A practical electrode-array myoprocessor for surface electromyography

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

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

Frequency study of facial electromyography signals with respect to emotion recognition

Emotional intelligence is one of the key research areas in human-computer interaction. This paper reports the development of an emotion recognition system using facial electromyogram (EMG) signals focusing the ambiguity on the frequency ranges used by different research works. The six emotional states (happiness, sadness, fear, surprise, disgust, and neutral) were elicited in 60 subjects using audio visual stimuli. Statistical features were extracted from the signals at high, medium, low, and very low frequency levels. They were then classified using four classifiers - naïve Bayes, regression tree, K-nearest neighbor, and fuzzy K-nearest neighbor, and the performance of the system at the different frequency levels were studied using three metrics, namely, % accuracy, sensitivity, and specificity. The post hoc tests in analysis of variance (ANOVA) indicate that the features contain significant emotional information at the very low-frequency range (<0.08 Hz). Similarly, the performance metrics of the classifiers also ensure better recognition rate at very low-frequency range. Though this range of frequency has not been used by researchers, the results of this work indicate that it should not be ignored. Further investigation of the very low frequency range to identify emotional information is still in progress.

Clinical applications of high-density surface EMG: A systematic review

High density-surface EMG (HD-sEMG) is a non-invasive technique to measure electrical muscle activity with multiple (more than two) closely spaced electrodes overlying a restricted area of the skin. Besides temporal activity HD-sEMG also allows spatial EMG activity to be recorded, thus expanding the possibilities to detect new muscle characteristics. Especially muscle fiber conduction velocity (MFCV) measurements and the evaluation of single motor unit (MU) characteristics come into view. This systematic review of the literature evaluates the clinical applications of HD-sEMG. Although beyond the scope of the present review, the search yielded a large number of "non-clinical" papers demonstrating that a considerable amount of work has been done and that significant technical progress has been made concerning the feasibility and optimization of HD-sEMG techniques. Twenty-nine clinical studies and four reviews of clinical applications of HD-sEMG were considered. The clinical studies concerned muscle fatigue, motor neuron diseases (MND), neuropathies, myopathies (mainly in patients with channelopathies), spontaneous muscle activity and MU firing rates. In principle, HD-sEMG allows pathological changes at the MU level to be detected, especially changes in neurogenic disorders and channelopathies. We additionally discuss several bioengineering aspects and future clinical applications of the technique and provide recommendations for further development and implementation of HD-sEMG as a clinical diagnostic tool.

Sampling, noise-reduction and amplitude estimation issues in surface electromyography

This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

Joint angle-dependence of elbow flexor activation levels during isometric and isokinetic maximum voluntary contractions

OBJECTIVE

The influences of elbow joint angle and the type of contraction on the activation levels of biceps brachii and brachioradialis during maximum voluntary isometric and isokinetic contractions were investigated.

DESIGN

A within-session repeated measures design.

BACKGROUND

Activation of synergistic elbow flexor muscles has been reported to be affected disparately by elbow joint angle and contraction type.

METHODS

Ten subjects performed concentric isokinetic, eccentric isokinetic, and isometric maximum voluntary contractions of the elbow flexor muscles. For the isokinetic contractions the activation levels of two ranges of motion were compared. For the isometric contractions the activation levels at two joint angles were compared. The activation levels of the biceps brachii and brachioradialis acquired simultaneously using bipolar surface electrodes and a surface electrode array were compared.

RESULTS

Results from the electrode array were similar to those acquired using conventional bipolar electrodes. The activation of biceps brachii was significantly affected by joint angle during concentric isokinetic and isometric maximum voluntary contractions. The activation of brachioradialis was significantly affected by joint angle only during eccentric isokinetic maximum voluntary contractions.

CONCLUSIONS

The results confirm that joint angle and contraction type contribute to the distinction between the activation of synergistic elbow flexor muscles during isometric and isokinetic contractions. Relevance The results point to the complexity of control of elbow joint synergists and raise questions about the plasticity of this dependency of elbow flexor activation on joint angle. Solutions to these questions are of importance in the areas of upper extremity rehabilitation and modeling the upper extremity neuromechanics.

