4264results about "Eye diagnostics" patented technology

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

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 present application provides a method for diagnosing and / or evaluating the presence or absence, severity or degree of the improvement of a retinal disease in a subject, which comprises determining and / or evaluating circulatory parameters, retinal function, retina morphology and / or visual relating quality of life (QOL).

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably 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.

According to the multi-mode visual stimulation and intelligent self-adaption combined myopia prevention and control system based on the AR glasses, all-weather, personalized and full-scene myopia suppression is realized through multi-module cooperation of sensory visual stimulation, visual function training, AI scene dynamic recognition and directional intervention, self-adaption closed-loop feedback, cloud / offline deployment and the like; through multi-dimensional visual stimulation and real-time physiological monitoring, eye axis growth is accurately regulated and controlled, myopia prevention and control precision is improved, out-of-focus and contrast setting are automatically optimized based on immediate refraction and adjustment feedback, personalized intervention is enhanced, lightweight AI and cloud pre-calculation are introduced, the endurance of equipment is effectively prolonged, an existing AR platform is compatible, the development process is simplified, and the development efficiency is improved. And the user compliance is enhanced.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention discloses a pupil image recognition and disease change evaluation system based on image recognition, and relates to the technical field of monitoring analysis, and the system comprises the steps: obtaining the identity information and historical pupil data of a patient, and building a pupil dynamic database corresponding to the patient; alternately irradiating the eyes of the patient, and collecting a dynamic image sequence corresponding to a pupil light reflex area of the patient; performing spatial-temporal characteristic analysis on the dynamic image sequence, establishing a pupil diameter dynamic change curve, and generating a light reflection sensitivity index of the pupil of the patient according to the pupil diameter dynamic change curve; constructing a pupil dynamic evaluation model according to the pupil diameter dynamic change curve and the light reflex sensitivity index of the patient pupil, and outputting a pupil grade evaluation result based on the pupil dynamic evaluation model; comprehensive analysis is conducted on pupil grade evaluation results, analysis results are uploaded to an intensive care central system, and a grading early warning mechanism is triggered. The application has the effect of reducing the occurrence of illness state delay.

The invention relates to the technical field of cognitive impairment detection, and discloses a cognitive impairment early warning method and device based on an electroencephalogram micro state and an eye movement trajectory, and the method comprises the following steps: S1, data acquisition, S2, electroencephalogram preprocessing, S3, electroencephalogram feature extraction, S4, eye movement feature extraction, S5, feature fusion, and S6, early warning judgment. According to the method, through independent convolution branch of electroencephalogram and eye movement features, a receptive field is expanded by utilizing cavity convolution to capture multi-scale features, and long-distance dependence is modeled by a self-attention layer; after tensor splicing, time sequence information is dynamically fused through a gating cycle unit (GRU), and cross-modal time correlation is captured. The method has the advantages that the multi-modal feature hierarchical extraction and self-adaptive modeling capability is enhanced, the complementarity fusion efficiency is optimized, meanwhile, by means of cavity convolution sparse connection, self-attention parameter sharing and GRU lightweight design, the model complexity and the calculation efficiency are balanced, and efficient feature representation is provided for cognitive impairment early warning.

The invention relates to the field of electroencephalogram signal processing, and particularly discloses a multi-mode electroencephalogram characteristic cognitive ability evaluation system based on a neural network, comprising: an acquisition module used for synchronously acquiring whole brain signals and eye movement data during pilot simulation training; the data preprocessing module is used for primarily processing the received whole brain signals and the eye movement data, monitoring the primarily processed data in real time by utilizing a preset monitoring mechanism, and triggering to generate interaction test starting signals if preset symbolic fluctuation occurs in the whole brain signals or the eye movement data; and the test interaction sub-module is used for generating arithmetic test questions according to a preset rule after receiving the interaction test starting signal, and synchronously recording answer correlation parameters of the pilot. By adopting the technical scheme of the invention, the deep-level reaction mechanism of the brain under the stimulation of the special complex cognitive task of flight training can be fully considered, and the cognitive load change of the pilot in the simulated training can be comprehensively and accurately reflected.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention relates to the technical field of medical equipment control, in particular to a control system and a control method of cornea crosslinking equipment. The control system comprises a multi-modal data fusion module which is configured to integrate a preoperative corneal topographic map curvature matrix, OCT layering thickness data and biomechanical parameters to generate a four-channel fusion tensor; the virtual cornea modeling module is configured to input a conditional generative adversarial network to generate a virtual cornea model under the condition of the four-channel fusion tensor; the dynamic energy planning module is configured to generate a thermodynamic diagram of energy distribution based on mechanical response prediction of the virtual cornea model; and an illumination control module configured to perform patterned illumination based on the thermodynamic diagram. According to the method, the virtual cornea model is constructed, the energy distribution thermodynamic diagram is generated, accurate patterning reference can be provided for irradiation treatment, and a better treatment effect is achieved.

