2600results about "Eye diagnostics" patented technology

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 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 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 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 provides a concentration training method and device based on visual tracking, and the method comprises the steps: collecting the real-time eye movement data of a user, including the staring point coordinate and the eye movement speed, and initializing the target trajectory of a training task; calculating a concentration deviation between the current gazing point and the target trajectory, and dynamically adjusting the target trajectory through nonlinear mapping based on the deviation to generate a dynamic trajectory; constructing a multi-feature fusion distraction scoring model based on the concentration deviation and the change trend of the concentration deviation in combination with the geometric features of the trajectory, and detecting and marking distraction events; when a distraction event is detected, self-adaptive visual correction stimulation is executed at the event position immediately, and a user is guided to return to a task; and after the training period is finished, dynamically adjusting the task difficulty coefficient of the next period according to the frequency and the average deviation of the distraction events in the period.

The invention discloses an attention monitoring method, device and equipment based on multi-modal sensing and a medium, and relates to the technical field of attention monitoring. The method comprises the following steps: collecting eye movement image data of eyeballs of a user; extracting fixation point stability features, eye movement track features, blink mode features and pupil dynamic features based on the eye movement image data; determining a concentration score of the user based on the fixation point stability feature, the blink mode feature, the pupil dynamic feature and the eye movement track feature; and if the concentration score is lower than a preset threshold value, sending reminding information to a user through the wearable device. The attention distraction behavior of the user in a continuous high attention demand scene can be accurately identified and prompted in time.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention discloses a portable eyeshade for positioning injured semicircular canals according to nystagmus, which relates to the field of clinical medicine and comprises an eyeshade body, a fixing plate and a high-speed camera. High-speed cameras are arranged on two sides in the eyeshade body, an infrared lighting module is arranged at one end of each high-speed camera, an inertial measurement unit is arranged in the eyeshade body on one side close to the two high-speed cameras, and a processor is arranged in the eyeshade body on the top of the inertial measurement unit; a fixing plate is movably installed at one end of the eyeshade body, connecting buckles are fixedly connected to the two sides of the eyeshade body, and a head band is movably installed on the outer sides of the connecting buckles. The high-speed camera, the infrared lighting module, the inertial measurement unit and the processor are integrated on the light-weight eyeshade body, the head band connected with the connecting buckle is matched to achieve wearable performance, large equipment is not needed, the nystagmus detection convenience and timeliness are improved, the head band is adjusted in an adaptive mode, the wearing oppression feeling of a patient can be reduced, and the examination experience is improved.

The utility model provides wearable equipment which comprises a shell and further comprises a processor, a display assembly and a lens cone assembly which are installed in the shell, and the display assembly is fixedly connected with the lens cone assembly and is in communication connection with the processor. The lens cone assembly comprises at least one lens cone, a lens and a first eye detector are arranged in each lens cone, the first eye detector is in communication connection with the processor, and the first eye detector is used for collecting eye movement state information and transmitting the collected eye movement state information to the processor. In the first direction, the display assembly and the first eye detector are arranged on the two sides of the lens respectively, and a display interface of the display assembly faces the lens. The wearable device provided by the embodiment of the utility model is convenient to operate and high in accuracy, and can provide timely treatment for a patient suffering from otolithiasis.

The invention discloses an English classroom attention regulation and control method combined with brain wave detection, and relates to the technical field of English classroom attention regulation and control, and the method comprises the steps: obtaining a multi-channel brain wave signal of a student in English classroom learning through a brain wave collection device, collecting visual gazing track data through an eye tracker, and obtaining a brain wave signal; capturing a face image sequence through a camera; performing frequency band separation on the multi-channel electroencephalogram signal, extracting energy of a theta wave and a beta wave of a prefrontal lobe, and calculating a theta / beta power ratio; performing de-noising and clustering processing on the visual gaze track data, identifying an effective gaze interval, calculating a reciprocal of a gaze point distribution variance in unit time, generating a visual focusing stability parameter, performing action unit identification on a facial image sequence, extracting smile frequency, eyebrow intensity and blink period characteristics, and outputting a cognitive emotion state score; and constructing a multi-modal attention fusion evaluation model based on the theta / beta power ratio, the visual focusing stability parameter and the cognitive emotion state score.

The invention relates to the technical field of emotion recognition, and discloses an emotion monitoring system and method based on a brain-computer interface, and the emotion monitoring method based on the brain-computer interface comprises the steps: synchronously collecting and preprocessing physiological signals through multiple sensors; calculating credibility scores and screening available modes; adopting a credibility weighted attention mechanism to obtain a fusion feature vector and calculating feature quality; constructing a multi-branch inference network to perform emotion recognition, and outputting an emotion state and an overall credibility score; and detecting distribution drift and carrying out individualized multi-branch inference network calibration. According to the invention, through cooperative work of multi-modal physiological signals, when the quality of a single-modal signal is reduced, the stability of the overall performance can be ensured, and the emotion recognition accuracy is improved; through an online continuous learning mechanism, model calibration can be performed according to an individual emotion expression mode of a user, physiological feature differences of different users are adapted, and a complete solution is provided for practical application of an emotion monitoring technology.

