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Active drag, useful mechanical power output and hydrodynamic force coefficient in different swimming strokes at maximal velocity
Abstract
By comparing the time of the same distance swum with and without an added resistance, under the assumption of an equal power output in both cases, the drag of 73 top swimmers was estimated. The active drag Fr(a.d.) at maximal swimming velocities varied considerably across strokes and individuals. In the females Fr(a.d.) ranged from 69.78 to 31.16 N in the front-crawl, from 83.04 to 37.78 N in dolphin, from 93.56 to 45.19 N in breaststroke, and from 65.51 to 37.79 N in back-stroke. In the males Fr(a.d.) ranged from 167.11 to 42.23 N in front-crawl, from 156.09 to 46.95 N in dolphin, from 176.87 to 55.61 N in breaststroke, and from 146.28 to 46.36 N in back-stroke. Also, the ratio of Fr(a.d.) to the passive drag Fr(a.d.) as determined for the analogical velocity in a tugging condition (in standard body position-front gliding) shows considerable individual variations. In the female swimmers variations in Fr(a.d.)/Fr(p.d.) ranged from 145.17 to 59.94% in front-crawl, from 192.39 to 85.57% in dolphin, from 298.03 to 124.50% in breaststroke, and from 162.87 to 85.61% in back-stroke. In the male swimmers variations in Fr(a.d.)/Fr(p.d.) ranged from 162.24 to 62.39% in front-crawl, from 191.70 to 70.38% in dolphin, from 295.57 to 102.83% in breaststroke, and from 198.82 to 74.48% in back-stroke. The main reason for such variations is found in the individual features of swimming technique and can be quantitatively estimated with the hydrodynamic force coefficient, which thus provides an adequate index of technique.
... Experimental mechanical methods used to estimate active drag, in succession to the biophysical method, include the Measurement of Active Drag System (MAD-System) (Hollander et al., 1986), the Velocity Perturbation Method (VPM) (S. V. Kolmogorov & Duplishcheva, 1992;Xin-Feng et al., 2007), the Assisted Towing Method (ATM) (Alcock & Mason, 2007), the Naval-Based Architecture method (NABA) (Webb et al., 2011), the Method of Instantaneous Hydrodynamic Reaction or Hydrodynamic Reaction Method (HRM) (S. V. Kolmogorov, 2008) and the Measured values of Residual Thrust (MRT) while swimming in a flume (Narita et al., 2017) developed from a similar method established by Takagi et al. (1999). ...
... The VPM method was first published in 1992 by Russian scientists (S. V. Kolmogorov & Duplishcheva, 1992), and revisited some years later by a Chinese research group with a similar conceptual approach (Xin-Feng et al., 2007). The VPM method estimates the active drag by calculating the difference in velocity between a free-swimming condition and a resisted swimming condition, whilst incorporating a known resistant force (the resistance of a hydrodynamic body or a resistive cable attached to the swimmer and towed behind them). ...
... Analysis of active drag force using the VPM (S. V. Kolmogorov & Duplishcheva, 1992), the ATM (Alcock & Mason, 2007) and the NABA method (Webb et al., 2011) enable determination of hydrodynamic resistance during maximal velocity free-swimming and all assume equal power conditions. Alternatively, the MAD-System (Hollander et al., 1986), HRM (S. ...
Free-swimming performance depends strongly on the ability to develop propulsive force and minimise resistive drag. Therefore, estimating resistive drag (passive or active) may be important to understand how free-swimming performance can be improved. The purpose of this narrative overview was to describe and discuss experimental methods of measuring or estimating active and passive drag relevant to competitive swimming. Studies were identified using a mixed-model approach comprising a search of SCOPUS and Web of Science data bases, follow-up of relevant studies cited in manuscripts from the primary search, and additional studies identified by the co-authors based on their specific areas of fluid dynamics expertise. The utility and limitations of active and passive drag methods were critically discussed with reference to primary research domains in this field, 'swimmer morphology' and 'technique analysis'. This overview and the subsequent discussions provide implications for researchers when selecting an appropriate method to measure resistive forces (active or passive) relevant to improving performance in free-swimming.
... Developed in the early 90s of the 20th century, the small perturbations method (SPM) for measuring swimmers' active drag at the average maximal swimming velocity (v 0max ) in different competitive swimming techniques (Kolmogorov and Duplishcheva, 1992) is currently actively used by specialists in the field of biomechanics of human aquatic locomotion, both as a basic variant of the method (originally developed) (Kolmogorov et al., 1997;Toussaint et al., 2004;Corrêa et al., 2007;Kjendlie and Stallman, 2008;Tomikawa and Nomura, 2009;Marinho et al., 2010), and in the form of its various and more or less successful modifications used (Bideau et al., 2002;Thorp and Wilson, 2002;Xin-Feng et al., 2007;Sacilotto et al., 2014). The main advantage of the basic variant of the SPM is its portability: the measuring kit is easy to move together with a coach or a specialist on training camps and competitions. ...
... In the basic variant of the method (Kolmogorov and Duplishcheva, 1992;Kolmogorov et al., 1997), to determine the average value of swimmer's active drag (F r(ad) ) the subject swims a 50 m trial with maximal velocity using a push-off from the wall. Time is recorded at a measured segment of 30 m of undisturbed swimming. ...
... In the basic variant of the method, several hydrodynamic bodies of various size ("family of bodies") are used, which makes it easy to select a body with an optimal value of F r(ab) , adequate to the subjects' sports qualification [in this variant, the total measurement error is 6-8 % (Kolmogorov and Duplishcheva, 1992)]. An objective criterion for the adequacy of the body size used is the percentage of the swimming velocity reduction relative to the velocity of free swimming -Δv 0 = (1-(v 0 2/v 0 1)) × 100 % = 4-6 % (Kolmogorov, 2008;Kolmogorov et al., 2021). ...
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 (v0max). For better reliability and accuracy of measurements, a standard variant of SPM was developed to measure automatically active drag (Fr(ad)),
dimensionless coeffcient of hydrodynamic force (Cx) and total external mechanical power Pto 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: v0max = 1.909 ± 0.017 m⋅s− 1, Fr(ad) = 106.892 ± 7.276 N, Cx = 0.311 ±
0.028 and Pto = 204.033 ± 13.221 W. For objective estimation of the results, a verifcation variant of SPM was
applied: v0max = 1.911 ± 0.018 m⋅s− 1, Fr(ad) = 107.033 ± 7.232 N, Cx = 0.311 ± 0.030 and Pto = 204.467 ±
12.982 W. The correlation analysis of the results obtained by the standard and verifcation variants of SPM
confrms high accuracy and reliability of the method used (r = 0.988 for v0max; r = 0.979 for Fr(ad); r = 0.988 for
Cx(ad); r = 0.979 for Pto). 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.
... The velocity perturbation method was used (Kolmogorov and Duplishcheva, 1992) to compute the active drag coefficient based on a drag method (CDa_VPM). Swimmers performed two 25 m maximum front crawl swimming trials with a push-off start after a standardized warm-up. ...
... One trial was performed at maximum speed in front crawl and the other at maximum speed in front crawl while towing a hydrodynamic body (i.e., a perturbation device). This hydrodynamic body was attached to the swimmers' waist with a belt at an 8 m distance (to minimize drafting effects of the perturbation device in the wake of the swimmer) (Kolmogorov and Duplishcheva, 1992). ...
... The literature reports four experimental approaches to measure the active drag: (1) the measuring active drag (MAD) system (Hollander et al., 1986); (2) the assistant towing method (ATM) at constant speed (Alcock and Mason, 2007), and at fluctuating speed (Mason et al., 2011); (3) the residual thrust method (MRT) (Narita et al., 2017), and; (4) the velocity perturbation method (VPM) (Kolmogorov and Duplishcheva, 1992). However, the MAD, ATM, and MRT methods are complex, time-consuming, and expensive systems. ...
The aim of this study was to analyze the agreement of the active drag coefficient measured through drag and propulsion methods. The sample was composed of 18 swimmers (nine boys: 15.9 ± 0.9 years; nine girls: 15.3 ± 1.2 years) recruited from a national swimming team. The velocity perturbation method was used as the drag measurement system and the Aquanex system as the propulsion system. For both sexes combined, the frontal surface area was 0.1128 ± 0.016 m 2 , swim velocity 1.54 ± 0.13 m•s-1 , active drag 62.81 ± 11.37 N, propulsion 68.81 ± 12.41 N. The level of the active drag coefficient agreement was calculated based on the mean values comparison, simple linear regression, and Bland Altman plots. The mean data comparison revealed non-significant differences (p > 0.05) between methods to measure the active drag coefficient. Both the linear regression (R 2 = 0.82, p < 0.001) and Bland Altman plots revealed a very high agreement. The active drag coefficient should be the main outcome used in the interpretation of the swimmers' hydrodynamic profile, because it is less sensitive to swimming velocity. Coaches and researchers should be aware that the active drag coefficient can also be calculated based on propulsion methods and not just based on drag methods. Thus, the swimming community can now use different equipment to measure the hydrodynamics of their swimmers.
... Two alternative methods have been developed to determine active drag: the Velocity Perturbation Method (VPM, Kolmogorov and Duplishcheva, 1992) and the Assisted Towing Method (ATM, Alcock and Mason, 2007). Both methods require that the swimmer swims with maximal effort in two conditions, one with and one without a known force (Fadded) acting on the swimmer. ...
... However, to what extent they influence the estimation of power and drag in front crawl swimming is currently unknown. Kolmogorov and Duplishcheva (1992) introduced the Velocity Perturbation Method (VPM) as an alternative method to determine drag in swimming. For the VPM, a swimmer swims an all-out trial under two conditions: one with a hydrodynamic body with known drag characteristics attached with a rope to the swimmer and one involving a free swim without a hydrodynamic body. ...
