A subjective intervention method for virtual reality motion sickness based on perceptual theory

Abstract

The present invention discloses a method for intervening in the subjective feeling of motion sickness in virtual reality based on perceptual theory, which relates to the technical field of virtual reality. The method comprises: acquiring proprioception parameter information of a current user, wherein the proprioception parameter information includes but is not limited to head movement data, eye movement data, and physiological data; performing real-time analysis on the currently acquired data using a data analysis algorithm to determine whether the user has early signs of motion sickness; determining, by the data analysis algorithm, that the user has early signs of motion sickness, and immediately initiating a visual perception adjustment step. Furthermore, by real-time capturing and analyzing the user's physiological signals in a virtual reality experience, it is possible to identify and intervene in the early stages of motion sickness, and to formulate an intervention method in a timely manner for users who have subjective feelings of motion sickness, thereby facilitating rapid relief of user discomfort.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and more particularly to a method for intervening in the subjective feeling of virtual reality motion sickness based on perceptual theory. Background Art

[0002] Virtual reality motion sickness, also known as simulator sickness or virtual reality sickness, refers to a series of uncomfortable symptoms such as dizziness, nausea, vomiting, and eyestrain caused by the incoordination between visual perception and body proprioception and vestibular sense when users use virtual reality (VR) technology. These symptoms seriously affect the user's usage time and acceptance of virtual reality equipment.

[0003] Currently, research on virtual reality motion sickness primarily focuses on hardware improvements (such as increasing display frame rates and reducing latency). While some progress has been made, it remains ineffective in completely eliminating motion sickness. Furthermore, there is a lack of effective real-time intervention methods for users who already experience motion sickness. Therefore, developing a method based on perceptual theory to effectively intervene in the subjective experience of motion sickness during virtual reality experiences is crucial. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method for intervening in the subjective feeling of virtual reality motion sickness based on perceptual theory.

[0005] The present invention provides a method for intervening in the subjective feeling of virtual reality motion sickness based on perceptual theory, comprising:

[0006] Step 101: Acquire proprioception parameter information of the current user, wherein the proprioception parameter information includes but is not limited to head movement data, eye movement data, and physiological data;

[0007] Step 102: Analyze the currently collected data in real time using a data analysis algorithm to determine whether the user has early data of motion sickness;

[0008] Step 103: When early-stage motion sickness data appears, determining a motion sickness level based on physiological data of the motion sickness, wherein the motion sickness level includes a first level, a second level, and a third level;

[0009] Step 104: Dynamically adjust the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level;

[0010] Step 105: Continuously collect user feedback information during the intervention process, and further optimize various parameters based on the user feedback information.

[0011] Preferably, the specific steps of using the data analysis algorithm to perform real-time analysis on the currently collected data to determine whether the user has early data of motion sickness are:

[0012] Training based on a large amount of labeled user data;

[0013] The collected real-time multi-dimensional data is used as input;

[0014] Build a decision tree model to determine whether the current user has early data on motion sickness.

[0015] Preferably, the specific steps of constructing the decision tree model to determine whether the current user has early data of motion sickness are:

[0016] Collect multi-dimensional data from many users;

[0017] Clean the collected multi-dimensional data and remove data records with obvious errors or too many missing values;

[0018] Calculate the correlation between each feature and the motion sickness annotation results;

[0019] Determine which features can be used as input features for the decision tree model;

[0020] Select a feature that best distinguishes motion sickness from non-motion sickness from the determined feature set as the root node;

[0021] For each branch divided by the root node, continue to select features with strong distinguishing ability from the remaining features as new internal nodes to further subdivide the branches;

[0022] Using the labeled training data set, a decision tree model is generated according to the above construction process;

[0023] Evaluate the trained decision tree model based on a reserved validation dataset to see whether its prediction accuracy, recall rate, F1 value and other indicators meet expectations;

[0024] After collecting the user's multi-dimensional data in real time, the multi-dimensional data is pre-processed and feature extracted in the same way as the training data;

[0025] The processed data starts from the root node of the decision tree, corresponds to different branch paths according to the values ​​of the corresponding features, and finally reaches the leaf node.

