551 results about "Athletes" patented technology

The invention discloses a three-dimensional attitude high-precision acquisition and reconstruction system for exercise training, which relates to the technical field of three-dimensional attitude acquisition and reconstruction and comprises a camera calibration module, an image acquisition module, a key point extraction module, a three-dimensional reconstruction module, an attitude optimization module, a data fusion module and a feedback generation module. The camera calibration module is used for performing internal and external parameter calibration on a multi-view camera assembly by adopting a self-adaptive joint calibration method to obtain a projection matrix parameter and a transformation matrix of each camera; the image acquisition module is used for synchronously acquiring multi-view video images of the athlete in the training process based on the calibration parameters, and performing timestamp alignment processing to obtain an original image sequence; and the key point extraction module is used for detecting and identifying a human body image by adopting a deep learning model, extracting two-dimensional coordinates of key human body joint points of each frame from an original image sequence, and obtaining a two-dimensional key point set under each view angle.

The invention discloses a Pick ball athlete action recognition and analysis system based on multi-modal data fusion, and the system comprises the steps: collecting the video image data and sensor data of a Pick ball athlete in the motion process, and carrying out the feature extraction of the body posture of the athlete in the video image data through a convolutional neural network; according to the method, a recurrent neural network is combined to perform action classification on a feature sequence, meanwhile, sensor data are fused, accurate recognition of actions of an athlete is achieved, meanwhile, detailed technical parameters of the athlete in the ball hitting action can be obtained by deeply analyzing the recognized actions, accurate guidance is provided for training of the athlete, and the training efficiency is improved. And the technical level and the competitive ability of athletes can be improved.

The invention discloses a Pick ball motion trail tracking and tactical decision-making auxiliary system which adopts a high-frame-rate camera, a micro sensor installed on a ball and an athlete motion sensor worn on an athlete to respectively collect video image data, ball motion data and athlete motion data in the Pick ball motion process. Precise positioning and trajectory tracking are carried out on a ball through a target detection and tracking algorithm based on deep learning, a tactical knowledge model is constructed by combining site information and athlete action data and applying a decision tree and a neural network algorithm, real-time tactical decision suggestions are provided for athletes and coaches, and the training efficiency is improved. And the competitive level and the training effect of the Pick ball sports can be improved.

The invention discloses a golf motion simulation method and system based on digital twinning, and the method comprises the steps: obtaining an original data set containing a timestamp and a space coordinate from the cooperative motion of a plurality of parts and equipment of an athlete body through a sensor network technology in combination with a high-speed image capture device, and obtaining a preliminary multi-source motion information record; aiming at the initial multi-source motion information record, correcting time synchronization deviation and space matching error by adopting a data preprocessing module, and determining a corrected unified data framework by comparing timestamps and coordinate mapping relationships of data sources; according to the corrected unified data framework, a feature extraction algorithm is used for separating and marking the action analysis details and key parameters at the ball hitting moment, and a feature vector set containing the body posture and the equipment state is obtained. According to the invention, a highly vivid virtual environment support is provided for exercise training and match analysis, and the simulation precision and application value of a virtual exercise scene are effectively improved.

The invention discloses a sports evaluation method and system based on computer vision, and belongs to the field of sports intelligent evaluation, and the method comprises the following steps: S1, collecting a motion image; s2, matching and fusing the moving images under multiple view angles; s3, processing the motion video stream; s4, data preprocessing; s5, motion tracks of the athlete and the target are obtained, and motion parameters of key parts of the athlete are extracted; s6, comparing the motion parameters of the key part of the athlete with preset data, outputting an evaluation result according to a comparison result, and meanwhile, evaluating whether the speed change and the route of the athlete are compliant or not by analyzing a motion track; and S7, encrypting the evaluation result, and uploading the encrypted evaluation result to a user interaction interface for display. By the adoption of the sports evaluation method and system based on computer vision, evaluation accuracy can be improved, meanwhile, more visual and effective training suggestions are provided for athletes, and therefore the competitive level is promoted to be improved.

