Electromyography Signals Of Muscles Research Articles

EVALUATION OF NECK FATIGUE CAUSED BY WEARING HELMET IN MOTION: AN ELECTROMYOGRAPHY STUDY

The increased weight of multifunction and intelligent helmet systems can cause an exacerbation of the neck workload. Further, increased weight of the helmet can reduce the wearing comfort, and prolonged use may cause neck fatigue and even neck injury. This study evaluates the fatigue of neck muscles caused by wearing a helmet in motion using electromyography (EMG) measurement. Additionally, a comprehensive evaluation model for neck fatigue was established effectively. Twenty healthy subjects were selected to run for 10[Formula: see text]min continuously in each task by wearing two different helmets, respectively, during which the EMG signals of different neck muscles and subjective scores were recorded and analyzed. Two-way repeated measures analysis of variance (ANOVA) and Pearson correlation analysis were used to obtain indicators of neck fatigue, based on which the support vector machine (SVM) was used to construct a classification model of neck fatigue. By using integral EMG (IEMG) and root mean square (RMS) for assessing the neck injury in sternocleidomastoideus and trapezius muscles, it was observed that the mean power frequency (MPF) and median frequency (MF) in trapezius and splenius capitis could be used as effective evaluation indicators of neck fatigue. The EMG activity in trapezius was the highly sensitivity to fatigue when wearing helmets in motion. The classifier for distinguishing three different neck fatigue levels was 91.67% accurate. This study demonstrates the feasibility of an EMG-based method to evaluate the neck fatigue caused by wearing a helmet in motion, and also provides an evaluation method for the optimal design of helmets in the future.

Normalization of the electromyographic signals of masticatory muscles during non-habitual chewing activity

There is no consensus on the most appropriate method for normalizing electromyography (EMG) signals from masticatory muscles during isotonic activity. Aim: To analyze the best method for data processing of the EMG signal of the masticatory muscles during isotonic activity (non-habitual chewing), comparing raw data and different types of normalization. Methods: This is a cross-sectional study. Women aged between 18 and 45 years were selected. Anthropometric data were collected (age, height, body mass index – BMI, masticatory preference) as well as EMG signal (root mean square – RMS) data for the anterior temporal and masseter bilaterally, and for the suprahyoid muscles, during isotonic (non-habitual chewing) and isometric tasks. EMG data were processed offline using Matlab® Software. The normalization of the EMG signal was carried out using the 2nd masticatory cycle, chosen at random, of the 20 cycles collected, the maximum RMS value, and the maximum voluntary contraction (MVC). To analyze the best method of data processing for the isotonic data, the coefficient of variation (CV) was calculated. Descriptive data analysis was adopted, using the mean and standard deviation. ANOVA with repeated measures was used to detect significant differences between the methods of normalization. Statistical significance was set at 5% (α<0.05). Results: The final sample of this research was composed of 86 women. The volunteers presented an average age of 27.83±7.71 years and a mean BMI of 22.85±1.91 Kg/m2. Regarding masticatory preference, 73.25% reported the right side, and 26.75% the left side. Considering the comparison between the methods, the %CV measure of the 2nd cycle showed the lowest variation coefficient during biting for all the muscles from the raw data, RMS Max, and MVC (p=0.001, p=0.003, and p=0.001 respectively). Conclusion: In conclusion, for non-habitual chewing activity, the results of this study recommend data processing using normalization with the second cycle during chewing.

