440 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 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 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.

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.

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 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 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 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;

The invention discloses a leisure physical exercise physical fitness test auxiliary method based on intelligent equipment, and relates to the technical field of physical fitness test, and the method comprises the following steps: firstly, collecting heart rate data of a user in exercise in real time through an intelligent heart rate monitoring chest strap, and preprocessing the obtained original heart rate data; the heart rate data is collected and preprocessed in real time, noise interference is eliminated, and the data accuracy is ensured; a data set is constructed and key motion features are extracted, so that the motion state distinguishing capability is improved; a machine learning model is utilized to intelligently evaluate the motion state, the sensitivity of the sensor is dynamically adjusted, and misjudgment is avoided. According to the method, the accuracy of the intelligent heart rate monitoring chest strap is improved, the sporter is helped to optimize training based on reliable data, unnecessary exercise adjustment or premature rest is avoided, the exercise efficiency is improved, and the health risk is reduced.

The invention belongs to the technical field of exercise training and analysis, and particularly relates to an intelligent badminton action analysis system and method based on a motion sensor, and the system comprises a sensor data collection module, a real-time data processing module, an action recognition analysis engine, an intelligent data storage module and a visualization module. The method comprises the following steps: receiving and buffering sensor data in real time; periodically extracting data segments by using a sliding window to carry out multi-feature (an acceleration peak value, an angular velocity peak value, a peak value ratio and the like) calculation; based on a predefined multi-feature threshold rule set, identifying action types (such as kicking, flat ball drawing and the like) and evaluating the quality; an intelligent strategy is adopted to selectively store data so as to optimize the system load; and finally, a result is visually fed back in real time. The method realizes objective, quantitative and real-time analysis of badminton actions, has the advantages of high recognition accuracy, high system real-time performance, efficient data processing and the like, and is suitable for autonomous training of athletes and auxiliary guidance of coaches.

An athlete fatigue recovery auxiliary system based on biofeedback belongs to the field of fatigue recovery assistance and comprises a data acquisition module, a processing extraction module, a state evaluation module, a scheme making module, an adjustment monitoring module and an evaluation feedback module. According to the method, the fatigue state of the athlete is comprehensively evaluated based on various physiological signals, and the physiological and exercise load state of the athlete can be effectively and accurately reflected. According to the method, personalized recovery suggestions can be provided according to individual differences of all athletes. The recovery strategy can be dynamically adjusted to meet the actual requirements of different athletes, so that the recovery effect is improved, and the risk of excessive recovery or insufficient recovery is reduced.

A method for providing visual guidance for sports training is disclosed. The method advantageously captures and analyzes the real-time motions of a novice athlete and compares them with the previously recorded motions of an expert athlete. Additionally, the method advantageously provides both embodied and detached visual guidance for the novice athlete and, in particular, provides feedback that helps the novice athlete to quickly and intuitively understand the differences between their real-time motions and the previously recorded motions of the expert athlete.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user / wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.

The invention discloses an intelligent management system for sports basketball training risk data, which relates to the technical field of sports data analysis and comprises an acquisition module for generating three-dimensional tracks of athletes, an identification module for identifying risk actions, an analysis module for analyzing kinematic characteristics of the risk actions and a feedback module. According to the invention, data acquisition is carried out through multi-view video capture and a three-dimensional reconstruction technology, through real-time analysis of features such as postures and joint angles of athletes, the system can provide customized action suggestions for each athlete, the training quality is effectively improved, iterative updating of a deep learning model is carried out according to an evaluation result of a feedback module, and the training efficiency is improved. Along with the promotion of the training process, the recognition precision and risk judgment capability of the system are continuously improved, and more accurate risk action recognition is ensured.