[0026] Preferably, when early data of motion sickness appears, the specific steps of determining the level of motion sickness based on physiological data of motion sickness include:

[0027] Obtain evaluation data for current early data;

[0028] If the evaluation data is in the first interval, it is determined to be the first level, indicating that the current user's motion sickness is mild.

[0029] If the evaluation data is in the second interval, it is determined to be the second level, indicating that the current user has moderate symptoms of motion sickness;

[0030] If the evaluation data is in the third interval, it is determined to be the third level, indicating that the current user has severe symptoms of motion sickness.

[0031] Preferably, the specific steps of obtaining the evaluation data of the current early data are:

[0032] Calculate the energy of theta, alpha, beta, and gamma frequency bands in specific brain areas;

[0033] Set the energy threshold for each frequency band. When the actual energy value exceeds the threshold, it is considered that there is a risk of motion sickness.

[0034] Calculate the frequency domain indicators of heart rate variability, including low-frequency power LF and high-frequency power HF;

[0035] A threshold value of the LF / HF ratio is set. When the ratio exceeds the threshold value, it is considered that there is a risk of motion sickness.

[0036] Calculate the rate of change of skin electrical signals;

[0037] Set a threshold for the rate of change of the skin electrode signal. When the rate of change exceeds the threshold, it is considered that there is a risk of motion sickness.

[0038] According to the threshold values ​​of the above indicators, a logical operation formula is used to determine the level of motion sickness, wherein the logical operation formula is:

[0039]

[0040] in, 、 、 They represent the EEG energy, the LF / HF ratio of HRV, and the threshold value of EDA change rate, respectively.

[0041] Preferably, the method for calculating the energy of the θ, α, β, and γ frequency bands in a specific brain region is:

[0042] According to the formula to obtain;

[0043] in, is the power spectral density value in the frequency band, and n is the number of samples.

[0044] Preferably, the specific method of dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level includes the following steps:

[0045] Get the original field of view of the current user ;

[0046] The ratio R of the reduced retinal peripheral area information is obtained;

[0047] According to the formula × (1-R) ​​to get the adjusted field of view.

[0048] Preferably, the specific method of dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level further includes the following steps:

[0049] Get the original visual scene parameters ;

[0050] The visual scene parameters are adjusted according to the current user's vestibular perception V and proprioception P, and expressed as ;

[0051] According to the formula = × ,in, are the adjusted visual scene parameters.

[0052] Preferably, the order of dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level is:

[0053] When the motion sickness level is determined to be level 1 or level 2, the user's field of view is preferably adjusted;

[0054] After obtaining the adjusted field of view, determine whether the current user's motion sickness is alleviated;

[0055] When motion sickness symptoms persist, choose to adjust visual scene parameters;

[0056] After obtaining the adjusted visual scene parameters, determine whether the current user's motion sickness is alleviated;

[0057] When motion sickness symptoms persist, the user's field of view and visual scene parameters are adjusted synchronously.

[0058] When the motion sickness level is determined to be the third level, the field of view of the current user and the visual scene parameters are adjusted synchronously.

[0059] Preferably, the steps of continuously collecting feedback information from users during the intervention process and further optimizing various parameters based on the feedback information from users are as follows:

[0060] After adjusting the current user's field of view or adjusting visual scene parameters, evaluate the changes in motion sickness symptoms after various field of view adjustments;

[0061] After motion sickness symptoms worsen, determine which changes in visual field parameters lead to the worsening of motion sickness symptoms;

[0062] Analyze the specific effects of different parameter adjustments on motion sickness symptoms;

[0063] Parameter factors that have a negative impact on motion sickness symptoms are further optimized and adjusted.

[0064] Beneficial effects:

[0065] By using data analysis algorithms to identify early signs of motion sickness in users, we can immediately initiate visual perception adjustment steps. Furthermore, by capturing and analyzing the user's physiological signals in real time during the VR experience, we can identify and intervene promptly in the early stages of motion sickness. This allows us to promptly develop intervention methods for users who experience subjective motion sickness, helping to quickly alleviate user discomfort and thereby improve user acceptance and satisfaction with VR technology.

