2671 results about "Rehabilitation" patented technology

The invention relates to the technical field of medical instruments, in particular to a postpartum rehabilitation magnetic therapy device and method.The postpartum rehabilitation magnetic therapy device comprises a placement plate used for being placed on a chair, a magnetic therapy coil used for placing the sacral region is embedded in one side of the top end of the placement plate, and an anus lifting assembly is arranged on the magnetic therapy coil; auxiliary massage assemblies for placing two sides of legs are connected to the top end of the placement plate and located on two sides of the anus lifting assembly, and the input end of the magnetic therapy coil is connected with the output end of the rectifier; through the use of the anus lifting assembly, under the magnetic therapy effect of the magnetic therapy coil, people can be helped to carry out anus lifting movement, so that postpartum disc bottom repair is more perfect, through the use of the auxiliary massage assembly, legs at the disc bottom position can be massaged up and down, surrounding muscles move along with the legs, and the postpartum repair effect is improved.

The invention discloses a human body motion posture evaluation method and system. Low-cost and high-precision human body posture analysis is realized through common camera equipment. The method comprises the following steps: acquiring a human body motion video through camera equipment; detecting key points of a human body by using a deep learning model, and extracting two-dimensional or three-dimensional coordinates of the human body; calculating joint angle, speed and acceleration based on the key point data; and analyzing attitude stability, motion symmetry and fluency characteristics, and generating a comprehensive evaluation result. The system evaluates the motion quality of the user through comparison between dynamic feature extraction and a standard motion template, and generates personalized improvement suggestions based on specific indexes, including direction adjustment and amplitude range adjustment. The method can be widely applied to scenes such as exercise and fitness, rehabilitation medical treatment and competitive training, and the comprehensiveness, the accuracy and the guiding effect of action evaluation are improved. By means of the method, the user can obtain motion quality feedback in real time, the posture is effectively optimized, and the exercise performance or rehabilitation effect is improved.

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses an AI auxiliary limb rehabilitation system, and belongs to the field of communication control systems, the AI auxiliary limb rehabilitation system combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like, comprehensively collects limb movement and physiological data, introduces gamification elements, improves the patient participation degree through target setting, an award mechanism and interesting tasks, and improves the rehabilitation effect. A personalized rehabilitation plan is generated by using a deep learning algorithm, an emotion recognition unit can adjust the plan according to a psychological state, environment self-adaption and real-time adjustment of training difficulty to ensure training safety and is challenging, data cloud storage facilitates remote monitoring, third-party equipment is compatible with extended monitoring ability, and the training efficiency is improved. A brand-new, efficient and personalized solution system scheme is provided for limb rehabilitation.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention provides an artificial intelligence auxiliary diagnosis method and system for a knee joint, and the method comprises the steps: synchronously collecting a multi-angle dynamic X-ray image and six-degree-of-freedom contact force distribution data in an articular cavity, dynamically tracking a patella sliding track, and generating a mechanical phase track; a personalized knee joint model is constructed in combination with the biological parameters of the lower limbs of the patient, the mechanical phase trajectory and the contact force data vector are superposed to generate a dynamic mechanical fingerprint, and then the dynamic mechanical fingerprint is mapped into a biomechanical attribute map; electromyographic signal time-frequency characteristics and tibia rotation angles in the movement process are collected, an electromyographic activation mode is constructed, and the electromyographic activation mode and a biomechanical attribute map are subjected to characteristic fusion to form a joint dynamics comprehensive contour; analyzing a stress wave conduction path by using a joint anomaly dynamic propagation model to identify a compensatory injury mode; a diagnosis map is generated according to the recognition result, a preset biomechanical constraint condition is matched to correct the diagnosis map, and an individualized rehabilitation strategy is formulated. The treatment effect is improved, and the recovery speed of a patient is increased.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

