FNPA), and electromyographic (EMG) activity was recorded from UE muscles.

RESULTS

Our findings demonstrated i) voluntary activation of clinically paralyzed muscles as evident from EMG readouts, ii) increased activity in these muscles during events engaging muscles above the injury level, iii) reduced spectral properties of paralyzed muscles in SCI compared to NI participants.

CONCLUSIONS

Functional EMG activity in clinically paralyzed muscles indicate presence of residual pathways across the injury establishing supralesional control over the sublesional neural circuitry.

SIGNIFICANCE

The findings may help explain the neurophysiological basis for UE recovery and can be exploited in designing rehabilitation techniques to facilitate UE recovery following cervical SCIs.

Translation of surface electromyography to clinical and motor rehabilitation applications: The need for new clinical figures

Advanced sensors/electrodes and signal processing techniques provide powerful tools to analyze surface electromyographic signals (sEMG) and their features, to decompose sEMG into the constituent motor unit action potential trains, and to identify synergies, neural muscle drive, and EEG-sEMG coherence. However, despite thousands of articles, dozens of textbooks, tutorials, consensus papers, and European and International efforts, the translation of this knowledge into clinical activities and assessment procedures has been very slow, likely because of lack of clinical studies and competent operators in the field. Understanding and using sEMG-based hardware and software tools requires a level of knowledge of signal processing and interpretation concepts that is multidisciplinary and is not provided by most academic curricula in physiotherapy, movement sciences, neurophysiology, rehabilitation, sport, and occupational medicine. The chasm existing between the available knowledge and its clinical applications in this field is discussed as well as the need for new clinical figures. The need for updating the training of physiotherapists, neurophysiology technicians, and clinical technologists is discussed as well as the required competences of trainers and trainees. Indications and examples are suggested and provide a basis for addressing the problem. Two teaching examples are provided in the Supplementary Material.

Cervical Spondylosis as a Hidden Contributing Factor to Fibromyalgia: A Case Report

The present case study describes the long-term symptomatic remission in a patient with fibromyalgia (FM) after multimodal spinal manipulation. A 44-year-old woman presented with a chronic headache, severe neck pain, shoulder pain, and back pain lasting for 2 years after experiencing domestic violence. She had sleep disorders, fatigue, and depressive mood. Her primary care physician diagnosed her with FM and comorbid depression. Despite treatment with non-steroidal anti-inflammatory drugs, muscle relaxants, anti-depressants, anti-epileptics, acupuncture, and aqua-therapy, she experienced no appreciable relief from her symptoms. The patient then sought a chiropractic evaluation and potential treatment for her symptoms. At presentation, widespread tenderness was palpable over the neck, shoulder, back, anterior chest, abdominal wall, and buttock. Radiographs showed loss of cervical lordosis, widespread degenerative spondylosis, and osteitis pubis. Surface electromyography (sEMG) revealed neck and thoracic paraspinal muscular spasms. The patient was diagnosed with FM based on the American College of Rheumatology diagnostic criteria and the associated comorbidities. Multimodal chiropractic approaches, which consisted of spinal manipulation, massage, and intermittent motorized cervical traction, were used twice weekly to relieve soft-tissues and intervertebral joints and stretch core musculatures. The patient's physical and mental complaints were mostly resolved near the end of 9 months of treatment. Her symptom alleviation was associated with corresponding change in normalized sEMG signal and cervical spine realignment at the 16th- and 26th-month follow-ups. Widespread pain in FM can lead to confused thinking and a lack of awareness of cervical spondylosis. In this example, it is assumed that the noxious cervical inputs triggered an ongoing FM process. Chiropractic treatment blocked noxious inputs coming from pain sources, corrected pain thresholds, and lowered excitability, thereby eradicating FM symptoms.