The invention relates to the technical field of intelligent exercise evaluation, and provides an artificial intelligence-based athlete total element evaluation system, which comprises a data acquisition device for acquiring multi-element data of an athlete; the data preprocessing and analysis module is used for preprocessing the collected multi-element data, extracting key features for different types of data and performing integration processing to form a fusion feature vector containing multiple elements; the AI comprehensive evaluation module is based on the fusion feature vector, and an artificial intelligence algorithm is used for integrating all elements to obtain an evaluation result of the athlete; and the result output and feedback module provides the result to a coach, an athlete or a manager in a visual form for decision support. The overall architecture of the system adopts a layered modular design, comprehensive, objective and scientific comprehensive evaluation of athletes is realized through deep fusion of an artificial intelligence technology, and accurate data support and decision basis are provided for coaches, athletes and managers.

The invention relates to the technical field of data processing and artificial intelligence, and particularly discloses a basketball player sport injury risk prediction and rehabilitation training planning system. The system comprises a multi-source data fusion sensing layer, an injury risk dynamic assessment engine, a rehabilitation planning adaptive generator and a man-machine collaborative interaction execution module. Multi-source data of physiology, biomechanics and the like are collected and fused in real time, the damage risk is dynamically evaluated through a depth time sequence diagram neural network, a self-adaptive rehabilitation training prescription is generated based on hierarchical reinforcement learning and a digital twinning model, meanwhile, the execution process is monitored, safety intervention is conducted, and the rehabilitation training accuracy is improved. Personalized dynamic adjustment and safe closed-loop management of the rehabilitation plan are realized, and the rehabilitation effect and safety are improved.

The invention discloses an athlete action analysis and training optimization method and system based on big data, and relates to the technical field of exercise training.Multiple array sensors are deployed to collect exercise data, including static data and dynamic data, of a trainee, and meanwhile medical images of the trainee are obtained; constructing a basic model of the trainer based on the motion data, wherein the basic model comprises a body model and a motion model; acquiring athlete big data to construct an athlete big database, and generating a shadow model based on coupling of the athlete big database and the body model; calculating the coincidence degree of the basic model and the shadow model, generating an optimal function unit based on the coincidence degree, and guiding training of other body function units based on the optimal function unit to obtain training data. According to the method, the trainee basic model and the athlete shadow model are constructed, a scientific target reference is set for personalized training, and the pertinence and operability of training optimization are greatly improved through data-driven personalized model construction.

The invention provides a tennis player sport injury risk early warning analysis method, which comprises the following steps of: preprocessing acquired multi-dimensional sport data, accurately modeling time sequence dependence and dynamically focusing key features by utilizing a model architecture combining a long-short term memory network and an attention mechanism; through interpretability constraint loss function and Grad-CAM thermodynamic diagram visualization, high-risk action stages and key features are automatically extracted from a model decision path, a causal analysis interpretation report is generated, and finally real-time prediction and interpretation of damage risk levels are realized. According to the method, the accuracy of risk identification, the interpretability of model output and the adaptability of actual deployment are effectively improved, and scientific auxiliary decision making of coaches and medical teams is facilitated.

A method for generating a probability for a first action of a sporting event by implementing a feature set, the method including: obtaining an initial set of data relating to the first action of a sporting event, the initial set of data including at least a position of a first player on a surface and a position of a target area on the surface; generating, by a machine learning model, an initial projected scoring probability based on the initial set of data; generating a feature set relating to the sporting event; and modifying, by the machine learning model, the initial projected scoring probability to an updated scoring probability using the feature set.

This application is applicable to the field of sports information processing technology, and provides a method and device for processing skeet shooting sports information. The method includes: obtaining first motion information of an athlete and second motion information of a gun body; obtaining video images of the athlete during the entire skeet shooting process sent by a high-speed camera, where the high-speed camera is synchronized with the clock of an inertial sensor node; determining the stage time of each shooting stage of the athlete based on the acquisition time of the video images, and performing shooting stage division and shooting action analysis on the first motion information and the second motion information according to the stage time. This application divides the motion information collected by the inertial sensor node through the video images collected by the high-speed camera, and then analyzes and guides the athlete's actions based on the motion data of each stage, which can improve the accuracy of action analysis.

