Swimming Posture Research Articles

Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings

AbstractVarious iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso‐ or macrozooplankton taxa (< 50 ind. m−3), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for > 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min−1 for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.

Retraction Note: Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis

Retraction Note: Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis

Muscular expression of pezo-1 differentially contributes to swimming and crawling production in the nematode C. elegans.

Mechanosensitive PIEZO ion channels are evolutionarily conserved proteins that are widely expressed in neuronal and muscular tissues. This study explores the role of the mechanoreceptor PEZO-1 in the body wall muscles of Caenorhabditis elegans, focusing on its influence on two locomotor behaviors, swimming and crawling. Using confocal imaging, we reveal that PEZO-1 localizes to the sarcolemma and plays a crucial role in modulating calcium dynamics that are important for muscle contraction. When we knocked down pezo-1 expression in striated muscles with RNA interference, calcium levels in head and tail muscles increased. While heightened, the overall trajectory of the calcium signal during the crawl cycle remained the same. While downregulation of pezo-1 led to an increase in crawling speed, it caused a reduction in swimming speed. Reduction in pezo-1 expression also resulted in the increased activation of the ventral tail muscles, and a disruption of dorsoventral movement asymmetry, a critical feature that enables propulsion in water. These alterations were correlated with impaired swimming posture and path curvature, suggesting that PEZO-1 has different functions during swimming and crawling.

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming

<p>Swimming is a sport that relies heavily on motor skills. Inability to maintain adequate bodily balance in water prevents swimmers from remaining afloat and propelling themselves. Due to the difficulty of attaching reflective stickers or LED (light-emitting diode) emitters to the body while adjusting the swimming posture, it is not possible to capture the posture like during a bicycle fitting; the refraction of water also affects the detection of the body’s posture. Addressing the shortcomings, this study developed a low-cost nonhardware posture detection system based on machine-learning models in MediaPipe. The system provides real-time and post analyses of posture angles and posture lines during front crawl swimming, thereby facilitating observation of the relationship between angle at which the arm enters the water and the body horizon. Two participants practicing front crawl were invited to test the proposed system. The experimental results confirmed that the proposed system provides effective detection and analyses of posture lines and angles in swimmers. The study also proposed the algorithm for optimizing posture angle detection to solve the problems of posture line distortion and angle calculation errors that arise when MediaPipe was used to detect a human skeleton above a water line. The system does not require the installation of hardware and is inexpensive to deploy, and it can be widely applied in front crawl swimming lessons to help learners adjust their arm’s entry angle and body horizon to reduce forward drag and increase speed.</p> <p> </p>

RETRACTED ARTICLE: Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis

RETRACTED ARTICLE: Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis

Diversified Teaching Strategies for College Swimming Courses in the Context of Artificial Intelligence

Abstract Swimming is a sport with a very significant exercise effect, so swimming courses in colleges and universities can promote the overall development of students’ physical fitness. In this paper, students’ swimming data are collected using intelligent sensors, and the collected data are noise-reduced and normalized by a low-pass filter. After the completion of data preprocessing, the swimming posture data features are extracted. The features are dimensionality reduced by the PCA method, combined with the BP neural network for training and accurate swimming posture recognition, and on this basis, a diversified teaching system for swimming courses in smart colleges and universities is constructed. After the students’ swimming data set was collected, the recognition effect of each swimming stroke was analyzed, and a comparison experiment was set up to investigate the practical effect and strategy of this teaching mode. The results show that the average accuracy of recognizing each stroke in this study can reach more than 0.988, and in the teaching test, the average difference between the performance of the experimental group and the control group is 7.6, and the P-value of the technical evaluation performance of the two groups of students in this project is 0.002<0.05, which significantly improves the swimming performance. This study can be combined with artificial intelligence technology to diversify the teaching of swimming courses in colleges and universities.

Boldness behavior and ecological adaptability of stone moroko (Pseudorasbora parva) in open-field testing

Abstract Boldness is closely related to fish species’ adaptative strategies and invasiveness and has become the focus of fish personality research. However, due to the lack of studies on behavioral stages, the ecological functions of certain behaviors remain unclear. To better understand the link between boldness and invasiveness in stone moroko (Pseudorasbora parva), an invasive Asian fish species, we exposed the fish to an open-field environment for 60 min and assessed their behavioral patterns, profiles, and stages. The results show that the boldness behavior of stone moroko was characterized by high activity, low average velocity, and diverse swimming postures. Five behavioral stages with significant differences were revealed: outbreak, inhibition, slow swimming, exploration, and adaptation. These observations suggest that stone moroko can quickly habituate to new environments. In addition, stone moroko spent more energy on exploring the living area and less on environmental risk assessment, as revealed by an analysis of their energy trade-off strategy. The behavioral strategies of stone moroko may explain their extremely high environmental adaptability and ability to invade multiple environments.