Adaptive whitening of the electromyogram to improve amplitude estimation

Previous research showed that whitening the surface electromyogram (EMG) can improve EMG amplitude estimation (where EMG amplitude is defined as the time-varying standard deviation of the EMG). However, conventional whitening via a linear filter seems to fail at low EMG amplitude levels, perhaps due to additive background noise in the measured EMG. This paper describes an adaptive whitening technique that overcomes this problem by cascading a nonadaptive whitening filter, an adaptive Wiener filter, and an adaptive gain correction. These stages can be calibrated from two, five second duration, constant-angle, constant-force contractions, one at a reference level [e.g., 50% maximum voluntary contraction (MVC)] and one at 0% MVC. In experimental studies, subjects used real-time EMG amplitude estimates to track a uniform-density, band-limited random target. With a 0.25-Hz bandwidth target, either adaptive whitening or multiple-channel processing reduced the tracking error roughly half-way to the error achieved using the dynamometer signal as the feedback. At the 1.00-Hz bandwidth, all of the EMG processors had errors equivalent to that of the dynamometer signal, reflecting that errors in this task were dominated by subjects' inability to track targets at this bandwidth. Increases in the additive noise level, smoothing window length, and tracking bandwidth diminish the advantages of whitening.

Electromyogram amplitude estimation with adaptive smoothing window length

Typical electromyogram (EMG) amplitude estimators use a fixed window length for smoothing the amplitude estimate. When the EMG amplitude is dynamic, previous research suggests that varying the smoothing length as a function of time may improve amplitude estimation. This paper develops optimal time-varying selection of the smoothing window length using a stochastic model of the EMG signal. Optimal selection is a function of the EMG amplitude and its derivatives. Simulation studies, in which EMG amplitude was changed randomly, found that the "best" adaptive filter performed as well as the "best" fixed-length filter. Experimental studies found the advantages of the adaptive processor to be situation dependent. Subjects used real-time EMG amplitude estimates to track a randomly-moving target. Perhaps due to task difficulty, no differences in adaptive versus fixed-length processors were observed when the target speed was fast. When the target speed was slow, the experimental results were consistent with the simulation predictions. When the target moved between two constant levels, the adaptive processor responded rapidly to the target level transitions and had low variance while the target dwelled on a level.

Influence of smoothing window length on electromyogram amplitude estimates

A systematic, experimental study of the influence of smoothing window length on the signal-to-noise ratio (SNR) of electromyogram (EMG) amplitude estimates is described. Surface EMG waveforms were sampled during nonfatiguing, constant-force, constant-angle contractions of the biceps or triceps muscles, over the range of 10%-75% maximum voluntary contraction. EMG amplitude estimates were computed with eight different EMG processor schemes using smoothing length durations spanning 2.45-500 ms. An SNR was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Over these window lengths, average +/- standard deviation SNR's ranged from 1.4 +/- 0.28 to 16.2 +/- 5.4 for unwhitened single-channel EMG processing and from 3.2 +/- 0.7 to 37.3 +/- 14.2 for whitened, multiple-channel EMG processing (results pooled across contraction level). It was found that SNR increased with window length in a square root fashion. The shape of this relationship was consistent with classic theoretical predictions, however none of the processors achieved the absolute performance level predicted by the theory. These results are useful in selecting the length of the smoothing window in traditional surface EMG studies. In addition, this study should contribute to the development of EMG processors which dynamically tune the smoothing window length when the EMG amplitude is time varying.

Influence of joint angle on the calibration and performance of EMG amplitude estimators

Multiple-channel electromyogram (EMG) amplitude estimators incorporating temporal whitening filters and/or spatial uncorrelation filters contain a characterization of the EMG waveform (specifically, auto- and cross-correlation information) which may vary with joint angle. This paper reports on an experimental study which investigated the influence of joint angle on these EMG amplitude estimators. It was found that little or no relative improvement in estimator performance resulted from altering either temporal whitening or spatial uncorrelation filters as a function of joint angle. Also, the absolute performance level of these estimators did not vary with joint angle.

