Power frequency spectrum analysis of surface EMG signals of upper limb muscles during elbow flexion – A comparison between healthy subjects and stroke survivors

Positional Analysis of Assisting Muscles for Handling-Assisted Exoskeletons

In order to better design handling-assisted exoskeletons, it is necessary to analyze the biomechanics of human hand movements. In this study, Anybody Modeling System (AMS) simulation was used to analyze the movement state of muscles during human handling. Combined with surface electromyography (sEMG) experiments, specific analysis and verification were carried out to obtain the position of muscles that the human body needs to assist during handling. In this study, the simulation and experiment were carried out for the manual handling process. A treatment group and an experimental group were set up. This study found that the vastus medialis muscle, vastus lateralis muscle, latissimus dorsi muscle, trapezius muscle, deltoid muscle and triceps brachii muscle require more energy in the process of handling, and it is reasonable and effective to combine sEMG signals with the simulation of the musculoskeletal model to analyze the muscle condition of human movement.

Preliminary Head-Supported Mass Performance Guidance for Dismounted Soldier Environments

INTRODUCTION

The helmet is an ideal platform to mount technology that gives U.S. Soldiers an advantage over the enemy; the total system is recognized quantitatively as head-supported mass (HSM). The stress placed on the head and neck is magnified by adding mass and increasing the center of mass offset away from the atlanto-occipital complex, the head's pivot point on the spine. Previous research has focused on HSM-related spinal degeneration and performance decrement in mounted environments. The increased capabilities and protection provided by helmet systems for dismounted Soldiers have made it necessary to determine the boundaries of HSM and center of mass offset unique to dismounted operations.

MATERIALS AND METHODS

A human subject volunteer study was conducted to characterize the head and neck exposures and assess the impact of HSM on performance in a simulated field-dismounted operating environment. Data were analyzed from 21 subjects who completed the Load Effects Assessment Program-Army obstacle course at Fort Benning, GA, while wearing three different experimental HSM configurations. Four variable groups (physiologic/biomechanical, performance, kinematic, and subjective) were evaluated as performance assessments. Weight moments (WMs) corresponding to specific performance decrement levels were calculated using the quantitative relationships developed between each metric and the study HSM configurations. Data collected were used to develop the performance decrement HSM threshold criteria based on an average of 10% total performance decrement of dismounted Soldier performance responses.

RESULTS

A WM of 134 N-cm about the atlanto-occipital complex was determined as the preliminary threshold criteria for an average of 10% total performance decrement. A WM of 164 N-cm was calculated for a corresponding 25% average total performance decrement.

CONCLUSIONS

The presented work is the first of its kind specifically for dismounted Soldiers. Research is underway to validate these limits and develop dismounted injury risk guidance.

Developing new intermuscular coordination patterns through an electromyographic signal-guided training in the upper extremity

BACKGROUND

Muscle synergies, computationally identified intermuscular coordination patterns, have been utilized to characterize neuromuscular control and learning in humans. However, it is unclear whether it is possible to alter the existing muscle synergies or develop new ones in an intended way through a relatively short-term motor exercise in adulthood. This study aimed to test the feasibility of expanding the repertoire of intermuscular coordination patterns through an isometric, electromyographic (EMG) signal-guided exercise in the upper extremity (UE) of neurologically intact individuals.

METHODS

10 participants were trained for six weeks to induce independent control of activating a pair of elbow flexor muscles that tended to be naturally co-activated in force generation. An untrained isometric force generation task was performed to assess the effect of the training on the intermuscular coordination of the trained UE. We applied a non-negative matrix factorization on the EMG signals recorded from 12 major UE muscles during the assessment to identify the muscle synergies. In addition, the performance of training tasks and the characteristics of individual muscles' activity in both time and frequency domains were quantified as the training outcomes.

RESULTS

Typically, in two weeks of the training, participants could use newly developed muscle synergies when requested to perform new, untrained motor tasks by activating their UE muscles in the trained way. Meanwhile, their habitually expressed muscle synergies, the synergistic muscle activation groups that were used before the training, were conserved throughout the entire training period. The number of muscle synergies activated for the task performance remained the same. As the new muscle synergies were developed, the neuromotor control of the trained muscles reflected in the metrics, such as the ratio between the targeted muscles, number of matched targets, and task completion time, was improved.

