Methodological aspects of SEMG recordings for force estimation--a tutorial and review

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks

The quadriceps are particularly susceptible to fatigue during repetitive lifting, lowering, and carrying (LLC), affecting worker performance, posture, and ultimately lower-back injury risk. Although robotic exoskeletons have been developed and optimized for specific use cases like lifting-lowering, their controllers lack the versatility or customizability to target critical muscles across many fatiguing tasks. Here, we present a task-adaptive knee exoskeleton controller that automatically modulates virtual springs, dampers, and gravity and inertia compensation to assist squatting, level walking, and ramp and stairs ascent/descent. Unlike end-to-end neural networks, the controller is composed of predictable, bounded components with interpretable parameters that are amenable to data-driven optimization for biomimetic assistance and subsequent application-specific tuning, for example, maximizing quadriceps assistance over multiterrain LLC. When deployed on a backdrivable knee exoskeleton, the assistance torques holistically reduced quadriceps effort across multiterrain LLC tasks (significantly except for level walking) in 10 human users without user-specific calibration. The exoskeleton also significantly improved fatigue-induced deficits in time-based performance and posture during repetitive lifting-lowering. Last, the system facilitated seamless task transitions and garnered a high effectiveness rating postfatigue over a multiterrain circuit. These findings indicate that this versatile control framework can target critical muscles across multiple tasks, specifically mitigating quadriceps fatigue and its deleterious effects.

The Kinematic and Electromyographic Analysis of Roller Skating at Different Speeds on a Treadmill: A Case Study

Elite athletes in speed roller skates perceive skating to be a more demanding exercise for the groin when compared to other cyclic disciplines, increasing their risk of injury. The objective of this study was to monitor the kinematic and electromyographic parameters of roller speed skaters, linearly, on a treadmill, and to compare different skating speeds, one at 20 km/h and one at 32 km/h, at a 1° inclination. The acquisition was carried out by placing an inertial sensor at the level of the first sacral vertebra, and eight surface electromyographic probes on both lower limbs. The kinematic and electromyographic analysis on the treadmill showed that a higher speed requires more muscle activation, in terms of maximum and average values and co-activation, as it not only increases the intrinsic muscle demand in the district, but also the athlete's ability to coordinate the skating technique. The present study allows us to indicate not only how individual muscle districts are activated during skating on a surface different from the road, but also how different speeds affect the overall district load distributions concerning effective force, which is essential for the physiotherapist and kinesiologist for preventive and conditional purposes, while also considering possible variations in the skating technique in linear advancement.

Consensus for experimental design in electromyography (CEDE) project: Application of EMG to estimate muscle force

Skeletal muscles power movement. Deriving the forces produced by individual muscles has applications across various fields including biomechanics, robotics, and rehabilitation. Since direct in vivo measurement of muscle force in humans is invasive and challenging, its estimation through non-invasive methods such as electromyography (EMG) holds considerable appeal. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, summarizes recommendations on the use of EMG to estimate muscle force. The matrix encompasses the use of bipolar surface EMG, high density surface EMG, and intra-muscular EMG (1) to identify the onset of muscle force during isometric contractions, (2) to identify the offset of muscle force during isometric contractions, (3) to identify force fluctuations during isometric contractions, (4) to estimate force during dynamic contractions, and (5) in combination with musculoskeletal models to estimate force during dynamic contractions. For each application, recommendations on the appropriateness of using EMG to estimate force and justification for each recommendation are provided. The achieved consensus makes clear that there are limited scenarios in which EMG can be used to accurately estimate muscle forces. In most cases, it remains important to consider the activation as well as the muscle state and other biomechanical and physiological factors- such as in the context of a formal mechanical model. This matrix is intended to encourage interdisciplinary discussions regarding the integration of EMG with other experimental techniques and to promote advances in the application of EMG towards developing muscle models and musculoskeletal simulations that can accurately predict muscle forces in healthy and clinical populations.

Consensus for experimental design in electromyography (CEDE) project: Checklist for reporting and critically appraising studies using EMG (CEDE-Check)

The diversity in electromyography (EMG) techniques and their reporting present significant challenges across multiple disciplines in research and clinical practice, where EMG is commonly used. To address these challenges and augment the reproducibility and interpretation of studies using EMG, the Consensus for Experimental Design in Electromyography (CEDE) project has developed a checklist (CEDE-Check) to assist researchers to thoroughly report their EMG methodologies. Development involved a multi-stage Delphi process with seventeen EMG experts from various disciplines. After two rounds, consensus was achieved. The final CEDE-Check consists of forty items that address four critical areas that demand precise reporting when EMG is employed: the task investigated, electrode placement, recording electrode characteristics, and acquisition and pre-processing of EMG signals. This checklist aims to guide researchers to accurately report and critically appraise EMG studies, thereby promoting a standardised critical evaluation, and greater scientific rigor in research that uses EMG signals. This approach not only aims to facilitate interpretation of study results and comparisons between studies, but it is also expected to contribute to advancing research quality and facilitate clinical and other practical applications of knowledge generated through the use of EMG.

