Technology and instrumentation for detection and conditioning of the surface electromyographic signal: state of the art

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1 ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

Development of Surface EMG for Gait Analysis and Rehabilitation of Hemiparetic Patients

BACKGROUND

The quantification of electromyographic activity using surface electrodes is invaluable for understanding gait disorders in patients with central nervous system lesions. We propose to evaluate a commercially available low-cost system compared to a reference system in participants with stroke-related movement disorders in functional situations.

METHODS

Three hemiparetic participants performed three functional tasks: two treadmill walks at different speeds and a sit-to-stand test. The vastus lateralis and gastrocnemius medialis muscles were equipped with two EMG sensors. The comparison between the two EMG systems was based on 883 identified cycles. Spearman's correlation coefficients (SCs), linear correlation coefficients (LCCs), and cross-correlation coefficients (CCCs) were calculated.

RESULTS

The main results indicate good to very good similarity of the EMG signals collected from the two tested sEMG systems. In the comfortable-walking condition, an SC of 0.894 ± 0.091 and an LCC of 0.909 ± 0.094 were noted. In the fast-walking condition, an SC of 0.918 ± 0.064 and an LCC of 0.935 ± 0.056 were observed. For the 1 min sit-to-stand test, an SC of 0.880 ± 0.058 and an LCC of 0.881 ± 0.065 were noted.

CONCLUSIONS

This study demonstrates good to very good similarity between the two sEMG systems, enabling the analysis of muscle activity during functional tasks.

Surface Electromyography-Based Recognition of Electronic Taste Sensations

Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.

The correlation of gait and muscle activation characteristics with locomotion dysfunction grade in elderly individuals

Objective

To investigate the differences and regularity of gait and muscle activation characteristics parameters in the Locomotion Dysfunction Grade (LDG) scale assessment in elderly individuals, and analyse the correlation between objective parameters and scale grading. Thus, to propose a novel detection mode for elderly individuals, which combined the LDG scale with objective detection. It can not only provide quantitative data for intelligent evaluation and rehabilitation, but also provided more accurate reference for the classification of care levels in elderly care policies.

Methods

Elderly individuals (n = 159) who underwent gait analysis and sEMG at the Chinese Rehabilitation Research Center from January 2019 to September 2023 were included. According to the LDG scale, the elderly individuals were divided into four groups, namely, the LDG4, LDG5, LDG6 groups and the healthy control group. Four indicators, namely, spatiotemporal, kinematic, dynamic gait parameters and muscle activation characteristics data, were collected. Changes in these characteristics of elderly individuals with lower extremity motor dysfunction were evaluated and analysed statistically.

Results

The spatiotemporal gait parameters were significantly lower in the LDG4, LDG5, LDG6 groups than in the healthy control group. The double support phase was positively correlated with the LDG, while the swing phase, step length and velocity were negatively correlated (P < 0.05). The movement angles of both hips, knees and ankles were significantly limited and negatively correlated with the LDG (P < 0.05). Compared with those in the healthy control group, the centre of pressure (COP) path length were greater, and the average COP velocity was significantly lower (P < 0.05) in the LDG4, LDG5, LDG6 groups. The regularity of muscle activation clearly changed. The root mean square of the gastrocnemius medialis was positively correlated with LDG (P < 0.05), while the tibialis anterior showed no regularity.

Conclusion

As the LDG increased, the differences in spatiotemporal, kinematic and dynamic gait parameters between elderly individuals with motor dysfunction and the healthy individuals gradually increased. The muscle activation characteristics parameters showed an abnormal activation pattern. These parameters were correlated with the LDG, providing a more comprehensive and objective assessment of lower extremity motor function in elderly individuals, improve assessment accuracy, and help accurate rehabilitation.

Equivalent weight: Application of the assessment method on real task conducted by railway workers wearing a back support exoskeleton

Commonly used risk indexes, such as the NIOSH Lifting Index, do not capture the effect of exoskeletons. This makes it difficult for Health and Safety professionals to rigorously assess the benefit of such devices. The community requires a simple method to assess the effectiveness of back-support exoskeleton's (BSE) in possibly reducing ergonomic risk. The method introduced in this work is termed "Equivalent Weight" (EqW) and it proposes an interpretation of the effect built on the benefit delivered through reduced activation of the erector spinae (ES). This manifests itself as an apparent reduction of the lifted load perceived by the wearer. This work presents a pilot study where a practical application of the EqW method is used to assess the ergonomic risk in manual material handling (MMH) when using a back support exoskeleton (StreamEXO). The results are assessed by combining observational measurements from on-site testing with five different workers and quantitative measures of the muscle activity reduction achieved during laboratory evaluation with ten workers. These results will show that when lifting, lowering, and carrying a 19 kg load the StreamEXO can reduce risk by up to two levels (from "high" to "low") in the target sub-tasks. The Lifting index (LI) was reduced up to 64% when examining specific sub-tasks and the worker's movement conduction.

Anthropometric, neuromuscular, physiologic and training variables as determinants to laboratory cycling performance

BACKGROUND

The goal of this study was to verify whether anthropometric, physiological and neuromuscular factors, as well as training characteristics, could predict cycling performance during maximal incremental and time-to-exhaustion tests.

METHODS

Twenty cyclists were evaluated: Anthropometric variables, knee extensor muscle activation and architecture, training history, and training volume were assessed. Second ventilatory threshold (VT2), maximal oxygen uptake (VO2MAX), and maximal power output (POMAX) were assessed during the incremental test. Muscle architecture of the vastus lateralis (VL) and rectus femoris (RF) muscles was evaluated bilaterally to calculate the mean thighs' muscle thickness, pennation angle and fascicle length, at rest condition. After that, time-to-exhaustion test at POMAX was performed. Muscle activation of the VL, RF and vastus medialis (VM) was evaluated of both legs.

