1708 results about "Rehabilitation" patented technology

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 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 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 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 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 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 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 relates to the technical field of rehabilitation medical treatment, and discloses a muscle group stress analysis method and device, a terminal and a storage medium, and the method comprises the steps: respectively obtaining motion data collected by inertial measurement units arranged on the body of a user, and recognizing gait events based on the motion data corresponding to the inertial measurement units of the lower limbs, dividing the continuous motion data into a plurality of gait cycles according to the gait event; fusing the motion data acquired by each inertial measurement unit to obtain a three-dimensional posture of each preset segment of the body, and constructing a three-dimensional motion track of the spine area of the user in combination with a human kinematics chain model; processing the three-dimensional motion track and the known muscle group stress data by adopting a singular value decomposition embedding regression method to obtain a preliminary target muscle group stress curve of each gait cycle; and correcting the initial target muscle group stress curve based on a human body multi-rigid-body dynamic model in combination with a control cost function to obtain a final target muscle group stress curve.

The invention provides a rehabilitation scheme generation system and method based on a personalized state model and reinforcement learning, and aims to solve the technical problems that an existing rehabilitation scheme is static and universal and cannot be dynamically adjusted according to daily fluctuating physical conditions of a user. Comprising a multi-mode biomechanical data acquisition module used for acquiring kinematics and dynamics data of a hand of a user; the user state modeling module is used for constructing and dynamically updating a personalized state model based on the collected data; the scheme generation module is used for determining a personalized rehabilitation scheme by utilizing a reinforcement learning model based on a personalized state model and the index; and the real-time feedback module is used for comparing the real-time action of the user with a target action generated based on the personalized state model and providing deviation correction feedback. According to the method, the optimal rehabilitation strategy can be dynamically generated, accurate and safe guidance is carried out, and the personalized rehabilitation effect and safety are improved.

The invention provides a brain organism closed-loop rehabilitation system and a control method thereof, belongs to the field of brain-computer interfaces and rehabilitation medical treatment, and is used for solving the problems of insufficient brain state perception, poor adaptability and low robustness of a brain organism rehabilitation system in related technologies. The method comprises the following steps: synchronously acquiring multi-modal physiological signals by cooperating with physiological signal monitoring, task induction and regulation and control equipment modules, removing artifacts through preprocessing and redundancy check, extracting neuroplasticity characteristics, dynamically adjusting weight decoding brain activity intentions, regulating and controlling stimulation parameters in a closed loop, and training an optimization model in combination with a multi-center federation; accurate and highly-adaptive rehabilitation regulation and control are realized, and the rehabilitation effect and the model generalization are improved.

The invention discloses an EIT-based respiratory muscle force and lung compliance closed-loop evaluation system, which is characterized by comprising a multi-frequency EIT injection / sampling unit, a signal processing unit and a signal processing unit, a pressure measurement and synchronization unit; an FPGA clock synchronization triggering unit; an EIT preprocessing unit; a compressed sensing reconstruction unit; a [delta] Z-Pressure fitting unit is adopted; a feature extraction unit; a control decision unit; and the self-adaptive NMES participates in a trend recording unit. According to the method, advanced technologies such as multi-frequency EIT, compressed sensing reconstruction, machine learning fitting, fuzzy + PID mixed control and federated learning are integrated, a new normal form for closed-loop evaluation and optimization of respiratory muscle force and lung compliance is created, and the method has great clinical value for rehabilitation of diseases such as COPD and neuromuscular respiratory failure.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention relates to the technical field of data processing, and discloses a patient rehabilitation trajectory prediction and personalized nursing plan generation method and system. The method comprises the steps of collecting multi-dimensional function evaluation data to calculate a rehabilitation rate index sequence, recognizing a rehabilitation stage and configuring a feature weight vector, predicting a multi-dimensional rehabilitation trajectory through a rehabilitation stage gating sequential network, constructing a nursing task dependency network based on prediction data, and generating a personalized nursing plan through multi-objective optimization. According to the method, the accuracy of rehabilitation track prediction and the individuation degree of the nursing plan are improved, and comprehensive optimization of rehabilitation benefits, safety risks and resource consumption is realized.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The invention relates to the field of medical instruments, in particular to an abduction assisting device for shoulder joints, which comprises a support, a support plate for bearing arms is hinged to the top of the support, and a driving mechanism, an adjusting mechanism, a fitting mechanism, a support mechanism and a locking mechanism are arranged on the support; the driving mechanism is used for driving the adjusting mechanism, the laminating mechanism and the supporting mechanism to operate synchronously; the adjusting mechanism is used for driving the supporting plate to rotate around the hinge point so as to adjust the abduction angle. The attaching mechanism comprises an air bag fixedly connected to the supporting plate, and the attaching mechanism is used for synchronously inflating or deflating when the angle is adjusted by the adjusting mechanism so as to be attached to the arm; the supporting mechanism is used for synchronously providing or releasing additional supporting force for the supporting plate when the adjusting mechanism adjusts the angle; the locking mechanism is used for locking the adjusting mechanism after angle adjustment is completed. The use process is simplified, the operation threshold is lowered, the rehabilitation preparation time can be saved, and the clinical use efficiency is improved.