The invention discloses a wireless high-density electroencephalogram and myoelectricity hybrid synchronous acquisition and monitoring system, and belongs to the field of human-computer interaction. The wireless high-density electroencephalogram and myoelectricity mixed synchronous collecting and monitoring system is composed of three parts, namely a high-density electroencephalogram and surface myoelectricity signal collecting system, a base station system and an upper computer system. The high-density electroencephalogram and surface electromyogram signal acquisition system is responsible for acquisition and primary processing of signals, and the base station system performs data interaction with the high-density electroencephalogram and surface electromyogram signal acquisition system in a wireless transmission mode and transmits the processed signals to the upper computer system for further analysis and display. According to the invention, a wireless and distributed architecture and a high-channel-number data acquisition scheme are integrated, the problems of efficient acquisition and synchronous processing of electroencephalogram and myoelectricity mixed signals are emphatically solved, and the application range in the fields of brain-computer interfaces, virtual reality, neural rehabilitation, cognitive training and the like is widened.

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

The invention discloses a digital intelligent tumor prevention and treatment management platform and management method, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting the multi-modal data of a patient in real time, carrying out the standardized cleaning and space-time alignment processing, and generating a space-time tagging data set; inputting the multi-modal joint feature representation into an anti-fact inference engine, simulating potential effects under intervention of different treatment schemes, calculating individual processing effects, and generating an anti-fact rehabilitation suggestion set; according to the anti-fact rehabilitation suggestion set, a dynamic causal enhancement decision model is constructed, real-time physiological data feedback of the patient is continuously received through a near-end strategy optimization algorithm, and a treatment scheme is dynamically adjusted. According to the method, through multi-modal data space-time alignment and feature fusion, in combination with a tumor biological mechanism and a machine learning algorithm, the accuracy and clinical credibility of individualized tumor treatment scheme recommendation are remarkably improved.

The invention discloses an anorectal postoperative intelligent follow-up visit management method and system based on a mobile terminal, and relates to the technical field of communication data. Comprising the steps that a dynamic follow-up survey matrix is constructed according to clinical indexes and preset weights of patients before and after an operation, and the follow-up survey matrix aims at determining the interval duration of all follow-up stages; the mobile terminal establishes a timeliness doctor-patient communication channel through the follow-up survey matrix, and the mobile terminal generates a personalized rehabilitation guidance scheme based on follow-up data. According to the method, the dynamic follow-up survey matrix is constructed, the comprehensive risk value is calculated according to the quantitative assignment of the clinical indexes and the weighting of the preset weight, and the follow-up interval is iteratively optimized in combination with the real-time recovery data, so that the precision of the follow-up period is realized, the dynamic adjustment reduces unnecessary follow-up disturbance to low-risk patients, and the follow-up efficiency is improved. Tracking frequency is enhanced for middle-high risk patients, and the defect that a traditional fixed follow-up visit mode is cut at a time is thoroughly overcome.

The invention belongs to the technical field of cerebral apoplexy rehabilitation medicine, and relates to evaluation of myoelectric medicine, in particular to an HD-sEMG-based evaluation method and evaluation system for hand dyskinesia of a cerebral apoplexy hemiplegia patient. According to the invention, the high-density surface myoelectricity data of the front arms of the healthy side and the affected side can be respectively collected when the stroke hemiplegia patient executes different hand actions, and the microscopic neuron characteristics are analyzed, so that the degree and progress of dyskinesia of the stroke hemiplegia patient can be accurately evaluated. Through statistical analysis of the number of motion units, the average discharge rate and the discharge mode, changes and rules of neurons in the rehabilitation process are revealed, the difference between the healthy side and the affected side is compared, and the motion function condition of the neurons is evaluated.