A Laguerre-Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state

With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG-force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre-Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.

Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left-right, up-down and open-close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.

Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data

Hip muscle weakness can be a precursor to or a result of lower limb injuries. Assessment of hip muscle strength and muscle motor fatigue in the clinic is important for diagnosing and treating hip-related impairments. Muscle motor fatigue can be assessed with surface electromyography (sEMG), however sEMG requires specialized equipment and training. Inertial measurement units (IMUs) are wearable devices used to measure human motion, yet it remains unclear if they can be used as a low-cost alternative method to measure hip muscle fatigue. The goals of this work were to (1) identify which of five pre-selected exercises most consistently and effectively elicited muscle fatigue in the gluteus maximus, gluteus medius, and rectus femoris muscles and (2) determine the relationship between muscle fatigue using sEMG sensors and knee wobble using an IMU device. This work suggests that a wall sit and single leg knee raise activity fatigue the gluteus medius, gluteus maximus, and rectus femoris muscles most reliably (p < 0.05) and that the gluteus medius and gluteus maximus muscles were fatigued to a greater extent than the rectus femoris (p = 0.031 and p = 0.0023, respectively). Additionally, while acceleration data from a single IMU placed on the knee suggested that more knee wobble may be an indicator of muscle fatigue, this single IMU is not capable of reliably assessing fatigue level. These results suggest the wall sit activity could be used as simple, static exercise to elicit hip muscle fatigue in the clinic, and that assessment of knee wobble in addition to other IMU measures could potentially be used to infer muscle fatigue under controlled conditions. Future work examining the relationship between IMU data, muscle fatigue, and multi-limb dynamics should be explored to develop an accessible, low-cost, fast and standardized method to measure fatiguability of the hip muscles in the clinic.

The effect of back squat depth and load on lower body muscle activity in group exercise participants

Les Mills BODYPUMPTM is a resistance training group exercise class with a low load, high repetition format. Squat training in BODYPUMPTM has two key variables: depth and load. The study aim was to determine the effect of these parameters on the mean and peak EMG amplitude of vastus lateralis, gluteus maximus, biceps femoris and lateral gastrocnemius. Ten female BODYPUMPTM participants (age 41 ± 9 years, height 161.9 ± 3.8 cm, mass 67.7 ± 7.0 kg) performed 1 × 7 squats under four conditions, representing every combination of two depths (90° knee angle and 125° knee angle) and two loads (23% bodyweight and 38% bodyweight). The main effect of depth was significant for mean and peak activity of vastus lateralis and gluteus maximus, and peak activity of biceps femoris and lateral gastrocnemius. The main effect of load was significant for mean and peak activity of gluteus maximus and lateral gastrocnemius. There was no depth * load interaction. These data can be used to inform BODYPUMPTM programme design and amplify the training effect of participation in group exercise classes.

A Biomechanical Comparison Between the Safety-Squat Bar and Traditional Barbell Back Squat

ABSTRACT

Johansson, DG, Marchetti, PH, Stecyk, SD, and Flanagan, SP. A biomechanical comparison between the safety-squat bar and traditional barbell back squat. J Strength Cond Res 38(5): 825-834, 2024-The primary objectives for this investigation were to compare the kinematic and kinetic differences between performing a parallel back squat using a traditional barbell (TB) or a safety-squat bar (SSB). Fifteen healthy, recreationally trained male subjects (23 + 4 years of age) performed the back squat with a TB and an SSB at 85% of their respective 1 repetition maximum with each barbell while instrumented for biomechanical analysis. Standard inverse dynamics techniques were used to determine joint kinematic and kinetic measures. A 2 × 3 (exercise × joint) factorial analysis of variance with repeated measures was used to determine the kinetic and kinematic differences between the squats while using the different barbells. Fisher's least significant difference post hoc comparisons showed that the TB resulted in significantly greater maximum hip flexion angle (129.33 ± 11.8° vs. 122.11 ± 12.1°; p < 0.001; d = 1.80), peak hip net joint extensor torque (2.54 ± 0.4 Nm·kg -1 vs. 2.40 ± 0.4 Nm·kg -1 ; p = 0.001; d = 1.10), hip net extensor torque mechanical energy expenditure (MEE; 2.81 ± 0.5 Nm·kg -1 vs. 2.58 ± 0.6 Nm·kg -1 ; p = 0.002; d = 0.97), and ankle net joint plantar flexor torque MEE (0.32 ± 0.09 J·kg -1 vs. 0.28 ± 0.06 J·kg -1 ; p = 0.029; d = 0.63), while also lifting significantly (123.17 ± 20.8 kg vs. 117.17 ± 20.8 kg; p = 0.005; d = 0.858) more weight than the SSB. The SSB resulted in significantly higher maximum knee flexion angles (116.82 ± 5.8° vs. 115.65 ± 5.6°; p = 0.011; d = 0.75) than the TB, with no significant difference in kinetics at the knee. The TB may be preferred to the SSB for developing the hip extensors and lifting higher maximum loads. The SSB may be advantageous in situations where a more upright posture or a lower load is preferred while creating a similar demand for the knee joint.