The invention relates to the technical field of physical examination scoring, in particular to a solid ball examination method and system based on AI, and the method comprises the following steps: based on a solid ball throwing video of an athlete, employing an inter-frame difference method, calculating the pixel change between continuous video frames, recognizing a motion area, separating a foreground from a background, and determining the positions and movement of the athlete and the solid ball; and generating motion area data. According to the method, the dynamic positions of the athlete and the solid ball can be accurately identified and tracked by applying the inter-frame difference method and the optical flow method, so that more accurate and continuous movement track information is provided, and key actions are identified and classified more accurately by using the time convolutional network and the time sequence clustering; accuracy and efficiency of action analysis are improved, and more objective and meticulous evaluation is realized on technical execution quality scoring and comprehensive technical evaluation through application of a support vector machine and a decision tree.

The invention provides a high-distance badminton training assisting system capable of prompting optimal starting time, and belongs to the technical field of physical training equipment. The technical problem that the learning efficiency is low due to the fact that a badminton beginner difficultly captures the high and far ball hitting starting opportunity is solved. According to the technical scheme, the system is composed of the following four modules: M1, an action recognition module; m2, a badminton tracking module; m3: a time sequence calculation module; and M4: a feedback module. The intelligent badminton racket has the beneficial effects that the intelligent badminton racket combines a wearable sensor, a camera system and an intelligent data analysis technology, accurately monitors key actions of an athlete and a falling track of a badminton, and calculates and prompts an optimal kicking starting opportunity in real time, so that the athlete is helped to realize high synchronization of racket swinging and badminton falling; and the ball hitting accuracy and competitive performance are improved.

The invention belongs to the technical field of motion monitoring, and provides an intelligent motion monitoring system and method oriented to water boat sports, a combined displacement sensor, an inertial sensor and a pressure sensing electronic fabric sensor are mounted on a boat body, a pressure sensor is mounted on a paddle, and an unmanned aerial vehicle monitoring system and a heart rate band are combined to monitor the motion of the boat. A multi-modal sensor system is integrated. Under the support of a deep learning algorithm, the invention realizes a multi-modal intelligent motion monitoring system. The system can comprehensively and accurately obtain the kinematics, dynamics and physiology parameters of the athlete in the water boat sports in real time, and does not interfere with the normal sports of the athlete. Through multi-dimensional data operation and coupling analysis, the most real athlete data model is constructed, and daily training of athletes is effectively guided.

The invention discloses an analysis method and system for AI recognition of physical exercise trajectory data. The method comprises the following steps: step 1, constructing a space-time-environment-biological feature fusion network, integrating collected multi-source data, capturing association of trajectory coordinates, skeleton joint angles and electromyographic signals, constructing a space-time dynamic graph, and comprehensively sensing the influence of a dynamic environment on a motion trajectory; 2, introducing a generative adversarial network to simulate extreme environment interference, and improving model robustness; forcing the model to learn a defensive decision through physical layer disturbance and behavior layer disturbance; distinguishing a real track from a generated track by using a discriminator, and trying to cheat the discriminator by a generator to enable the model to adapt to a noise environment; 3, cross-athlete skill migration is conducted, a parameter freezing strategy and dynamic weight adjustment are adopted, the basic exercise mode recognition capacity is reserved, and meanwhile individual differences are adapted; and step 4, in combination with deep reinforcement learning and an interpretable rule engine, carrying out instant tactical adjustment, and generating an action through a DRL strategy network.

The invention discloses a Pick ball athlete action analysis method based on computer vision, and belongs to the technical field of data processing. Standard action videos corresponding to action types of Pick ball athletes are collected by arranging a camera, and the standard action videos are analyzed and learned by utilizing a computer vision technology; according to the technical scheme, the motion analysis method and the motion analysis device, the model capable of analyzing the motion of the Pick ball athlete is formed, then the to-be-analyzed video data of the to-be-analyzed Pick ball athlete is collected, the to-be-analyzed video data is analyzed through the obtained model, and finally the motion analysis result is obtained, so that the normalization of the technical motion can be effectively evaluated; scientific training suggestions are provided for athletes, and athletic performance is improved.

