143 results about "Fixation point" patented technology

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention provides an on-line examination anti-cheating method and system based on AI fusion sight tracking, and relates to the technical field of on-line education safety, and the method comprises the steps: extracting a head motion feature sequence of an examinee, calculating the coordinates and duration of a sight fixation point, analyzing the fixation behavior of a functional region, and recognizing a periodic jump mode in a fixation migration feature map. The gazing mode mutation point is identified by combining autoregression spectrum analysis and wavelet analysis, so that the cheating behavior is accurately identified, the safety and fairness of the online examination are effectively improved, and the misjudgment rate is reduced.

The invention discloses a Parkinson's disease assessment method and system based on face video multi-modal physiological feature fusion, and relates to multiple technical fields of computer vision, physiological signal processing and the like, and the method comprises the following steps: S1, based on a continuous face video stream, extracting rPPG signals; then heart rate variability key parameters are calculated, and heart rate variability characteristics are obtained; s2, analyzing the dynamic change of an eye fixation point in the face video based on IPAST, extracting key eye movement behavior parameters, and obtaining eye movement behavior characteristics through a convolution gating loop unit; and S3, inputting the heart rate variability characteristics and the eye movement behavior characteristics into a multi-modal fusion network structure, and outputting a continuous risk score or an illness state label for assisting a doctor in early Parkinson risk assessment. According to the method, multiple physiological signals acquired through videos are deeply integrated, a multi-modal feature collaborative analysis framework is constructed, the subjective limitation of traditional scale evaluation is broken through, and the one-sidedness defect of single biomarker detection is overcome.

The invention relates to the technical field of large model cheating detection, in particular to an online interview large model cheating detection system and method based on eye movement tracking, and the detection method comprises the steps: S1, system initialization and environment adaptation; s2, eye movement tracking calibration and baseline establishment; s3, collecting and preprocessing real-time eye movement data; s4, executing an anomaly detection algorithm; and S5, carrying out risk assessment and outputting a result. According to the scheme, based on the WebGazer.js technology, the mapping relation between the fixation point and the screen coordinates is established through nine-point initialization calibration, and the personalized coefficient k is generated in combination with the pupil diameter and the facial features of the candidate, so that the eye movement tracking error is greatly reduced, and the large model cheating detection precision requirement can be met without special hardware. The design not only reduces the deployment cost, but also breaks through the hardware limitation, so that the technology can adapt to mainstream notebook equipment, is convenient for large-scale popularization and application, and solves the contradiction that high precision and low cost cannot be achieved at the same time in the traditional scheme.

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 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 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 intelligent perception, and mainly relates to a user interaction intention perception method and system based on intelligent glasses, and the method comprises the steps: obtaining the abnormal degree of an eye jump drop point according to the main eye jump and the subsequent correction eye jump times after detecting that the current eye jump drop point of a user enters an interaction region of an interactive object; acquiring a pupil diameter change sequence after the main eye jump, and acquiring a pupil transient response score based on the pupil diameter change sequence; after a certain time from the falling point time of the main eye jump, acquiring a real-time double-variable contour ellipse area based on information of a plurality of latest fixation points, and acquiring a fixation stability score based on the real-time double-variable contour ellipse area; obtaining a real-time gaze residence time according to the falling point time of the main eye jump, and obtaining a residence time evaluation score based on the real-time gaze residence time; fusing the abnormal degree, the pupil instantaneous response score, the stability score and the evaluation score to obtain a dynamic interaction intention score of the user, and determining the interaction intention of the user based on the intention score. The perception accuracy of the user interaction intention is improved.

The invention discloses a focus depth determination method and a related device, and relates to the technical field of rendering, and the method comprises the steps: collecting eye movement data of a user according to a preset eye movement sampling rate in a process that the user watches a three-dimensional scene, so as to obtain multi-frame eye movement data collected in a target duration, and determining a focus depth of the user according to the multi-frame eye movement data, determining a fixation point position of the sight line of the user in the target three-dimensional model, generating a sphere with the radius as a target value by taking the fixation point position as a sphere center, obtaining a target vertex set in the sphere in the target three-dimensional model, and performing vertex density analysis on the target vertex set to obtain a focus depth for rendering the three-dimensional scene. According to the method, the focus depth dynamically adjusted along with the sight line of the user can be generated, the detail resolution capability of the user on the fixation point is improved, and the watching experience of the user on the three-dimensional scene is further improved.