High accuracy intelligent real-time framework for detecting infant drowning based on deep learning

Timely detection of swimming accidents plays a critical role in keeping the safety of infants in pools. However, the targets in the infant drowning detection are generally small, densely-distributed and often immersed in complicated environment with lighting various and toy disturbances, and thus there are currently few efficient, accurate and comprehensive solutions for such a task. Aiming at this problem, a novel intelligent real-time framework for detecting infant drowning is proposed. Processing of the framework includes two stages: in stage I, an attention mechanism-integrated YOLOv5-alike model is designed to extract infant portrait foreground, which can reduce the interferences caused by background noise and improve the accuracy of detecting tiny objects. On this basis, in stage II, Single Shot Multi-box Detector (SSD) is utilized to filter out the falsely-detected targets in stage I and recognize swimming posture accurately. Live videos are collected from ten infant swimming pools to generate training dataset of totally 7,296 swimming images and 7,723 portrait images of infants for stage I and II respectively. And a set of 1,222 infant swimming images from another two pools are annotated to test the trained framework. Experimental results show that the Mean Average Precision (mAP) of the framework is 97.17%, and the processing speed can reach 43 frames per second, which outperforms any previous related network and has significant application value in practical infant drowning detection. Additionally, a web-based platform is developed to put the framework into practice, whose preliminary test results demonstrate its strong application potential.

Kinematic and hydrodynamic mechanisms of Misgurnus anguillicaudatus during routine turning

Although traditional underwater thrusters are technologically advanced and widely used, they have limitations in propulsion efficiency, flexibility, and noise. Studying the swimming mechanisms of aquatic organisms can provide new insight into submarine propulsion. The kinematics and hydrodynamic mechanisms of Misgurnus anguillicaudatus in the turning process were explored experimentally through particle image velocimetry. Morphological characteristics of Misgurnus anguillicaudatus locomotion were analyzed using the swimming posture and extracted a body trunk curve. The kinematic characteristics of Misgurnus anguillicaudatus during turning maneuvers were further explored through quantified kinematic parameters. The hydrodynamic mechanism of the turning process was analyzed from the perspective of transient kinetic energy, vortex evolution, and pressure characteristics. The body trunk of Misgurnus anguillicaudatus maintained a fluctuating pattern from the beginning of the movement. Relying on periodic body undulations and periodic tail wagging enables the fish to maintain a continuous maneuvering state. The tail wagging in different directions generates a pair of positive and negative vortices and local high-kinetic-energy regions. The combination of pressure and viscous mechanisms creates vorticity. Jets are generated at the interface between converging vortices and opposite spins. The thrust jets provide thrust, and the side jets provide angular momentum to the fish body and the surrounding additional mass. The pull of the negative pressure area on the body along the trough is the main thrust mechanism that enables Misgurnus anguillicaudatus to swim.

Modeling of swimming posture dynamics for a beaver-like robot

A bionic underwater robot swims to generate its propulsion in water, which in turn directly determines its movement stability. Exploring the relationship between the swimming force and the posture is important. This study aims to analyze and model the swimming posture dynamics of a beaver-like robot. The posture dynamics is decomposed into three parts: leg dynamics, body hydrodynamics, and body posture dynamics. First, the leg dynamic model of the beaver-like robot is established using the rigid-fluid integration method. Then, the overall fluid dynamics of the robot is modeled via numerical calculation methods to obtain the forces of the water on the robot body during swimming. Lastly, the swimming posture dynamic model of the robot is constructed to describe the relationship between the leg movement and body posture. The swimming process of the beaver-like robot with bionic alternating and synchronous trajectory is simulated with ADAMS 2019. The proposed modeling method and the swimming posture dynamic model are verified by comparing the simulation and theoretical calculation results of robot posture, which could be used for the swimming posture control of a bionic underwater robot.

OTIMIZAÇÃO DA POSTURA NA NATAÇÃO BASEADA NA MECÂNICA ESPORTIVA

ABSTRACT Introduction Speed and posture are two determining factors for the success of swimmers. Posture optimization can make athletes’ actions more standardized and improve their speed at a competitive level. Objective Explore the optimization of swimming posture based on sports mechanics. Methods In the controlled experiment, the control group was given routine training, while the experimental group was given sport mechanics-based swimming posture optimization training. A duration of 60 minutes daily, three times a week. The experiment was completed in 6 weeks. Data by computerized cinematography were extracted using the reflective marking ball technique. Its mechanical motion and time consumption characteristics were collected before and after the experiment, cataloged, compared, and discussed statistically. Results Daily and posture optimization training can improve athletes’ body extension range. The effect of the experimental group in improving the range of body extension was shown to be greater than the standard training and slightly greater than the daily training by shortening the action time. Conclusion The posture optimization scheme for swimming based on sports mechanics can improve athletes’ extension range and speed. Physical education teachers and coaches should further optimize and promote the swimming posture optimization scheme to improve the performance of their athletes. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Polyionic liquids supramolecular hydrogel with anti-swelling properties for underwater sensing