Spectral properties of physiological mirror activity: an investigation of frequency features and common input between homologous muscles

During unilateral contractions, muscular activation can be detected in both active and resting limbs. In healthy populations, the latter is referred to as physiological mirror activity (pMA). The study of pMA holds implications for clinical applications as well as the understanding of bilateral motor control. However, the underlying mechanisms of pMA remain to be fully resolved. A commonality of prevailing explanatory approaches is the concept of shared neural input. With this study, we, therefore, aimed to investigate neural input in the form of multiple analyses of surface electromyography (sEMG) recordings in the frequency domain. For this purpose, 14 healthy, right-handed males aged 18–35 years were recruited. All participants performed a pinch-force task with the dominant hand in a blockwise manner. In total, 9 blocks of 5 contractions each were completed at 80% of maximum force output. Muscle activity was recorded via sEMG of the first dorsal interosseous muscle of the active and resting hand. We analyzed (1) spectral features as well as (2) intermuscular coherence (IMC). Our results demonstrate a blockwise increase in median frequency, mean frequency, and peak frequency in both hands. Frequency ratio analyses revealed a higher low-frequency component in the resting hand. Although we were able to demonstrate IMC on an individual level, results varied greatly and grand-averaged IMC failed to reach significance. Taken together, our findings imply an overlap of spectral properties between active and passive hands during repeated unilateral contractions. Combined with evidence from previous studies, this suggests a common neural origin between active and resting hands during unilateral contractions possibly resulting from a reduction in interhemispheric inhibition due to high force demands. Nevertheless, the exploratory nature of this study necessitates the classification of our results through follow-up studies.

Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.

Teaching Essential EMG Theory to Kinesiologists and Physical Therapists Using Analogies Visual Descriptions, and Qualitative Analysis of Biophysical Concepts

Electromyography (EMG) is a multidisciplinary field that brings together allied health (kinesiology and physical therapy) and the engineering sciences (biomedical and electrical). Since the physical sciences are used in the measurement of a biological process, the presentation of the theoretical foundations of EMG is most conveniently conducted using math and physics. However, given the multidisciplinary nature of EMG, a course will most likely include students from diverse backgrounds, with varying levels of math and physics. This is a pedagogical paper that outlines an approach for teaching foundational concepts in EMG to kinesiologists and physical therapists that uses a combination of analogies, visual descriptions, and qualitative analysis of biophysical concepts to develop an intuitive understanding for those who are new to surface EMG. The approach focuses on muscle fiber action potentials (MFAPs), motor unit action potentials (MUAPs), and compound muscle action potentials (CMAPs) because changes in these waveforms are much easier to identify and describe in comparison to the surface EMG interference pattern (IP).

Automatic Assessment of Abdominal Exercises for the Treatment of Diastasis Recti Abdominis Using Electromyography and Machine Learning

Diastasis Recti Abdominis (DRA) is a medical condition in which the two sides of the rectus abdominis muscle are separated by at least 2.7 cm. This happens when the collagen sheath that exists between the rectus muscles stretches beyond a certain limit. The recti muscles generally separate and move apart in pregnant women due to the development of fetus in the womb. In some cases, this intramuscular gap will not be closed on its own, leading to DRA. The primary treatment procedures of DRA involve different therapeutic exercises to reduce the inter-recti distance. However, it is tedious for the physiotherapists to constantly monitor the patients and ensure that the exercises are being done correctly. The objective of this research is to analyze the correctness of such performed exercises using electromyogram (EMG) signals and machine learning. To the best of our knowledge, this is the first work reporting the objective evaluation of rehabilitation exercises for DRA. Experimental studies indicate that the surface EMG signals were effective in classifying the correctly and incorrectly performed movements. An extensive analysis was carried out with different machine learning models for classification. It was inferred that the RUSBoosted Ensembled classifier was effective in differentiating these movements with an accuracy of 92.3%.

Analysis of Trunk Neuromuscular Activation During Equine-Assisted Therapy in Older Adults

The three-dimensional movement of the horse in physical therapy is a valuable kinesio-therapeutic phenomenon that simultaneously affects several body systems, including particularly the neuromuscular system. However, the effects of equine-assisted services (EAS) on neuromuscular activation patterns in older adults have not been thoroughly investigated. In this study, we evaluated the impact of a 10-weeks EAS program on trunk muscles in older adults who used a saddle and placed their feet in stirrups for the first 15 minutes and out of stirrups for the remaining 15 minutes of 30-minute EAS sessions. We gathered electromyographic (EMG) data of the trunk muscles five times each on the first, fifth, and 10th sessions: pre-EAS and post-EAS on a stationary horse and at 1-minute, 15-minutes, and 30-minutes on a horse in motion. Participants were 20 adults, aged 60-79 years. We analyzed normalized EMG data with 5 (session time) by 3 (session number) analyses of variance (ANOVAs) with repeated measures and with Bonferroni's testing (p ≤ .05). There was a significant difference over the number of interventions for the right thoracic paravertebral muscle (p = .025) and session time effect for the left trapezius (p = .042), right thoracic paravertebral (p < .001), right and left multifidus (p < .001), and right and left rectus abdominis muscles (p < .001). Thus, trunk muscles in older adults showed complex neuromuscular activation synchronized with the horse's movement, which was influenced by session time and number of interventions. The practical implication of these findings is that EAS can reduce fall risk among elderly adults of both sexes.