The invention discloses a Rhatish culture VRAR virtual-real fusion interaction method based on eye movement tracking, and particularly relates to the technical field of virtual reality and augmented reality man-machine interaction. The method comprises the following steps: acquiring an eye image of a user through a near-infrared image acquisition unit, and inputting the eye image into a double-branch convolutional neural network model to estimate a two-dimensional fixation point of a screen; performing nonlinear correction on a fixation point based on the user personalized sight line offset correction model established in the offline calibration stage; in the online interaction stage, the corrected fixation point is combined with equipment pose data to be converted into three-dimensional space rays, the distance and angle relation between the three-dimensional space rays and an interaction target is calculated, and an interaction instruction is triggered when a threshold condition is continuously met. According to the method and the system, accurate, stable and natural eye movement interaction of the virtual contents of the Rhatish culture and the entity exhibits in the VR / AR environment is realized, and the user experience and the system robustness are improved.

The invention provides a fitting method of a 3D eye model and electronic equipment, and aims to improve the accuracy of the eye model. Comprising the following steps: in response to an eye model fitting instruction, obtaining eye images obtained when eyes of a user watch fixation points at different positions; obtaining eyeball parameters of the user by using the image position coordinates of the pupil center of the user in each eye image and the standard visual axis vector corresponding to each eye image; obtaining a current eyeball rotation angle based on the eyeball parameters of the user and the image position coordinates of the pupil center in the current eye image every specified duration; according to the current eyeball rotation angle and the eyeball parameters, current eyeball model parameters are obtained, and the eyeball model parameters comprise the position vector of the eyeball center, the position vector of the pupil center, the position vector of the curvature center and the visual axis vector; and updating a preset initial eye model by using the current eyeball model parameters of the user to obtain a current 3D eye model of the user.

The invention discloses an automobile cabin software control method and system based on multi-mode interaction, and relates to the field of multi-mode data, and the method comprises the steps: S1, multi-source signal synchronous collection and timestamp calibration; a user voice instruction, a gesture track and eye fixation point information are collected in real time through a distributed sensor array; according to sensor data, a three-level synchronization architecture is combined with a PTP precise time protocol to achieve microsecond-level clock synchronization, a unified time service timestamp is embedded in a data packet header, and the timestamp error is controlled within + / -0.5 ms; s2, dynamic environment interference compensation; the method comprises the following steps: constructing an environmental noise perception model; the construction of the environmental noise perception model comprises the following steps: starting a regional strong light suppression algorithm for a strong light scene with the illumination intensity greater than 10,000 lux; activating a motion compensation module for a bumpy scene with a vertical acceleration greater than 0.3 g, and outputting an environment compensation multi-modal data stream; s3, analyzing time-space association semantics; and S4, confidence decision and instruction execution.

The invention discloses an eye movement tracking stability enhancing method, system and device and a storage medium, and belongs to the field of human-computer interaction and computer vision. The method comprises the following steps: acquiring an eye image and an original sight fixation point coordinate; performing quantitative evaluation on the image from five dimensions of definition, contrast and the like to generate an eye image quality evaluation grade; calculating the moving speed and acceleration of the current point based on the historical sight fixation point sequence; fusing, and judging and correcting the original coordinates. Through multi-modal information fusion and hierarchical adaptive correction, noise jump caused by poor image quality can be accurately distinguished and filtered out, meanwhile, the real eye movement dynamic state is effectively reserved, and the output stability and reliability of an eye movement tracking system are remarkably improved.

The invention belongs to the technical field of pilot ability assessment, and particularly discloses a pilot ability assessment method based on dynamic time warping and hierarchical clustering, which comprises the following steps of: acquiring eye movement data of a tested pilot in a flight simulation task process, preprocessing the eye movement data, extracting behavior indexes of each stage of a flight task, and calculating a pilot ability assessment result; generating a fixation area number sequence according to the fixation point position; in the selection target evaluation stage, a dynamic time warping algorithm is used for carrying out nonlinear alignment on the gaze sequences of all the pilots, and an eye movement difference degree matrix between the pilots is generated; based on the difference degree matrix, adopting a hierarchical clustering algorithm to group the pilots; and outputting an ability evaluation result of the pilot according to the distribution of the pilot in the difference degree matrix and the deviation information of the pilot and the teacher watching sequence. According to the method, structured comparison and capability grade evaluation of complex cognitive behaviors can be realized, and an evaluation result has relatively high objectivity and interpretability.