The invention provides a multichannel transcranial direct current stimulation cognitive function assessment method and system, and belongs to the field of neuroscience and brain cognitive function assessment. The method comprises the steps that electroencephalogram signal data of a subject under multi-channel transcranial direct current stimulation are collected and preprocessed; respectively performing micro-state analysis, brain function connectivity analysis and oscillation power analysis on the preprocessed data; constructing a linear mixed effect model, and inputting the results of the micro-state analysis, the brain function connectivity analysis and the oscillation power analysis into the model for quantitative evaluation of the cognitive function state; and a time sequence database is constructed, and the time sequence database is used for storing cross-time-point analysis results and analyzing the long-term change trend of the cognitive function based on a statistical model. Dynamic, accurate and high-temporal-spatial-resolution evaluation of the cognitive state of the subject is achieved, the long-term change trend of the cognitive state is dynamically monitored, and technical support is provided for early warning and rehabilitation effect tracking of neurodegenerative diseases.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention relates to the technical field of medical rehabilitation monitoring, in particular to a rehabilitation cloud platform system for cardiopulmonary function monitoring. According to the system, an intelligent monitoring terminal collects physiological data; the cloud computing platform adopts wavelet threshold denoising and an index moving average method to remove noise, combines dynamic time warping to achieve multi-physiological signal time sequence alignment, constructs a double-flow model, generates a health state index and a comprehensive health risk index, introduces a multi-scale branch and differential attention mechanism, and achieves the multi-scale health state index and the comprehensive health risk index. High-precision anomaly detection is realized through prediction deviation analysis, multi-target reinforcement learning is adopted to balance health improvement and risk control, and a personalized training scheme is generated; the remote medical support module supports a doctor to monitor patient data in real time and intervene in an adjustment scheme; the user interaction end provides a real-time monitoring data visualization and individuation rehabilitation plan. Through multi-modal data fusion, time sequence synchronous optimization and adaptive reinforcement learning, accurate monitoring, dynamic rehabilitation optimization and remote medical collaboration are realized.

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 discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

The invention provides a neurosurgery tumor patient personalized nursing intervention method based on big data. The neurosurgery tumor patient personalized nursing intervention method comprises the steps that physiological parameters, tumor characteristic data, behavior data and environment data of a patient are collected through a multi-mode sensor and an electronic medical record system; preprocessing the data to generate a standardized data set; extracting dynamic time sequence features and spatial correlation features based on a multi-modal fusion feature extraction algorithm; calculating an intensity parameter, a frequency parameter and a priority parameter of nursing intervention through a personalized intervention parameter generation algorithm according to the features; generating a personalized nursing intervention scheme in combination with the nursing rule base; a dynamic adjustment scheme is fed back through real-time data, and intervention parameters are corrected through a feedback compensation mechanism; and outputting the corrected intervention scheme to the nursing terminal equipment for execution. Accurate personalized nursing intervention can be achieved, the nursing scheme is dynamically adjusted, and the nursing effect and rehabilitation quality of neurosurgical tumor patients are improved.

According to the human body activity distinguishing device and method, through an intelligent insole system integrating an inertial sensor and a plantar pressure sensor and in combination with a multi-scale time window analysis method, gait data of a user are accurately collected and processed, daily activity modes such as walking, running and going upstairs and downstairs and changes of the modes are recognized, and the human body activity distinguishing effect is achieved. And real-time health feedback and rehabilitation support are provided. The intelligent insole system comprises an upper computer system, sensor integrated insole hardware and a module communicating with a user terminal and a cloud server. According to the system, multi-modal data is collected through an inertial sensor and a plantar pressure sensor, data processing is achieved in combination with data fusion and a multi-scale sliding time window method, finally, a random forest classifier is used for classifying activity modes, and the functions of health monitoring, posture improvement suggestion, falling risk assessment and the like are achieved. The exercise performance of the user is improved, and personalized health management is provided.

The invention relates to the technical field of rehabilitation data prediction, and discloses a motion function rehabilitation prediction method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, an electromyographic signal and a functional near infrared spectrum signal of a target user, pre-processing to obtain a target electroencephalogram signal, a target electromyographic signal and a target functional near infrared spectrum signal; respectively carrying out feature extraction to obtain three time-frequency features, and carrying out source positioning processing on the target electroencephalogram signal and the target functional near infrared spectrum signal to obtain brain source intensity; and fusing the three time-frequency features to obtain a target time-frequency feature, inputting the target time-frequency feature and the brain source intensity into a multi-modal data rehabilitation prediction model, and outputting a motor function rehabilitation prediction result. According to the method, the relationship between the three signals of the target user and the motion function of the target user is modeled in the time sequence, so that the long-term evolution of the motion function in the time sequence is quickly evaluated, and the accuracy of a prediction result is improved.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention relates to the technical field of physiological information processing, and discloses a personalized burn rehabilitation path planning method and system, and the method comprises the steps: obtaining historical rehabilitation data and real-time evaluation data, constructing a state time sequence, extracting a score fluctuation vector and an emotion change vector, and carrying out the window adjustment processing of a fluctuation feature vector set, a rhythm factor and a training factor are calculated in each window range, so that path analysis has the capability of synchronously evaluating rhythm change and a training state, the phenomenon of excessive path adjustment caused by short-time abnormal fluctuation is avoided, a rhythm feature sequence is input into a whale optimization algorithm to execute path updating processing, and the path updating efficiency is improved. The method ensures that the path generation process has the dynamic adjustment capability, enhances the adaptability of the algorithm to the recovery rhythm change, performs grading processing on each stage according to the stage sequence set, can effectively output the rehabilitation path matrix, and realizes the unified expression of the multi-stage rhythm and the training grade.

