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Propulsive force is a determinant of swimming performance. Several methods have been proposed to estimate the propulsive force in human swimming; however, their practical use in coaching is limited. Herein, we propose a novel method for estimating the propulsive force generated by swimmers’ hands using an inertial measurement unit (IMU) and pressure sensors. In Experiment 1, we use a hand model to examine the effect of a hand-mounted IMU on pressure around the hand model at several flow velocities and water flow directions. In Experiment 2, we compare the propulsive force estimated using the IMU and pressure sensors (FIMU) via an underwater motion-capture system and pressure sensors (FMocap). Five swimmers had markers, pressure sensors, and IMUs attached to their hands and performed front crawl swimming for 25 m twice at each of nine different swimming speeds. The results show that the hand-mounted IMU affects the resultant force; however, the effect of the hand-mounted IMU varies with the flow direction. The mean values of FMocap and FIMU are similar (19.59 ± 7.66 N and 19.36 ± 7.86 N, respectively; intraclass correlation coefficient(2,1) = 0.966), and their waveforms are similar (coefficient of multiple correlation = 0.99). These results indicate that the IMU can estimate the same level of propulsive force as an underwater motion-capture system.

Swimming intra-cycle velocity fluctuation has often been assessed using the coefficient of variation, which requires a mathematical assumption of a positive linear relationship between the velocity mean and standard deviation. As this assumption has never been tested, the current study aimed to investigate the within-participant relationship between the mean and standard deviation of the intra-cycle velocity. Cross-sectional study. The intra-trial mean and standard deviation of one stroke cycle centre of mass velocity (vCMmean and vCMSD, respectively) were obtained from 80 front crawl trials (10 participants × eight swimming speeds) using whole-body three-dimensional motion analysis. The linear mixed-effect model and intra-class correlation analysis were used to test the linear relationship between vCMmean and vCMSD (n = 80) and the absolute agreement between vCMmean and vCMSD relative to those during the fastest trial (n = 70). Neither the linear regression model (95 % confidence interval range of the fixed effect of vCMmean: −0.003–0.031) nor the intra-class correlation coefficient (ICC = 0.07; p = 0.26) verified linear relationships between vCMmean and vCMSD, which violated the background assumption of coefficient of variation calculation. When investigating the intra-cycle velocity fluctuation, the coefficient of variation should not be used alone. Researchers and practitioners should always interpret/report the obtained results together with the mean and standard deviation to avoid misleading conclusions and feedback because the coefficient of variation obtained from one cycle velocity data is likely biased by mean velocity.

The purpose of this systematic review was to investigate the relationship between anthropometric characteristics, biomechanical variables and performance in the conventional swimming techniques in young and adolescent swimmers. A database search from 1 January 2001 to 30 June 2021 was done according to the PRISMA statement, with 43 studies being selected for analysis. Those manuscripts were divided in butterfly, backstroke, breaststroke and front crawl techniques as main categories. The results showed the importance of the anthropometric variables for the performance of the young swimmer, although there was a lack of variables common to the studies that analysed the butterfly, backstroke and breaststroke techniques. For the front crawl technique there is a consensus among studies on the advantage of having higher height and arm span values, variables that concurrently with high body mass and lean body mass values, contribute positively to better stroke length and stoke index values.

The projected frontal area (PFA) is a useful indicator of swimming drag. However, it is inherently limited because it only considers observable frontal areas from a frontal view. To address this limitation, we determined a new indicator, the projected and occluded frontal area (POFA), which includes occluded frontal areas relative to the swimming direction. This study aimed to examine the difference between the PFA and POFA, focusing on the tibial and femoral segments during front crawl. Twelve competitive male swimmers performed a 15-meter front crawl at 1.20 m·s−1. The three-dimensional positions of the reflective markers attached to the swimmers’ bodies were collected using an underwater motion-capture system. The body shape of each swimmer was obtained using a body scanner. Two types of digital human models were created: a whole-body model with vertex colors divided into eight body segments and a segment-specific model extracted from the whole-body model. To reconstruct identical motions in both models, the joint angle data obtained through inverse kinematics computations using motion-capture data and the whole-body model were applied to the segment-specific models. The PFA and POFA were determined through image processing of a series of parallel frontal images from whole-body and segment-specific models, respectively. The PFA of the tibial and femoral segments was substantially smaller than the corresponding POFA (p < 0.001), with underestimation ratios of 86.1 % and 42.3 %, respectively. These results suggest that PFA is not a fully reliable indicator for evaluating swimming drag, at least in the tibial and femoral segments.

