Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis

Eccentric exercise-induced delayed onset trunk muscle soreness alters high-density surface EMG-torque relationships and lumbar kinematics

We aimed to assess high-density surface electromyography (HDsEMG)-torque relationships in the presence of delayed onset trunk muscle soreness (DOMS) and the effect of these relationships on torque steadiness (TS) and lumbar movement during concentric/eccentric submaximal trunk extension contractions. Twenty healthy individuals attended three laboratory sessions (24 h apart). HDsEMG signals were recorded unilaterally from the thoracolumbar erector spinae with two 64-electrode grids. HDsEMG-torque signal relationships were explored via coherence (0-5 Hz) and cross-correlation analyses. Principal component analysis was used for HDsEMG-data dimensionality reduction and improvement of HDsEMG-torque-based estimations. DOMS did not reduce either concentric or eccentric trunk extensor muscle strength. However, in the presence of DOMS, improved TS, alongside an altered HDsEMG-torque relationship and kinematic changes were observed, in a contraction-dependent manner. For eccentric trunk extension, improved TS was observed, with greater lumbar flexion movement and a reduction in δ-band HDsEMG-torque coherence and cross-correlation. For concentric trunk extensions, TS improvements were observed alongside reduced thoracolumbar sagittal movement. DOMS does not seem to impair the ability to control trunk muscle force, however, perceived soreness induced changes in lumbar movement and muscle recruitment strategies, which could alter motor performance if the exposure to pain is maintained in the long term.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression

Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.

A Wireless 2-Channel Layered EMG/NIRS Sensor System for Local Muscular Activity Evaluation

A wireless 2-channel layered sensor system that enables electromyography (EMG) and near-infrared spectroscopy (NIRS) measurements at two local positions was developed. The layered sensor consists of a thin silver electrode and a photosensor consisting of a photoemitting diode (LED) or photodiode (PD). The EMG and NIRS signals were simultaneously measured using a pair of electrodes and photosensors for the LED and PD, respectively. Two local muscular activities are presented in detail using layered sensors. In the experiments, EMG and NIRS signals were measured for isometric constant and ramp contractions at each forearm using layered sensors. The results showed that local muscle activity analysis is possible using simultaneous EMG and NIRS signals at each local position.

A Wireless Multi-Layered EMG/MMG/NIRS Sensor for Muscular Activity Evaluation

A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode and piezo-film, respectively. NIRS measurements are performed using the photosensor. Muscular activity is then analyzed in detail using the three types of data obtained. In experiments, the EMG, MMG and NIRS signals were measured for isometric ramp contraction at the forearm and cycling exercise of the lateral vastus muscle with stepped increments of the load using the layered sensor. The results showed that it was possible to perform simultaneous EMG, MMG and NIRS measurements at a local position using the proposed sensor. It is suggested that the proposed sensor has the potential to evaluate muscular activity during exercise, although the detection of the anaerobic threshold has not been clearly addressed.

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

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

People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations

We quantified the relationship between spatial oscillations in surface electromyographic (sEMG) activity and trunk-extension torque in individuals with and without chronic low back pain (CLBP), during two submaximal isometric lumbar extension tasks at 20% and 50% of their maximal voluntary torque. High-density sEMG (HDsEMG) signals were recorded from the lumbar erector spinae (ES) with a 64-electrode grid, and torque signals were recorded with an isokinetic dynamometer. Coherence and cross-correlation analyses were applied between the filtered interference HDsEMG and torque signals for each submaximal contraction. Principal component analysis was used to reduce dimensionality of HDsEMG data and improve the HDsEMG-based torque estimation. sEMG-torque coherence was quantified in the δ(0–5 Hz) frequency bandwidth. Regional differences in sEMG-torque coherence were also evaluated by creating topographical coherence maps. sEMG-torque coherence in the δ band and sEMG-torque cross-correlation increased with the increase in torque in the controls but not in the CLBP group (p = 0.018, p = 0.030 respectively). As torque increased, the CLBP group increased sEMG-torque coherence in more cranial ES regions, while the opposite was observed for the controls (p = 0.043). Individuals with CLBP show reductions in sEMG-torque relationships possibly due to the use of compensatory strategies and regional adjustments of ES-sEMG oscillatory activity.