... While Kolmogorov and Duplishcheva (1992) stated that only the power output needs to be equal between both trials, the underlying assumptions are slightly different when comparing the power balances for the condition without and with the hydrodynamic body. ...
... Passive drag (D p ) is the evaluation of the drag produced during the displacement of a towed body (i.e., without relative movement of the body segments in the aquatic environment) (Pendergast et al., 2006). Active drag (D a ) is the water resistance induced to a body while swimming (Kolmogorov and Duplishcheva, 1992). Studies on D a are more common because during a race swimmers spend most of their time performing strokes . ...
... Based on experimental methods, D a can be measured through three approaches: 1) measurement of active drag (MAD) (Hollander et al., 1986); 2) velocity perturbation method (VPM) (Kolmogorov and Duplishcheva, 1992); 3) assisted towing method (ATM) (Alcock and Mason 2007), and; 4) measurement of residual thrust (MRT) (Narita et al., 2017). To determine D a through experimental studies, it was found that MAD, VPM, and ATM are now commonly used to obtain D a values accurately to assess swimmer technique (Toussaint et al., 2004;Formosa et al., 2012;Hazrati et al., 2016). ...
... Thus, these variables depend not only on their propulsive abilities but also on their ability to reduce to a minimum the drag forces that involve the body in a hydrodynamic way (Taı €ar et al., 1999). Studying active drag becomes relevant simply because it corresponds to the very act of swimming in a cyclical way, which consists almost of the entire race in high competition (Kolmogorov and Duplishcheva, 1992). Considering the importance that the measurement of drag has on swimming performance, it can be said that the evidence in the literature has not been systematically or narratively summarized, specially including studies based on both numerical and experimental measurements. ...
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.
... Swimming drag related research has focused essentially on assessing active drag in front crawl, existing scarce information on the hydrodynamic constrains of the other conventional swimming techniques. Interestingly, Kolmogorov and Duplishcheva (1992) evidenced active drag coefficients as good indicators of swimming technique and performance potential and Kolmogorov, Rumyantseva, Gordon, and Cappaert (1997) observed few differences between male and female swimmers and swimming techniques. Even if there are arguments supporting the passive drag practical utility, particularly concerning its predictive capacity of swimmers' performance (Chatard et al., 1990), only few studies considered the drag coefficients for both passive drag and active drag, with the topic remaining controversial (Havriluk, 2007;Zamparo, Gatta, Pendergast, & Capelli, 2009). ...
... Then, the cross-sectional area was determined by the sum of the triangles' areas, with the mean of three independent digitizing trials accepted as final value. This variable was also assessed using the human body volume to the power 2/3 (Kolmogorov & Duplishcheva, 1992). Active drag was assessed using the velocity perturbation method (Kolmogorov & Duplishcheva, 1992) assuming a constant mechanical power output at maximal free swimming and at maximal swimming towing a known added resistance. ...
... This variable was also assessed using the human body volume to the power 2/3 (Kolmogorov & Duplishcheva, 1992). Active drag was assessed using the velocity perturbation method (Kolmogorov & Duplishcheva, 1992) assuming a constant mechanical power output at maximal free swimming and at maximal swimming towing a known added resistance. Active drag and active drag coefficient values were calculated using the Newtonian equations that describe 1 D and 2 D (corresponding to situations 1 and 2, respectively): ! ...
... Swimming drag related research has focused essentially on assessing active drag in front crawl, existing scarce information on the hydrodynamic constrains of the other conventional swimming techniques. Interestingly, Kolmogorov and Duplishcheva (1992) evidenced active drag coefficients as good indicators of swimming technique and performance potential and Kolmogorov, Rumyantseva, Gordon, and Cappaert (1997) observed few differences between male and female swimmers and swimming techniques. Even if there are arguments supporting the passive drag practical utility, particularly concerning its predictive capacity of swimmers' performance (Chatard et al., 1990), only few studies considered the drag coefficients for both passive drag and active drag, with the topic remaining controversial (Havriluk, 2007;Zamparo, Gatta, Pendergast, & Capelli, 2009). ...
... Then, the cross-sectional area was determined by the sum of the triangles' areas, with the mean of three independent digitizing trials accepted as final value. This variable was also assessed using the human body volume to the power 2/3 (Kolmogorov & Duplishcheva, 1992). Active drag was assessed using the velocity perturbation method (Kolmogorov & Duplishcheva, 1992) assuming a constant mechanical power output at maximal free swimming and at maximal swimming towing a known added resistance. ...
... % − $ % ) , providing the active drag measure in situation 1 (free swimming at maximal velocity). This is a reliable method and measurements have been verified with a maximum potential error of 6-8% based on Strukhal number estimations (Kolmogorov & Duplishcheva, 1992). ...
... The drag factor (k) was estimated using the velocity perturbation method (VPM). 6 Starting in the pool, two 25-m maximal front crawl swim tests were performed with or without towing a hydrodynamic body with a known drag coefficient. 6 The mean swimming velocity (v) of each test was calculated based on the time necessary to cover the distance between the 12th and 22nd m points of the pool. ...
... 6 Starting in the pool, two 25-m maximal front crawl swim tests were performed with or without towing a hydrodynamic body with a known drag coefficient. 6 The mean swimming velocity (v) of each test was calculated based on the time necessary to cover the distance between the 12th and 22nd m points of the pool. The swimming training zones used (intensities: Z1, Z2, Z3, and Z4) 7 for the IIST consisted of 400 m at Z1 (Z1), 300 m at Z2 (Z2), 200 m at Z3 (Z3), and 100 m at Z4 (Z4) with 30-second recovery intervals. ...
... The η d was lower with wetsuit (Z1 = −13% [2%]; Z2 = −17% [7%]; Z3 = −20% [5%]; Z4 = −15% [7%]), a reasonable result due to the v increment. 6,10 The OW swimmers tend to swim faster with wetsuits for the same perceived swimming intensity with a lower C. The OW3 (6 times female OW swimmer of the year by FINA) showed the highest reduction in C, but not the largest improvement in v. ...
Aim: We investigated how Arena Powerskin R-EVO Closed Back (swimsuit) and Arena Carbon Triwetsuit (full-sleeve, wetsuit), both approved by the Fédération Internationale de Natation (FINA) regulations, affect biomechanics and energetics of 3 Elite female open water (OW) swimmers at maximal and 4 submaximal swimming intensities.
Methods: Three Elite female OW swimmers (OW1 = 24 y, 1.64 m, 60 kg; OW2 = 23 y, 1.69 m, 65 kg; OW3 = 27 y, 1.63 m, 64.5 kg) were tested 1 week prior to a FINA/CNSG Marathon Swim World Series event and 40 days before the 18th FINA World Championships 2019. Each OW swimmer completed 2 identical testing sessions, one with a swimsuit and other with a wetsuit, involving shoulder flexion power output (SFPO) assessed from medicine ball throw (MBT), maximal performance and drag coefficient assessment, and an incremental intermittent swim test (IIST) at 4 different relative intensities.
Results: Estimated peak oxygen uptake was 4.4 L·min−1 for OW1, 5.6 L·min−1 for OW2, and 5.0 L·min−1 for OW3. Despite a distinct behavior observed on index of coordination for OW3, a null index of synchronization, increased stroke rate (mean difference = 2%–8%), reduced drag factor (minimum = −14%; maximum = −30%), lower energy cost (mean difference = −2% to −6%), and faster performance (mean difference = 2% to 3%) were observed with the wetsuit compared with swimsuit for all Elite OW swimmers.
Conclusion: The wetsuit enhances submaximal swimming performance and this increase is dependent on the OW swimmer’s characteristics. The higher stroke rate and lower stroke length detected with wetsuit could be related to movement constraints imposed by the suit.
... The active drag (D a ), and the active drag coefficient (C Da ) were computed based on the velocity perturbation method (Kolmogorov & Duplishcheva, 1992). Swimmers performed two all-out trials of 25m at front crawl with a push-off start. ...
... Swimmers performed two all-out trials of 25m at front crawl with a push-off start. One trial was carried out towing a hydrodynamic body (i.e., a perturbation device) and one other without towing it (Kolmogorov & Duplishcheva, 1992). A camera (Sony x3000, Tokyo, Japan) was used to record the swimmer's displacement time between the 11 th and 24 th meter. ...
... The power to overcome drag (P d ) was computed as (Kolmogorov & Duplishcheva, 1992): ...
Purpose: The aims of this study were to classify, identify and follow-up young swimmers’ performance and its biomechanical determinants during two competitive seasons (in seven different moments of assessment—M), and analyze the individual variations of each swimmer. Method: Thirty young swimmers (14 boys: 12.70 ± 0.63 years-old; 16 girls: 11.72 ± 0.71 years-old) were recruited. A set of anthropometric, kinematic, efficiency, hydrodynamic and mechanical power variables were assessed. Results: The cluster solution (i.e., number of ideal clusters for this sample) resulted in three clusters, which were named as: cluster 1 (“talented”), cluster 2 (“proficient”), and cluster 3 (“non-proficient”). The performance improved between moments of assessment in all clusters (cluster 1—M1: 68.07 ± 6.62s vs M7: 61.46 ± 3.43s; cluster 2—M1: 73.14 ± 4.87s vs M7: 65.33 ± 2.97s; cluster 3—M1: 82.60 ± 4.18s vs M7: 70.09 ± 3.48s). Anthropometric features also increased between moments of assessment, and remaining biomechanical variables (kinematic, efficiency, hydrodynamic and mechanical power) also increased between M1 and M7, in all clusters. Cluster 1 increased their swimmer’s membership between M1 and M7 (4 to 11), cluster 2 decreased (12 to 5), and cluster 3 maintained (14). Conclusion: It can be concluded that the cluster formation depends on different determinant factors during two competitive seasons, and young swimmers are prone to change from one cluster to another over this period of time.