Velocity variation in swimming is commonly assessed using quantification methods that fail to consider the movement mechanical nature in the aquatic environment. The current study proposes the relative power to velocity variation method to evaluate the velocity variation effect in efficiency, accounting the swimmers hydrodynamic drag into the calculation. Twenty regional level swimmers (12 males) performed three 25 m front crawl trials (one at 88 and other at 100 % of their maximum velocity, and another at maximum pace using the velocity perturbation method). Mean, maximum and minimum velocities, stroke rate, length and index, and indexes of coordination and synchronization were obtained for each cycle. The power to overcome drag, power to velocity variation, total mechanical power, intracycle velocity variation (assessed by the coefficient of variation) and difference between maximum and minimum absolute velocities were also computed. Power regressions were performed between mean velocity, standard deviation and the absolute and relative power to velocity variation. Results showed that the absolute and relative power to velocity variation values increased (a = 2.82, b = 1) and decreased (a = 0.07, b = −2.00, respectively) with the mean velocity increment, while both rose with mean velocity standard deviation (a = 142.31, b = 2.00 and a = 1.14, b = 2.00, respectively). The relative power to velocity variation method offers an advanced understanding of front crawl efficiency and enables predicting its effect on overall swimming performance.

Human movements, such as walking and running, are able to generate rhythmic motor patterns, with the consequent appearance of hidden time-harmonic structures. Such harmonic structures are represented (at comfortable speed) by the occurrence of the golden ratio as ratio of durations of specific walking and running gait sub-phases. Preliminary experimental evidences suggest that front crawl swimming may behave, under this point of view, like walking and running. This paper aims to demonstrate that a mathematical connection between the golden ratio and the front crawl swimming stroke actually exists, at a pace that plays the role of the comfortable speed in walking and running. Generalized Fibonacci sequences are used to this purpose. They rely on the durations of aggregate phases of the front crawl swimming stroke with a clear physical meaning, while characterizing self-similarity of front crawl strokes in its simple nature and enhanced (stronger) variant. Experimental data on front crawl swimmers illustrate the theoretical derivations, suggesting that the pace playing the role of the comfortable speed in walking and running is the middle/long-distance one, while showing that the self-similarity level increases with the swimming technique and the enhanced self-similarity is associated with the performance of top-level swimmers.

An animal's trajectory is a fundamental object of interest in movement ecology, as it directly informs a range of topics from resource selection to energy expenditure and behavioral states. Optimally inferring the mostly unobserved movement path and its dynamics from a limited sample of telemetry observations is a key unsolved problem, however. The field of geostatistics has focused significant attention on a mathematically analogous problem that has a statistically optimal solution coined after its inventor, Krige. Kriging revolutionized geostatistics and is now the gold standard for interpolating between a limited number of autocorrelated spatial point observations. Here we translate Kriging for use with animal movement data. Our Kriging formalism encompasses previous methods to estimate animal's trajectories—the Brownian bridge and continuous‐time correlated random walk library—as special cases, informs users as to when these previous methods are appropriate, and provides a more general method when they are not. We demonstrate the capabilities of Kriging on a case study with Mongolian gazelles where, compared to the Brownian bridge, Kriging with a more optimal model was 10% more precise in interpolating locations and 500% more precise in estimating occurrence areas.

The purpose of this study was to determine the effect of breathing laterality on hip roll kinematics in submaximal front crawl swimming. Eighteen elite competitive swimmers performed three 100 m front crawl trials at a consistent sub-maximal speed (70% of seasonal best time) in a 25 m pool. Each trial was performed with one of three different breathing conditions: (1) unilateral breathing (preferred side), (2) bilateral breathing (alternating left/right-side every 3 strokes) and (3) simulated non-breathing using a swim snorkel. A waist-mounted triaxial accelerometer was used to determine continuous hip roll angle throughout the trial, from which peak hip roll angles (Ө) and average angular velocities (ω) were calculated. Two-way repeated measures ANOVAs were used to identify significant main effects for laterality (preferred vs. non-preferred breathing sides) and condition (unilateral, bilateral and snorkel breathing) for both Ө and ω. Peak hip roll to the preferred side was significantly greater (p < 0.001) in the unilateral condition, while ω to the non-preferred side was significantly greater in the unilateral (p < 0.01) and bilateral (p < 0.04) conditions. Significant same-side differences were also found between the different breathing conditions. The results demonstrate that breathing laterality affects hip roll kinematics at submaximal speeds, and that unilateral and snorkel breathing are associated with the least and most symmetric hip roll kinematics, respectively. The findings show that a snorkel effectively balances and controls bilateral hip rotation at submaximal speeds that are consistent with training, which may help to minimize and/or correct roll asymmetries that are the result of unilateral breathing.