The preparation of hydrogel-based wearable sensors for underwater application with high mechanical properties and electrical conductivity is an urgent challenge. Here, a supramolecular hydrogel based on polyionic liquids was designed and prepared for underwater sensing. The introduction of functional ionic liquid structures effectively increased the supramolecular interaction in the hydrogel network, which made the hydrogel successfully resist the interference of external water molecules. Depending on the effect of charge and hydrophobic interactions, this supramolecular hydrogel sensor exhibited high tensile (759 %), high tensile strength (0.23 MPa), high sensitivity (GF = 10.76) and extensive antibacterial properties, even in seawater environment. The obtained hydrogel sensor successfully monitored the swimming posture, which was helpful to digitally reflect the limb movement of athletes during underwater sports. This work made progress in the field of underwater wearable sensors based on hydrogels, and this design of multifunctional hydrogel provided a new idea for the development of functional sensors.

De novo non-synonymous CTR9 variants are associated with motor delay and macrocephaly: human genetic and zebrafish experimental evidence.

CTR9 is one of five genes that form the PAF1 complex, which binds to RNA polymerase II and plays critical roles in transcriptional elongation and transcription-coupled histone modifications including histones H3K4me3 and H3K36me3. In this study, de novo CTR9 non-synonymous variants (p.(Glu15Asp) and p.(Pro25Arg)) were detected in two unrelated patients with macrocephaly, motor delay, and intellectual disability. A pull-down assay showed that the mutant CTR9 proteins had stronger affinities to the PAF1 protein than the wild-type protein. Functional analyses using zebrafish showed that the knockout of the ctr9 gene caused motor defects and enlargement of the telencephalon, which is homologous to the mammalian cerebrum. The rescue experiment, in which the human CTR9 mutants were introduced into ctr9-knockout zebrafish, failed to maintain the swimming posture of the ctr9-knockout fish, suggesting that the human CTR9 mutant proteins do not function normally in vivo. In addition, the overexpression of human CTR9 mutant mRNA caused telencephalon enlargement in zebrafish larvae, suggesting that the human CTR9 mutant proteins interfered with normal endogenous CTR9 function. We concluded that the two missense variants in CTR9 (p.(Glu15Asp) and p.(Pro25Arg)) cause a new syndrome involving macrocephaly, motor delay and intellectual disability through the loss of the normal function of CTR9 and the inhibition of the normal intrinsic CTR9 function of the contralateral allele.

Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model.

Swimming monitoring based on acceleration sensor is an emerging research direction in the field of human motion recognition. As a public sport, swimming has a wide audience. The swimming monitoring system can facilitate people to monitor and record their own swimming data, so as to formulate a reasonable training plan. Aiming at the defects of single modal information representation ability, high contingency, and easy to be influenced by the outside world, this paper adopts the underwater posture training model of swimmers to perform multimodal information fusion. In this paper, a multimodal information fusion method based on evolutionary neural network is proposed, and an intelligent perception information processing model of the intelligent subject system is constructed. Aiming at the defect that the accuracy and speed of the underwater posture monitoring of swimmers cannot be guaranteed in a complex environment, an evolutionary neural network optimized by a multimodal adaptive genetic algorithm is constructed to perform multimodal information fusion to ensure the effectiveness of the system in the face of complex information. Regarding attitude detection, it mainly uses the three dimensions of the angle of movement, the influence of gravity, and the strength and speed of the movement to measure. The MPU6050 module processor has a wide range of applications and is a mold processing tool with high performance and level. It completes the data processing, data calculation, and data storage of the inspection system in this paper. This paper further studies the working principle, structure, and operation process of this module and adjusts the time error in the detection of carrier motion and attitude so that the processing function of this module can play an optimal state. Four kinds of swimming posture measurement experiments were carried out on the swimmers, and the experimental data were analyzed. The whole system is controlled by the host computer man-machine interaction software remotely and in real time through commands. The experimental results show that the system realizes the detection of the basic posture, meets the basic requirements of the system design, and provides a certain foundation for the follow-up research.