RESULTS

Cyclists' height (r2=0.37), experience time and training volume (r2=0.46) were predictors of POMAX (P<0.02), while cadence (r2=0.30) was the only predictive variable for the time-to-exhaustion performance (P<0.01).

CONCLUSIONS

These results suggest that training characteristics and experience are important when training for incremental cycling conditions, whereas cadence (and its determinant variables) should be looked at during maximal and exhaustive conditions.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

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.

Electromyography: Processing, Muscles' Electric Signal Analysis, and Use in Myofunctional Orthodontics

Electromyography, commonly known as EMG, utilizes superficial or needle electrodes to record and analyze the fundamental electrical characteristics of skeletal muscles, determining whether the muscles are contracting. The motor unit, which consists of a collection of group muscle fibers and the motor neurons that govern them, is the structural basis of EMG. Three types of electrode are used in EMG which are needle electrode, fine wire electrode, and surface electrode. A significant amount of literature indicates that the correction of muscle function affects the relationships between teeth within the same jaw and between the jaws on opposing sides. The mechanism of action in myofunctional appliance therapy is linked to neuromuscular and skeletal adaptations resulting from altered function in the orofacial region. Both myofunctional therapy and orthodontics aim to address abnormal muscular behavior, restore abnormal muscle activity, and maintain proper alignment in various areas, including the lips, lower jaw, and tongue. This knowledge is essential for functions such as swallowing, speaking, chewing, and respiration as well as for minimizing incorrect movements and positioning. This article aims to describe the application of surface EMG as a diagnosis tool for assessing muscle activities in various orthodontic disorders, such as class II malocclusion open bite, crossbite, maxillary constriction, cleft lip and palate (CLP), and temporomandibular dysfunction, in patients. The electrodes used in EMG can be utilized to detect bioelectric activity in the muscles of the jaws and abnormalities in jaw movement. Analyzing EMG data is vital for obtaining a comprehensive understanding of the masticatory muscle system.

Robust neural decoding for dexterous control of robotic hand kinematics

BACKGROUND

Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand.

METHODS

High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time.

RESULTS

Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers.

CONCLUSIONS

This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

A diagnostic model of nerve root compression localization in lower lumbar disc herniation based on random forest algorithm and surface electromyography

Objective

This study aimed to investigate the muscle activation of patients with lumbar disc herniation (LDH) during walking by surface electromyography (SEMG) and establish a diagnostic model based on SEMG parameters using random forest (RF) algorithm for localization diagnosis of compressed nerve root in LDH patients.

Methods

Fifty-eight patients with LDH and thirty healthy subjects were recruited. The SEMG of tibialis anterior (TA) and lateral gastrocnemius (LG) were collected bilaterally during walking. The peak root mean square (RMS-peak), RMS-peak time, mean power frequency (MPF), and median frequency (MF) were analyzed. A diagnostic model based on SEMG parameters using RF algorithm was established to locate compressed nerve root, and repeated reservation experiments were conducted for verification. The study evaluated the diagnostic efficiency of the model using accuracy, precision, recall rate, F1-score, Kappa value, and area under the receiver operating characteristic (ROC) curve.

Results

The results showed that delayed activation of TA and decreased activation of LG were observed in the L5 group, while decreased activation of LG and earlier activation of LG were observed in the S1 group. The RF model based on eight SEMG parameters showed an average accuracy of 84%, with an area under the ROC curve of 0.93. The RMS peak time of TA was identified as the most important SEMG parameter.

Conclusion

These findings suggest that the RF model can assist in the localization diagnosis of compressed nerve roots in LDH patients, and the SEMG parameters can provide further references for optimizing the diagnosis model in the future.

Dynamic electromyography findings of the lower leg muscles during walking in spastic cerebral palsy children with hindfoot valgus

BACKGROUND

Hindfoot valgus is one of the most prevalent foot deformities in cerebral palsy children. Investigating the muscle activation patterns of cerebral palsy children with hindfoot valgus is crucial to understand their abnormal gait different from typically developing children.

METHODS

Electromyography data of 20 cerebral palsy children with hindfoot valgus and 20 typically developing children were recorded for tibialis anterior, peroneal longus, and gastrocnemius medialis. The activation onset and offset times, normalized peak electromyography amplitude, average electromyography amplitude and integral electromyography amplitude for 20 completed cycles were averaged for data analysis. The co-activation index and activation percentage of peroneal longus were used to evaluate the co-activation level for tibialis anterior and peroneal longus muscles.

FINDINGS

Compared with typically developing children, the activation onset of tibialis anterior and the activation offset of tibialis anterior, peroneal longus, and gastrocnemius medialis were significantly delayed in cerebral palsy children; moreover, the muscle activation durations of tibialis anterior, peroneal longus, and gastrocnemius medialis were significantly longer, and the normalized average electromyography amplitude of tibialis anterior, peroneal longus and gastrocnemius medialis, and the normalized integral electromyography amplitude of tibialis anterior were significantly lower in cerebral palsy children. Furthermore, for cerebral palsy children, the co-activation index was greater, and the peroneal longus muscles activation percentage was lower in the stance phase and greater in the swing phase than that of typically developing children.

INTERPRETATION

The lower leg muscle activation patterns in cerebral palsy children were found to be abnormal.