The present invention discloses a risk warning method for sports training injuries of athletes, which belongs to the field of sports risk warning technology and includes the following steps: S1: collecting limb movement information and limb movement trajectory of athletes in a sports training state, and determining the sports training information of the athletes in the sports training state; S2: pre-processing the collected sports training information of the athletes in the sports training state, and determining sports training characterization data of the athletes in the sports training state; S3: analyzing and evaluating the sports training characterization data of the athletes in the sports training state. The present invention solves the existing problem that it is impossible to provide timely risk warnings for sports training injuries of athletes, resulting in poor sports training effects of athletes. The present invention can provide timely risk warnings for sports training injuries of athletes, avoid injuries to athletes during sports training, facilitate the prevention of sports training injuries of athletes, and improve the sports training effects of athletes.

Disclosed are an athlete evaluation method and a system and device for performing the same. The athlete evaluation method may include acquiring location data of a plurality of participants, computing a reference location of a specific sport participant on the basis of location data of other sport participants, generating relative location data of the specific participant from the location data of the specific participant in consideration of the reference location, generating heatmap data of the specific participant on the basis of the relative location data, and computing a performance index of the specific participant.

Disclosed are methods and systems for creating an avatar (102) of a real-world athlete (104) using artificial intelligence. A corresponding method may comprise providing (802) an avatar (102) corresponding to a real-world athlete (104), receiving (806) athlete feedback (300) from the real-world athlete (104) in response to a behavior of the avatar (102), and adjusting (808) the avatar (102) based on the received athlete feedback (300).

The invention relates to a method for calculating the change in the anaerobic energy balance ΔW(t) of an athlete over a time interval ∆t. The method comprises the following steps: determining the power output P(t) of the athlete during the time interval ∆t; determining the athlete's critical power CP(t) at time t, calculated from historical power output data P(t) collected during the athlete's sporting activities; and calculating the change in the anaerobic energy balance ΔW(t) according to the formula: ∆W(t) = (CP(t) - P(t)) × v(t) × ∆t, where v(t) is the energy flux rate. Abstract figure: Figure 2

The invention provides a video data processing method, system and device for sports events and a medium. The method comprises the following steps: firstly, extracting shallow semantic features and deep semantic features of each frame of image in video data; determining the perceptual attention of the track contour features during the movement of the target athlete according to the movement association relationship of the track pixels in the movement process of the target athlete and the difference degree of the semantic segmentation mask information between the adjacent image frames; determining significant factors of track behavior characteristics when the target athlete moves according to the behavior dependency relationship of the target athlete and the global trend information of the motion track of the target athlete in the video data; performing feature cascade fusion on the trajectory contour feature and the trajectory behavior feature according to the perceptual attention and the saliency factor to obtain a cascade fusion feature, and performing label processing on the motion trajectory of the target athlete in the video data based on the cascade fusion feature. By adopting the scheme of the invention, the feature fusion of the contour and the behavior during the motion can be realized in the label processing of the video data.

The invention discloses a method and a device for analyzing running exercise conditions. The method comprises the steps that inspection data and test data are collected through a sensor laid on a runway, the inspection data are used for obtaining a stiffness coefficient and a baseline offset, the test data are used for obtaining a plurality of accelerations, and an acceleration time curve is obtained; basic parameters are obtained, wherein the basic parameters comprise the weight, time and stride frequency of an athlete; according to the acceleration and the basic parameters, obtaining a step length, an acceleration change rate, a data relationship between the acceleration and the step length and a data relationship between the acceleration and a stride frequency; acceleration ability and dynamic performance are evaluated, and athlete ability is optimized. Through the method and the device, the problem of incomplete analysis of running conditions of athletes in related technologies is solved.