Upper Limb Stroke Rehabilitation Using Surface Electromyography: A Systematic Review and Meta-Analysis

Background

Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke.

Aim

Synthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke.

Methods

PubMed, SCOPUS, and PEDro databases were systematically searched for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols.

Results

Twenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to non-sEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I² 55%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I² 73%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline.

Conclusion

This review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions.

Direct Effect of Local Cryotherapy on Muscle Stimulation, Pain and Strength in Male Office Workers with Lateral Epicondylitis, Non-Randomized Clinical Trial Study

Background: Local cryotherapy (LC) is one of the physiotherapeutic methods used in the conservative treatment of lateral epicondylitis (LE). The aim of the study was to verify the direct effect of a single LC procedure on the clinical symptoms of lateral epicondylitis enthesopathy (pain, pain free grip, PFG) and its effect on the bioelectrical properties of the wrist extensor muscles at rest, on maximal contraction and isometric contraction during fatigue. Methods: The study group was 28 men (35.4 ± 6.13 years) with confirmed unilateral epicondylitis. The performed procedures included the assessment of pain (visual analogue scale, VAS), PFG and ARMS (root-mean-square amplitude) and mean frequencies (MNF) of the sEMG signal before (T0) and after (T1) LC on the side with enthesopathy (ECRE) and without enthesopathy (ECRN/E). Results: There was an increase in the ARMS values of the signals recorded during rest and MVC from the ECR muscles both with and without enthesopathy (p = 0.0001, p = 0.006), an increased PFG after LC only on the side with LE (p < 0.0001) and decreased pain (p < 0.0001). During isometric fatigue contraction, a higher ARMS on both the ECRE side (p < 0.0001) and the ECRN/E side (p < 0.0001) was observed after LC treatment, and a lower MNF was observed on both the ECRN/E side (p < 0.0001) and the ECRE side (p < 0.0001) after LC. Conclusions: LC reduces the pain and increases PFG and muscle excitation expressed by ARMS and seems to delay muscle fatigue.

Methodological Considerations in Assessing Interlimb Coordination on Poststroke Gait: A Scoping Review of Biomechanical Approaches and Outcomes

Objective: To identify and summarize biomechanical assessment approaches in interlimb coordination on poststroke gait. Introduction: Interlimb coordination involves complex neurophysiological mechanisms that can be expressed through the biomechanical output. The deepening of this concept would have a significant contribution in gait rehabilitation in patients with an asymmetric neurological impairment as poststroke adults. Inclusion criteria: Poststroke adults (>19 years old), with assessment of interlimb coordination during gait, in an open context, according to the Population, Concept, Context framework. Methods: A literature search was performed in PubMed, Web of Science™, Scopus, and gray literature in Google Scholar™, according to the PRISMA-ScR recommendations. Studies written in Portuguese or English language and published between database inception and 14 November 2021 were included. Qualitative studies, conference proceedings, letters, and editorials were excluded. The main conceptual categories were “author/year”, “study design”, “participant’s characteristics”, “walking conditions”, “instruments” and “outcomes”. Results: The search identified 827 potentially relevant studies, with a remaining seven fulfilling the established criteria. Interlimb coordination was assessed during walking in treadmill (n = 3), overground (n = 3) and both (n = 1). The instruments used monitored electromyography (n = 2), kinetics (n = 2), and kinematics (n = 4) to assess spatiotemporal parameters (n = 4), joint kinematics (n = 2), anteroposterior ground reaction forces (n = 2), and electromyography root mean square (n = 2) outcomes. These outcomes were mostly used to analyze symmetry indices or ratios, to calculate propulsive impulse and external mechanical power produced on the CoM, as well as antagonist coactivation. Conclusions: Assessment of interlimb coordination during gait is important for consideration of natural auto-selected overground walking, using kinematic, kinetic, and EMG instruments. These allow for the collection of the main biomechanical outcomes that could contribute to improve better knowledge of interlimb coordination assessment in poststroke patients.