The invention discloses a perioperative cognitive impairment predicting and monitoring device, relates to the technical field of medical instruments, and aims to solve the problem of low accuracy of perioperative cognitive impairment predicting and monitoring for children in the prior art. An electroencephalogram signal acquisition module, an eye movement tracking module, a behavior interaction module, a physiological parameter monitoring module and a data processing and analyzing module are integrated in the head-mounted VR glasses; the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal of a child, processing the electroencephalogram signal and then transmitting the electroencephalogram signal to the data processing and analyzing module; the eye movement tracking module is used for capturing motion images of eyeballs of children, obtaining fixation points, eye jump frequencies and pupil diameter change parameters of the eyeballs and transmitting the parameters to the data processing and analyzing module. The method has the advantage that the accuracy of predicting and monitoring the cognitive impairment in the perioperative period of children is improved.

The invention provides an eye movement trajectory analysis method and system based on hybrid clustering and time constraint, and belongs to the technical field of computer vision. Calculating a time difference and a moving speed between continuous original eye movement data points; comparing the moving speed with a speed threshold value, and classifying the moving speed into candidate fixation points and glancing points; extracting spatial features and time features of the candidate fixation points, performing standardization processing, and performing weighted fusion to obtain spatial-temporal feature vectors; clustering the spatio-temporal feature vectors to obtain preliminary clustering labels of the candidate fixation points; performing time constraint processing on each cluster; calculating the duration of the clustering cluster after the time constraint processing, and generating a final clustering label of the candidate fixation point; and outputting a final classification label of each original eye movement data point in combination with the classification result of the glancing points.

The invention discloses a multi-task fixation point estimation method, which relates to the technical field of computer vision and deep learning, and comprises the following steps of: firstly, constructing a data set for providing associated data from tasks to labels and supporting multi-task cooperative training, and then constructing an MTHGaze model, through a semi-structured framework, deep learning, a geometric constraint fitting model and key tasks of cooperative processing are fused, a loss function is designed and trained, defects of each task are solved through targeted loss function design, and a scientific training process enables the model to be iteratively optimized. Finally, the targets of improving the estimation precision of the fixation point, ensuring the physical rationality and enhancing the environmental robustness are achieved; and finally, performing fixation point mapping. According to the estimation method, through data set construction, data consistency can be guaranteed, training noise can be reduced, a real reference can be provided for loss function calculation, error back propagation can be realized, and Euclidean distance errors can be minimized through L2 norms of real fixation point coordinates and prediction coordinates.

According to an aspect of the present invention, there is provided a computer graphics processing and selective visual display system, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

The invention discloses an ADHD screening method and system based on mobile terminal eye movement tracking and a multi-modal agent. The method comprises the following steps: (1) collecting and preprocessing mobile terminal eye movement data; (2) performing fixation point estimation based on a lightweight MGazeNet model; (3) eye movement feature extraction based on a multi-normal form interaction task; and (4) carrying out comprehensive diagnosis analysis based on machine learning and a large model agent. The invention further provides an ADHD screening system based on mobile terminal eye movement tracking and the multi-modal intelligent agent. The ADHD screening system sequentially comprises the following modules: (1) a front-end data acquisition and preprocessing module; (2) an end side eye movement tracking reasoning module; (3) an interactive task control and feature calculation module; and (4) a multi-mode intelligent diagnosis and analysis module. The ADHD screening method effectively solves the problems that traditional ADHD screening depends on expensive professional equipment and subjective scales, so that the popularity rate is low, and objectivity is poor, screening portability and diagnosis objective accuracy are improved, and the intelligent level of ADHD auxiliary diagnosis and user interaction experience are improved.