The invention relates to the technical field of attention deficit hyperactivity disorder detection, and discloses a device for detecting cognitive and developmental conditions of children with hyperactivity disorder, which comprises a handle, a head-mounted device, a bracelet, a hierarchical adaptive algorithm module, a multi-dimensional execution module and a data fusion and analysis module, a multi-mode sensing module is arranged in each of the handle, the head-mounted device and the bracelet; a motion capture unit, a high-sensitivity key array, a tactile feedback module and a wireless transmission module are integrated in the handle, and a wireless electroencephalogram acquisition module, a miniature near infrared spectrum module and an eye movement tracking module are integrated in the head-mounted device. According to the invention, through deep fusion of a multi-dimensional cognitive evaluation system and gamepad hardware, the device not only breaks through the limitation of the traditional neuropsychological test in methodology, but also realizes refined capture of ADHD heterogeneity characteristics in the technical level, and provides a hardware basis for establishing an objective and quantitative diagnostic tool with high ecological efficiency.

The invention relates to the technical field of intelligent visual detection, in particular to visual field detection equipment, system and method based on extended reality (XR), and the method comprises the steps that an image feature extraction module collects image data in real time, extracts features and eliminates optical distortion; the fixation deviation correction module analyzes eye movement data in real time, quantifies fixation deviation and triggers visual prompt and correction; the adaptive stimulation generation module dynamically encrypts to generate a test point image and adaptively adjusts the test point image according to visual field analysis and test requirements; the dynamic adjustment image module predicts and optimizes a test point presentation sequence according to image features and fixation points, automatically encrypts a high-probability defect area and quickly jumps to a key screening area; and the visual field defect identification module extracts image features, calculates the defect probability of each region in real time by using a Bayesian network in combination with subject response data, and locates a high-risk region. The system provides an efficient and accurate visual field detection scheme through the synergistic effect of multiple modules.

The invention relates to the field of aviation human factor engineering and state monitoring, and particularly discloses a pilot cognitive load real-time monitoring system based on multi-modal data, which comprises multi-modal data acquisition, dynamic preprocessing and time-space synchronization, adaptive feature extraction, a cognitive load intelligent calculation engine, and a multi-dimensional visual early warning and distributed collaborative integration platform. The multi-modal data acquisition module obtains four types of data and dynamically optimizes the precision; carrying out differential denoising on the preprocessing module and the space-time synchronization module; the adaptive feature extraction module extracts multiple types of features and performs weight adjustment and screening; the cognitive load intelligent calculation engine uses a hybrid model to adaptively adjust parameters and predict a trend; the multi-dimensional visual early warning module provides a load curve, a dynamic threshold adjustment value and multi-stage early warning; the distributed collaborative integration platform supports encryption transmission and cross-system docking. The method solves the problem that the prior art depends on subjective evaluation and single physiological indexes and cannot monitor the cognitive state of the pilot in real time, and realizes high-precision, high-adaptability and high-timeliness monitoring.

The invention discloses a strabismus detection method based on causal feature gating, and the method comprises the steps: carrying out the modeling analysis of a causal relationship between a key point coordinate and a strabismus category through a causal discovery algorithm, obtaining a causal relationship model used for determining the causal relationship, screening the key point coordinate based on the causal relationship model, and obtaining a strabismus detection result. Extracting a plurality of causal variables from a plurality of samples of the training sample set according to the causal variable set; and introducing a causal feature gating module between adjacent network layers of the classification model, training the classification model by using a plurality of causal variables, and performing adaptive adjustment on gating parameters in the training process of the classification model until the classification model converges or reaches a preset training round number, thereby obtaining a final classification model. According to the embodiment of the invention, the accuracy of strabismus classification can be improved, and the interpretability and medical application value of the model can be remarkably enhanced.

The invention relates to the technical field of physiological signal analysis, in particular to a sleep disorder intelligent diagnosis system based on a multi-modal knowledge graph, which comprises a signal lag analysis module, an abnormal signal identification module, a graph construction module, a graph path generation module and a diagnosis result output module. According to the method, difference comparison and normalized sequence analysis in a time window are performed on multiple types of physiological response data, and derivative trend identification and mutation section calibration are combined, so that collaborative identification of signal delay response and abnormal change positions is realized, and multi-dimensional information of duration, frequency and fluctuation amplitude in abnormal events is further structured; the method comprises the following steps: constructing a traceable symptom expression vector, extracting structural features of the traceable symptom expression vector in a time sequence evolution process through a cross-time window entity evolution path, identifying a growth trend of association strength between symptoms according to an attribute change direction and a path connection mode, and judging a fluctuation mode and an abnormal evolution tendency of a sleep state of a user in a specific time period.

