Search Results

During muscle fatigue analysis some standard indexes are calculated from the surface electromyogram (EMG) as root mean square value (RMS), mean (F_mean ), and median power frequency (F_median ). However, these parameters present limitations and principal component analysis (PCA) appears to be an adequate alternative. In this context, we propose two indexes based on PCA to enhance the quantitative muscle fatigue analysis during cyclical contractions. Signals of vastus lateralis muscle were collected during a maximal exercise test. Twenty-four subjects performed the test starting at 12.5 W power output with increments of 12.5 W⋅min^–1, maintaining cadence of 50 rpm until voluntary exhaustion. The epochs of myoelectric activation were identified and used to estimate the power spectra. PCA was then applied to the power spectra of each subject. The standard (ST) and Euclidean (ED) distances were employed to estimate the alteration occurred due to fatigue. For comparison, the standard indexes were calculated. ST, ED, and RMS value were adequate for muscle fatigue analysis. Among these parameters, ST was more sensitive with higher effect size. Moreover, the F_mean and F_median were not sensitive to fatigue. The proposed method based on PCA of EMG in frequency domain allowed producing fatigue indexes suitable for cyclical contractions.

Purpose: The study aimed to identify the variables that differentiate judo athletes at national and regional levels. Multivariable analysis was applied to biomechanical, anthropometric, and Special Judo Fitness Test (SJFT) data. Method: Forty-two male judo athletes from 2 competitive groups (14 national and 28 state levels) performed the following measurements and tests: (1) skinfold thickness, (2) circumference, (3) bone width, (4) longitudinal length, (5) stabilometric tests, (6) dynamometric tests, and (7) SJFT. The variables with significant differences in the Wilcoxon rank-sum test were used in stepwise logistic regression to select those that better separate the groups. The authors considered models with a maximum of 3 variables to avoid overfitting. They used 7-fold cross validation to calculate optimism-corrected measures of model performance. Results: The 3 variables that best differentiated the groups were the epicondylar humerus width, the total number of throws on the SJFT, and the stabilometric mean velocity of the center of pressure in the mediolateral direction. The area under the receiver-operating-characteristic curve for the model (based on 7-fold cross validation) was 0.95. Conclusion: This study suggests that a reduced set of anthropometric, biomechanical, and SJFT variables can differentiate judo athlete’s levels.