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring

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

Can Increased Locomotor Task Difficulty Differentiate Knee Muscle Forces After Anterior Cruciate Ligament Reconstruction?

Changes in knee mechanics following anterior cruciate ligament (ACL) reconstruction are known to be magnified during more difficult locomotor tasks, such as when descending stairs. However, it is unclear if increased task difficulty could distinguish differences in forces generated by the muscles surrounding the knee. This study examined how knee muscle forces differ between individuals with ACL reconstruction with different graft types (hamstring tendon and patellar tendon autograft) and "healthy" controls when performing tasks with increasing difficulty. Dynamic simulations were used to identify knee muscle forces in 15 participants when walking overground and descending stairs. The analysis was restricted to the stance phase (foot contact through toe-off), yielding 162 separate simulations of locomotion in increasing difficulty: overground walking, step-to-floor stair descent, and step-to-step stair descent. Results indicated that knee muscle forces were significantly reduced after ACL reconstruction, and stair descent tasks better discriminated changes in the quadriceps and gastrocnemii muscle forces in the reconstructed knees. Changes in quadriceps forces after a patellar tendon graft and changes in gastrocnemii forces after a hamstring tendon graft were only revealed during stair descent. These results emphasize the importance of incorporating sufficiently difficult tasks to detect residual deficits in muscle forces after ACL reconstruction.

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

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

The Effect of ICA and Non-negative Matrix Factorization Analysis for EMG Signals Recorded From Multi-Channel EMG Sensors

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions-independent component analysis and non-negative matrix factorization-were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

Muscle Force Estimation Based on Neural Drive Information From Individual Motor Units

Estimation of muscle contraction force based on the macroscopic feature of surface electromyography (SEMG) has been widely reported, but the use of microscopic neural drive information has not been thoroughly investigated. In this study, a novel method is proposed to process individual motor unit (MU) activities (firing sequences and action potential waveforms) derived from the decomposition of high density SEMG (HD-SEMG), and it is applied to muscle force estimation. In the proposed method, a supervised machine learning approach was conducted to determine the twitch force of each MU according to its action potential waveforms, which enables separate calculation of every MU's contribution to force. Thus, the muscle force was predicted through a physiologically meaningful muscle force model. In the experiment, HD-SEMG data were recorded from the abductor pollicis brevis muscles of eight healthy subjects during their performance of thumb abduction with the force increasing gradually from zero to four force levels (10%, 20%, 30%, 40% of the maximal voluntary contraction), while the true muscle force was measured simultaneously. When the proposed method was used, the root mean square difference (RMSD) of the error of the estimated force with respect to the measured force was reported to be 8.3% ± 2.8%. The proposed method also significantly outperformed the other four common methods for force estimation (RMSD: from 11.7% to 20%, ), demonstrating its effectiveness. This study offers a useful tool for exploiting the neural drive information towards muscle force estimation with improved precision. The proposed method has wide applications in precise motor control, sport and rehabilitation medicine.

Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury

Background

Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.

Methods

Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).

Results

The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.

Conclusions

This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation

Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.

Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

Automated Channel Selection in High-Density sEMG for Improved Force Estimation

Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of 30% for force estimation while reducing the dimensionality by 57% for a subset of three channels.

Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control

Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.

Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network

In this study, research was carried out on the end-effector force estimation of two representative multi-muscle contraction tasks: elbow flexion and palm-pressing. The aim was to ascertain whether an individual muscle or a combination of muscles is more suitable for the end-effector force estimation. High-density surface electromyography (HD-sEMG) signals were collected from four primary muscle areas of the upper arm and forearm: the biceps brachii (BB), brachialis (BR), triceps brachii (TB), brachioradialis (BRD), and extensor digitorum communis (EDC). The wrist pulling and palm-pressing forces were measured in elbow flexion and palm-pressing tasks, respectively. The deep belief network (DBN) was adopted to establish the relation between HD-sEMG and the measured force. The representative signals of the four primary areas, which were considered as the input signal of the force estimation model, were extracted by HD-sEMG using the principle component analysis (PCA) algorithm, and fed separately or together into the DBN. An index termed mean impact value (MIV) was proposed to describe the priority of different muscle groups for estimating the end-effector force. The experimental results demonstrated that, in multi-muscle isometric contraction tasks, the dominant muscles with the highest activation degree could track variations in the end-effector force more effectively, and are more suitable than a combination of muscles. The main contributions of this research are as follows: (1) To fuse the activation information from different muscles effectively, DBN was adopted to establish the relationship between HD-sEMG and the generated force, and achieved highly accurate force estimation. (2) Based on the well-trained DBN force estimation model, an index termed MIV was presented to evaluate the priority of muscles for estimating the generated force.

An Investigation of Dimensionality Reduction Techniques for EMG-based Force Estimation

In this paper, extracted features in time and frequency domain, from high-density surface electromyogram (HD-sEMG) signals acquired from the long head and short head of biceps brachii, and brachioradialis during isometric elbow flexion are used to estimate force induced at the wrist using an artificial neural network (ANN). Different hidden layer sizes were considered to investigate its effect on the model accuracy. Also, we applied two dimensionality reduction techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), on the feature set and investigated their effects on force estimation accuracy.

PCA-Based Channel Selection in High-Density EMG for Improving Force Estimation

In this paper, a method for selecting channels to improve estimated force using fast orthogonal search (FOS) has been proposed. Surface electromyogram (sEMG) signals acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii, and brachioradialis during isometric contractions are used to estimate force induced at the wrist using the FOS algorithm. The method utilizes principle component analysis (PCA) in the frequency domain to select the channels with the highest contribution to the first principal component (PC). Our analysis demonstrates that our proposed method is capable of reducing the dimensionality of the data (the number of channels was reduced from 21 to 9) while improving the accuracy of the estimated force.

Comparison of Signal Processing Methods for Reducing Motion Artifacts in High-Density Electromyography During Human Locomotion

Objective: High-density electromyography (EMG) is useful for studying changes in myoelectric activity within a muscle during human movement, but it is prone to motion artifacts during locomotion. We compared canonical correlation analysis and principal component analysis methods for signal decomposition and component filtering with a traditional EMG high-pass filtering approach to quantify their relative performance at removing motion artifacts from high-density EMG of the gastrocnemius and tibialis anterior muscles during human walking and running. Results: Canonical correlation analysis filtering provided a greater reduction in signal content at frequency bands associated with motion artifacts than either traditional high-pass filtering or principal component analysis filtering. Canonical correlation analysis filtering also minimized signal reduction at frequency bands expected to consist of true myoelectric signal. Conclusions: Canonical correlation analysis filtering appears to outperform a standard high-pass filter and principal component analysis filter in cleaning high-density EMG collected during fast walking or running.

A Novel HD-sEMG Preprocessing Method Integrating Muscle Activation Heterogeneity Analysis and Kurtosis-Guided Filtering for High-Accuracy Joint Force Estimation

This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.

Heterogeneity Counts More than Power for HD-sEMG-Based Joint Force Estimation

The aim of the proposed work is to utilize the heterogeneity information, in input signal extraction, to improve the joint force estimation from high-density surface electromyography (HD-sEMG). For this purpose, joint force and HD-sEMG signals from biceps brachii and brachialis were collected synchronously during isometric elbow flexion. The input signal of the force model was obtained after the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas (maximum heterogeneity) was selected; and last, the selected principal component was low-pass filtered to obtain the input signal. The force model was built using a polynomial fitting technique. The conventional power-based input signal was compared with our obtained input signal. According to the obtained results, the proposed heterogeneity-based input signal can reduce the force estimation error significantly than the power-based input signal. The proposed heterogeneity-based input signal extraction methods had more neuromuscular control information and will be tested in more muscles and force tasks in future works.