The invention discloses an intelligent rehabilitation bed body dynamic supporting system based on pressure feedback, and relates to the technical field of intelligent rehabilitation bed body dynamic supporting, and the intelligent rehabilitation bed body dynamic supporting system comprises a sensing acquisition module, a data fusion module, an individual modeling module, a strategy generation module, an execution driving module, a state feedback module and a remote interaction module; the sensing and collecting module is used for collecting pressure, temperature, humidity and heat distribution information of a contact area of a bedridden patient and a bed body in real time in a distributed multi-mode sensing mode and outputting original sensing signals. The data fusion module is used for performing multi-source data fusion processing on the original sensing signals from the sensing acquisition module by adopting a Kalman filtering algorithm, extracting comprehensive health indexes reflecting the microcirculation state of local tissues, and outputting fused physiological evaluation data; and the individual modeling module is used for training the body type, the weight, the medical history information and the historical pressure distribution data of the patient by adopting a machine learning model.

The invention provides a wearable exercise and rehabilitation monitoring system based on multi-modal AI, which relates to the field of electrical digital data processing and comprises a distributed multi-modal sensing acquisition module, a hierarchical wireless data aggregation and transmission module, a 3D human body dynamics reconstruction and modeling module and an intelligent action evaluation and feedback module. The distributed multi-modal sensing acquisition module is responsible for realizing multi-dimensional cooperative sensing of human body motion through heterogeneous sensors, and the hierarchical wireless data aggregation and transmission module adopts a master-slave base station architecture to realize efficient aggregation and low-delay transmission of heterogeneous sensor data. The 3D human body dynamics reconstruction and modeling module is used for realizing high-fidelity 3D skeleton model reconstruction based on inverse kinematics solution of multi-sensor fusion; the intelligent action evaluation and feedback module is used for providing personalized training guidance by constructing a standard action library and realizing multi-dimensional similarity quantitative scoring; according to the system, deep fusion of the multi-modal sensor can be realized, and accurate motion monitoring is provided.

The invention discloses a postpartum pelvic floor function evaluation system based on three-dimensional image reconstruction, which belongs to the technical field of medical image processing and biomechanical analysis and comprises an ultrasonic image acquisition module, a three-dimensional reconstruction module, a tissue elasticity analysis module, a rehabilitation scheme generation module and a visual display module. Performing three-dimensional space registration and feature point extraction through a multi-angle ultrasonic image sequence, and realizing three-dimensional reconstruction of a pelvic floor anatomical structure with the precision of 0.2 mm by using a multi-scale fusion network; a deep learning neural network is adopted to extract tissue features, and elastic modulus distribution is calculated through a strain analysis model; an individualized rehabilitation training plan is automatically generated according to an evaluation result, and the rehabilitation effect is improved by 75%; according to the postpartum pelvic floor function evaluation system and method, the three-dimensional visual display and rehabilitation process tracking functions are provided, accurate evaluation and scientific rehabilitation guidance of the postpartum pelvic floor function are achieved, the screening efficiency is improved by 320%, and the clinical application value is remarkable.

The invention discloses a brain-controlled rehabilitation equipment-oriented control strategy generation method, device and equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the following steps: acquiring a multi-channel electroencephalogram signal, and preprocessing the multi-channel electroencephalogram signal to obtain a target electroencephalogram signal comprising a steady-state visual evoked potential signal and a motor imagery signal, the target electroencephalogram signal corresponds to a preset action category; decoding the steady-state visual evoked potential signal by adopting filter group task related component analysis to obtain a correlation score vector; decoding the motor imagery signal by adopting a Mangban dynamic routing space-time network model to obtain a classification score vector; performing posterior probability distribution conversion and weighted fusion on the correlation score vector and the classification score vector to obtain fusion probability distribution; and generating a target control strategy according to the action category corresponding to the highest probability value in the fusion probability distribution. The control strategy obtained by the invention can consider both intention recognition precision and rehabilitation nerve activation effect.

The invention discloses a rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, and particularly relates to the technical field of medical assessment management. The assessment management system comprises a patient information acquisition module, a dynamic scale adaptation module, an automatic data acquisition and calculation module, a data analysis and report generation module, a rehabilitation management module and a database storage center, and the patient information acquisition module is used for acquiring patient information and storing the acquired information in the database storage center. According to the rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, through dynamic multi-scale adaptation, the most suitable assessment scale combination can be selected according to individual differences and rehabilitation stages of patients, the rehabilitation conditions of the patients are comprehensively and accurately reflected, and the accuracy of assessment results is improved.

