94 results about "Training - action" patented technology

The invention discloses an intelligent rehabilitation training system based on multi-parameter detection and a control method, and relates to the technical field of intelligent rehabilitation training, the system comprises a user information acquisition unit, a multi-source parameter acquisition unit, a rehabilitation training plan customization unit, a feedback and control unit and a quantitative evaluation unit; multi-source detection parameters corresponding to movement, physiology and environment of a target user are collected in real time through a preset sensor, the health-related data and the multi-source detection parameters are fused, a rehabilitation training domain knowledge graph is accessed to construct a training scheme recommendation model, and a personalized rehabilitation training plan of the target user is output; according to the achievable degree of the target user for each rehabilitation training action, real-time feedback is provided through a visual interface and voice prompt, and the target user is guided to adjust the training action; a brand new solution is provided for rehabilitation training based on multi-parameter detection, the training state of the user can be monitored in real time, a personalized training scheme is generated, and the rehabilitation effect and efficiency are remarkably improved.

The invention belongs to the technical field of teaching video processing, and particularly relates to an interactive physical education system and method based on video image processing. According to the interactive physical education teaching method based on video image processing, training actions of a user can be captured in real time and are compared with standard actions for analysis, so that correct actions and deviation actions can be accurately recognized, continuous correct time periods and continuous deviation time periods can be determined through analysis of key nodes, and the accuracy of physical education teaching is improved. According to the method, the deviation action is subjected to orderliness analysis, independent deviation and progressive deviation are distinguished, targeted correction suggestions can be provided according to the type of the deviation action, the user is helped to improve the action and improve the training effect, in addition, the training action of the user can be fed back in real time according to the improvement amount, and the training effect is improved. The user can know the training effect in time, adjustment is carried out according to feedback, through continuous correction analysis and feedback, the user can gradually improve the motion quality, and the better training effect is achieved.

The invention discloses an evaluation method and device for joint rehabilitation training, and relates to the technical field of rehabilitation image processing. Receiving a monitoring request sent by a target user, and determining a to-be-evaluated part and rehabilitation duration according to the monitoring request; inputting the rehabilitation duration and the to-be-evaluated part into a preset training database for matching to obtain a first training plan; generating a first training video according to the first training plan, and obtaining a second training video corresponding to the target user; extracting a plurality of training images from the second training video, and processing a target training image to obtain a first training action; when the first training actions are in the preset action table, the first training actions are summarized into a first set, and a first number is obtained; and when the first number is not equal to a preset first number, determining that the target user is in a training abnormal state, and sending the training abnormal state to the target user. By implementing the technical scheme provided by the invention, the problem that the rehabilitation personnel are difficult to accurately evaluate the normalization of own actions in the training process is solved.

The invention discloses an upper limb rehabilitation training method and system based on a human body motion model, and relates to the technical field of rehabilitation training. Constructing a human body motion model and outputting simulation data; calculating a physiological index coefficient, and further calculating the exercise intensity of each training action; calculating a motion mode complexity index of each training action so as to obtain and calculate the training duration of each training action; and calculating a motion deviation value, and further calculating a comprehensive dynamic deviation index. Presetting a threshold value and judging whether the rehabilitation training plan is suitable for the current state of the patient; whether a rehabilitation training plan needs to be changed; according to the method, the suitability of the rehabilitation training plan can be evaluated in real time, and the training scheme is dynamically adjusted; the training is always fit with the condition of the patient, and the rehabilitation efficiency and success rate are improved.

The invention discloses a balance training coordination management method based on foot data analysis, and relates to the technical field of intelligent health and motion analysis, and the method comprises the steps: extracting gait features and fatigue indexes in a user foot comprehensive data set through a deep learning model, analyzing the balance capability and fatigue state of a user, and obtaining a quantitative evaluation report; according to the quantitative evaluation report, defining a reward function of balance ability improvement, recommending targeted training actions and recovery actions for the user, and obtaining a complete training plan, the personalized customization of the training plan is realized, the training intensity is ensured to be matched with the actual ability of the user, the problem of overtraining or insufficient training is avoided, and the training efficiency is improved. And the fatigue risk is reduced while the balance ability of the user is improved.