Using force or EMG envelope as feedback signal for motor control system

PURPOSE

This work studied muscle neuro-mechanics during symmetrical up-going ramp (UGR) and down-going ramp (DGR).

AIM

to evaluate during the modulation of muscular action the outcome of force feedback (FF) or neural feedback (NF) on the behavior of the trailing signals - i.e. the EMG envelope (eEMG) for FF or force signal for NF.

METHOD

Subjects: 20. Investigated muscles: dorsal interosseous (FDI) and tibialis anterior (TA). Detected signals: force and EMG. Visual feedback: force (FF), eEMG (NF). Effort triangles: ramps duration 7.5 s, vertex at 50 and 100 % of the maximal voluntary action. Eventually, each subject performed FF50%, FF100%, NF50% and NF100% per each muscle. In each condition the areas beneath the force and eEMG signals were computed to calculate the ratios between the DGR and UGR values during the different tasks (force area DGR / force area UGR; eEMG area DGR / eEMG area UGR). Electro-mechanical coupling efficiency (EMCE) was estimated through the eEMG area / force area ratio for both UGR and DGR in each condition.

RESULTS

a) FF. FDI: eEMG area ratio was 0.84 ± 0.15 and 0.73 ± 0.17 for FF50% and FF100%, respectively. TA: eEMG area ratio was 0.88 ± 0.11 and 0.91 ± 0.17 for FF50% and FF100%, respectively. b) NF: FDI: force area ratio was 1.18 ± 0.13 and 1.17 ± 0.13 for NF50% and NF100%, respectively. TA: force area ratio was 1.17 ± 0.21 and 1.07 ± 0.19 for NF50% and NF100%, respectively. c) DGR EMCE was greater than UGR EMCE in all four tasks.

CONCLUSION

The influence of UGR on deployed EMCE in the following force decrement phase underpins the changes of trailing signals area during DGR. This underlines the necessity of a careful evaluation of the features of FF or NF for experimental studies or rehabilitation purposes involving the motor control system.

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data). Using a deep multimodal convolutional neural network, consisting of two CNN models, one with high-density (HD) EMG and one with motion data recorded by an Inertial Measurement Unit (IMU), our proposed TL technique significantly improved force modeling compared to leave-one-subject-out (LOSO) and even intra-subject scenarios. The TL approach increased the average R squared values of the force modeling task by 60.81%, 190.53%, and 199.79% compared to the LOSO case, and by 13.4%, 36.88%, and 45.51% compared to the intra-subject case for isotonic, isokinetic and dynamic conditions, respectively. These results show that it is possible to adapt to a new user with minimal data while improving performance significantly compared to the intra-subject scenario. We also show that TL can be used to generalize on a new experimental condition for a new user.

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.

Monitoring Active Patient Participation During Robotic Rehabilitation: Comparison Between a Robot-Based Metric and an EMG-Based Metric

While rehabilitation robots present a much-needed solution to improving early mobilization therapy in demanding clinical settings, they also present new challenges and opportunities in patient monitoring. Aside from the fundamental challenge of quantifying a patient's voluntary contribution during robot-led therapy motion, many sensors cannot be used in clinical settings due to time and space limitations. In this paper, we present and compare two metrics for monitoring a patient's active participation in the motion. The two metrics, each derived from first principles, have the same biomechanical interpretability, i.e., active work by the patient during the robotic mobilization therapy, but are calculated in two different spaces (Cartesian vs. muscle space). Furthermore, the sensors used to quantify these two metrics are fully independent from each other and the associated measurements are unrelated. Specifically, the robot-based work metric utilizes robot-integrated force sensors, while the EMG-based work metric requires electrophysiological sensors. We then apply the two metrics to therapy performed using a clinically certified, commercially available robotic system and compare them against the specific instructions given to the healthy subjects as well as against each other. Both metric outputs qualitatively match the expected behavior of the healthy subjects. Additionally, strong correlations (median [Formula: see text]) are shown between the two metrics, not only for healthy subjects (n = 12) but also for patients (n = 2), providing solid evidence for their validity and translatability. Importantly, the robot-based work metric does not rely on any sensors outside of those integrated into the robot, thus making it ideal for application in clinical settings.

Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength

The need for developing a simple and effective assessment tool for muscle mass has been increasing in a rapidly aging society. This study aimed to evaluate the feasibility of the surface electromyography (sEMG) parameters for estimating muscle mass. Overall, 212 healthy volunteers participated in this study. Maximal voluntary contraction (MVC) strength and root mean square (RMS) values of motor unit potentials from surface electrodes on each muscle (biceps brachii, triceps brachii, biceps femoris, rectus femoris) during isometric exercises of elbow flexion (EF), elbow extension (EE), knee flexion (KF), knee extension (KE) were acquired. New variables (MeanRMS, MaxRMS, and RatioRMS) were calculated from RMS values according to each exercise. Bioimpedance analysis (BIA) was performed to determine the segmental lean mass (SLM), segmental fat mass (SFM), and appendicular skeletal muscle mass (ASM). Muscle thicknesses were measured using ultrasonography (US). sEMG parameters showed positive correlations with MVC strength, SLM, ASM, and muscle thickness measured by US, but showed negative correlations with SFM. An equation was developed for ASM: ASM = -26.04 + 20.345 × Height + 0.178 × weight - 2.065 × (1, if female; 0, if male) + 0.327 × RatioRMS(KF) + 0.965 × MeanRMS(EE) (SEE = 1.167, adjusted R² = 0.934). sEMG parameters in controlled conditions may represent overall muscle strength and muscle mass in healthy individuals.

Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing

Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

Continuous grip force estimation from surface electromyography using generalized regression neural network

BACKGROUND

Grip force estimation is highly required in realizing flexible and accurate prosthetic control.

OBJECTIVE

This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees.

METHODS

Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE).

RESULTS

The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training.

CONCLUSIONS

The proposed method has the potential for precise force control of prosthetic hands.

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach

Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics. For classification and estimation of force, support vector machine and support vector regression techniques are applied, respectively. We devise a genetic algorithm for parameter optimization and feature extraction within the proposed methods to improve the estimation accuracy. These are then analyzed and compared to the output of our estimation model to provide a reliable comparison. Our extensive experimental results reveal a high estimation accuracy for lower limb muscles to regulate robot impedance parameters. Although the identification method sounds similar to traditional ones, knee joint properties can be estimated by the machine learning approach from the surface Electromyography without perturbations.

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).

Embodiment of a virtual prosthesis through training using an EMG-based human-machine interface: Case series

Therapeutic strategies capable of inducing and enhancing prosthesis embodiment are a key point for better adaptation to and acceptance of prosthetic limbs. In this study, we developed a training protocol using an EMG-based human-machine interface (HMI) that was applied in the preprosthetic rehabilitation phase of people with amputation. This is a case series with the objective of evaluating the induction and enhancement of the embodiment of a virtual prosthesis. Six men and a woman with unilateral transfemoral traumatic amputation without previous use of prostheses participated in the study. Participants performed a training protocol with the EMG-based HMI, composed of six sessions held twice a week, each lasting 30 mins. This system consisted of myoelectric control of the movements of a virtual prosthesis immersed in a 3D virtual environment. Additionally, vibrotactile stimuli were provided on the participant’s back corresponding to the movements performed. Embodiment was investigated from the following set of measurements: skin conductance response (affective measurement), crossmodal congruency effect (spatial perception measurement), ability to control the virtual prosthesis (motor measurement), and reports before and after the training. The increase in the skin conductance response in conditions where the virtual prosthesis was threatened, recalibration of the peripersonal space perception identified by the crossmodal congruency effect, ability to control the virtual prosthesis, and participant reports consistently showed the induction and enhancement of virtual prosthesis embodiment. Therefore, this protocol using EMG-based HMI was shown to be a viable option to achieve and enhance the embodiment of a virtual prosthetic limb.

Robust muscle force prediction using NMFSEMD denoising and FOS identification

In this paper, an aliasing noise restraint technique and a system identification-based surface electromyography (sEMG)-force prediction model are proposed to realize a type of robust sEMG and muscle force prediction. For signal denoising, a novel non-negative matrix factorization screening empirical mode decomposition (NMFSEMD) and a fast orthogonal search (FOS)-based muscle force prediction model are developed. First, the NMFSEMD model is used to screen the empirical mode decomposition (EMD) results into the noisy intrinsic mode functions (IMF). Then, the noise matrix is computed using IMF translation and superposition, and the matrix is used as the input of NMF to obtain the denoised IMF. Furthermore, the reconstruction outcome of the NMFSEMD method can be used to estimate the denoised sEMG. Finally, a new sEMG muscle force prediction model, which considers a kind of candidate function in derivative form, is constructed, and a data-training-based linear weighted model is obtained. Extensive experimental results validate the suggested method's correction: after the NMFSEMD denoising of raw sEMG signal, the signal-noise ratio (SNR) can be improved by about 15.0 dB, and the energy percentage (EP) can be greater than 90.0%. Comparing with the muscle force prediction models using the traditional pretreatment and LSSVM, and the NMFSEMD plus LSSVM-based method, the mean square error (MSE) of our approach can be reduced by at least 1.2%.

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.

A Modular MRI-Compatible Pipette Simulator to Evaluate How Design Effects the Basilar Thumb Joint Mechanics

People who frequently use micropipettes experience hand and upper limb disorders. The basilar thumb joint, also known as the first carpometacarpal or trapeziometacarpal joint, is commonly affected by osteoarthritis (OA). Mechanical factors are associated with OA initiation and progression. We developed a MRI-compatible modular micropipette simulator to improve understanding of how micropipette design affects basilar thumb joint contact mechanics. The micropipette simulator also addresses limitations of current techniques for studying pipetting and basilar thumb joint mechanics. Its modularity will allow future studies to examine handle design parameters such as handle diameter, cross-sectional shape, and other features. A micropipette simulator with a cylindrical handle (length 127 mm, diameter 25 mm) was used with one subject to demonstrate the system's feasibility. Contact areas were within the range of prior data from basilar thumb joint models in power grasp and lateral pinch, and contact pressures were the same order of magnitude.