The invention discloses a heart rehabilitation action precision assessment method and system of wearable sensor equipment, and relates to the technical field of heart rehabilitation assessment, and the method comprises the following steps: utilizing a multi-mode sensor array module integrated with an electrocardio sensor, an inertial measurement unit IMU, a myoelectricity sensor and a pressure sensor; synchronously collecting multi-dimensional data of heart activity, motion posture, muscle electric signals and limb pressure; according to the method, multiple sensors are integrated, multi-dimensional data such as heart activity, movement postures, muscle electric signals and limb pressure are comprehensively collected, accurate acquisition of related information of rehabilitation actions is achieved, heterogeneous data are deeply fused through an improved self-adaptive weighted fusion algorithm, a more comprehensive and accurate rehabilitation action characteristic spectrum is generated, and the rehabilitation action characteristic spectrum is more accurate. And the transfer learning and meta-learning mechanism is introduced to construct a personalized evaluation model, individual differences of patients are fully considered, and an accurate evaluation standard is customized for each patient.

The invention discloses a rehabilitation state evaluation system for a patient with language psychological disorder based on multi-agent collaboration, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize comprehensive evaluation of language and psychology and improve the evaluation accuracy and efficiency. The system overcomes the defects of poor dialect adaptation, multi-mode splitting, single evaluation dimension and weak interpretability in the prior art, and meets the clinical requirements of accurate and efficient evaluation on patients with language psychological disorders. The technical scheme is as follows: the system comprises a base model construction unit, a special evaluation agent construction unit and a collaborative scheduling unit, the base model construction unit is used for constructing a base model by taking Transform-XL and Wav2Vec2.0 as a basic framework and fusing a multi-modal attention mechanism so as to realize cross-modal information interaction; and the special evaluation agent construction unit is used for constructing a dialect special evaluation agent, a text special evaluation agent and a chart special evaluation agent based on the base model.

The invention discloses a neural signal-based wounded rehabilitation effect evaluation model construction method, and relates to the technical field of medical rehabilitation, and the method comprises the steps: synchronously collecting electroencephalogram, myoelectricity and motion parameters, extracting multi-source features to construct feature vectors corresponding to training actions, and according to the feature vector data of multiple training actions of a wounded, constructing a neural signal-based wounded rehabilitation effect evaluation model; and capturing dynamic changes of neural signals and action control ability in the training action sequence based on the sequence model, and constructing an evaluation model. According to the invention, time-dependent learning is carried out on the corresponding characteristics of each training action by using the sequence model, dynamic changes of neural signals and action control ability along with the training actions can be captured, an improvement trend in a rehabilitation process is embodied, and an evaluation model aiming at an action training effect is constructed; the action indexes output by the evaluation model can reflect the instant effect and gradual improvement condition of each action in training, and help to identify rehabilitation inflection points and training bottlenecks.

The invention relates to the technical field of crossing of artificial intelligence and sports science, in particular to a motion posture evaluation method and system based on artificial intelligence, and the method comprises the following steps: S1, multi-modal motion data collection: collecting a motion video of a user through a camera device, and synchronously obtaining basic information of the user; according to the motion posture evaluation method and system based on artificial intelligence, 9 types of core parameters are extracted by collecting 3-5 basic motions, dynamic mapping rules of the parameters and evaluation standards are established by combining age and injury history dynamic correction standards, individual differences such as height, body type, motion basis and rehabilitation progress are adapted, and the evaluation accuracy is improved. According to the method, users of different body types can obtain safe and efficient evaluation direction guidance according with own physiological structures, the body type related evaluation misjudgment rate is reduced, sports injuries caused by disjunction of the evaluation direction and the body type are avoided, meanwhile, the users can more easily reach the action standard matched with themselves, and the obtaining feeling and effect of training and rehabilitation are improved.

The invention relates to a gait monitoring algorithm which is used for a traditional four-foot walking aid, a six-axis gyroscope is installed in the middle of four feet of the walking aid, pressure sensor data is installed at the bottom of a handle and the bottoms of the four feet, and the gait monitoring algorithm comprises a sensor data acquisition module used for acquiring original sensor data related to gaits of a user, acquiring three-axis acceleration information; acquiring triaxial angular velocity information according to gyroscope sensor data; acquiring handle pressure and ground contact pressure information according to data of a pressure sensor; the gait feature extraction system is used for extracting gait features from original sensor data and comprises a gait feature extraction module used for extracting gait feature values from the original sensor data, the gait feature values comprise stride frequency, and the number of steps per second is calculated by analyzing periodic fluctuation of pressure or acceleration; and calculating the distance between the two steps through acceleration integration or the ground contact interval between the left foot and the right foot.