Methodology of surface electromyography in gait analysis: review of the literature

Gait analysis is a significant diagnostic procedure for the clinicians who manage musculoskeletal disorders. Surface electromyography (sEMG) combined with kinematic and kinetic data is a useful tool for decision making of the appropriate method needed to treat such patients. sEMG has been used for decades to evaluate neuromuscular responses during a range of activities and develop rehabilitation protocols. The sEMG methodology followed by researchers assessed the issues of noise control, wave frequency, cross talk, low signal reception, muscle co-contraction, electrode placement protocol and procedure as well as EMG signal timing, intensity and normalisation so as to collect accurate, adequate and meaningful data. Further research should be done to provide more information related to the muscle activity recorded by sEMG and the force produced by the corresponding muscle during gait analysis.

Semi-automated Detection of Polysomnographic REM Sleep without Atonia (RSWA) in REM Sleep Behavioral Disorder

We aimed at evaluating semi-automatic detection and quantification of polysomnographic REM sleep without atonia (RSWA). As basic requirements, we defined lower time demand, the possibility of comparison of several evaluations and ease of examination for neurologists. We focused on well-known primary processing of surface electromyographic signals and selected recordings that were free of technical artifacts that could compromise automated signal detection. Thus we created a comprehensive method consisting of several modules (data preprocessing, signal filtration, envelopes creation, detection of ECG QRS complexes, iterative RSWA detection, detection evaluation and interactive visualization). The original dataset consisted of 7 individual polysomnography (PSG) recordings of individual human adult subjects with REM sleep behavior disorder (RBD). RSWA detection was performed with three different methods for envelope creation (envelope by moving average filter, envelope by Savitzky-Golay filtration and peaks interpolation). Best RSWA detection was achieved using the envelope by moving average filter (average precision 64.24±12.34 % and recall 91.63±10.07 %). The lowest precision was 42.86 % with 100 % recall.

A Fatigue Involved Modification Framework for Force Estimation in Fatiguing Contraction

To alleviate the negative impacts of muscle fatigue on a force estimation model, a modification framework taking use of fatigue index was put forward in this paper. Muscle force and surface electromyography were first collected using high-density electrode grid and dynamometer. Then, multi-step signal pre-processing and a nonnegative matrix factorization-based signal optimization were conducted, with fatigue indices being extracted in the same time. Next, a degree 4 polynomial fitting model was employed to undertake the training process, and the relationship between the generated model parameters and fatigue indices was built up. In the end, the parameter-index relationship was applied on different testing sets to complete fatigue-modified force estimation. Significant improvement was found in most testing cases across different sexes and ages. Relative decreases of 36.5%, 20.7%, and 20.4% in the percentage root mean square error were achieved by young males, young females, and elderly males. The proposed method can boost the performances of force estimation models, thereby contributing to the development of a variety of fields including biomechanical study, rehabilitation treatment, and prosthesis research.

Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation

To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.

HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion

OBJECTIVE

To investigate the activation heterogeneity of skeletal muscles and realize the joint force estimation during the elbow flexion task.

APPROACH

When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from a [Formula: see text] grid covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to biceps brachhi (BB) and brachialis (BR) and their contribution vectors using a fast, independent component analysis (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from the activation level and activation region, initially. Then, the contribution combinations of two sources were classified into several major clusters using the K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as the force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted.

MAIN RESULTS

Ten subjects were recruited in this research. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from the activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the specific area where the contribution difference of BB and BR to the HD-sEMG signals were relatively small.

SIGNIFICANCE

The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.

