A versatile program for the analysis of electromyographic data

Fethke, Nathan B.; Anton, Daniel C.; Fuller, Helen J. A.; Cook, Thomas M.

doi:10.1037/e577072012-004

“…After the normalization, all of the EMG signals from the worktasks were analyzed with a custom-signal processing package written in LabVIEW 7.1 (National Instruments, Austin, TX, USA) (33). The continuous signals from each participant were parsed into 30 discrete work cycles with the aid of digital video recordings obtained at the time of the measurement (figure 1).…”

Section: Data Analysis Proceduresmentioning

confidence: 99%

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Fethke¹,

Anton²,

Cavanaugh³

et al. 2007

Scand J Work Environ Health

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“…Surface EMG processing was performed using LabVIEW software (Fethke et al, 2004). Digitally sampled sEMG recordings for each muscle group were root-mean-square (RMS) processed with a 250 sample window length with no window overlap.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

“…All processing was accomplished using LabVIEW software (Fethke et al, 2004) which accepted user-defined processing parameters and completed spectral analysis of the processed data. To investigate the influence of processing parameters, the 200 data records were repeatedly analyzed using a series of techniques.…”

Section: Analysis Of Semg Datamentioning

confidence: 99%

See 1 more Smart Citation

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

Gant¹,

Fethke²,

Gerr³

2012

PsycEXTRA Dataset

0

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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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“…All sEMG processing was accomplished using LabVIEW software (Fethke et al, 2004). Digitally sampled sEMG recordings were visually scanned for transient artifacts, smoothing with a 100-sample moving-window average and a 90-sample overlap 2)…”

Section: Surface Emg Normalization Proceduresmentioning

confidence: 99%

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

Gant

¹

,

Fethke

²

,

Gerr

³

2012

Proceedings of the Human Factors and Ergonomics Society Annual

2

0

View full text Add to dashboard Cite

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

show abstract

A versatile program for the analysis of electromyographic data

Cited by 6 publications

References 0 publications

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

Bootstrap exploration of the duration of surface electromyography sampling in relation to the precision of exposure estimation

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

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

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