... The active drag (D a , N) and the coefficient of active drag (C Da , dimensionless) were computed by the Velocity Perturbation Method (Kolmogorov & Duplishcheva, 1992). Swimmers were invited to perform two maximal trials in front-crawl. ...
... Swimmers were invited to perform two maximal trials in front-crawl. In one trial, swimmers were towing a hydrodynamic body (perturbation device) (Kolmogorov & Duplishcheva, 1992). The swim velocity was calculated between the 11 th and 24 th metre as v = d/t. ...
... where P d is the power to overcome drag (W), D a is the swimmers' active drag at maximal velocity, and v is the swim velocity (m/s) (Kolmogorov & Duplishcheva, 1992). The external mechanical power (P ext , W) and the mechanical power to transfer kinetic energy to water (P k , W) were computed respectively as: ...
The aim of this study was to analyse the detraining process that occurs during a season break, and its influence on the performance, anthropometrics, and biomechanics of young swimmers. The sample included 54 young swimmers (22 boys: 12.79 ± 0.71 years; 32 girls: 11.78 ± 0.85 years). Performance for the 100 m freestyle and anthropometric and biomechanical variables were evaluated as main determinants. Performance impaired significantly for boys (2.17%) and girls (1.91%). All anthropometric variables increased between moments of assessment for boys and girls. Overall, the boys enhanced all biomechanical variables during the detraining period, and girls showed mixed results. For both sexes, the stroke index was the variable with the highest increase (boys: Δ = 16.16%; d = 0.89; p = 0.001; girls: Δ = 19.51%; d = 1.06; p = 0.002). Hierarchical linear modelling showed that the height retained the amount of impairment in the performance. One unit of increase in the height (cm) led to less 0.41 s impairment in the performance. Present data indicated that during an 11-weeks detraining period, young swimmers impaired their performance, but the determinant factors showed an impaired relationship. This increase in the determinant factors is mainly related to the increase in the swimmers’ anthropometrics. Moreover, the increase in height was responsible for retaining the performance impairment.
... Active drag is an important determinant of performance in nondisabled swimmers (3), and it is highly influenced by the swimmer's technique (11,12,13,14). In nondisabled swimmers, active drag has often been viewed as a measure of "skill" because swimmers who perform arm strokes and leg kicks while causing minimal disturbance to the water are considered to have a better "technique" than those whose movements cause more disturbance. ...
... In nondisabled swimmers, active drag has often been viewed as a measure of "skill" because swimmers who perform arm strokes and leg kicks while causing minimal disturbance to the water are considered to have a better "technique" than those whose movements cause more disturbance. It has also been suggested that the ratio of passive to active drag, termed the thrust deduction (15), or the reciprocal of this, called the technique drag index (12) may provide a useful measure of the swimmer's effectiveness in producing propulsion. If two swimmers with similar active drag are compared at the same speed, the one with the higher thrust deduction is more effective as they have created less of an increase in drag with their propulsive actions. ...
... In each case, the lowest drag figure over the three trials was used in the subsequent analysis to reflect the lowest drag that the swimmer was able to achieve. As hydrodynamic drag is related to the square of the towing or swimming speed (9,12), each swimmer's passive and active drag were divided by their v MAX squared to allow interswimmer comparisons (equations 2 and 3). Hydrodynamic drag is also a function of body size (9). ...
Introduction:
Paralympic classification should provide athletes with an equitable starting point for competition by minimising the impact their impairment has on the outcome of the event. As swimming is an event conducted in water, the ability to overcome drag (active and passive) is an important performance determinant. It is plausible that the ability to do this is affected by type and severity of physical impairment, but the current World Para Swimming classification system does not objectively account for this component. The aim of this study was to quantify active and passive drag in Para swimmers and evaluate the strength of association between these measures and type of physical impairment, swimming performance and sport class.
Methods:
Seventy-two highly-trained Para swimmers from sport classes S1 to S10 and fourteen highly-trained non-disabled swimmers were towed by a motorised winch whilst the towing force was recorded. Passive drag was measured with the arms held by the side; active drag was determined during freestyle swimming using an assisted towing method.
Results:
Active and passive drag were higher in Para swimmers with central motor and neuromuscular impairments than for non-disabled swimmers and were associated with severity of swim-specific impairment (sport class) and maximal freestyle performance in these swimmers (r = -.40 to -.50, p ≤ .02). Para swimmers with anthropometric impairments showed similar active and passive drag to non-disabled swimmers, and between swimmers from different sport classes.
Conclusion:
Para swimmers with central motor and neuromuscular impairments are predisposed to high active drag during freestyle swimming that impacts on their performance. It is recommended that drag measures be considered in revised classification for these swimmers, but not for those with anthropometric impairments.
... Breaststroke swimming has the worst hydrodynamics and the larger active drag at a given swimming speed compared to the other strokes (Kolmogorov and Duplisheva, 1992;Kolmogorov et al., 1997). Therefore, technique differences in spatiotemporal stroke parameters, like stroke length and stroke rate, or synchronization differences of the arms and the kick in the stroke cycle, are very exemplar when fatigue is induced or a different tactic is imposed according to the swimming event Thompson et al., 2000). ...
... Finally, it must be noted that a significant amount of propulsion in breaststroke comes from the leg kick (Gourgoulis et al., 2018;Mason et al., 1989). Since breaststroke has the larger fluctuation of the active/ passive drag ratio (Kolmogorov and Duplisheva, 1992) the synchronization of the arm and leg movements should be considered in order to extract further conclusions (Seifert and Chollet, 2008). The faster velocity of the hand in the Y axis during the recovery phase for the maximal condition (Fig. 2) is probably linked to that. ...
The aim of the current study was to compare the arm-stroke kinematics during maximal and sub-maximal breaststroke swimming using both discrete and continuous data analysis. Nine male breaststrokers swam 2 x 25 m with maximal and sub-maximal intensity and their full body 3-D kinematics were obtained using eight video cameras. The arm-stroke was divided into five phases: recovery, glide, out-sweep, in & down-sweep and in & up-sweep. The statistical treatment of selected discrete variables was conducted using t-test, while the analysis of their equivalent time series, when applicable, was conducted using Statistical Parametric Mapping. Sub-maximal trial, compared to maximal, presented lower swimming velocity, greater stroke length and less stroke rate. Moreover, the absolute and relative duration of the glide phase was longer, while the relative duration of all the other phases was shorter. The resultant hand velocity during the arm recovery was slower, as well as the hand velocity time series in the transverse and longitudinal axis which were slower from ∼45% to ∼60% and from ∼5% to ∼15% of the stroke cycle, respectively. Both discrete and continuous data analysis revealed that the main discriminating factor between the two conditions concerns to the adjustment of the glide and the recovery phase and consequently the continuation of the propulsive movements.
... The slope of the load-velocity regression line was then acquired as -V0/L0. Da was computed using the velocity perturbation method (VPM) proposed by Kolmogorov and Duplishcheva (1992). For this analysis, V0, mean force and velocity data from a semi-tethered swimming trial were used. ...
... Furthermore, the present study estimated the maximum free-swimming velocity from the load-velocity profiling rather than actually measuring it. Nevertheless, Da for both males and females in the present study showed similar values as the original VPM study by Kolmogorov and Duplishcheva (1992) who reported Da of 82.79 ± 35.90 N and 53.17 ± 11.70 N for males and females whose level was similar to the swimmers in the present study. These similarities indirectly imply that the slight modification in the method did not critically affect the outcomes. ...
The purpose of this study was to investigate the relationship between swimming load-velocity slope and the active drag (Da) in front crawl. 19 female and 22 male swimmers were recruited and performed three 25 m front crawl sprints with different external loads (1, 3, 5 kg for females and 1, 5, and 9 kg for males) assigned by a robotic resistance device. The mean swimming velocity was plotted against the external load to establish the load-velocity profile for each swimmer. Da was obtained by the velocity perturbation method. The relationship between the load-velocity slope and Da was assessed using the Pearson correlation coefficient, which showed a very large correlation (r = 0.84, p < 0.001) and an extremely large correlation (r = 0.93, p < 0.01) for female and male swimmers, respectively, indicating that the load-velocity slope is an indicator of Da in front crawl swimming.
... The C Da (dimensionless) was computed based on the velocity perturbation method (Kolmogorov & Duplishcheva, 1992). Swimmers performed two all-out trials of 25-m front crawl with a push-off start. ...
... Swimmers performed two all-out trials of 25-m front crawl with a push-off start. One trial was carried out towing a hydrodynamic body (i.e., a perturbation device) and the other without towing it (Kolmogorov & Duplishcheva, 1992). Between trials swimmers had 10 min for full recovery. ...
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.
... The objective quantitative criterion for the adequacy of this magnitude is the percent of the velocity change (%∆υ 0 ) while swimming under impact of the perturbation force in relation to the velocity of free swimming (decrease or increase of the velocity depending on the variant of the method). Due to the metrological modeling based on the mathematical model of the method, the optimal range of the magnitude was determined as %∆υ 0 = 4-6% (in this case the maximum experimental error doesn't exceed ±3%) [1,2,9]. ...