The embodiment of the invention discloses a model training method, a control method, equipment, a medium and a program product. The method comprises the steps of obtaining a training sample set; inputting a next training state in the training sample into the initial action network to obtain a next training action output by the initial action network; inputting the next training action, the current training state in the training sample, the current training action and the next training state into the initial value network to obtain a current target Q value output by the initial value network; if the iteration termination condition is not met, updating the initial value network, obtaining an updated initial value network, updating the initial action network and obtaining an updated initial action network according to the current target Q value, and returning to execute the step of obtaining the training sample set; and if the iteration termination condition is satisfied, determining the initial action network as a target control model. According to the model training method, rapid convergence of the target control model is realized, and the stability of the controlled equipment is improved.

The invention discloses an action model training method and device based on residual learning and computer equipment, and belongs to the field of artificial intelligence. The method comprises the following steps: acquiring a first reference trajectory of a reference hand and a second reference trajectory of an object; on the basis of the first reference track and the wrist posture and the finger posture of the mechanical dexterous hand, an imitation reward is determined, imitation training is conducted on the to-be-trained action model, and an imitation action model is obtained; determining a residual award based on the second reference trajectory and the object interaction state, and performing residual training on the simulation action model to obtain a residual action model; obtaining an action model based on the simulation action model and the residual error action model; when the action model is applied, the mechanical dexterous hand simulates the action of the reference hand. Through staged training, the first stage simulates hand movement, the second stage carries out fine adjustment on interaction constraints of object interaction tasks, and precise simulation of the mechanical dexterous hand on a reference hand and dynamic adaptation of the mechanical dexterous hand on complex tasks are achieved.

The invention belongs to the field of evaluation assistance, and particularly relates to an interactive teaching evaluation assistance system and device based on multi-modal fusion, and the system collects multi-dimensional information through a portrait module, and constructs a user training action portrait library in combination with multi-modal feature extraction and a knowledge graph algorithm; the strategy recommendation module generates an optimal guidance scheme through a correction scheme matching model according to the portrait library, the real-time information and the teaching guidance strategy library; the auxiliary adjustment module collects training action images in real time, and obtains a guided continuous correction action sequence and a standard score by using a spatial-temporal feature extraction model and an evaluation algorithm; the traceability feedback module is combined with the action standard knowledge base to obtain transverse and longitudinal spatial-temporal change chains of non-standard actions through a forward reasoning model, and feeds back the transverse and longitudinal spatial-temporal change chains to the instructor to adjust the scheme in real time until the actions reach the standard; according to the method, accurate evaluation and personalized scheme matching of multi-modal data fusion are realized, and scientificity and interactivity of evaluation assistance are effectively improved.

The present invention discloses a method and device for capturing human posture balance based on a smart wearable device, wherein the method comprises: obtaining a first training posture of a first target user; obtaining a first initial training action; collecting skeletal posture data for the first initial training action to generate a first skeletal posture collection; constructing a human posture balance correction database; inputting the first initial training action into the human posture balance correction database for training screening to obtain first preset skeletal posture information; judging whether the first skeletal posture collection meets the first preset skeletal posture information; if not, generating a first compensation instruction, and performing action compensation on the first initial training action. The method solves the technical problem in the prior art that it is impossible to collect all-round dynamic data of the user's balance rehabilitation training action, so that the user's rehabilitation action cannot meet the expected standard, thereby affecting the user's training efficiency.