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

The Evaluation of Facial Muscles by Surface Electromyography in Very Preterm Infants

BACKGROUND

It is reported that 40% of preterm infants have problems with eating. Neonatal feeding disorders may be one of the factors increasing neonatal mortality. The aim of our study was to evaluate the muscles involved in suckling and swallowing in premature newborns using surface electromyography (sEMG). We would like to objectively describe the tension of muscles engaged in feeding in order to properly plan the therapy. Another aim was to compare sEMG measurements to gestational age, birth weight, and umbilical blood pH to show which parameters put children at risk of feeding problems.

METHODS

Sixteen preterm neonates with gestational age less than 32 weeks, birth weight less than 1500 g, and oral feeding difficulties were analyzed for muscle response and electrical activity of nerves using sEMG (surface electromyography).

RESULTS

We found a negative correlation indicating that preterm infants with a younger gestational age had higher suprahyoid muscle tension, and a positive correlation was found between pH value and suprahyoid muscles. The lower the pH value, the lower the tension in the suprahyoid muscles.

CONCLUSIONS

sEMG may be a helpful diagnostic tool in the evaluation of the masticatory system of premature infants. Due to the abnormal tone of the muscles responsible for swallowing, it is advisable to rehabilitate as early as possible.

Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring

Driving is a ubiquitous activity that requires both motor skills and cognitive focus. These aspects become more problematic for some seniors, who have underlining medical conditions and tend to lose some of these capabilities. Therefore, driving can be used as a controlled environment for the frequent, non-intrusive monitoring of bio-physical and cognitive status within drivers. Such information can then be utilized for enhanced assistive vehicle controls and/or driver health monitoring. In this paper, we present a novel multi-modal smart steering sleeve (S3) system with an integrated sensing platform that can non-intrusively and continuously measure a driver's physiological signals, including electrodermal activity (EDA), electromyography (EMG), and hand pressure. The sensor suite was developed by combining low-cost interdigitated electrodes with a piezoresistive force sensor on a single, flexible polymer substrate. Comprehensive characterizations on the sensing modalities were performed with promising results demonstrated. The sweat-sensing unit (SSU) for EDA monitoring works under a 100 Hz alternative current (AC) source. The EMG signal acquired by the EMG-sensing unit (EMGSU) was amplified to within 5 V. The force-sensing unit (FSU) for hand pressure detection has a range of 25 N. This flexible sensor was mounted on an off-the-shelf steering wheel sleeve, making it an add-on system that can be installed on any existing vehicles for convenient and wide-coverage driver monitoring. A cloud-based communication scheme was developed for the ease of data collection and analysis. Sensing platform development, performance, and limitations, as well as other potential applications, are discussed in detail in this paper.

Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography

Repetitive or tiring tasks and movements during manual work can lead to serious musculoskeletal disorders and, consequently, to monetary damage for both the worker and the employer. Among the most common of these tasks is overhead working while operating a heavy tool, such as drilling, painting, and decorating. In such scenarios, it is desirable to provide adaptive support in order to take some of the load off the shoulder joint as needed. However, even to this day, hardly any viable approaches have been tested, which could enable the user to control such assistive devices naturally and in real time. Here, we present and assess the adaptive Paexo Shoulder exoskeleton, an unobtrusive device explicitly designed for this kind of industrial scenario, which can provide a variable amount of support to the shoulders and arms of a user engaged in overhead work. The adaptive Paexo Shoulder exoskeleton is controlled through machine learning applied to force myography. The controller is able to determine the lifted mass and provide the required support in real time. Twelve subjects joined a user study comparing the Paexo driven through this adaptive control to the Paexo locked in a fixed level of support. The results showed that the machine learning algorithm can successfully adapt the level of assistance to the lifted mass. Specifically, adaptive assistance can sensibly reduce the muscle activity's sensitivity to the lifted mass, with an observed relative reduction of up to 31% of the muscular activity observed when lifting 2 kg normalized by the baseline when lifting no mass.

Rapid Detection of Cardiac Pathologies by Neural Networks Using ECG Signals (1D) and sECG Images (3D)

Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. On the other hand, surface electrocardiography (sECG) is a little-explored technique for this diagnosis. sECGs are three-dimensional images (two dimensions in space and one in time). In this way, the signals were taken in one-dimensional format and analyzed using neural networks. Following the transformation of the one-dimensional signals to three-dimensional signals, they were analyzed in the same sense. For this research, two models based on LSTM and ResNet34 neural networks were developed, which showed high accuracy, 98.71% and 93.64%, respectively. This study aims to propose the basis for developing Decision Support Software (DSS) based on machine learning models.

A generic neural network model to estimate populational neural activity for robust neural decoding

BACKGROUND

Robust and continuous neural decoding is crucial for reliable and intuitive neural-machine interactions. This study developed a novel generic neural network model that can continuously predict finger forces based on decoded populational motoneuron firing activities.

METHOD

We implemented convolutional neural networks (CNNs) to learn the mapping from high-density electromyogram (HD-EMG) signals of forearm muscles to populational motoneuron firing frequency. We first extracted the spatiotemporal features of EMG energy and frequency maps to improve learning efficiency, given that EMG signals are intrinsically stochastic. We then established a generic neural network model by training on the populational neuron firing activities of multiple participants. Using a regression model, we continuously predicted individual finger forces in real-time. We compared the force prediction performance with two state-of-the-art approaches: a neuron-decomposition method and a classic EMG-amplitude method.

RESULTS

Our results showed that the generic CNN model outperformed the subject-specific neuron-decomposition method and the EMG-amplitude method, as demonstrated by a higher correlation coefficient between the measured and predicted forces, and a lower force prediction error. In addition, the CNN model revealed more stable force prediction performance over time.

CONCLUSIONS

Overall, our approach provides a generic and efficient continuous neural decoding approach for real-time and robust human-robot interactions.