A fall detection and prevention system and method. The system includes one or more sensors (204), strategically positioned to cover an optimal monitored area, for monitoring individuals within an indoor environment in real time,and gathering sensor data essential for personnel fall detection; one or more processors (206), responsible for real-time processing of sensor data,implementation of fall detection and prevention algorithms to analyze patterns associated with falls, and generating necessary responses; and a communication module, configured to trigger the necessary responses in response to detected falls. The comprehensive system and method combine technological innovation and user-friendly features for effective fall detection and prevention, ensuring a safer environment, for high-risk patients, senior citizens and during periods of rehabilitation.

The invention belongs to the field of intelligent rehabilitation medical instruments, and particularly relates to a brain-computer interface-based multi-modal child sensory integration training device, which comprises a brain-computer interface head ring, an intelligent touch floor and AR interactive glasses, the brain-computer interface head ring is connected with a biological signal acquisition module, the intelligent touch floor is connected with a motion trail analysis unit, the AR interactive glasses are connected with a universe scene engine, and the biological signal acquisition module, the motion trail analysis unit and the universe scene engine are connected with a digital twin generator. The digital twin generator is connected with a dynamic mode switching decision tree, and the dynamic mode switching decision tree is connected with a multi-mode feedback actuator; the brain-computer interface head ring is located on the head of the child and collects electroencephalogram, myoelectricity and electrocardiosignals through a biological signal collection module; and the biological signal acquisition module sends a signal to the digital twin generator through wireless transmission. According to the invention, the training efficiency can be improved, the evaluation dimension can be expanded, potential safety hazard early warning can be realized, and the compliance can be enhanced.

The invention relates to the technical field of artificial intelligence and biological characteristic analysis, in particular to an AI-based personalized postpartum rehabilitation medical beauty evaluation system and method. The method comprises the following steps: generating a standardized action sequence according to a rehabilitation stage and a historical record of a user and guiding execution; collecting dynamic biological characteristic data in real time; aligning data and distributing dynamic weights by using a multi-modal fusion model guided by time sequence semantics, constructing a three-dimensional response graph by combining a tension response function, a collaborative exponential function and a stable fluctuation function, and generating individual embedded vectors through a graph neural network; identifying a potential high-risk time period through map potential energy offset analysis and disturbance sensitivity factors; integrating a user state, a historical response and an intervention knowledge graph, recommending a dynamically adaptive intervention scheme by using a multi-channel attention network, and continuously optimizing through a feedback closed loop; precise evaluation and personalized intervention recommendation of the postpartum rehabilitation process are realized through multi-modal fusion and dynamic modeling, and the rehabilitation efficiency and safety are improved.

The invention discloses a hand function rehabilitation closed-loop training system based on a multi-dimensional evaluation model, and relates to the technical field of hand rehabilitation, a data set acquisition module collects key physiological parameters such as a joint pressure coupling coefficient, and quantifies the biomechanical relevance between joint movement and pressure distribution; the LSTM neural network synchronously generates an action completion degree score and a joint pressure coupling coefficient prediction value, and dual functions of rehabilitation quality evaluation and abnormity early warning are realized; a confidence index is generated based on a feature consistency factor of a score fluctuation standard deviation and a prediction-actual measurement deviation, and the credibility of an evaluation result is accurately identified; switching response modes in real time according to a confidence level grading interval, and dynamically adjusting training difficulty and auxiliary force parameters; a confidence quantitative model and a rehabilitation strategy engine are deeply coupled to form an'evaluation-verification-regulation 'integrated framework, so that the rehabilitation safety and effectiveness are remarkably improved.

