1923 results about "Walking gait" patented technology

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention discloses an exercise and cognition ability assessment method, system and device, and the method comprises the steps: obtaining real-time exercise information, including real-time joint angle information, real-time gait information, real-time attention information and real-time decision speed information, of a user; constructing a physiological-behavior coupling model based on a hidden Markov model, and generating a joint evaluation index by inputting real-time motion information; performing feature extraction on the real-time motion information by using a hierarchical feature extraction model to obtain a motion feature vector and a cognitive feature vector; performing fusion processing on the feature vector and the joint evaluation index, and outputting abnormal state information including motion abnormality and cognitive abnormality; behavior abnormity is detected through the dynamic threshold model, a real-time feedback instruction is generated in combination with a preset feedback rule, and finally user training task parameters are dynamically adjusted according to the instruction. According to the method, through multi-dimensional data fusion and a dynamic evaluation mechanism, cooperative monitoring and self-adaptive training regulation and control of motion and cognitive competence are realized.

The invention belongs to the technical field of robot control, and discloses a high-gear-ratio humanoid robot robust walking control method and system based on potential dynamics self-adaption, and the method comprises the steps: designing a control strategy training frame based on deep reinforcement learning, and carrying out the optimization through an Actor-Critic structure and a PPO algorithm; constructing a potential dynamic adaptive network LDAN, and extracting environment and ontology dynamic parameters through a variational auto-encoder; designing a multi-dimensional reward function; constructing a periodic gait library by using von Mises distribution and motion capture data; gradually introducing terrain disturbance and dynamic change through curriculum type simulation training; the trained strategy and the LDAN module are deployed on the high-gear-ratio driven humanoid robot, the problems that the high-gear-ratio humanoid robot is poor in motion stability, poor in adaptability and insufficient in action expression in a variable environment are solved, and the high-gear-ratio driven humanoid robot has the advantages of being high in robustness, high in natural expressivity and high in migration ability.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention provides a multi-path cooperation method, device and equipment for a cluster quadruped inspection robot, and the method comprises the steps: obtaining gait dynamics data and topographic data, and recognizing a topographic adaptive rhythm based on the spectrum decomposition of a foot end contact force sequence; converting the topographic data into a potential energy gradient field to construct a dynamic potential energy diagram, and generating a natural path network; a cooperative trigger point is set based on the stable support moment, a path ripple field is formed, and a multi-machine cooperative potential well is constructed; decomposing the path network into a dominant task chain and an auxiliary task chain, and weaving to form an initial collaborative path; rigid nodes and flexible sections are identified through topological transformation, and random disturbance is injected to reconstruct a self-adaptive collaborative path; gait phase information is extracted to construct a synchronous network, a potential energy flow vector is used for phase alignment to generate a group resonance frequency, and a resonance cooperation path is formed; and finally, an execution instruction is generated based on the resonance cooperation path and the terrain adaptability rhythm, and multi-path cooperation of the cluster quadruped inspection robot in the complex terrain environment is achieved.

The invention provides a multi-modal gait recognition method and system, and aims to solve the problems of accuracy and robustness of gait recognition in a complex environment. According to the method, two types of modal information of a gait contour map and a skeleton Gaussian heat map are combined, global-local shallow gait features are extracted by using a multi-scale convolution feature extraction module based on residual connection, and adaptive weight distribution and fusion are performed on the features of the two types of modals through a cross-modal attention fusion module. Furthermore, a sliding window Transform module is introduced to perform deep gait feature extraction on the fused features, and a long-range space-time dependency relationship of gait information is modeled to obtain a gait feature vector with high resolution. Experimental results show that the gait recognition precision of the method in a complex scene is remarkably superior to that of an existing method, and particularly, the gait recognition precision is excellent under extreme conditions of shielding, complex backgrounds and the like. The invention provides a new solution for high-precision gait recognition in an open environment.

The invention discloses an interaction test method for gait simulation of a humanoid robot, which is applied to a test platform. Comprising the following steps: controlling a current gait simulation model corresponding to a virtual robot to perform a gait simulation test in a virtual test environment in a current preset time period; the virtual test data is sent to the physical robot; controlling the physical robot to execute corresponding gait operation according to the virtual test data in the actual test environment, and sending the actual test data to the virtual robot; updating the current gait simulation model based on the virtual test data and the real test data; and continuing to perform the gait simulation test based on the updated gait simulation model, and ending the updating operation until the gait simulation test is ended, so as to generate a complete gait scheme. Therefore, by constructing a closed-loop interaction mechanism between the virtual robot and the physical robot, dynamic adjustment of the gait simulation model is achieved, the gait of the physical robot is restored, and the stability and precision of the gait of the physical robot are improved.

