55 results about "Rehabilitation interventions" patented technology

The invention discloses a standing and walking test method and system based on an inertial sensor and a myoelectricity technology, and relates to the technical field of gait evaluation and rehabilitation detection, and the method comprises the following steps: completing data acquisition by using the inertial sensor and the myoelectricity technology; comprehensively evaluating the difference between the left and right support phases and the swing phase through a first algorithm, and judging whether the gait is symmetrical and stable or not; and evaluating the muscle coordination of the patient through a second algorithm. According to the method, synchronous data fusion of the behavior layer and the neural control layer is achieved, a data foundation is laid for follow-up construction of an accurate and multi-dimensional gait and myoelectricity evaluation model, joint evaluation of two core indexes of gait symmetry and stability is achieved, the detection capacity of neurological gait disorders is improved, and the method is suitable for being used for the field of neurological gait disorders. Reliable criteria are provided for individualized rehabilitation intervention, modeling and difference measurement of time sequence regulation and control characteristics are achieved, and the recognition capacity for neuromuscular control disorders is improved.

The invention discloses an emotion rehabilitation intervention method based on face recognition. The method comprises the steps that S1, a user face image sequence is collected through a front camera; s2, performing image preprocessing to generate a standardized image tensor set; s3, facial dynamic features are extracted through an improved BiFormer network, emotional states are recognized, and a multi-dimensional emotional state sequence and a facial expression coding sequence are generated; s4, jointly analyzing the emotion change trend and the expression behavior mode of the user through a multi-mode RETNet network, and outputting a psychological state grade and a future emotion fluctuation score sequence of the user; s5, matching an emotion rehabilitation intervention strategy through a preset rule engine; s6, executing the emotion rehabilitation intervention strategy, collecting user data in real time, and generating a user response scoring sequence; and S7, performing incremental updating on the multi-modal RETNet network and a preset rule engine based on the user response score sequence. According to the invention, the accuracy of emotional state recognition and the intelligent level of intervention strategy pushing are improved.

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention relates to the technical field of medical data analysis. By providing the multi-modal fusion ACLR post-rehabilitation intelligent evaluation method and system, the method comprises the steps that peripheral modal data, central modal data and rehabilitation intervention data of an ACLR patient are synchronously collected and processed, and a multi-source heterogeneous data set is obtained; performing space-time alignment processing on the multi-source heterogeneous data set through a preset unified timestamp to generate a time-synchronized cross-modal data set; the cross-modal data set is processed through the trained brain-muscle-bone multi-modal fusion model, and three-dimensional comprehensive evaluation indexes including the structure safety degree, the function recovery degree and the nerve recombination degree are generated; and performing rehabilitation parameter dynamic optimization processing according to the three-dimensional comprehensive evaluation index, and generating a personalized blood flow limitation training pressure value and proprioceptive training intensity, so as to achieve the technical effects of multi-dimensional quantification of a rehabilitation state, dynamic adaptation of training parameters, nerve-muscle-bone collaborative intervention, improvement of a rehabilitation curative effect and reduction of a re-injury risk.

The invention provides a nerve regulation and control system and method for a spinal cord ventral epidural region, and the system employs a microprocessor to generate multiple paths of pulse width modulation signals, and drives a voltage-controlled current source after amplitude adjustment. Stimulation current with adjustable frequency, pulse width and amplitude is output to act on a spinal cord ventral nerve structure. Real-time monitoring of stimulation current of each channel is realized through combination of voltage sampling and a single-pole multi-throw analog switch, feedback information of a body position, myoelectricity, pressure and other multi-mode sensors is fused, a closed-loop control mechanism is constructed, and dynamic adjustment and individualized optimization of electrical stimulation parameters are realized. The system has the advantages of being more accurate in electrical stimulation positioning, more sensitive in response, more intelligent in control and the like, and is suitable for intelligent rehabilitation intervention in scenes of spinal cord injury, cerebral palsy, neurological dysfunction and the like.