The embodiment of the invention provides an emotion detection method based on a multi-mode electroencephalogram eye movement signal. According to the method, a dual-path electroencephalogram modal encoder is used for determining time sequence characteristics of an electroencephalogram in a time domain path, determining frequency domain characteristics of an amplitude spectrum of the electroencephalogram in a frequency domain path, and obtaining dual-path time domain and frequency domain fused electroencephalogram characteristics based on the time sequence characteristics and the frequency domain characteristics; the dual-path eye movement modal encoder is used for determining pupil features of the eye movement data in the pupil diameter path, determining eye movement fixation features of the eye movement data in the fixation point path, and obtaining fused eye movement features based on the pupil features and the eye movement fixation features; and the multi-modal expert mixing module is used for dynamically performing modal calling on the multi-modal mixed characteristics which are subjected to modal alignment and are fused with the electroencephalogram characteristics and the eye movement characteristics, performing emotion detection by utilizing an expert with a modal corresponding to the multi-modal mixed characteristics, and outputting an emotion classification result. According to the embodiment of the invention, alignment is realized through multi-task pre-training, and the depressive emotion is accurately recognized.

The invention discloses a sight tracking method and system based on cascade deep learning and distance correction, and a storage medium. The method comprises the following steps: processing an eye image through a cascade deep learning model, and obtaining pupil center coordinates and cornea reflection light spot coordinates of two eyes; calculating a binocular pixel distance as a distance correction factor based on the pupil center coordinates, and calculating a relative position difference vector based on the pupil and light spot coordinates; establishing a three-dimensional nonlinear polynomial regression model taking the relative position difference vector and the distance correction factor as input; obtaining calibration data through a multi-distance sampling strategy and solving a model coefficient; and in the real-time tracking stage, calculating the coordinates of the fixation point of the screen by using the solved model. According to the method, the distance correction factor and the three-dimensional mapping model are introduced, so that geometric errors caused by forward and backward movement of the head of the user are effectively eliminated, meanwhile, the feature point positioning precision is ensured through cascaded deep learning detection, and the precision and robustness of a sight line tracking system are remarkably improved.

The invention discloses an image rendering method and system, and the method comprises the following steps: collecting a fixation point of a user through eye movement tracking, and mapping the fixation point into a fixation position in a three-dimensional space; the system constructs a hierarchical sensing area model with the gazing position as the center, and rendering priorities of different areas are divided. A Gaussian kernel is generated after original three-dimensional point cloud data is subjected to structural analysis, and a weighted three-dimensional Gaussian point cloud data set is generated in combination with the priority of a sensing area. The system schedules Gaussian point cloud by using a spatial index structure, and dynamically generates pixel resolution, sampling step length and illumination parameters according to a gazing distance. And finally, differential rendering is performed on the target point set through a three-dimensional Gaussian sputtering technology, and a current frame image is generated. The method can be widely applied to real-time image rendering scenes needing high quality and high efficiency.

Method for designing a mechatronic component, characterized by the following features: The component is repeatedly simulated by software (10) with certain design parameters (11) that take on variable values, A fixation point (20) of the designer (30) is captured on a screen, while the software (10) displays the component to the designer (30) on the screen. A neural circuit with LTC neurons is trained to infer the resulting fixation point (20) of the designer (30) from the current values ​​of the design parameters (11), and The values ​​of the design parameters (11) are modified by the neural circuit and fed back to the software (10).

The invention relates to the technical field of augmented reality, in particular to a guide AR glasses interaction method and system based on gesture recognition, and the method comprises the steps: obtaining a control instruction triggered when a user eyeball fixation point is located in each page region in a historical period, analyzing the triggering frequency of the control instruction of each page region, and obtaining the control instruction of each page region; screening a control instruction corresponding to the page area, constructing an instruction triggering rule set exclusive to the page area, collecting a user image, identifying an age group and determining a dynamic inclusion coefficient; an eyeball fixation point is obtained in real time, a corresponding page area is marked, a demand control instruction is obtained from a corresponding instruction triggering rule set according to the marked page area, and a coverage area is determined based on a containing coefficient to generate an optimized gesture action sample; the determination is performed preferentially based on the optimized gesture action sample. Based on different functions of different areas of the page, the judgment standard is adaptively optimized, the accuracy of gesture judgment and recognition is improved, the response speed is increased, and the misjudgment rate is reduced.