A SEMG-Force Estimation Framework Based on a Fast Orthogonal Search Method Coupled with Factorization Algorithms

A novel framework based on the fast orthogonal search (FOS) method coupled with factorization algorithms was proposed and implemented to realize high-accuracy muscle force estimation via surface electromyogram (SEMG). During static isometric elbow flexion, high-density SEMG (HD-SEMG) signals were recorded from upper arm muscles, and the generated elbow force was measured at the wrist. HD-SEMG signals were decomposed into time-invariant activation patterns and time-varying activation curves using three typical factorization algorithms including principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF). The activation signal of the target muscle was obtained by summing the activation curves, and the FOS algorithm was used to create basis functions with activation signals and establish the force estimation model. Static isometric elbow flexion experiments at three target levels were performed on seven male subjects, and the force estimation performances were compared among three typical factorization algorithms as well as a conventional method for extracting the average signal envelope of all HD-SEMG channels (AVG-ENVLP method). The overall root mean square difference (RMSD) values between the measured forces and the estimated forces obtained by different methods were 11.79 ± 4.29% for AVG-ENVLP, 9.74 ± 3.77% for PCA, 9.59 ± 3.81% for ICA, and 9.51 ± 4.82% for NMF. The results demonstrated that, compared to the conventional AVG-ENVLP method, factorization algorithms could substantially improve the performance of force estimation. The FOS method coupled with factorization algorithms provides an effective way to estimate the combined force of multiple muscles and has potential value in the fields of sports biomechanics, gait analysis, prosthesis control strategy, and exoskeleton devices for assisted rehabilitation.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

OBJECTIVE

To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle.

APPROACH

Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique.

MAIN RESULTS

Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number.

SIGNIFICANCE

The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Robust and accurate decoding of motoneuron behaviour and prediction of the resulting force output

KEY POINTS

The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the in vivo cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation.

ABSTRACT

The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibres represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allow for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the in vivo cat. Our results showed that the decomposition method in this animal preparation was highly accurate, with conventional two-source validation providing rates of agreement equal to or superior to those found in humans. Multidimensional reconstruction of the motor unit action potential provides the ability to accurately track the same motor unit across multiple contractions. Additionally, correlational analyses demonstrate that the composite spike train provides better estimates of whole muscle force than conventional estimates obtained from the electromyographic signal. Lastly, stark differences are observed between the modes of activation, in particular tendon vibration produced quantal interspike intervals at integer multiples of the vibration period.

Relationship between Isometric Muscle Force and Fractal Dimension of Surface Electromyogram

The relationship between fractal dimension of the surface electromyogram (sEMG) and the intensity of muscle contraction is still controversial in simulated and experimental conditions. To support the use of fractal analysis to investigate myoelectric fatigue, it is crucial to establish the interdependence between fractal dimension and muscle contraction intensity. We analyzed the behavior of fractal dimension, conduction velocity, mean frequency, and average rectified value in twenty-eight volunteers at nine levels of isometric force. sEMG was obtained using bidimensional arrays in the biceps brachii muscle. The values of fractal dimension and mean frequency increased with force unless a plateau was reached at 30% maximal voluntary contraction. Overall, our findings suggest that, above a certain level of force, the use of fractal dimension to evaluate the myoelectric manifestations of fatigue may be considered, regardless of muscle contraction intensity.

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

Surface electromyogram-controlled powered hand/wrist prostheses return partial upper-limb function to limb-absent persons. Typically, one degree of freedom (DoF) is controlled at a time, with mode switching between DoFs. Recent research has explored using large-channel EMG systems to provide simultaneous, independent and proportional (SIP) control of two joints-but such systems are not practical in current commercial prostheses. Thus, we investigated site selection of a minimum number of conventional EMG electrodes in an EMG-force task, targeting four sites for a two DoF controller. In a laboratory experiment with 10 able-bodied subjects and three limb-absent subjects, 16 electrodes were placed about the proximal forearm. Subjects produced 1-DoF and 2-DoF slowly force-varying contractions up to 30% maximum voluntary contraction (MVC). EMG standard deviation was related to forces via regularized regression. Backward stepwise selection was used to retain those progressively fewer electrodes that exhibited minimum error. For 1-DoF models using two retained electrodes (which mimics the current state of the art), subjects had average RMS errors of (depending on the DoF): 7.1-9.5% MVC for able-bodied and 13.7-17.1% MVC for limb-absent subjects. For 2-DoF models, subjects using four electrodes had errors on 1-DoF trials of 6.7-8.5% MVC for able-bodied and 11.9-14.0% MVC for limb-absent; and errors on 2-DoF trials of 9.9-11.2% MVC for able-bodied and 15.8-16.7% MVC for limb-absent subjects. For each model, retaining more electrodes did not statistically improve performance. The able-bodied results suggest that backward selection is a viable method for minimum error selection of as few as four electrode sites for these EMG-force tasks. Performance evaluation in a prosthesis control task is a necessary and logical next step for this site selection method.