Effects of workload level on the timing of concentric-eccentric contractions during cycling

BACKGROUND

The mechanical energy required to drive the cranks during cycling depends on concentric and eccentric muscle actions. However, no study to date provided clear evidence on how workload levels affect concentric and eccentric muscle actions during cycling. Therefore, the aim of this study was to investigate the workload effects on the timing of lower limb concentric and eccentric muscle actions, and on joint power production.

METHODS

Twenty-one cyclists participated in the study. At the first session, maximal power output (POMAX) and power output at the first (POVT1) and second (POVT2) ventilatory thresholds were determined during an incremental cycling test. At the second session, cyclists performed three trials (2-min/each) in the workloads determined from their POMAX, POVT1 and POVT2, acquiring data of lower limb muscle activation, pedal forces and kinematics. Concentric and eccentric timings were computed from muscles' activations and muscle-tendon unit excursions along with hip, knee and ankle joints' power production.

RESULTS

Longer rectus femoris eccentric activation (62%), vastus medialis concentric (66%) and eccentric activation (26%) and biceps femoris concentric (29%) and eccentric (133%) activation at POMAX were observed compared to POVT1. Longer positive (12%) and shorter negative (12%) power were observed at the knee joint for POMAX compared to POVT1.

CONCLUSIONS

We conclude that, to sustain higher workload levels, cyclists improved the timing of power transmission from the hip to the knee joint via rectus femoris eccentric, vastus medialis concentric and eccentric and biceps femoris concentric and eccentric contractions.

A Way of Bionic Control Based on EI, EMG, and FMG Signals

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.

Development of an Integrated EEG/fNIRS Brain Function Monitoring System

In this study, a fully integrated electroencephalogram/functional near-infrared spectroscopy (EEG/fNIRS) brain monitoring system was designed to fulfill the demand for a miniaturized, light-weight, low-power-consumption, and low-cost brain monitoring system as a potential tool with which to screen for brain diseases. The system is based on the ADS1298IPAG Analog Front-End (AFE) and can simultaneously acquire two-channel EEG signals with a sampling rate of 250 SPS and six-channel fNIRS signals with a sampling rate of 8 SPS. AFE is controlled by Teensy 3.2 and powered by a lithium polymer battery connected to two protection circuits and regulators. The acquired EEG and fNIRS signals are monitored and stored using a Graphical User Interface (GUI). The system was evaluated by implementing several tests to verify its ability to simultaneously acquire EEG and fNIRS signals. The implemented system can acquire EEG and fNIRS signals with a CMRR of -115 dB, power consumption of 0.75 mW/ch, system weight of 70.5 g, probe weight of 3.1 g, and a total cost of USD 130. The results proved that this system can be qualified as a low-cost, light-weight, low-power-consumption, and fully integrated EEG/fNIRS brain monitoring system.

Time Evolution of the Skin-Electrode Interface Impedance under Different Skin Treatments

A low and stable impedance at the skin-electrode interface is key to high-fidelity acquisition of biosignals, both acutely and in the long term. However, recording quality is highly variable due to the complex nature of human skin. Here, we present an experimental and modeling framework to investigate the interfacial impedance behavior, and describe how skin interventions affect its stability over time. To illustrate this approach, we report experimental measurements on the skin-electrode impedance using pre-gelled, clinical-grade electrodes in healthy human subjects recorded over 24 h following four skin treatments: (i) mechanical abrasion, (ii) chemical exfoliation, (iii) microporation, and (iv) no treatment. In the immediate post-treatment period, mechanical abrasion yields the lowest initial impedance, whereas the other treatments provide modest improvement compared to untreated skin. After 24 h, however, the impedance becomes more uniform across all groups (<20 kΩ at 10 Hz). The impedance data are fitted with an equivalent circuit model of the complete skin-electrode interface, clearly identifying skin-level versus electrode-level contributions to the overall impedance. Using this model, we systematically investigate how time and treatment affect the impedance response, and show that removal of the superficial epidermal layers is essential to achieving a low, long-term stable interface impedance.

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention.

Effectiveness of a combined transcranial direct current stimulation and virtual reality-based intervention on upper limb function in chronic individuals post-stroke with persistent severe hemiparesis: a randomized controlled trial

BACKGROUND

Functional impairments derived from the non-use of severely affected upper limb after stroke have been proposed to be mitigated by action observation and imagination-based techniques, whose effectiveness is enhanced when combined with transcranial direct current stimulation (tDCS). Preliminary studies in mildly impaired individuals in the acute phase post-stroke show intensified effects when action is facilitated by tDCS and mediated by virtual reality (VR) but the effectiveness in cases of severe impairment and chronic stroke is unknown. This study investigated the effectiveness of a combined tDCS and VR-based intervention in the sensorimotor function of chronic individuals post-stroke with persistent severe hemiparesis compared to conventional physical therapy.

METHODS

Twenty-nine participants were randomized into an experimental group, who received 30 minutes of the combined tDCS and VR-based therapy and 30 minutes of conventional physical therapy, or a control group, who exclusively received conventional physical therapy focusing on passive and active assistive range of motion exercises. The sensorimotor function of all participants was assessed before and after 25 one-hour sessions, administered three to five times a week, using the upper extremity subscale of the Fugl-Meyer Assessment, the time and ability subscales of the Wolf Motor Function Test, and the Nottingham Sensory Assessment.

RESULTS

A clinically meaningful improvement of the upper limb motor function was consistently revealed in all motor measures after the experimental intervention, but not after conventional physical therapy. Similar limited effects were detected in the sensory function in both groups.

CONCLUSION

The combined tDCS and VR-based paradigm provided not only greater but also clinically meaningful improvement in the motor function (and similar sensory effects) in comparison to conventional physical therapy.