The application discloses a brain-computer interface-based attention-enhanced cognitive closed-loop rehabilitation intervention method, which comprises the following steps: firstly, collecting electroencephalogram signals and related behavior information synchronously and completing time alignment processing; secondly, pre-processing and extracting features of the collected signals; on the basis, constructing a multi-dimensional brain state fusion model to realize comprehensive evaluation of attention and cognitive participation state; further generating control instructions through dynamic threshold determination and a gated decision mechanism, and adaptively adjusting cognitive task parameters and intervention intensity according to the control instructions; and updating system parameters through a feedback mechanism, so as to form a closed-loop intervention process. The application solves the problems of single attention representation, unstable state determination and lack of adaptive closed-loop regulation in the existing brain-computer interface cognitive intervention method, and realizes an attention-enhanced cognitive closed-loop intervention mechanism based on multi-dimensional brain state fusion.

The invention relates to the technical field of rehabilitation training, in particular to a rehabilitation training device for old people after heart diseases. The method comprises the following steps: a health data acquisition unit acquires physiological data, motion data and subjective feeling data of an elderly patient; the health data transmission and storage unit encrypts the data acquired by the health data acquisition unit in real time by using an encryption communication module, and stores the encrypted data; the intelligent analysis and decision-making unit dynamically evaluates rehabilitation risks based on a state space sequence model and generates a personalized training scheme; and the rehabilitation intervention unit performs rehabilitation training intervention based on the rehabilitation risk assessment result. According to the method, the state space model is used for describing the potential dynamic process of the heart load, real-time physiological observation data and the historical trend are continuously fused through online Bayesian reasoning, model parameters are dynamically updated, and therefore accurate tracking of individual heart state evolution is achieved.

The present application relates to the technical field of rehabilitation intervention method, specifically to a burn scar personalized rehabilitation intervention method based on edge computing, comprising the following steps: obtaining skin temperature and tendon compression data of traction path, matching time to identify traction asynchronous section, analyzing release rhythm lag and marking lag path, identifying interference section, reconstructing alternating decreasing release rhythm, pairing scar recovery and rehabilitation stage label, and obtaining individualized traction intervention structure content.In the present application, by synchronously comparing skin temperature and tendon compression changes, identifying rhythm response time difference positioning abnormal section, introducing path level rhythm lag analysis and contralateral interference discrimination, making traction rhythm adjustment based on multi-source physiological behavior correlation, reconstructing the path rhythm with conflict through nonlinear decreasing release, reducing interference risk, pairing the reconstructed rhythm with scar recovery status and rehabilitation stage, making traction control logic dynamically adapt to individual recovery changes, and improving intervention process stability and pertinence.

This application relates to the field of interdisciplinary technology, and in particular to a stroke rehabilitation training system based on a mixed reality digital human. The system includes: a data acquisition module configured to: acquire multimodal data of the subject performing standard movement tasks under the guidance of the rehabilitation therapist's digital human; the multimodal data includes: visual data, electromyographic data, and inertial data; perform perception and heterogeneous spatial registration on the multimodal data; and drive the subject's mirror digital human for synchronous display in real time; and a processing module configured to: identify the subject's movement intentions, assess upper limb motor function, detect compensatory movements, and assess lower limb motor function based on the multimodal data; and generate real-time prompts, visual feedback, and training reports, thus achieving an integrated closed loop of stroke rehabilitation training, functional assessment, and feedback adjustment. It is applicable to stroke rehabilitation training, functional grading assessment, and home-based continuous rehabilitation intervention.

The application discloses a somatosensory interaction fault-tolerant method suitable for ASD children, relates to the technical field of children's mental health, and comprises the following steps: S1, constructing an ASD child motion feature library; S2, gradientized motion fault-tolerant determination; S3, real-time emotion self-adaptive adjustment; S4, interactive rhythm dynamic adaptation; and S5, positive feedback reinforcement. The somatosensory interaction fault-tolerant method suitable for ASD children accurately adapts to the core development characteristics of ASD children, such as small motion amplitude, poor continuity, weak limb control, sensory sensitivity and short attention maintenance, greatly reduces the somatosensory interaction triggering and completion threshold, reduces the frustration, anxiety and emotional resistance of children from the root, significantly improves the interaction compliance and active participation willingness of ASD children, realizes stress-free, sustainable and personalized somatosensory interaction rehabilitation intervention, and effectively solves the technical pain points that existing somatosensory solutions are not suitable for ASD children and cannot be stably used.

