Single motor unit and spectral surface EMG analysis during low-force, sustained contractions of the upper trapezius muscle.

Intramuscular and surface electromyographic (EMG) activities were recorded from the left and right upper trapezius muscle of eight healthy male subjects during 5-min long static contractions at 2% and 5% of the maximal voluntary contraction (MVC) force. Intramuscular signals were detected by wire electrodes while surface EMG signals were recorded with linear adhesive electrode arrays. The surface EMG signals were averaged using the potentials extracted from the intramuscular EMG decomposition as triggers. The conduction velocity of single motor units (MUs) was estimated over time from the averaged surface potentials while average rectified value and mean power spectral frequency were computed over time from 0.5 s epochs of surface EMG signal. It was found that (1) MUs were progressively recruited after the beginning of sustained contractions of the upper trapezius muscle at 2% and 5% MVC, (2) the conduction velocity of the MUs active since the beginning of the contraction significantly decreased over time, and (3) although the CV of single MUs significantly decreased, the mean power spectral frequency of the surface EMG did not show a consistent trend over time. It was concluded that spectral surface EMG analysis, being affected by many physiological mechanisms, may show limitations for the objective assessment of localized muscle fatigue during low force, sustained contractions. On the contrary, single motor unit conduction velocity may provide an early indication of changes in muscle fiber membrane properties with sustained activity.

Trapezius muscle motor unit activity in symptomatic participants during finger tapping using properly and improperly adjusted desks.

Work-related musculoskeletal disorders in the neck-shoulder area and upper extremities are common among computer users, especially women. We compared temporal changes of motor unit (MU) activation in the trapezius muscle during finger tapping using both appropriate and inappropriate ergonomic desk adjustments. Sixteen intensive and nonintensive computer users with either moderate or severe musculoskeletal disorders participated in the study. Six-channel intramuscular electromyographic (EMG) signals and 2-channel surface EMG were recorded from 2 positions of the trapezius muscle. A statistically significant increase in activity was observed with a desk adjusted 5 cm higher than appropriate and was attributable mainly to increased duration of MU activity. Participants with severe symptoms activated more MUs, and these were also active longer. In women, on average, MUs were active nearly twice as long as in men during the same tapping task. This study demonstrates that it is possible to evaluate ergonomic topics on the MU level and that incorrectly adjusted office equipment, in addition to motor demands imposed by the work task, results in prolonged activity of MUs. A potential application of this research is an increased awareness that certain individuals who work with incorrectly adjusted office equipment may be at greater risk of developing work-related musculoskeletal disorders.

Motor unit identification in two neighboring recording positions of the human trapezius muscle during prolonged computer work.

Work-related shoulder-neck pain is a major health risk in computer operators. To understand the physiological mechanisms behind the development of these disorders, EMG recordings of some minutes up to several hours must be accurately decomposed. For this reason we developed EMG-LODEC, an automatic decomposition software program, especially designed for multi-channel long-term recordings of signals detected during slight muscle movements. The subjects executed a 30-min computer task to simulate real work conditions while working at an ergonomically designed workstation. Six-channel intramuscular EMG signals were recorded from two positions of the upper trapezius muscle. The EMG signals were decomposed into individual motor unit action potential trains using EMG-LODEC. The study design enabled us first to study the dependence of intramuscular analysis on the insertion points and second to test the accuracy of the decomposition technique under laboratory conditions during a real experiment. The two positions yielded 887 motor units--452 located in position 1 and 435 in position 2. Although the numbers of detected action potentials were strongly correlated between the two insertion positions, different motor units were mostly recorded. In particular, the detection of continuously active motor units is specific for the selected insertion points and may not be representative of a muscle, not even for parts with common functions. The approach for the quantitative evaluation of the decomposition technique was to independently decompose two signals that were simultaneously detected by separate sets of wire electrodes placed close to each other in the muscle. Common trains discovered in each signal were compared for consistency. A cross-correlation analysis was performed to find corresponding motor unit pairs that were concurrently active. Concurrently active motor units were found in six subjects. For these motor units the extent of simultaneous occurrence of motor unit action potentials between the two positions ranged from 23% to 78% depending on the distinction of the single motor units and the number of superimposed motor unit action potentials. High concordance was seen in 3 out of the 15 motor unit pairs. Based on the results, EMG-LODEC is capable of providing reliable decompositions with satisfying accuracy and reasonable processing time. EMG-LODEC is suitable for the study of motor unit discharge patterns and recruitment order in subjects with and without musculoskeletal pain during long-term measurements to study work-related musculoskeletal disorders.

A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients.

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.