282 results about "Training program" patented technology

The invention discloses a respiratory training device based on multi-modal data fusion. The device comprises a multi-modal sensor, a data processing server and an interaction module, the multi-modal sensor is used for collecting multi-modal data of a user in real time; the data processing server extracts feature data of the user according to the multi-modal data; constructing a physiological feature sub-graph by taking each physiological feature as a node, constructing an emotional feature sub-graph by taking the emotional feature as a node, constructing a physiological genetic map of the user, and inputting the physiological genetic map into a physiological state analysis model to obtain a physiological state of the user; generating a respiratory training scheme according to the physiological state, wherein the respiratory training scheme comprises a multi-sensory training guide step, target key feature data and training intensity; or adjusting the current breathing training scheme based on the difference value between the extracted key feature data and the target key feature data and the training intensity; the interaction module is used for displaying the breathing training scheme. According to the invention, accurate and effective respiration monitoring can be provided for the user.

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

A training program recommendation system may monitor a user while the user is performing an assessment exercise activity. An exercise chatbot may present the user with an assessment query. The assessment query may request exercise information for the user. A training program recommendation system may apply a health assessment model to the exercise information to determine a health parameter and a user scaling factor. A training program recommendation system may apply a recommendation model to the health parameter and a user response to the assessment query. The recommendation model generates a training program recommendation for the user based at least in part on at least one of the health parameter or the user scaling factor. The training program recommendation includes a training program. A training program recommendation system may provide the training program recommendation to the user.

The invention discloses an intelligent analysis system and method for rehabilitation treatment of lumbar intervertebral disc protrusion, and relates to the technical field of medical informationization, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal medical data of a patient; the feature adaptive optimization module is used for screening a rehabilitation prediction feature set and dynamically optimizing a feature weight; the rehabilitation prediction analysis module is used for generating a rehabilitation process prediction result; the training scheme generation module is used for generating a rehabilitation training scheme; the feedback data acquisition module is used for monitoring feedback data in the training process; the rehabilitation evaluation and adjustment module is used for evaluating a rehabilitation process state and dynamically adjusting a rehabilitation training scheme; and the incremental learning updating module is used for updating the feature correlation prediction model. Through mutual cooperation and mutual promotion of the functional modules, the precision, intelligence and standardization level of lumbar intervertebral disc protrusion rehabilitation treatment is improved, and powerful technical support is provided for improvement of the rehabilitation treatment effect.

The invention belongs to the technical field of artificial intelligence, and discloses an operation behavior characteristic-driven rehabilitation training effect dynamic evaluation method, which comprises the following steps of: extracting compensatory behavior characteristics and recessive behavior mode characteristics by acquiring multi-dimensional operation behavior data and physiological sensor data of a patient on a simulated driving device; and constructing a behavior pattern migration feature set. And calculating a behavior pattern migration index based on the compensatory behavior characteristics in the characteristic set, and accurately identifying the progressive transformation process of the patient from the compensatory behavior to the normal behavior. Further constructing a rehabilitation capability dynamic evaluation model, generating a real-time rehabilitation capability score, dynamically adjusting the complexity of a simulated driving scene, collecting adaptive behavior data, constructing a behavior mode migration trend curve and a rehabilitation effect prediction model, and realizing closed-loop optimization of a personalized training scheme. According to the method, subjectivity and static limitation of traditional rehabilitation evaluation are broken through, and the accuracy and effectiveness of neural rehabilitation are remarkably improved.

The present invention discloses an eloquence training method, device, and medium based on data retrieval and fusion. The method comprises: sorting the resources by calculating the matching degree between the query word and the resource to obtain a first resource; obtaining the user's eloquence ability evaluation result, and sorting the first resource according to the result to obtain knowledge feedback suggestions; filtering the resources according to learning habits to generate personalized feedback suggestions; evaluating eloquence information according to eloquence indicators, and generating evaluation feedback suggestions according to the evaluation results; and generating an eloquence training method based on the above feedback suggestions to train the user. The present invention proposes an eloquence training method, device, and medium based on data retrieval and fusion. By considering multiple different dimensions, corresponding feedback suggestions are generated, and a comprehensive eloquence training method is constructed to train the user. This can solve the problem of difficulty in generating personalized and comprehensive eloquence training programs based on the user's actual usage needs.