This invention discloses a proactive rehabilitation intervention method for the elderly based on a motion-cognitive coupling mechanism, comprising the following steps: collecting user training data and preprocessing the training data; inputting the preprocessed data into a dual-stream neural network state evaluation model to generate a state vector; inputting the state vector into a reinforcement learning decision agent, which outputs a decision action, which is then sent to a task presentation engine; a task paradigm generator generating a training task based on the decision action; a reality engine generating a 3D scene containing moving targets; simulating corresponding road surface disturbances through a motion platform; and presenting auditory instructions through headphones. This invention organically integrates a deeply coupled dual-task paradigm, multimodal state perception, reinforcement learning-based dynamic difficulty optimization, and closed-loop control process by constructing a real-time adaptive closed loop driven by intelligent algorithms, thereby achieving precise and proactive intervention in the patient's rehabilitation process.

This invention discloses a dance rehabilitation training system integrating EEG and EEG sensors. The system includes an EEG headband and EEG sensors, each with built-in EEG and EEG acquisition modules. The dance rehabilitation training process includes: device wearing and initialization; personalized physiological baseline acquisition; simultaneous acquisition and preprocessing of dual-modal signals; multimodal feature fusion and emotion recognition, obtaining emotional state through dual-model fusion and smoothing; personalized safety threshold decision-making and intervention level determination; multi-channel dance intervention execution; emergency stress handling mechanism; closed-loop iteration and real-time control; and finally, generation of an evaluation report. This invention solves the problems of static intervention parameters, low feedback accuracy, and easy triggering of stress responses in traditional dance training by using dual-modal signal fusion, personalized safety threshold modeling, and closed-loop real-time control, achieving real-time, precise, and safe personalized rehabilitation intervention.

1. The name of this design product: Virtual Digital Human Rehabilitation Intervention Training System Equipment. 2. Purpose of this design product: Equipment used for patient intervention training and rehabilitation assessment. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The application discloses a postoperative care scheme optimization method and an intelligent chip for liver cancer, and relates to the technical field of postoperative care for liver cancer. The method comprises the following steps: performing multidimensional detection on a liver cancer tumor tissue sample of a target user to obtain a multidimensional detection data set, determining a tumor immune microenvironment type, and generating an initial postoperative care scheme; obtaining a predicted recurrence and metastasis risk probability of the target user within a preset rehabilitation period; evaluating the initial postoperative care scheme based on baseline state detection information of the target user to generate a care scheme strength and a baseline state score; formulating an adaptive rehabilitation intervention rhythm and an adaptive risk monitoring mechanism according to the predicted recurrence and metastasis risk probability, the care scheme strength and the baseline state score; and performing care on the target user according to the initial postoperative care scheme and dynamically optimizing the postoperative care scheme according to the adaptive rehabilitation intervention rhythm and the adaptive risk monitoring mechanism. The application realizes individualized and accurate adaptation of the postoperative care scheme for liver cancer.

The application discloses a standing-up and walking test method and system based on inertial sensors and electromyography technology, relates to the technical field of gait assessment and rehabilitation detection, and comprises the following steps: data acquisition is completed by using inertial sensors and electromyography technology; through a first algorithm, differences between left and right support phases and swing phases are comprehensively evaluated to determine whether the gait is symmetrical and stable; and through a second algorithm, muscle coordination of a patient is evaluated. The method realizes synchronous data fusion of a behavior layer and a neural control layer, lays a data foundation for subsequent construction of a precise and multi-dimensional gait and electromyography evaluation model, realizes joint evaluation of two core indexes of gait symmetry and stability, improves detection capability of neurological gait disorders, provides reliable criteria for individualized rehabilitation intervention, realizes modeling and difference measurement of timing control characteristics, and improves recognition capability of neuromuscular control disorders.

