22 results about "Blink frequency" patented technology

The invention relates to the technical field of screen display adjustment and visual health protection, and discloses a screen color temperature intelligent adjustment method and system, and the method comprises the steps: obtaining a user face image sequence and ambient light perception data; according to the face image sequence, eye features are extracted, a dynamic change trend is analyzed, blinking frequency and eyelid closing duration data are calculated, and a visual fatigue evaluation value is obtained; according to the ambient light sensing data and the visual fatigue evaluation value, correcting the fatigue degree and classifying fatigue stages to obtain color temperature and brightness adjustment parameters; and transmitting the adjustment parameter to a screen display driver for gradual change processing to obtain a final screen display configuration. According to the method, personalized accurate adjustment of the screen parameters is realized, visual fatigue is effectively relieved, and the use experience and the visual health protection effect are improved.

The invention discloses an LED display wireless charger based on an AI blinking algorithm and a control method, blinking parameters including the average blinking frequency, the single blinking duration and the eye closing interval are preset in an AI blinking algorithm module, the AI blinking algorithm module carries out randomization processing on the blinking parameters based on a linear congruent random number algorithm, and the random processing is carried out on the blinking parameters according to the linear congruent random number algorithm. Adjusting the fluctuation time length of each blinking interval and the blinking time length, setting parameter constraint logic, and generating a blinking control signal representing a natural blinking effect; the main MCU module is used for controlling the charging state of the circular charging coil and outputting a control instruction to the LED eye module according to the blinking control signal, so that the on-off time sequence of an LED lamp in the LED eye module is controlled, and the natural blinking effect of human eyes is simulated. Compared with the prior art, the non-coil empty idle area can be fully utilized, the natural blinking effect of human eyes can be achieved, the structure is simple, cost is low, and application requirements are well met.

The invention relates to a data evaluation method and device based on artificial intelligence, computer equipment and a medium, and is suitable for smart televisions, laptops, projectors, tablet personal computers and the like, and the method comprises the following steps: collecting video streams related to a user, and obtaining environment illumination intensity; performing face key point detection on the video stream to obtain key point information; performing blink detection and frequency calculation based on the key point information to obtain a blink frequency; performing head posture estimation on the key point information, and calculating continuous fixation duration based on a posture estimation result; generating a watching distance of the user to the screen of the target equipment; integrating the environment illumination intensity, the continuous fixation duration, the blinking frequency and the watching distance to obtain index data; evaluating the face image of the user, and obtaining a target index threshold value based on the target age; and performing data evaluation on the index data based on the target index threshold to generate a health evaluation result. According to the invention, the accuracy of health assessment processing of eye using behaviors is improved.

The invention discloses a public work mechanical vehicle operation and driver behavior monitoring system and method, and aims at solving the problem that in the operation process of a public work mechanical vehicle, the eye closing time is prolonged and the blinking frequency is reduced due to fatigue of a driver, and consequently misoperation is caused. A driver face video stream is collected; the embedded edge computing terminal is connected with the vehicle-mounted camera, performs eye key point detection on each frame of image and calculates an eye height ratio to obtain a fatigue trigger signal, and encapsulates a video stream and vehicle operation data into an event packet; the vehicle-mounted loudspeaker is connected with the embedded edge computing terminal and outputs voice warning in a cab according to the fatigue triggering signal; and the wireless communication module uploads the event packet to a ground server, and automatically enters a breakpoint resuming mode when the signal is interrupted.

Provided in the embodiments of the present application are a blinking guidance method, a method for updating a screen display parameter, and an electronic device. In the blinking guidance method, after the current operating state of an electronic device is acquired, when the current operating state of the electronic device satisfies a first predetermined state, the electronic device performs blinking measurement on a user who uses the electronic device, so as to obtain a blinking frequency of the user; and when the blinking frequency of the user is less than or equal to a blinking threshold value, the electronic device performs blinking guidance on the user, such that when the blinking frequency of the user is relatively low, the user is prompted to appropriately blink, thereby improving the stability of a tear film, and relieving dry eyes and visual discomfort.

The invention relates to the technical field of intelligent health equipment, in particular to multi-mode asthenopia quantification and light-nerve regulation sleep optimization equipment which comprises an asthenopia quantification module, a light regulation module, a nerve regulation module and a central control module. The visual fatigue state is accurately quantified from multiple dimensions such as blink frequency, tear secretion, eye myoelectricity and a visual regulation function through multiple monitoring units, compared with single-index evaluation, the accuracy and comprehensiveness are greatly improved, light regulation and control and nerve regulation and control technologies are combined, and the visual fatigue state is accurately quantified. Through the synergistic effect of external visual environment optimization and internal nerve regulation, the vicious circle of asthenopia and insomnia is broken through, the intervention effect is more remarkable, personalized and intelligent regulation of light regulation and nerve regulation is achieved based on an asthenopia quantification result, the requirements of different users are met, and the user experience and the intervention effect are improved.

