Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm

Abstract

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation

“…Dideriksen et al (2010b) and Venugopal et al (2017) solved this issue by introducing a simple PID controller for adjusting the model input, such that the error between desired and actual force output is minimized. This method corresponds to the introduction of an artificial feedback loop in Figure 1.…”

“…Contessa and De Luca (2013) introduced a similar force feedback loop, although they implemented the feedback in an offline instead of online fashion, updating the input signal in hindsight and re-running the simulation if the force output deviated too strongly from the desired output. While all three articles (Dideriksen et al, 2010b; Contessa and De Luca, 2013; Venugopal et al, 2017) include some remarks on physiological feedback processes, neither model was meant to replicate properties of actual physiological feedback control, but rather to account for the fact that the rate coding and force generation model components had not been adjusted to each other. One significant drawback of this method is that the feedback loop unpredictably distorts the characteristics of the assumed rate coding model, effectively leading to a different model being used in simulation than the one that has been initially described.…”

“…Figure 5 illustrates the proposed force control scheme, as opposed to the scheme used traditionally (Dideriksen et al, 2010b; Contessa and De Luca, 2013; Venugopal et al, 2017). In this article, we focus on describing a comprehensive feed-forward model of muscular EMG and force generation, and hence we only consider the first of the two components here.…”

“…Illustration of two physiological force control schemes. Traditionally, authors have followed approach (A) , in which the controller needs to compensate for the nonlinearity of the muscular force generation model in addition to external disturbances and time-varying parameters (Dideriksen et al, 2010b; Contessa and De Luca, 2013; Venugopal et al, 2017). This nonlinearity is not a physiological property but rather results from the fact that the rate coding model is not adjusted to the force generating capacity of the simulated muscle (or vice versa).…”

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes

“…This approach provides an infinitely wide dictionary of MUAPs, permits to take into consideration the electrode position and the neuromuscular jitter. Moreover, biophysical EMG simulation can be complemented by a force generation model in order to establish a complete model of the muscle electrical and mechanical responses [2], [3]. In this paper, we present a new biophysical MUAP simulation model.…”