The invention discloses an intelligent management method for breast cancer rehabilitation training and related equipment. The method comprises the following steps: generating rehabilitation game tasks based on breast cancer postoperative rehabilitation training actions, wherein each rehabilitation game task comprises at least one breast cancer postoperative rehabilitation training action; under the condition that the patient user executes the game task, motion sensing game video information including the patient user is collected in real time, and actual game actions of the patient user are recognized based on the motion sensing game video information; and performing comparative analysis on the actual game action and the corresponding preset rehabilitation training action, and quantifying the completion degree of the preset rehabilitation training action of the patient user so as to display the completion degree to the patient user in real time. The problems that the postoperative functional rehabilitation of the breast cancer mainly guides a patient to carry out standardized functional training through a physical therapist, but the training mode is relatively mechanical and lacks real-time feedback and interaction, the subjective participation intention of the patient is not strong, and the rehabilitation compliance is low can be solved.

The invention relates to a sports competition training action deviation detection and immediate correction method. The method comprises the following steps: collecting multi-joint dynamic data in a training process; on the basis of a preset inertial conduction continuity criterion, inertial change among all joints of the body is analyzed, and when it is detected that inertial conduction of any joint node is interrupted or an impact value is lower than a threshold value, the joint node is identified as a trigger point of action chain interruption; performing causal priority judgment on the action deviation based on the identified trigger point, determining whether the deviation is a dominant deviation or a subordinate deviation caused by an upstream joint, and if the deviation is the subordinate deviation, tracing an upstream cause and repositioning an intervention target; judging whether the current training stage is in a correction window stage suitable for feedback or not for the deviation which is confirmed to be intervened, and if not, delaying feedback output; and in the window period, trend judgment is performed on the deviation direction, if the deviation causes negative technical degradation, correction feedback is output, and if the deviation trend has positive evolution potential, the deviation is marked as acceptable deviation and is not corrected.

The invention relates to the technical field of training teaching, in particular to an exercise training action teaching system. The system comprises a training data acquisition module, a trainee action difference analysis module, a trainee training teaching module and a demonstration teaching feedback adjustment module, and can acquire corresponding training action postures and acceleration data in real time by configuring an inertial sensor on sports equipment corresponding to a trainee; meanwhile, the corresponding body consumption load condition is obtained; performing student action difference feature analysis on the training action postures and the acceleration data based on the body consumption load condition to obtain training action difference features corresponding to different students in the daily exercise training process; training teaching resources and time limiting conditions are obtained, and student training teaching analysis and teaching feedback adjustment are carried out, so that an exercise training teaching feedback adjustment strategy corresponding to the student is generated. According to the invention, scientific and personalized action teaching services can be provided for exercise training learners, so that the exercise training effect is improved.

The application belongs to the field of evaluation assistance, and particularly relates to an interactive teaching evaluation assistance system and device based on multi-modal fusion. The system collects multi-dimensional information through a portrait module, combines multi-modal feature extraction and a knowledge graph algorithm to construct a user training action portrait library. A strategy recommendation module generates an optimal guidance scheme from a portrait library, real-time information and a teaching guidance strategy library through a correction scheme matching model. An auxiliary adjustment module collects training action images in real time, uses a space-time feature extraction model and an evaluation algorithm to obtain a continuous correction action sequence and a standard score after guidance. A traceability feedback module combines an action standard knowledge base, obtains a horizontal and vertical space-time change chain of non-standard actions through a forward reasoning model, and feeds back to the instructor to adjust the scheme in real time until the action meets the standard. The application realizes precise evaluation and personalized scheme matching of multi-modal data fusion, and effectively improves the scientificity and interactivity of evaluation assistance.

One or more radio access network nodes may determine learning model configuration information for training a learning model corresponding to a user equipment in an idle mode. The node may broadcast a training configuration resource indication in the information block, the training configuration resource indication indicating resources available for broadcasting the learning model training configuration or indicating resources available for broadcasting the training results. While idle, the user equipment may decode the training configuration according to the training configuration resource indication and perform a training action indicated in the training configuration. The learning model may be trained based on the training action while the user device is idle. While idle, the user equipment may estimate radio parameters using a model trained while the user equipment is idle, and transmit the estimated radio parameters to the node for establishing a connection with the node.

