1477 results about "Training effect" patented technology

The invention provides an intelligent rehabilitation training method and system based on artificial intelligence and virtual reality, a user wears an intelligent wearable device to collect multi-modal data such as electroencephalogram, myoelectricity, physiological features and motion signals, the multi-modal data is preprocessed and then input into an artificial intelligence training model, and key features are extracted and fused by using a graph convolutional network and an attention mechanism algorithm. And constructing a digital twinborn model by using the fusion features, performing real-time dynamic mapping and predictive simulation, and generating a customized training scheme by means of a reinforcement learning algorithm in combination with a rehabilitation target and a physical state of the user. A user is trained in the virtual reality interaction model, the system monitors actions and physiological states in real time, the digital twin model synchronously acts, and the scene is dynamically adjusted. After training, the rehabilitation effect is evaluated according to the physiological indexes, the motion data and the twinning optimization analysis result, and an optimization training scheme and a digital twinning model are fed back. Precision, individuation and intelligentization of rehabilitation training are achieved, and the training effect and quality are improved.

The invention relates to the field of man-machine interaction training, virtual reality and intelligent evaluation, and particularly discloses a psychological exercise ability training method and system based on mixed reality and behavior analysis. The method comprises the steps that a multi-scene psychological training environment is constructed and presented in mixed reality equipment, action behavior data, eye movement path data and operation response time data of a pilot in the training process are collected, multi-modal original behavior characteristics are constructed, and then a psychological exercise ability label set is constructed; and realizing capability-task modeling through matching of a capability label and a task structure, generating a personalized training path and a capability difference atlas based on difference analysis with a preset capability standard, adjusting task parameter configuration data, dynamically updating a training scene, generating a capability evolution trajectory, and outputting a training analysis result. In this way, accurate ability modeling, task dynamic adaptation and visual tracking of the training effect can be achieved, and the individuation and intelligence level of psychological exercise ability training is remarkably improved.

The invention provides a VR-based dynamic visual function training method and device, a medium, a program product and a terminal. An initial training task type and configuration parameters are generated according to a visual evaluation result of a patient, a sighting mark and a stimulation signal for visual function training are constructed, and training is executed through a VR unit. In the training process, feedback data of a patient is collected in real time, a real-time evaluation result is analyzed and generated, task types, configuration parameters and sighting marks are dynamically adjusted, and new stimulation signals are generated to continue training, so that dynamic optimization of training content is achieved. Through real-time evaluation and dynamic adjustment, the problem that the training content is not matched with the requirements of the patient is solved, the defects of a traditional method in the aspects of individuation and adaptability are overcome, the training effect is remarkably improved, the requirements of the patient are better met, the training time is shortened, and the efficiency is improved. The device is especially suitable for rehabilitation training of various visual function defects, and has remarkable advantages in the aspect of dynamic visual function recovery.

The invention relates to a muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals, which comprises the following steps of: (1) acquiring the surface electromyogram signals of a measured muscle area by using a non-invasive sensor, and acquiring muscle mechanics parameters at the same time; (2) carrying out filtering, amplification and noise reduction preprocessing on the collected surface electromyogram signals to eliminate interference and enhance signal quality; and (3) extracting multi-dimensional features from the preprocessed surface electromyogram signals and muscle mechanics parameters, establishing a frequency domain data set by using the feature values, and calibrating the muscle mechanics parameters. The method has the advantage that the prediction accuracy and reliability are enhanced. Muscle strength and muscle fatigue states of different individuals can be reflected more accurately, and accurate prediction of individual differences is realized. The adopted surface electromyogram signal acquisition mode is non-invasive, and a plug-in electrode is not needed, so that the test discomfort is reduced, and the acceptability and participation degree of a testee are increased; by establishing an individual model and combining with real-time signal acquisition, real-time monitoring and prediction of muscle strength and muscle fatigue can be realized, and a user can obtain feedback of a muscle state at any time, so that the exercise intensity, posture and training plan are adjusted, and the training effect is optimized.