The Influence of the sEMG Amplitude Estimation Technique on the EMG–Force Relationship

The estimation of the sEMG–force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.

Analysis on Muscle Forces of Extrinsic Finger Flexors and Extensors in Flexor Movements with sEMG and Ultrasound

The coupling relationship between surface electromyography (sEMG) signals and muscle forces or joint moments is the basis for sEMG applications in medicine, rehabilitation, and sports. The solution of muscle forces is the key issue. sEMG and Muscle-Tendon Junction (MTJ) displacements of the flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and extensor digitorum (ED) were measured during five sets of finger flexion movements. Meanwhile, the muscle forces of FDS, FDP, and ED were calculated by the Finite Element Digital Human Hand Model (FE-DHHM) driven by MTJ displacements. The results showed that, in the initial position of the flexion without resistance, the high-intensity contraction of the ED kept the palm straight and the FDS was involved. The sEMG-force relationship of FDS was linear during the flexion with resistance, while FDP showed a larger sEMG amplitude than FDS, with no obvious linearity with its muscle forces. sEMG-MTJ displacement relationships for FDS and FDP were consistent with the trend of their own sEMG-force relationships. sEMG of ED decreased and then increased during the flexion with resistance, with no obvious linear relationship with muscle forces. The analysis of the proportion of muscle force and integrated EMG (iEMG) reflected the different activation patterns of FDS and ED.

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.

Clarify Sit-to-Stand Muscle Synergy and Tension Changes in Subacute Stroke Rehabilitation by Musculoskeletal Modeling

Post-stroke patients exhibit distinct muscle activation electromyography (EMG) features in sit-to-stand (STS) due to motor deficiency. Muscle activation amplitude, related to muscle tension and muscle synergy activation levels, is one of the defining EMG features that reflects post-stroke motor functioning and motor impairment. Although some qualitative findings are available, it is not clear if and how muscle activation amplitude-related biomechanical attributes may quantitatively reflect during subacute stroke rehabilitation. To better enable a longitudinal investigation into a patient's muscle activation changes during rehabilitation or an inter-subject comparison, EMG normalization is usually applied. However, current normalization methods using maximum voluntary contraction (MVC) or within-task peak/mean EMG may not be feasible when MVC cannot be obtained from stroke survivors due to motor paralysis and the subject of comparison is EMG amplitude. Here, focusing on the paretic side, we first propose a novel, joint torque-based normalization method that incorporates musculoskeletal modeling, forward dynamics simulation, and mathematical optimization. Next, upon method validation, we apply it to quantify changes in muscle tension and muscle synergy activation levels in STS motor control units for patients in subacute stroke rehabilitation. The novel method was validated against MVC-normalized EMG data from eight healthy participants, and it retained muscle activation amplitude differences for inter- and intra-subject comparisons. The proposed joint torque-based method was also compared with the common static optimization based on squared muscle activation and showed higher simulation accuracy overall. Serial STS measurements were conducted with four post-stroke patients during their subacute rehabilitation stay (137 ± 22 days) in the hospital. Quantitative results of patients suggest that maximum muscle tension and activation level of muscle synergy temporal patterns may reflect the effectiveness of subacute stroke rehabilitation. A quality comparison between muscle synergies computed with the conventional within-task peak/mean EMG normalization and our proposed method showed that the conventional was prone to activation amplitude overestimation and underestimation. The contributed method and findings help recapitulate and understand the post-stroke motor recovery process, which may facilitate developing more effective rehabilitation strategies for future stroke survivors.

The Importance of Lifting Height and Load Mass for Muscular Workload during Supermarket Stocking: Cross-Sectional Field Study

High physical work demands increase the risk of musculoskeletal disorders and sickness absence. Supermarket work involves a high amount of manual material handling. Identifying specific ergonomic risk factors is an important part of occupational health and safety efforts in the supermarket sector. In this cross-sectional field study among 64 supermarket workers, we used electromyography during the workday to determine the influence of lifting height and load mass on muscular workload of the low-back and neck/shoulder muscles during un-restricted manual material handling (grocery stocking). We found a significant effect of load mass, i.e., higher loads associated with higher muscular workload in the low-back and neck/shoulder muscles. We demonstrated a significant interaction between start and end position, i.e., lifts performed from 'Low' start positions to 'High' end positions demonstrated the highest low-back muscular workload, whereas 'High' positions were associated with increased neck/shoulder workload. In conclusion, lifting higher loads and lifting goods from low to high positions (low-back) and at high positions (neck/shoulder) are associated with higher muscular workload. These results can be used to guide highly warranted preventive initiatives to reduce the physical workload during supermarket work.

Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

High-Density Electromyography Provides Improved Understanding of Muscle Function for Those With Amputation

Transtibial amputation can significantly impact an individual's quality of life including the completion of activities of daily living. Those with lower limb amputations can harness the electrical activity from their amputated limb muscles for myoelectric control of a powered prosthesis. While these devices use residual muscles from transtibial-amputated limb as an input to the controller, there is little research characterizing the changes in surface electromyography (sEMG) signal generated by the upper leg muscles. Traditional surface EMG is limited in the number of electrode sites while high-density surface EMG (HDsEMG) uses multiple electrode sites to gather more information from the muscle. This technique is promising for not only the development of myoelectric-controlled prostheses but also advancing our knowledge of muscle behavior with clinical populations, including post-amputation. The HDsEMG signal can be used to develop spatial activation maps and features of these maps can be used to gain valuable insight into muscle behavior. Spatial features of HDsEMG can provide information regarding muscle activation, muscle fiber heterogeneity, and changes in muscle distribution and can be used to estimate properties of both the amputated limb and intact limb. While there are a few studies that have examined HDsEMG in amputated lower limbs they have been limited to movements such as gait. The purpose of this study was to examine the quadriceps muscle during a slow, moderate and fast isokinetic knee extensions from a control group as well as a clinical patient with a transtibial amputation. HDsEMG was collected from the quadriceps of the dominant leg of 14 young, healthy males (mean age = 25.5 ± 7 years old). Signals were collected from both the intact and amputated limb muscle of a 23 year old clinical participant to examine differences between the affected and unaffected leg. It was found that there were differences between the intact and amputated limb limb of the clinical participant with respect to muscle activation and muscle heterogeneity. While this study was limited to one clinical participant, it is important to note the differences in muscle behavior between the intact and amputated limb limb. Understanding these differences will help to improve training protocols for those with amputation.

Technical field measurements of muscular workload during stocking activities in supermarkets: cross-sectional study

Multiple studies have reported high prevalence of musculoskeletal disorders among supermarket workers. Technical field measurements can provide important knowledge about ergonomic risk factors for musculoskeletal disorders in the physical working environment, but these measurements are lacking in the supermarket sector. Therefore, using wearable electromyography and synchronous video recording in 75 supermarket workers, this cross-sectional study measured muscular workload during stocking activities in six different types of general store departments and during the thirteen most common work tasks across five different supermarket chains. Our results showed that muscular workload varies, especially for the low-back muscles, across (1) supermarket chains, (2) departments, and (3) specific stocking activities. Highest workloads of the low-back and neck/shoulders were seen in the fruit and vegetables department and during heavy, two-handed lifts of parcels (especially without using technical aids). In conclusion, physical work demands during supermarket stocking activities differ between chains, departments, and work tasks. These results can be used by company representatives and work environment professionals to specifically address and organize the stocking procedures to reduce the muscular workload during supermarket stocking.

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.

Chronic Multi-Electrode Electromyography in Snakes

Knowledge about body motion kinematics and underlying muscle contraction dynamics usually derives from electromyographic (EMG) recordings. However, acquisition of such signals in snakes is challenging because electrodes either attached to or implanted beneath the skin may unintentionally be removed by force or friction caused from undulatory motion, thus severely impeding chronic EMG recordings. Here, we present a reliable method for stable subdermal implantation of up to eight bipolar electrodes above the target muscles. The mechanical stability of the inserted electrodes and the overnight coverage of the snake body with a “sleeping bag” ensured the recording of reliable and robust chronic EMG activity. The utility of the technique was verified by daily acquisition of high signal-to-noise activity from all target sites over four consecutive days during stimulus-evoked postural reactions in Amazon tree boas and Western diamondback rattlesnakes. The successful demonstration of the chronic recording suggests that this technique can improve acute experiments by enabling the collection of larger data sets from single individuals.

Normalisation of a biarticular muscle EMG signal using a submaximal voluntary contraction: Choice of the standardised isometric task for the rectus femoris, a pilot study

BACKGROUND

Electromyography (EMG) signal amplitude is often altered by factors related to the participants and the measurement system. To overcome this issue, a normalisation of the EMG signal amplitude can be performed. Recently, it has been demonstrated that a submaximal voluntary contraction (subMVC) normalisation approach, inspired by grade 3 of manual muscle testing, could produce reliable results. However, rectus femoris (RF) normalisation resulted in low reliability. While the normalisation task chosen for this biarticular muscle was to maintain a knee extension against gravity (ISO-K), a hip flexion isometric task (ISO-H) could also be applied.

RESEARCH QUESTION

This pilot study aimed to assess the impact of the normalisation task on the RF EMG signal quality and related intra-rater within-day reliability during ISO-K and ISO-H, and intra-rater between-day reliability of the EMG signal amplitude during gait.

