Proceedings of the 12th International Conference on Kinanthropology

Proceedings of the 12th Conference of Sport and Quality of Life 2019 gatheres submissions of participants of the conference. Every submission is the result of positive evaluation by reviewers from the corresponding field. Conference is divided into sections – Analysis of human movement; Sport training, nutrition and regeneration; Sport and social sciences; Active ageing and sarcopenia; Strength and conditioning training; section for PhD students

A comparison of the internal load in soccer training process of goalkeepers

The main topic of our research was to determine the internal load of goalkeepers in the soccer training process. We focused on the analysis of the achieved heart rate values. In this way we wanted to extend the knowledge of the impact of the various methodical forms on the internal load on soccer goalkeepers, there by supporting the effort to improve the youth training process. We assumed the goalkeepers reached a significantly different level of heart rate in different methodical forms. Six goalkeeper´s (U16, U17, U19) internal load were analysed by POLAR Team2. To determine statistical significance, we used the Wilcoxon T-test and then we calculated Cohen r (effect size). We found significantly different mean heart rate values in individual methodical forms (T=0;p≤0.05;r=0.9).Vnútorné zaťaženie brankárov v tréningovom procese skúmame z pohľadu dosiahnutých hodnôt srdcovej frekvencie. Chceli sme rozšíriť poznatky o vplyve rozdielnych metodických foriem na vnútorné zaťaženie brankárov vo futbale a tým podporiť možnosti vylepšenia ich tréningového procesu. Predpokladali sme, že brankári budú dosahovať významne vyššie hodnoty srdcovej frekvencie počas prípravnej hry v hernom tréningu ako počas prípravného cvičenia v hernom nácviku. Sledovaný súbor tvorili šiesti brankári v kategórií mladšieho a staršieho dorastu (U16, U17, U19). Pomocou športtesterov POLAR PRO sme zistili vnútorné zaťaženie brankárov, ktoré sme spracovali kompatibilným softwarom POLAR Team2. Následne sme získané údaje vyhodnotili Wilcoxonovým T-testom a Cohenovým r. Zistili sme významne rozdielne priemerné hodnoty srdcovej frekvencie v jednotlivých metodických formách (T=0;p≤0.05;r=0.9), čím sme štatisticky aj vecne logicky potvrdili náš predpoklad

Maturity Status and Relative Age of Elite Taekwondo Youth Competitors—Case Study on Croatian National Team

This study examines the maturity status and relative age effect in elite youth taekwondo Croatian National Team athletes. Measurements of biological age, maturity offset, and body composition were taken from a sample of 17 junior athletes. Differences in maturity status were observed among athletes of the same chronological age, with variations in sitting height and age at peak height velocity. Male athletes generally exhibited higher values in body height, percentage of body fat, muscle mass, and total body water. No significant relative age effect was found. These findings highlight the importance of considering individual biological age and maturity status for talent development and training program adjustments. Further research involving athletes from different countries is recommended to validate these results and enhance the understanding of youth taekwondo athlete development

Physiological Response of the Performance of Young Football Players During Small-Sided Games

The main aim of the present study was to examine the cardiovascular response, time-motion characteristics, game performance and rated of perceived exertion (RPE) during small-sided games (SSGs) with different number of players. The research group consisted of elite youth male soccer players (n = 18) (aged 16.5 ±0.71 years, maximum heart rate (HRmax) 196.42 ±5.31 beats.min−1) from the FC DAC 1904 Dunajská Streda U17 soccer club. Cardiovascular response measurements included heart rate (HR) expressed in minimum, mean and maximum values and time spent in different intensity zones. Total distance covered, high intensity running and the number of accelerations and decelerations were captured by GPS. Individual game performance and the number of technical-tactical actions were recorded during every SSGs. After the game time we collected the ratings of perceived exertion scores from each player. Results showed that SSG with small number of players (3 vs. 3) triggered the highest HR response with mean value 168.00 ±8.48 beats.min−1, players spent the most time in maximal intensity zone 0:09:06 minutes, of SSG duration. This format of SSG was the mostintense for the players´ cardiovascular system, but we can’t find statistically significant differences between the HR values in SSGs. External load was the most demanding in SSG1 too, like in internal load. The highest scores in individual game performance were recorded in SSG2. In RPE scores SSG1 was the most difficult from the players point of view. In conclusion, the present research demonstrates the effectiveness of SSG1 in training sessions. Therefore, the coaching staff has the possibility to choose between SSGs during training sessions according to their physical, technical, tactical and psychological objectives

Predicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental data

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to o er acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 di erent machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results o er high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed

Predicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental data

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to o er acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 di erent machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results o er high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed

Intensity of Soccer Players’ Training Load in Small-Sided Games with Various Content Focus

The aim of this research was to make reference to the difference in heart rate values (HR) of soccer players in small-sided games (SSG) with various content focus. We assumed that the aim at the content in small-sided games would significantly affect the HR values of participating soccer players. The research group was comprised of players (n = 6) of the soccer club FK DAC 1904 Dunajská Streda (U15) at the age of 14 ± 0.7 years. The HR values were evaluated on the basis of collected data, which we obtained using sport testers and special software POLAR TEAM2. In order to find out the statistical significance of the difference in HR was used the One-Way ANOVA and the Bonferroni post hoc test. The level of statistical significance was set at 5 %. We found out that by the change of the small-sided game’s content focus, the internal reaction of players’ organism to training load was at different level. In the SSG3, with the emphasis on the improvement of individual’s final offensive game activity – shooting, was recorded the highest achieved HRavg value of the monitored players (181.83 ±7.11 beats.min−1). This form of the SSG was the most intense. However, there were no significant differences in HR values among the individual forms of the SSG. Our recommendation is to employ in the systematic training process small forms of small-sided games with various content focus, because by the means of it we can adequately prepare the players for the match load itself