... In the male subjects, the observation is analogous. The biomechanical mechanism of this phenomenon can be clarified by the analysis of C x(ad) , the values of which depend on the swimming stroke and are observed within the determined quantitative range (Both in the female and male subjects, statistical differences between the magnitudes of C x(ad) have not been revealed only between dolphin and backstroke, that was repeatedly observed previously [1,2,10]). Consequently, C x(ad) apparently depends on the individual features of the athlete's body (i.e., physical build), but to much greater extent, it depends on the inherent to every athletic stroke specific biomechanical system of motions, cinematic characteristics of which are strictly regulated by the competition rules. ...
Abstract
Hydrodynamic characteristics of elite female and male swimmers were determined by the four variants of the perturba�tion method at the phase of decrease in training load before the 2016 Olympic Games in Rio de Janeiro and the 2017 World
Championship in Budapest. Further, the ten best swimmers of both genders were selected by their maximal swimming ve�locity (v0max) in all athletic strokes (80 subjects altogether). As a result of the proper processing of the data, statistical models
of quantitative values of active drag force (Fr(ad), the dimensionless hydrodynamic coefficient (Cx(ad)) and total external me�chanical power (Pto) were determined. In all four strokes, due to their essential superiority in Pto, men have higher levels of
v0max than women. Naturally enough that at higher v0max elite male athletes have greater Fr(ad), too. Again, in terms of Cx(ad),
there was no statistical difference between women and men within each of the strokes. Consequently, regardless of their
gender, elite swimmers may be stated to be equally successful in mastering proficient swimming techniques of all athletic
strokes. Besides having an independent scientific significance, the statistical models of Fr(ad), Cx(ad) and Pto allow to increase
considerably the quality of the individual analysis of these indicators on athletes of different performance levels. The key
criterion for such analysis at the phase of decrease in training load is Cx(ad), which determines the hydrodynamic efficiency
of the individual swimming technique in any of athletic strokes in terms of quantity.
Keywords: swimming velocity, active hydrodynamic resistance (active drag), mechanical power
... Participation in this study occurred during the preparation process for the World Swimming Championships 2017, in Budapest. Since all the considered variables-Equation (1)-are possibly affected by the training period [7], all tests were carried out at the end of the specific training period (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) days before competing at the World Championship). This period allowed for subjectively accommodating individual adaptations to tapering, aiming to test all the subjects in a similar conditioning state. ...
... To experimentally determine the total external mechanical power (P to ), active hydrodynamic resistance force (F r(ad) ), or active drag force, and the dimensionless coefficient of active drag force (C x(ad) ) were assessed using the method of small perturbations, usually known as the velocity perturbation method-VPM (for more methodological details, see [24,25]). A 30 m swimming test was performed twice at maximum velocity (v 0max ) by each swimmer using the swimming technique of his/her specialty. ...
Eight elite swimmers—four females and four males—were studied, each of whom specialized in different swimming techniques and ranked among the top 10 in the world in the 100 m event in their swimming specialty. Methods included a complex of physiological, biomechanical and hydrodynamic procedures, as well as mathematical modeling. During the special preparation period for the 2017 Swimming World Championship, all subjects performed an 8 × 100 m swimming step-test using their main swimming technique. The relationships between velocity, mechanical and metabolic power were obtained and analyzed for each swimming technique. It was found that, at the last stage of the test, in all swimming techniques, men demonstrated higher values of metabolic power (Pai = 3346–3560 W) and higher mechanical efficiency (eg = 0.062–0.068) than women (Pai = 2248–2575 W; eg = 0.049–0.052). As for propelling efficiency, women (ep = 0.67–0.71) and men (ep = 0.65–0.71) did not differ from each other. Results showed that the frontal component of active drag force is the main reason for the existing differences in maximal swimming velocity between different techniques, since no relevant differences were observed for mechanical and propelling efficiencies among swimming techniques.
... Due to the complexity of unsteady flow mechanics in human swimming, it is currently impossible to measure D and T directly. Thus, researchers have established indirect methods to estimate these forces, such as the 'energetics' approach di Prampero et al., 1974;Zamparo et al., 2009), the MAD-system (Toussaint, 1990;Toussaint & Beek, 1992;Toussaint, Beelen et al., 1988;Toussaint & Hollander, 1994;Toussaint et al., 1991), velocity perturbation method (Kolmogorov & Duplishcheva, 1992), assisted towing method (Formosa et al., 2012), MRT method Narita et al., 2017Narita et al., , 2018. These methods enable researchers to estimate D (and consequently T, when the swimmer maintains a constant velocity) acting on the whole body but do not provide information on the sources of the total forces. ...
... Methods to estimate D during a whole-body swimming condition include the velocity perturbation method (Kolmogorov & Duplishcheva, 1992) and the assisted towing method (Formosa et al., 2012). Both methods require two testing conditions; one with freeswimming and the other with passive or active towing with a known force. ...
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]; Vˉ: 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]; Vˉhand: 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.
... The active drag coefficient (C Da , dimensionless) was computed with the velocity perturbation method (Kolmogorov and Duplishcheva, 1992). Swimmers were invited to perform two maximal trials at front crawl: one trial towing a hydrodynamic body (perturbation device) and the other without (Kolmogorov and Duplishcheva, 1992). ...
... The active drag coefficient (C Da , dimensionless) was computed with the velocity perturbation method (Kolmogorov and Duplishcheva, 1992). Swimmers were invited to perform two maximal trials at front crawl: one trial towing a hydrodynamic body (perturbation device) and the other without (Kolmogorov and Duplishcheva, 1992). The swim velocity was calculated as follows: v = d/t. ...
The purpose of this study was to understand the relationship between the coaches’ demographics (academic degree and/or coaching level and/or coaching experience) and young swimmers’ performance and technical ability. The sample was composed by 151 young swimmers (75 boys and 76 girls: 13.02 ± 1.19 years old, 49.97 ± 8.77 kg of body mass, 1.60 ± 0.08 m of height, 1.66 ± 0.09 m of arm span), from seven different clubs. Seven coaches (one per club) were responsible for the training monitoring. Performance and a set of biomechanical variables related to swim technique and efficiency were assessed. The swimmers’ performance was enhanced according to the increase in the coaches’ academic degree (1: 75.51 ± 10.02 s; 2: 74.55 ± 9.56 s; 3: 73.62 ± 7.64 s), coaching level (1: 76.79 ± 11.27 s; 2: 75.06 ± 9.31 s; 3: 73.65 ± 8.43 s), and training experience (≤5-y training experience: 75.44 ± 9.57 s; >5-y training experience: 74.60 ± 9.54 s). Hierarchical linear modeling retained all coaches’ demographics characteristics as main predictors (being the academic degree the highest: estimate = -1.51, 95% confidence interval = -0.94 to -2.08, p = 0.014). Hence, it seems that an increase in the demographics of the coaches appears to provide them with a training perspective more directed to the efficiency of swimming. This also led to a higher performance enhancement
... Similarly, an increasing strength method obtained with "dry-methods" showed some limits on the "transferability" on specific technical swim movements 9,15 . Recently, several in-water methods 5,16 were used to assess the strength and the power of the swimmers through the assessment of the drag, providing conflicting results 5,16,17 . The strength and power estimates from swimming velocity doesn't seem adequate 8,18,19 because the swimming velocity was related to muscle power, and both propulsion efficiency and drag coefficient of swimmer 5 . ...
... Similarly, an increasing strength method obtained with "dry-methods" showed some limits on the "transferability" on specific technical swim movements 9,15 . Recently, several in-water methods 5,16 were used to assess the strength and the power of the swimmers through the assessment of the drag, providing conflicting results 5,16,17 . The strength and power estimates from swimming velocity doesn't seem adequate 8,18,19 because the swimming velocity was related to muscle power, and both propulsion efficiency and drag coefficient of swimmer 5 . ...
Вивчено вплив метода силового тренування на потужність плавання 20 спортсменів-ветеранів, яких було умовно розподілено на дві групи – силової (n = 10, ST) і плавальної (n = 10, SW) підготовки. Тренувальні заняття проводилися упродовж 6 тиж. і включали в групі SW плавальну підготовку та силову з подальшим плаванням з максимальною швидкістю. Результати в обох групах оцінювали на основі максимальної–механічної–зовнішньої потужності (ММЕР), застосуванням ергометра для вимірювання сили, швидкості і потужності в воді. В групі ST спостерігали значне підвищення ММЕР (5,79 %; р < 0,05) разом із збільшенням сили (11,70 %; p < 0,05) і зниженням швидкості (4,99 %; р < 0,05). В групі SW виявлено зниження ММЕР, сили і швидкості (7,31, 4,16 % і 4,45 %; р < 0,05). Дослідження показало, що метод, заснований на поєднанні силового тренування (на суші)з подальшим швидким плаванням, істотно збільшує потужність плавання у спортсменів-ветеранів.
... where D is the drag force (in N), ρ is the density of water (in kg/m 3 ), v is the swimming speed (in m/s), S is the projected frontal surface area (FSA) of the swimmers (in m 2 ), and CD is the coefficient of drag (changing according to shape, orientation, and Reynolds number). There are two types of drag: (i) active (DA) -the water resistance induced to a body while swimming (Kolmogorov and Duplishcheva, 1992), and (ii) passive (DP)drag produced during the displacement of a towed body, i.e., without relative movement of the body segments in the aquatic environment (Pendergast et al., 2005). ...
