Influence of estimators of spectral density on the analysis of electromyographic and vibromyographic signals

Robust fatigue markers obtained from muscle synergy analysis

This study aimed to utilize the nonnegative matrix factorization (NNMF) algorithm for muscle synergy analysis, extracting synergy structures and muscle weightings and mining biomarkers reflecting changes in muscle fatigue from these synergy structures. A leg press exercise to induce fatigue was performed by 11 participants. Surface electromyography (sEMG) data from seven muscles, electrocardiography (ECG) data, Borg CR-10 scale scores, and the z-axis acceleration of the weight block were simultaneously collected. Three indices were derived from the synergy structures: activation phase difference, coactivation area, and coactivation time. The indicators were further validated for single-leg landing. Differences in heart rate (HR) and heart rate variability (HRV) were observed across different fatigue levels, with varying degrees of disparity. The median frequency (MDF) exhibited a consistent decline in the primary working muscle groups. Significant differences were noted in activation phase difference, coactivation area, and coactivation time before and after fatigue onset. Moreover, a significant correlation was found between the activation phase difference and the coactivation area with fatigue intensity. The further application of single-leg landing demonstrated the effectiveness of the coactivation area. These indices can serve as biomarkers reflecting simultaneous alterations in the central nervous system and muscle activity post-exertion.

A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2-10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction.

Identification of the best strategy to command variable stiffness using electromyographic signals

OBJECTIVE

In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG signal samples collected from the operator. However, this control strategy may be fatiguing. In this study, we proposed and experimentally validated a novel stiffness control strategy, based on the average muscle co-contraction estimated from EMG samples collected in the previous 1 or 2 s.

APPROACH

Nine subjects performed a tracking task with their right wrist in five different sessions. In four sessions a haptic device (Hi-5) applied a sinusoidal perturbing torque. In Baseline session, co-contraction reduced the effect of the perturbation only by stiffening the wrist. In contrast, during aided sessions the perturbation amplitude was also reduced (mimicking the effect of additional stiffening provided by EMG-driven robotic device) either proportionally to the co-contraction exerted by the subject sample-by-sample (Proportional), or according to the average co-contraction exerted in the previous 1 s (Integral 1s), or 2 s (Integral 2s). Task error, metabolic cost during the tracking task, perceived fatigue, and the median EMG frequency calculated during a sub-maximal isometric torque generation tasks that alternated with the tracking were compared across sessions.

MAIN RESULTS

Positive effects of the reduction of the perturbation provided by co-contraction estimation was identified in all the investigated variables. Integral 1s session showed lower metabolic cost with respect to the Proportional session, and lower perceived fatigue with respect to both the Proportional and the Integral 2s sessions.

SIGNIFICANCE

This study's results showed that controlling the stiffness of an EMG-driven robotic device proportionally to the operator's co-contraction, averaged in the previous 1 s, represents the best control strategy because it required less metabolic cost and led to a lower perceived fatigue.

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle

We hypothesized that a shift in diaphragm muscle (DIAm) EMG power spectral density (PSD) to higher frequencies reflects recruitment of more fatigable fast-twitch motor units and motor unit recruitment is reflected by EMG non-stationarity. DIAm EMG was recorded in anesthetized rats during eupnea, hypoxia-hypercapnia (10% O(2)-5% CO(2)), airway occlusion, and sneezing (maximal DIAm force). Although power in all frequency bands increased progressively across motor behaviors, PSD centroid frequency increased only during sneezing (p<0.05). The non-stationary period at the onset of EMG activity ranged from ∼80 ms during airway occlusion to ∼150 ms during eupnea. Within the initial non-stationary period of EMG activity 80-95% of motor units were recruited during different motor behaviors. Motor units augmented their discharge frequencies progressively beyond the non-stationary period; yet, EMG signal became stationary. In conclusion, non-stationarity of DIAm EMG reflects the period of motor unit recruitment, while a shift in the PSD towards higher frequencies reflects recruitment of more fatigable fast-twitch motor units.

Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity

In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent performance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.

A wavelet multiscale based method to separate the high and low frequency components of mechanomyographic signals

The study of mechanomyographic (MMG) signals during dynamic contraction requires a criterion to select the cut-off frequency of the filter utilized to separate the low frequency (L.F) component (basically due to gross movement of the muscle or of the body) and the high frequency (HF) component (related with the vibration of the muscle fibers during contraction). To date, there is not an established criterion to carry out this selection. In this study, we propose a wavelet multiscale based method to aid to select a suitable cut-off frequency to separate correctly the LF and HF components. This method has been tested in an animal model, with the signal acquired during spontaneous ventilations with a capacitive accelerometer applied on the costal wall. This signal, as the MMG signals during dynamic contractions, has a LF component that is related with the movement of the thoracic cage, and a HF component that could be related with the vibration of diaphragm muscle fibers during contraction. The results obtained in the two respiratory tests analyzed indicate that cut-off frequencies around 10 and 3 Hz, respectively, must be employed to eliminate the LF component. The proposed wavelet multiscale method appears to be suitable to carry out a preliminary study of the MMG frequency content in dynamic contraction protocols.

Experimentally verified model of mechanomyograms recorded during single motor unit contractions

The aim of the paper is to create a model which enables to observe the mechanomyographic (MMG) wave generated during single motor unit contractions in a muscle, while the muscle is immersed in paraffin oil. The muscle model is described as a rheological membrane. Both the muscle and the medium models have been built by using Stiff-Finite-Element-Method (SFEM), which allows one to simulate the muscle surface displacement and the acoustic propagation of this effect in the oil. Such a modelling enables one to determine the impact of the rheological properties of the liquid environment on the shape of the MMG wave. In order to verify the model, the MMG signals and the contraction forces have been recorded in vivo from the medial gastrocnemius muscle of a rat. In these experiments single motor units were stimulated with various stimulation frequencies. A piezotransducer, immersed in paraffin oil, has been used to record the MMG signal recording. The signals recorded during individual twitches of the motor units have been used to estimate the parameters of the model. Subsequently, the model has been experimentally verified. The signals recorded in experiments during unfused and fused tetani have been compared with the simulated model responses in the analogous stimulation program. It has been observed that the MMG signals obtained with the proposed linear model have been consistent with the results of in vivo experiments.