The invention discloses an emotion recognition model training method, an emotion recognition method, equipment and a storage medium. The emotion recognition model training method comprises the following steps: acquiring a predicted emotion category output by an emotion category component in an emotion recognition model for a physiological signal sample; comparing the predicted emotion category with an emotion category label to obtain a category label comparison result; when it is detected that the category label comparison result is that categories are matched, obtaining predicted emotion intensity output by an emotion intensity component in the emotion recognition model for the physiological signal sample, and comparing the predicted emotion intensity with an emotion intensity label to obtain an intensity label matching result; and training the emotion intensity component according to an intensity label matching result. Therefore, interference of wrong emotion classification samples on emotion intensity learning can be effectively avoided, it is ensured that intensity estimation is carried out in an accurate category context, and the precision and reliability of intensity recognition are further improved.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

The invention belongs to the technical field of brain function monitoring, and discloses a three-in-one non-invasive brain function monitoring system and method, a medium, equipment and application, and the monitoring method comprises the following steps: monitoring brain oxygen saturation of a critical patient; carrying out transcranial Doppler ultrasonic measurement on intracranial blood vessel blood flow velocity and ultrasonic measurement on the inner diameter of the optic nerve sheath; monitoring a quantitative electroencephalogram of the patient; various brain function monitoring series indexes and continuous changes of the indexes along with disease evolution are obtained, the pathogenic mechanism and pathogenesis of the indexes are found in time, and primary diseases and related complications of the primary diseases are treated. According to the invention, the brain oxygen saturation can avoid too low brain oxygen or low brain oxygen for a long time and too high brain oxygen due to oxygen utilization disorder; cerebral blood flow can be monitored through a transcranial Doppler (TCD) technology to find an optimal craniocerebral perfusion pressure (CPP), and a cerebral blood flow self-adjusting function is enabled to be in an optimal state; the electroencephalogram can avoid early warning of over-sedation or abnormal discharge and the like. Through organic combination of the three, noninvasive, real-time and dynamic monitoring of critical patients is realized.

A vision test can be implemented in a virtual reality (VR) environment using a computer device that includes a head-mounted display (HMD). The computer device can obtain historical vision data of a patient user. Based on the historical vision data, the computer device determines an ordered sequence of vision tests including a first vision test for the patient user. The first vision test is followed by a set of one or more subsequent vision tests of the ordered sequence of vision tests. The computer device executes a user application configured to enable the ordered sequence of vision tests by rendering a user interface on the display. A first visual stimulus of the first vision test is displayed on the user interface. The computer device obtains a user response to the first visual stimulus, and dynamically adjusts one or more subsequent visual stimuli based on the user response.

Aspects of the present disclosure provide techniques for quantitatively assessing tear-film dynamics associated with contact lenses. An example method includes projecting an image of one or more shapes on a tear film surface of the contact lens worn on the eye, capturing video data, comprising a plurality of image frames, of the one or more shapes projected on the tear film surface of the contact lens over a period of time, performing image segmentation on a plurality of reflection patterns included in the plurality of image frames, generating a plurality of maps of the tear film surface of the contact lens indicating changes to the tear film surface of the contact lens during the period of time, and outputting, based on the plurality of maps, one or more metrics quantifying the changes to the tear film surface of the contact lens over the period of time.

Detection method (50) of an activity of a first eye (30) of a user of a pair of glasses (10) comprising a first (22a) and a second (22b) electrode facing the first eye (30) and configured to detect respective electrostatic charge variations generated by a movement of the first eye (30). The detection method, performed by a control unit (21, 15) of the glasses (10), comprises the steps of: acquiring (S10-S14), through the first (22a) and second (22b) electrodes, a first electrostatic charge variation signal (SQ,1a) indicative of a difference between the electrostatic charge variations; verifying (S16, S18) the presence of one or more blink patterns in the first electrostatic charge variation signal (SQ,1a), each blink pattern being indicative of a respective voluntary blink or involuntary blink; if the first electrostatic charge variation signal (SQ,1a) has said one or more blink patterns, for each blink pattern determining (S20, S22; S20', S22) by the control unit (21, 15) whether said blink pattern is indicative of a respective voluntary blink and detecting a involuntary blink or a voluntary blink.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

The embodiment of the invention relates to the field of data processing, and provides a multimode fusion emotion recognition method and device based on electroencephalogram and eye movement signals, and the method comprises the steps: extracting electroencephalogram signals corresponding to K reference users used during model training, and obtaining a first electroencephalogram signal set, obtaining an eye movement signal set corresponding to K reference users used during model training, and obtaining a first eye movement signal set; performing feature fusion on first electroencephalogram signals in the first electroencephalogram signal set and first eye movement signals in the first eye movement signal set to obtain a first mixed feature information set; training an initial model by using the first mixed feature information in the first mixed feature information set to obtain a target model; performing emotion mode recognition on a target user by using the target model to obtain a target emotion mode recognition result; and the accuracy of the target model in performing emotion mode recognition on the target user is improved.