The invention relates to the technical field of human resources, in particular to a human resource intelligent allocation optimization system, which comprises an occupational analysis and prediction module, an occupational path planning module, an emergency allocation management module and a human resource optimization module. According to the invention, by integrating the occupational history and skill data of the medical personnel, accurate prediction and dynamic deployment of occupational paths are realized, talent matching is more accurate through accurate occupational conversion point analysis, resource waste is reduced, and occupational training recommendation is customized for hospital business targets, so that the pertinence and effectiveness of training are improved; the talent training process is optimized, the training effect of professional talents is improved, under the emergency condition, the system can respond in time through real-time data monitoring, rapid and effective manpower allocation is carried out, it is ensured that a hospital can keep efficient operation before emergency medical requirements, through continuous data updating and feedback, a manpower resource allocation strategy is continuously optimized, and the training effect of the professional talents is improved. And the adaptive capacity and the management efficiency of the hospital in a changeable environment are improved.

The invention belongs to the technical field of data processing, and relates to an intelligent exercise training method based on Internet of Things, which comprises the following steps: firstly, acquiring exercise state information and heart rate state information selected by a user, and transmitting the heart rate state information and the exercise state information to edge computing equipment; secondly, the edge computing device constructs a heart rate prediction model according to the real-time heart rate state information and the motion state information of the user, and meanwhile the edge computing device feeds back heart rate prediction information output by the heart rate prediction model in real time to the user in real time; meanwhile, the edge computing device evaluates the fatigue degree of the user according to the heart rate prediction information and the heart rate state information of the user at the time pointed by the heart rate prediction information; and finally, when the fatigue degree of the user in the motion process is abnormal, the edge computing device outputs a fatigue alarm to the user. By means of the technical scheme, real-time data collection, analysis and feedback in the exercise training process are achieved; meanwhile, the invention further designs a device for improving the training effect and electronic equipment.

The invention provides an intelligent fitness guidance system and method based on AI posture analysis and MR man-machine interaction. The system comprises a 3D depth global camera module used for capturing RGBD image data of a user; the AI-based fitness plan recommendation module is used for planning a fitness plan for the user; the dynamic adjusting module is used for analyzing the physical fitness change of the user and dynamically adjusting the training plan according to the actual physical fitness condition of the user; the instant feedback module is used for collecting feedback of the user on the training effect; the AI-based body-building action analysis module is used for identifying and analyzing body-building actions of the user based on the multi-modal body-building data of the user; the AI-based virtual coach interaction module is used for performing real-time communication with the user based on the fitness knowledge large model; and the fitness action demonstration module based on 3DMR imaging is used for projecting a virtual 3D fitness coach and a virtual character similar to the posture of the user. According to the invention, intelligent and personalized fitness guidance can be provided for the user.

The invention provides a physical training posture correction method based on machine vision, which comprises the following steps: acquiring an athlete training image sequence through a multi-view image acquisition system and preprocessing the athlete training image sequence, extracting three-dimensional posture key points of a human body by utilizing a depth posture recognition model, constructing a skeleton model and calculating real-time kinematics characteristic parameters, and performing multi-dimensional comparison with standard parameters to generate a posture deviation evaluation report, thereby generating a personalized multi-modal correction feedback scheme, and establishing a personal athlete movement feature database to realize adaptive standard parameter optimization. According to the method, the training postures of athletes can be accurately recognized and corrected in real time, the training effect can be improved, sports injuries can be prevented, and training individuation and scientificity are improved.

The invention relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

The invention relates to the technical field of spine rehabilitation training, and discloses a computer vision recognition and guidance system for spine rehabilitation training actions. The system comprises a data acquisition module for acquiring user spine motion three-dimensional dynamic image data by using a multi-camera array; the action recognition module tracks bone joint points based on an improved algorithm to generate a real-time action sequence; the posture evaluation module is used for extracting spine curvature and joint angle features through a multi-scale convolutional neural network; the guidance instruction generation module inputs the features into a reinforcement learning model to generate a personalized instruction; and the feedback execution module constructs a fuzzy self-adaptive control model to correct the motion trail of the spine. According to the system, training actions can be accurately recognized, personalized guidance is provided, real-time correction is achieved, the spine rehabilitation training effect is effectively improved, subjectivity and errors of manual evaluation are reduced, and rehabilitation requirements of different patients are met.