The main objective of this study was to confirm that the passive drag coefficient is less dependent on swimming speed than the passive drag, Froude, and Reynolds numbers, even as swimming speed increases. The sample consisted of 12 young proficient non-competitive swimmers (seven males and five females: 20.4 ± 1.9 years). Passive drag was measured with a low-voltage isokinetic engine at 1.2, 1.4, 1.6 and 1.8 m/s. The frontal surface area was measured using digital photogrammetry. Passive drag showed significant differences with a strong effect size over the four towing speeds measured (F = 116.84, p < 0.001, η 2 = 0.91) with a quadratic relationship with speed. The Froude and Reynolds numbers had similar trends, but with linear relationships. Conversely, the passive drag coefficient showed non-significant differences across the four towing speeds (F = 3.50, p = 0.062, η 2 = 0.33). This strongly suggests that the passive drag coefficient should be the variable of choice for monitoring the hydrodynamic profile of swimmers rather than the absolute value of passive drag.
... Thus, several indirect methods have been used to estimate it, for example, by assisted or resisted swimming protocols. [6][7][8] The resisted swimming has also been recently used for the load-velocity profile as a performance assessment tool in sprint swimming in adult athletes. The load-velocity profile is a widely used method to estimate maximal performance in multiple sports, such as sprint running and strength exercises. ...
Purpose:
The present study aimed to establish differences in load-velocity profiling, active drag (AD), and drag coefficient (Cd) between 3 age groups of female swimmers.
Methods:
Thirty-three swimmers (11, 13, or 16 y old) were recruited. The individual load-velocity profile was determined for the 4 competitive swimming strokes. The maximal velocity (V0), maximal load (L0), L0 normalized to the body mass, AD, and Cd were compared between the groups. A 2-way analysis of variance and correlation analysis were conducted.
Results:
Compared with their younger counterparts, 16-year-old swimmers generally had larger V0, L0, and AD, which was particularly evident when comparing them with 11-year-old swimmers (P ≤ .052). The exception was breaststroke, where no differences were observed in L0 and AD and Cd was smaller in the 16-year-old group than the 11-year-old group (P = .03). There was a negative correlation between Cd and V0 for all groups in backstroke (P ≤ .038) and for the 11-year-old group and 13-year-old group in breaststroke (P ≤ .022) and front crawl (P ≤ .010). For the 16-year-old group, large correlations with V0 were observed for L0, L0 normalized to the body mass, and AD (P ≤ .010) in breaststroke and for L0 and AD with V0 in front crawl (P ≤ .042). In butterfly, large negative correlations with V0 were observed in the 13-year-old group for all parameters (P ≤ .027).
Conclusions:
Greater propulsive force is likely the factor that differentiates the oldest age group from the younger groups, except for breaststroke, where a lower Cd (implying a better technique) is evident in the oldest group.
... Unlike other swimming strokes, stroke rate has been shown to be the most discriminative feature of velocity in the breast-stroke (Pai et al., 1984;Chollet et al., 1996). Some studies (Clarys, 1979;Kolmogorov and Duplischeva, 1992) have suggested that the ratio of stroke rate to stroke length, the velocity, and the active drag are more dependent on swimming technique and thus, on the inter-limb coordination, than on anthropometric characteristics. In their study, Seifert and Chollet (2007) have discovered how men typically show shorter temporal gaps, body glide, and recovery but higher body propulsion with respect to women. ...
Introduction
Dynamics-on-graph concepts and generalized finite-length Fibonacci sequences have been used to characterize, from a temporal point of view, both human walking & running at a comfortable speed and front-crawl & butterfly swimming strokes at a middle/long distance pace. Such sequences, in which the golden ratio plays a crucial role to describe self-similar patterns, have been found to be subtly experimentally exhibited by healthy (but not pathological) walking subjects and elite swimmers, in terms of durations of gait/stroke-subphases with a clear physical meaning. Corresponding quantitative indices have been able to unveil the resulting hidden time-harmonic and self-similar structures.
Results
In this study, we meaningfully extend such latest findings to the remaining two swimming strokes, namely, the breast-stroke and the back-stroke: breast-stroke, just like butterfly swimming, is highly technical and involves the complex coordination of the arm and leg actions, while back-stroke is definitely similar to front-crawl swimming. An experimental validation with reference to international-level swimmers is included.
... where where 9.8 m/s , , , , , , are the gravity, the time period of the combustion, the dynamic viscosity of the water, the effective acting area of the combustion, the mass of the copebot, time, the gas amount and the pre-inclined wing angle, respectively [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] . Note that represents X, Y, and , which are the displacement in x-direction, the displacement in y-direction, the rotational angle, respectively. ...
It has been a great challenge to develop robots that are able to perform complex movement patterns with high speed and, simultaneously, high accuracy. Copepods are animals found in freshwater and saltwater habitats that can have extremely fast escape responses when a predator is sensed by performing explosive curved jumps. Here, we present a design and build prototypes of a combustion-driven underwater soft robot, the "copebot", that, like copepods, is able to accurately reach nearby predefined locations in space within a single curved jump. Because of an improved thrust force transmission unit, causing a large initial acceleration peak (850 Bodylength*s-2), the copebot is 8 times faster than previous combustion-driven underwater soft robots, whilst able to perform a complete 360{\deg} rotation during the jump. Thrusts generated by the copebot are tested to quantitatively determine the actuation performance, and parametric studies are conducted to investigate the sensitivities of the input parameters to the kinematic performance of the copebot. We demonstrate the utility of our design by building a prototype that rapidly jumps out of the water, accurately lands on its feet on a small platform, wirelessly transmits data, and jumps back into the water. Our copebot design opens the way toward high-performance biomimetic robots for multifunctional applications.
... where where 9.8 m/s , , , , , , are the gravity, the time period of the combustion, the dynamic viscosity of the water, the effective acting area of the combustion, the mass of the copebot, time, the gas amount and the pre-inclined wing angle, respectively [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] . Note that represents X, Y, and , which are the displacement in x-direction, the displacement in y-direction, the rotational angle, respectively. ...
It has been a great challenge to develop robots that are able to perform complex movement patterns with high speed and, simultaneously, high accuracy. Copepods are animals found in freshwater and saltwater habitats that can have extremely fast escape responses when a predator is sensed by performing explosive curved jumps. In this study, we present a design and build prototypes of a combustion-driven underwater soft robot, the "copebot," which, similar to copepods, is able to accurately reach nearby predefined locations in space within a single curved jump. Because of an improved thrust force transmission unit, causing a large initial acceleration peak (850 body length·s-2), the copebot is eight times faster than previous combustion-driven underwater soft robots, while able to perform a complete 360° rotation during the jump. Thrusts generated by the copebot are tested to quantitatively determine the actuation performance, and parametric studies are conducted to investigate the sensitivity of the kinematic performance of the copebot to the input parameters. We demonstrate the utility of our design by building a prototype that rapidly jumps out of the water, accurately lands on its feet on a small platform, wirelessly transmits data, and jumps back into the water. Our copebot design opens the way toward high-performance biomimetic robots for multifunctional applications.
... It is also hypothesised that females' difficulty switching to superposition mode is due to their inability to reach the 1.8 m.s −1 swimming velocity threshold, where this change in coordination typically occurs (Seifert, Boulesteix, et al., 2004). Consequently, they face less hydrodynamic resistance than males, as drag rises as velocity increases (Kolmogorov & Duplishcheva, 1992). ...
Several constraints, including environmental (e.g., aquatic resistance, temperature and viscosity), organismic (e.g., anthropometry, buoy- ancy) and task-related (e.g., imposed swim speed or stroke rate) impact motor coordination and swimming performance. As motor coordination requires structurally organising intra- and inter-limb coupling, the purpose of this review was to update the literature concerning coordination between the upper-limbs in front crawl swimming. We focused on the effects of biomechanical, physiologi- cal, and personal (gender, skill level, and age) factors on coordination and performance. In fact, it could be highlighted that upper-limbs coordination varies with organismic, task and environmental con- straints, resulting in several available motor solutions that should be adopted according to how each swimmer deals with occurring con- straints. As such, there is no ideal or optimal coordination pattern that youth, learners and less-skilled swimmers should imitate.
... However, most of the current study swimmers are younger and/or females, which commonly do not show superposition coordination (Seifert et al., 2004;Silva et al., 2019a). Although catch-up coordination could provide higher variations due to the propulsion lag, it allowed swimmers to reduce hydrodynamic resistance due to the higher relative time spent in streamlined position (Kolmogorov & Duplishcheva, 1992). Along with race parameters, we believe that swimmers were able to maximise propulsive and minimise drag forces, reflected by the greater stroke length, compared to other studies . ...
Intra- and inter-cycle velocity variations are of utmost importance for achieving enhanced swimming performances. However, intra-cycle events can impact and interfere with the subsequent cycles, making relevant to study several consecutive cycles allowing a better understanding of this possibility. We have assessed front crawl intra- and inter-cyclic velocity variations and overall biomechanical variables in sprint front crawl swimming. Twenty-seven elite swimmers performed 25 m all-out front crawl, were videotaped using moving cameras placed at the sagittal plane and were grouped according to their sprint mean velocity. Coefficient of variation, root mean square error and mean velocity differences between two consecutive paired cycles allowed assessing intra- and inter-cycle velocity variations. Visual inspection was performed to analyse possible variability causes and independent-measures t-test allowed comparing groups. Sprint front crawl was characterised by intra- (11.12 ± 2.98) and inter-cycle velocity variation (2.27 ± 0.80 of inter-cycle velocity coefficient of variation and 0.031 ± 0.014 of root mean square error), with no differences between fastest and slowest swimmers. Front crawl intra-cycle velocity variation was not related to mean velocity and cycle sequence but considered swimmers' personal strategy. Despite some abnormal oscillations within cycles, inter-cycle velocity variation was not caused by intra-cycle velocity variation, mean velocity or cycle sequence.