The invention belongs to the technical field of medical treatment, and discloses a chronic fatigue syndrome screening and diet-exercise intervention method which has the advantages of being high in individuation degree, low in rehabilitation cost, flexible in intervention implementation field, flexible in intervention training time and small in space requirement. And the compliance of the patient in completing the exercise and diet combined intervention rehabilitation task is greatly improved. The method can effectively cope with patient low compliance challenges caused by infrastructure deficiency and motivation disorder, significantly improves training enthusiasm and participation willingness of CFS patients, has significant effect on promoting rehabilitation of the CFS patients, enables the patients to obtain efficient medical services without going to medical rehabilitation institutions personally, and has good application prospects. Therefore, the current situation that basic medical resources are extremely deficient is relieved to a certain extent, and favorable conditions are created for implementation and popularization of exercise and diet combined intervention training.

The invention discloses a gait analysis method, system and equipment. The method comprises the following steps: acquiring a pressure value of each measuring point in a sole area in real time when a tested person stands or walks; generating a pressure distribution map and a change curve map based on the acquired pressure values; gait-related parameters are calculated and an electronic report is generated. In order to improve image visibility and analysis precision, a perceptual disturbance guided multi-scale color coding mechanism is adopted, and a high-resolution and vision enhanced pressure distribution diagram is generated through the steps of interpolation expansion, normalization, disturbance enhancement, HSV color wheel mapping and the like. The system also supports two analysis modes of balanced standing and dynamic walking, can output multi-dimensional gait indexes including pressure parameters, gait cycle parameters, pressure center tracks and the like, is matched with a built-in high-density sensor array and a man-machine interaction module of the system, is widely suitable for scenes of rehabilitation medical treatment, sports science, old-age health management and the like, and has wide application prospects. The method has the advantages of high detection precision, visual result, comprehensive analysis and the like.

The present application relates to the technical field of speech recognition, in particular to a speech recognition and training method for speech disorder rehabilitation patients. The present application first extracts the text in the speech data, and obtains the problem words and the standard scores of the text of the patient's pronunciation; further constructs the feature vector of each text, and classifies the standard pronunciation text and the text of the patient's pronunciation; further obtains the pronunciation disorder degree of each problem word of the patient according to the text difference and the feature vector difference between the pronunciation word class cluster and the corresponding standard word class cluster, in combination with the standard score of the text of the patient and the proportion of the problem word; finally, according to the distribution of the problem word in the sentence, in combination with the pronunciation disorder degree and the appearance frequency of the problem word, the pronunciation difficulty of each problem word is obtained, the personalized pronunciation disorder of the patient is accurately identified, and personalized training schemes are formulated for relevant personnel, thereby providing support for improving the intervention effect.

An emotional disorder recognition control model acquisition method and system based on near-infrared brain imaging map characteristics, a controller and a control method, and the method comprises the following steps: step 100, fNIRS-based multi-dimensional nerve marker acquisition and analysis, step 200, an integrated handheld diagnostic device, step 300, a dual-mode high-speed intelligent analysis system, step 300, a near-infrared brain imaging map characteristic analysis system, and step 300, a near-infrared brain imaging map characteristic analysis system. And step 400, a closed-loop AI training and database iteration system realizes a link of collection-analysis-diagnosis-display by fusing technologies of signal collection, data preprocessing and normalization, characteristic parameter extraction, intelligent report display and the like, so that the transformation of mood disorder screening from subjectivity to objectivity and from experience driving to data driving is promoted, and the mood disorder screening efficiency is improved. Therefore, the embedded analysis system for the rehabilitation medical scene is provided.

The invention discloses an intelligent rehabilitation monitoring method and device for a heart failure patient, and relates to the technical field of rehabilitation data processing.The method comprises the steps that on the basis of multiple collection time scales, monitoring item time sequence data sets are obtained, and the monitoring item time sequence data sets comprise the time sequence data set, corresponding to the multiple collection time scales, of each monitoring item; health feature value extraction is carried out on the time sequence data set of the monitoring items, and feature value sequences corresponding to different collection time scales are generated; constructing a multi-time-scale multivariate sequential sequence, and quantifying a physiological system elasticity coefficient value of the heart failure patient by using a preset event elasticity extraction mechanism; and inputting the multi-time-scale multivariate sequential sequence and the elastic coefficient value as monitoring features into a heart failure rehabilitation trajectory prediction model, outputting a health index sequence in a future intervention window, and performing elastic loss critical point identification as an early intervention point. Therefore, the accuracy and foresight of monitoring the rehabilitation health trend of the heart failure patient are improved.