Identification and classification of upper limb motions using PCA

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

The role of electromechanical delay in modelling the EMG-force relationship during quasi-dynamic contractions of the upper-limb

There is a discontinuity in published electromechanical delays (EMD) in upper-limb muscles and the state-of-the-art in modelling end-point force from electromyographic signals collected from one or more muscles. Published values are typically in the range of 10 to 30ms, depending on the nature of the contraction. In published literature where the EMG-force relationship is modelled, generally a delay of 100ms or more is induced during linear enveloping to match the EMD. The implications of EMD on end-point force prediction were considered using inter-session end-point force modelling with a support-vector-regression model. The delays were estimated using the first-order cross-correlation and the force and EMG signal were temporally aligned. The results show the delays vary by 20ms or more but did produce a notable trend based on elbow joint angle. We conclude that for upper-limb biomechanics modelling, the best practice is to align the force and EMG signals based on the induced delay during linear enveloping.

Comparison of Constant-Posture Force-Varying EMG-Force Dynamic Models About the Elbow

Numerous techniques have been used to minimize error in relating the surface electromyogram (EMG) to elbow joint torque. We compare the use of three techniques to further reduce error. First, most EMG-torque models only use estimates of EMG standard deviation as inputs. We studied the additional features of average waveform length, slope sign change rate and zero crossing rate. Second, multiple channels of EMG from the biceps, and separately from the triceps, have been combined to produce two low-variance model inputs. We contrasted this channel combination with using each EMG separately. Third, we previously modeled nonlinearity in the EMG-torque relationship via a polynomial. We contrasted our model versus that of the classic exponential power law of Vredenbregt and Rau (1973). Results from 65 subjects performing constant-posture, force-varying contraction gave a "baseline" comparison error (i.e., error with none of the new techniques) of 5.5 ± 2.3% maximum flexion voluntary contraction (%MVC_F). Combining the techniques of multiple features with individual channels reduced error to 4.8 ± 2.2 %MVC_F, while combining individual channels with the power-law model reduced error to 4.7 ± 2.0 %MVC_F. The new techniques further reduced error from that of the baseline by ≈ 15 %.

Functional connectivity in the neuromuscular system underlying bimanual coordination

Neural synchrony has been suggested as a mechanism for integrating distributed sensorimotor systems involved in coordinated movement. To test the role of corticomuscular and intermuscular coherence in bimanual coordination, we experimentally manipulated the degree of coordination between hand muscles by varying the sensitivity of the visual feedback to differences in bilateral force. In 16 healthy participants, cortical activity was measured using EEG and muscle activity of the flexor pollicis brevis of both hands using high-density electromyography (HDsEMG). Using the uncontrolled manifold framework, coordination between bilateral forces was quantified by the synergy index R_V in the time and frequency domain. Functional connectivity was assessed using corticomuscular coherence between muscle activity and cortical source activity and intermuscular coherence between bilateral EMG activity. The synergy index increased in the high coordination condition. R_V was higher in the high coordination condition in frequencies between 0 and 0.5 Hz; for the 0.5- to 2-Hz frequency band, this pattern was inverted. Corticomuscular coherence in the beta band (16-30 Hz) was maximal in the contralateral motor cortex and was reduced in the high coordination condition. In contrast, intermuscular coherence was observed at 5-12 Hz and increased with bimanual coordination. Within-subject comparisons revealed a negative correlation between R_V and corticomuscular coherence and a positive correlation between R_V and intermuscular coherence. Our findings suggest two distinct neural pathways: 1) corticomuscular coherence reflects direct corticospinal projections involved in controlling individual muscles; and 2) intermuscular coherence reflects diverging pathways involved in the coordination of multiple muscles.