... However, due to the complexity of unsteady flow mechanics in human swimming, it is currently impossible to measure propulsion and drag directly. Thus, researchers have established indirect methods to estimate these forces, such as the MADsystem (Hollander et al., 1986), velocity perturbation method (Kolmogorov and Duplishcheva, 1992), assisted towing method (Formosa et al., 2012), MRT (measured values of residual thrust) method (Narita et al., 2017). These methods enable researchers to estimate drag (and consequently propulsion, assuming the swimmer maintains a constant velocity ignoring force and velocity fluctuations within a stroke cycle) acting on the whole body but do not provide information on the sources of the total forces. ...
PurposeThis 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.ResultsMean 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.
... One trial is performed as free swimming while the other is performed towing a hydrodynamic body creating a known resistance. The two trials are analyzed with the equal power assumption, where the difference in swimming velocity is attributed to the added resistance created by the hydrodynamic body, allowing calculation of active drag (Kolmogorov & Duplishcheva 1992). Although the two methods are commonly used to determine active drag, they have been found to produce significantly different results (Toussaint, Roos & Kolmogorov 2004 (1990) found that SL is the determining performance characteristic among Olympic swimmers. ...
Aim The aim of this study was to ascertain the effect of resisted sprint training in swimming on maximal swimming velocity and performance characteristics. The aim was also to examine how maximal swimming velocity is related to maximal swim power and maximal dry-land power. Method Eighteen competitive national level swimmers (9 male and 9 female; age: 18.3 ± 2.3 years, body mass: 72 ± 8.3 kg, height: 177.2 ± 4.6 cm, mean ± SD) were recruited to this study. Subjects were assigned to either resisted sprint training (RST) or unresisted sprint training (UST). Sprint training was performed two times per week during 6 weeks as 8x15m with a 2min send-off interval. RST performed sprint training using individualized load corresponding 10% of maximum drag load (L 10), UST performed sprint training with no added resistance. A test-battery including dry-land strength assessment; maximal strength (MxS) and explosive strength (ExS), a timed 25m front-crawl swim and in-water force-velocity profiling was performed prior and following the training intervention. Maximal swim power (P max), maximum drag load (F 0), theoretical maximum velocity (v 0) and slope of force-velocity curve (S Fv) was computed though force-velocity profiling. Results No significant within group differences occurred in neither RST nor UST following the 6-week intervention period in: swimming velocity, MxS, ExS, P max , F 0 , v 0 , and S Fv. Strong correlations were found between swimming velocity and MxS (r = 0.75), ExS (r = 0.82) and P max (r = 0.92). Conclusion Resisted sprint training in swimming using L 10 did in the present study not elicit any improvements in maximal swimming velocity or examined performance characteristics. Resisted sprint training does not appear to be a superior method of improving swimming performance compared to unresisted sprint training. MxS, ExS and P max can be used as robust predictors of swim performance, however only P max was found to be casually related to swimming velocity. Acknowledgments I would like to express gratitude to my supervisor Dr. Lennart Gullstrand for guidance and feedback along the journey. Great thank you to Johan Wallberg for providing me with literature and valuable advice, also thank you to Carl Jenner for support and interesting discussions on the topic. Special thanks to Juan Alonso for supplying me with equipment and teaching me how to operate it. Thank you to Manni Svensson at 1080 Motion for showing interest in the project and to Prof. Peter Schantz for precious feedback in the finishing stages. Lastly, big thank you to all the swimmers who volunteered to participate in the project. Abbreviation Dictionary C D-hydrodynamic force coefficient ExS-dry-land explosive strength e g-gross efficiency e p-propelling efficiency F p-propulsive force F d-drag force F-v-force-velocity F 0-theoretical maximum force (maximum drag load) L 10-load corresponding 10% of maximum drag load L opt-load corresponding to maximal power output MxS-dry-land maximal strength P d-power to overcome drag (useful power) P k-power lost in giving water kinetic energy P i-power input (metabolic power) P max-maximal swim power P o-mechanical power output RST-resisted sprint training SI-stroke index SL-stroke length SR-stroke rate (stroke frequency) S Fv-slope of force-velocity curve UST-unresisted sprint training v 0-theoretical maximum velocity ∆%-delta in % (change in %)
... On the other hand, every fin can be examined for exactly the same kinematic measurement conditions. Active Drag Evaluation System used in [1] is based on Velocity Perturbation Method presented by [3] and used for active drag computation. In the case of force measurements [9], swimmers were linked via a non-elastic wire to a force sensor with a belt placed around the hip joint. ...
This article presents a methodology for an evaluation of the dynamic ability and efficiency of diving fins. There is paucity in the literature on the process of selecting optimal fins. As a result, there are efforts made to develop a methodology for selecting fins that meet the proposed criteria. Analysis exists on the two most popular types of fins within the commercial market. The experiment took place in a test water tunnel fully equipped with a measuring system and strain gauges for recording forced interaction between the moving fin and flowing water. The tested fins rested on an artificial leg, which moved respectively, thereby developing movement algorithms. This forced fluid flow was implemented by a pump that was able to control the fluids velocity, and a non-invasive method involving an ultrasonic flow meter was used to measure the fluids velocity. Finally, the fin efficiency was calculated as the ratio of multiplication of generated thrust to electrical energy consumption whilst also considering the mechanical efficiency of the leg manipulator. The results of these experiments are discussed in depth and a methodology is created for the subsequent stage in which a new type of fins called biomimetic will be analyzed and compared.
... Besides the indirect approaches, there are three methods for assessing D A that can be used for both front crawl and backstroke: the velocity perturbation and the assisted towing methods (Kolmogorov and Duplishcheva, 1992;Alcock and Mason, 2007) that only estimate D A at the maximal effort of swimmers; and the measuring residual thrust (MRT) method (Narita et al., 2017) that can be used to quantify D A in both front crawl and backstroke at controlled v. The MRT method is conducted in a flume with two wires attached to the swimmer's body, which are connected to load cells at front and back of the flume, thereby fixing the swimmer at a certain location in the flume and measuring the force needed for the wires to fix the swimmer at the specific location (residual thrust). ...
The purpose of this study was to investigate differences in Froude efficiency (η
F
) and active drag (D
A
) between front crawl and backstroke at the same speed. η
F
was investigated by the three-dimensional (3D) motion analysis using 10 male swimmers. The swimmers performed 50 m swims at four swimming speeds in each technique, and their whole body motion during one upper-limb cycle was quantified by a 3D direct linear transformation algorithm with manually digitized video footage. Stroke length (SL), stroke frequency (SF), the index of coordination (IdC), η
F
, and the underwater body volume (UWV
body
) were obtained. D
A
was assessed by the measuring residual thrust method (MRT method) using a different group of swimmers (six males) due to a sufficient experience and familiarization required for the method. A two-way repeated-measures ANOVA (trials and techniques as the factors) and a paired t-test were used for the outcomes from the 3D motion analysis and the MRT method, respectively. Swimmers had 8.3% longer SL, 5.4% lower SF, 14.3% smaller IdC, and 30.8% higher η
F
in front crawl than backstroke in the 3D motion analysis (all p < 0.01), which suggest that front crawl is more efficient than backstroke. Backstroke had 25% larger D
A
at 1.2 m⋅s-1 than front crawl (p < 0.01) in the MRT trial. A 4% difference in UWV
body
(p < 0.001) between the two techniques in the 3D motion analysis also indirectly showed that the pressure drag and friction drag were probably larger in backstroke than in front crawl. In conclusion, front crawl is more efficient and has a smaller D
A
than backstroke at the same swimming speed.
... An alternative method is a semi-tethered swimming approach, in which a swimmer is required to swim with a known external load applied by a pully system (Dominguez-Castells et al., 2013;Cuenca-Fernández et al., 2020), a floating object (Kolmogorov and Duplishcheva, 1992;Morais et al., 2020), or a resistance device (Gonjo et al., 2020). This method allows researchers to conduct a similar assessment as bespoken on-land force-velocity studies. ...
The purposes of this study were to establish test-retest reliability of load-velocity profile outcome measurements in front crawl swimming calculated from three and five different external loads, and if outcome results were comparable between calculation methods. Seven females and eight males performed 25 m semi-tethered swimming with five progressive external loads (females 1,2,3,4 and 5 kg and males 1,3,5,7 and 9 kg) and 50 m front crawl on two different days (all with maximal effort). The mean velocity during three cycles in mid-pool was calculated and plotted as a function of the load to generate the load-velocity profile, expressed as the linear regression. The axes intercepts were defined as the theoretical maximum velocity (V0) and load (L0). The coefficient of determination and the slope of the load-velocity relationship were also calculated. The intra-class correlation coefficient (ICC) showed excellent agreement (ICC ≥ 0.902) for all variables. The coefficient of variation was ≤ 3.14% and typical error was rated as “good” for all variables. A difference was found between the first and second day in V0 from both three and five loads and 50 m front crawl time (p < 0.05), but no difference was found for the other variables, suggesting a systematic change on V0 due to swimmers’ condition. Bland-Altman plots showed small biases for all variables for both the three and five calculation. In conclusion, the load-velocity profile for front crawl swimming can be calculated with high reliability from both five and three external loads to a comparable extent.
... These measure the effective power delivered by an athlete and also their energy consumption by monitoring oxygen consumption (VO 2 ). 22 Alternatively, it is possible to simultaneously measure active drag [23][24][25] and metabolic power ( _ E) using VO 2 directly during swimming. 26 This enables athletes' aerobic energy expenditure and propulsion to be measured. ...