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

Design and Characterization of a Textile Electrode System for the Detection of High-Density sEMG

Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks.

Multimodal Data Fusion of Electromyography and Acoustic Signals for Thai Syllable Recognition

Speech disorders such as dysarthria are common and frequent after suffering a stroke. Speech rehabilitation performed by a speech-language pathologist is needed to improve and recover. However, in Thailand, there is a shortage of speech-language pathologists. In this paper, we present a syllable recognition system, which can be deployable in a speech rehabilitation system to provide support to the limited speech-language pathologists available. The proposed system is based on a multimodal fusion of acoustic signal and surface electromyography (sEMG) collected from facial muscles. Multimodal data fusion is studied to improve signal collection under noisy situations while reducing the number of electrodes needed. The signals are simultaneously collected while articulating 12 Thai syllables designed for rehabilitation exercises. Several features are extracted from sEMG signals and five channels are studied. The best combination of features and channels is chosen to be fused with the mel-frequency cepstral coefficients extracted from the acoustic signal. The feature vector from each signal source is projected by spectral regression extreme learning machine and concatenated. Data from seven healthy subjects were collected for evaluation purposes. Results show that the multimodal fusion outperforms the use of a single signal source achieving up to [Formula: see text] of accuracy. In other words, an accuracy improvement up to [Formula: see text] can be achieved when using the proposed multimodal fusion. Moreover, its low standard deviations in classification accuracy compared to those from the unimodal fusion indicate the improvement in the robustness of the syllable recognition.

Effects of Caffeine Chewing Gum on Exercise Tolerance and Neuromuscular Responses in Well-Trained Runners

ABSTRACT

Dittrich, N, Serpa, MC, Lemos, EC, De Lucas, RD, and Guglielmo, LGA. Effects of caffeine chewing gum on exercise tolerance and neuromuscular responses in well-trained runners. J Strength Cond Res 35(6): 1671-1676, 2021-This study aimed to investigate the effects of caffeinated chewing gum on endurance exercise, neuromuscular properties, and rate of perceived exertion on exercise tolerance. Twelve trained male runners (31.3 ± 6.4 years; 70.5 ± 6.6 kg; 175.2 ± 6.2 cm; 9.4 ± 2.7% body fat; and V̇o2max = 62.0 ± 4.2 ml·kg-1·min-1) took part of the study. The athletes performed an intermittent treadmill test to determine maximal aerobic speed and delta 50% (Δ50%) intensity. In the following visits, they performed 2 randomized time to exhaustion tests (15.4 ± 0.7 km·h-1) after the ingestion of 300 mg of caffeine in a double-blind, crossover, randomized design. Maximal voluntary contraction of the knee extensor associated to surface electromyographic recording and the twitch interpolation technique were assessed before and immediately after the tests to quantify neuromuscular fatigue of the knee extensor muscles. Caffeine significantly improved exercise tolerance by 18% (p < 0.01). Neuromuscular responses decreased similarly after time to exhaustion in both exercise conditions; however, athletes were able to run a longer distance in the caffeine condition. The performance improvement induced by caffeine seems to have a neuromuscular contribution because athletes were able to run a longer distance with the same neuromuscular impairment.

Reliability of knee extensor neuromuscular structure and function and functional tests' performance

INTRODUCTION

The aim of this study was to evaluate the intra and inter-rater and inter-analyzer reliability of neuromuscular variables and functional tests.

METHODS

Cross-sectional crossover design. Two independent raters and analyzers evaluated twenty-two healthy subjects. Knee-extensor strength was assessed from three maximal voluntary isometric contractions. Muscle activation was obtained from the vastus lateralis (VL), rectus femoris (RF), and vastus medialis (VM) muscles. VL and RF muscles' architecture [fascicle length (FL), pennation angle (PA), muscle thickness (MT)] was obtained at rest by ultrasound. The time from five sit-to-stand (STS) trials, and the distance from the 6-min walk test (6MWT) were obtained. Intraclass correlation coefficient was determined and classified as strong (r = 0.75-1.00), moderate (r = 0.40-0.74), and weak (r < 0.40).

RESULTS

Strong intra-rater reliability values were observed for strength (r = 0.97), muscle activation [VL (r = 0.91); RF (r = 0.92); VM (r = 0.80)], VL [FL (r = 0.90); PA (r = 0.94); MT (r = 0.99)] and RF [MT (r = 0.85)] muscle architecture, STS (r = 0.95), and 6MWT (r = 0.98). Inter-rater reliability also presented strong values for strength (r = 0.97), muscle activation [VL (r = 0.94); RF (r = 0.79); VM (r = 0.78)], muscle architecture VL [PA (r = 0.81) and MT (r = 0.88)] and RF [MT (r = 0.80)], STS (r = 0.93), and 6MWT (r = 0.98). A moderate correlation VL muscle architecture [FL (r = 0.69)]. Inter-analyzer muscle architecture reliability presented strong VL [FL (r = 0.77); PA (r = 0.76); MT (r = 0.91)] and RF [MT (r = 0.99)].

CONCLUSION

The high intra and inter-rater and inter-analyzer reliability values for most variables is evidence that they can be used for clinical evaluation. Muscle architecture might need a longer training period by different raters and analyzers to increase reliability.

A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model

The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg-Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.

