Influence of smoothing window length on electromyogram amplitude estimates

St-Amant, Yves; Rancourt, Denis; Clancy, Edward A.

doi:10.1109/10.678614

Cited by 53 publications

(24 citation statements)

References 15 publications

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“…Summary measures of sEMG data also vary, depending research objectives (Farfán, Politti, & Felice, 2010;Soderberg & Knutson, 2000). Processing techniques have been shown to influence summary measures of sEMG data (Waly, Asfour, & Kahalil, 1996;St-Amant, Rancour, & Clancy, 1998).…”

Section: Discussionmentioning

confidence: 99%

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Spectral Analysis of Root-Mean-Square Processed Surface Electromyography Data as a Measure of Repetitive Muscular Exertion

Gant

Fethke

Gerr

2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Highly repetitive motion is associated with upper extremity musculoskeletal disorders (UEMSDs) among industrial workers, especially when encountered concurrently with forceful exertions. Current methods of estimating occupational exposure to "repetitiveness" provide information about the repetitiveness of joint motion, but fail to provide complete information about the repetitiveness of muscular exertion, a more biomechanically meaningful measure of repetition. This thesis introduces an innovative digital signal processing method, from which muscular exertion frequency was estimated. Specifically, time series recordings of muscle activity obtained with surface electromyography (sEMG) were processed with standard root-mean-square (RMS) amplitude calculations and then transformed from the time domain into the frequency domain. The mean power frequencies of the RMS-processed sEMG signals (MPF EMG ) were then calculated to estimate muscular exertion frequency.In a laboratory-based validation study involving repetitive isometric hand gripping exertions, MPF EMG was compared to established measures of muscular exertion frequency and joint motion frequency across a range of known exertion frequencies, exertion intensities (i.e. forces), and exertion durations (i.e., duty cycles). Strong linear relationships were observed between MPF EMG and external measures of muscular exertion frequency. However, performance of MPF EMG as a measure of muscular exertion frequency may be improved with an increase of the signal to noise ratio in the sEMG data. Signal processing parameters were therefore investigated and alternative techniques were explored. Alternative processing parameters were suggested to minimize difference between MPF EMG and established methods of muscular exertion frequency.A second laboratory-based validation study compared MPF EMG to an established measure of muscular exertion frequency and a widely-used measure of joint movement frequency during a simulated industrial task. Although a stronger linear relationship was observed between metrics of joint motion frequency and established measures of 2 muscular exertion, the differences between measures were not meaningful and the relationship between MPF EMG and established measures was moderate-to-strong.The final phase of this thesis explores the application of the newly proposed techniques to field-based data collected during a study of ironworkers involved in construction stud-welding tasks. Limitations in data collection limited the analysis of MPF EMG in this study.The research presented in this thesis introduces a novel metric based on the frequency analysis of RMS processed sEMG data, and presents evidence that MPF EMG has potential to be a valuable assessment technique of exposure to repetitive muscular exertion.Abstract Approved: _____________________________________ Thesis Supervisor

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Section: Discussionmentioning

confidence: 99%

“…Three window lengths were examined: 100, 180, and 250 samples, as these window lengths have been utilized in previous research (Farfán et al, 2010;St-Amant et al, 1998). For each window length, five window overlaps were employed: 0% (i.e., no overlap), 10%, 30%, 50%, and 90%.…”

Section: Exploration Of Rms-processing Parametersmentioning

confidence: 99%

Spectral Analysis of Root-Mean-Square Processed Surface Electromyography Data as a Measure of Repetitive Muscular Exertion

Gant

Fethke

Gerr

2012

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Another class of reaching models are stochastic models that take into account the noise inherent to the motor system. It has been consistently observed that the standard deviation of neuromotor commands increases with its mean (Sutton and Sykes 1967;Schmidt et al 1979;Clamman 1969;Matthews 1996;St-Amant et al 1998;Clancy and Hogan 1999;Osu et al 2004). In line with this evidence, it was suggested that the brain minimizes the variance of the final arm position in the presence of such signal-dependent motor noise (Harris and Wolpert 1998;Hamilton et al 2002).…”

Section: Computational Approachmentioning

confidence: 92%

Movement curvature planning through force field internal models

Petreska

Billard

2009

Biol Cybern

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Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one's body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model's parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvature.

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“…Some researchers have applied techniques such as whitening [12,13] or high pass filtering [14] to reduce the variance of the EMG amplitude estimates in force estimation applications.…”

Section: Introductionmentioning

confidence: 99%