The invention discloses a biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention relates to the technical field of robot control methods, in particular to a simulation-to-reality migration-oriented robot gait optimization method, which comprises the following steps of: acquiring input characteristics of a robot; constructing a gait control model by adopting a near-end strategy optimization reinforcement learning algorithm; training the gait control model in parallel in a physical simulation environment; the input feature matrix is input into the trained gait control model, a joint target position control instruction is generated, and the robot is controlled to execute gait actions; executing domain randomization operation by applying simulation to a reality checking tool; the simulation parameters are adjusted according to the difference parameters, and optimized gait control model parameters are output. The uncertainty of the real environment is covered by applying simulation to reality troubleshooting tool execution domain randomization operation, the systematized difference positioning process is combined, the simulation and real machine difference parameters are recognized, and the accuracy of the gait control model is improved. And action malformation, poor stability or hardware damage of the gait strategy of simulation training during real machine deployment can be prevented.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention provides a muscle stimulation adjusting method and system based on gait detection. The method comprises the following steps: acquiring lower limb biomechanical data; performing phase recognition according to a dynamic programming algorithm, and constructing a pressure center track to perform feature extraction to obtain gait features; carrying out feature analysis on the gait features through a reverse dynamics model and a machine learning model, and carrying out output judgment according to a double-model collaborative decision-making mechanism to obtain a stimulation parameter decision-making result; generating an electrical stimulation waveform according to the decision result, and applying differential electrical stimulation to the target muscle in the gait cycle to obtain a stimulation scheme; and monitoring stimulated myoelectricity feedback signals and gait changes, and carrying out parameter updating by calculating a signal change rate and analyzing a motion track error to obtain an updating scheme. According to the method, through a double decision-making mechanism of the reverse dynamics model and the machine learning model and a real-time feedback regulation mechanism, the gait phase recognition precision is improved, and precise regulation and control of stimulation parameters are realized.

The invention discloses a control method and device for straight-knee walking of a humanoid robot and a storage medium. The control method and device are used for improving the straight-knee walking performance of the humanoid robot in different scenes. Collecting human body gait data; performing action redirection processing on the human body gait data; modeling the walking task, constructing an adversarial network, and generating a maximum expected discount return function; constructing a speed tracking reward function; constructing a soft boundary Wasserstein loss function of the discriminator, and constructing a style reward function according to the output of the discriminator; a PD controller is constructed; physical parameters of the target humanoid robot in straight knee walking training are selected, and prior distribution is set for each physical parameter; performing live-action test motion on the target humanoid robot, and collecting operation data corresponding to the physical parameters; and combining the collected operation data with the prior distribution corresponding to the physical parameters, and feeding back the updated posterior distribution to the simulation learning stage to optimize the control strategy.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.

The invention provides a quadruped robot fault-tolerant control method and system based on residual learning, and the method comprises the steps: constructing an ontology mechanism model based on phase information, dividing the phases of a supporting stage and a swinging stage based on a diagonal gait, and designing foot end tracks through combining a Bezier curve and a sine curve; designing a six-dimensional reward function including speed tracking, posture balance, foot movement direction, energy consumption control, body contact constraint and foot end contact excitation; a data-driven model based on a heterogeneous actor-commentator architecture is constructed, an actor network integrates terrain information, ontology sensing data and damage parameter estimation values, a commentator network integrates privilege information for strategy evaluation, and network parameters are optimized based on a near-end strategy optimization algorithm; and on the basis of a residual learning thought, a final motion instruction is generated by coupling the correction output by the data driving model with the ontology mechanism model.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

The invention discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention discloses a three-degree-of-freedom pectoral fin cooperative motion control and gait optimization method, which comprises the following steps of: firstly, establishing a motion gait model of a three-degree-of-freedom rigid pectoral fin, and acquiring hydrodynamic feedback and attitude information of the pectoral fin under different input parameter combinations in real time by utilizing a multi-mode sensing system so as to construct a pectoral fin-environment interaction model; secondly, training a high-level strategy network by adopting a deep reinforcement learning algorithm, and optimizing a control signal cbase (t) by taking propulsive efficiency and attitude stability as reward functions; then, constructing a multi-layer hierarchical neural control network to decode cbase (t), outputting an incremental control signal delta c (t), and introducing a central mode generator network to generate a reference trajectory # imgabs0 # of each degree of freedom; finally, dynamic compensation of external disturbance and modeling errors is achieved based on a sliding mode control method of an extended state observer, delta c (t) is adjusted online through a performance evaluation index function J (t) to correct control parameters, pectoral fin motion is kept in the optimal propulsion and stable state all the time, closed-loop optimization is achieved, and the propulsion efficiency and attitude control capacity of the robotic fish are improved.