A method to prevent intoxicated operation is described. The method includes providing instructions to a vehicle user to position a vehicle user body portion posture in a predefined alignment. The method further includes obtaining the vehicle user body portion posture from a first vehicle detector, and determining whether the vehicle user body portion posture is in the predefined alignment. The method includes activating a plurality of vehicle visual indicators to illuminate in a predefined manner when the vehicle user body portion posture is in the predefined alignment. The method further includes providing instructions to the vehicle user to move vehicle user eyes to track the plurality of vehicle visual indicators, and obtaining a vehicle user eye movement from a second vehicle detector. The method further includes determining whether the vehicle user eye movement meets a predetermined condition, and actuating a control action accordingly.

The invention discloses a psychological health monitoring and self-adaptive nursing decision-making system and method based on electroencephalogram signals, and relates to the field, and the method comprises the following steps: S1, collecting electroencephalogram signals and eye movement characteristics, and generating a multi-modal original signal data set; s2, electroencephalogram alpha wave asymmetry and eye movement characteristics are extracted, and delta-alpha spectrum overlapping pathological interference is eliminated; s3, inputting an evaluation model and a rule base, matching a DLB pathological marker and a symptom association rule, and marking a mental health risk level; and S4, matching an intervention strategy, and dynamically updating the evaluation model and the symptom association rule. The multi-modal fusion strategy effectively covers an association path of a DLB patient from molecular pathology to clinical symptoms, further provides a high-reliability data basis for subsequent risk level marking, realizes extraction of pure alpha wave energy and correction of prefrontal lobe alpha wave asymmetry aiming at frequency spectrum overlapping interference of delta waves and alpha waves in electroencephalogram signals, and improves the accuracy of risk level marking. Emotion abnormal degree misjudgment caused by false increase of the FAA value is avoided.

The invention provides a visual fatigue relieving method based on ambient light self-adaption and an AI algorithm, and relates to the technical field of visual health protection. The visual fatigue relieving method based on ambient light self-adaption and the AI algorithm comprises the following specific steps: S1, data acquisition: acquiring a user eye image sequence in real time through a high-frame-rate camera; and S2, parameter extraction: when the user uses the eye for the first time, testing the eyes by combining the adjustable light source with ambient light. Multi-dimensional features (such as eyeball movement, blinking mode, pupil function and the like) of eyes are collected in real time through a high-frame-rate camera, an individualized base line is established and a fatigue rate value is dynamically calculated in combination with data of an ambient light sensor, and conversion from passive response to active prevention is realized. Through a hierarchical intervention strategy (such as brightness adjustment, blue light control and forced rest), the visual load is remarkably reduced, the intervention efficiency is improved, and the problem of insufficient adjustment hysteresis and individual adaptability in the prior art is solved.

The invention discloses a depression detection method based on cross-modal knowledge distillation, belongs to the field of depression identification, and solves the defects of an existing depression detection method in the aspects of single-modal data and time sequence model interpretation. The method comprises the following steps: constructing a multi-modal teacher model, wherein the teacher model integrates electroencephalogram and pupil area signals; constructing a single-modal student model which only uses pupil area signals and learns multi-modal features from the teacher model through a knowledge distillation process; training the student model by using a knowledge distillation process, and aligning the middle layer features and output probability distribution of the student model with the teacher model through feature layer distillation and probability distribution layer distillation; and in a test stage, inputting a pupil area signal to be detected into the distilled student model, and outputting a depression classification result. According to the method, the performance of the single-mode model is improved, the dependence on multi-mode data is reduced, and the model interpretability is enhanced.