The invention relates to the technical field of data processing, and in particular to a water sports training interactive method, system and medium based on virtual reality. The method comprises: in response to a training start instruction triggered by a user, a standard action model of a training task is played cyclically through a display, and N motion data are acquired through an inertial motion capture device; based on the motion data and the standard action model, the action difference of M key actions is determined, and then a target key action and a target key part corresponding to the target key action are determined; surface electromyography signals of the target key part are obtained, and an expected improvement degree of the target key action is determined based on the surface electromyography signals and the action difference degree, and the target key action with an expected improvement degree greater than an improvement degree threshold is used as a training action; in response to a reinforcement training instruction triggered by the user, interactive training is performed on the training action. The invention can improve the training effect of water sports.

The invention discloses a video-based omni-directional remote rehabilitation training system, a video-based omni-directional remote rehabilitation training method and a medium, which are characterized in that a digital standardized training video library classified according to four stages of PT physical therapy is established, and more than 700 digital standardized training videos such as joint activity, balance training, gait correction and the like are covered; it is ensured that the training content is scientific and covers the whole period and all directions of rehabilitation training; rehabilitators generate personalized training schemes in combination with rehabilitation scene types (hospitalization / home / community / old-age care institutions) and clinical data and in combination with remote video evaluation of the rehabilitators, and training requirements in different environments are met. By capturing actions of limb joint angles, joint point motion trails and muscle force changes, a training action deviation rate is calculated and is fed back and output to a rehabilitation teacher in a grading manner, so that the training standardability is remarkably improved; rehabilitators can manage multiple patients online at the same time, remotely check training percentage data, adjust schemes and generate rehabilitation notes, and efficient, accurate and personalized rehabilitation guidance services are provided for the patients.

The present invention relates to the field of muscle function assessment based on precise measurement of perioral muscle strength, specifically a device and method for rhythmic training assessment of perioral muscle strength. The method comprises the following steps: 1) collecting the resistance value signal of the perioral muscle strength during training using a thin film pressure sensor; 2) preprocessing the resistance value signal to obtain perioral muscle strength training information for a single movement; 3) calculating the effectiveness of the single training movement; 4) determining whether the training time has reached the overall training time; if so, executing step 5); otherwise, repeating steps 1) to 3); and 5) comprehensively evaluating the training movements throughout the entire training process and outputting an evaluation score for this training session. The present invention employs automated analysis methods to evaluate each training movement in real time and output the overall training effectiveness results.

The utility model discloses a split type upper and lower limb rehabilitation training device, which relates to the technical field of rehabilitation instruments, and comprises a base, a height adjusting mechanism, a stretching mechanism, two pressing mechanisms, a leg exercising mechanism and a supporting mechanism, fixing bolt holes are symmetrically formed in the positions, close to the front ends and the rear ends of the two sliding grooves, of the top wall of the base at equal intervals, the height adjusting mechanism is arranged on the sliding groove in the right side, the supporting mechanism is arranged on the sliding groove in the left side, and the leg exercising mechanism is arranged on the position, between the height adjusting mechanism and the supporting mechanism, of the top wall of the base. The stretching mechanism is arranged on the left side of the height adjusting mechanism, by adjusting the training device in multiple aspects, different patients can adjust the training device to proper training positions, so that the training device is suitable for being used by different patients, and by increasing different training actions, the training device can be used by patients with different requirements.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent that is interacting with an environment. Implementations of the system use previously learned skills to explore states of the environment to collect and store training data, which is then used to train an action selection system. The system includes a set of skill action selection subsystems, each configured to select actions for the agent to perform for a respective skill. The set of skill action selection subsystems is used to explore states of the environment to collect the training data, keeping their individual action selection policies unchanged. A scheduler neural network selects the skill neural networks to use. The action selection system is trained on the stored training data.