CONCLUSION

These findings suggest that our protocol can increase the repertoire of readily available muscle synergies and improve motor control by developing the activation of new muscle coordination patterns in healthy adults within a relatively short period. Furthermore, the study shows the potential of the isometric EMG-guided protocol as a neurorehabilitation tool for aiming motor deficits induced by abnormal intermuscular coordination after neurological disorders.

TRIAL REGISTRATION

This study was registered at the Clinical Research Information Service (CRiS) of the Korea National Institute of Health (KCT0005803) on 1/22/2021.

An EMG-Based Objective Function for Human-in-the-Loop Optimization

Advancements in wearable robots aim to improve the users' motion, performance, and comfort by optimizing, mainly, energetic cost (EC). However, EC is a noisy measurement with a physiological delayed response that requires long evaluation periods and wearing an uncomfortable mask. This study aims to estimate and minimize an EMG-based objective function that describes the natural energetic expenditure of individuals walking. This objective is assessed by combining multiple electromyography (EMG) variables from the EMG intensity and muscle synergies. To evaluate this objective function simply and repeatedly, we prescribed step frequency (SF) via a metronome and optimized this frequency to minimize muscle activity demands. Further, a linear mixed-effects model was fitted for EC, with the EMG variables as fixed-effects and a random intercept that varies by participant. After the model was fitted to the data, a cubic polynomial was used to identify the optimal SF that reduces the overall EMG-based objective function. Our analysis outlines that the proposed objective function is comparable to the EC during walking, the primary objective function used in human-in-the-loop optimization. Thus, this EMG-based objective function could be potentially used to optimize wearable robots and improve human-robot interaction.

Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker

Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.

Analysis of Muscular Electrical Activity and Blood Perfusion of Upper Extremity in Patients with Hemiplegic Shoulder Pain: A Pilot Study

Background

Hemiplegic shoulder pain (HSP) is a common symptom for post-stroke patients, which has a severely adverse impact on their rehabilitation outcomes. However, the cause of HSP has not been clearly identified due to its complicated multifactorial etiologies. As possible causes of HSP, the abnormality of both muscular electrical activity and blood perfusion remains lack of investigations.

Objective

This study aimed to analyze the alteration of muscular electrical activity and blood perfusion of upper extremity in patients with HSP by using surface electromyography (sEMG) and laser speckle contrast imaging (LSCI) measurement techniques, which may provide some insight into the etiology of HSP.

Methods

In this observational and cross-sectional study, three groups of participants were recruited. They were hemiplegic patients with shoulder pain (HSP group), hemiplegic patients without shoulder pain (HNSP group), and healthy participants (Healthy group). The sEMG data and blood perfusion data were collected from all the subjects and used to compute three different physiological measures, the root-mean-square (RMS) and median-frequency (MDF) parameters of sEMG recordings, and the perfusion unit (PU) parameter of blood perfusion imaging.

Results

The RMS parameter of sEMG showed significant difference (p < 0.05) in the affected side between HSP, HNSP, and Healthy groups. The MDF parameter of sEMG and PU parameter of blood perfusion showed no significant difference in both sides among the three groups (p > 0.05). The RMS parameter of sEMG showed a statistically significant correlation with the pain intensity (r = -0.691, p =0.012).

Conclusion

This study indicated that the muscular electrical activity of upper extremity had a correlation with the presence of HSP, and the blood perfusion seemed to be no such correlation. The findings of the study suggested an alternative way to explore the mechanism and treatment of HSP.

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke

Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.

Muscle Co-Contraction Detection in the Time-Frequency Domain

Background: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.

Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: A non-linear dimensionality reduction and clustering

The Short-Time Fourier transform (STFT) is a helpful tool to identify muscle fatigue with clinical and sports applications. However, the choice of STFT parameters may affect the estimation of myoelectrical manifestations of fatigue. Here, we determine the effect of window length and overlap selections on the frequency slope and the coefficient of variation from EMG spectrum features in fatiguing contractions. We also determine whether STFT parameters affect the relationship between frequency slopes and task failure. Eighty-eight healthy adult men performed one-leg heel-rise until exhaustion. A factorial design with a window length of 50, 100, 250, 500, and 1000 ms with 0, 25, 50, 75, and 90% of overlap was used. The frequency slope was non-linearly fitted as a task failure function, followed by a dimensionality reduction and clustering analysis. The STFT parameters elicited five patterns. A small window length produced a higher slope frequency for the peak frequency (p < 0.001). The contrary was found for the mean and median frequency (p < 0.001). A larger window length elicited a higher slope frequency for the mean and peak frequencies. The largest frequency slope and dispersion was found for a window length of 50 ms without overlap using peak frequency. A combination of 250 ms with 50% of overlap reduced the dispersion both for peak, median, and mean frequency, but decreased the slope frequency. Therefore, the selection of STFT parameters during dynamic contractions should be accompanied by a mechanical measure of the task failure, and its parameters should be adjusted according to the experiment's requirements.

Electromyographic biofeedback improves upper extremity function: a randomized, single-blinded, controlled trial

OBJECTIVE

To examine the effects of a 6-week surface electromyographic biofeedback intervention on the re-learning of upper extremity motor function in subjects with paretic upper extremity after stroke.

DESIGN

A randomized controlled trial.

SETTING

State Centre of Attention to Brain Injury, Madrid, Spain.

PARTICIPANTS

Thirty-eight participants in the sub-acute post-stroke stage were recruited and randomly allocated into either the surface electromyographic biofeedback (sEMG-BFB) or sham biofeedback (BFB) groups.

INTERVENTIONS

The sEMG-BFB group (n=19) received the intervention focused on re-learning scapulothoracic control during arm-reaching tasks involving shoulder abduction. The sham BFB group (n=19) received a sham intervention.

OUTCOME MEASURES

Upper extremity motor function assessed using the Fugl-Meyer Assessment-Upper Extremity Scale (66 points), the glenohumeral active range of motion, and the electromyographic amplitude signal of the middle deltoid and upper trapezius muscles were collected at baseline, after the intervention, and at the one-month follow-up.

RESULTS

Compared with the sham BFB group, the sEMG-BFB group experienced significant increases in upper extremity motor function after the intervention. The mean differences between groups were as follows: 4.79 points (95% CI 2.92 to 6.66) after the intervention; 6.55 points (95% CI 3.75 to 9.34) at the one-month follow-up; improved active range of motion 15.75 points (95% CI 6 to 30) after the intervention and electromyographic activity in the upper trapezius muscle changed in favour of the sEMG-BFB.

CONCLUSIONS

In the short term, a 6-week sEMG-BFB intervention effectively improved paretic upper limb motor function. Future research is needed to determine if the sEMG-BFB intervention has any long-term effects. Clinical trial number registration: NCT02974465 (ClinicalTrials.gov).

Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study

Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.

Dynamics of directional tuning and reference frames in humans: A high-density EEG study

Recent developments in EEG recording and signal processing have made it possible to record in an unconstrained, natural movement task, therefore EEG provides a promising approach to understanding the neural mechanisms of upper-limb reaching control. This study specifically addressed how EEG dynamics in the time domain encoded finger movement directions (directional tuning) and posture dependence (movement reference frames) by applying representational similarity analysis. High-density EEG covering the entire scalp was recorded while participants performed eight-directional, center-out reaching movements, thereby allowing us to explore directional selectivity of EEG sources over the brain beyond somatosensory areas. A majority of the source processes exhibited statistically significant directional tuning during peri-movement periods. In addition, directional tuning curves shifted systematically when the shoulder angle was rotated to perform the task within a more laterally positioned workspace, the degree of tuning curve rotation falling between that predicted by models assuming extrinsic and shoulder-based reference frames. We conclude that temporal dynamics of neural mechanisms for motor control can be studied noninvasively in humans using high-density EEG and that directional sensitivity of motor and non-motor processing is not limited within the sensorimotor areas but extends to the whole brain areas.