Classification of tracheal stenosis with asymmetric misclassification errors from EMG signals using an adaptive cost-sensitive learning method

Upper airway obstruction is characterized by loss of normal airway architecture resulting from various disorders such as infections and asthma. Early detection of airway obstruction is essential to prevent medical deterioration. The objective of this study was to non-invasively identify early-stage tracheal stenosis, using electromyography (EMG) signals of inspiratory muscles. The identification of tracheal stenosis has been defined as an asymmetric misclassification cost problem. Specifically, the EMG signals are used as input to a ResNet-like architecture for tabular data with an Adaptive Cost-Sensitive Learning (AdaCSL) algorithm. The electrical activity of the external intercostal muscle of four healthy individuals was recorded while they breathed through two different tubes, one simulating a narrowed airway and the other simulating a normal airway. Two experiment settings were designed. The first setting aimed to classify tracheal stenosis in a specific subject by training the model on data from other subjects, reflecting the case of diagnosing a new subject. To overcome multi-subject variations, the second setting aimed to classify tracheal stenosis by mixing all subjects’ training and test data. The ResNet-like architecture with an AdaCSL algorithm was significantly better in the first experiment setting with costs that were 43%, 48%, and 59% lower than the cost of the second-best alternative for three different misclassification cost values. It also achieved a lower cost in the second experiment setting over other classifiers. The experiments emphasize the capability of using inspiratory muscle EMG signals to diagnose respiratory disease and demonstrate the usefulness of the AdaCSL algorithm for personalized monitoring.

Classification of muscle activity patterns in healthy children using biclustering algorithm

In recent years, there has been major interest in recognising electromyography (EMG) patterns. This work proposes a new method based on a biclustering algorithm which can group strides showing homogeneous EMG activation intervals. The surface EMG signals of biceps femoris, rectus femoris, semitendinosus, lateral gastrocnemius, and medial gastrocnemius muscles of 17 healthy children aged between 4 and 11 years old were obtained using a Trigno EMG wireless system. The data set was tested for different values of parameter α (the threshold describing when the multiple node deletion step is used) and δ (the threshold that limits the value of the mean square residue). The highest number of coincidences of muscle activation was observed in 6 to 7-year-old subjects. This was not affected by their anthropometrics or gender. The obtained biclusters reflect actual differences between the subjects' gait parameters, namely stride length, stride time, and walking speed. These results can be used to develop strategies for finding homogeneous groups of patients.

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning.

In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features: 1) experimental measurements - muscle signals and joint angles, and 2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra- and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

Imaging-based musculoskeletal models alter muscle and joint contact forces but do not improve the agreement with experimentally measured electromyography signals in children with cerebral palsy

BackgroundMusculoskeletal simulations are used to estimate muscle-tendon and joint contact forces (JCF). Personalizing the model’s femoral geometry has been shown to improve the accuracy of JCF calculations. It is, however, unknown if the personalized geometry improves the agreement between estimated muscle activations and experimentally measured electromyography (EMG) signals. Research questionDoes personalizing the musculoskeletal geometry improve the agreement between estimated muscle activations and EMG signals in terms of timing? MethodsWe retrospectively analysed data from Bosmans et al. [5], which included three-dimensional motion capture data, EMG signals of eight lower limb muscles on each leg, and magnetic resonance imaging (MRI) data from seven children with cerebral palsy. For each patient we created a generic-scaled model and MRI-based model, which accounted for the subject-specific musculoskeletal geometry. We calculated muscle activations, muscle-tendon forces and JCF. Muscle activations were compared to the EMG signals using coefficient of determination and cosines similarity. ResultsMRI-based models altered the magnitude of muscle activations and had a large impact on JCF but did not change the muscle activations profiles and therefore did not improve the agreement with EMG signals. SignificanceMRI-based models do not alter the shape of muscle activations. Hence, if detailed muscle activations are a desired output of the simulations, EMG-informed modeling approaches should be used for musculoskeletal simulation in children with cerebral palsy. Furthermore, our study highlighted that altered JCF does not necessarily mean accurate muscle activations. To improve patient-specific simulations, future work should focus on developing methods to estimate cost functions representative for the neural control of children with cerebral palsy.

