António J. Silva

Biomechanics of Competitive Swimming Strokes

Competitive swimming is one of the most challenging sports to perform scientific research. Not only the research of human movement is quite complex, because human beings are not so determinists as other (bio)mechanical systems; but also, assessing human beings in aquatic environment becomes even more as this is not their natural environment and other physical principles have to be considered. On regular basis, for human movement analysis, including the ones made on aquatic environments, experimental and numerical methods are used. Experimental methods are characterized by attaching bio-sensors to the subjects being analyzed, acquiring the biosignal and thereafter processing it. Numerical methods are characterized by the introduction of selected input data, processing data according to given mechanical equations and thereafter collecting the output data. Both methods groups aim to perform kinematics analysis, kinetics analysis, neuromuscular analysis and anthropometrical/inertial analysis. These method groups are also used for biomechanical analysis of competitive swimming. A swimming event can be decomposed in four moments or phases: (i) the starting phase; (ii) the swimming phase; (iii) the turning phase and; (iv) the finishing phase. During any swimming event, a swimmer spends most of his/her absolute or relative time in the swimming phase. Therefore, the swimming phase is the most (but not the only one) determinant moment of the swimming performance. In this sense, a large part of the biomechanical analysis of competitive swimming is dedicated to the four competitive swimming strokes: (i) the Front Crawl; (ii) the Backstroke; (iii) the Breaststroke and; (iv) the Butterfly stroke. The aim of this chapter has two folds: (i): to perform a biomechanical characterization of the four competitive swimming strokes, based on the kinematics, kinetics and neuromuscular analysis; (ii) to report the relationships established between all the domains and how it might influence the swimming performance.

Effect of wearing a swimsuit on hydrodynamic drag of swimmer

The purpose of this study was to analyse the effect of wearing a swimsuit on swimmers passive drag. A computational fluid dynamics analysis was carried out to determine the hydrodynamic drag of a female swimmers model (i) wearing a standard swimsuit; (ii) wearing a last generation swimsuit and; (iii) with no swimsuit, wearing light underwear. The three-dimensional surface geometry of a female swimmers model with different swimsuit/underwear was acquired through standard commercial laser scanner. Passive drag force and drag coefficient were computed with the swimmer in a prone position. Higher hydrodynamic drag values were determined when the swimmer was with no swimsuit in comparison with the situation when the swimmer was wearing a swimsuit. The last generation swimsuit presented lower hydrodynamic drag values, although very similar to standard swimsuit. In conclusion, wearing a swimsuit could positively influence the swimmers hydrodynamics, especially reducing the pressure drag component.

Modelling Hydrodynamic Drag in Swimming using Computational Fluid Dynamics

In the sports field, numerical simulation techniques have been shown to provide useful information about performance and to play an important role as a complementary tool to physical experiments. Indeed, this methodology has produced significant improvements in equipment design and technique prescription in different sports (Kellar et al., 1999; Pallis et al., 2000; Dabnichki & Avital, 2006). In swimming, this methodology has been applied in order to better understand swimming performance. Thus, the numerical techniques have been addressed to study the propulsive forces generated by the propelling segments (Rouboa et al., 2006; Marinho et al., 2009a) and the hydrodynamic drag forces resisting forward motion (Silva et al., 2008; Marinho et al., 2009b). Although the swimmer’s performance is dependent on both drag and propulsive forces, within this chapter the focus is only on the analysis of the hydrodynamic drag. Therefore, this chapter covers topics in swimming drag simulation from a computational fluid dynamics (CFD) perspective. This perspective means emphasis on the fluid mechanics and

Validation of predictive equations of the cross-sectional area of the human trunk

The objective of this study was to develop and validate predictive equations of the cross-sectional area of the human trunk. The models were developed for males according to their level of expertise. The sample comprised 152 male subjects, all of them with a background in competitive or recreational swimming. Their ages ranged between 10 and 32 years. Two different groups of subjects were used to estimate and validate the equation. The following anthropometric characteristics were assessed: (i) body weight, (ii) height, (iii) biacromial diameter, (iv) sagittal thoracic diameter, (v) chest circumference, and (vi) cross-sectional area of the trunk. Predictive models were developed using stepwise multiple linear regression analysis. One of the models used level of expertise as a dummy variable. All models included sagittal thoracic diameter and chest circumference as independent variables (0.32 ≤ R2 ≤ 0.48; P 0.05). The simple linear regressions were moderate (0.23 ≤ R2 ≤ 0.55; 0.01 ≤ P ≤ 0.001), and the Bland-Altman criterion was met in all cases. Therefore, our findings suggest that the models developed for male swimmers according to their level of expertise are able to provide a valid prediction of the cross-sectional area of the trunk.

Modelling propelling force in swimming using numerical simulations

Daniel A. Marinho1,2, Tiago M. Barbosa2,3, Vishveshwar R. Mantha2,4, Abel I. Rouboa2,5 and Antonio J. Silva2,4 1University of Beira Interior, Department of Sport Sciences, Covilha 2Research Centre in Sports, Health and Human Development, Vila Real 3Polytechnic Institute of Braganca, Department of Sport Sciences, Braganca 4University of Tras-os-Montes and Alto Douro, Department of Sport Sciences, Exercise and Health, Vila Real 5University of Tras-os-Montes and Alto Douro, Department of Engineering, Vila Real Portugal

Validação de equações preditivas da área de secção transversa do tronco

The objective of this study was to develop and validate predictive equations of the cross-sectional area of the human trunk. The models were developed for males according to their level of expertise. The sample comprised 152 male subjects, all of them with a background in competitive or recreational swimming. Their ages ranged between 10 and 32 years. Two different groups of subjects were used to estimate and validate the equation. The following anthropometric characteristics were assessed: (i) body weight, (ii) height, (iii) biacromial diameter, (iv) sagittal thoracic diameter, (v) chest circumference, and (vi) cross-sectional area of the trunk. Predictive models were developed using stepwise multiple linear regression analysis. One of the models used level of expertise as a dummy variable. All models included sagittal thoracic diameter and chest circumference as independent variables (0.32 ≤ R2 ≤ 0.48; P 0.05). The simple linear regressions were moderate (0.23 ≤ R2 ≤ 0.55; 0.01 ≤ P ≤ 0.001), and the Bland-Altman criterion was met in all cases. Therefore, our findings suggest that the models developed for male swimmers according to their level of expertise are able to provide a valid prediction of the cross-sectional area of the trunk.

Technological Dynamics and National Innovation System: A Quantitative Focus of a Neoschumpeterian Approach

This paper aims at analyzing the dynamic technological transition of a Naional Innovation System (subsequently abbreviated as NIS) through a theoretical model of complex system, presented in a non-linear difference equation. This paper shall also specify the way through which a hypothetical technological paradigm may be explored and the forces that shape the economical, social and institutional spheres.Putting together the results presented by the analysis of the complexity with the characteristics proposed by the evolutionist theory related to technologic dynamics inside the NIS, it is possible to systemize in a better way the results of the variation in the time of the economical changes presented.