Experimental Electromyography Research Articles

EMG model calibration and trajectory tracking of rehabilitation exoskeleton by asynchronous tele-training

ABSTRACT This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. The therapist’s rehabilitation exercises are quantitatively assessed using a monocular vision system. The resultant metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. The exoskeleton utilises these metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilising EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements including shoulder flexion, elbow flexion, and rowing-like movements. Computer simulations demonstrate the system’s ability to reliably, robustly, and effectively follow desired trajectories. This research offers a promising approach for remote personalized rehabilitation.

EMG-Constrained and Ultrasound-Informed Muscle-Tendon Parameter Estimation in Post-Stroke Hemiparesis.

Secondary morphological and mechanical property changes in the muscle-tendon unit at the ankle joint are often observed in post-stroke individuals. These changes may alter the force generation capacity and affect daily activities such as locomotion. This work aimed to estimate subject-specific muscle-tendon parameters in individuals after stroke by solving the muscle redundancy problem using direct collocation optimal control methods based on experimental electromyography (EMG) signals and measured muscle fiber length. Subject-specific muscle-tendon parameters of the gastrocnemius, soleus, and tibialis anterior were estimated in seven post-stroke individuals and seven healthy controls. We found that the maximum isometric force, tendon stiffness and optimal fiber length in the post-stroke group were considerably lower than in the control group. We also computed the root mean square error between estimated and experimental values of muscle excitation and fiber length. The musculoskeletal model with estimated subject-specific muscle tendon parameters (from the muscle redundancy solver), yielded better muscle excitation and fiber length estimations than did scaled generic parameters. Our findings also showed that the muscle redundancy solver can estimate muscle-tendon parameters that produce force behavior in better accordance with the experimentally-measured value. These muscle-tendon parameters in the post-stroke individuals were physiologically meaningful and may shed light on treatment and/or rehabilitation planning.

Tutorial on MUedit: An open-source software for identifying and analysing the discharge timing of motor units from electromyographic signals

We introduce the open-source software MUedit and we describe its use for identifying the discharge timing of motor units from all types of electromyographic (EMG) signals recorded with multi-channel systems. MUedit performs EMG decomposition using a blind-source separation approach. Following this, users can display the estimated motor unit pulse trains and inspect the accuracy of the automatic detection of discharge times. When necessary, users can correct the automatic detection of discharge times and recalculate the motor unit pulse train with an updated separation vector. Here, we provide an open-source software and a tutorial that guides the user through (i) the parameters and steps of the decomposition algorithm, and (ii) the manual editing of motor unit pulse trains. Further, we provide simulated and experimental EMG signals recorded with grids of surface electrodes and intramuscular electrode arrays to benchmark the performance of MUedit. Finally, we discuss advantages and limitations of the blind-source separation approach for the study of motor unit behaviour during tonic muscle contractions.

Larger and Denser: An Optimal Design for Surface Grids of EMG Electrodes to Identify Greater and More Representative Samples of Motor Units.

The spinal motor neurons are the only neural cells whose individual activity can be noninvasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IEDs). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56 ± 14; range: 39-79) and the percentage of early recruited motor units within these samples (29 ± 14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies.

Using different matrix factorization approaches to identify muscle synergy in stroke survivors.

Over the past several decades, many scholars have investigated muscle synergy as a promising tool for evaluating motor function. However, it is challenging to obtain favorable robustness using the general muscle synergy identification algorithms, namely non-negative matrix factorization (NMF), independent component analysis (ICA), and factor analysis (FA). Some scholars have proposed improved muscle synergy identification algorithms to overcome the shortcomings of these approaches, such as singular value decomposition NMF (SVD-NMF), sparse NMF (S-NMF), and multivariate curve resolution-alternating least squares (MCR-ALS). However, performance comparisons of these algorithms are seldom conducted. In this study, experimental electromyography (EMG) data collected from healthy individuals and stroke survivors were applied to assess the repeatability and intra-subject consistency of NMF, SVD-NMF, S-NMF, ICA, FA, and MCR-ALS. MCR-ALS presented higher repeatability and intra-subject consistencies than the other algorithms. More synergies and lower intra-subject consistencies were observed in stroke survivors than in healthy individuals. Thus, MCR-ALS is considered a favorable muscle synergy identification algorithm for patients with neural system disorders.

