The use of accelerometers is discussed to evaluate standing vertical jump. Two accelerometers, mounted on each ankle and connected to a wearable system, were used for signal acquisition, and a piezoelectric platform was used to verify the results. Fifty-one subjects were enrolled, subdivided into a group of healthy subjects and 2 groups who had different surgery for Achilles tendon rupture. Each subject performed 5 countermovement and 5 squat jumps; 11 subjects also performed 5 countermovement jumps with voluntary leg rotations during the flight phase. A training set was used to assess signal processing, and a validation set was used to verify its accuracy. A peak detection algorithm was developed to quantify flight time from the acceleration modulus, and its results were compared with platform data. The Pearson correlation coefficient of ankle accelerations and the integral of each signal were adopted to describe, respectively, the movement coordination and the limbs rotation during the flight time. The flight times obtained from the accelerometers and force plate were highly correlated (Spearman's coefficient >0.95); they were compared, for each jump, and the maximum mean error, for subject, was 4.8%. The movement coordination was in good agreement with subjects' clinical features and with the different jump phases. The signal integral presented significant differences, among jumps, related to leg rotations (p < 0.0005). The method proposed allows the monitoring of standing vertical jump using the fight time and gives information on the legs coordination and on the motor strategies of the lower limbs. Therefore, it can be used to obtain performance reference also outside labs, both in clinical and sport settings.
The purpose of the study was (a) to assess the accuracy of the regression equations available in the literature to estimate the actual peak power (PPac) of the countermovement jump (CMJ) executed by young male soccer players, (b) to develop new regression equations from this population, and (c) to verify whether regression equations obtained from age-based subgroups could increase the accuracy of the estimation (PPes) of PPac. In all, 117 young players (age: 13.6 ± 2.4 years) were enrolled in the study. Each subject performed 5 CMJs on a force platform. The new regression equations were obtained from the entire experimental sample (G1) and 3 age-based subsamples (G2 = prepubertal, G3 = peripubertal, G4 = postpubertal) using 2 different approaches: the best jump and the mean values achieved by each subject. All the equations in the literature underestimated the peak power (p < 0.00005) in all the groups. The approach based on the mean values was more accurate (adjusted R = 0.925, SEE = 302.9 W) than the one based on the best jump (adjusted R = 0.892; SEE = 360.8 W). Moreover, calculating the regression equations from the 3 age-based subsamples, SEE resulted improved (15.5% in G2, 5.6% in G3 and 0.9% in G4). Regression equations must be derived from homogeneous populations, in terms of gender, sports practice, and age. The approach based on the mean values for each subject was more accurate than the approach used in the literature up to now. In practical applications, regression equation estimates cannot be used to assess the performance of a single subject, because errors may exceed 50%, whereas they may be useful for group comparisons.
The flight time was the parameter more sensitive to detect differences in the jump performance related to training and age. Adopting a normalization procedure it was possible to highlight that only the flight time and the peak power are sensitive to training effects on young adult male soccer players.
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