How do swimmers control their front crawl swimming velocity? Current knowledge and gaps from hydrodynamic perspectives

The aim of this study was to review the literature on front crawl swimming biomechanics, focusing on propulsive and resistive forces at different swimming velocities. Recent studies show that the resistive force increases in proportion to the cube of the velocity, which implies that a proficient technique to miminise the resistive (and maximise the propulsive) force is particularly important in sprinters. To increase the velocity in races, swimmers increase their stroke frequency. However, experimental and simulation studies have revealed that there is a maximum frequency beyond which swimmers cannot further increase swimming velocity due to a change in the angle of attack of the hand that reduces its propulsive force. While the results of experimental and simulation studies are consistent regarding the effect of the arm actions on propulsion, the findings of investigations into the effect of the kicking motion are conflicting. Some studies have indicated a positive effect of kicking on propulsion at high swimming velocities while the others have yielded the opposite result. Therefore, this review contributes to knowledge of how the upper-limb propulsion can be optimised and indicates a need for further investigation to understand how the kicking action can be optimised in front crawl swimming.Abbreviations: C: Energy cost [kJ/m]; Ė: Metabolic power [W, kJ/s]; Fhand: Fluid resultant force exerted by the hand [N]; Ftotal: Total resultant force [N] (See Appendix A); Fnormal: The sum of the fluid forces acting on body segments toward directions perpendicular to the segmental long axis, which is proportional to the square of the segmental velocity. [N] (See Appendix A); Ftangent: The sum of the fluid forces acting on body segments along the direction parallel to the segmental long axis, which is proportional to the square of the segmental velocity. [N] (See Appendix A); Faddmass: The sum of the inertial force acting on the body segments due to the acceleration of a mass of water [N] (See Appendix A); Fbuoyant: The sum of the buoyant forces acting on the body segments [N] (See Appendix A); D: Fluid resistive force acting on a swimmer's body (active drag) [N]; T: Thrust (propulsive) force acting in the swimming direction in reaction to the swimmer's actions [N]; Thand: Thrust force produced in reaction to the actions of the hand [N]; Tupper_limb: Thrust force produced in reaction to the actions of the upper limbs [N]; Tlower_limb: Thrust force produced in reaction to the actions of the lower limbs [N]; Mbody: Whole-body mass of the swimmer [kg]; SF: Stroke frequency (stroke number per second) [Hz]; SL: Stroke length (distance travelled per stroke) [m]; v: Instantaneous centre of mass velocity of the swimmer [m/s]; V - : Mean of the instantaneous centre of mass velocities in the swimming direction over the period of the stroke cycle [m/s]; a: Centre of mass acceleration of the swimmer [m/s2]; V - hand: Mean of the instantaneous magnitudes of hand velocity over a period of time [m/s]; Ẇtot: Total mechanical power [W]; Ẇext: External mechanical power [W]; Ẇd: Drag power (mechanical power needed to overcome drag) [W, Nm/s]; α: Angle of attack of the palm plane with respect to the velocity vector of the hand [deg]; ηo: Overall efficiency [%]; ηp: Propelling efficiency [%]; MAD-system: Measuring Active Drag system; MRT method: Measuring Residual Thrust method.

SwimmerNET: Underwater 2D Swimmer Pose Estimation Exploiting Fully Convolutional Neural Networks

Professional swimming coaches make use of videos to evaluate their athletes' performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer's body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively.

Numerical and experimental methods used to evaluate active drag in swimming: A systematic narrative review

Introduction: In swimming, it is necessary to understand and identify the main factors that are important to reduce active drag and, consequently, improve the performance of swimmers. However, there is no up-to-date review in the literature clarifying this topic. Thus, a systematic narrative review was performed to update the body of knowledge on active drag in swimming through numerical and experimental methods.

Methods: To determine and identify the most relevant studies for this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was used.

Results: 75 studies related to active drag in swimming and the methodologies applied to study them were analyzed and kept for synthesis. The included studies showed a high-quality score by the Delphi scale (mean score was 5.85 ± 0.38). Active drag was included in seven studies through numerical methods and 68 through experimental methods. In both methods used by the authors to determine the drag, it can be concluded that the frontal surface area plays a fundamental role. Additionally, the technique seems to be a determining factor in reducing the drag force and increasing the propulsive force. Drag tends to increase with speed and frontal surface area, being greater in adults than in children due to body density factors and high levels of speed. However, the coefficient of drag decreases as the technical efficiency of swimming increases (i.e., the best swimmers (the fastest or most efficient) are those with the best drag and swimming hydrodynamics efficiency).

Conclusion: Active drag was studied through numerical and experimental methods. There are significantly fewer numerical studies than experimental ones. This is because active drag, as a dynamical phenomenon, is too complex to be studied numerically. Drag is greater in adults than in children and greater in men than in women across all age groups. The study of drag is increasingly essential to collaborate with coaches in the process of understanding the fundamental patterns of movement biomechanics to achieve the best performance in swimming. Although most agree with these findings, there is disagreement in some studies, especially when it is difficult to define competitive level and age. The disagreement concerns three main aspects: 1) period of the studies and improvement of methodologies; 2) discrimination of methodologies between factors observed in numerical vs. experimental methods; 3) evidence that drag tends to be non-linear and depends on personal, technical, and stylistic factors. Based on the complexity of active drag, the study of this phenomenon must continue to improve swimming performance.

Young Swimmers' Classification Based on Performance and Biomechanical Determinants: Determining Similarities Through Cluster Analysis

The aim of this study was to classify and identify young swimmers' performance, and biomechanical determinant factors, and understand if both sexes can be clustered together. Thirty-eight swimmers of national level (22 boys: 15.92 ± 0.75 years and 16 girls: 14.99 ± 1.06 years) were assessed. Performance (swim speed at front crawl stroke) and a set of kinematic, efficiency, kinetic, and hydrodynamic variables were measured. Variables related to kinetics and efficiency (p < .001) were the ones that better discriminated the clusters. All three clusters included girls. Based on the interaction of these determinant factors, there are girls who can train together with boys. These findings indicate that not understanding the importance of the interplay between such determinants may lead to performance suppression in girls.

A Coupled Biomechanical-Smoothed Particle Hydrodynamics Model for Horse Racing Tracks

Distal limb injuries are common in racing horses and track surface properties have been associated with injury risk. To better understand how track surfaces may contribute to equine limb injury, we developed the first 3D computational model of the equine hoof interacting with a racetrack and simulated interactions with model representations of 1) a dirt surface and 2) an all-weather synthetic track. First, a computational track model using the Smoothed Particle Hydrodynamics (SPH) method with a Drucker-Prager (D-P) elastoplastic material model was developed. It was validated against analytical models and published data and then calibrated using results of a custom track testing device applied to the two racetrack types. Second, a sensitivity analysis was performed to determine which model parameters contribute most significantly to the mechanical response of the track under impact-type loading. Third, the SPH track model was coupled to a biomechanical model of the horse forelimb and applied to hoof-track impact for a horse galloping on each track surface. We found that 1) the SPH track model was well validated and it could be calibrated to accurately represent impact loading of racetrack surfaces at two angles of impact; 2) the amount of harrowing applied to the track had the largest effect on impact loading, followed by elastic modulus and cohesion; 3) the model is able to accurately simulate hoof-ground interaction and enables study of the relationship between track surface parameters and the loading on horses’ distal forelimbs.

Effects of Currents on Human Freestyle and Breaststroke Swimming Analyzed by a Rigid-Body Dynamic Model

Swimming is a kind of complex locomotion that involves the interaction between the human body and the water. Here, to examine the effects of currents on the performance of freestyle and breaststroke swimming, a multi-body Newton-Euler dynamic model of human swimming is developed. The model consists of 18 rigid segments, whose shapes and geometries are determined based on the measured data from 3D scanning, and the fluid drags in consideration of the current are modeled. By establishing the interrelations between the fluid moments and the swimming kinematics, the underlying mechanism that triggers the turning of the human body is uncovered. Through systematic parametric analyses, the effects of currents on swimming performance (including the human body orientation, swimming direction, swimming speed, and propulsive efficiency) are elucidated. It reveals that the current would turn the human body counterclockwise in freestyle swimming, while clockwise in breaststroke swimming (which means that from the top view, the human trunk, i.e., the vector pointing from the bottom of feet to the top of the head, rotates counterclockwise or clockwise). Moreover, for both strokes, there exists a critical current condition, beyond which, the absolute swimming direction will be reversed. This work provides a wealth of fundamental insights into the swimming dynamics in the presence of currents, and the proposed modeling and analysis framework is promising to be used for analyzing the human swimming behavior in open water.

Arm-pull thrust in human swimming and the effect of post-activation potentiation

The aim of this study was to analyse the front-crawl arm-pull kinetics and kinematics, comparing it before and after post-activation potentiation (PAP), and the associations between variables describing of the arm-pull kinetics. Twelve male competitive swimmers were randomly assigned to perform two different warm-ups in a crossover manner: (i) non-PAP (control condition); and (ii) PAP (experimental condition). PAP consisted of 2 × 5 arm-pulls with resistance bands by both upper-limbs. Eight minutes later, participants underwent a 25 m all-out trial in front-crawl arm-pull. Kinetics (i.e., peak thrust, mean thrust and thrust-time integral) and kinematics (i.e., speed and speed fluctuation) were collected by an in-house customised system composed of differential pressure sensors, speedo-meter and underwater camera. There was a significant and large improvement of the arm-pull kinetics after completing the warm-up with PAP sets (0.010 < P < 0.054, 0.50 < d < 0.74). There were non-significant and small effects of PAP on speed (P = 0.307, d = 0.18) and speed fluctuation (P = 0.498, d = 0.04). Correlation coefficients among kinetic variables were significant with large associations (0.51 < R < 0.90, 0.001 < P < 0.088). In conclusion, warm-ups including PAP conditioning sets elicit a large improvement in the thrust, but with small improvement in performance. Variables used to characterise thrust are strongly correlated and hence can be used interchangeably.

Studying the effects of asymmetry on freestyle swimming using smoothed particle hydrodynamics

The use of asymmetrical strokes is common in freestyle swimming because of breathing and strength laterality. In this study, the asymmetrical freestyle swimming performance of a male elite level swimmer who breathed every second arm stroke (unilaterally) was investigated. A laser body scan and multi-angle video footage of the athlete were used to generate a swimming biomechanical model. This model was then used in a Smoothed Particle Hydrodynamics (SPH) fluid simulation of swimming through a virtual pool. The results from this study enabled the kinematic asymmetry to be related to the consequential fluid dynamic asymmetry. The intra-cyclic fluctuations in the streamwise forces and speed were also examined. Hand angles of attack were compared along with the lift and drag contributions of the hands to generating the streamwise thrust. From this study, connections between asymmetry and the resultant swimming performance were identified.

Dynamic simulation of flat water kayaking using a coupled biomechanical-smoothed particle hydrodynamics model

Kayak racing performance is known to be dependent on technique, strength and equipment, but the relationship between these factors and performance is not well understood. Complete experimental measures of stroke technique and the interactions between the water and the paddle and the boat are not practical in a racing environment. Instead, simulation using computational fluid dynamics can be used to study this system. A coupled biomechanical-Smoothed Particle Hydrodynamics (B-SPH) model of the kayaking athlete is presented. Verification and validation of the model are confirmed using drag force data from the literature and a spatial resolution study. Using this model and stroke kinematics (developed from the combination of literature data and digitised motion of an amateur level athlete from video), calculations are made of (a) the fluid response to interactions with the paddle and kayak; (b) speed of the kayak; and (c) magnitudes of force and impulse on the paddle and the hands. Key features of the fluid response are related to the loading on the athlete and the speed of the kayak. Perturbations to stroke technique are explored to give new insights into the relationships between technique and racing performance.

Investigating mixing and emptying for aqueous liquid content from the stomach using a coupled biomechanical-SPH model

Computational modelling of gastric emptying reveals the complex flow patterns that occur. The resulting mixing is substantial in the inferior stomach but much lower near the fluid's top surface.

Individual-Environment Interactions in Swimming: The Smallest Unit for Analysing the Emergence of Coordination Dynamics in Performance?

Displacement in competitive swimming is highly dependent on fluid characteristics, since athletes use these properties to propel themselves. It is essential for sport scientists and practitioners to clearly identify the interactions that emerge between each individual swimmer and properties of an aquatic environment. Traditionally, the two protagonists in these interactions have been studied separately. Determining the impact of each swimmer's movements on fluid flow, and vice versa, is a major challenge. Classic biomechanical research approaches have focused on swimmers' actions, decomposing stroke characteristics for analysis, without exploring perturbations to fluid flows. Conversely, fluid mechanics research has sought to record fluid behaviours, isolated from the constraints of competitive swimming environments (e.g. analyses in two-dimensions, fluid flows passively studied on mannequins or robot effectors). With improvements in technology, however, recent investigations have focused on the emergent circular couplings between swimmers' movements and fluid dynamics. Here, we provide insights into concepts and tools that can explain these on-going dynamic interactions in competitive swimming within the theoretical framework of ecological dynamics.