The application relates to the technical field of rehabilitation training, in particular to a home environment cognitive rehabilitation training action guiding system based on visual interaction. The system can realize the following steps through mutual cooperation among multiple modules: acquiring a rehabilitation training image of a target patient in each unit training period within each preset rehabilitation training period, and acquiring a cognitive recovery score corresponding to each preset rehabilitation training period; determining a target motion vector sequence and a target state value sequence; performing interval division and clustering on the training action period; determining an action proficiency index corresponding to each training action period; and determining a target recommended guiding value corresponding to a training action represented by each target cluster. The application quantifies the target recommended guiding value corresponding to different training actions by analyzing the self condition of the target patient in the rehabilitation training process, thereby improving the rationality of cognitive rehabilitation training action recommendation and the guiding effect of cognitive rehabilitation training action.

The invention provides a hand rehabilitation training control system and method, and relates to the technical field of rehabilitation medicine. The system comprises a client, a main control unit, a power mechanism and a plurality of training suites, the client is used for determining a target training function, generating a training instruction according to the target training function and issuing the training instruction to the main control unit, and the training instruction is used for indicating a target training suite corresponding to the target training function; and the main control unit is used for controlling the power mechanism to rotate according to the training instruction so as to drive the target training suite to execute the training action. According to the rehabilitation training device, the multiple training suites can share one main control unit and one power mechanism, the equipment complexity and cost are reduced, different training suites can execute different training actions, the training requirements of multiple parts such as hand, wrist and forearm training are met, personalized and precise rehabilitation training tasks are completed, and the rehabilitation training efficiency is improved. The problem that an existing hand function rehabilitation training device cannot meet comprehensive and systematic hand function rehabilitation requirements is solved.

This invention discloses a multi-level adjustable automatic arm integrated training frame, including a training frame, a smart screen fixedly connected to the outer wall of the training frame, rotation limit components on both sides of the smart screen, an angle sensor fixedly connected to the bottom of the training frame, and a training device below the rotation limit components, the training device including a fixing frame. This multi-level adjustable automatic arm integrated training frame, through the use of the rotation limit components and the training device, enables the training arm to perform multi-angle three-dimensional adjustment. Through an automatically controllable movable arm structure, it provides two degrees of freedom: vertical swing and left-right rotation. It achieves free and automated setting of resistance fulcrums within a range of multiple curved surfaces in space, realizing the automation of the integrated training frame. It can complete most training movements, greatly reducing the required fixed equipment and improving the utilization efficiency of equipment and space.

The application discloses a rehabilitation training method and system based on a human motion model, relates to the technical field of rehabilitation training, and mainly has the following scheme: collecting multiple data of a patient; constructing a human motion model and outputting simulation data; calculating a physiological index coefficient, and further calculating the motion intensity of each training action; calculating the motion mode complexity index of each training action, and then obtaining the training duration of each training action; calculating a motion deviation value, and then calculating a comprehensive dynamic deviation index. A preset threshold value is used to determine whether the rehabilitation training plan is suitable for the current state of the patient, and whether the rehabilitation training plan needs to be changed. The method can evaluate the adaptability of the rehabilitation training plan in real time, dynamically adjust the training scheme, and ensure that the training always conforms to the state of the patient, thereby improving the rehabilitation efficiency and success rate.

The invention discloses a dance teaching analysis system and method based on artificial intelligence, and belongs to the field of educational appliances. Training data in a training process and physique change data in the training process are acquired and imported into a training matching analysis strategy for training matching analysis; dance training action data are acquired and imported into a training difficulty analysis strategy for training difficulty analysis, training safety analysis is performed according to a training matching analysis result obtained through analysis and a training difficulty analysis result, and training process early warning is performed through an obtained training safety analysis result. The training condition and physical change data in the training process are comprehensively analyzed, the safety in the dance training process is evaluated, the danger in the dance process is comprehensively analyzed and evaluated according to dancers and dance actions, and human body injuries caused by forced dance training of mismatched persons are avoided.