Electromyography-controlled lower extremity exoskeleton to provide wearers flexibility in walking

Objective:In recent years, there have been significant developments in lower extremity robotic exoskeletons intended for gait rehabilitation. However, wearers may not be sufficiently motivated to participate in traditional rehabilitation robot training as the training pattern is usually predefined and rigid. Enabling wearers to actively control the exoskeleton to assist them in walking may improve rehabilitation treatments. Methods:This paper presents an electromyography (EMG)-based gait pattern adaptation method that allows subjects to control the exoskeleton via EMG signals of thigh muscles (quadriceps femoris and hamstrings muscles). Six healthy subjects participated in the initial experiment on a treadmill based lower extremity rehabilitation robot system. In a single walking routine, six widely used adaptation gait patterns were tested. Results:The results indicate that all of the subjects were able to change the gait pattern of the exoskeleton and achieved the adaptation goals stably within average 16 strides by generating EMG signals. The muscle activation during the adaptation condition is significantly higher than that in fixed normal walking condition (p<0.05). The subjects gave positive evaluation on the designed system. Conclusion:With this method, the subjects were involved in the control loop and actively participated in the training. Significance:The proposed EMG-based two-layer admittance control algorithm is novel, which enabled subjects to adjust gait trajectories continuously and smoothly.

Prediction of leg muscle activities from arm muscle activities in arm and leg cycling.

Functional electrical stimulation (FES) driven leg cycling is usually controlled by previously established stimulation patterns. We investigated the potential utilization of a particular computational method for controlling electrical stimulation of lower limb muscles by real-time electromyography (EMG) signals of arm muscles during hybrid arm and leg cycling. In hybrid arm and leg cycling, arm cranking is performed voluntarily, while leg cycling is driven by FES. In this study, we investigate arm and leg cycling movements of able-bodied persons when both arm and leg cycling is performed voluntarily without FES. We present a neural network-based model in which the input of the neural network is given by a time series of upper limb muscle activities (EMG), and the output provides potential lower limb muscle activities. The particular neural network was a nonlinear autoregressive exogen (NARX) neural network. The measured EMG signals of the lower limb muscles were compared to the signals that were predicted by the neural network. The neural network was trained with data recorded from four participants. Our preliminary results show notable differences between the predicted and the experimentally measured lower limb muscle activities. The prediction was good only for 60% of the movement time. We conclude that-while including arm cycling in the movement-simpler control modalities or further consideration of applying machine-learning techniques has to be taken into account to improve voluntary upper limb-controlled FES assisted leg cycling.

Drop jumps versus sled towing and their effects on repeated sprint ability in young basketball players

BackgroundThe aim of the investigation was to compare the occurrence of post-activation performance enhancement (PAPE) after drop jumps, or heavy sled towing, and the subsequent effect on repeated sprint ability (RSA).MethodsTen young basketball players (17 ± 1 yrs) performed, in randomized order, RSA test with changes of direction after a standardized warm up followed by drop jumps, heavy sled towing, or no exercise (control condition). Neuromuscular assessments composed of two maximal voluntary contractions of the knee extensors, peripheral nerve stimulation, and surface electromyography (EMG), responses were recorded before and immediately after the RSA. The EMG signal of leg muscles during sprinting were also recorded as well as the blood lactate concentration.ResultsThe drop jumps improved the RSA mean time (P = 0.033), total time (P = 0.031), and slowest time (P = 0.029) compared to control condition, while heavy sled towing did not change RSA outcomes (P > 0.05). All conditions exhibited a decrease of doublet high frequency stimulation force (pre-post measurement) (P = 0.023) and voluntary activation (P = 0.041), evidencing the occurrence from peripheral and central components of fatigue after RSA, respectively, but no difference was evident between-conditions. There was a significantly greater EMG activity during sprints for the biceps femoris after drop jumps, only when compared to control condition (P = 0.013).ConclusionRepeated drop jumps were effective to induce PAPE in the form of RSA, while heavy sled towing had no effect on RSA performance in young basketball players. Furthermore, both conditioning activities exhibited similar levels of fatigue following the RSA protocol. Thus, drop jumps may be used as an alternative to induce PAPE and thus improve performance during sprints in young male basketball players.

Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning.

Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error ≤ 0.14 Nm/kg, normalized root mean square error ≤ 8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.

Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles

Bruxism is known as the rhythmical clenching of the lower jaw (mandibular) by involuntary contraction of the masticatory muscles (masseter muscles) together with parafunctional grinding of the teeth that usually occur during sleep. It affects patients’ quality of life adversely due to tooth wear, tooth loss, and pain and fatigue in the jaw muscles. It is a common condition that is difficult to diagnose and treat. Bruxism diagnosis is often made by monitoring electromyography (EMG) activity of the masseter muscles during sleep. In this study, fatigue and pain in lower jaw muscles are examined together with teeth grinding and clenching activities. 13 time- and frequency-related features that are widely used in the literature were extracted from EMG signals. Correlations between the features were determined through five different algorithms in addition to Artificial Neural Network, and the features with the highest correlation were grouped in different combinations. As a result of tests performed on these groups of features, most effective features to be used in surface electromyography (sEMG) signal analysis were identified. K-nearest Neighbor, Support Vector Machine and Artificial Neural Network algorithms are shown to increase the accuracy of bruxism diagnosis.

EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee.

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.

Muscle contributions to medial and lateral tibiofemoral compressive loads during sidestep cutting

The tibiofemoral compressive forces experienced during functional activities are believed to be important for maintaining tibiofemoral stability. Previous studies have shown that both knee-spanning and non-knee-spanning muscles contribute to tibiofemoral joint compressive forces during walking. However, healthy individuals typically engage in more vigorous activities (e.g. jumping and cutting) that provide greater challenges to tibiofemoral stability. Despite this, no previous studies have investigated how both knee-spanning and non-knee-spanning muscles contribute to tibiofemoral compressive loading during such tasks. The present study investigated how muscles contributed to the medial and lateral compartment tibiofemoral compressive forces during sidestep cutting. Three-dimensional marker trajectories, ground reaction forces and muscle electromyographic signals were collected from eight healthy males whilst they completed unanticipated sidestep cutting. OpenSim was used to perform musculoskeletal simulations to compute the contribution of each lower-limb muscle to compressive loading of each compartment of the knee. The greatest contributors to medial compartment loading were the vasti, gluteus maximus and medius, and the medial gastrocnemius. The greatest contributors to lateral compartment loading were the vasti, adductors, medial and lateral gastrocnemius, and the soleus. The soleus displayed the greatest potential for unloading the medial compartment, whereas the gluteus maximus and medius displayed the greatest potential for unloading the lateral compartment. These findings may help to inform interventions aiming to modulate compressive loading at the knee.

Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses

Objective. The aim of the study was to characterize the accuracy in the identification of motor unit discharges during natural movements using high-density electromyography (EMG) signals and to investigate their correlation with finger kinematics. Approach. High-density EMG signals of forearm muscles and finger joint angles were recorded concurrently during hand movements of ten able-bodied subjects. EMG signals were decomposed into motor unit spike trains (MUSTs) with a blind-source separation method. The first principle component (FPC) of the low-pass filtered MUST was correlated with finger joint angles. Main results. On average, motor units were identified during each individual finger task with an estimated decomposition accuracy 85%. The FPC extracted from discharge rates was strongly associated to the joint angles (), and preceded the joint angles on average by ms. Moreover, the FPC outperformed two time-domain features (the EMG envelop and the root mean square of EMG) in estimating joint angles. Significance. These results indicated the possibility of identifying individual motor unit behavior in dynamic natural contractions. Moreover, the strong association between motor unit discharge behaviors and kinematics proves the potential of the approach for the simultaneous and proportional control of prostheses.

Clinical utility of portable electrophysiology to measure fatigue in treatment-naïve non-small cell lung cancer.