The invention relates to a multi-mode upper limb function recovery path prediction and rehabilitation intervention method for a stroke patient and a related device. The method comprises the following steps: acquiring a movement track, speed, accelerated speed and a joint angle of the stroke patient during a standard upper limb movement task through an inertial measurement unit; the method comprises the following steps: acquiring iconography data of a stroke patient through an HIS system, and extracting key brain region injury volume and position and cortical spinal bundle fiber bundle tracking data of the iconography data according to a Destrieux map; inputting the trained multi-mode adaptive attention model, and outputting an upper limb function recovery path of the stroke patient at the time point after rehabilitation; and inputting the upper limb function recovery path of the stroke patient at the time point after rehabilitation and a predefined rehabilitation intervention scheme knowledge base into the personalized rehabilitation intervention model, and outputting a rehabilitation intervention scheme of the stroke patient. According to the method, prediction of the upper limb function recovery path of the stroke patient and a rehabilitation intervention scheme are realized, and the stroke rehabilitation efficiency and effect are improved.

The invention discloses a core muscle group unbalance evaluation and active rehabilitation control method and system in a weightless environment, and the system part comprises an underwater bioelectricity sensing module, an embedded evaluation and control module, and an active rehabilitation execution module. An underwater core muscle group unbalance evaluation and control strategy based on surface myoelectricity feature extraction and principal component analysis dimensionality reduction is adopted, and the strategy comprises the steps that firstly, bioelectricity signals of a trunk core muscle group are collected through a waterproof sensor; extracting time domain, frequency domain and nonlinear features, and calculating a lateral asymmetry index and a front-back balance index; reducing the dimension of the high-dimensional index into two core components, namely an unbalance amplitude and an unbalance mode, by using a PCA model; judging the current physiological state according to the scores of the two components; and finally, controlling the underwater work clothes or the exoskeleton to execute differentiated rehabilitation actions (amplitude remodeling or mode induction) on the basis. And in the rehabilitation intervention process, continuously and circularly monitoring until the muscle group control indexes return to the baseline range.

The invention relates to the technical field of psychological accompanying devices, in particular to an AI social worker digital device for psychological accompanying. The device comprises a device main body, and the device main body is internally provided with a voice interaction module which is used for realizing natural voice conversation with a convalescent person; the data acquisition and preprocessing module is used for acquiring and providing preprocessed multi-modal data to the communication and storage module; the emotion recognition and psychological assessment module is used for recognizing the multi-modal data of the convalescent and generating an assessment report; according to the culture combination module, a large language model is adopted as a base model, fine adjustment is carried out on text data in the recovery situation knowledge base and rehabilitation dialogue corpora marked with core elements of an oriented recovery mode, and a standard verbal skill library covering multiple rehabilitation intervention scenes is established according to the text data and the rehabilitation dialogue corpora; and the recovery scene simulation and generation unit is used for storing a dialogue template constructed based on mental rehabilitation practice.

The invention discloses a postpartum rehabilitation management method and system fusing weakness assessment and pain prediction, and relates to the field of postpartum management, and the method comprises the steps: classifying a puerpera into a preset rehabilitation classification matrix according to weakness degree classification and pain risk classification, and generating a corresponding rehabilitation classification code; according to the rehabilitation classification code, a personalized initial rehabilitation scheme is called and combined from a preset modular intervention scheme library, and the initial rehabilitation scheme at least comprises one or more of a targeted exercise prescription, a nutrition prescription, a pain pre-rehabilitation intervention measure and a psychological social support plan. According to the invention, through pain risk prediction based on multi-dimensional postpartum weakness quantitative evaluation and multi-factor fusion, postpartum rehabilitation classification precision is realized, and intervention pertinence is improved. A modular intervention scheme is called based on the rehabilitation classification code, a personalized rehabilitation scheme matched with individual differences is generated, and the rehabilitation requirements of lying-in women with different weakness degrees and pain risks are met.

The invention discloses an information processing method and device for evaluating a stroke rehabilitation treatment scheme. The method comprises the following steps: performing causal effect identification based on a causal effect inference model; performing neural rehabilitation prognosis score estimation based on the causal effect recognition result; and performing neurological rehabilitation prognosis score adjustment based on the score estimation result to screen out a plurality of treatment exposure comparison objects. Through a cause and effect inference model and an analysis method based on a prognosis scoring framework, the method can adapt to various rehabilitation intervention modes accepted by stroke patients in a real clinical environment, and through simulation of prognosis risks under different rehabilitation paths, curative effect comparison and individualized prognosis evaluation under non-random data are realized. And the scientificity and the accuracy of rehabilitation scheme decision making are improved.