The invention discloses an intelligent asthenopia control method and system based on eye movement and electroencephalogram data. The method comprises the steps that eye movement time sequence data and electroencephalogram rhythm signals of a user are synchronously obtained through a multi-mode sensing unit; inputting the eye movement time sequence data and the electroencephalogram rhythm signal into a multi-modal fusion decision model, and generating a fatigue level through feature weighting based on an attention mechanism; at least one intervention mode is dynamically selected according to the asthenopia level, and the intervention modes comprise the first mode, the second mode and the third mode; in the first mode, a display interface adjusting instruction containing natural light simulation parameters is generated, and the display interface adjusting instruction is integrated with a vegetation color system compensation algorithm; and mode 2: starting a visual training program with ecological images, and generating a dynamic guide mark simulating natural motion on an interface. According to the invention, through a data-environment-physiology multi-dimensional collaborative innovative architecture, the spanning of asthenopia regulation from passive response to ecological active restoration is realized.

The invention relates to a self-adaptive visual training method based on eye features, and belongs to the technical field of visual health and artificial intelligence. In order to solve the problems that traditional visual training equipment is heavy in structure, single in training mode and lack of personalized regulation and control and concentration state monitoring, the visual training equipment obtains eye images of a user in real time through an image acquisition module, adopts visual intelligent analysis, extracts double features of eye contours and pupil positions, calculates the visual concentration degree, and improves the visual training efficiency. And a concentration state is judged by combining a dynamic self-adaptive threshold value. When the concentration degree is insufficient, the control module triggers intervention mechanisms such as prompt or training pause and the like, and the remote interaction module uploads data to the cloud platform to generate a personalized training scheme. Intelligent and personalized regulation and control of the training process are achieved, the training effect and compliance are improved, the system is compact in structure and suitable for various scenes, and an efficient solution is provided for vision health.

The invention discloses an auditory cognitive impairment evaluation and screening system, which belongs to the technical field of data analysis, and specifically comprises the following steps: setting basic test parameters and initial stimulation parameters, and synchronously acquiring voice, eye movement trajectory data and brain wave signals by combining space-time anchoring marks to form a multi-modal data set with a timestamp; a multi-stage cognitive test is carried out in an adaptive test engine, and stimulation parameters are dynamically adjusted by using a forgetting curve prediction algorithm according to real-time accuracy and response time; based on the adjusted stimulation parameters, performing time domain alignment on the multi-modal data set by adopting a dynamic time warping algorithm and taking a space-time anchoring mark as a benchmark, extracting features and combining the features into a cross-modal feature vector; and the cross-modal feature vectors are input into a pre-trained LSTM-decision tree fusion diagnosis model, and a quantitative evaluation report of obstacle type and degree grading is output, so that the accuracy of cognitive disorder diagnosis is improved.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention discloses a glaucoma optic nerve fiber layer dynamic tracking method and system, and relates to the field of medical image analysis, and the method comprises the steps: obtaining visual field examination data and OCT scanning data; calculating a defect change value and an optic nerve fiber layer thickness change value; the thickness change value and the defect change value are paired to form a joint change data set; judging the state of the sampling point, and determining the sampling point as a joint progress area, a structural risk area or a functional risk area; analyzing the dynamic change trend of each region, and evaluating the progress risk level; calculating a time sequence correlation metric value and a time delay parameter between the thickness change sequence and the defect change sequence to form a space coupling strength distribution diagram; adjusting the boundary of the risk area based on the distribution map, and marking a dynamic boundary adjustment area; and dynamically adjusting the density and the frequency of the OCT sampling points in the next period according to the adjustment area and the coupling strength distribution result. According to the invention, accurate monitoring and early warning of glaucoma conditions are realized.

