Daniel A. Marinho

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.

Modelling the relationship between biomechanics and performance of young sprinting swimmers

Abstract The aim of this study was to compute a swimming performance confirmatory model based on biomechanical parameters. The sample included 100 young swimmers (overall: 12.3u2009±u20090.74 years; 49 boys: 12.5u2009±u20090.76 years; 51 girls: 12.2u2009±u20090.71 years; both genders in Tanner stages 1–2 by self-report) participating on a regular basis in regional and national-level events. The 100u2005m freestyle event was chosen as the performance indicator. Anthropometric (arm span), strength (throwing velocity), power output (power to overcome drag), kinematic (swimming velocity) and efficiency (propelling efficiency) parameters were measured and included in the model. The path-flow analysis procedure was used to design and compute the model. The anthropometric parameter (arm span) was excluded in the final model, increasing its goodness-of-fit. The final model included the throw velocity, power output, swimming velocity and propelling efficiency. All links were significant between the parameters included, but the throw velocity–power output. The final model was explained by 69% presenting a reasonable adjustment (models goodness-of-fit; x2/dfu2009=u20093.89). This model shows that strength and power output parameters do play a mediator and meaningful role in the young swimmers’ performance.

Growth influences biomechanical profile of talented swimmers during the summer break

This study aimed to analyse the effect of growth during a summer break on biomechanical profile of talented swimmers. Twenty-five young swimmers (12 boys and 13 girls) undertook several anthropometric and biomechanical tests at the end of the 2011–2012 season (pre-test) and 10 weeks later at the beginning of the 2012–2013 season (post-test). Height, arm span, hand surface area, and foot surface area were collected as anthropometric parameters, while stroke frequency, stroke length, stroke index, propelling efficiency, active drag, and active drag coefficient were considered as biomechanical variables. The mean swimming velocity during an all-out 25 m front crawl effort was used as the performance outcome. After the 10-week break, the swimmers were taller with an increased arm span, hand, and foot areas. Increases in stroke length, stroke index, propelling efficiency, and performance were also observed. Conversely, the stroke frequency, active drag, and drag coefficient remained unchanged. When controlling the effect of growth, no significant variation was determined on the biomechanical variables. The performance presented high associations with biomechanical and anthropometric parameters at pre-test and post-test, respectively. The results show that young talented swimmers still present biomechanical improvements after a 10-week break, which are mainly explained by their normal growth.

Modelling Swimming Hydrodynamics to Enhance Performance

Swimming assessment is one of the most complex but outstanding and fascinating topics in biomechanics. Computational fluid dynamics (CFD) methodology is one ...

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.

Start and turn performances of elite sprinters at the 2016 European Championships in swimming

Abstract The aim of this study was to examine the performance characteristics of male and female finalists in the 100-m distance at the 2016 European Championships in swimming (long-course-metre). The performances of all 64 (32-males and 32-females) were analysed (8 swimmers per event; Freestyle, Backstroke, Breaststroke and Butterfly). A set of start and turn parameters were analysed. In the start main outcome, male swimmers were faster in Butterfly (5.71 ± 0.14s) and females in Freestyle (6.68 ± 0.28s). In the turn main outcome, male and female swimmers were faster in Freestyle (males: 9.55 ± 0.13s; females: 10.78 ± 0.28s). A significant and strong stroke effect was noted in the start and turn main outcome, in both sexes. In the start plus the turn combined, males and females were faster in Freestyle (males: 15.40 ± 0.20s; females: 17.45 ± 0.54s). The start and the turn combined accounted almost one-third of the total race time in all events, and non-significant differences (p > 0.05) were noted across the four swim strokes. Once this research made evident the high relevance of start and turns, it is suggested that coaches and swimmers should dedicate an expressive portion of the training perfecting these actions.

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.

The transfer of strength and power into the stroke biomechanics of young swimmers over a 34-week period

Abstract The purpose of this study was to learn the interplay between dry-land strength and conditioning, and stroke biomechanics in young swimmers, during a 34-week training programme. Twenty-seven swimmers (overall: 13.33u2009±u20090.85 years old; 11 boys: 13.5u2009±u20090.75 years old; 16 girls: 13.2u2009±u20090.92 years old) competing at regional- and national-level competitions were evaluated. The swimmers were submitted to a specific in-water and dry-land strength training over 34 weeks (and evaluated at three time points: pre-, mid-, and post-test; M1, M2, and M3, respectively). The 100-m freestyle performance was chosen as the main outcome (i.e. dependent variable). The arm span (AS; anthropometrics), throwing velocity (TV; strength), stroke length (SL), and stroke frequency (SF; kinematics) were selected as independent variables. There was a performance enhancement over time (M1 vs. M3: 68.72u2009±u20095.57u2005s, 66.23u2009±u20095.23u2005s; Δu2009=u2009−3.77%; 95% CI: −3.98;−3.56) and an overall improvement of the remaining variables. At M1 and M2, all links between variables presented significant effects (pu2009<u2009.001), except the TV–SL and the TV–SF path. At M3, all links between variables presented significant effects (pu2009≤u2009.05). Between M1 and M3, the direct effect of the TV to the stroke biomechanics parameters (SL and SF) increased. The model predicted 89%, 88%, and 92% of the performance at M1, M2, and M3, respectively, with a reasonable adjustment (i.e. goodness-of-fit M1: χ2/dfu2009=u20093.82; M2: χ2/dfu2009=u20093.08; M3: χ2/dfu2009=u20094.94). These findings show that strength and conditioning parameters have a direct effect on the stroke biomechanics, and the latter one on the swimming performance.