We propose a model for surface electromyography (EMG) signal generation with cylindrical description of the volume conductor. The model is more general and complete with respect to previous approaches. The volume conductor is described as a multilayered cylinder in which the source can be located either along the longitudinal or the angular direction, in any of the layers. The source is represented as a spatio-temporal function which describes the generation, propagation, and extinction of the intracellular action potential at the end-plate, along the fiber, and at the tendons, respectively. The layers are anisotropic. The volume conductor effect is described as a two-dimensional spatial filtering. Electrodes of any shape or dimension are simulated, forming structures which are described as spatial filters. The analytical derivation which leads to the signal in the temporal domain is performed in the spatial and temporal frequency domains. Numerical issues related to the frequency-based approach are discussed. The descriptions of the volume conductor and of the source are applied to the cases of signal generation from a limb and a sphincter muscle. Representative simulations of both cases are provided. The resultant model is based on analytical derivations and constitutes a step forward in surface EMG signal modeling, including features not described in any other analytical approach.

Two physiological factors are assumed in this paper to mainly determine the myoelectric manifestations of fatigue: (1) the decrease of the conduction velocity (CV) of motor unit action potentials (MUAP) (peripheral fatigue), and (2) the increase of MU synchronization by the central nervous system (central fatigue). To describe separately the peripheral and central components of the myoelectric manifestations of fatigue, we investigated the following indexes: (1) mean spectral frequency - MNF, (2) median spectral frequency - MDF, (3) root mean square - RMS, (4) average rectified value - ARV, (5) estimation of muscle fiber conduction velocity - ECV, (6) percentage of determinism - %DET, (7) spectral indexes defined as the ratio between signal spectral moments - FI(k), (8) MNF estimated by autoregressive analysis - MNF(AR), (9) MNF estimated by Choi-Williams time-frequency representation - MNF(CWD), (10) MNF estimated by continuous wavelet transform - MNF(CWT), (11) signal entropy - S, (12) fractal dimension - FD. The indexes were tested with a set of synthetic EMG signals, with different CV distribution and level of MU synchronization. The indexes were calculated on epochs of 0.5s. It was observed that ECV is uncorrelated with the level of simulated synchronization (promising index of peripheral fatigue). On the other hand FD was the index least affected by CV changes and most related to the level of synchronism (promising index of central fatigue). A representative application to some experimental signals from vastus lateralis muscle during an isometric endurance test supported the results of the simulations. The vector (ECV, FD) is suggested to provide selective indications of peripheral and central fatigue. The description of EMG fatigue by a bi-dimensional vector opens new perspectives in the assessment of muscle properties, with potential application in both clinical and sport sciences.

Quantifying muscle force and fatigue is important in designing ergonomic work stations, in planning appropriate work-rest patterns, and in preventing/assessing the progress of disorders. In 14 subjects (seven males, seven females), muscle force and fatigue were estimated by subjective perception (based on Borg scale CR10) and objective indexes extracted from surface electromyogram (EMG). The experimental protocol consisted of an isometric task selective for the upper trapezius muscle at different force levels (10-80% of maximal voluntary contraction--MVC, in steps of 10%MVC) and one fatiguing contraction (constant force level at 50%MVC until exhaustion). Surface EMG signals were detected by a two-dimensional (2D) array of electrodes placed half way between C7 and the acromion. The following variables were calculated from EMG signals: muscle fibre conduction velocity (CV), root mean square value (RMS), mean frequency of the power spectrum (MNF), fractal dimension (FD), and entropy. All detected signals were also used to build topographical maps of RMS. Both subjective and objective indications of force and fatigue can provide information on exerted force and endurance time (ET). In particular, Borg ratings, RMS, and entropy were significantly related to force, and the rate of change of CV, MNF, FD, and Borg ratings were predictive of the endurance time. Moreover, significant differences were found in Borg ratings between males and females. The correlation coefficient of pairs of topographical maps of RMS was high (of the order of 0.8). This reflects a characteristic spatial-temporal recruitment of upper trapezius motor units that is not affected by force levels or fatigue.

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Interpretation of surface electromyograms (EMG) is usually based on the assumption that the surface representation of action potentials does not change during their propagation. This assumption does not hold for muscles whose fibers are oblique to the skin. Consequently, the interpretation of surface EMGs recorded from pinnate muscles unlikely prompts from current knowledge. Here we present a complete analytical model that supports the interpretation of experimental EMGs detected from muscles with oblique architecture. EMGs were recorded from the medial gastrocnemius muscle during voluntary and electrically elicited contractions. Preliminary indications obtained from simulated and experimental signals concern the spatial localization of surface potentials and the myoelectric fatigue. Specifically, the spatial distribution of surface EMGs was localized about the fibers superficial extremity. Strikingly, this localization increased with the pinnation angle, both for the simulated EMGs and the recorded M-waves. Moreover, the average rectified value (ARV) and the mean frequency (MNF) of interference EMGs increased and decreased with simulated fatigue, respectively. The degree of variation in ARV and MNF did not depend on the pinnation angle simulated. Similar variations were observed for the experimental EMGs, although being less evident for a higher fiber inclination. These results are discussed on a physiological context, highlighting the relevance of the model proposed here for the interpretation of gastrocnemius EMGs and for conceiving future experiments on muscles with pinnate geometry.

Surface electromyograms (EMGs) recorded with a couple of electrodes are meant to comprise representative information of the whole muscle activation. Nonetheless, regional variations in neuromuscular activity seem to occur in numerous conditions, from standing to passive muscle stretching. In this study, we show how local activation of skeletal muscles can be automatically tracked from EMGs acquired with a bi-dimensional grid of surface electrodes (a grid of 8 rows and 15 columns was used). Grayscale images were created from simulated and experimental EMGs, filtered and segmented into clusters of activity with the watershed algorithm. The number of electrodes on each cluster and the mean level of neuromuscular activity were used to assess the accuracy of the segmentation of simulated signals. Regardless of the noise level, thickness of fat tissue and acquisition configuration (monopolar or single differential), the segmentation accuracy was above 60%. Accuracy values peaked close to 95% when pixels with intensity below approximately 70% of maximal EMG amplitude in each segmented cluster were excluded. When simulating opposite variations in the activity of two adjacent muscles, watershed segmentation produced clusters of activity consistently centered on each simulated portion of active muscle and with mean amplitude close to the simulated value. Finally, the segmentation algorithm was used to track spatial variations in the activity, within and between medial and lateral gastrocnemius muscles, during isometric plantar flexion contraction and in quiet standing position. In both cases, the regionalization of neuromuscular activity occurred and was consistently identified with the segmentation method.

Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.

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