The invention discloses a depressive symptom assessment method and system based on multi-modal physiological data, relates to the technical field of artificial intelligence, and solves the problems of relatively high subjectivity, single data dimension, insufficient linkage and lack of a dynamic induction mechanism during depressive symptom assessment in the prior art. The method comprises the following steps: performing emotion induction on a subject by using a VR scene, collecting multi-dimensional physiological signals, behavior performance and subjective feedback information at the same time, and realizing feature extraction and multi-modal feature fusion; a depression risk assessment model trained by a deep neural network model is utilized to predict the current emotional state, depression symptom severity and potential abnormal reaction characteristics of a subject, and self-iterative learning of the depression risk assessment model and regulation and control of a VR situation push strategy are realized. The problems of single emotion induction mode and unstable reaction are solved, and the defects of high subjectivity and limited data dimension in a traditional evaluation method are effectively overcome.

The invention provides a brain function evaluation method and system based on fNIRS and eye movement feature fusion, and the method comprises the steps: testing a to-be-tested person under a pre-constructed social scene normal form, and obtaining test data which comprises functional near infrared spectrum data and eye movement data; extracting time dynamic characteristics of brain function activation and brain function network space organization mode characteristics from the functional near infrared spectrum data, and constructing a near infrared brain function characteristic set; for a near-infrared brain function feature set corresponding to the functional near-infrared spectrum data and an eye movement space-time feature set corresponding to the eye movement data, constructing features in each near-infrared brain function feature set and features in the eye movement space-time feature set into feature pairs, and calculating a correlation coefficient of each feature pair, feature fusion and screening are realized based on correlation coefficients; and inputting the screened features into a classification evaluation model to obtain the severity of the behavioral dysfunction and a brain function analysis value.

The invention discloses a hidden multi-mode brain state real-time monitoring and closed-loop intervention system, and relates to the technical field of intelligent wearable equipment. The system comprises a cap body, and further comprises a multi-point electrode array used for collecting EEG (electroencephalogram) signals, EOG (eye movement) signals and EMG (electromyography) signals of a human body; the brain blood oxygen monitoring module is used for monitoring brain blood oxygen saturation; the signal receiving and processing module is used for preprocessing and analyzing data acquired by the multi-point electrode array and the brain blood oxygen monitoring module; the multi-mode stimulation module is used for implementing sensory and nerve stimulation on the user according to a judgment result of the state judgment module so as to intervene in a drowsiness or attention decline state; and the power supply module and the communication unit are used for providing a working power supply for the modules needing power supply in the system and carrying out data communication with external terminal equipment. The system can monitor the waking degree of the user with high precision and carry out closed-loop feedback intervention by various stimulation means.

The disclosure describes a reading system and a reading method for fundus images, the reading system comprising: an input module for receiving fundus images; a screening module for outputting screening results based on the fundus images, the screening results at least including quality control judgment results and lesion judgment results; a first classification module for classifying the fundus images into screening qualified images and first images to be quality controlled based on the quality control judgment results, and taking at least one image from the first images to be quality controlled and the screening qualified images as an image to be quality controlled; and a quality control module for outputting quality control results based on the image to be quality controlled. According to the disclosure, the screening accuracy of the reading system can be improved.

The invention discloses an evaluation and feedback method and system combining eye movement and visual and auditory algorithms, and relates to the technical field of eye movement and visual and auditory. The evaluation and feedback method combining the eye movement and the visual and auditory algorithm comprises the following steps: acquiring human eye image information by wearing head-mounted eye movement tracking equipment, and mapping to obtain a real-time human eye staring position under a scene camera coordinate system in combination with a scene image acquired by a scene camera in the equipment; a scene camera in the head-mounted eye movement tracking equipment and acoustic acquisition equipment arranged in an environment are utilized to analyze and process acquired scene images and sound signals through a visual / auditory processing algorithm, so that scene contents are identified, detected, segmented and positioned. According to the method, the problems that most of current researches are still limited to single-mode analysis, a joint modeling and intelligent evaluation mechanism for multi-mode sensing signals is lacked, and the requirement for panoramic, real-time and intelligent feedback of individual states is difficult to meet are solved.