The invention provides a motion capture method and device based on physical training and a storage medium. The method comprises the steps that athletes are numbered; collecting action data, analyzing whether the action data is abnormal, if so, performing early warning, otherwise, entering the next step; action quality parameters are collected, and action quality is analyzed; training environment parameters are collected, the environment state is analyzed, and early warning is conducted if the environment is unqualified. According to the invention, abnormity can be early warned in time, the motion quality can be accurately evaluated, and the training effect and safety can be improved.

The invention provides a sports action correcting and monitoring system based on image recognition and big data, relates to the technical field of artificial intelligence, and integrates multi-source data, eliminates space-time differences and improves the accuracy and reliability of an analysis result through a data synchronization and fusion module by using a big data processing technology; the biomechanical solving module calculates a lower limb joint torque vector in real time based on a Lagrange equation, and real-time monitoring of the joint torque is achieved; the load optimization module adopts an Adam optimization algorithm, optimizes the stride width and the knee joint buckling angle through a gradient descent method, minimizes the maximum value of the joint torque, reduces the joint load of an athlete, and prevents potential injury; the feedback display module transmits the optimized action parameters to the wearable device in real time, the athlete is guided to adjust the action in real time, and the high efficiency and safety of training are ensured.

The invention relates to the technical field of competition data processing, in particular to a marathon competition data processing system and method based on big data, in the marathon competition data processing system and method based on big data, physical consumption and health risks of athletes are comprehensively evaluated by combining environmental factors, the body loads of the athletes can be comprehensively known, and the health of the athletes is improved. Therefore, possible risks in the match can be more effectively judged, decision support is provided, match track resources are dynamically adjusted by combining a health risk assessment result and match track condition analysis, state changes of athletes are analyzed by adopting an isolated forest algorithm, match resource allocation is dynamically adjusted, and match resource scheduling is more scientific and real-time. According to the method and the system, the running flexibility and the coping capacity of the competition are enhanced, the load and resource requirements of different competition segments can be accurately predicted through the real-time health data and the time sequence analysis of the long-short-term memory network, the competition resource allocation is more reasonable and scientific, excessive or insufficient allocation of the resources is avoided, and the accuracy and the real-time performance of the competition resource scheduling are improved.

The invention relates to the technical field of data analysis, in particular to a physical exercise trajectory data analysis system based on AI recognition. Comprising a data acquisition module for acquiring multi-dimensional data of a table tennis player in a movement process through a multi-modal sensor; according to the method, a multi-dimensional complete data set is formed by accurately capturing the biomechanical data of the acceleration, the angular velocity and the force output of the ping-pong athlete in the actions of running, jumping and swinging, a basis is provided for accurate analysis of the force exerting skill of the ping-pong athlete, and the force exerting skill of the ping-pong athlete is accurately analyzed through the AI model. The exercise efficiency, physical fitness condition and action precision of the ping-pong athlete are predicted, so that the power generation skill and promotion space of the ping-pong athlete are better analyzed, a personalized training plan is generated according to the defects of the athlete, the ping-pong athlete is assisted in identifying and improving the defects, and the training efficiency is improved. Powerful support is provided for improving the power generation efficiency and the ball hitting stability of the ping-pong athletes, and the training effect of the power generation skills of the ping-pong athletes can be improved.

