Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study

Abstract

In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG-force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80% MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

“…For upper limb movement, most studies conducted have characterized hand and arm movements based on EMG signals recorded on the biceps brachii muscle during isometric contractions [65,76,99]. Later, biceps brachii and triceps brachii muscles were selected in the analysis of motion pattern EMG signals in isotonic and isomteric contractions [29].…”

Sensitivity evaluation of HOS parameters by data fusion from HD-sEMG grid

“…In contrast, we observe a low value of skewness in the middle of the grid. This reflects the contribution of the MU (at the center of the muscle) which is close to the detection system [2], [6]. Then with the increase of the contraction level from 20% to 50%-MVC we observe a clear increase in the skewness due to the effect of the new recruited MUs.…”

“…Therefore, a single monopolar electrode or even bipolar detection system is hardly sufficient to obtain a reliable signal that reflects the muscle, because of the variability of surface EMG that's caused by a number of factors such as: the timing and intensity of muscle contraction, the distance of the electrode from the active muscle area, the electrode and amplifier properties and the quality of contact between the electrodes and the skin [1], [2]. Since EMG signals constitute a summation of the motor unit (MU) action potentials, it occurs within the detection area of the electrode constructive and destructive superimpositions highly dependent on the MU spatial distribution, causing a change in the sEMG amplitude.…”

“…HD-sEMG is a non-invasive technique to measure electrical muscle activity with multiple closely spaced electrodes overlying a restricted area of the skin. In our study we used a simulated 64 electrode grid (8x8) by a developed sEMG-force model using parallel computing [1], [2]. The aim of the proposed study is to evaluate the sensitivity of HOS parameters according to contraction level variation by the data fusion from the simulated HD-sEMG grid.…”

“…The aim of the proposed study is to evaluate the sensitivity of HOS parameters according to contraction level variation by the data fusion from the simulated HD-sEMG grid. In fact, in precedent study, interesting results have been obtained with few electrodes [1], [2]. Multisensor data fusion is a technology that enables combining information from several sources in order to form a unified picture [4], [5].…”