Recognizing fish behavior in aquaculture with graph convolutional network

Analyzing fish shoal behaviors is one of the concerned problems for scientists who study fish welfare and stress. However, most shoal behavior exploring methods with manual parameters are subjective and not widely available in various conditions. Therefore, this study introduced graph technology, built 29,505 shoal behavioral graphs and presented a graph neural network for analyzing four shoal behaviors (normal, resting, abnormal, and circular state) by calculating the multiple swimming indexes and swimming posture from videos. In the proposed model, motion characteristics of the shoal and swimming posture of individuals in shoal were utilized to construct a shoal graph, and then the graph convolution network (GCN) model was trained and tested. Results indicated that the model could effectively improve the identification rate of fish shoals’ special behaviors, with an overall accuracy of 97.3% under the ideal condition, 92.3% for the practicable scheme that track fish by machine learning technology, compared with the artificial neural network, modified kinetic energy model and simulation feature point selection model, the accuracy of special behaviors increased by 1.6%, 57.7%, and 34.0%, respectively. Besides, the main factors that affected the accuracy of the analyzer were explored. The analyzer is sensitive to (1) the precision of tracking results, (2) edge connection in the graph and (3) features of the model’s input. In addition, by interpreting the principle of the GCN model, it assigns greater weights for dispersion in normal swimming state recognition, and swimming postures are the most significant indicators to determine whether a shoal is in an abnormal state or not. In summary, the model can be used to help researchers explore the basal behavioral mechanisms in aquaculture.

RETRACTED ARTICLE:Abnormal anti-cyclone in ocean atmosphere and sports swimming strength training based on target tracking algorithm

Using the best tropical cyclone path data set provided by the Shanghai Typhoon of the China Meteorological Administration, the 2474 site data provided by the CMA, and the China ground precipitation daily value grid data set (V2.0), based on objective weather analysis techniques, this paper first conducts TC precipitation Separate and propose a decomposition method for TC precipitation influencing factors to analyze the climate characteristics of continental tropical cyclone precipitation and its influencing factors, then use ERSSTv5 sea temperature data to study the characteristics of TC precipitation in the four phases of ENSO through synthetic analysis methods, and use the abnormal cause diagnosis method analyzes its possible causes. From the point of view of the magnitude, the EPW and CPC phases show an abnormal decrease, while the CPW and EPC phases show an abnormal increase; from the perspective of the spatial distribution characteristics of abnormal TC precipitation, the spatial distribution of EP-type ENSO presents a southwest-northeast distribution, while the CP-type ENSO presents uniformity throughout the region. At the same time, in order to improve the special physical abilities of swimmers, the physical strength training of swimmers should be adjusted according to different swimming styles and closely coordinated with special techniques. At present, the physical training methods of swimmers cannot fully meet the functional requirements of various special swimmers. Therefore, in various swimming events, how to carry out the highly combined design of physical training methods under different swimming postures is a problem worthy of discussion. Based on this, this article simulates the athlete’s physical training through the target tracking algorithm, which can improve the athlete’s professionalism.

ESTABLISHMENT OF SWIMMING POSTURE TEACHING MODEL BASED ON INTEGRATED ALGORITHM

ABSTRACT As people’s lives become better and better, more and more citizens are no longer satisfied with simple food-and-clothing problems, but gradually move towards the spiritual life they are yearning for. Among this, physical health is also an important part of it. So, at this stage, a lot of people will go swimming to exercise their body and mind. In this paper, the integration algorithm was used, and the self-learning ability of the integration algorithm was used. This algorithm was used to study the swimming posture model. This model can play an important role in the teaching of traditional swimming posture.

An untethered soft robotic fish using SMA wires and its performance analysis

The authors have developed a bionic robotic fish having a soft tail by employing intelligent materials as actuators. Two sets of shape memory alloy (SMA) wires are stitched to the two sides of the soft tail, allowing the tail to bend on either side. The designed structure is expected to have the ability to mimic the straight swimming posture of a real fish in the water, and also to turn left and right using the soft body and caudal fins (BCF propulsion). The body and the head part are designed by a 3D CAD software, whose streamline ability is verified through the fluid simulation, and are printed by a high-precision 3D printer. A micro Arduino computer is set inside the fish body for the total swimming control. The infrared (IR) communication is also installed for the detailed analysis of the swimming motion and the speed. By studying the relationship between the control signals and the swimming behaviour of the robotic fish, the performance of the swimming motion was examined for determining the parameter values to realise real-fish swimming.

A real-time fuzzy logic biofeedback controller for freestyle swimming body posture adjustment.

Wearable body area networks (BANs) have been widely used in activity measurements for kinematic information collection. This paper presents the design and implementation of a wearable device used as a training tool in freestyle swimming. The device supplies a close-loop control mechanism via a fuzzy logic controller. Swimming posture data is collected quantitatively and audibly fed back to swimmers in real time through bone conductors. Two recreational swimmers were invited to participate in a series of experiments including 7 days of baseline capability test (no feedback), 7 days of feedback training, and 2 days of retention test. It was found that both swimmers could well adapt to the feedback instructions. A maximum of 7.62% of lap time improvement and 29.64% of trunk roll improvement were observed in FB training, and such pattern was maintained after feedback was removed. We conclude that real-time fuzzy logic feedback can be used to improve recreational swimmers performance.