Systems and methods for compensating for tilt of an intraocular lens (IOL) and systems and methods for adjusting an IOL are disclosed herein. For example, one of the methods may include capturing one or more optical coherence tomography (OCT) images of a subject's eye while implanting the IOL within the subject's eye. The method may further include generating a fixation target such that the fixation target is visible to an eye of the subject; and moving the fixation target until the lateral plane of the IOL is perpendicular or substantially perpendicular to the optical axis of the ophthalmic system. The method may also include directing a laser beam generated by a laser of the ophthalmic system at the IOL.

Methods and systems for administration of intoxication testing within a vehicle. In some implementations, the method may comprise displaying a fixed eye target on a graphical user interface of the vehicle and displaying a first prompt on the graphical user interface inducing the driver to perform a first movement sequence in which the driver is to stare at the fixed eye target while moving the driver's head in a first direction. A second prompt may be displayed on the graphical user interface inducing the driver to perform a second movement sequence in which the driver is to stare at the fixed eye target while moving the driver's head in a second direction. Received images of the driver's eyes may then be used to perform a nystagmus test and to assess whether the driver is intoxicated.

The invention relates to the technical field of medical technology, in particular to a method for evaluating and adjusting pain in maternal and child anesthesia, which comprises the following steps of: constructing a physiological signal acquisition subsystem, a pretreatment subsystem, a self-adaptive evaluation subsystem and a closed-loop analgesia control subsystem to realize continuous, objective and specific evaluation of pain states of maternal and child patients under anesthesia; and the analgesic medicine is accurately and automatically adjusted. The continuous, objective and specific pain assessment can be realized, the analgesic medicine can be accurately and automatically adjusted, and the safety and comfort of maternal and child anesthesia are improved.

The invention discloses a method for evaluating eye fatigue in real time when digital equipment is used. The method comprises the following steps: acquiring data in the process of using the digital equipment by a user in real time, and dividing eye using situations; calculating the key visual load indexes, and performing linear combination on the key visual load indexes by adopting different weights according to eye use scenes to obtain an eye fatigue comprehensive coefficient; dynamically calibrating the comprehensive eyestrain coefficient in combination with twelve-hour and five-element theories of traditional Chinese medicine; according to a set fatigue threshold value, formulating a reminding intervention strategy: according to the collected historical eye use data, adopting a multiple regression or machine learning model to adjust the weight of each key visual load index under different eye use situations of each user; and dynamically adjusting the eye fatigue comprehensive coefficient. According to the invention, through multi-dimensional data fusion, evaluation is more objective and accurate; the method has a machine learning adaptive capability, can continuously optimize model parameters according to historical data of each user, and realizes real personalized evaluation.

The invention discloses an intelligent diopter training system and method based on a multi-modal physiological feedback and reinforcement learning algorithm. The intelligent diopter training system comprises a multi-modal physiological data acquisition module, a central processing PC, a user terminal and a cloud data platform. Visual behavior data, ciliary muscle state data and autonomic nerve reaction data of a user are collected in real time through a multi-modal physiological data collection module, multi-modal physiological data fusion and feature extraction are carried out, and a refraction state evaluation model is used for calculating a comprehensive score of a refraction state grade, visual fatigue and adjustment sensitivity of the current user. A personalized training parameter set of the next training period is dynamically decided and generated based on a reinforcement learning algorithm, and the user terminal presents visual training stimulation according to the current personalized training parameter set. The cloud data platform stores the data and generates a visual training effect trend chart, a diopter change prediction report and personalized training suggestions to complete training scheme personalization, training means intellectualization and feedback evaluation comprehensiveness.

According to an embodiment disclosed in the present application, proposed is a method for estimating an eye protrusion value, the method including: obtaining an image representing at least one eye of the subject, wherein the image includes a plurality of pixels assigned a value corresponding to at least one of brightness and color; obtaining a pre-processed image by performing a previously stored pre-processing for the image; obtaining a depth image corresponding to the pre-processed image by applying the pre-processed image to a pre-trained depth image generation model, wherein the depth image includes a plurality of pixels, wherein each of the plurality of pixels of the depth image is assigned a depth value representing a relative distance of an object corresponding to each of a plurality of pixels of the pre-processed image; and estimating an eye protrusion value for the eye of the subject by applying both the pre-processed image and the depth image to a pre-trained eye protrusion value estimation model.