The invention discloses a pilot visual fatigue detection system and method based on artificial intelligence, and belongs to the technical field of flight safety monitoring, and the method comprises the steps: obtaining a pilot face video through an image sensor, carrying out the face positioning and tracking through a neural network, extracting a blinking frequency and a critical flash fusion frequency from an eye region, and carrying out the detection of the visual fatigue of a pilot. The head posture movement is analyzed; fusing the multi-dimensional physiological data with the behavior features, and inputting the fused data into a pre-trained visual fatigue evaluation model to obtain an initial fatigue index; performing dynamic calibration on the initial fatigue index in combination with the historical fatigue data of the pilot individual and the current flight mission load, and generating an optimized visual fatigue score; and finally, grading early warning is started according to the grading grade, personalized and foresight fatigue state intervention is realized, and the flight safety is effectively improved.

The application provides a high beam adaptive control method, system, device and medium, relates to the technical field of vehicle lighting control, and the method comprises the following steps: acquiring the driving environment of a target vehicle, and generating a first light scheme of a high beam; in combination with the first vehicle feature of the target vehicle and the second vehicle feature of an oncoming vehicle, and the distance between the target vehicle and the oncoming vehicle, generating an adjustment index of the high beam, adjusting the first light scheme according to the adjustment index, and generating a second light scheme; in combination with the driving time length of the driver of the target vehicle, the blink frequency of the driver and the pupil change rate, generating a visual fatigue parameter of the driver, calculating the light sensitivity coefficient of the driver according to the visual fatigue parameter; in combination with the visual fatigue parameter and the driving environment, generating a driving risk coefficient, and in combination with the driving risk coefficient and the light sensitivity coefficient, adjusting the second light scheme to generate a target light scheme. The application has the technical effect that the safety of night driving is ensured.

The invention discloses a blink fatigue detection method and system based on deep learning, and the method comprises the steps: obtaining an eye image data set with an eye opening and closing state label, carrying out the preprocessing, and dividing a training set, a verification set, and a calibration data set; performing training and performance verification on the convolutional neural network model by using the training set and the verification set to obtain a blink detection model; the calibration data set is used for conducting INT8 quantification processing on the blink detection model, and the blink detection model is converted into a light-weight blink detection model and then deployed to an embedded device; taking a continuous video frame sequence which is acquired by a camera in real time and contains a face image as input data of a lightweight blink detection model for detection, and obtaining an effective blink frequency; the number of effective blinking times is counted, the blinking frequency in unit time is calculated, the blinking frequency is compared with a preset multi-level fatigue level threshold value level by level to determine the corresponding fatigue level, and whether the user is in a fatigue state or not is judged. According to the invention, blink fatigue detection can be efficiently and accurately carried out on low-power-consumption equipment.

The embodiment of the invention provides a blink guiding method, a screen display parameter updating method and electronic device.In the blink guiding method, after the current running state of the electronic device is obtained, when the current running state of the electronic device conforms to a first preset state, the electronic device carries out blink detection on a user using the electronic device, and the screen display parameter updating method is used for updating the screen display parameter. And obtaining the blinking frequency of the user. When the blinking frequency of the user is smaller than or equal to the blinking threshold value, the electronic equipment guides blinking of the user, so that when the blinking frequency of the user is low, the user is prompted to blink properly, the tear film stability is improved, and eye dryness and visual discomfort are relieved.

The invention relates to the technical field of vision detection, in particular to novel medical equipment for vision detection and a control method thereof. Determining that the to-be-detected child is located at a designated position, and triggering a detection program to start according to the age of the to-be-detected child Displaying a three-color sphere based on preset parameters, controlling the sphere to move based on a vision value locking algorithm, determining eyeball following times, and obtaining a vision value; on the basis of an amblyopia qualitative detection algorithm, controlling the sphere to move to obtain retinal imaging response delay time and child blinking frequency; based on the vision value, the retinal imaging response delay time and the child blinking frequency, a detection result is obtained through a child amblyopia detection algorithm model. According to the method, a vision value locking algorithm, an amblyopia qualitative detection algorithm and a child amblyopia detection algorithm model are adopted, the improved E word table quantitative logic and the improved OKN qualitative logic are fused through algorithms, quantitative and qualitative synchronous detection of child amblyopia is achieved, the detection efficiency is 2.7 times that of a traditional method, and the detection accuracy rate reaches 95% or above.