The present application discloses a method, device, storage medium and processor for adjusting the difficulty of a sports course. The method comprises: obtaining the user's feeling information about the target action of the fitness course during the process of the user following the fitness course in the application program, wherein the fitness course comprises multiple classes, each class comprises multiple actions, each action is respectively associated with a preset action set, and each preset action set comprises multiple actions of the same type but different difficulty; selecting a first replacement action from the preset action set associated with the target action according to the feeling information, replacing the first action to be adjusted in the fitness course with the first replacement action, and obtaining an updated course; and displaying the updated course in the application program. Through the present application, the problem that the difficulty of the training action in the fitness course is difficult to match the user's own situation in the related art is solved.

The application discloses a kind of elbow joint rehabilitation training technical field's two-way assisted elbow joint rehabilitation training device and training method, including support base, turnover support and sliding slot base, the rear end of the turnover support can be rotatably connected in the front end of support base, sliding block and drive motor are slidably installed in the top of the sliding slot base, controller is further arranged in the support base, and electromyographic sensor is arranged on the support base and turnover support, patient can be helped to carry out passive training by the device, active training mode can also be provided, patient is forced to actively use muscle strength, impedance training is provided in the later stage of patient recovery, resistance is provided to patient training action, impedance is formed, impedance type rehabilitation training is generated for patient, effectively help patient to finally restore normal state of elbow joint muscle, and in the same stroke, three different training modes can also be combined with each other, to achieve the best training effect.

The application provides a lip muscle training device and method, relates to the field of lip muscle training equipment, and aims to solve the problems of poor guidance and difficulty in quantitative recording of the current lip muscle training. A guide plate with multiple guide grooves is arranged, a lip baffle plate is pulled by a flexible cable to slide a sliding block in the guide groove, the relative positions of the multiple guide grooves are different, the lip muscle active closure of the trainer is guided to pull the sliding block to move in different directions, the training action is guided to be shaped, and the number of times that the sliding block is moved to the position in the guide groove is collected by a counting component, so that the training process is recorded quantitatively.

The invention discloses a model training method and a virtual coach guidance strategy generation method, and relates to the technical field of computers. The method comprises the steps of obtaining a current state after a user executes a current training action; wherein the current state comprises an action quality score, a fatigue degree score, a self-satisfaction degree score, an understanding degree score and any multiple items in the sensor data of the sports equipment, inputting the current state into a DQN model, and obtaining a Q value of each strategy in a preset action space; selecting a current strategy from the action space based on the Q value of each strategy; executing the current strategy; after the user responds to the current strategy to execute the next training action, the next state of the user is obtained; and adjusting parameters of the DQN model based on the current state, the next state and the current strategy. According to the embodiment, more targeted training guidance can be provided for the user, and the training quality of the user is improved.

The present invention relates to the technical field of orthopedic rehabilitation management, and particularly to an adaptive orthopedic rehabilitation management system. The system includes a training module, a collection module, an evaluation module, and an adjustment module. The training module is used to assist the user in performing training actions; the collection module respectively collects the acting muscle strength, acting range, and breathing frequency of the user when performing training actions; the evaluation module is configured to generate an evaluation result for the process of the user performing training actions based on the difference between the acting range and the standard acting range, the difference between the acting muscle strength and the standard muscle strength, and the breathing frequency; the adjustment module is configured to correct the training level of the next training action based on the evaluation result, or output a rehabilitation reminder; through the comprehensive analysis of muscle strength and acting range, the orthopedic rehabilitation degree can be effectively evaluated, and then the rehabilitation training intensity can be accurately and adaptively adjusted, effectively improving the adaptability and timeliness of the orthopedic rehabilitation management system.