Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG

Selezionato dalla rivista COMPUTERS IN BIOLOGY AND MEDICINE come Meritorious paper per l'anno 201

Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

EMG Modeling

The aim of this chapter is to describe the approaches used for modelling electromyographic (EMG) signals as well as the principles of electrical conduction within the muscle. Sections are organized into a progressive, step-by-step EMG modeling of structures of increasing complexity. First, the basis of the electrical conduction that allows for the propagation of the EMG signals within the muscle is presented. Second, the models used for describing the electrical activity generated by a single fibre described. The third section is devoted to modeling the organization of the motor unit and the generation of motor unit potentials. Based on models of the architectural organization of motor units and their activation and firing mechanisms, the last section focuses on modeling the electrical activity of a complete muscle as recorded at the surface

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces

Analysis of crosstalk signals in a cylindrical layered volume conductor – Influence of the anatomy, detection system and physical properties of the tissues

A comparison of the ability of different spatial filters to reduce the amount of crosstalk in a surface electromyography (sEMG) measurement was conducted. It focused on the influence of different properties of the muscle anatomy and detection system used on the amount of crosstalk present in the measurements. An analytical model was developed which enabled the simulation of single fibre action potentials (SFAPs). These fibres were grouped together in motor units (MUs). Each MU has characteristics which, along with the SFAPs, are used to obtain the motor unit action potential (MUAP). A summation of the MUAPs from all the MUs in a muscle leads to the electromyogram (EMG) signal generated by the muscle. This is the first model which simulates a complete muscle for crosstalk investigation. Previous studies were done for single fibres (Farina&Rainoldi 1999; Farina et al. 2002e; Farina et al. 2004a) or MUs (Dimitrova et al. 2002; Dimitrov et al. 2003; Winter et al. 1994). Lowery et al. simulated a complete muscle, but only investigated one spatial filter (Lowery et al. 2003a). This model is thus the first of its kind. EMG signals were generated for limbs with different anatomical properties and recorded with various detection systems. The parameters used for comparison of the recorded signals are the average rectified value (ARV) and mean frequency (MNF), which describe the amplitude and frequency components of an EMG signal, respectively. These parameters were computed for each EMG signal and interpreted to make recommendations on which detection system results in the best crosstalk rejection for a specific experimental set-up. The conclusion is that crosstalk selectivity in an sEMG measurement is decreased by increasing the thickness of the fat layer, increasing the skin conductivity, decreasing the fibre length, increasing the interelectrode distance of the detection system, placing the detection electrodes directly above the end-plate area or an increased state of muscle contraction. Varying the contraction force strength or placing the detection electrodes directly above the tendon area has no influence on the crosstalk selectivity. For most of the conditions investigated, the normal double differential (NDD) detection system results in the best crosstalk reduction. The only exceptions are a set-up with poor skin conductivity where NDD and double differential (DD) performed comparably, and the two simulations in which the muscle length is varied, where the DD filter performed best. Previous studies have found DD to be more selective for crosstalk rejection than NDD (Dimitrov et al. 2003; Farina et al. 2002a; Van Vlugt&Van Dijk 2000). Possible reasons for the contradictory results are the high value of skin conductivity currently used or influences of the muscle geometry.Dissertation (MEng(Bio-Engineering))--University of Pretoria, 2007.Electrical, Electronic and Computer Engineeringunrestricte

tutorial surface emg detection in space and time best practices

Abstract This tutorial is aimed to non-engineers using, or planning to use, surface electromyography (sEMG) as an assessment tool in the prevention, monitoring and rehabilitation fields. Its first purpose is to address the issues related to the origin and nature of the signal and to its detection (electrode size, distance, location) by one-dimensional (bipolar and linear arrays) and two-dimensional (grids) electrode systems while avoiding advanced mathematical, physical or physiological issues. Its second purpose is to outline best practices and provide general guidelines for proper signal detection. Issues related to the electrode-skin interface, signal conditioning and interpretation will be discussed in subsequent tutorials

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

A finite element approach to study skeletal muscle tissue

This dissertation investigates force generation in muscle using a finite element (FE) approach to model electrical activity and mechanical force production within skeletal muscle. The work proposes new FE models design/formulations to answer specific research questions related to skeletal muscle properties. The focus is on two specific determinants of skeletal muscle force: the activation and the connective tissue. A FE model was created and designed to study the impact of the dielectric and geometric (pennation) properties of the muscle tissues on the electric activation signal detected on the skin surface by bipolar electrodes (surface electromyography, sEMG). The model shows that when considering parallel muscle fibres the tissue, attenuated mainly frequencies in the physiological range (92-542 Hz). This study revealed a strong impact of the muscle fibres pennation angle, on the detected signal (low pass filtering effect); suggesting that the low pass filtering behaviour observed in experimental data is due to the geometry (curvature or pennation) rather than the dielectric properties. The model informed recommendations for sEMG experimental protocol to increase the inter-electrodes distance when measuring sEMG of pennated muscles. A micromechanical model of the muscle tissue was created to explore the influence of the connective tissue properties (endomysium) on the total muscle force production. The constitutive model was used to study the mechanical consequence of clustering of fibres due to the remodelling of the motor units, which occurs with ageing. An FE model with a bundle of 19 fibres was designed and simulated activating 21% and 37% of the fibres in a distributed and clustered pattern. Results showed for both activation levels that the pattern of the strain distribution changed with an increased deformation toward the centre of the bundle. This could lead to excessive unbalanced stresses if higher deformations are involved. The micromechanical model can be used to study muscle force determinants at a fascicle level. It showed the importance of the fibre distribution during the muscle activation and the consequences of age related alterations on force production