The present disclosure discloses a method for detecting the refractive power of the human eye and related products. The method uses a light-feeding device for detection, and the light-feeding device includes a red light source and a projection light path for projecting red light to the human eye. The method includes: using the projection light path including a diffraction optical element device and a conjugate mirror to form an intermediate image plane of the red light; adjusting the distance between the conjugate mirror and the intermediate image plane according to the clarity of the fixation point observed by the human eye in the projection light path; and determining the refractive power of the human eye according to the distance when the human eye clearly observes the fixation point. Using the scheme of the present disclosure, the detection of the refractive power of the human eye can be completed during the light-feeding operation, thereby improving the efficiency of the detection of the refractive power of the human eye.

The invention provides a fixation point tracking method and device based on a three-dimensional sight line, and relates to the field of intelligent perception. The method comprises the steps of performing simplified modeling on an eyeball model based on an eyeball vision structure, and obtaining key construction parameters in a three-dimensional space where a user is located according to the eyeball model; obtaining an eye image of the user, and constructing a three-dimensional pupil section parameter corresponding to the user based on the key construction parameter and by performing reverse geometric projection on an elliptical contour in the eye image; calculating eyeball center parameters and eyeball radius parameters based on the three-dimensional pupil section parameters; and determining gaze direction information of the user according to the eyeball center parameter and the eyeball radius parameter, and obtaining a gaze point corresponding to the user based on the fitting polynomial so as to track the gaze point of the user. According to the method and the device, the problem that when a user has a high-dynamic scene with a quick watching switching behavior, response lag still occurs to a sight line focus tracking effect, and the dynamic recognition precision is relatively low is solved.

The embodiment of the invention provides a fixation point rendering method, near-to-eye display equipment and a storage medium, and belongs to the field of eye movement tracking. The method comprises the following steps: acquiring a human eye image obtained by acquiring human eyes of a wearer by near-to-eye display equipment; dividing the human eye image into a plurality of grid units according to a preset row number and a preset column number; according to the plurality of grid units, determining a target grid unit watched by a human eye watching point of the wearer; according to the target grid unit, position information of a human eye fixation point is determined, and according to the position information of the human eye fixation point, the human eye fixation point is rendered and displayed in a user interface currently displayed by the near-to-eye display device. According to the technical scheme of the embodiment of the invention, the jitter amplitude of the fixation point and the jitter frequency of the fixation point are reduced, and the visual experience of a user is improved.

The invention discloses a spatial orientation real-time analysis method based on eye movement, relates to the related technical field of flight training, and aims to overcome the defects of low analysis accuracy and poor adaptability in the prior art. The method comprises the following steps: acquiring pilot eye movement data through eye movement tracking equipment, acquiring head space orientation data through a flight helmet space sensor, and acquiring environment data containing instrument visual complexity and aircraft attitude from a virtual flight simulator; performing time synchronization on the three types of data; extracting features such as coordinates of a fixation point from the synchronous eye movement data; comparing the head orientation data with the reference data to generate deviation data; inputting the eye movement characteristics, the deviation data and the environment data into an evaluation model; the model dynamically adjusts parameters according to environment data, and calculates pilot space orientation capability indexes in combination with eye movement characteristics and deviation data. According to the method, accurate integration of multi-dimensional data can be realized, the analysis comprehensiveness and reliability are improved, a dynamic flight scene is adapted, and real-time accurate evaluation is realized.

The invention discloses a rendering method and device of wearable equipment, equipment and a storage medium, relates to the technical field of wearable equipment, and discloses the rendering method of the wearable equipment, the method is applied to the wearable equipment, an eye tracking device is arranged, and the eye tracking device is used for monitoring eye movement. The method comprises the following steps: analyzing an eye tracking image collected by an eye tracking device, and determining a gazing point location and gazing parameters; determining a device offset state according to the gaze parameter; and when the state is not shifted, determining a target rendering area according to the fixation point location and the equipment interaction information, and performing image enhancement rendering in the area. Through the above mode, whether deviation exists between the equipment and the eyes of the user is determined based on the gaze parameter, the rendering area is determined by combining the gaze point location and the equipment interaction information when deviation does not occur, rendering is enhanced in the area, visual delay and scene deviation are effectively reduced, motion sickness of the user is relieved, and user experience is improved.