The application discloses a multi-modal fusion physiological fatigue recognition method and device, and the method comprises the following steps: constructing a standardized speech data set and a physiological information data set; the physiological information data set comprises physiological index information and biochemical index information; the physiological index information comprises heart rate information, heart rate variability information, skin electricity information, eye movement information and blink frequency information; the biochemical index information comprises saliva information, urine information and fingertip blood sample information; processing the standardized speech data set and the physiological information data set to obtain multi-dimensional physiological fatigue feature information; training a preset fatigue recognition model by using the multi-dimensional physiological fatigue feature information to obtain an optimized fatigue recognition model; and processing the multi-dimensional physiological fatigue feature information to be processed by using the optimized fatigue recognition model to obtain a multi-modal fusion physiological fatigue recognition result. The multi-modal fusion physiological fatigue recognition method constructed by the application has higher accuracy, and the robustness and generalization of model migration are better.

The application discloses a kind of pilot visual fatigue detection system and method based on artificial intelligence, belong to flight safety monitoring technical field, first by image sensor obtains pilot face video, face positioning and tracking are carried out by neural network, and then blink frequency and critical flicker fusion frequency are extracted from eye area, and head posture motion is analyzed;After the fusion of multidimensional physiological data and behavior characteristics, the initial fatigue index is obtained by inputting the pre-trained visual fatigue evaluation model;Then, in combination with the individual historical fatigue data of the pilot and the current flight task load, the initial fatigue index is dynamically calibrated to generate an optimized visual fatigue score;Finally, according to the score level, a graded warning is started, a personalized and forward-looking fatigue state intervention is realized, and flight safety is effectively improved.

The invention discloses a screen brightness adjusting method and system based on double-stage blink frequency calibration, and the method comprises the steps: carrying out the initial calibration, monitoring the initial blink frequency of a user under the conditions of first use and the like, and generating a personalized basic baseline through statistical processing and coefficient compensation; dynamically running, periodically updating the dynamic baseline based on historical data in the rolling time window, and monitoring the current blinking frequency in real time; adjusting triggering, and when the current blinking frequency is continuously lower than a threshold value set by the dynamic baseline, controlling the screen brightness to smoothly and gradually change to a target value; the system comprises an image acquisition and feature extraction module, a dual-stage calibration and decision module, a secure storage module and a brightness adjustment execution module. According to the method, the personalized dynamic baseline is constructed through double-stage calibration, the problem of misjudgment caused by a fixed threshold value is solved, self-adaptive visual fatigue intervention is realized, and the privacy security of a user is guaranteed.

The invention relates to the technical field of data processing, and provides an attention guiding method and system based on multi-sensory feedback, the attention guiding method is used for guiding a user to concentrate attention in a remote classroom, a remote conference or remote training, and the attention guiding method comprises the following steps: collecting behavior characteristic information of the user in real time through a camera and a sensor, the behavior characteristic information at least comprises the sight direction, the head posture and the blinking frequency of the user; analyzing and judging the attention state of the user according to the behavior characteristic information; if it is monitored that the attention of the user is reduced, intervention at least including tactile vibration is triggered, and the user is guided to concentrate attention. According to the attention guiding method based on multi-sensory feedback, attention monitoring and regulation serve as the core, the attention state of the user is analyzed by capturing the behavior characteristic information of the user, and therefore the user is intervened through the vibration signals, and the user is prompted or guided to concentrate attention.

This invention proposes a video-based ocular surface monitoring method and system, belonging to the field of ocular surface monitoring technology. The method includes: acquiring ocular surface video sequences; using a geometrically constrained cascaded localization strategy to identify anatomical structures in each frame of the image, obtaining the location information of key anatomical structures, and calculating the palpebral fissure height; calculating the average tear meniscus height and the standard coefficient of variation of the tear meniscus height based on the palpebral margin position; calculating the corneal exposed area; detecting blinking events based on the temporal changes in palpebral fissure height from consecutive frames of the video sequence, and calculating the blinking frequency and the proportion of incomplete blinks; constructing a tear fluid dynamic balance model, substituting the average tear meniscus height, the standard coefficient of variation of the tear meniscus height, the corneal exposed area, the blinking frequency, and the proportion of incomplete blinks into the tear fluid dynamic balance model to calculate the tear fluid dynamic balance index, and performing a graded assessment of ocular surface health status based on this index. This invention enables a quantitative and comprehensive assessment of ocular surface health status.