METHODS

Twenty-four asymptomatic participants were asked to perform ISO-K and ISO-H tasks with both legs and then to walk at self-spontaneous speed, in two identical sessions one week apart. A wireless EMG system was used to record the EMG signal of bilateral RF during each task.

RESULTS

Signal-to-noise ratio during ISO-K and ISO-H was ≥ 15 dB in respectively 51% and 98% of all task repetitions. Intra-rater within-day reliability was acceptable using ISO-K (ICC = 0.71 (0.57; 0.83)) with high %SEM of 35%, and excellent using ISO-H (ICC = 0.94 (0.90; 0.96)) with high %SEM of 34%. Intra-rater between-day reliability during gait was acceptable using ISO-K (ICC = 0.74 (0.61; 0.81)) with a high %SEM of 49%, and excellent using ISO-H (ICC = 0.87 (0.76; 0.93)) with a high %SEM of 38%.

SIGNIFICANCE

The reliability (ICC) of RF EMG signal normalisation was higher using ISO-H than using ISO-K. However, even if signal-to-noise ratio was notably improved using ISO-H, %SEM remains high whatever the normalisation task used. Some additional improvements might thus still be needed to obtain a normalisation protocol allowing more reproducible measurements.

Validity and Reliability of Surface Electromyography Features in Lower Extremity Muscle Contraction in Healthy and Spinal Cord-Injured Participants

Background: Spinal cord injury (SCI) has a significant impact on motor control and active force generation. Quantifying muscle activation following SCI may help indicate the degree of motor impairment and predict the efficacy of rehabilitative interventions. In healthy persons, muscle activation is typically quantified by electromyographic (EMG) signal amplitude measures. However, in SCI, these measures may not reflect voluntary effort, and therefore other nonamplitude-based features should be considered. Objectives: The purpose of this study was to assess the correlation of time-domain EMG features with the exerted joint torque (validity) and their test-retest repeatability (reliability), which may contribute to characterizing muscle activation following SCI. Methods: Surface EMG (SEMG) and torque were measured while nine uninjured participants and four participants with SCI performed isometric contractions of tibialis anterior (TA) and soleus (SOL). Data collection was repeated at a subsequent session for comparison across days. Validity and test-retest reliability of features were assessed by Spearman and intraclass correlation (ICC) of linear regression coefficients. Results: In healthy participants, SEMG features correlated well with torque (TA: ρ > 0.92; SOL: ρ > 0.94) and showed high reliability (ICC_mean = 0.90; range, 0.72-0.99). In an SCI case series, SEMG features also correlated well with torque (TA: ρ > 0.86; SOL: ρ > 0.86), and time-domain features appeared no less repeatable than amplitude-based measures. Conclusion: Time-domain SEMG features are valid and reliable measures of lower extremity muscle activity in healthy participants and may be valid measures of sublesional muscle activity following SCI. These features could be used to gauge motor impairment and progression of rehabilitative interventions or in controlling assistive technologies.

Alteration in HDEMG Spatial Parameters of Trunk Muscle Due to Handle Design during Pushing

Previous research identifies that pushing and pulling is responsible for approximately 9-18% of all low back injuries. Additionally, the handle design of a cart being pushed can dramatically alter a worker's capacity to push (≅9.5%). Surprisingly little research has examined muscle activation of the low back and its role in muscle function. Therefore, the purpose of this study was to examine the effects of handle design combination of pushing a platform truck cart on trunk muscle activity. Twenty participants (10 males and 10 females, mean age = 24.3 ± 4.3 years) pushed 475 lbs using six different handle combinations involving handle orientation (vertical/horizontal/semi-pronated) and handle height (hip/shoulder). Multichannel high-density EMG (HDsEMG) was recorded for left and right rectus abdominis, erector spinae, and external obliques. Pushing at hip height with a horizontal handle orientation design (HH) resulted in significantly less (p < 0.05) muscle activity compared to the majority of other handle designs, as well as a significantly higher entropy than the shoulder handle height involving either the semi-pronated (p = 0.023) or vertical handle orientation (p = 0.028). The current research suggests that the combination of a hip height and horizontal orientation handle design may require increased muscle demand of the trunk and alter the overall muscle heterogeneity and pattern of the muscle activity.

High-volume intermittent maximal intensity isometric exercise caused great stress, although central motor fatigue did not occur

To establish whether very high-volume, high-intensity isometric exercise causes stress to the body and how it affects peripheral and central fatigue. Nineteen physically active healthy male subjects (21.2 ± 1.7 years; height – 1.82 ± 0.41 m, body weight – 79.9 ± 4.5 kg; body mass index – 24.3 ± 2.1 kg/m2) volunteered to participate in this study. They participated in two experiments 3–5 days apart. Each experiment comprised six series of 60-s maximum voluntary contraction (MVC) force (knee extension) achieved as rapidly as possible. This very high-volume, high-intensity exercise (HVHIE) was performed at different quadriceps muscle lengths: short (SL) and long (LL). The MVC and the electrically stimulated contractile properties of the muscle were measured prior to HVHIE, immediately after and 3 min after each series, and at 3, 10, and 30 min after the end of HVHIE. We found that HVHIE caused high levels of stress (cortisol levels approximately doubled, heart rate and the root mean square successive difference of interval (RMSSD) decreased by about 75%); lactate increased to 8–11 mmol/L, voluntary and 100 Hz stimulation-induced force (recorded immediately after HVHIE) decreased by 55% at LL and 40% at SL. However, the central activation ratio during MVC did not change after either exercise. Isometric HVHIE performed using one leg caused high levels of stress (RMSSD decreased, cortisol increased after HVHIE equally at SL and LL; La increased more while exercising at LL) and the voluntary and electrostimulation-induced muscle force significantly decreased, but muscle central activation during MVC did not decrease.

Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

Accurate and long-term prediction of elbow flexion force can be used to recognize the intended movement and help wearable power-assisted robots to improve control performance. Our study aimed to find a proper relationship between electromyography and flexion force. However, the existing methods must incorporate biomechanical models to produce accurate and timely predictions of flexion force. Elbow flexion force is largely determined by the contractile properties of muscles, and the relationship between flexion force and the motor function of muscles has to be thoroughly analyzed. Therefore, based on the investigation on the contributions of different muscles to the flexion force, original electromyography signals were decomposed into non-linear and non-stationary parts. We selected the mean absolute value (MAV) of the non-linear part and the variance of the non-stationary part as inputs for an Informer prediction model that does not require detailed a priori knowledge of biomechanical models and is optimized for processing time sequences. Finally, a long-term flexion force probability interval is proposed. The proposed framework performs well in predicting long-term flexion force and outperforms other state-of-the-art models when compared to experimental results.

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.

Effects of Power and Ballistic Training on Table Tennis Players' Electromyography Changes

The aim of the present study was to analyze the effects of ballistic and power training on table tennis players' electromyography (EMG) changes. Thirty male table tennis players, who were able to perform top spin strikes properly, were randomly assigned to three groups: power training (PT; n = 10); ballistic training (BT; n = 10); and no training (CON = control group; n = 10). PT and BT were performed 3 times weekly for 8 weeks. Before and after training programs, a one-repetition maximum test (1RM) and the EMG activity of all the subjects' upper/lower body muscles while performing top spin strokes were analyzed. After training, significant interactions (group × time) were observed in increasing 1RM strength in upper/lower muscles (p < 0.05). However, neither training type had any significant effect on muscle EMG activity. These findings suggest that there should not necessarily be any significant change in the EMG signal after BT and PT despite the increase in muscle strength.

Human muscle activity and lower limb biomechanics of overground walking at varying levels of simulated reduced gravity and gait speeds

Reducing the mechanical load on the human body through simulated reduced gravity can reveal important insight into locomotion biomechanics. The purpose of this study was to quantify the effects of simulated reduced gravity on muscle activation levels and lower limb biomechanics across a range of overground walking speeds. Our overall hypothesis was that muscle activation amplitudes would not decrease proportionally to gravity level. We recruited 12 participants (6 female, 6 male) to walk overground at 1.0, 0.76, 0.55, and 0.31 G for four speeds: 0.4, 0.8, 1.2, and 1.6 ms^-1. We found that peak ground reaction forces, peak knee extension moment in early stance, peak hip flexion moment, and peak ankle extension moment all decreased substantially with reduced gravity. The peak knee extension moment at late stance/early swing did not change with gravity. The effect of gravity on muscle activity amplitude varied considerably with muscle and speed, often varying nonlinearly with gravity level. Quadriceps (rectus femoris, vastus lateralis, & vastus medialis) and medial gastrocnemius activity decreased in stance phase with reduced gravity. Soleus and lateral gastrocnemius activity had no statistical differences with gravity level. Tibialis anterior and biceps femoris increased with simulated reduced gravity in swing and stance phase, respectively. The uncoupled relationship between simulated gravity level and muscle activity have important implications for understanding biomechanical muscle functions during human walking and for the use of bodyweight support for gait rehabilitation after injury.

A novel sEMG-based force estimation method using deep-learning algorithm

This paper discusses the problem of force estimation represented by surface electromyography (sEMG) signals collected from an armband-like collection device. The scheme is proposed for the sake of two dimensions of sEMG signals: spatial and temporal information. From the point of space, first, appropriate channel number across all subjects is investigated. During this progress, an electrode channel selection method based on Spearman’s rank order correlation coefficient is utilized to detect signals from active muscle. Then, to reduce the computation and highlight the channel information, linear regression (LR) algorithm is conducted to weight each channel. Besides, the recurrent neural network (RNN) is used to capture the temporal information and model the relation between sEMG and output force. Experiments conducted on four subjects demonstrate that six channels are enough to characterize the muscle activity. By combining the selected channels with different weight coefficients, LR algorithm can fit the output force better than simply averaging them. Furthermore, RNN with long short-term memory cell shows the superiority in time series modeling, which can improve our results to a greater degree. Experimental results prove the feasibility of the proposed method.

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.