The embodiment of the invention discloses a supply strategy prediction method, system and device and a storage medium. The method comprises the steps of obtaining monitoring data, a current position, a historical supply state, a supply station identifier and a current environment parameter of an athlete; calculating a first replenishment distance according to the historical replenishment state and the replenishment station identifier, determining the road condition of the target competition section according to the current position and the competition map, and determining a target prediction model according to the road condition; inputting the first supply distance, the monitoring data and the current environment parameters into a prediction model, and generating supply prompt information; determining a second replenishment distance from the current position to the next nearest replenishment station based on the match map, and calculating predicted replenishment time according to the average allocation speed and the second replenishment distance; and binding the replenishment prompt information, the predicted replenishment time and the participating number to generate a predicted replenishment strategy. The corresponding supply strategy can be predicted based on the actual condition of the athlete, so that the athlete can accurately judge the optimal supply type and supply amount at each supply station.

Aspects of the subject disclosure may include, for example, obtaining current video content of a player repeatedly performing a physical skill, analyzing the current video content based on previous video content, the previous video content comprises other video content of the player repeatedly performing the physical skill, determining biomechanic metrics of the player performing the physical skill based on the analysis, and determining each biomechanic metric of a portion of the biomechanic metrics does not satisfy a respective biomechanic metric success rate. Further embodiments include generating a first image of the player performing the physical skill from the current video content, generating a second image of the player performing the physical skill from the previous video content, and presenting the first image and the second image simultaneously and indicating the portion of the biomechanics that did not satisfy the respective biomechanic metric success rate. Other embodiments are disclosed.

A modular training system for use by athletes to improve their stick-handling skills. More specifically, a modular training system comprising a mounting device and a bridging component. The mounting device can have a main body with two side projections that are structured and configured to together define a central channel therebetween; and at least one base portion located below the main body. Each side projection may have at least one tab projecting into the central channel for releasably securing the bridging component in a horizontal configuration within the central channel. The bridging component can have a tube, a base extending along a lower outer surface of the tube, and a plurality of projecting rails extending along left and right outer surfaces of the tube.

The invention discloses an exercise risk assessment system based on big data, and relates to the technical field of exercise risk assessment. The exercise risk assessment system based on the big data comprises an exercise data acquisition and integration module, a data extraction and fusion module and a risk assessment module. According to the method, the sports data of the athlete are obtained through the wearable device, big data, training logs and other multi-angle modes, standardization processing is carried out, the method is not limited to manually filled sports information, scientificity is high, the multi-dimensional sports data are fused to form comprehensive features, one-sidedness of subsequent evaluation is avoided, and the method is suitable for popularization and application. And based on the obtained comprehensive characteristic dynamic evaluation, fatigue loss accumulation caused by long-time movement is considered, so that the risk evaluation result is more accurate, and the purpose of dynamically evaluating the strategy is achieved.

The invention discloses an in-site exercise risk comprehensive real-time assessment system and method, and relates to the technical field of exercise risk assessment, the system comprises a real-time monitoring module, a data fusion module, a data processing module, a risk assessment module and an optimization suggestion early warning module; the data fusion module processes the initial motion information collected by the real-time monitoring module to obtain target motion information, and the data processing module constructs a motion risk field based on the target motion information on the basis of four dimensions of a motion field terrain energy field, an athlete kinetic energy field, a ball kinetic energy field and an athlete behavior psychological field; according to the method and the system, a motion risk field is established, a motion risk assessment model is established through the motion risk field, the motion risk assessment model is solved to obtain a risk reliability index, and an alarm is given when the risk reliability index exceeds a threshold value, so that high-precision motion state monitoring, comprehensive risk assessment and real-time risk early warning can be realized.

The systems and processes described herein can provide dynamic and realistic route generation based on actual route data within the game environment. The system provides for generating a route database for use with a sports simulation game application. The present disclosure also provides for generation of routes during runtime of the game application. The route generation system can help address the problem of generating realistic and lifelike routes based on real life movements of athletes.