EMG variability during maximum voluntary isometric and anisometric contractions is reduced using spatial averaging

Electromyography (EMG) is a commonly used tool that can be plagued with poor signal-to-noise ratios. One result of poor signal-to-noise ratios is increased within- and between-subject variability of quantified EMG variables, for example, the integrated EMG. Methods that reduce within- and between-subject variability of quantified EMG variables can increase the statistical power of an experimental design and aid in the functional interpretation of experimental results. The purpose of this investigation was to determine the effectiveness of spatially averaging the surface EMG signal to reduce the variability of the quantified EMG obtained during maximum voluntary contractions (MVC). The present study extends the work of earlier investigators describing the enhanced signal characteristics obtained by spatially averaging the surface EMG measured during submaximum voluntary isometric contractions and stretch reflexes. Ten subjects performed maximum voluntary isometric and anisometric (concentric and eccentric) contractions of the elbow flexors. Four electrodes, forming two pairs of bipolar electrodes were placed over both the biceps brachii and brachioradialis muscles. Four rectified and integrated EMG signals from the electrode array were compared. Data from each subject's contraction condition and from each muscle were used to compute a coefficient of variation that was considered representative of the within-subject variability. These data were analysed with a multifactorial repeated measures analysis of variance (ANOVA). The results revealed a muscle-specific, statistically significant superiority of one of the methods in reducing the variability of the rectified and integrated EMG signal. Summing the rectified and integrate signals from each bipolar pair of electrodes in the array was shown to reduce significantly the within-subject variability.

Multiple site electromyograph amplitude estimation

Temporal whitening of individual surface electromyograph (EMG) waveforms and spatial combination of multiple recording sites have separately been demonstrated to improve the performance of EMG amplitude estimation. This investigation combined these two techniques by first whitening, then combining the data from multiple EMG recording sites to form an EMG amplitude estimate. A phenomenological mathematical model of multiple sites of the surface EMG waveform, with analytic solution for an optimal amplitude estimate, is presented. Experimental surface EMG waveforms were then sampled from multiple sites during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Results showed that SNR performance: 1) increased with the number of EMG sites, 2) was a function of the sampling frequency, 3) was predominantly invariant to various methods of determining spatial uncorrelation filters, 4) was not sensitive to the intersite correlations of the electrode configuration investigated, and 5) was best at lower levels of contraction. A moving average root mean square estimator (245-ms window) provided an average +/- standard deviation (A +/- SD) SNR of 10.7 +/- 3.3 for single site unwhitened recordings. Temporal whitening and four combined sites improved the A +/- SD SNR to 24.6 +/- 10.4. On one subject, eight whitened combined sites were achieved, providing an A +/- SD SNR or 35.0 +/- 13.4.

The effects of skinfold thickness on the selectivity of surface EMG

We investigated the effects of skinfold thickness and electrode orientation on the ability to record selectively from a localized region of a muscle using arrays of surface electrodes. EMG activity elicited by electrical stimulation and by voluntary contraction of the biceps muscle was recorded from subjects with skinfold thicknesses ranging from 2 mm to 21 mm. The selectivity of the surface electrodes increased as the skinfold thickness decreased; action potentials were more rapidly attenuated and underwent less low-pass filtering. As a result, the EMG recorded during a voluntary contraction at one site became less highly correlated with that recorded at neighboring sites as skinfold thickness decreased. We were able to determine the axis of action potential propagation (muscle fiber direction) through comparison of the amplitude and delay of cross-correlation peaks from pairs of colinear electrodes oriented at angles to one another, although the thicker the skinfold the lower the resolution. It was clear that the ability to localize EMG signal sources deteriorated as the amount of subcutaneous fat between the surface recording site and the active muscle fibers increased.

Pattern recognition and interpretation of electromyogram data from cat jaw muscle

This study investigates the effect of emotional behavior on the masseteric muscle EMG response patterns. Two experimental protocols are utilized: (1) does not elicit emotional behavior (stick chewing) and (2) elicits emotional behavior (hypothalamic stimulation). The Karhunen-Loève transform is used to compute features which exactly represent the correlated patterns of mean-zero observations, with data compression and noise immunity. Using nonparametric tests, it is found that the populations of biting and hissing features are significantly different (p < 0.05), with increased statistical significance as the size of the training set is increased. No statistically significant difference is seen in a test of the two biting populations.