The invention discloses an intelligent driving school mode virtual driving scene generation method based on an AI model, relates to the technical field of virtual driving scene generation, and is used for solving the problems of feedback lag and poor individualized adaptability of a training scheme caused by difficulty in dynamically adapting to the current stress level and operation habit of a driver. The method comprises the steps of monitoring the instantaneous speed and direction control amplitude of a simulated vehicle in real time, calculating a longitudinal G value and a steering change rate, collecting physiological data of a tested driver to analyze heart rate variability and cognitive load degree, generating driving behavior characteristics and stress characteristics by using an LSTM model, and judging whether to trigger a specific scene or not through a support vector machine based on the characteristics. The method comprises the following steps of: acquiring response time and misoperation frequency, generating a risk response score through fusion function joint quantification, and judging a driving level grade according to a score threshold value, thereby realizing screening and marking of tested drivers, dynamically adapting to actual performance, and improving pertinence and effectiveness of virtual training.

The invention relates to the technical field of vision intervention, in particular to an eye muscle regulation ability training method and a visual training graph generation method thereof.According to the eye muscle regulation ability training method, feature information of a trainee and distance information of training are obtained, and a preset image generation network model is used for generating a corresponding visual training graph; the training device is used for training the eye muscle adjusting ability of a trainee. According to the method, the initial distance point location is matched based on the eyesight data of the trainee, the corresponding vision training graph is generated according to the current distance point location and the feature data of the trainee, the training intensity is dynamically adjusted, a personalized training scheme is achieved, the eye muscle adjusting ability is helped to be improved, and the training effect is improved. The problem of poor effect or overtraining caused by inappropriate training intensity is avoided, and the technical problems that in an existing eye muscle training method, due to insufficient individual pertinence, the training effect is difficult to quantify, and training scientificity and systematicness are insufficient are solved.

A learning device includes processing circuitry configured to acquire speech data of a speaker, information on the speaker, conversation data of a listener, information on the listener, and emotion information of the listener and create a learned model of estimating a quick response of the listener to a conversation of the speaker using the acquired information with a quick response included in the conversation data of the listener as correct answer data.

The invention relates to the technical field of flight training, and particularly discloses an intelligent auxiliary training method and system for the driving skill of a helicopter pilot, and the method comprises the steps: deploying a plurality of sensors in a simulated helicopter cockpit for collecting operation, physiology, eye movement and other original data; after data standardization preprocessing, constructing a trainee training state comprehensive data model by using a multi-modal data fusion algorithm; dynamically generating a simulated flight scene by using VR and AR; carrying out comprehensive evaluation by adopting a multi-modal evaluation model based on deep learning in combination with multi-source data and student scene performance, and generating a multi-dimensional report; an evaluation result is fed back in real time through multiple channels such as voice and touch, and customized training resources are pushed; and formulating an exclusive training plan by using a personalized learning path planning algorithm based on multiple training data. Through multi-source data fusion and an intelligent algorithm, the training state of the trainee is comprehensively and accurately evaluated, a personalized training scheme is provided, and the driving skill training efficiency and quality of the helicopter pilot are effectively improved.

A system includes a data lake including data associated with training and operation of particular equipment. The system also includes one or more processors configured to execute instructions to use a training skill model to determine training skill scores for a member of an entity associated with use of the particular equipment, to use an operation skill model to determine operation skill scores for use of the particular equipment by the member, to use an anomalous state probability model to determine anomalous state profiles associated with the particular equipment for the member, and to use a decision engine to determine a training program for the member based on the anomalous state profiles.

A connected hockey training system that records and generates digital displays of the position and parameters of a smart hockey puck. The system is also configured to providing training programs about which the smart hockey puck is used to track progress of those training tasks and generate a score or other statistical parameters related thereto, such as feedback on stickhandling and shooting performance.

A reinforcement learning agent training device for automating a collage generation process includes a memory storing a reinforcement learning agent training program; and a processor configured to execute the reinforcement learning agent training program stored in the memory, wherein the reinforcement learning agent training program includes determining an action for a collage when state information including a canvas, a material, a target, and a remaining number of times is input; rendering a second canvas by applying the action to the first canvas; updating a reward based on similarity between the first canvas and the second canvas and the target; and training a reinforcement learning agent by repeating the determining of the action, the rendering of the second canvas, and the updating of the reward.

The invention relates to the technical field of medical health and artificial intelligence, in particular to a system and method for improving the cognitive function of old people based on emotion voice perception training. Comprises: a voice stimulation presentation module for controlling playing of target voice stimulation and masking voice; the training program control module is used for controlling and adjusting the voice stimulation presentation module; the reaction collection module collects the reaction of the patient and outputs the reaction to the feedback and enhancement module; the feedback and strengthening module is used for providing training feedback according to the input of the reaction acquisition module; the data processing and evaluation module is used for processing the input of the feedback and enhancement module; and the user interface and visualization module is used for providing operation and result display for patients and researchers. According to the method, emotional voice perception training and voice recognition training in a noise environment are combined for the first time, a comprehensive cognitive intervention mode based on acoustic signal processing and emotional rhythm recognition is established, and the limitation that a traditional cognitive training visual channel is poor in dependence and migration effect is broken through.