Equivalent Weight: Connecting Exoskeleton Effectiveness with Ergonomic Risk during Manual Material Handling

Occupational exoskeletons are becoming a concrete solution to mitigate work-related musculoskeletal disorders associated with manual material handling activities. The rationale behind this study is to search for common ground for exoskeleton evaluators to engage in dialogue with corporate Health & Safety professionals while integrating exoskeletons with their workers. This study suggests an innovative interpretation of the effect of a lower-back assistive exoskeleton and related performances that are built on the benefit delivered through reduced activation of the erector spinae musculature. We introduce the concept of "equivalent weight" as the weight perceived by the wearer, and use this to explore the apparent reduced effort needed when assisted by the exoskeleton. Therefore, thanks to this assistance, the muscles experience a lower load. The results of the experimental testing on 12 subjects suggest a beneficial effect for the back that corresponds to an apparent reduction of the lifted weight by a factor of 37.5% (the perceived weight of the handled objects is reduced by over a third). Finally, this analytical method introduces an innovative approach to quantify the ergonomic benefit introduced by the exoskeletons' assistance. This aims to assess the ergonomic risk to support the adoption of exoskeletons in the workplace.

Association between Oxygen Consumption and Surface Electromyographic Amplitude and Its Variation within Individual Calf Muscles during Walking at Various Speeds

Surface electromyography (EMG) has been used to estimate muscle work and physiological burden of the whole body during human movements. However, there are spatial variations in surface EMG responses within individual muscles. The aim of this study was to investigate the relation between oxygen consumption and surface EMG responses of lower leg muscles during walking at various speeds and to quantify its spatial variation within an individual muscle. Nine young males walked on a treadmill at four speeds: preferred minus 1 km/h, preferred, preferred plus 1 km/h, and preferred plus 2 km/h, and the metabolic response was measured based on the expired gas. High-density surface EMG of the tibialis anterior (TA), medial gastrocnemius (MG), lateral gastrocnemius, and soleus muscles was performed using 64 two-dimensional electrode grids. Correlation coefficients between oxygen consumption and the surface EMG amplitude were calculated across the gait speeds for each channel in the electrode grid and for individual muscles. Mean correlation coefficients across electrodes were 0.69–0.87 for the four individual muscles, and the spatial variation of correlation between the surface EMG amplitude and oxygen consumption within an electrode grid was significantly greater in MG muscle than in TA muscle (Quartile deviations: 0.24 for MG and 0.02 for TA, p < 0.05). These results suggest that the physiological burden of the whole body during gait at various speeds can be estimated from the surface EMG amplitude of calf muscles, but we need to note its spatial distribution within the MG muscle.

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human-Robot Collaborative Technologies

Patients affected by neurological pathologies with motor disorders when they are of working age have to cope with problems related to employability, difficulties in working, and premature work interruption. It has been demonstrated that suitable job accommodation plans play a beneficial role in the overall quality of life of pathological subjects. A well-designed return-to-work program should consider several recent innovations in the clinical and ergonomic fields. One of the instrument-based methods used to monitor the effectiveness of ergonomic interventions is surface electromyography (sEMG), a multi-channel, non-invasive, wireless, wearable tool, which allows in-depth analysis of motor coordination mechanisms. Although the scientific literature in this field is extensive, its use remains significantly underexploited and the state-of-the-art technology lags expectations. This is mainly attributable to technical and methodological (electrode-skin impedance, noise, electrode location, size, configuration and distance, presence of crosstalk signals, comfort issues, selection of appropriate sensor setup, sEMG amplitude normalization, definition of correct sEMG-related outcomes and normative data) and cultural limitations. The technical and methodological problems are being resolved or minimized also thanks to the possibility of using reference books and tutorials. Cultural limitations are identified in the traditional use of qualitative approaches at the expense of quantitative measurement-based monitoring methods to design and assess ergonomic interventions and train operators. To bridge the gap between the return-to-work rehabilitation and other disciplines, several teaching courses, accompanied by further electrodes and instrumentations development, should be designed at all Bachelor, Master and PhD of Science levels to enhance the best skills available among physiotherapists, occupational health and safety technicians and ergonomists.

Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users

Wearable devices play an increasing role in the rehabilitation of patients with movement disorders. Although information about muscular activation is highly interesting, no approach exists that allows reliable collection of this information when the sensor is applied autonomously by the patient. This paper aims to demonstrate the proof-of-principle of an innovative sEMG sensor system, which can be used intuitively by patients while detecting their muscular activation with sufficient accuracy. The sEMG sensor system utilizes a multichannel approach based on 16 sEMG leads arranged circularly around the limb. Its design enables a stable contact between the skin surface and the system's dry electrodes, fulfills the SENIAM recommendations regarding the electrode size and inter-electrode distance and facilitates a high temporal resolution. The proof-of-principle was demonstrated by elbow flexion/extension movements of 10 subjects, proving that it has root mean square values and a signal-to-noise ratio comparable to commercial systems based on pre-gelled electrodes. Furthermore, it can be easily placed and removed by patients with reduced arm function and without detailed knowledge about the exact positioning of the sEMG electrodes. With its features, the demonstration of the sEMG sensor system's proof-of-principle positions it as a wearable device that has the potential to monitor muscular activation in home and community settings.

Electromyography activities in patients with lower lumbar disc herniation

BACKGROUND

Lumbar disc herniation (LDH) can affect lower limb muscle function resulting in an abnormal gait. This study aims to use surface electromyography (SEMG) to evaluate patients with L4/L5 and L5/S1 LDH throughout muscle movement.

METHODS

Twenty L4/L5 LDH patients (L5 Group), twenty L5/S1 LDH patients (S1 Group), and twenty healthy controls (Healthy) were recruited for the study. SEMG of bilateral tibialis anterior (TA) and lateral gastrocnemius (LG) muscles of patients were recorded using the DELSYS Wireless EMG System (TrignoTM Wireless Systems, Delsys Inc., USA). Root-mean-square (RMS), mean power frequency (MPF), and median frequency (MF) were compared between bilateral limbs in each participant.