Analysis of video and speed data is used to evaluate the efficiency of human underwater flykick. The authors show that by coupling Lighthill’s theory of fish locomotion with human musculoskeletal modelling, it is possible to evaluate the effectiveness of the mechanical and hydrodynamic propulsive components of human underwater flykick. This allows the effect of subtle variances in technique to be assessed by measurement of athlete motion alone. This is demonstrated in an experimental case study of an elite athlete performing two different techniques; one more knee-based or thunniform, and the second more undulatory or carangiform/anguilliform. In finding the mean kinematics of each technique, it is first shown that maintaining stroke-by-stroke consistency of technique leads to an increase in propulsive efficiency. It is further demonstrated that in changing technique, an athlete may swim at the same kick rate but have different propulsive efficiency. This demonstrates the need to determine the energy cost in order to evaluate differing techniques. For the sprint athlete in this case study, it was shown to be more effective to swim with a thunniform technique when at higher velocities and a more anguilliform at lower velocities.
... Due to the difficulty of quantifying the complex, unsteady flow profile around the whole body during swimming, it is currently not possible to measure both propulsive and resistive forces directly [3]. For this reason, there have been several estimative methods established that allow researchers to assess propulsive and/or resistive forces together with a swimming velocity, such as the MAD system [4], the velocity perturbation method [5], and the assisted towing method [6]. However, each method has disadvantages. ...
The purpose of this study was to establish the relationships between 50 m sprint swimming performance and variables acquired from a swimming load-velocity profile established by semi-tethered butterfly swimming. Twelve male elite swimmers participated in the present study and performed 50 m sprint and semi-tethered butterfly swimming with different loads. The mean velocity among all upper-limb cycles was obtained from the 50 m swimming (race velocity), and maximum load and velocity were predicted from the load-velocity profile established by the semi-tethered swimming test. There was a very large correlation (r=0.885, p<0.01) and a high intra-class correlation (0.844, p<0.001) between the race velocity and the predicted maximum velocity. Significant correlations were also observed between the predicted maximum load and the 50 m time as well as the race velocity (r=− 0.624 and 0.556, respectively, both p<0.05), which imply that an ability to achieve a large tethered swimming force is associated with 50 m butterfly performance. These results indicate that the load-velocity profile is a useful tool for predicting and assessing sprint butterfly swimming performance.
... High variability in the intracyclic velocity as well as changes in the swimming velocity over the entire distance are linked with an increase in the incurred energy costs. This is caused by the need to overcome the varying resistance force (Kolmogorov & Duplischeva, 1992). Reducing the intracyclic velocity variability was recognized as an indicator for the increased effectiveness of swimming (Schnitzler et al., 2008). ...
... Then numerous research ex- amining passive drag have emerged as shown in the review of Scurati et al. [26]. The mean drag experienced during swimming is still not fully understood and continue being investigated [10,18,33,24]. A simple way to reduce the drag is to swim in the wake of another swimmer [37,32,4]. ...
The skill to swim fast results from the interplay between generating high thrust while minimizing drag. In front crawl, swimmers achieve this goal by adapting their inter-arm coordination according to the race pace. A transition has been observed from a catch-up pattern of coordination (i.e. lag time between the propulsion of the two arms) to a superposition pattern of coordination as the velocity increases. Expert swimmers choose a catch-up coordination pattern at low velocities with a constant relative lag time of glide during the cycle and switch to a maximum propulsion force strategy at higher velocities. This transition is explained using a burst-and-coast model. At low velocities, the choice of coordination can be understood through two parameters: the time of propulsion and the gliding effectiveness. These parameters can characterize a swimmer and help to optimize their technique.
... Ⅰ 緒 言 競泳競技において，推進力や抵抗力は競技成績 を決定する主要な要因であることから，水泳に関 する研究では，古くから泳者の身体に働く抵抗 力や，泳者が発揮する推進力を定量化する試み がなされてきた (Formosa et al., 2011;Hollander et al., 1986;Kolmogorov and Duplishcheva, 1992;Kudo et al., 2008;Morouço et al., 2011;Narita et al., 2018aNarita et al., , 2018bSchleihauf et al., 1983; 清 水 ほ か，2000； Takagi and Wilson, 1999;Tsunokawa et al., 2015;角 川ほか，2012；Xin-Feng et al., 2007 (Hollander et al., 1986;Toussaint et al., 1988;Van der Vaart et al., 1987) (Kudo et al., 2008;Takagi and Wilson, 1999;Tsunokawa et al., 2015 Ceccon, S., Ceseracciu, E., Sawacha, Z., Gatta, G., Cortesi, ...
Through pressure measurement and underwater motion capture analysis, the aim of this study was to clarify how propulsive forces, Froude efficiency, and stroke parameters change with swimming velocity during front-crawl swimming. Eight male swimmers performed 2 trials, once using pressure measurement and underwater motion capture analysis and once using a MAD system. In the analysis using pressure measurement and underwater motion capture, each swimmer performed 16-m front-crawl swimming 10 times at various velocities. During the trials, pressure forces acting on the hand and hand kinematics were analyzed to obtain the hand propulsive forces at each velocity. In the analysis using the MAD system, each swimmer performed 25-m front-crawl swimming 10 times at various velocities while pushing the pad set under the water, and the propulsive force at each velocity was obtained from the pushing force of the pad. This revealed that the mean propulsive force increased exponentially with the increase in mean swimming velocity, and the propulsive index n was 2.62 on average for the 8 participants. Maximal propulsive forces and maximal propulsive powers at maximum were significantly correlated with the results obtained using the MAD system. Froude efficiency varied considerably among the participants, being 0.54 ± 0.05 on average for all trials.
... Fixed swimming changes the effort created in the body to attempt to overcome the fixed force [10], whereas free swimming does not allow for OPP to be determined. Additionally, applying a load to the body requires the load to remain outside of the wake, or the load generated will be reduced by water disturbance [11]. As power is dependent upon the length and volume of the muscle mass being used to generate power, the assessment of lean body mass is essential to identify real power contributions. ...
Background: Front crawl and backstroke share similar trunk rotating characteristics and tempt coaches to transfer teaching parts from one stroke to the other intuitively. However, the degree of similarity has yet to be determined. The coordination of the pelvis and the 7th cervical vertebrae (C7), during yaw and roll rotation, when sprint swimming front crawl, and backstroke was studied. Methods: Thirty-four swimmers were assessed on their performance in25m-sprint of each stroke. Using inertial sensors, each segment’s time series of angular displacement was calculated. Their amplitudes, mean autocorrelation values, max cross-correlation coefficient, phase lag, and relative power at the main frequency were analyzed. For all comparisons, the p-value was set to 0.05. Results: Pelvis yaw and roll and C7 roll amplitudes were greater at backstroke, C7 yaw was greater at front crawl. Autocorrelations ranged from 0.79 to 0.82 except for the pelvis at front crawl in yaw which was 0.72±0.16. Relative power at the main frequency ranged from 47% to 52% except for the yaw pelvis’ at the front crawl which was lower (32.81±14.09%). Backstroke had larger mean values in all cases and roll had larger mean values than yaw. Cross-correlation between the two segments yielded higher values at roll. At roll direction, the leading segment in the front crawl was the pelvis while in backstroke, it was the C7 which was true in all cases. In all cases, the coupling was slightly deviating from in-phase mode except from backstroke yaw which yield phase lag values of -13.35±1.14% of stroke cycle time. Conclusions: Although both strokes share similar characteristics their intersegmental coupling differs. The findings of the study imply that proper focus should be given to enhance only a positive transfer of learning between the two strokes.
Aim
The purposes of this study were to investigate if the load-velocity (L-V) profile parameters-force capacity and efficiency-differ between swimmers of different performance level, and to investigate if efficiency is the key performance indicator between international elite and national elite level swimmers.
Method
Fifty-four swimmers (27 female and 27 male) of either regional level, national elite or international elite level, participated in this study. The swimmers performed three 25 m semi-tethered maximum effort swims with ascending loads (1 kg, 5% and 10% of body mass). Mean velocity during three stroke cycles mid-effort was calculated and plotted as a function of the external added load. A linear regression was established, expressing the relationship between load and velocity, with the intercepts between the axes and the regression line being defined as the theoretical maximum velocity (V0) and load (force capacity, L0). The slope of the regression line (slopeLV) serves as an index of efficiency.
Results
A statistically significant difference was found between the three performance levels for all L-V profile variables for front crawl: V0 (F [2, 51] = 7.76, p<0.001), L0 (F [2, 51] = 5.18, p=0.009), and slopeLV (F [2, 51] = 3.36, p=0.043). A paired t-test revealed no difference in slopeLV between matched international elite and national elite level swimmers (t [9] = 1.42, p=0.188), but a near significant difference in L0 (t [9] = 2.11, p=0.064). Both slopeLV and L0 for front crawl had a strong correlation with personal best in 100 m front crawl (PB100).