The invention belongs to the technical field of virtual reality, and discloses a virtual rich real environment training system based on benign stress, comprising: a database module for storing cognitive tasks and virtual scenes; the acceptance testing module is used for testing a preset cognitive task of the user and generating a scene acceptance index; and the initial training task generation module is used for generating a training task comprising two groups of scene parameters and one cognitive task according to the index. And the training execution module is used for constructing a dynamically changing virtual training scene according to the two groups of parameters and executing a cognitive task in the virtual training scene. And the training task updating module is used for generating a new training task based on the execution result and the acceptability index. And the training planning module formulates an overall training plan and controls an execution process. Personalized training content is provided by evaluating individual differences of the users, it is ensured that the training difficulty is matched with the capabilities of the users, and the training effect is optimized while the user participation degree and the possibility of insisting in training for a long time are improved.

The invention relates to the field of medical informatization, in particular to a multifunctional acupuncture point training simulation system and an application method thereof. Simulation tissue, force feedback, an acupoint simulation database and an intelligent evaluation technology are fused, and a vivid acupuncture training environment is created. The system simulates human tissues layer by layer, accurately collects acupuncture pressure and resistance, feeds back force feeling in real time, intelligently evaluates acupuncture performance in combination with acupuncture point characteristic data, and generates a detailed evaluation report. The virtual reality display module visually displays the acupuncture process and provides all-around feedback information. According to the innovative design, the acupuncture training effect and efficiency are remarkably improved, a safe and efficient learning platform is created for students, the modernization and standardization process of acupuncture education is promoted, and new power is injected for traditional medical inheritance and development.

The invention discloses an intelligent training method for preventing lower limb thrombus and a related device, and the method comprises the steps: inputting the physiological parameters of a patient, the initial exercise ability evaluation information and the personalized rehabilitation target information into a DQN multi-mode fusion model, and generating an ankle pump exercise training scheme meeting the rehabilitation demands of the patient; monitoring ankle pump motion execution information of the patient in real time through wearable equipment; according to the ankle pump motion training scheme and the ankle pump motion execution information of the patient, a lower limb thrombus training effect evaluation result of the patient is calculated, and the lower limb thrombus training effect evaluation result comprises the blood circulation improvement degree, the joint motion range and the gait characteristics; according to a lower limb thrombus training effect evaluation result of the patient, predicting lower limb thrombus potential recovery disorder and a falling risk level of the patient, and starting a grading alarm program according to the potential recovery disorder and the falling risk level. The training effect is comprehensively evaluated through the multi-modal data fusion training method, and the lower limb thrombus rehabilitation efficiency is improved.

The invention discloses a caesarean section training method and system based on mixed reality, and the method comprises the steps: obtaining the medical image data of the pelvis and abdomen of a pregnant woman, recognizing and marking the boundary and region of a key tissue structure, constructing a three-dimensional model of the key tissue structure, and generating key indexes of caesarean section training. Importing the constructed three-dimensional model into an MR system, collecting action data, generating a real-time feedback prompt, receiving a training ending signal, generating an evaluation result, recording operations which do not conform to an evaluation standard, and providing targeted improvement suggestions for trainees. According to the method, the quality and efficiency of operation training can be effectively improved, a safe, efficient and immersive training platform is provided for trainees through precise processing of medical images, efficient reconstruction of three-dimensional models, real-time feedback and personalized operation evaluation, personalized guidance can be carried out according to the performance of the trainees, and the training efficiency of the trainees is improved. And the training effect of surgical skills is further improved.

The invention discloses a Pick ball motion trail tracking and tactical decision-making auxiliary system which adopts a high-frame-rate camera, a micro sensor installed on a ball and an athlete motion sensor worn on an athlete to respectively collect video image data, ball motion data and athlete motion data in the Pick ball motion process. Precise positioning and trajectory tracking are carried out on a ball through a target detection and tracking algorithm based on deep learning, a tactical knowledge model is constructed by combining site information and athlete action data and applying a decision tree and a neural network algorithm, real-time tactical decision suggestions are provided for athletes and coaches, and the training efficiency is improved. And the competitive level and the training effect of the Pick ball sports can be improved.