The application discloses a kind of multimodal AI algorithm and block chain data security system, it is related to the technical field of children autism rehabilitation intervention, including the following steps: S1 data information acquisition;S2 data preprocessing;S3 behavior recognition and quantitative mapping;S4 emotional state quantitative score;S5 emotion guidance and rehabilitation training matching;S6 gamification guidance training;S7 algorithm self-optimization iteration.The application realizes non-contact multimodal behavior data acquisition, relies on CNN+Transformer fusion model to complete the joint extraction of static features and dynamic time series features, realizes the high accuracy of behavior recognition and the quantitative docking of professional rehabilitation evaluation, effectively improves the objectivity and reference value of behavior analysis;Through emotional state quantitative score and personalized rehabilitation adaptation algorithm, dynamically match exclusive emotion guidance scheme and gamification rehabilitation task, and adjust task difficulty according to age, symptom degree, rehabilitation stage, significantly improve the pertinence of rehabilitation training and child compliance.

The invention discloses a pulmonary fibrosis risk AI assessment and rehabilitation intervention system after severe pneumonia, which belongs to the technical field of medical health data processing and comprises a radiomics feature extraction module, a multi-modal risk assessment module, a personalized rehabilitation scheme generation module and a dynamic feedback adjustment module. The image omics feature extraction module is used for carrying out dual-channel feature extraction based on a Vision Transform and a three-dimensional convolutional neural network, and identifying an early fibrosis marker; the multi-modal risk assessment module fuses radiomics characteristics, acute-stage illness state data and inflammatory factor monitoring data to generate a pulmonary fibrosis risk score; the personalized rehabilitation scheme generation module generates a rehabilitation intervention scheme according to the risk hierarchy; according to the system, risk assessment precision, rehabilitation scheme personalization and intervention effect optimization are achieved through a closed-loop cooperative system, the occurrence rate of pulmonary fibrosis is remarkably reduced, and the long-term pulmonary function and life quality of a patient are improved.

This invention belongs to the fields of rehabilitation medicine and intelligent computing, specifically disclosing an artificial intelligence-based personalized treatment recommendation system for stroke rehabilitation. It includes a multimodal data acquisition module, a feature extraction and clustering modeling module, a personalized path formulation module, and a recommended path generation and feedback optimization module. This system enables dynamic monitoring and intelligent analysis of stroke patient rehabilitation data. By fusing multidimensional data such as images, biomarkers, clinical behavior, and cognitive emotions, a multimodal aggregated neural network model is constructed. Rehabilitation paths are formulated based on medical data clustering results, and the recommended paths are dynamically adjusted and optimized in conjunction with patient treatment feedback. Compared to traditional methods, this invention improves the scientific rigor and adaptability of personalized treatment, possessing advantages such as real-time updates of rehabilitation paths, closed-loop system management, and high model robustness. It significantly enhances the efficiency and accuracy of rehabilitation interventions, providing technical support for the intelligent development of rehabilitation medicine.

The invention relates to the technical field of medical information processing, and discloses a fracture child rehabilitation exercise compliance change prediction method and system, and the method comprises the steps: constructing a rehabilitation exercise compliance longitudinal time sequence of a target child at a preset follow-up time node; extracting stage transition features and identifying a compliance stage state; synchronously constructing a psychological-behavioral multi-index synchronous time sequence, wherein indexes comprise pain, terrorism and self-effectiveness scores; analyzing the index change synchronism and consistency, and identifying a psychological-behavior coupling state; carrying out collaborative modeling by taking the two states as input characteristics, and establishing an incidence relation; and predicting the compliance significant change risk of the next follow-up visit stage based on the modeling result and outputting the result. According to the method, the periodical change of compliance can be accurately described, an internal driving mechanism is disclosed, a prediction result is accurate and explainable, and a scientific basis is provided for clinical rehabilitation intervention.

