3608results about "Sleep inducing/ending devices" patented technology

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

The invention discloses a cognitive function rehabilitation training system and method based on virtual reality, and the system comprises a cognitive function test unit which is used for carrying out the cognitive impairment test evaluation of a patient based on a virtual reality module, and generating a cognitive function evaluation report of the patient; the cognitive training unit is used for generating a personalized cognitive training scheme based on the cognitive function evaluation report, carrying out targeted cognitive training in a virtual reality environment, and collecting physiological data and training performance data of the patient in the cognitive training process; the feedback unit is used for comprehensively analyzing the physiological data and the training performance data and generating a staged feedback report; and the rehabilitation effect tracking unit is used for analyzing rehabilitation effects of the patient in different time periods based on the periodic feedback report and optimizing a subsequent cognitive training scheme based on an analysis result. And the interestingness and interactivity of rehabilitation training are improved.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention belongs to the technical field of biological calculation, and discloses an intelligent music regulation and control system and method based on electroencephalogram signal multi-modal feature recognition, electroencephalogram data are obtained through anti-interference collection and adaptive filtering processing, multi-modal feature vectors are constructed based on physiological feature extraction and information fusion, and the multi-modal feature vectors are extracted and subjected to multi-modal feature recognition. And accurate recognition of the emotional state and cognitive load evaluation are realized. A music-emotion mapping network is constructed through music feature analysis and emotion semantic matching, a personalized music regulation and control strategy is formulated, music parameter dynamic adjustment and audio stream reconstruction are realized, and a music output stream is generated. A closed-loop optimization feedback mechanism is formed by adopting electroencephalogram feedback collection and adjustment effect evaluation, user data security grading and privacy protection processing are implemented, a security specification database is constructed, the effect of a music output stream is continuously evaluated, and a comprehensive evaluation report is generated. Immediate emotion regulation is provided for the user, a healthier emotion management mode is established for the user through long-term data accumulation, and psychological toughness is improved.

The invention provides a motion fear intervention device based on virtual reality. The motion fear intervention device comprises a VR scene generation module; the subjective evaluation module presents the TSK scale to the user to obtain a TSK scale score of the user, and obtains a TSK grade corresponding to the TSK scale score according to the TSK scale score; the biological signal acquisition module is used for acquiring biological feedback of a user and calculating a comprehensive exercise fear grade according to the following formula: the comprehensive exercise fear grade = alpha * TSK grade + beta * HRV score + gamma * GSR score + delta * pupil change score + belonging to * EMG score; the dynamic intervention module is used for generating scene parameters in real time according to the comprehensive exercise fear level and inputting the scene parameters into the VR scene generation module to generate a corresponding scene; the dynamic intervention module is also used for providing forward excitation. According to the method and the device, the currently displayed VR hierarchical scene is adjusted through biological feedback, and closed-loop feedback adjustment of motion scene stimulation is realized.

The invention provides a system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention relates to the technical field of child cognitive training, and discloses an ADHD child cognitive training system. The system comprises a multi-modal sensing module, an attention evaluation module, a dynamic task generation module, a behavior feedback module and a self-adaptive adjustment module. The multi-modal perception module collects multi-dimensional behavior data, the attention evaluation module extracts attention fluctuation features, the dynamic task generation module generates a cognitive training task sequence, the behavior feedback module performs emotion state classification processing to generate feedback signals, and the self-adaptive adjustment module generates personalized training difficulty steps. According to the system and the method, through comprehensive data acquisition, precise attention assessment, dynamic task generation, real-time emotion feedback and difficulty adaptive adjustment, personalized and precise cognitive training of ADHD children is realized, the training effect is effectively improved, and the problems of lack of personalization, insufficient state monitoring, unscientific difficulty adjustment and the like of an existing training method are solved.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention discloses a data-driven self-adaptive cognitive competence training method and a data-driven self-adaptive cognitive competence training device. The method comprises the following steps: collecting initial cognitive competence data of a user; establishing a mapping relationship between the training tasks and the cognitive ability dimensions, and associating a plurality of predefined training tasks to the corresponding cognitive ability dimensions respectively; based on the user cognitive competence vector, selecting a training task matched with the current cognitive competence level of the user from a training task library through an adaptive recommendation algorithm; providing the training task matched with the current cognitive ability level of the user for the user to execute; and updating the user cognitive ability vector according to the behavior feedback data. According to the technical scheme, the training task is finely matched through the multi-dimensional cognitive vector, the difficulty is adjusted in real time, and the load is prevented from being too high or too low; and by utilizing behavior data closed-loop updating, a capability increasing curve can be accurately quantified, task sorting and rhythm are dynamically optimized, and a learning motivation is stimulated.