Conclusion
Efficiency was not found to be the key performance indicator between matched international elite and national elite swimmers in this study, and neither was force capacity. Nevertheless, a significant difference in all front crawl L-V profile parameters was found between performance level groups, but post hoc analyses indicated no difference between adjacent performance levels neither in L0 nor slopeLV. There was however a strong correlation between both slopeLV, and L0, to the swimmers' PB100. All these findings imply that efficiency and force capacity seem to be of equal importance for high performance, but swimmers use different strategies to reach the high swim velocity. Abbreviation dictionary BM-body mass (kg) CD-coefficient of drag FP-propulsive force FD-drag force (in this study used as a representation of all resistive forces) L0-theoretical maximum load LCM-long course meter, i.e. a 50 m pool L-V-load-velocity rL0-theoretical maximum load relative to body mass rslopeLV-slope of regression line of velocity measurements and load relative to body mass SCM-short course meters, i.e. a 25 m pool SL-stroke length slopeLV-slope of the regression line of the load-velocity measurements SR-stroke rate V0-theoretical maximum velocity vmax-maximum velocity VPM-velocity perturbation method WA-World Aquatics, formerly known as FINA (Fédération Internationale de Natation)
The swimming pool experience is a fertile ground to challenge current knowledge and catalyse research into factors governing swimming performance that may inform individualised swimming training. This paper discusses the perspective and contributions of a swimming scientist, analyst, and coach on the main current trends of scientific and technological developments, allowing a deeper knowledge about determining factors of swimming performance, its evaluation difficulties, and utility for coaching daily tasks. After equating the complexity of an integrative approach to 'swimming performance', five main topics were selected: (i) the swimming economy and energy profile characteristics of each swimmer and swimming technique; (ii) the associated intra-cycle velocity variation profile; (iii) the propulsive force generation capacity; (iv) the drag force imposed on the swimmer; and (v) the internal load characterisation, opening perspectives for understanding the muscle activity pattern. It was concluded that, all together, scientific developments in these domains have allowed for an almost complete picture of the complex network of factors that explain swimming performance (velocity to cover a given distance, which can be further decomposed into a specific combination of stroke length and frequency), favouring the objectivity of diagnosing strengths and weaknesses of an individual profile.
A novel method aimed at evaluating the active drag profile during front-crawl swimming is proposed. Fourteen full trials were conducted with each trial using a stationary load cell set-up and a commercial resistance trainer to record the tension force in a rope, caused by an athlete swimming. Seven different stroke cycles in each experiment were identified for resampling time dependent data into position dependent data. Active drag was then calculated by subtracting resistance trainer force data away from the stationary load cell force data. Mean active drag values across the stroke cycle were calculated for comparison with existing methods, with mean active drag values calculated between 76 and 140 N depending on the trial. Comparing results with established active drag methods, such as the Velocity Perturbation Method (VPM), shows agreement in the magnitude of the mean active drag forces. Repeatability was investigated using one athlete, repeating the load cell set-up experiment, indicating results collected could range by 88 N depending on stroke cycle position. Variation in results is likely due to inconsistencies in swimmer technique and power output, although further investigation is required. The method outlined is proposed as a representation of the active drag profile over a full stroke cycle.
Purpose:
The aim of this study was to assess the interaction of kinematic, kinetic, and energetic variables as speed predictors in adolescent swimmers in the front-crawl stroke.
Design:
Ten boys (mean age [SD] = 16.4 [0.7] y) and 13 girls (mean age [SD] = 14.9 [0.9] y) were assessed.
Methods:
The swimming performance indicator was a 25-m sprint. A set of kinematic, kinetic (hydrodynamic and propulsion), and energetic variables was established as a key predictor of swimming performance. Multilevel software was used to model the maximum swimming speed.
Results:
The final model identified time (estimate = -0.008, P = .044), stroke frequency (estimate = 0.718, P < .001), active drag coefficient (estimate = -0.330, P = .004), lactate concentration (estimate = 0.019, P < .001), and critical speed (estimate = -0.150, P = .035) as significant predictors. Therefore, the interaction of kinematic, hydrodynamic, and energetic variables seems to be the main predictor of speed in adolescent swimmers.
Conclusions:
Coaches and practitioners should be aware that improvements in isolated variables may not translate into faster swimming speed. A multilevel evaluation may be required for a more effective assessment of the prediction of swimming speed based on several key variables rather than a single analysis.
The purpose of the presents study was to investigate the reliability of the active drag (Da) assessment using the velocity perturbation method (VPM) with different external resisted forces. Eight male and eight female swimmers performed 25 m sprints with five isotonic loads (1–2–3–4–5 kg for females; 1–3–5–7–9 kg for males), which were repeated twice on different days. The mean velocity and semi-tethered force were computed for each condition, and the free-swimming maximum velocity was estimated with load-velocity profiling. From the obtained variables, Da at the maximum free-swimming condition was calculated using VPM. Absolute and typical errors and the intra-class correlation (ICC) were calculated to assess test–retest reliability. 95% confidence interval (95% CI) lower bound of ICC was larger than 0.75 in 3, 4 (females only) and 5 kg trials in both sexes (corresponding to 37–60 N additional resistance; all p < 0.001), which also showed small absolute and relative typical errors (≤ 2.7 N and ≤ 4.4%). In both sexes, 1 kg load trial (16–17 N additional resistance) showed the lowest reliability (95% CI of ICC; − 0.25–0.83 in males and 0.07–0.94 in females). These results suggested that a tethered force of 37–60 N should be used to assess Da using VPM.
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.
Despite the increasing importance of the underwater segment of start and turns in competition and its positive influence on the subsequent surface swimming, there is no evidence on how the transition from underwater to surface swimming should be performed. Therefore, the aim of the present study was to examine the role of segmental, kinematic and coordinative parameters on the swimming velocity during the pre-transition and transition phases. A total of 30 national male swimmers performed 4 × 25 m (one each stroke) from a push start at maximum velocity while recorded from a lateral view by two sequential cameras (50 Hz), and their kinematic and coordinative swimming parameters were calculated by means of two-dimensional direct linear transformation (DLT) algorithms. Unlike pre-transition, backward regression analysis of transition significantly predicted swimming velocity in all strokes except breaststroke (R2 ranging from 0.263 in front crawl to 0.364 in butterfly). The inter-limb coordination was a predictor in butterfly stroke (p = 0.006), whereas the body depth and inclination were predictors in the alternate strokes (front crawl (p = 0.05) and backstroke (p = 0.04)). These results suggest that the body position and coordinative swimming parameters (apart from kicking or stroking rate and length) have an important influence on the transition performance, which depends on the swimming strokes.
Purpose:
The aims of this study were to 1) compare active drag (Da) calculation between a single land-based measurement of frontal surface area (FSA) and in-water FSA measures obtained at key events of the arm pull (1, right upper-limb catch; 2, right upper-limb insweep; 3, right upper-limb exit and left upper-limb catch; 4, left upper-limb insweep; and 5, left upper-limb exit and right upper-limb catch) at front crawl swimming, and 2) compare mechanical power variables computed based on these two approaches.
Methods:
Seventeen swimmers (11, male; 6, female; 16.15 ± 0.94 yr old) were recruited. The FSA was measured based on two approaches: (i) nonvariation, that is, assuming a constant value, and (ii) variation, that is, calculated in each key event of the front crawl swim. Active drag based on a nonvariation of the FSA was measured using the Velocity Perturbation method. Active drag based on a variation approach was measured in each key event of the front crawl according to the law of linear motion. Paired t-test (P ≤ 0.05), simple linear regression models, and Bland-Altman plots between assessment methods (variation vs nonvariation) were computed.
Results:
The FSA (variation) was higher than when assuming a nonvariation (0.1110 ± 0.010 vs 0.0968 ± 0.010 m, Δ = 15.69%, t = 4.40, P < 0.001, d = 0.95). Active drag (variation) was also significantly higher than when assuming a nonvariation (88.44 ± 25.92 vs 75.41 ± 15.11 N, Δ = 16.09%, t = 3.66, P = 0.002, d = 0.61).
Conclusions:
Besides the FSA, swim velocity also changes during the front crawl arm pull. The variation of both variables had a significant effect on the active drag measurement and consequently on mechanical power and total power input variables.
Propulsive arm forces of 32 male and 9 female swimmers were measured during front crawl swimming using arms only, in a velocity range between 1.0 m s-1 and 1.8 m s-1. At constant velocity, the measured mean propulsive force Fp equals the mean active drag force (Fd). It was found that Fd is related to the swimming velocity v raised to the power 2.12 +/- 0.20 (males) or 2.28 +/- 0.35 (females). Although many subjects showed rather constant values of Fd/v2, 12 subjects gave significantly (p less than 0.01) stronger or weaker quadratic relationships. Differences in drag force and coefficient of drag between males and females (drag: 28.9 +/- 5.1 N, 20.4 +/- 1.9 N, drag coefficient: 0.64 +/- 0.09, 0.54 +/- 0.07 respectively) are especially apparent at the lowest swimming velocity (1 m s-1), which become less at higher swimming velocities. Possible explanations for the deviation of the power of the velocity from the ideal quadratic dependency are discussed.
Propulsive arm forces of twelve elite male swimmers during a front crawl swimming-like activity were measured. The swimmers pushed off against grips which are attached to a 23 m tube at 0.8 m under the water surface. The tube was fixed to a force transducer. Since at constant speed, mean propulsive force equals mean drag force this method also provides the mean active drag on a moving swimmer. The mean propulsive force at a speed of v = 1.48 m s-1 appeared to be 53.2 +/- 5.8 N which is two to three times smaller than what is reported by other authors for active drag but which is in agreement with values reported for passive drag on a (towed) swimmer who is not moving. Discrepancies with indirect active drag measurements are discussed.
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The peculiarities of teaching children to swim in the period of adaptation to the water medium. Thesis, Moscow Regional Pedagogical Institute
- S V Kolmogorov
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The influence of anthropometric data on the swimmers hydrodynamics. J. Theory Practice Phys Usage of modelling for water resistance to swimmers body movement research. 1. Theory Practice Phys. Cult
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Dependence of the resistance coefficient on the stream velocity, age and anthropometric indices
- Gordon
The influence of anthropometric data on the swimmers hydrodynamics
- Onoprienko
The peculiarities of teaching children to swim in the period of adaptation to the water medium
- Kolmogorov
Usage of modelling for water resistance to swimmers body movement research
- Onoprienko