The application discloses a combined traditional Chinese and western medicine internal medicine chronic disease rehabilitation intervention system and application, and belongs to the technical field of medical rehabilitation. The system comprises a multi-source data acquisition module, a traditional Chinese and western medicine fusion diagnosis module, a personalized rehabilitation intervention module, a real-time monitoring feedback module and a data storage and analysis module, and the modules are bidirectionally connected to form a closed-loop management; a traditional Chinese and western medicine fusion diagnosis model is innovatively constructed, deep fusion of western medicine physiological indexes and traditional Chinese medicine syndromes is realized, and traditional Chinese medicine fuzzy syndromes are converted into quantifiable data; personalized rehabilitation schemes containing traditional Chinese medicine, western medicine and lifestyles are generated according to individual differences of patients, the schemes are dynamically adjusted through real-time monitoring data, and rehabilitation effects are quantitatively evaluated; and the system is applied to rehabilitation intervention of various internal medicine chronic diseases and is suitable for different scenes. The application completely avoids defects of the prior art, solves problems of insufficient traditional Chinese and western medicine fusion, insufficient personalization of rehabilitation schemes and incapability of quantifying intervention effects, improves rehabilitation effects and reduces a chronic disease recurrence rate.

This application provides a knowledge graph-based rehabilitation diagnosis and treatment system and method for cerebrovascular diseases. It maps multimodal rehabilitation data onto a pre-defined knowledge graph, generating a feature subgraph. Starting with the core functional impairment node in the patient's feature subgraph, a multi-hop relationship expansion search is performed in the knowledge graph to obtain a set of candidate rehabilitation intervention nodes. Based on the empirical validity data of each candidate rehabilitation intervention node in a historical patient group with similar characteristics to the current patient, the topological association strength of the knowledge graph, and the feasibility constraints of the target patient's current medical scenario, the personalized fit of each candidate rehabilitation intervention node is determined. The results of ranking each candidate rehabilitation intervention node according to all personalized fits form a draft recommendation of a structured rehabilitation plan for the target patient. Using the scheme of this application, multimodal rehabilitation data and dynamic medical knowledge of patients can be integrated to conduct intelligent rehabilitation plan recommendations.

The present application provides a nerve regulation system and method for the ventral epidural region of the spinal cord, wherein the nerve regulation system for the ventral epidural region of the spinal cord generates a multi-channel pulse width modulation signal by using a microprocessor, drives a voltage-controlled current source after amplitude adjustment, and outputs a stimulation current with adjustable frequency, pulse width and amplitude to act on the ventral nerve structure of the spinal cord. Real-time monitoring of the stimulation current of each channel is realized by combining voltage sampling and single-pole multi-throw analog switches, while the feedback information of multiple modal sensors such as body position, electromyography and pressure is integrated to construct a closed-loop control mechanism, realize dynamic adjustment and individual optimization of the electric stimulation parameters. The precise activation ability of motor and autonomic nerve function is enhanced, and the present application has the advantages of more accurate electric stimulation positioning, more sensitive reaction, more intelligent control and the like, and is suitable for intelligent rehabilitation intervention in scenes such as spinal cord injury, cerebral palsy, and nerve dysfunction.

The application discloses a system and method for intervention of children with autism spectrum disorder based on a COM-B model, and relates to the technical field of child rehabilitation intervention.The technical solution points of the system and method are as follows: the COM-B model based on ability, opportunity and motivation is taken as a theoretical core, and a recreational sports game is taken as an intervention coupling agent, a closed-loop intervention of evaluation, design, implementation, analysis and adjustment is realized through cooperation of various modules of the system, individual characteristics of children with autism spectrum disorder are accurately captured by using baseline evaluation, a personalized game scheme is designed, the scheme is implemented after standardized training, and a dynamic optimization scheme is quantitatively analyzed in combination with process data.The application fills the gap of the theoretical framework of the prior art, adapts to individual differences and interest changes of children with autism spectrum disorder, improves the standardization and reliability of intervention, effectively improves core symptoms such as social communication and repetitive stereotyped behavior of children with autism spectrum disorder, and enhances the physical and mental adaptation ability.The application has a wide application scene and is easy to popularize.