Cancer-related fatigue (CRF) biology remains poorly understood. Responsible mechanisms may be central or peripheral and originate anywhere from the brain to muscle fiber. Objective measurement is complex and previously limited to specialized laboratories. Portable electroencephalography (EEG) and electromyography (EMG) may enhance objective measurement. This study evaluated the feasibility and acceptability of portable EMG-EEG in CRF assessment. A prospective observational feasibility study compared ten outpatients with inoperable, treatment-naïve non-small cell lung cancer and CRF to ten healthy volunteers. All completed a sustained isometric hand-grip contraction at 30% maximal level until self-perceived exhaustion. 128-channel EEG and 2-channel EMG signals of forearm muscles were recorded. Device acceptability was evaluated by questionnaire. The task was evaluated in two stages; first and last 20s. CRF cohort perceived exhaustion earlier than volunteers (mean 137 ± 76s vs 208 ± 51s). As fatigue progressed, EMG amplitude increased significantly (CRF p =0.02; volunteers: p =0.04) in both groups as did EMG beta band power (CRF p =0.008; volunteers: p =0.006). The increase was significantly less in CRF (amplitude p =0.032; beta power: p =0.014). EEG beta band power in the contralateral motor cortex increased significantly (CRF p =0.03; volunteers: p =0.019) in both cohorts but to greater extent (p =0.024) in CRF. One hundred percent device acceptability was reported. A laboratory-based evaluation was successfully adapted to the outpatient setting during routine visits. High acceptability supports clinical utility. In CRF, a higher degree of cortical activation was required to drive a much lower level of muscle performance. This suggests impairment of both central and peripheral mechanisms in CRF.

Electromyographic Signal-Driven Continuous Locomotion Mode Identification Module Design for Lower Limb Prosthesis Control

The purpose of current research work is to extract physiological information form surface electromyographic signal (sEMG) in efficient manner for different human locomotion and utilize it for lower limb prosthesis control. The proposed locomotion mode identification approach conserves the novelty in terms of its dependency on only single muscle electromyographic signal and independency on human gait phase. For current study, 18 healthy subjects of 21–42-year age group were engaged and their sEMG signal form two lower limb muscles has been recorded for three daily life locomotion’s. The presented approach of locomotion mode identification covers the wide group of designing factors. Here, twelve different window sizes, twelve types of feature vectors and six classifiers were compared on the ground of predictive performance and stability. The results show the best performance of overlapped windowing technique with window size of 256 ms and a shift of 32 ms. LDA emerges as best performing classifier (p value < 0.05) with a classification accuracy ranging from 89 to 99% for diverse feature subsets. Feature vector carrying time domain information reflected better performance. The multifactorial analysis reveals that the choice of feature vector as the most dominant source of performance variation (39.17% of total variance) and muscle selection as the least (1.35% of total variance). The proposed locomotion mode identification approach proves its applicability for rehabilitation and lower limb prosthesis control applications. Also, the protocol leads the researches for determining the appropriate values of designing factors involves in the model.

Developing control algorithms of a voluntary cough for an artificial bioengineered larynx using surface electromyography of chest muscles: A prospective cohort study.

This prospective cohort study investigates the prediction of a voluntary cough using surface electromyography (EMG) of intercostal and diaphragm muscles, to develop control algorithms for an EMG-controlled artificial larynx. The Ear Institute, London. Electromyography onset compared to voluntary cough exhalation onset and to 100ms (to give the artificial larynx the time to close the bioengineered vocal cords) before voluntary cough exhalation onset, in twelve healthy participants. In the 189 EMG of intercostal muscle-detected voluntary coughs, 172 coughs (91% CI 70-112) were detected before onset of cough exhalation and 128 coughs (67.6% CI 33.7-101.7) 100ms before onset of cough exhalation. In the 158 EMG of diaphragm muscle-detected voluntary coughs, 149 coughs (94.3% CI 76.3-112.3) were detected before onset of cough exhalation and 102 coughs (64.6% CI 26.6-102.6) 100ms before onset of cough exhalation. More coughs were detected before onset of cough exhalation when combining EMG activity of intercostal and diaphragm muscles and comparing this to intercostal muscle activity alone (183 coughs [96.8% CI 83.8-109.8] vs 172 coughs, P=.0294). When comparing the mentioned combination to diaphragm muscle activity alone, the higher percentage of detected coughs before cough exhalation onset was not found to be significant (183 coughs vs 149 coughs, P=.295). In addition, more coughs were detected 100ms before onset of cough exhalation with the mentioned combination of EMG activity and comparing this to intercostal muscles alone (149 coughs [78.8% CI 48.8-108.8] vs 128 coughs, P=.0198) and to diaphragm muscles alone (149 coughs vs 102 coughs, P=.0038). Most voluntary coughs can be predicted based on combined EMG signals of intercostal and diaphragm muscles, and therefore, these two muscle groups will be useful in controlling the bioengineered vocal cords within the artificial larynx during a voluntary cough.

Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called "muscle excitations"), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called "included" muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called "excluded" muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called "synergy extrapolation"). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

The human ankle has a critical role in locomotion and estimating its impedance is essential for human gait rehabilitation. The ankle is the first major joint that regulates the contact forces between the human body and the environment, absorbing shocks during the stance, and providing propulsion during walking. Its impedance varies with the level of the muscle activation. Hence, characterizing the complex relation between the ankle impedance and the lower leg’s muscle activation levels may improve our understanding of the neuromuscular characteristics of the ankle. Most ankle–foot prostheses do not have a degree of freedom in the transverse plane, which can cause high amounts of shear stress to be applied to the socket and can lead to secondary injuries. Quantifying the ankle impedance in the transverse plane can guide the design for a variable impedance ankle–foot prosthesis that can significantly reduce the shear stress on the socket. This paper presents the results of applying artificial neural networks (ANN) to learn and estimate the relation between the ankle impedance in the transverse plane under non-load bearing condition using electromyography signals (EMG) from the lower leg muscles. The Anklebot was used to apply pseudorandom perturbations to the human ankle in the transverse plane while the other degrees of freedom (DOF) in the sagittal and frontal planes were constrained. The mechanical impedance of the ankle was estimated using a previously proposed stochastic identification method that describes the ankle impedance as a function of the applied disturbances torques and the ankle motion output. The ankle impedance with relaxed muscles and with the lower leg’s muscle activations at 10 and 20% of the maximum voluntary contraction were estimated. The proposed ANN effectively predicts the ankle impedance within 85% accuracy (±5 Nm/rad absolute) for nine out of ten subjects given the root-mean-squared (rms) of the EMG signals. The main contribution of this paper is to quantify the relationship between lower leg muscle EMG signals and the ankle impedance in the transverse plane to pave the way towards designing and controlling this degree of freedom in a future ankle–foot prosthesis.

Multiparameter Electromyography Analysis of the Masticatory Muscle Activities in Patients with Brainstem Stroke at Different Head Positions.

The performance of the masticatory muscle is frequently affected and presents high heterogeneity poststroke. Surface electromyography (EMG) is widely used to quantify muscle movement patterns. However, only a few studies applied EMG analysis on the research of masticatory muscle activities poststroke, and most of which used single parameter—root mean squares (RMS). The aim of this study was to fully investigate the performance of masticatory muscle at different head positions in healthy subjects and brainstem stroke patients with multiparameter EMG analysis. In this study, 15 healthy subjects and six brainstem stroke patients were recruited to conduct maximum voluntary clenching at five different head positions: upright position, left rotation, right rotation, dorsal flexion, and ventral flexion. The EMG signals of bilateral temporalis anterior and masseter muscles were recorded, and parameters including RMS, median frequency, and fuzzy approximate entropy of the EMG signals were calculated. Two-way analysis of variance (ANOVA) with repeated measures and Bonferroni post hoc test were used to evaluate the effects of muscle and head position on EMG parameters in the healthy group, and the non-parametric Wilcoxon signed rank test was conducted in the patient group. The Welch–Satterthwaite t-test was used to compare the between-subject difference. We found a significant effect of subject and muscles but no significant effect of head positions, and the masticatory muscles of patients after brainstem stroke performed significantly different from healthy subjects. Multiparameter EMG analysis might be an informative tool to investigate the neural activity related movement patterns of the deficient masticatory muscles poststroke.