According to the human body activity distinguishing device and method, through an intelligent insole system integrating an inertial sensor and a plantar pressure sensor and in combination with a multi-scale time window analysis method, gait data of a user are accurately collected and processed, daily activity modes such as walking, running and going upstairs and downstairs and changes of the modes are recognized, and the human body activity distinguishing effect is achieved. And real-time health feedback and rehabilitation support are provided. The intelligent insole system comprises an upper computer system, sensor integrated insole hardware and a module communicating with a user terminal and a cloud server. According to the system, multi-modal data is collected through an inertial sensor and a plantar pressure sensor, data processing is achieved in combination with data fusion and a multi-scale sliding time window method, finally, a random forest classifier is used for classifying activity modes, and the functions of health monitoring, posture improvement suggestion, falling risk assessment and the like are achieved. The exercise performance of the user is improved, and personalized health management is provided.

The invention discloses an old man gait abnormity monitoring system based on body surface electromyographic signals, and relates to the technical field of gait abnormity monitoring, and the system comprises a configuration module which is used for obtaining a foot bearing device set of a user, and constructing three groups of foot bearing device test scenes; the inertial measurement module is used for collecting gait data of a user and establishing gait key parameters; the myoelectricity data acquisition module is used for establishing a myoelectricity test data set; the labeling module is used for establishing a gait cycle window and establishing a myoelectricity-gait fusion fingerprint spectrum; and the early warning module is used for reading the real-time acquired data and outputting an early warning signal. The technical problems that in the prior art, monitoring of the gait abnormity of the old man is not accurate enough, abnormity is difficult to find in time and early warning is difficult, and consequently the old man faces a high falling risk and cannot be protected in time when walking are solved, the accuracy and timeliness of monitoring of the gait abnormity of the old man are improved, and the safety of the old man is improved. Therefore, the walking safety of the old people is guaranteed.

The invention discloses a quadruped robot inspection method based on multi-modal sensing fusion. The method comprises the following steps of: 1, initializing a global task, loading a basic navigation map, and generating the basic navigation map comprising topographic features, forbidden areas and parking positions; 2, autonomous navigation and dynamic correction of positioning deviation are realized according to laser radar SLAM data, and the positioning deviation is dynamically corrected according to real-time observation data; 3, dynamically switching or adjusting the gait strategy according to the terrain category, training the gait strategy of the quadruped robot to be matched with the terrain category, and generating a self-adaptive motion control instruction to adapt to the terrain in real time; and step 4, synchronously realizing parking area structured data acquisition and dynamic abnormal information perception through multi-sensor fusion, and realizing target state monitoring and abnormal event response in the inspection task. According to the invention, through collaborative innovation of the bionic motion platform and multi-mode intelligent detection, all-terrain coverage, total-factor perception and full-process autonomous intelligent inspection in a complex parking lot environment is realized.

The invention relates to the technical field of quadruped robot control, and discloses a quadruped robot anti-disturbance motion control method based on a cost weight adaptive mechanism, which comprises the following steps: constructing a quadruped robot state equation based on a single rigid body dynamic model, a model prediction control problem with cost weight, friction cone constraint and gait constraint is formed through discretization; a cost weight self-adaptive mechanism based on disturbance observation is introduced on the basis of a model prediction control framework, the disturbance intensity is estimated by taking the attitude error of the fuselage and the corresponding change rate as disturbance observation values, and the cost weight corresponding to the attitude in the model prediction control problem is dynamically adjusted; by solving a model prediction control problem, an expected ground reaction force and a target state are calculated in real time, and motion control of the quadruped robot is realized in combination with a gait planner and a joint controller. According to the invention, the motion stability and anti-disturbance performance of the quadruped robot under external disturbance are improved based on a cost weight adaptive mechanism of disturbance observation.

A method includes obtaining image frames capturing one or more people in at least one scene and identifying features of the image frames. The method also includes providing the identified features to a trained spatiotemporal transformer machine learning model configured to generate a set of features for each of the one or more people. The set of features for each person includes facial features of the person and pose features of the person over time. The method further includes performing face identification using the facial features to generate one or more first embeddings representing at least one face of at least one person and performing gait identification using the pose features to generate one or more second embeddings representing at least one gait of at least one person. In addition, the method includes identifying at least one of the one or more people based on the first and second embeddings.