The invention provides a dynamic sports data visualization system and method, and relates to the technical field of data visualization analysis. Competition behavior data of athletes in a sports event are captured in real time; determining a plurality of exercise interaction nodes of the athlete in the current exercise item, and determining the action evolution trend of each exercise interaction node according to the action tendency track of the athlete; performing feedback evaluation on the real-time state data to obtain a motion feedback strategy of the athlete, and determining an action linkage attribute of the athlete according to the motion feedback strategy and the motion posture information of the athlete in the current sports event; and inputting the action evolution trend and the action linkage attribute into a preset action prediction model for real-time training, and visually displaying the output action prediction data. According to the method and the device, the dynamic sports data of the athletes can be subjected to linkage analysis in a data environment with real-time capture capability and a multi-modal perception basis, so that the effectiveness of collaborative interaction sports dynamic data visualization processing is improved.

The invention relates to a ball game real-time AI driving joint data generation method and system, and the method comprises the steps: obtaining main skeleton joint point data and motion joint point data, carrying out the motion noise elimination and track smoothing processing, and obtaining whole body motion training data; performing model training on a preset self-attention neural network model based on the whole body exercise training data to obtain a ball game human body joint data prediction model; acquiring role main body skeleton data of a real-time game scene, inputting the role main body skeleton data into the ball game human body joint data prediction model for joint point data prediction, and outputting joint point action parameters; and performing fusion processing on the role main body skeleton data and the joint point action parameters, and outputting role driving event data. The joint point motion data of the athlete can be predicted in real time.

The invention provides a throwing action real-time guidance method based on causal inference and self-adaption, and the method comprises the steps: synchronously collecting the kinematics and electromyography data of an athlete through a multi-mode sensor, constructing and applying a dynamic causal graph model which integrates biomechanical priori knowledge and online self-adaption capability, processing the data in real time to generate deep causal enhancement state representation; and based on the model, performing autoregressive pre-judgment on the future motion trajectory of the athlete, and performing quantitative evaluation on the pre-judged trajectory by using a multi-objective cost function integrating athletic performance, injury risk and technical style. When the evaluation cost exceeds an intervention threshold value, a risk source is traced back to determine a kinematic error, and an error correction instruction is generated according to a preset error-instruction mapping relation; the fatigue state of an athlete is monitored in real time, model parameters are finely adjusted on line, a correction instruction is transmitted to the athlete in real time, and prospective and personalized intelligent guidance is achieved.

The invention relates to the technical field of kayak motion evaluation, and discloses a kayak motion posture evaluation method based on vision. The method comprises the following steps: synchronously acquiring dynamic image data of an athlete in different exercise stages through a multi-angle image acquisition device, and constructing an original exercise posture data set; key frame extraction and preprocessing are carried out on the dynamic image data, and a multi-dimensional visual feature space is constructed based on the preprocessed data; constructing and training an attitude quality evaluation model by using the space, and extracting attitude evaluation indexes; establishing a corresponding relation between the kayak motion posture scoring grade and the evaluation index; obtaining real-time training image data, obtaining a dynamic self-adaptive attitude evaluation index by combining with the model, and further obtaining a real-time attitude scoring grade; and if the grade is lower than a preset standard value, generating a posture adjustment suggestion. The method can capture attitude information in multiple dimensions, forms objective evaluation, feeds back and adjusts the direction in real time, and is suitable for kayak exercise training scenes.

The invention relates to the technical field of exercise training, in particular to an intelligent environment adaptability exercise training system and training method. The system comprises an environment simulation module, an intelligent wearable device, a data processing and analysis platform and an augmented reality technology, and the environment simulation module simulates a plateau training mode by accurately controlling indoor environment parameters to help athletes adapt to a high-altitude environment. The intelligent wearable device monitors the physiological data and the motion trail of the athlete in real time, and provides real-time feedback and decision-making assistance. The method comprises the steps of environment simulation setting, intelligent wearable device monitoring, data processing and analysis, augmented reality technology training and training scheme generation. The invention aims to improve the environmental adaptability of athletes by simulating extreme environmental conditions such as high altitude, high temperature, low temperature and the like, so that the competitive performance is enhanced, and the adaptive capacity and competitive level of the athletes in actual competitions are enhanced.