RESULTS

Reduced MPF and MF was found in TA measurements of the L5 Group and LG measurements of the S1 Group. The MPF and MF of the TA of symptomatic limbs of the L5 Group were reduced when compared to asymptomatic limbs (p= 0.006, p= 0.012, p< 0.05), and there were no significant differences in LG measurements (p> 0.05). The LG MPF and MF of the S1 Group in symptomatic limbs were reduced when compared to asymptomatic limbs (p= 0.006, p= 0.017, p< 0.05), and there were no significant differences in TA measurements (p> 0.05). Although there were no significant differences in RMS between bilateral limbs of the L5 and S1 Groups, we found some changes in RMS curves. First, compared to asymptomatic limbs of L4/L5LDH patients, β-peaks in the TA of symptomatic limbs appeared earlier. Second, two peaks in the LG of symptomatic limbs were found in L5/S1 LDH patients.

CONCLUSION

TA is affected in patients with LDH of L4/L5, and LG is affected in patients with LDH of L5/S1. As demonstrated, SEMG can identify LDH-related muscle dysfunction.

Effects of compression garments on surface EMG and physiological responses during and after distance running

BACKGROUND

The few previous studies that focused on the effects of compression garments (CG) on distance running performance have simultaneously measured electromyogram, physiological, and perceptual parameters. Therefore, this study investigated the effects of CG on muscle activation and median frequency during and after distance running, as well as blood-lactate concentration and rating of perceived exertion (RPE) during distance running.

METHODS

Eight healthy male recreational runners were recruited to randomly perform two 40 min treadmill running trials, one with CG, and the other with control garment made of normal cloth. The RPE and the surface electromyography (EMG) of 5 lower extremity muscles including gluteus maximus (GM), rectus femoris (RF), semitendinosus (ST), tibialis anterior (TA), and gastrocnemius (GAS) were measured during the running trial. The blood-lactate levels before and after the running trial were measured.

RESULTS

Wearing CG led to significant lower muscle activation (p  <  0.05) in the GM (decreased 7.40%-14.31%), RF (decreased 4.39%-4.76%), and ST (decreased 3.42%-7.20%) muscles; moreover, significant higher median frequency (p< 0.05) in the GM (increased 5.57%) and ST (increased 10.58%) muscles. Wearing CG did not alter the RPE values or the blood-lactate levels (p > 0.05).

CONCLUSION

Wearing CG was associated with significantly lower muscle activation and higher median frequency in the running-related key muscles during distance running. This finding suggested that wearing CG may improve muscle function, which might enhance running performance and prevent muscle fatigue.

Effect of electrode characteristics on electromyographic activity of the masseter muscle

OBJECTIVES

The study investigated effects of electrode material, inter-electrode distance (IED), and conductive gel on electromyographic (EMG) activity recorded from the masseter muscle.

MATERIALS AND METHODS

EMG was recorded unilaterally, as ten volunteers performed standardized oral tasks. Ag/AgCl and Ag coated with Au were the gel-based; Ag alloy coated with graphene, pure Ag coated with graphene and silver nanowire embedded electrodes were the gel-free materials tested. Ag/AgCl electrodes were tested at three different IEDs (i.e. 15 mm, 20 mm, 25 mm). An electrode relative performance index (ERPI) was defined and calculated for each of the standardized oral tasks that the volunteers performed. ERPI values obtained for the different oral tasks with different electrode materials and IEDs were compared using two-way repeated-measures ANOVA.

RESULTS

ERPI values were not significantly influenced by IED. However, for the electrode materials statistically significant differences were found in ERPI values for all oral tasks. Of the gel-free electrode materials tested, pure silver electrodes coated with graphene had the highest ERPI values followed by Ag alloy electrodes coated with graphene and silver nanowire embedded electrodes.

CONCLUSIONS

Within the limitations of the study, IED between 15 and 25 mm has a negligible effect on masseter muscle EMG. Graphene coated and silver nanowire embedded electrodes show promise as gel-free alternatives.

A review on muscle activation behaviour during gait in shallow water and deep-water running and surface electromyography procedures

BACKGROUND

Surface electromyography (sEMG) can provide information on muscle activation patterns during gait.

OBJECTIVES

To characterize electromyographic activity during gait in shallow water and during deep-water running compare to on land and to review and analyse underwater surface-electromyographic (sEMG) procedures.

SEARCH METHODS

Eight databases (MEDLINE, EMBASE, WEB OF SCIENCE, SPORT Discus, CINAHL, SCOPUS, SCIELO, and LILACS) were searched from their inception to the December of 2019.

SELECTION CRITERIA

The selected studies had to be related to electromyographic analysis of gait in an aquatic environment.

DATA COLLECTION AND ANALYSIS

The studies that met the inclusion criteria were reviewed by two independent reviewers and divided into four groups.

RESULTS

Ten studies met the inclusion criteria. Lower muscle activation was found with treadmill water walking compared to treadmill land walking. With deep-water running, the leg muscles (tibialis anterior and gastrocnemius lateralis) have lower muscle activation when compared to on land running, but the trunk and thigh muscles have higher activation.

CONCLUSION

If gait is performed on an aquatic treadmill, the muscles assessed had lower muscle activation when compared to land. During deep-water running activities, lower activation of the distal leg muscles and a higher activation thigh muscles were found when compared to on land. Studies did not follow standard processes in sEMG procedures.