The present invention concerns a method of operation of a gymnastic machine comprising a system (2) for applying a force exerted by a user when performing a gymnastic exercise; a brake system (3) to exert a brake action in response to the force exerted by the user; a transmission group (4) of the force exerted by the user to the brake system (3); at least one detection system (6) of the force exerted by a user when performing the gymnastic exercise; and at least one logic control unit (U) connected to said brake system (3) and to said detection system (6); the method including the step of: selecting a training program that comprises the passage from at least one first target (Target_1) to at least one second target (Target_2) or setting in the machine at least one first target (Target_1) and at least one second target (Target_2), wherein the target is the value of a parameter associated with the gymnastic exercise; selecting or setting in the machine a passage mode (MODE 0, MODE 1, MODE 2, MODE 3) from the first target (Target_1) to the second target (Target_2), wherein: in a first passage mode (MODE 0), the logic control unit adjusts a brake parameter corresponding to the brake action exerted by the brake system (3) with a feedback control as a function of at least one signal from the detection system (6); in a second passage mode (MODE 1, MODE 2, MODE 3), the control logic unit maintains the brake parameter at a constant value (P_const) for a predefined period of time (T) or until the second target (Target 2) is reached.

To provide a system.SOLUTION: Provided is a personal training system using generative AI, comprising: means for generating a personalized training program based on the user's fitness level and goals; means for providing training guidance to the user based on the generated training program; and means for monitoring the user's training performance and modifying the training program when necessary.SELECTED DRAWING: Figure 1

Provided are a multi-parameter group biofeedback training method, apparatus, and system, and a storage medium. The method includes: receiving a predetermined training program and a predetermined training prescription that are set in a training management module, the training program being a combination of individual trainings, and the training prescription being a combination of long-term, time-sequenced phased trainings or training programs; and presenting the training scenario to a subject based on the training or the training prescription to execute the training, and collecting in real-time physiological data and / or target indicator data selected for the training. The training prescription includes predetermined training programs for a plurality of phases and a transition rule between the training programs for the plurality of phases. The training program includes one or more training tasks in a predetermined execution sequence. Each training task has a corresponding training scenario.

The invention belongs to the technical field of tobacco chain small object detection, and particularly discloses an anchor frame tobacco chain small object detection method based on optimized YOLOv10, and the method comprises the following steps: S1, constructing a data set, and customizing a user-defined data set; s2, optimizing the anchor frame; step S3, integrating to a YOLOv10 model system structure; s4, a training program; and S5, evaluating the performance of the optimization model. According to the anchor frame tobacco chain small object detection method based on the optimized YOLOv10, small target detection in the tobacco chain is detected by refining the anchor frame in the YOLOv10 model, and small impurities can be better adapted, so that the detection performance is remarkably improved, the influence of optimization and improvement on the overall performance is minimum, and the detection accuracy is improved. Therefore, the detection method is feasible in real-time industrial application in which the precision is preferentially considered.

To determine the type of cells which an existing scattergram cannot determine.SOLUTION: The present invention relates to a method for analyzing cells which analyzes a cell in a living body sample by a deep learning algorithm of a neural network structure, the method including the steps of flowing the cell in a flow passage, acquiring a signal intensity related to individual cells passing through the flow passage, inputting the numerical value data corresponding to the signal intensity related to the acquired individual cells into the deep learning algorithm, and determining the type of cells of which signal intensity has been acquired on a cell-by-cell basis on the basis of the result output from the deep learning algorithm.SELECTED DRAWING: Figure 1

Systems and methods for creating personalized exercise programs are disclosed. An image capture device and a computer device are used to capture images of a user while the user performs athletic movements. The images may then be evaluated to create a human movement screen score. The human movement screen score, goal and time commitment information may then be used to create a personalized exercise program tailored to the specific user.

A learning device includes processing circuitry configured to acquire speech data of a speaker, information on the speaker, conversation data of a listener, and information on the listener, and create a learned model of estimating a quick response of the listener to a conversation of the speaker using the information acquired with a quick response included in the conversation data of the listener as correct answer data.

