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Highly repetitive motion is associated with upper extremity musculoskeletal disorders among industrial workers.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 measures of muscular exertion frequency and joint motion frequency across a range of known exertion frequencies, intensities, and durations. 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. 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 a measure of muscular exertion frequency and a 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 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 explored the application of the 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.