A peel-off convolution kernel compensation method for surface electromyography decomposition

Objective:Estimating discharge patterns of motor units by electromyography (EMG) decomposition has shown promising perspectives in neurophysiologic investigations and human–machine interfaces. However, the number of decoded motor units is still limited, especially under high excitation and noisy conditions, which is a major barrier to the broad application of EMG decomposition. In this work, we applied a peel-off decomposition strategy to the convolution kernel compensation (CKC) algorithm to extract more motor units non-invasively. Methods:EMG signals were firstly decomposed into motor unit spike trains (MUSTs) using the CKC. After each decomposition, the motor unit action potentials (MUAPs) were reconstructed and subtracted from the EMG signals. Then the same CKC decomposition was applied to the residual signals. This peel-off procedure was repeated until no valid motor units were identified. The proposed decomposition strategy was validated on synthetic EMG signals by convolving simulated MUSTs and experimentally extracted MUAPs under multiple excitations and noise conditions. Then the decomposition performance was evaluated on experimental data. Main results:Compared with the classic CKC method, the number of the identified motor units from synthetic signals was significantly increased by 7 to 43 in each decomposition. Moreover, the average agreement between identified MUST and ground truth was 0.80 ± 0.20, indicating a high decomposition accuracy. From experimental EMG signals, the peel-off method could identify more motor units (>20) with high confidence than the classic method (<5) across three excitation levels. Conclusion and Significance:These results demonstrate the efficiency of the proposed method in identifying more motor units from EMG signals, extending the potential applications of surface EMG decomposition for neural decoding.

Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model

Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results.

Toward the definition of a minimum input model for an EMG analysis in clinics

Neuro-musculoskeletal (NMS) modeling based on electromyographic (EMG) signals allows for a better understanding of the principles of human movement [1]. Through appropriate modeling software (CEINMS) it is possible to estimate the muscular forces and joint moments calibrated on the single individual. The possibility of having highly subject-specific models allows an improvement in the treatment of a great variety of neurodegenerative diseases and can be fundamental for the general understanding of the neuro-mechanics of human locomotion. Fifteen walking trials of four able body subjects (mean age = 60 ± 2.16, mean BMI = 26.66 ± 4.13) were acquired with a motion capture system (BTS, 60 Hz), synchronized with a force plate (Bertec, 960 Hz) and a 15 channels surface electromyography (sEMG) system (FreePocketEMG, 1000 Hz). The CAST protocol was adopted as marker set, while the following muscles were acquired: Gluteus Maximus, Gluteus Medius, Tensor Fasciae Latae, Sartorius, Adductor Longus, Rectus Femoris, Biceps Femoris, Semitendinosus, Vastus Lateralis, Vastus Medialis, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis, Gastrocnemius Medialis and Soleus. The data were processed in MatLab for the extraction of four muscle synergies by applying the ‘Non-negative Matrix Factorization’ method [2]. Then, 'Statistical Parametric Mapping' [3] and 'Cosine Similarity' were used as metrics to assign each synergy to the EMG signals of just one muscle for each different muscle groups (Ankle Plantar-flexors, Ankle Dorsi-flexors, Knee Extensors and Knee Flexors). Afterwards data were processed in OpenSim; scaling, inverse kinematics, inverse dynamics, muscle analysis were run. Finally, CEINMS was executed to calculate muscle activations and torques first by informing the model with a complete setup of experimental EMGs data then with a reduced setup (the foru EMGs that best matched with muscle synergies). Activation signals of the muscle-tendon units and the angular moments of the knee and ankle obtain from models developed by CEINMS were compared for different setup with the experimental EMG signals. Comparison was done for both different muscle setups for each subject. In Fig. 1a are shown the values of cosine similarities of the muscles able to better approximate each primitive for one subject. In Fig. 1 b-e are displayed the pattern of primitives and the chosen experimental EMG signals for the same subject during one walking trial. The implemented methodology allowed the analysis of the muscular forces developed during walking with an acquisition protocol that includes a reduced number of EMG signals, and a sensitivity analysis for the adoption of NMS models based on a limited number of inputs was conducted. Results seem promising but a validation on a wider sample of subjects including pathological subjects is needed.

EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation.

Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called “synergy extrapolation” or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were “unmeasured” for walking datasets collected from two individuals post-stroke–one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.

A Musculoskeletal Model Customized for Sagittal and Frontal Knee Kinematics With Improved Knee Joint Stability.

Current lower limb musculoskeletal (MSK) models focus on sagittal plane kinematics. However, abnormal gait is typically associated with sagittal plane motions crossing into other planes, limiting the use of current MSK models. The purpose of this study was twofold, first, to extend the capability of a full-body MSK model from the literature to include frontal knee plane kinematics during healthy gait, and second, to propose and implement a realistic muscle discretization technique. Two MSK model constructs were derived-the first construct (Knee2_SM) allowed two degrees-of-freedom (sagittal and coronal) at the knee and the second construct (Knee2_MM) implemented multiline elements for all the lower limb muscles in conjunction with two knee degrees-of-freedom. Motion analysis data of normal gait cycle from 10 healthy adults were used to compare joint kinematics, muscle moment arms, muscle forces, and muscle activations, between new constructs and the original model. Knee varus-valgus trajectories were estimated with the mean peak values ranging from 9.49 deg valgus to 1.57 deg varus. Knee2_MM predicted a significant difference (p < 0.05) in moment arms and forces in those muscles responsible for medial-lateral stability of the knee. The simulated muscle activations generated by the Knee2_MM model matched more closely to the experimental electromyography (EMG) when qualitatively compared. This study enhances the capability of the sagittal plane full-body MSK model to incorporate knee varus-valgus motion while keeping the joint stability intact and improving muscle prediction.

Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations.

The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs remains similar even though more muscles or movements are involved, where multiple motor neuron populations are activated. The objective of this study was to develop a segment-wise decomposition strategy to increase the number of MU decoded from multiple motor neuron populations. The EMG signals were divided into several segments depending on the number of involved movements. The motor neurons, activated during each movement, were regarded as a population. The convolution kernel compensation (CKC) method was applied individually for each segment to decode the motor unit discharges from each motor neuron population. The MU filters were obtained in each segment and filtrated to estimate the MU spike trains (MUSTs) from the global EMG signals. The decomposition performance was validated on synthetic and experimental EMG signals. From synthetic EMG signals generated by two motor neuron populations, the proposed segment-wise CKC (swCKC) decoded significantly more MUs during low and medium excitation levels, with an increased rate of 16.3% to 75.4% compared with the conventional CKC. From experimental signals recorded during ten motor tasks, 133±24 MUs with the pulse-to-noise ratio of 36.6±6.5 dB were identified for each subject by swCKC, whereas the conventional CKC identified only 43±12 MUs. These results indicate the feasibility and superiority of the proposed swCKC to decode MU activities across motor neuron populations, extending the potential applications of EMG decomposition for neural decoding during multiple motor tasks.

Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson’s disease

Electromyography (EMG)-driven neuromusculoskeletal modeling (NMSM) enables simulating the mechanical function of multiple muscle-tendon units as controlled by nervous system in the generation of complex movements. In the context of clinical assessment this may enable understanding biomechanical factor contributing to gait disorders such as one induced by Parkinson’s disease (PD). In spite of the challenges in the development of patient-specific models, this preliminary study aimed at establishing a feasible and noninvasive experimental and modeling pipeline to be adopted in clinics to detect PD-induced gait alterations. Four different NMSM have been implemented for three healthy controls using CEINMS, an OpenSim-compatible toolbox. Models differed in the EMG-normalization methods used for calibration purposes (i.e. walking trial normalization and maximum voluntary contraction normalization) and in the set of experimental EMGs used for the musculotendon-unit mapping (i.e. 4 channels vs. 15 channels). Model accuracy assessment showed no statistically significant differences between the more complete model (non-clinically viable) and the proposed reduced one (clinically viable). The clinically viable reduced model was systematically applied on a dataset including ten PD’s and thirteen healthy controls. Results showed significant differences in the neuromuscular control strategy of the PD group in term of muscle forces and joint torques. Indeed, PD patients displayed a significantly lower magnitude on force production and revealed a higher amount of force variability with the respect of the healthy controls. The estimated variables could become a measurable biomechanical outcome to assess and track both disease progression and its impact on gait in PD subjects.

EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling

Glenohumeral stability is essential for a healthy function of the shoulder. It is ensured partly by the scapulohumeral muscular balance. Accordingly, modelling muscle interactions is a key factor in the understanding of occupational pathologies, and the development of ergonomic interventions. While static optimization is commonly used to estimate muscle activations, it tends to underestimate the role of shoulder’s antagonist muscles. The purpose of this study was to implement experimental electromyographic (EMG) data to predict muscle activations that could account for the stabilizing role of the shoulder muscles. Kinematics and EMG were recorded from 36 participants while lifting a box from hip to eye level. Muscle activations and glenohumeral joint reactions were estimated using an EMG-assisted algorithm and compared to those obtained using static optimization with a generic and calibrated model. Muscle activations predicted with the EMG-assisted method were generally larger. Additionally, more interactions between the different rotator cuff muscles, as well as between primer actuators and stabilizers, were predicted with the EMG-assisted method. Finally, glenohumeral forces calculated from a calibrated model remained within the boundaries of the glenoid stability cone. These findings suggest that EMG-assisted methods could account for scapulohumeral muscle co-contraction, and thus their contribution to the glenohumeral stability.

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8dB, corresponding to an estimated decomposition accuracy 75%. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.

Foot Pronation Contributes to Altered Lower Extremity Loading After Long Distance Running.

