Wavelet analysis of electromyography for back muscle fatigue detection during isokinetic constant-torque exertions.

STUDY DESIGN

An investigation of the effects of human trunk extensor muscle fatigue on the temporal change in frequency content of the electromyogram as quantified using the Fourier and wavelet transforms during the performance of repetitive dynamic trunk extension.

OBJECTIVE

To evaluate whether alterations in the Fourier and wavelet transform measures were consistent with a shift of the signal power to lower frequencies, and to determine which measures were more highly correlated with the decline in maximal trunk extension torque.

SUMMARY OF BACKGROUND DATA

Objective assessment of trunk muscle fatigue is likely to play a more important role in the rehabilitation and prevention of low back injuries, given the association between lack of trunk muscle endurance and acquisition of low back pain. Validation of new methods designed to quantify the level of fatigue using the surface electromyogram is necessary before these techniques can be used in industrial rehabilitation settings. The wavelet transform is a recent development in the signal processing of electromyograms that shows promise as a method for assessment of fatigue.

METHODS

Trunk muscle electromyograms obtained from study participants performing repetitive isokinetic trunk extension endurance tests were analyzed using the wavelet and the traditional Fourier methods. Trunk extension torque was controlled at 35% and 70% of the participants' maximal voluntary contraction while they exerted at 5 and 10 repetitions per minute. The decline in maximal trunk extension torque was measured once per minute. Linear regression quantified the rate of change in Fourier and wavelet measures caused by fatigue, whereas Pearson's correlation coefficient determined their association with the decline in maximum torque.

RESULTS

Changes in the characteristics of the electromyogram were consistent with a shift to lower frequencies: The signal power at higher frequencies was reduced, whereas the power at lower frequencies was elevated. The amount of change was dependent on the task conditions (exertion level and repetition rate). The wavelet-based measures demonstrated as strong an association with the decline in maximal torque output as the Fourier-based measures.

CONCLUSIONS

This study demonstrates that assessment of trunk muscle fatigue during isokinetic movementis possible using both Fourier and wavelet measurements. However, the methods were not as likely to change significantly during lower rates of exertion. These methods, when implemented in a controlled setting, may be used to document the rehabilitation process and guide preventive exercise training.