The embodiment of the invention provides a cognitive training method and system based on an eye movement tracking control technology, and relates to the field of eye movement tracking, and the method comprises the steps: obtaining test data, corresponding to a plurality of eye movement test tasks, of a tested person; wherein the various eye movement test tasks comprise eye movement test tasks corresponding to each cognitive ability to be evaluated, aiming at each cognitive ability, according to eye movement test data, related to the cognitive ability, of the testee, index values of various ability evaluation indexes, corresponding to the cognitive ability, of the testee are determined; and determining the ability score corresponding to the cognitive ability, and determining a matched target cognitive training game according to the ability scores corresponding to various cognitive abilities of the testee. According to the method, the cognitive competence is evaluated based on the eye movement data of the testee, and the matched game is selected according to the evaluation result to train the cognitive competence, so that the interestingness of cognitive training is enhanced, and the training effect of cognitive training is remarkably improved.

The invention relates to the field of swimming physical training analysis, and particularly discloses a swimming underwater physical training effect analysis system based on training data, which is characterized in that video data, motion data and physiological data of a swimmer are acquired by integrating an underwater camera, an inertial measurement unit, a myoelectricity sensor, a heart rate sensor and a blood oxygen sensor; space-time synchronous analysis of multi-modal data is realized; on the basis of joint angle change consistency and muscle activation synchronism, whether the action of the swimmer is standard or not is judged, AR real-time feedback is conducted, and real-time correction of the swimmer in training is facilitated; the training load of the swimmer is evaluated based on the underwater speed, the stroke, the stroke frequency, the swimming distance and the swimming duration, and overrun early warning is carried out, so that making of a personalized training plan is facilitated, and meanwhile, overtraining is avoided; by monitoring the heart rate, the oxyhemoglobin saturation, the muscle activation duration and the joint activity condition of the swimmer, whether the swimmer has the injury risk or not is judged, early warning is triggered, and the injury occurrence rate is reduced.

The invention discloses a spine load monitoring system for dynamically adjusting a rehabilitation plan after an operation, and relates to the technical field of spine load monitoring, a monitoring data acquisition module collects preoperative and postoperative multi-modal data, and provides a comprehensive basis for the rehabilitation plan; a rehabilitation training initial parameter setting module determines expected recovery effect data and initial parameters of each stage, so that rehabilitation training is more targeted; in the rehabilitation process, the recovery state evaluation module evaluates training and recovery effects in real time and discovers problems in time; the rehabilitation training initial parameter adjusting module adjusts parameters when the recovery effect does not reach the standard, it is ensured that the rehabilitation plan always conforms to the condition of the patient, and the problem that in the prior art, a personalized rehabilitation plan cannot be made accurately according to preoperative and postoperative multi-modal monitoring data of the patient is solved. The training effect is difficult to dynamically evaluate in the rehabilitation process, and rehabilitation training initial parameters are difficult to adjust in time to guarantee the effectiveness and safety of the rehabilitation process.

The invention discloses an intelligent shooting range training system based on real-time data acquisition, relates to the technical field of intelligent shooting range training based on real-time data acquisition, and is used for solving the problem that an existing intelligent shooting range training system based on real-time data acquisition only pays attention to a single aspect of a training effect. The system is difficult to provide personalized training suggestions according to specific conditions of trainees; and a traditional training method may lack scientific verification. Comprising a real-time information acquisition module, an information analysis module, a scene analysis module and an optimization processing module. The real-time information is acquired through the real-time information acquisition module, and the behavior and physiological data are analyzed by the information analysis module to obtain comprehensive and deep analysis, so that the pertinence and effectiveness of training are improved; the method comprises the following steps: identifying different scene mode factors and setting corresponding evaluation weights; personalized improvement suggestions are provided for trainees, the best physiological state can be kept, the training effect is comprehensively improved, and the training experience is optimized.

The invention relates to the technical field of training simulation, and discloses a VR-based gas safety emergency training system, which comprises a three-dimensional scene construction module, an interactive data acquisition module, an interactive data processing module, an examination data analysis module and an examination evaluation module, entity drilling is replaced, the same scene can be repeatedly trained for infinite times, and the training cost is remarkably reduced; the VR technology provides multi-sensory stimulation, the intuitive cognition of students on danger is enhanced, the safety awareness and the emergency response capability are effectively improved, and no danger exists; by collecting operation data of students, the system automatically calculates key coefficients such as equipment operation precision, process compliance, disposal timeliness and path planning rationality, and scientific quantification and accurate scoring of training effects are achieved; and automatic grading evaluation is carried out according to a grading result, and a customized remedy module is triggered for students with insufficient abilities, so that a'training-assessment-intervention 'closed loop is formed, and the overall training efficiency is improved.

The invention provides a personalized training plan generation method and system in meta-universe smart sports, and relates to the technical field of smart sports, and the method comprises the steps: obtaining user meta-universe scene sports data, calculating an actual sports load based on a human body biomechanical model, carrying out the time sequence analysis through a neural network, and predicting a physiological limit threshold value; and constructing a training knowledge graph, generating a stage training plan adaptive to the user through a graph neural network, and finally presenting the stage training plan in a meta-universe virtual scene form. According to the method, personalized, safe and scientific training guidance can be provided, and the training effect and the user experience are improved.

The invention discloses a concentration training method and system based on multi-modal fusion, and the method comprises the steps: collecting an electroencephalogram signal, an eye movement signal and a head movement signal through a wearable device in a concentration training process, and calculating an electroencephalogram frequency characteristic parameter, an eye movement parameter and a head movement modal parameter in a one-to-one correspondence manner; calculating a concentration score according to the electroencephalogram frequency characteristic parameters, the eye movement parameters and the head movement modal parameters so as to evaluate the concentration; and adjusting a concentration training strategy according to a concentration evaluation result. According to the method, the electroencephalogram, eye movement and head movement signals are collected at the same time through the wearable device, the corresponding characteristic parameters are calculated, the concentration score is comprehensively evaluated, and a comprehensive, accurate and stable evaluation result is provided. On the basis of real-time evaluation, task difficulty and types are dynamically adjusted, challenging and interestingness are ensured, the user participation degree and the training effect are improved, and concentration research and technical progress are promoted.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention relates to a method and device for determining a rope skipping recommendation plan based on a large model, and belongs to the technical field of training guidance. Determining a class exercise intensity score and an after-class exercise intensity score of the user; determining an after-class exercise quality score, a training effect score, a training stability score and a physique score; determining a physique similarity score, an exercise intensity similarity score and an exercise attitude similarity score; determining plan recommendation degree scores of the training plan of the user i in the current semester and the training plan of the user r in the current semester; according to the plan recommendation degree score, determining a training plan recommended by the semester; constructing a fine tuning data set, loading the large model, and finely tuning the large model to enable the large model to learn how to generate a rope skipping recommendation plan; and outputting a rope skipping recommendation plan through the large model. According to the method, the accurate rope skipping recommendation plan can be generated by fully utilizing the historical use data, and dynamic and personalized rope skipping guidance is provided for the user.

The invention provides a cooperative control method and system for integrated rehabilitation equipment, and the method comprises the steps: obtaining to-be-trained data and communication data, carrying out the preprocessing, and taking the sub-equipment in each rehabilitation equipment as a node to construct a training sub-graph; according to the training sequence, sequentially filling the preprocessed data to be trained into the corresponding training sub-graphs to obtain initial training sub-graphs; then the target rehabilitation device with the highest intelligent degree is determined, the convenience of user operation is improved, the initial training sub-graph of the target rehabilitation device serves as a main graph, and the remaining initial training sub-graphs serve as auxiliary graphs; mapping all the auxiliary graphs into the main graph according to a principle of combining nodes with the same training effect to form a complete training graph; and converting the complete training graph by using a corresponding communication conversion strategy to enable the rehabilitation equipment to complete rehabilitation training according to the complete training graph. According to the invention, a plurality of existing rehabilitation devices can be synchronously trained, the user operation is convenient, and the economic and human input can be reduced.