The invention relates to a gynecological postoperative intelligent rehabilitation management method and system, and relates to the technical field of rehabilitation management. Determining a basic recovery score of the patient based on the preoperative information; determining a rehabilitation adjustment coefficient based on the intraoperative information; based on the basic recovery score and the rehabilitation management adjustment coefficient, physiological feature tags of the patient are determined, and the physiological feature tags comprise a low-risk tag, a medium-risk tag and a high-risk tag; based on the physiological feature tag and the operation type, determining a rehabilitation path according to historical rehabilitation data; and when the physiological feature tags are different risk tags, determining a rehabilitation management strategy according to the rehabilitation path and based on different patient information. According to the method, quantitative indexes are adopted to carry out risk layering on the patients, the situation of one-step operation in traditional rehabilitation management is avoided, and therefore the pertinence and adaptability of a rehabilitation scheme are improved. Meanwhile, the method can improve the effective utilization rate of data.

The invention discloses a cognitive impairment intervention system and method based on general artificial intelligence, and the method comprises the steps: S1, collecting the physiological, behavior and cognitive state data of a patient, and carrying out the data preprocessing; s2, inputting a dual convolutional neural network to extract spatial-temporal features, and generating comprehensive cognitive state features; s3, time sequence clustering analysis is carried out, the key fluctuation period of the cognitive state is recognized, the decline trend is predicted, and the risk level is adjusted; s4, analyzing the emotional state of the patient in combination with a dynamic emotion recognition algorithm of micro-expression time sequence mapping, and optimizing an intervention scheme based on a user-defined emotion tag; s5, adjusting the intervention scheme by using an incremental learning method according to real-time feedback of the patient; and S6, optimizing the dual convolutional neural network by long-term training data, improving feature extraction and intervention precision, and realizing personalized long-term intelligent intervention. According to the method, multi-modal data and adaptive optimization are fused, accurate detection, personalized intervention and long-term optimization are realized, the rehabilitation efficiency is improved, and the life quality is improved.

The invention provides a construction method of a brain rehabilitation cognitive training effect prediction model. The method improves the accuracy and individuation level of cognitive training effect prediction. Firstly, state data of a subject is obtained and preprocessed; thirdly, obtaining unified features by adopting an extraction layer, carrying out combined modeling of a spatial topological relation and dynamic time sequence evolution by adopting a fusion layer based on the unified features and generating spatio-temporal features, and adjusting model parameters by adopting an adaptation layer according to baseline features of a subject to ensure personalized prediction; and finally, in a model operation process, triggering online updating through concept drift detection, and generating a confidence interval and an interpretability score of a prediction result based on Bayesian reasoning. According to the method, through state data processing, personalized adaptation, dynamic adaptation and Bayesian reasoning, the precision, stability and interpretability of brain rehabilitation cognitive training effect prediction are remarkably improved, and powerful support is provided for personalized treatment and clinical decision making.

The invention discloses a figure modeling and fitness action teaching method and system based on non-contact action capture, and the method carries out the real-time monitoring of a human body posture through a millimeter wave radar, carries out the 3D human body posture reconstruction and analysis through combining with a human body posture frame of Google image processing, and achieves the high-precision action capture. The method can be used for human body modeling, body building action scoring and action guidance, action evaluation and real-time feedback in body building teaching can be effectively supported, the millimeter wave radar is adopted for human body posture capture, external markers or wearable equipment are not needed, constraint on user activities is avoided, and use convenience and comfort are improved. The 3D posture of the human body can be accurately reconstructed in combination with a deep learning neural network, and real-time processing and analysis are carried out. The method not only can realize accurate motion capture, but also can be combined with fitness motion scoring and guidance, is widely applied to the fields of virtual fitness, exercise rehabilitation and the like, and has great application potential.