This invention relates to a method and system for evaluating the online reading quality of students. The method includes: Q1. During online reading, collecting data on the user's viewing distance, blink frequency, and heart rate at the same moment, and performing normalization processing to obtain normalized data on the user's viewing distance, blink frequency, and heart rate at the same moment. Then, using an ELMAN neural network regression prediction algorithm based on the sardine optimization algorithm, the method predicts the user's viewing distance, blink frequency, and heart rate at the next moment, obtaining data on the user's viewing distance, blink frequency, and heart rate at the next moment. This invention not only evaluates the user's reading quality from multiple aspects, ensuring the accuracy of the reading quality assessment, but also assesses and reminds users of their reading quality in advance, thereby improving the user's enthusiasm and stability in reading.

There is provided a method for threat detection based on facial image analysis, the method comprises identifying a face from a facial image acquired from a camera, inferring an emotion corresponding to the face to obtain an emotion-based threat score based on the inferred emotion, obtaining an eye-based threat score based on a blink frequency of eyes on the face and a number of pupil movements in the face, and detecting whether a person corresponding to the face is in a threat situation based on a total threat score calculated by combining the emotion-based threat score and the eye-based threat score.

This invention relates to an intervention system for dry eye syndrome associated with screen-related syndromes. The system includes a personal computer with a front-facing camera and a humidifier. The personal computer uses its front-facing camera to collect dynamic changes in the user's eye state. An analysis unit on the personal computer determines the user's total blink count based on these dynamic changes. The analysis unit then determines a composite index representing poor eye-use behavior associated with dry eye syndrome based on the number of incomplete blinks and blink frequency within the total blink count over a predetermined time period. The analysis unit determines intervention reminders based on changes in these composite indexes relative to baseline data and generates an intervention plan to drive the humidifier. The intervention plan is then sent to the humidifier, which performs humidification according to the intervention plan. This invention addresses the problem of failing to alleviate symptoms of dry eye syndrome associated with screen-related syndromes due to neglecting individual differences and environmental factors.

The invention discloses a multi-parameter eyelid dynamic driving screen brightness adjusting method and system. The method comprises the following steps: acquiring an eyelid movement track of a user in real time through a front-facing camera; extracting blink frequency, duration and amplitude as multi-dimensional dynamic parameters; dynamically distributing the weight according to the real-time change rate of each parameter, and when the change rate exceeds a preset threshold value, improving the weight of the corresponding parameter; inputting the weighted parameters into a Sigmoid type nonlinear function to calculate the target brightness; and finally, the screen brightness is adjusted in a stepped smooth mode. The system comprises a collaboration module for realizing the functions. According to the method, the real-time physiological state of the user is used as a unique core drive, accurate response and rapid intervention of the nonlinear accumulation process of visual fatigue are achieved through dynamic weight distribution and nonlinear mapping, and the visual comfort degree of use of the mobile terminal is remarkably improved.

The application relates to the technical field of vehicle control, and particularly discloses a tunnel vehicle control method, a tunnel vehicle control system and a vehicle. The tunnel vehicle control method comprises the following steps: obtaining a basic heart rate, a basic blink frequency and a basic skin electricity level of a driver of the vehicle before the vehicle enters a tunnel; obtaining a first heart rate, a first blink frequency and a first skin electricity level of the driver when the vehicle is driving in the tunnel; and adjusting an output direction and output power of an air conditioner assembly of the vehicle according to a difference between the first heart rate and the basic heart rate, a difference between the first blink frequency and the basic blink frequency, and a difference between the first skin electricity level and the basic skin electricity level. Compared with the prior art, the application can actively intervene in the driver with a negative mental state in the tunnel.

The invention discloses an abnormal behavior recognition and data quality evaluation method in vestibular function inspection. The invention discloses an abnormal behavior recognition and data quality evaluation method in vestibular function inspection. The method comprises the following steps: synchronously acquiring eye movement data and head movement data of a subject, and extracting multi-dimensional fusion features including eye closing duration, blinking frequency, angular velocity RMS and static state proportion; on the basis of the features, abnormal behaviors such as eye closing interference, head shaking or attention distraction are recognized through a rule engine or a machine learning classifier; and dynamically calculating a data quality score of 0-100 scores in combination with an abnormal category, a confidence coefficient and a continuity penalty, and according to a score grade (the score is high quality when greater than or equal to 90, and the score is available and lt when 70-89 is greater than or equal to 90; and 70 is unreliable) carrying out visual marking and operation prompting on the original eye movement record. According to the method, semantic recognition of abnormal behaviors and quantitative evaluation of data reliability are realized, and the standardization level and clinical diagnosis efficiency of vestibular examination are remarkably improved.