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.

A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study

Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users' intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with potential skin irritations and discomfort. Alternative dry solid metallic electrodes also face long-term usability and comfort challenges due to their inflexible and non-breathable structures. This is critical when the anatomy of the targeted body region is variable (e.g., residual limbs of individuals with amputation), and conformal contact is essential. In this study, textile electrodes were developed, and their performance in recording EMG signals was compared to gel electrodes. Additionally, to assess the reusability and robustness of the textile electrodes, the effect of 30 consumer washes was investigated. Comparisons were made between the signal-to-noise ratio (SNR), with no statistically significant difference, and with the power spectral density (PSD), showing a high correlation. Subsequently, a fully textile sleeve was fabricated covering the forearm, with 14 textile electrodes. For three individuals, an artificial neural network model was trained, capturing the EMG of 7 distinct finger movements. The personalized models were then used to successfully control a myoelectric prosthetic hand.

Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

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.

Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5-10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)-but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.

Consensus for experimental design in electromyography (CEDE) project: Terminology matrix

Consensus on the definition of common terms in electromyography (EMG) research promotes consistency in the EMG literature and facilitates the integration of research across the field. This paper presents a matrix developed within the Consensus for Experimental Design in Electromyography (CEDE) project, providing definitions for terms used in the EMG literature. The definitions for physiological and technical terms that are common in EMG research are included in two tables, with key information on each definition provided in a comment section. A brief outline of some basic principles for recording and analyzing EMG is included in an appendix, to provide researchers new to EMG with background and context for understanding the definitions of physiological and technical terms. This terminology matrix can be used as a reference to aid researchers new to EMG in reviewing the EMG literature.

Using Surface Electromyography to Evaluate the Efficacy of Governor Vessel Electroacupuncture in Poststroke Lower Limb Spasticity: Study Protocol for a Randomized Controlled Parallel Trial

Background. Lower limb spasticity is a common complication after stroke, which seriously affects the quality of life and rehabilitation of patients. There are different treatment methods for poststroke spasticity. It has been found in clinical practice that governor vessel electroacupuncture (GV-EA) can effectively relieve poststroke upper extremity spasticity, but the efficacy of treatment of lower extremity spasticity needs to be further verified. This study aims to design a randomized controlled trial to evaluate the efficacy of GV-EA in the treatment of poststroke lower limb spasticity. Methods/Design. This is a randomized, controlled trial. Patients (N = 177) will be randomized to receive routine therapeutic drug and rehabilitation treatment plus GV-EA (experimental group) or routine therapeutic drug and rehabilitation treatment plus EA (control group 1) or routine therapeutic drug and rehabilitation treatment (control group 2). All patients will receive 20 sessions of treatment for 4 weeks. The primary outcomes are the RMS value and the Modified Ashworth Scale. Secondary outcomes include the Fugl-Meyer Assessment for Lower Extremity (FMA-LE) and the Modified Barthel Index score. All outcome measures will be evaluated at the beginning and after the intervention (4 weeks). Discussion. This trial will observe the clinical effect of GV-EA on lower extremity spasticity after stroke, especially its influence on surface electromyography characteristics, and provide high-quality experimental evidence for the clinical application of GV-EA based on surface electromyography in the treatment of poststroke lower limb spasticity. Trial Registration. China Clinical Trials Registry No. ChiCTR1900027969. Registered on 7 December 2019.

The influence of resistance training on neuromuscular function in middle-aged and older adults: A systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Deterioration of neuromuscular function is a major mechanism of age-related strength loss. Resistance training (RT) improves muscle strength and mass. However, the effects of RT on neuromuscular adaptations in middle-aged and older adults are unclear.