Muscle Activity After Stroke: Perspectives on Deploying Surface Electromyography in Acute Care

After a stroke, clinicians and patients struggle to determine if and when muscle activity and movement will return. Surface electromyography (EMG) provides a non-invasive window into the nervous system that can be used to monitor muscle activity, but is rarely used in acute care. In this perspective paper, we share our experiences deploying EMG in the clinic to monitor stroke survivors. Our experiences have demonstrated that deploying EMG in acute care is both feasible and useful. We found that current technology can be used to comfortably and non-obtrusively monitor muscle activity, even for patients with no detectable muscle activity by traditional clinical assessments. Monitoring with EMG may help clinicians quantify muscle activity, track recovery, and inform rehabilitation. With further research, we perceive opportunities in using EMG to inform prognosis, enable biofeedback training, and provide metrics necessary for supporting and justifying care. To leverage these opportunities, we have identified important technical challenges and clinical barriers that need to be addressed. Affordable wireless EMG system that can provide high-quality data with comfortable, secure interfaces that can be worn for extended periods are needed. Data from these systems need to be quickly and automatically processed to create round-ready results that can be easily interpreted and used by the clinical team. We believe these challenges can be addressed by integrating and improving current methods and technology. Deploying EMG in the clinic can open new pathways to understanding and improving muscle activity and recovery for individuals with neurologic injury in acute care and beyond.

A gel-free Ti3C2Tx-based electrode array for high-density, high-resolution surface electromyography

Wearable sensors for surface electromyography (EMG) are composed of single- to few-channel large-area contacts, which exhibit high interfacial impedance and require conductive gels or adhesives to record high-fidelity signals. These devices are also limited in their ability to record activation across large muscle groups due to poor spatial coverage. To address these challenges, we have developed a novel high-density EMG array based on titanium carbide (Ti3C2Tx) MXene encapsulated in parylene-C. Ti3C2Tx is a two-dimensional nanomaterial with excellent electrical, electrochemical, and mechanical properties, which forms colloidally stable aqueous dispersions, enabling safe, scalable solutions-processing. Leveraging the excellent combination of metallic conductivity, high pseudocapacitance, and ease of processability of Ti3C2Tx MXene, we demonstrate the fabrication of gel-free, high-density EMG arrays which are ~8 μm thick, feature 16 recording channels, and are highly skin-conformable. The impedance of Ti3C2Tx electrodes in contact with human skin is 100-1000x lower than the impedance of commercially-available electrodes which require conductive gels to be effective. Furthermore, our arrays can record high-fidelity, low-noise EMG, and can resolve muscle activation with improved spatiotemporal resolution and sensitivity compared to conventional gelled electrodes. Overall, our results establish Ti3C2Tx-based bioelectronic interfaces as a powerful platform technology for high-resolution, non-invasive wearable sensing technologies.

Development of Soft sEMG Sensing Structures Using 3D-Printing Technologies

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 sEMG sensing structures. The structures are directly 3D-printed without the need for an additional post-processing step using a low-cost, consumer grade multi-material FDM printer. A comparison between the gold standard Ag/AgCl gel electrodes and the 3D-printed EMG electrodes with a comparable contact area shows that there is no significant difference in the EMG signals' amplitude. The sensors are capable of distinguishing a variable level of muscle activity of the biceps brachii. Furthermore, as a proof of principle, sEMG data of a 3D-printed 8-electrode band are analyzed using a patten recognition algorithm to recognize hand gestures. This work shows that 3D-printed sEMG electrodes have great potential in practical applications.

Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode-skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples. The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial.

Domingues R, Lopes C, Borges J, Haueisen J, Vaz F. Dry Electrodes for Surface Electromyography Based on Architectured Titanium Thin Films

Electrodes of silver/silver chloride (Ag/AgCl) are dominant in clinical settings for surface electromyography (sEMG) recordings. These electrodes need a conductive electrolyte gel to ensure proper performance, which dries during long-term measurements inhibiting the immediate electrode’s reuse and is often linked to skin irritation episodes. To overcome these drawbacks, a new type of dry electrodes based on architectured titanium (Ti) thin films were proposed in this work. The architectured microstructures were zigzags, obtained with different sputtering incidence angles (α), which have been shown to directly influence the films’ porosity and electrical conductivity. The electrodes were prepared using thermoplastic polyurethane (TPU) and stainless-steel (SS) substrates, and their performance was tested in male volunteers (athletes) by recording electromyography (EMG) signals, preceded by electrode-skin impedance measurements. In general, the results showed that both SS and TPU dry electrodes can be used for sEMG recordings. While SS electrodes almost match the signal quality parameters of reference electrodes of Ag/AgCl, the performance of electrodes based on TPU functionalized with a Ti thin film still requires further improvements. Noteworthy was the clear increase of the signal to noise ratios when the thin films’ microstructure evolved from normal growth towards zigzag microstructures, meaning that further tailoring of the thin film microstructure is a possible route to achieve optimized performances. Finally, the developed dry electrodes are reusable and allow for multiple EMG recordings without being replaced.

Action interference in simultaneous and proportional myocontrol: comparing force- and electromyography

Myocontrol, that is, control of a prosthesis via muscle signals, is still a surprisingly hard problem. Recent research indicates that surface electromyography (sEMG), the traditional technique used to detect a subject's intent, could proficiently be replaced, or conjoined with, other techniques (multi-modal myocontrol), with the aim to improve both on dexterity and reliability. Objective. In this paper we present an online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. Approach. Twenty sEMG and FMG sensors in total were used to enforce simultaneous and proportional control of hand opening/closing, wrist pronation/supination and wrist flexion/extension of 12 intact subjects. Main results and Significance. We found that FMG yields in general a better performance than sEMG, and that the main drawback of the sEMG array we used is not the inability to perform a desired action, but rather action interference, that is, the undesired concurrent activation of another action. FMG, on the other hand, causes less interference.

Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles’ activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method’s validity showing its potential in diagnostic and rehabilitation fields.