To provide a system for providing expert knowledge and appropriate assistance to children having developmental disorder and their families.SOLUTION: Provided is a system for providing children having developmental disorder and their families with a learning environment and learning method, whole-day action management, communication skill training, and introduction to reliable experts. The system includes: means for providing a learning environment that can be customized to suit the characteristics of children; means for managing the actions of children and proposing appropriate schedules and routines; means for providing training programs for assisting to improve communication skills; and information provision means for promoting linkage for collaboration with experts or service providers.SELECTED DRAWING: Figure 1

1. Name of the product in this design: Computer with graphical interface and training program (Dynaxis). 2. The purpose of this design product is for program operation and the operation of training programs provided by therapists to patients. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design points: Change state diagram 1. 5. If the design points are not involved, omit the rear view; if the design points are not involved, omit the left view; if the design points are not involved, omit the right view; if the design points are not involved, omit the top view; if the design points are not involved, omit the bottom view. 6. The purpose of a graphical user interface is to enable program execution. 7. Human-computer interaction method of graphical user interface: Main view: The displayed graphical user interface is the login interface; Change state diagram 1: The displayed graphical user interface is the main interface of the system after successfully logging in by entering the device account and password in the main view; Change state diagram 2: The displayed graphical user interface is the prompt interface of the current training task when the start button in the plan bar is clicked in Change state diagram 1.

The systems and methods described herein present a virtual coach and / or real-time modified workout that tailors, in real-time (or near real-time), an ongoing workout as the user progresses through different segments of the workout. For example, a workout system may guide, prompt, or otherwise cause a user to experience different branches or paths of an overall workout based on their needs, interest, or performance (e.g., metrics captured by sensors) during the workout and / or during an overall training program or plan. Further, the workout system may modify voice instructions presented to the user during a workout, such as when the user progresses between workout segments.

A non-transitory computer-readable recording medium stores therein a training program that causes a computer to execute a process including first inputting a first image capturing a target compound to an encoder of an auto-encoder including a latent space that is isometric with respect to an input space, second inputting a latent variable output by the encoder and a typical compound model corresponding to a typical case of a three-dimensional structure of the target compound to a decoder of the auto-encoder, and updating parameters of the encoder and the decoder, based on a reconfiguration error between a second image reconfigured based on an output of the encoder and the first image.

The invention provides a neural feedback training scheme automatic screening and effect suggestion method. The method comprises the following steps: acquiring biological data of a testee by using a brain wave collection device; wherein the brain wave collecting device comprises a household brain wave collecting device; transmitting the biological data to a brainwave database of a remote cloud system through a network; the brain wave database converts the biological data into a corresponding training parameter suggestion; and remotely feeding back the training parameter suggestions to a neural feedback training module for the testee to perform heart and brain training. Therefore, suggested training parameters are provided through a software interface, and superior and inferior brain area networks, digital therapy suggested training schemes and related health education are arranged, so that the effects of long-distance feedback and improvement of cognitive competence by brain training at home are achieved.

The invention discloses a physical fitness assessment index dynamic generation method oriented to a military training outline, and aims to solve the problem that existing military physical fitness assessment indexes are static and uniform and cannot adapt to individual differences and real-time states. The method comprises the following steps: carrying out digital analysis on a military training outline to extract a benchmark assessment index; acquiring and constructing individual feature vectors and real-time state data streams of soldiers; on the basis of an adjustment factor calculation model, a dynamic adjustment factor is calculated by fusing the outline benchmark, the individual features and the real-time state data; adjusting a reference index by using the factor, applying a safety boundary constraint, and generating a personalized dynamic assessment index set; and performing index fine adjustment and recording feedback according to real-time data in the assessment process. According to the method, the conversion of the assessment indexes from static unification to dynamic individuation is realized, the scientificity, fairness and safety of assessment are improved, and a continuously optimized closed loop is formed.

Methods and systems for generating physiological metrics for display on a computing device are disclosed herein. In some implementations, an exemplary system comprises a torso wearable device including a wireless transmitter and a sensor, and non-transitory computer-readable media (CRM). The CRM, when executed by a computing device in communication with the wireless transmitter, can perform operations comprising: receiving the input signals representing respiration of the user; displaying a graphical user interface to the user; receiving one or more user inputs associated with a desired metric; displaying on the graphical user interface a first visualization corresponding to the desired metric; and displaying on the graphical user interface a second visualization corresponding to a physiological metric based on the input signals generated from the sensor. The second visualization can change in real time based on real time angular displacement of the sensor in response to the respiration of the user.

This invention provides a method and system for training and evaluating pilots' cognitive abilities, relating to the fields of cognitive science and computer-aided training technology. Based on a training and evaluation system integrating personnel management, evaluation management, and training management, it completes a phased cycle of initial assessment, competency training, periodic assessment, and targeted training. The system collects operational data and adaptively adjusts training difficulty based on difficulty parameters, allowing assessment results to directly drive training program configuration. Furthermore, through the fusion analysis of multiple rounds of assessment and training data, it generates a digital profile of cognitive competence, achieving precise quantification and continuous improvement of pilots' cognitive abilities. The training effects of this invention can be transferred to real-world mission scenarios, making it suitable for large-scale application.