METHODS

Randomised controlled RT interventions (≥2 weeks) involving adults aged ≥50 years were identified. Primary outcome measures were voluntary activation (VA), electromyographic (EMG) activity during maximal voluntary contraction (MVC), and antagonist coactivation. Data were pooled using a weighted random-effect model. Sub-analyses were conducted by muscle or muscle group and health status of participants. Sensitivity analysis was based on study quality. P < 0.05 indicated statistical significance.

RESULTS

Twenty-seven studies were included. An effect was found for VA (standardised mean difference [SMD] 0.54, 0.01 to 1.07, P = 0.04), This result remained significant following sensitivity analysis involving only studies that were low risk of bias. Subgroup analyses showed an effect for plantar flexor VA (SMD 1.13, 0.20 to 2.06, P = 0.02) and VA in healthy participants (SMD 1.04, 0.32 to 1.76, P = 0.004). There was no effect for EMG activity or antagonist coactivation of any muscle group (P > 0.05).

DISCUSSION

Resistance training did not alter EMG activity or antagonist coactivation in older adults. Sensitivity analysis resulted in the effect for VA remaining significant, indicating that this finding was not dependent on study quality. Studies predominantly involved healthy older adults (78%), limiting the generalisability of these findings to clinical cohorts. Future research should determine the effects of RT on neuromuscular function in people with sarcopenia and age-related syndromes.

Age Differences in Motor Recruitment Patterns of the Shoulder in Dynamic and Isometric Contractions. A Cross-Sectional Study

Aging processes in the musculoskeletal system lead to functional impairments that restrict participation. Purpose: To assess differences in the force and motor recruitment patterns of shoulder muscles between age groups to understand functional disorders. A cross-sectional study comparing 30 adults (20–64) and 30 older adults (>65). Surface electromyography (sEMG) of the middle deltoid, upper and lower trapezius, infraspinatus, and serratus anterior muscles was recorded. Maximum isometric voluntary contraction (MIVC) was determined at 45° glenohumeral abduction. For the sEMG signal registration, concentric and eccentric contraction with and without 1 kg and isometric contraction were requested. Participants abducted the arm from 0° up to an abduction angle of 135° for concentric and eccentric contraction, and from 0° to 45°, and remained there at 80% of the MIVC level while isometrically pushing against a handheld dynamometer. Differences in sEMG amplitudes (root mean square, RMS) of all contractions, but also onset latencies during concentric contraction of each muscle between age groups, were analyzed. Statistical differences in strength (Adults > Older adults; 0.05) existed between groups. No significant differences in RMS values of dynamic contractions were detected, except for the serratus anterior, but there were for isometric contractions of all muscles analyzed (Adults > Older adults; 0.05). The recruitment order varied between age groups, showing a general tendency towards delayed onset times in older adults, except for the upper trapezius muscle. Age differences in muscle recruitment patterns were found, which underscores the importance of developing musculoskeletal data to prevent and guide geriatric shoulder pathologies.

Clinical Relevance of State-of-the-Art Analysis of Surface Electromyography in Cerebral Palsy

Surface electromyography (sEMG) can be used to assess the integrity of the neuromuscular system and its impairment in neurological disorders. Here we will consider several issues related to the current clinical applications, difficulties and limited usage of sEMG for the assessment and rehabilitation of children with cerebral palsy. The uniqueness of this methodology is that it can determine hyperactivity or inactivity of selected muscles, which cannot be assessed by other methods. In addition, it can assist for intervention or muscle/tendon surgery acts, and it can evaluate integrated functioning of the nervous system based on multi-muscle sEMG recordings and assess motor pool activation. The latter aspect is especially important for understanding impairments of the mechanisms of neural controllers rather than malfunction of individual muscles. Although sEMG study is an important tool in both clinical research and neurorehabilitation, the results of a survey on the clinical relevance of sEMG in a typical department of pediatric rehabilitation highlighted its limited clinical usage. We believe that this is due to limited knowledge of the sEMG and its neuromuscular underpinnings by many physiotherapists, as a result of lack of emphasis on this important methodology in the courses taught in physical therapy schools. The lack of reference databases or benchmarking software for sEMG analysis may also contribute to the limited clinical usage. Despite the existence of educational and technical barriers to a widespread use of, sEMG does provide important tools for planning and assessment of rehabilitation treatments for children with cerebral palsy.