397 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 cluster-based training method includes: in response to a hardware fault in the training node, selecting a target standby node from the plurality of standby nodes, and obtaining a target training snapshot of the model training task in the training node, in which the target training snapshot includes training state data of the model training task; and initializing the target standby node based on a container image of a model training program in the training node and the training state data to replace the training node with the target standby node to continue executing the model training task.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an olfaction training program by an online service executing on a server. A first object and an aroma of the first object is transmitted to the client device of a user that presents the first object in a virtual reality environment and disperses the first aroma. In response, the user interacts with the vital reality environment. The user response is transmitted back to the online service. The user response is stored in a database along with the first data and the first aroma. The online service determines a second object and an aroma of the second object based on the user response. The second object and the aroma of the second object is transmitted to the client device.

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 discloses a children mode and education content processing method and device based on artificial intelligence, and the method comprises the steps: when a smart television is in a children watching mode, controlling to start an eye protection mode, automatically starting blue light filtering, and automatically adjusting the brightness to be suitable for children watching, and filtering television programs only allowing the playing of children television programs; starting AI intelligent content screening and recommendation, screening the education content of the children of the right age, and outputting and playing; receiving interactive learning feedback data of the user; and detecting and recording use detail data of the smart television in the child mode in real time, analyzing the use detail data, identifying the interest point and learning progress of the current child user, and generating a visual report. Through a special child watching mode, the education content and parent control functions are integrated, the equipment use condition of the child can be effectively supervised, and the recommended education cultivation scheme is output through the AI technology, so that convenience is provided for the use of the user.

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.

The invention relates to a multi-mode stimulation dysphagia training optimization method. According to the technical scheme, the method comprises the following steps that basic physiological data including age, gender and medical history of a patient and real-time swallowing data including throat muscle activity, electrophysiological signals and swallowing pressure of the patient are collected; performing fusion processing on the real-time swallowing data S1.1 by using a multi-modal data fusion technology to generate a personalized swallowing function evaluation report; on the basis of an evaluation result, a personalized dysphagia training scheme is generated through an intelligent algorithm, and the personalized dysphagia training scheme comprises a combination of multiple stimulation means such as tactile sensation, electrical stimulation, light stimulation and sound stimulation; constructing a real-time data acquisition and feedback system, and monitoring multi-modal data including muscle activity, sound signals, facial expressions and respiratory rate of a patient in a training process in real time by using a sensor; through a dynamic time warping (DTW) algorithm, real-time monitoring data is compared with an expected index in a training scheme, and the stimulation intensity and mode are adjusted in real time.

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.

The invention relates to the technical field of vision health care and myopia prevention and control, and discloses a myopia adjusting system for AI intelligent vision training, which comprises a vision training device for dynamically adjusting the vision of a user; the AI algorithm module is used for generating a personalized training scheme according to the vision data of the user and adjusting training parameters in real time; the cloud data processing module is used for storing and analyzing vision data and training records of a user, and the invention further discloses a myopia adjusting method for AL intelligent vision training, and the method comprises the following steps: S1, collecting the vision data of the user, and inputting the vision data into the AI algorithm module; a personalized training scheme is generated by using an AI algorithm, multiple training modes are supported, training parameters are adjusted in real time through a dynamic focusing assembly, the optimization effect is ensured, the system automatically generates a training report for a user to check and adjust, meanwhile, real-time monitoring is achieved through a range finder and a wearing sensor, and training safety and intelligence are ensured.

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.

The invention discloses an intelligent detection exercise rehabilitation system, and particularly relates to the technical field of exercise rehabilitation. The system comprises a personalized rehabilitation intelligent planning module for making a personalized rehabilitation plan for each patient by using artificial intelligence and a big data technology, and an intelligent wearable device module for realizing high-precision data acquisition and long-time monitoring through an advanced flexible sensor and an energy collection technology. By integrating personalized rehabilitation intelligent planning, intelligent wearable equipment, intelligent interactive feedback and a data security and privacy protection module, comprehensive rehabilitation support is provided for a patient, artificial intelligence and a big data technology are utilized by the system, comprehensive assessment of the exercise ability and the health state of the patient is achieved, and the rehabilitation effect of the patient is improved. And immersive rehabilitation training experience is provided through the virtual reality technology, the system can dynamically adjust the training scheme through the emotion recognition technology and the adaptive learning algorithm, and individuation and effectiveness of the rehabilitation process are ensured.

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.