The invention provides a football and training assembly. The football comprises a ball body and an identification area arranged on the surface of the ball body. And the identification area comprises a ball outlet guide line, a ball placing line and a ball hitting point group which are respectively arranged on the surface of the ball body. The ball outlet guide line and the ball placing line are located on different sections. And each section passes through the center of the sphere. The ball hitting point set comprises a plurality of ball hitting datum points distributed at intervals. The section where the distribution path of the ball hitting datum points is located coincides with the section where the ball outlet guide line is located. The horizontal midline section is defined as a section passing through the center of the sphere. When the section where the ball outlet guide line is located and the section where the ball placing line is located are both perpendicular to the ground, the section of the horizontal center line is parallel to the ground, and the ball hitting datum points are distributed downwards from the section of the horizontal center line. According to the football and training assembly provided by the invention, the training efficiency and the training effect of the place kick can be improved.

The invention relates to the technical field of medical rehabilitation, in particular to a lung rehabilitation training auxiliary system based on virtual reality. Comprising an equipment wearing module, an education module, a virtual scene generation module, a rehabilitation training scheme customization module, a data acquisition and monitoring module, a real-time feedback and guidance module, a training data recording and analysis module and a user interaction and management module. The motion sensing device, the respiration monitoring device and the like are utilized, by means of the interaction function of the motion sensing device, the respiration monitoring device and the like, limb actions and respiration conditions of a patient are captured in real time, interaction with elements in the virtual environment is carried out, real-time feedback and accurate guidance of training actions and respiration states of the patient are achieved, the training interestingness is greatly improved, and the training effect is improved. And the lung rehabilitation training auxiliary system based on virtual reality customizes personalized rehabilitation training schemes fitting own conditions for different patients, training item selection, intensity arrangement, frequency setting and the like can be varied from person to person, and the promotion effect of rehabilitation training on lung function recovery is played to the maximum extent.

The utility model relates to the technical field of sitting posture correction, and discloses a child sitting posture training device which comprises a correction assembly, the correction assembly comprises a first cushion block and a second cushion block, the first cushion block is provided with a first top face and a first bottom face which are oppositely arranged, and the second cushion block is provided with a second top face. The second cushion block is provided with a second top surface, a second bottom surface and a plurality of side surfaces, the second top surface and the second bottom surface are oppositely arranged, the side surfaces are arranged between the second top surface and the second bottom surface, at least two adjacent side surfaces are arranged at a preset included angle, the second bottom surface is detachably connected with the first bottom surface, and the size of the second bottom surface is smaller than that of the first bottom surface; when the first cushion block is located at the supporting position, a child patient can sit on the second top face and step on the first bottom face, the two legs of the child patient can abut against the two side faces arranged at the preset included angle respectively, when the first cushion block is located at the table top position, the two legs of the child patient can abut against the two side faces arranged at the preset included angle respectively in the sitting posture state, and the first top face is used for containing objects. Adjustability and use flexibility are good, operation is convenient, and the training effect is good.

The invention provides a multifunctional rehabilitation robot real-time monitoring and intelligent regulation and control system for ICU-AW patients. Comprising a trial operation data transmission self-inspection module, an initial data acquisition module, a rehabilitation data real-time monitoring module and a rehabilitation training parameter real-time regulation and control module. According to the method, the basic physiological data of the user is collected and analyzed to obtain the personalized rehabilitation characteristic coefficient, so that the training parameters of the robot are initialized, the suitability of the training parameters and the requirements of the patient is ensured, the rehabilitation efficiency is improved, and training injuries are prevented. In addition, the system also performs pilot run self-inspection, so that the data transmission quality is ensured, and the analysis efficiency and accuracy are improved. In addition, the system monitors physiological and training data in rehabilitation training in real time, provides immediate feedback, dynamically adjusts training intensity and rhythm, optimizes the training effect and reduces the training risk.