The purpose of this study was to identify intra- and inter-individual variabilities during short course 50 m sprints. Swimming velocity ( SV ), stroke frequency ( SF ), and stroke length ( SL ) for each stroke cycle in 189 male and 160 female swimmers’ 50 m time trials (with their specialised stroke) were analysed. The inter-individual variability for each kinematic variable was analysed using the inter-individual standard deviation of the Gaussian Process regression. Intra-participant variability was analysed using k-means clustering with kinematic data extracted from the first, mid-, and last strokes. In all strokes and both sexes, swimmers showed large inter-individual kinematic variabilities at the first and last strokes, which justified the need to separate these strokes from the clean-swimming segment in race analyses. Intra-individual kinematic patterns were categorised into four clusters with different within-lap SV patterns. Particularly, many front crawl and backstroke swimmers showed a faster velocity in mid-pool than in the transition, while many butterfly swimmers showed the fastest SV in the transition. This might suggest a greater difficulty in the transition technique in alternating strokes than in butterfly. Race analyses should focus on not only the overall trend but also individual variabilities to investigate the swimmers’ behaviour during swimming races.

Command-like descending neurons can induce many behaviors, such as backward locomotion, escape, feeding, courtship, egg-laying, or grooming (we define ‘command-like neuron’ as a neuron whose activation elicits or ‘commands’ a specific behavior). In most animals, it remains unknown how neural circuits switch between antagonistic behaviors: via top-down activation/inhibition of antagonistic circuits or via reciprocal inhibition between antagonistic circuits. Here, we use genetic screens, intersectional genetics, circuit reconstruction by electron microscopy, and functional optogenetics to identify a bilateral pair of Drosophila larval ‘mooncrawler descending neurons’ (MDNs) with command-like ability to coordinately induce backward locomotion and block forward locomotion; the former by stimulating a backward-active premotor neuron, and the latter by disynaptic inhibition of a forward-specific premotor neuron. In contrast, direct monosynaptic reciprocal inhibition between forward and backward circuits was not observed. Thus, MDNs coordinate a transition between antagonistic larval locomotor behaviors. Interestingly, larval MDNs persist into adulthood, where they can trigger backward walking. Thus, MDNs induce backward locomotion in both limbless and limbed animals. When we choose to make one kind of movement, it often prevents us making another. We cannot move forward and backward at the same time, for example, and a horse cannot simultaneously gallop and walk. These ‘antagonistic’ behaviors often use the same group of muscles, but the muscles contract in a different order. This requires exquisite control over muscle contractions. Neurons located in the central nervous system form circuits to produce distinct patterns of muscle contractions and to switch between these patterns. Smooth, rapid switching between behaviors is important for animal escape and survival, as well as for performing fine movements. However, we know little about how the activity of the neuronal circuits enables this. Carreira-Rosario, Zarin, Clark et al. set out to identify the underlying neuronal circuitry that allows larval fruit flies to transition between crawling forward and backward. Results from a combination of genetics and microscopy techniques revealed that a neuron called the Mooncrawler Descending Neuron (MDN) induces a switch from forward to backward travel. MDN activates a neuron that stops the larvae crawling forward, and at the same time activates a different neuron that is only active when the larvae crawl backward. Carreira-Rosario et al. also found that MDN triggers backward crawling in the six-limbed adult fly. Understanding how a single neuron – in this case MDN – can trigger a smooth switch between opposing behaviors could be beneficial for the medical and robotics fields. In the medical field, understanding how movement is generated could help to improve therapies that fix damage to the relevant neuronal circuits. Understanding how behavioral transitions occur may also help to design autonomous robots that can navigate complex terrain.