The invention relates to the technical field of medical diagnosis, in particular to an eye white image acquisition and disease auxiliary diagnosis method and device, and the method comprises the steps: obtaining a video frame collected by a camera in real time, obtaining eye socket detection frame information in the video frame through a target detection algorithm, and adjusting the three-axis displacement of a motor to be positioned to an eye socket based on the eye socket detection frame information. The method comprises the following steps: completing image acquisition of eye white area maps in five directions, automatically marking abnormal parts, establishing a dynamic coordinate system and a mapping area which take a pupil center as a pole, calculating polar radius and polar angle of polar coordinate parameters, calculating a spatial weight value of mapping abnormity through a distance attenuation function, calculating a comprehensive gain value of the mapping area, and normalizing the comprehensive gain value. Generating an eye white abnormity thermodynamic diagram and performing hierarchical display; according to the invention, eye sockets are automatically positioned, eye white area maps in five directions are adaptively acquired, hierarchical display of eye white abnormity thermodynamic diagrams is realized, batch acquisition of eye white images of patients is satisfied, diagnosis efficiency is improved, and doctors are assisted to complete disease diagnosis.

The invention discloses a senile dementia early warning, evaluation and health management system and method based on artificial intelligence, and relates to the field of artificial intelligence and medical health. The system comprises a data acquisition module for acquiring Chinese speech, electroencephalogram signals, facial expression images and eye movement data of a patient; the data preprocessing module is used for preprocessing and storing various data; the feature extraction and modeling module is used for extracting a feature set and constructing a one-dimensional classification model; the intelligent diagnosis module inputs the feature set to a one-dimensional classification model to obtain a disease probability and a preliminary diagnosis result, and a final result is obtained through comprehensive diagnosis after a preliminary diagnosis threshold value is compared; and the user interaction module realizes diagnosis visualization, allows a user to define a preliminary diagnosis threshold value, imports data to retrain and updates a one-dimensional classification model. Through multi-modal data fusion and intelligent analysis, potential relations among different modal data are fully mined, and the accuracy of senile dementia diagnosis and the system adaptability are effectively improved.

The embodiment of the invention relates to the field of intelligent wearable equipment, in particular to intelligent ear vagus nerve regulation and control glasses and a control method, control device and regulation and control system thereof, and the method comprises the following steps: acquiring pupil dynamic characteristic parameters monitored by the intelligent ear vagus nerve regulation and control glasses; calling a trained machine learning model to analyze real-time physiological state information according to the pupil dynamic characteristic parameters; according to the real-time physiological state information obtained through analysis, generating an electrical stimulation control instruction corresponding to the real-time physiological state information; and controlling an ear vagus electrode in the intelligent ear vagus nerve regulation and control glasses to implement nerve regulation based on the electrical stimulation control instruction. According to the method, the limitation of fixed parameter output of a traditional ear vagus nerve stimulation device can be broken through, the individualization and accuracy of an ear vagus nerve stimulation scheme are improved, and the problem that the treatment effect is insufficient or discomfort is caused is solved.

The invention discloses a distance measurement system based on artificial intelligence, and the system can achieve the automatic grabbing of size parameters of glasses. According to the system, a camera is adopted to capture a glasses image, a gyroscope and an artificial intelligence algorithm are adopted to recognize the spatial position of glasses, spatial coordinates of feature points are obtained, the pixel scale # imgabs0 # of the image is obtained subsequently based on the physical geometrical relationship of the glasses and the real pupil distance of a user, and finally the size parameters of the glasses are obtained. The glasses parameters comprise a glasses width, a pupil distance, a front inclination angle, a surface bending angle and a glasses-eye distance; the camera is an apple panel built-in camera; the gyroscope can obtain the pitch angle # imgabs1 #, the deflection angle # imgabs2 # and the inclination angle # imgabs3 #, so that the head posture of a glasses wearer is obtained, and the size measurement precision of the glasses is greatly improved. According to the system, automatic measurement of glasses parameters is realized, the working intensity of manual glasses matching is greatly reduced, and digital upgrading of high-end customized glasses is realized.