Ivy M1, Zuniga JM2, Sikora A1, Ino E1, Aguero G1, Cho E1The Effect of Different Regression-Based Algorithms on Frequency Based EMG Fatigue. 1Department of Exercise Science and Pre-Health Professions, Creighton University, Omaha, NE, USA. 2Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.


Purpose: The purpose of this study was to determine if there were significant differences between the electromyographic (EMG) mean power frequency at the fatigue threshold (MPFFT) and D-max methods when assessing neuromuscular fatigue (NMF). Methods: Twenty-two adults (17 men, 5 women; mean ± standard (SD): age = 21.1 ± 2.8 years, body weight = 78.0 ± 12.7 kg, height = 177.4 ± 9.6cm) volunteered to participate in the investigation. Each participant performed an incremental cycle ergometry test to fatigue while EMG signals were measured from the vastus lateralis (VL) muscle. Mean, SD, and range values were calculated for the power outputs determined by the MPFFT and D-max methods. The relationships for EMG frequency and power output for each participant were examined using linear regression (SPSS software program, Chicago, IL). An alpha level of p≤0.05 was considered significant for all statistical analyses. A paired dependent t-test was used to determine if there were significant mean differences in power outputs determined by the MPFFT method (Mean ± SD; 161.9 ± 44.9 W) and D-max method (168.9 ± 36.6 W). Results: The results of the dependent t-test indicated that there were not significant mean differences (p>0.05) between the MPFFT and D-max values (p=0.29). The zero-order correlation for the power outputs determined by the MPFFT and D-max methods showed that the two methods were fairly correlated (r=0.69). Conclusion: The result of the present investigation suggests that the two regression-based algorithms can be used to calculate neuromuscular fatigue.