The present disclosure describes systems, devices, and methods of benefiting a cognitive state or function of a brain of a subject. The systems can include a portable system comprising a single headset device to be worn by a subject, comprising a pair of glasses configured to provide a visual stimulus to the subject, and a pair of supra-aural headphones configured to provide an auditory stimulus to the subject.

The invention relates to the technical field of natural language processing and image processing, and discloses an emotional music adjusting method and terminal based on dynamic perception and multi-modal fusion. The method comprises the following steps: acquiring multi-dimensional feature data of a user, processing an unstructured text through a dynamic sensing field based on a large language model, and carrying out dynamic weight distribution on the multi-dimensional feature data to generate fusion features; collecting facial image data of a user, extracting facial emotion features through a visual converter model, and mapping the facial emotion features to a music feature space; the features of the two modes of text and image, namely fusion features and facial emotion features, are input into a music generation module, and personalized treatment music is generated through feature fusion coding, music structure planning and a layered decoding architecture. And music generation parameters are adjusted through a dynamic feedback mechanism to form closed-loop control. According to the method and the computer terminal, full-process intelligent processing from user feature capturing to emotional music generating is achieved, and the treatment effect and the user experience are improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

A system and a method provide closed-loop sedation, anesthesia, or analgesia by monitoring EEG and automatically adjusting delivery of sedative, anesthetic, and / or analgesic drugs to maintain a desired or predetermined level of cortical at all echelons of care. The system and the method further monitor a subject's cortical activity to detect occurrence of burst suppression which can be indicative of unsafe depth of anesthesia or sedation. Further, the system and the method provide for alteration or cessation of administration of anesthesia or sedation based on the occurrence of burst suppression to mitigate harm to the subject.

A wireless implantable neuromuscular stimulator includes an antenna for producing an induced current in response to being disposed in an electromagnetic field. The antenna includes a substrate having an upper surface and a lower surface. An upper coil including a plurality of coil turns is disposed on the upper surface of the substrate. A lower coil including a plurality of coil turns is disposed on the lower surface of the substrate. The upper and lower coils are electrically connected to each other in parallel. The parallel connection can be facilitated by a plurality of connectors that extend through the substrate and electrically connect the upper coil to the lower coil. In one example configuration, connectors connect each coil turn of the upper coil to a corresponding turn of the lower coil.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

The invention relates to the technical field of medical rehabilitation, and discloses a vestibular function and cognitive function rehabilitation training system and method based on virtual reality, and the rehabilitation training system comprises a hardware platform, a software platform and a server. The hardware platform comprises VR interaction equipment, a head motion tracking sensor and calculation and display equipment; the software platform is integrated with a user management module, a visual stimulation module, a vestibular rehabilitation training module, a cognitive evaluation and training module, a dynamic visual acuity evaluation module and a data management and analysis module. By combining black and white chess grid visual stimulation, standardized vestibular rehabilitation tasks, spatial cognitive testing and DVA objective monitoring, synchronous evaluation and personalized collaborative intervention of vestibular functions and cognitive functions of dizzy patients are realized. According to the method, the problems of boring traditional rehabilitation means, subjective evaluation and lack of quantitative feedback are solved, and the scientificity, interestingness and curative effect testability of rehabilitation training are remarkably improved.

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

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention relates to a child attention evaluation and training method and system, and the method comprises the steps: obtaining a brain wave mode, an eyeball movement track, an emotion activation level and autonomic nervous system function data of a child in real time, and generating multi-dimensional biological signal data; based on the multi-dimensional biological signal data, generating evaluation task data; generating dynamic task parameters according to biological signal data and behavior performance data in the evaluation task data; generating an attention feedback signal based on the dynamic task parameters; according to the attention feedback signal, a training task integrating work memory, suppression control and cognitive flexibility is constructed, and cognitive function training data is generated; and based on the cognitive function training data, synchronously sharing the attention performance data of the children to the parent terminal and the teacher terminal, generating life situation intervention suggestions, and iteratively optimizing evaluation parameters according to a long-term effect evaluation result. Therefore, the accuracy of children attention evaluation and training can be improved.

The invention relates to the technical field of emotion recognition and regulation, and discloses a multi-modal emotion feedback regulation system, which comprises the following modules: an emotion recognition module used for acquiring an emotion state of a user through multi-modal data, the multi-modal data including but not limited to physiological signals, voice signals, facial expressions, action recognition and text analysis results; and the emotion analysis and modeling module is used for performing analysis and modeling on the emotion state of the user through a deep learning model according to the acquired multi-modal data, and generating time sequence data of the emotion of the user. By integrating a multi-mode emotion recognition technology and combining various data sources such as physiological signals, voice, facial expressions, actions and text data, the emotion state of the user can be comprehensively and accurately evaluated. Compared with traditional single-mode recognition, the multi-dimensional emotion recognition method has higher accuracy and adaptability, so that emotion regulation feedback can provide personalized feedback strategies more accurately according to individual requirements of users.