This study presents an investigation of the changes in foot posture, joint kinematics, joint moments and joint contact forces in the lower extremity following a 5 k treadmill run. A relationship between knee and ankle joint loading and foot posture index (FPI) is developed. Twenty recreational male heel-strike runners participated in this study. All participants had a history of running exercise and were free from lower extremity injuries and foot deformities. Foot posture was assessed from a six-item FPI to quantitatively classify high supination to high pronation foot poses. The FPI is scored using a combination of observations and foot palpations. The three-dimensional marker trajectories, ground reaction force and surface electromyography (EMG) were recorded at pre and post-gait sessions conducted over-ground and 5 k running was conducted on a treadmill. Joint kinematics, joint moments and joint contact forces were computed in OpenSim. Simulated EMG activations were compared against experimental EMG to validate the model. A paired sample t-test was conducted using a 1D statistical parametric mapping method computed temporally. Hip joint moments and contact forces increased during initial foot contact following 5 k running. Knee abduction moment and superior-inferior knee contact force increased, whereas the knee extension moment decreased. Ankle plantarflexion moment and ankle contact forces increased during stance. FPI was found to be moderately correlated with peak knee and ankle moments. Recreational male runners presented increased static foot pronation after 5 k treadmill running. These findings suggest that following mid distance running foot pronation may be an early indicator of increased lower limb joint loading. Furthermore, the FPI may be used to quantify the changes in knee and ankle joint moments.

Simulation of electromyographic recordings following transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood. We aim to develop a biophysically detailed computational model to study the potential mechanisms underlying the generation of EMG signals following TMS. Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive morphologically detailed layer 5 corticomotoneuronal cells, which in turn project to a pool of motoneurons. EMG signals are modeled as the sum of motor unit action potentials. EMG recordings from the first dorsal interosseous muscle were performed in four subjects and compared with simulated EMG signals. Our model successfully reproduces several characteristics of the experimental data. The simulated EMG signals match experimental EMG recordings in shape and size, and change with stimulus intensity and contraction level as in experimental recordings. They exhibit cortical silent periods that are close to the biological values and reveal an interesting dependence on inhibitory synaptic transmission properties. Our model predicts several characteristics of the firing patterns of neurons along the entire pathway from cortical layer 2/3 cells down to spinal motoneurons and should be considered as a viable tool for explaining and analyzing EMG signals following TMS. NEW & NOTEWORTHY A biophysically detailed model of EMG signal generation following transcranial magnetic stimulation (TMS) is proposed. Simulated EMG signals match experimental EMG recordings in shape and amplitude. Motor-evoked potential and cortical silent period properties match experimental data. The model is a viable tool to analyze, explain, and predict EMG signals following TMS.

Separation of electrocardiographic from electromyographic signals using dynamic filtration

Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golayfilter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0–30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals.

Evaluation of blood flow and electromyographic activity in the perioral muscles.

Although the electromyographic (EMG) activity of the perioral muscles, including the orbicularis oris and mentalis muscles, has been described in individuals with lip incompetence during lip sealing, blood flow through these muscles remains to be elucidated. The purpose of this study was to examine the blood flow associated with EMG activity in the perioral muscles using laser speckle imaging in individuals with lip incompetence. Blood flow and EMG activity of the superior and inferior orbicularis oris and mentalis muscles were measured with the lips in contact (C condition) and apart (O condition) in lip incompetence (experimental) and control subjects (n = 15 in each group; mean age: 29.5 years). The change ratios of blood flow and EMG activity in the C condition versus O condition (C/O ratios) were calculated and plotted in a scattergram. The Mann-Whitney U-test, Wilcoxon signed-rank test, discriminant analysis using the Mahalanobis generalized distance, and Spearman correlation were used for statistical analysis. In the experimental group, blood flow and EMG activity in all muscles were significantly greater in the C condition than in the O condition. The plots of C/O ratios in the experimental group showed a distinct and wide distribution and were significantly different than those in the control group. In both groups, a significant positive correlation was observed between blood flow and EMG activity in the mentalis muscle. The present findings suggest that observing blood flow in the mentalis muscle is an effective and easily performed method of evaluating lip incompetence.

Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury

Abstract Following neurological injuries, such as spinal cord injury (SCI), voluntary muscle activity detection from electromyography (EMG) signals can be problematic due to spurious involuntary spikes produced by physiological and extrinsic/accidental origins. The purpose of this study was to demonstrate the usefulness of the integrated profile for voluntary muscle activity detection in the presence of spurious background spikes for SCI patients. Two onset detection methods (the sample entropy analysis and the integrated profile) were evaluated using both semi-synthetic and experimental surface EMG signals with spurious background spikes recorded from SCI patients. For all the examined signal to noise ratios, the integrated profile method was able to achieve comparable performance with the sample entropy analysis-based method but while taking significantly shorter running time (P

Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion.

This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during overground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing 1) the elastic interplay between the knee and ankle joints, 2) the individual muscle contribution to joint stiffness, and 3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions, including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion.