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

Profiling biomechanical abilities during sprint front-crawl swimming using IMU and functional clustering of variabilities

This study aims to profile biomechanical abilities during sprint front crawl by identifying technical stroke characteristics, in light of performance level. Ninety-one recreational to world-class swimmers equipped with a sacrum-worn IMU performed 25 m all-out. Intra and inter-cyclic 3D kinematical variabilities were clustered using a functional double partition model. Clusters were analysed according to (1) swimming technique using continuous visualisation and discrete features (standard deviation and jerk cost) and (2) performance regarding speed and competition calibre using respectively one-way ANOVA and Chi-squared test as well as Gamma statistics. Swimmers displayed specific technical profiles of intra-cyclic (smoothy and jerky) and inter-cyclic stroke regulation (low, moderate and high repeatability) significantly discriminated by speed (p < 0.001, η2 = 0.62) and performance calibre (p < 0.001, V = 0.53). We showed that combining high levels of both kinds of variability (jerky + low repeatability) are associated with highest speed (1.86 ± 0.12 m/s) and competition calibre (ℽ = 0.75, p < 0.001). It highlights the crucial importance of variabilities combination. Technical skills might be driven by a specific alignment of stroke pattern and its associated dispersion according to the task constraints. This data-driven approach can assist eyes-based technical evaluation. Targeting the development of an explosive swimming style with a high level of body stability should be considered during training of sprinters.

Which Phases of the Stroke Cycle Are Propulsive in Front Crawl Swimming?

Purpose: The aim of this study was fourfold: (1) to quantify acceleration, velocity, and phase overlap for each phase of the stroke cycle (SC) during 200 m front crawl; (2) for each variable, to identify any differences between the four SC phases; (3) to investigate changes in variables during the 200 m; (4) to explore any association between performance and each variable. Methods: Ten swimmers performed a 200 m maximum swim. Four SCs were analyzed, one for each 50 m, using three-dimensional methods. Each SC was split into four phases: entry, pull, push, and recovery. Center of mass (CM) acceleration; maximum, minimum, and average CM velocity; phase duration, and, overlap of a phase of one arm with each phase of the opposite arm were calculated. Results and Conclusion: Phase velocities were positively correlated with performance and decreased during the 200 m. The acceleration data showed high within and between-swimmer variability. When the entry of one arm overlapped with the pull, and sometimes push, phase of the opposite arm, it was propulsive for the whole body. The pull was the slowest phase and overlapped predominantly with the opposite arm's recovery. The push phase was often propulsive for the whole body, regardless of the overlaps with the other arm, and together with the entry were the fastest phases. The recovery of each arm was mostly resistive for the whole body, except the short period of overlap with the opposite arm's push phase.

Estimating Active Drag Based on Full and Semi-Tethered Swimming Tests

During full tethered swimming no hydrodynamic resistance is generated (since v = 0) and all the swimmer's propulsive force (FP) is utilized to exert force on the tether (FT = FP). During semi-tethered swimming FP can be made useful to one of two ends: exerting force on the tether (FST) or overcoming drag in the water (active drag: Da). At constant stroke rate, the mean propulsive force (FP) is constant and the quantity FP - FST (the "residual thrust") corresponds to Da. In this study we explored the possibility to estimate Da based on this method ("residual thrust method") and we compared these values with passive drag values (Dp) and with values of active drag estimated by means of the "planimetric method". Based on data obtained from resisted swimming (full and semi-tethered tests at 100% and 35, 50, 60, 75, 85% of the individual FT), active drag was calculated as: DaST = kaST.vST2 = FP - FST ("residual thrust method"). Passive drag (Dp) was calculated based on data obtained from passive towing tests and active drag ("planimetric method") was estimated as: DaPL = Dp.1.5. Speed-specific drag (k = D/v2) in passive conditions (kp) was )25 kg.m-1 and in active conditions (ka) )38 kg.m-1 (with either method); thus, DaST > Dp and DaST > DaPL. In human swimming active drag is, thus, about 1.5 times larger than passive drag. These experiments can be conducted in an ecological setting (in the swimming pool) by using basic instrumentation and a simple set of calculations.

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

A comparison of load cell and pressure sensors to measure in-water force in young competitive swimmers

The purpose of this study was to compare the in-water force of young competitive swimmers using tethered swimming and differential pressure sensors. Thirty-one swimmers (16 girls and 15 boys) were randomly assigned to perform two in-water tests. Swimmers completed two maximum bouts of 25 m front crawl with a differential pressure system and a 30 s maximum bout with an attached load cell (tethered-swimming). The peak force (FPEAK, in N) of dominant and non-dominant upper limbs was retrieved for further analysis. Comparison between methods revealed significant differences in all force variables (p ≤ 0.05) and the biases (mean differences) were large in girls (FPEAK dominant, 45.89 N; FPEAK non-dominant, 43.79 N) and boys (FPEAK dominant, 67.26 N; FPEAK non-dominant, 61.78 N). Despite that, simple linear regression models between the two methods showed significant relationships with a moderate effect in all variables for girls, whereas in boys a high and moderate effect was verified for FPEAK of dominant and non-dominant limbs (respectively). It seems that using pressure sensors and tethered swimming leads to different FPEAK values in young competitive, where correction factors are needed to compare data between both methods.

Biomechanical framework for the inverse dynamic analysis of swimming using hydrodynamic forces from swumsuit

This study aims to integrate an open-source software capable of estimating hydrodynamic forces solely from kinematic data with a full-body biomechanical model of the human body to enable inverse dynamic analyses of swimmers. To demonstrate the methodology, intersegmental forces and joint torques of the lower limbs were computed for a six-beat front crawl swimming motion, acquired at LABIOMEP-UP. The hydrodynamic forces obtained compare well with existing numerical literature. The intersegmental forces and joint torques obtained increase from distal to proximal joints. Overall, the results are consistent with the limited literature on swimming biomechanics, which provides confidence in the presented methodology.

Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review

Swimming is a time-based sport and hence strongly dependent from velocity. Most studies about swimming refer to velocity as discrete variable, i.e., 0-D (no time dimension). However, it was argued that using swimming velocity as a continuous variable (1-D, with time dimension) with Statistical Parametric Mapping (SPM) can bring deeper and detailed insights about swimming performance. Therefore, the aim of this study was to perform a systematic review about the current body of knowledge of using Statistical Parametric Mapping in a swimming context. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to identify relevant articles. After screening, nine articles related to Statistical Parametric Mapping (SPM) analysis in swimming were retained for synthesis. Results showed that four articles (44.4%) aimed to understand the kinematics, isokinetic joint torque or electromyographic (EMG) pattern of the swimmer's shoulder either on land or during front crawl trials. Two articles (22.2%) focused on understanding the swimming velocity while performing the breaststroke stroke. One article (11.1%) analyzed the swimmers' propulsion at front-crawl stroke, another one (11.1%) compared swimming velocity during a complete stroke cycle in young swimmers of both sexes as a discrete variable and as a continuous variable. Also, one article (11.1%) analyzed the underwater undulatory velocity. In an EMG context, some findings verified in SPM are not possible to be discovered with traditional 0-D statistical methods. Studies about swimming velocity (breaststroke, freestyle, and underwater undulatory velocity) and propulsion (front-crawl) also highlighted the SPM advantages in comparison to traditional statistical methods. By using SPM, researchers were able to verify specifically where within the stroke cycle significant differences were found. Therefore, coaches can get more detailed information to design specific training drills to overcome hypothetical handicaps.

Performance Tiers within a Competitive Age Group of Young Swimmers Are Characterized by Different Kinetic and Kinematic Behaviors

The present study aimed to analyze swimmers' in-water kinetic and kinematic behaviors according to different swimming performance tiers within the same age group. An amount of 53 highly trained swimmers (girls and boys: 12.40 ± 0.74 years) were split up into 3 tiers based on their personal best performance (i.e., speed) in the 50 m freestyle event (short-course): lower-tier (1.25 ± 0.08 m·s^-1); mid-tier (1.45 ± 0.04 m·s^-1); and top-tier (1.60 ± 0.04 m·s^-1). The in-water mean peak force was measured during a maximum bout of 25 m front crawl using a differential pressure sensors system (Aquanex system, Swimming Technology Research, Richmond, VA, USA) and defined as a kinetic variable, while speed, stroke rate, stroke length, and stroke index were retrieved and considered as kinematic measures. The top-tier swimmers were taller with a longer arm span and hand surface areas than the low-tier, but similar to the mid-tier. While the mean peak force, speed and efficiency differed among tiers, the stroke rate and stroke length showed mixed findings. Coaches should be aware that young swimmers belonging to the same age group may deliver different performance outcomes due to different kinetic and kinematic behaviors.

Development of the technology to measure active drag ofswimmers by the method of small perturbations

The research aimed at further improvement of the technology of measuring swimmers' active drag by the small perturbation method (SPM) at the average maximal swimming velocity (v_0max). For better reliability and accuracy of measurements, a standard variant of SPM was developed to measure automatically active drag (F_r(ad)), dimensionless coefficient of hydrodynamic force (C_x) and total external mechanical power P_to in different competitive swimming techniques during a single 50 m trial. The study involved twelve elite swimmers who specialized in front crawl at the distances of 100 and 200 m (780-850 FINA Points). From our measurements, in front crawl the results are following: v_0max = 1.909 ± 0.017 m·s^-1, F_r(ad) = 106.892 ± 7.276 N, C_x = 0.311 ± 0.028 and P_to = 204.033 ± 13.221 W. For objective estimation of the results, a verification variant of SPM was applied: v_0max = 1.911 ± 0.018 m·s^-1, F_r(ad) = 107.033 ± 7.232 N, C_x = 0.311 ± 0.030 and P_to = 204.467 ± 12.982 W. The correlation analysis of the results obtained by the standard and verification variants of SPM confirms high accuracy and reliability of the method used (r = 0.988 for v_0max; r = 0.979 for F_r(ad); r = 0.988 for C_x(ad); r = 0.979 for P_to). The measurement error, including the method error and the devices error, does not exceed 2.4 %. This method has also a systematic error that reduces the measured data in the same direction from the actual values (min = -2.1 %; max = -2.9 %). This error should be taken into account when analyzing and evaluating the measured hydrodynamic characteristics of swimmers.

Differences in Race Characteristics between World-Class Individual-Medley and Stroke-Specialist Swimmers

The purpose of the present study was to investigate differences between world-class individual medley (IM) swimmers and stroke-specialists using race analyses. A total of eighty 200 m races (8 finalists × 2 sexes × 5 events) at the 2021 European long-course swimming championships were analysed. Eight digital video cameras recorded the races, and the video footage was manually analysed to obtain underwater distance, underwater time, and underwater speed, as well as clean-swimming speed, stroke rate, and distance per stroke. Each lap of the IM races was compared with the first, second, third, and fourth laps of butterfly, backstroke, breaststroke, and freestyle races, respectively. Differences between IM swimmers and specialists in each analysed variable were assessed using an independent-sample t-test, and the effects of sex and stroke on the differences were analysed using a two-way analysis of variance with relative values (IM swimmers' score relative to the mean specialists' score) as dependent variables. Breaststroke specialists showed faster clean-swimming speed and longer distance per stroke than IM swimmers for both males (clean-swimming speed: p = 0.011; distance per stroke: p = 0.023) and females (clean-swimming speed: p = 0.003; distance per stroke: p = 0.036). For backstroke and front crawl, specialists exhibited faster underwater speeds than IM swimmers (all p < 0.001). Females showed faster relative speeds during butterfly clean-swimming segments (p < 0.001) and breaststroke underwater segments than males (p = 0.028). IM swimmers should focus especially on breaststroke training, particularly aiming to improve their distance per stroke.

Relationship Between Hand Kinematics, Hand Hydrodynamic Pressure Distribution and Hand Propulsive Force in Sprint Front Crawl Swimming

Purpose

This study investigated the relationship between hand kinematics, hand hydrodynamic pressure distribution and hand propulsive force when swimming the front crawl with maximum effort.

Methods

Twenty-four male swimmers participated in the study, and the competition levels ranged from regional to national finals. The trials consisted of three 20 m front crawl swims with apnea and maximal effort, one of which was selected for analysis. Six small pressure sensors were attached to each hand to measure the hydrodynamic pressure distribution in the hands, 15 motion capture cameras were placed in the water to obtain the actual coordinates of the hands.

Results

Mean swimming velocity was positively correlated with hand speed (r = 0.881), propulsive force (r = 0.751) and pressure force (r = 0.687). Pressure on the dorsum of the hand showed very high and high negative correlations with hand speed (r = −0.720), propulsive force (r = −0.656) and mean swimming velocity (r = −0.676). On the contrary, palm pressure did not correlate with hand speed and mean swimming velocity. Still, it showed positive correlations with propulsive force (r = 0.512), pressure force (r = 0.736) and angle of attack (r = 0.471). Comparing the absolute values of the mean pressure on the palm and the dorsum of the hand, the mean pressure on the dorsum was significantly higher and had a larger effect size (d = 3.71).

Conclusion

It is suggested that higher hand speed resulted in a more significant decrease in dorsum pressure (absolute value greater than palm pressure), increasing the hand propulsive force and improving mean swimming velocity.

Vision-Based System for Automated Estimation of the Frontal Area of Swimmers: Towards the Determination of the Instant Active Drag: A Pilot Study

Swimmers take great advantage by reducing the drag forces either in passive or active conditions. The purpose of this work is to determine the frontal area of swimmers by means of an automated vision system. The proposed algorithm is automated and also allows to determine lateral pose of the swimmer for training purposes. In this way, a step towards the determination of the instantaneous active drag is reached that could be obtained by correlating the effective frontal area of the swimmer to the velocity. This article shows a novel algorithm for estimating the frontal and lateral area in comparison with other models. The computing time allows to obtain a reasonable online representation of the results. The development of an automated method to obtain the frontal surface area during swimming increases the knowledge of the temporal fluctuation of the frontal surface area in swimming. It would allow the best monitoring of a swimmer in their swimming training sessions. Further works will present the complete device, which allows to track the swimmer while acquiring the images and a more realistic model of conventional active drag ones.