2354 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 discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention discloses a gait sub-phase prediction method and device based on deep learning model fusion. The method comprises the following steps: synchronously collecting multi-source gait data of a wearer walking at various speeds; preprocessing the collected multi-source gait data, dividing a time window by adopting a sliding overlapping window technology, and constructing a supervised learning training data set based on double tags; hierarchically fusing CNN, Bi-LSTM and MHA, and constructing a gait sub-phase prediction model; automatically searching an optimal hyper-parameter combination by using a Bayesian optimization algorithm; a stable prediction time (Pstable) index is defined, and model performance is comprehensively evaluated through one-by-one subject rejection cross validation and a speed migration robustness experiment in combination with a classical index; a modular gait sub-phase prediction system architecture is constructed based on a Simulink platform, and a multi-thread buffer mechanism is integrated to process data streams in real time; and solidifying model parameters to an online reasoning module, and dynamically outputting a prediction result.

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 a biped robot complex terrain self-adaptive gait planning method and a biped robot, and belongs to the technical field of robotics.The biped robot complex terrain self-adaptive gait planning method comprises the steps that a fractional order PID controller is constructed, and the fractional order PID controller comprises a multi-sensor network module, a self-adaptive module and a fractional order calculus control module; constructing a reinforcement learning model for biped robot gait planning, setting a state, an action, a decision and a reinforcement learning reward function of reinforcement learning, performing continuous iteration and optimization adjustment on the reinforcement learning model by using a strategy gradient method, updating strategy parameters, and outputting control parameters of the biped robot; and performing decision fusion on control parameters output by the fractional order PID controller and the reinforcement learning model through a comprehensive loss function to obtain globally optimal biped robot control parameters. According to the method, more accurate gait planning can be provided for the biped robot in complex terrains, and better walking flexibility and adaptability are achieved.

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 quadruped robot motion control method and system based on reinforcement learning, and the method can simulate and analyze the motion behavior of a quadruped robot in a virtual environment through building a simulation model of the quadruped robot, remarkably reduces the research and development cost, remarkably shortens the research and development period, converts a balance control problem into a Markov decision process, defines a state, an action and a reward function, and improves the reliability of the quadruped robot. A more intelligent and adaptive balance strategy can be realized, a deterministic strategy gradient algorithm and a simulation model are adopted for interactive training, an efficient balance control strategy can be obtained, robot behaviors are adjusted in real time to keep balance, a walking task is decomposed into two sub-tasks of foothold selection and gait generation by utilizing a hierarchical control idea, and the walking efficiency is improved. Through reinforcement learning, walking flexibility and stability of the robot on irregular terrains are improved, the irregular terrains are built on a simulation platform for experimental verification, robot performance can be safely and repeatedly evaluated, and a strategy is further optimized.

The invention discloses a quadruped robot autonomous decision control system based on multi-modal perception, and belongs to the technical field of robot motion, and the system specifically comprises the steps: collecting visual, force and sound information through multiple sensors of a camera, a force sensor and a microphone array, and carrying out the preprocessing; according to the processed information, a diagonal gait, leg track and motion path planning scheme is generated by adopting a motion planning strategy; generating a specific control instruction to drive the robot to move, and monitoring and adjusting in real time; after the task is completed, the motion performance of the robot is evaluated, and a motion planning and control method is optimized according to an evaluation result; the autonomous motion ability and adaptability of the quadruped robot in a complex environment 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 sports equipment and rehabilitation, in particular to a split type treadmill control method capable of correcting gaits in a self-adaptive mode and a treadmill. The method comprises the following steps: acquiring air pressure and air pressure variation of each air bag in air-floating treadmills on two sides of the treadmill in a static scene or a dynamic scene to determine a static contact area or a dynamic contact area of the air-floating treadmills and feet, and converting the acquired air pressure variation into foot pressure; calculating a sole load index, a pressure ratio of the inner side to the outer side of a foot and a foot pressure gravity center according to the foot pressure, and adjusting the air pressure of all air bags of the air-floating treadmill to realize that the hardness grade and the inclination angle of the air-floating treadmill adapt to gait characteristics and training requirements of a user; by monitoring the air pressure and the air pressure variation in the air bag of the air floating treadmill in the dynamic scene and adjusting the air pressure of the air bag in the dynamic contact area, the foot pressure gravity center is always located in the gravity center shifting area, and the problems that an existing treadmill cannot adapt to training requirements, is poor in adaptability and is long in gait correction scheme adjusting period are solved.

The invention discloses a foot drop assisting method based on a flexible ankle joint exoskeleton, and belongs to the field of rehabilitation medical assistance. According to the method, an unhealthy side ankle joint angle is input into a self-adaptive oscillator to learn related parameters in the self-adaptive oscillator, and an affected side ankle joint angle is predicted; meanwhile, a CNP model is adopted to predict the ankle joint angle of the shank on the affected side according to the gait phase estimated by the self-adaptive oscillator, two prediction results of the ankle joint angle of the shank on the affected side are subjected to weighted fusion to finally obtain the expected angle of the ankle joint on the affected side, and the fused ankle joint angle prediction output is more stable and continuous. And a weighted fusion result is smoothed. According to the method, the normal walking mode based on the unhealthy side ankle joint angle of the patient can be dynamically learned online in real time, and personalized affected side ankle joint angle reference tracks at each moment are generated for different patients, so that the abnormal motion mode of the patient is inhibited, and the patient is guided to reach the normal gait in the rehabilitation training process.

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.

Footwear articles for walking are provided. In one example, a footwear article may include a midsole with a lower surface of constant curvature extending from a heel of the midsole to a toe of the midsole, wherein the lower surface maintains the constant curvature throughout a stance phase of a walking gait. In this way, a smooth step-to-step transition and a smaller range of oscillation of the center of mass of the wearer is achieved, and energy expenditure during walking is reduced. In turn, a wearer of the footwear article may smoothly walk for extended periods of time with reduced fatigue.

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 provides a full-scale humanoid robot coordination control method and system, and the method comprises the steps: building a nonlinear model prediction controller, carrying out the optimization solution through sequential quadratic optimization, and optimizing the system input and system state; a trunk pose task, a swing leg and support leg task and an arm task are extracted from the optimized trajectory, and a plurality of tracking tasks are mapped to a joint space according to an allocated priority weight by using a high-frequency hybrid whole body motion control HWBC algorithm, so that the robot can coordinately process the motions of the arms and the feet, the system response is improved, and the system reliability is improved. The method has the ability of multi-gait robust motion.

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 relates to the technical field of motion monitoring, in particular to a running posture monitoring method based on an image recognition technology, which comprises the following steps: deploying cameras at different angles, synchronously recording whole body actions of a user during running, extracting a joint angle change sequence, and constructing a running three-dimensional action model. According to the method, the consistency and the difference of the actions are recognized by comparing the amplitude and the speed of the joint movement, so that the detection of the action defects is facilitated, and the stability and the deviation of the gait can be quantitatively evaluated by analyzing the foot landing angle and the stride change; analysis of the motion change frequency and amplitude allows coaches and athletes to adjust and optimize gaits in a targeted manner in the training process, the risk of sports injury is reduced, and through continuous simulation and motion symmetry evaluation, recognition of sports defects and adjustment of the camera angle and shooting speed, the training effect of the coaches and the athletes is improved. And the data capture and analysis process in the training process is more efficient and targeted.

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.