Embodiments can relate to a prosthetic foot system. The system can include an ankle joint housing co-locating a rotation sub-assembly, a torsional shock absorbing sub-assembly, and a vertical shock absorbing sub-assembly. The system can include a foot component attached to the ankle joint housing. The system can be configured as a co-designed architecture to functionally integrate at least two functions of: (i) torsional shock absorption, (ii) multi-axial motion with stiffness modulation in single gait cycle, (iii) active dorsiflexion, and (iv) vertical shock absorption by causing the at least two functions to operate in concert.

The invention provides a shoe sole wear resistance detection device, and belongs to the technical field of wear resistance detection, the shoe sole wear resistance detection device comprises an instrument body and a shoe sole sample formed by punching a shoe sole, and further comprises a simulation detection assembly and a dust removal cleaning assembly; under the guidance of the guide ring, the first support arm can periodically move up and down, the process simulates the dynamic change of the pressure borne by the sole in different gait stages when a person walks, and in combination with the rotation of the sole sample, not only is the vertical pressure change simulated, but also the friction force in different directions can be simulated through relative sliding. The stress state of the shoe sole in the walking process is restored, so that the detection is closer to the actual use scene; the dust removal system is driven by the power of the detection device to operate, so that all-directional coverage of the whole abrasion area is realized, the retention time of dust in the detection area is reduced, dust interference can be avoided, the state of a sole sample before and after detection is only influenced by abrasion of a grinding wheel, and the detection result more truly reflects the abrasion resistance of the sole.

The invention discloses a humanoid robot reinforcement learning gait control method and system, and belongs to the technical field of robot control. Comprising the following steps: constructing a model prediction control optimization problem based on a linear inverted pendulum model, and generating a robot mass center track and a foothold sequence; collecting state-action pair data of model prediction control, and training a neural network model by adopting supervised learning; designing a reinforcement learning strategy network, taking the output of the neural network model as a physical guidance reference, calculating a training reward based on the constructed composite reward function, training the strategy network in a high-concurrency simulation environment by adopting a near-end strategy optimization algorithm, and outputting a joint action instruction; and verifying the trained strategy and the joint action instruction output by the trained strategy in a plurality of simulation environments, and deploying the trained strategy and the joint action instruction to a real machine for dynamic gait control. According to the method, the stability and adaptability of gait control of the humanoid robot are improved through a method of combining model predictive control and reinforcement learning.

The invention relates to the technical field of medical informatics, in particular to a construction method of an osteoporotic fracture risk prediction model. The method comprises the following steps: acquiring an included angle between a foot and the ground, a step speed and a CT image of a target person in a monitoring process; dividing pixel points in the CT image into a plurality of bone tissue areas; according to the shape distribution characteristics of the edge lines in the bone tissue area, bone characteristic values of the bone structure are obtained; obtaining a posture control factor of each gait according to the included angles between the left and right feet of the target person in each gait and the ground; determining an abnormal posture index of each gait in combination with the posture control factor and the corresponding step speed; obtaining a control disorder coefficient of each time period according to the numerical distribution characteristics and the change characteristics of all the posture control factors in each time period; and determining an osteoporosis evaluation value by integrating the bone characteristic value and the disorder control coefficient of the skeleton structure. The accuracy of the bone comprehensive evaluation result of the target person is improved.

The invention relates to the technical field of sports artificial intelligence and data management, in particular to a physical exercise AI standing long jump evaluation data management system which comprises a dynamic environment calibration module, a multi-mode identity recognition module, a three-dimensional posture analysis module and the like. The dynamic environment calibration module uses a multispectral sensor array and a deep learning algorithm to establish a dynamic coordinate system without fixing a marker; the multi-mode identity recognition module fuses gait and face features to realize non-contact identity authentication; the three-dimensional attitude analysis module performs high-precision modeling and analysis on the long jump action through a multi-view camera and an advanced algorithm; the intelligent violation determination engine combines rules and an AI model, automatically identifies violation behaviors and can dynamically adjust determination criteria. The method gets rid of the limitation of a traditional evaluation mode, realizes high-precision and intelligent evaluation, effectively solves the problems of poor environmental adaptability, inaccurate identity authentication, unguaranteed data security and the like, and is scientific and reliable in evaluation result and safe and efficient in data management.