Surface EMG-force modelling for the biceps brachii and its experimental evaluation during isometric isotonic contractions

Cao, Hua; Boudaoud, Sofiane; Marin, F.; Marque, Catherine

doi:10.1080/10255842.2013.867952

Cited by 17 publications

(16 citation statements)

References 49 publications

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“…An attempt to physiologically explain this trend should be linked to the MU recruitment strategies. In fact, with force increase, more fast type MU, localized closer to the surface for the Biceps Brachii, are recruited [7], which results in relative high amplitude MU action potential trains. And since the Laplacian arrangement generates highly asymmetric MU action potentials with important positive wave, the direct consequence should be the apparition of PDF shape deformation toward positive amplitude sEMG values explaining the positive skewness and kurtosis according to force increase.…”

Section: Discussionmentioning

confidence: 98%

Evaluation of HD-sEMG Probability Density Function deformations in ramp exercise

Harrach

Boudaoud

Gamet

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The aim of the present study is to propose a subject-specific screening approach of High Density surface EMG (HD-sEMG) Probability Density Function (PDF) shape evolution in experimental conditions following a ramp exercise from 0% to 50% of the Maximum Voluntary Contraction (MVC) during 25 seconds of isometric contractions of the Biceps Brachii from six healthy subjects. This method uses High Order Statistics (HOS), namely the kurtosis and the skewness for PDF shape screening examined on selectively positioned Laplacian sEMG channels obtained on an 8×8 HD-sEMG grid. For each subject, the position of the Laplacian channels was chosen based on the level of muscle activation obtained from the Signal to Noise Ratio (SNR) matrix computed for the 64 sEMG signals of the grid in order to obtain independent Laplacian configurations localized in areas with high SNRs indicating high muscle activation. Afterwards, we used the Principal Component Analysis (PCA) to obtain the principal trend of the kurtosis and the skewness computed from the selected Laplacian signals according to force level variation. The obtained results show a globally common increasing HOS trend according to force increase from 0% to 50% MVC for all the subjects regardless of the anatomical, instrumental and physiological variability that usually strongly influences these trends.

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Section: Discussionmentioning

confidence: 98%

Evaluation of HD-sEMG Probability Density Function deformations in ramp exercise

Harrach

Boudaoud

Gamet

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…The activation level α , which represents the activation level of muscle units bio-electrically, is commonly constructed from sEMG rms by fitting to specific activation-force patterns for various skeletal muscles 8, 30 .…”

Section: Methodsmentioning

confidence: 99%

“…Zajac 6 and Winters 3 improved the earlier models by introducing the effect of the pennation angle of skeletal muscles. These sEMG-driven models have been adapted for biceps brachii and proved effective for emulating nerval controls, contractions as well as other muscular behaviors 7, 8 . Despite the successful applications of the sEMG-driven models in neurophysiology and biomechanics, the sEMG devices are intricate, susceptible to variations in moisture and positions of electrodes, thus are used in well-conditioned laboratories instead of fields.…”

Section: Introductionmentioning

confidence: 99%

A Bio-mechanical Model for Elbow Isokinetic and Isotonic Flexions

Tao

2017

Sci Rep

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A new bio-mechanical model for elbow flexions is proposed to quantify the elbow torque generated as a function of the upper-arm circumferential strain and influencing factors of elbow angle and angular velocity. The upper-arm circumferential strain is used to represent the contractile intensity of the dominant flexor, biceps brachii, whose behavior is described by Hill’s theory. Experiments with thirteen healthy subjects were conducted to determine the influencing factors. The temporal distributions of torque and elbow angle were measured by Biodex ®3 simultaneously, while the upper-arm circumference was obtained by a wearable anthropometric measurement device. Within the experimental range, the change of angular velocity has been found to have no effect on the torque generated. The new model was further verified experimentally with reasonable agreements obtained. The mean relative error of the torque estimated from the model is 15% and 22%, for isokinetic and isotonic flexions, respectively. The verified model establishes the relationship between the torque generated and circumference strain of the upper arm, for the first time, thus provide a scientific foundation for the anthropometric measurement technology as an alternative to sEMG for monitoring force/torque generation during elbow flexions.

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“…The end-effector force estimation based on sEMG mainly includes two key procedures: muscle activation information extraction from raw sEMG signals, and establishment of a force estimation model. In early studies, the muscle activation information that was used to estimate the endeffector force was extracted from an individual channel or several channels of sEMG signals (Hayashibe and Guiraud, 2013;Cao et al, 2015). For example, Mobasser et al (2007) placed two sEMG electrodes on the biceps brachii (BB) and triceps brachii (TB) to perform force estimation at the wrist during the elbow flexion-extension task.…”

Section: Introductionmentioning

confidence: 99%

Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network

Chen

Cao

et al. 2020

Front. Neurosci.

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In this study, research was carried out on the end-effector force estimation of two representative multi-muscle contraction tasks: elbow flexion and palm-pressing. The aim was to ascertain whether an individual muscle or a combination of muscles is more suitable for the end-effector force estimation. High-density surface electromyography (HD-sEMG) signals were collected from four primary muscle areas of the upper arm and forearm: the biceps brachii (BB), brachialis (BR), triceps brachii (TB), brachioradialis (BRD), and extensor digitorum communis (EDC). The wrist pulling and palm-pressing forces were measured in elbow flexion and palm-pressing tasks, respectively. The deep belief network (DBN) was adopted to establish the relation between HD-sEMG and the measured force. The representative signals of the four primary areas, which were considered as the input signal of the force estimation model, were extracted by HD-sEMG using the principle component analysis (PCA) algorithm, and fed separately or together into the DBN. An index termed mean impact value (MIV) was proposed to describe the priority of different muscle groups for estimating the end-effector force. The experimental results demonstrated that, in multi-muscle isometric contraction tasks, the dominant muscles with the highest activation degree could track variations in the end-effector force more effectively, and are more suitable than a combination of muscles. The main contributions of this research are as follows: (1) To fuse the activation information from different muscles effectively, DBN was adopted to establish the relationship between HD-sEMG and the generated force, and achieved highly accurate force estimation. (2) Based on the well-trained DBN force estimation model, an index termed MIV was presented to evaluate the priority of muscles for estimating the generated force.

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Surface EMG-force modelling for the biceps brachii and its experimental evaluation during isometric isotonic contractions

Cited by 17 publications

References 49 publications

Evaluation of HD-sEMG Probability Density Function deformations in ramp exercise

Evaluation of HD-sEMG Probability Density Function deformations in ramp exercise

A Bio-mechanical Model for Elbow Isokinetic and Isotonic Flexions

Upper Limb End-Effector Force Estimation During Multi-Muscle Isometric Contraction Tasks Using HD-sEMG and Deep Belief Network

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