The invention discloses a smart park management and control method and system based on a digital twinborn technology, and belongs to the technical field of smart park management and control. The control method comprises the following steps: step (1), setting an acquisition node in each playground in a target Olympic park to obtain preprocessed data; (2) extracting the preprocessed data in the step (1), and carrying out digital twinning on the target Olympic park; (3) constructing an ideal digital geometric model according to the sensor; step (4), calculating an estimated anomaly occurrence probability of each node based on historical operation data, and establishing an ideal geometric model; (5) collecting the anomaly occurrence probability, and calculating the difference value between the anomaly occurrence probability and the anomaly probability of the corresponding node of the ideal digital geometric model; and step (6), carrying out visualization on the alarm position where the abnormity will occur. And the safety of athletes in the Olympic park during exercise is improved.

The invention relates to the technical field of intelligent sports data analysis, in particular to an intelligent sports data analysis method and system based on the Internet of Things and a medium. The method comprises the following steps: acquiring real-time state data of physical exercise of an athlete through an intelligent bracelet worn by the athlete; based on an electronic video recording device, motion posture data mapping is carried out according to the real-time state data of the athletes in physical exercise, and motion posture mapping data is obtained; performing instantaneous explosive power evaluation on the motion attitude mapping data, performing abnormal load limit coupling processing, and performing motion risk level evaluation to obtain motion risk level data; making an intelligent motion feedback strategy based on the motion risk level data to obtain the intelligent motion feedback strategy; and sending the intelligent motion feedback strategy to the intelligent bracelet to execute sports data analysis. The intelligent sports data analysis technology is optimized, so that the intelligent sports data analysis technology is more perfect.

The invention discloses a motion state data acquisition method, system and device and a medium. The method comprises the following steps: acquiring sensor data monitored by a wearable device worn by a clapping hand of a user and user information; the sensor data collected every time are sequentially stored in a sliding window according to the time sequence, and whether the ball hitting action exists in the sliding window or not is recognized; when the ball hitting action exists in the sliding window, the position of the sliding window is adjusted so that the ball hitting moment of the ball hitting action can be located at the middle position of the window; and analyzing the sensor data in the adjusted sliding window according to the user information to obtain motion state data of the user. According to the method, the exercise index data of the athlete can be evaluated conveniently, economically, quickly and accurately, so that targeted training can be performed.

The invention discloses a motion capture method and system for physical education training based on image recognition, and the method comprises the steps: collecting a motion video image of an athlete through a multi-angle image sensor, and recognizing the body contour of the athlete in the motion video image in real time through a Center Net target detection algorithm; if detecting that the athlete extracts the key node, outputting the position of the key node; delimiting an ROI region of each key node according to a prediction result to obtain an ROI region image; and establishing a 3D attitude estimation model based on an HR-Net neural network, extracting an action trajectory of the marked region from the ROI region image, calculating biomechanical parameters based on the action trajectory, and generating personalized scores and improvement suggestions. Multi-dimensional evaluation from an absolute standard to individual differences is realized, and personalized scores and improvement suggestions are generated.

The invention relates to a multi-modal medical information intelligent integration and decision support system for acupuncture rehabilitation, which belongs to the technical field of medical information, and comprises a data acquisition and synchronization unit used for acquiring physiological data, psychological data and behavior data of athletes; the data acquisition and synchronization unit is also used for performing timestamp alignment processing on the acquired physiological data, psychological data and behavior data to generate a data stream with a global timestamp; the state characterization and evaluation unit is used for extracting multi-modal features based on the data flow generated by the data acquisition and synchronization unit; the state characterization and evaluation unit is further used for processing the multi-modal features through a preset disposable physiological resource dynamic evaluation model so as to estimate a current state vector representing the current physiological state of the athlete. Advanced algorithms such as a convolutional neural network and wavelet transform are used for extracting deep features from original data, noise and redundant information are filtered out, and highly-condensed feature vectors are generated;