The invention provides a sleep-aiding medicine pillow dynamic regulation and control method and system based on physiological parameter analysis. According to the method, pressure data and sleep bio-electricity signals of a contact area of a user are collected in real time, a characteristic frequency band proportion and a heart rate fluctuation parameter are analyzed, and a correlation model is established; calculating a bioelectricity signal energy integral value of the characteristic frequency band based on the model, and quantizing to generate a bioelectricity intensity index; the heat conduction efficiency is changed by adjusting the molecular density of the temperature-sensitive material, and a contact layer heat transfer dynamic instruction is generated; pressure feedback and instruction waveform characteristics are combined, the micropore on-off duty cycle is adjusted, and the opening duration of unit time is controlled; the drug release rate is synchronously adjusted, so that the response waveform and the heat conduction instruction are dynamically balanced, and closed-loop regulation is formed. According to the application, through multi-parameter dynamic analysis and feedback adjustment, accurate cooperative control of heat conduction and medicine release of the medicine pillow is realized, and the matching degree of sleep-aiding substance release and sleep rhythm is optimized.

The invention relates to the field of psychological health, and provides a personalized mind-correcting and pressure-reducing system and method based on multi-modal physiological signals. The system comprises a multi-mode physiological signal acquisition module, an emotion recognition module, a mind content generation module and a virtual / augmented reality presentation module. The acquisition module is used for acquiring and preprocessing various physiological signals by using an advanced sensor; the emotion recognition module establishes a signal and emotion mapping relation according to a deep learning algorithm and classifies the signal and emotion mapping relation; the content generation module generates content according to the emotion and preference of the user by means of a big language model and a retrieval enhancement technology; and the presentation module is used for providing immersive experience by combining Unity 3D with VR / AR (Virtual Reality / Augmented Reality) equipment. The system has the advantages of being capable of accurately monitoring emotions in real time, intelligently matching contents, providing immersion experience and forming closed-loop feedback, having expansibility, achieving the emotion recognition accuracy rate of 95%, and being capable of effectively improving mental health and life quality.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

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 embodiment of the invention provides a user state adjusting system based on multi-sensory focusing and neural remodeling, and the system comprises a physiological data interaction module which is configured to collect a multi-modal physiological signal of a user; the intelligent decision-making module is internally provided with a pre-trained neural state evaluation model so as to evaluate the current neural physiological activation state of the user according to the multi-modal physiological signal and execute feedback regulation logic based on the evaluated current neural physiological activation state of the user so as to generate a multi-modal feedback control instruction; the multi-mode feedback output module is configured to generate at least one sensory regulation stimulus based on the multi-mode feedback control instruction; and the nerve focusing guide module is configured to position a nerve physiological reflex area strongly related to the user state based on the subjective state description of the user in combination with the multi-modal physiological signal of the user, so as to generate man-machine interaction guidance to focus the attention of the user on a target reflex area and form a closed-loop regulation link of'perception feedback and re-perception '.

The invention discloses a cognitive enhancement training method and system based on brain signal pattern recognition. The method comprises the following steps: acquiring offline brain signal data of a user, preprocessing the offline brain signal data, and inputting the preprocessed offline brain signal data into a pre-selected decoding model for model training so as to construct a decoding model for the user; based on multiple preset cognitive states, constructing task adjustment logic to perform bidirectional adjustment on the cognitive training task for each cognitive state; in the cognitive training process of the user, on-line brain signal data of the user is decoded based on the decoding model so as to obtain the current cognitive state of the user, and the next cognitive training test is adjusted in real time according to task adjustment logic so as to perform EEG neural regulation and fNIRS neural regulation on the user and further perform cognitive enhancement on the user. According to the method, the current cognitive state of a user is recognized in real time by pre-constructing a decoding model, and bidirectional adjustment is performed on a task according to preset task adjustment logic, so that cognitive enhancement is performed on the user.

The invention relates to the field of psychotherapy, and discloses a pain psychotherapy simulation system based on a virtual reality technology, which is used for solving the problem that when a female patient experiences a physiological cycle, a patient biofeedback signal collected by the system can be interfered, and comprises the following steps: combining a head-mounted display, a handle and other control equipment; the method comprises the steps that a patient enters an immersive virtual world through equipment, physiological data of the patient are obtained, the physiological data comprise heart rate data, skin conductivity and brain wave data, an initial disease pain index is calculated according to the physiological data of the patient, a regulatory factor is calculated according to physiological period information, and the initial disease pain index is calculated according to the regulatory factor; the actual disease pain index is calculated according to the initial disease pain index and the regulatory factor, and psychological intervention is performed according to the actual disease pain index, so that the accuracy of evaluating the state of the treated part of the patient by the system is effectively improved, and the use experience of the patient is improved.

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