[0066] When the level of motion sickness is determined to be level one or level two, the symptoms of motion sickness are alleviated by adjusting the field of view. By reducing the amount of peripheral vision information, the user's comfort in the VR environment can be improved, and the discomfort caused by visual information overload can be reduced, thereby improving the overall user experience. The foveal field of view (i.e., the central area of ​​the field of view) has higher visual resolution and richer detailed information, while the amount of peripheral vision information is relatively small. By reducing the amount of peripheral vision information, the user can focus more on important information in the center of the field of view, thereby improving visual efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the intervention method of the present invention. DETAILED DESCRIPTION

[0068] Application Scenarios: Currently, research on virtual reality motion sickness primarily focuses on hardware improvements (such as increasing display frame rates and reducing latency). While some progress has been made, it remains ineffective in completely eliminating motion sickness. Furthermore, there is a lack of effective real-time intervention methods for users who already experience motion sickness. Therefore, developing a method based on perceptual theory to effectively mitigate subjective motion sickness during the user experience of virtual reality is crucial.

[0069] like Figure 1 A method for intervening in the subjective feeling of virtual reality motion sickness based on perceptual theory is shown, including:

[0070] Step 101: Acquire proprioception parameter information of the current user, which includes but is not limited to head movement data, eye movement data, and physiological data. It should be noted that the physiological data includes but is not limited to electroencephalogram (EEG), heart rate, skin conductivity, etc., and is collected using specialized physiological signal acquisition equipment such as an EEG cap, ECG electrodes, and EDA sensors.

[0071] Head movement data includes angular velocity, angular acceleration, linear velocity, etc., and eye movement data includes eye rotation speed and angle, etc., all of which can be obtained based on existing sensors and other equipment.

[0072] Step 102: Using a data analysis algorithm to perform real-time analysis on the currently collected data to determine whether the user is experiencing early signs of motion sickness. It should be noted that the collected proprioception parameter information data is analyzed in real time using a data analysis algorithm to analyze whether the user is experiencing early signs of motion sickness. For example, the amplitude of electrical activity fluctuations in an electroencephalogram (EEG) and changes in heart rate can be used to assess the activity of the autonomic nervous system. Changes in human skin resistance can also be used to detect changes in the human nervous system, thereby determining whether motion sickness is occurring. When at least one of the above data shows abnormal fluctuations, it can be determined as early signs of motion sickness.

[0073] For example, the normal angular velocity of head rotation is within a certain range. If it exceeds the upper limit of the range, it is judged that there may be signs of motion sickness; the normal heart rate range is generally 60-100 beats / minute. When it is continuously higher than 100 beats / minute or other abnormal conditions, it indicates that there may be a risk of motion sickness.

[0074] Step 103: When early-stage motion sickness data appears, the motion sickness level is determined based on the motion sickness physiological data. The motion sickness levels include level 1, level 2, and level 3. It should be noted that, among the aforementioned data, physiological data is the primary factor reflecting the user's motion sickness symptoms. Therefore, when early-stage motion sickness data appears, the number of abnormalities in the physiological data is obtained to classify the motion sickness level.

[0075] For example, when one physiological data abnormality occurs, it is confirmed as the first level; when two physiological data abnormalities occur, it is confirmed as the second level; when three or more physiological data abnormalities occur, it is confirmed as the third level.

[0076] Step 104: Dynamically adjust the presentation of visual information in the virtual reality scene based on the current user's motion sickness level. It should be noted that after the motion sickness level is determined, the presentation of visual information is adjusted in real time according to the corresponding level standard to alleviate the current user's motion sickness level.

[0077] Step 105: Continuously collect user feedback during the intervention process, and further optimize various parameters based on the user feedback. It should be noted that during the adjustment process, user feedback is collected in real time. For example, when adjusting a certain parameter, the changes in the electroencephalogram, electrocardiogram, and skin electrical response are continuously monitored to determine which parameters have a greater impact on the current user, so as to facilitate subsequent optimization of the corresponding parameters.

[0078] It should be understood that before a user begins to experience VR content, the VR device's sensors must first be ensured to function properly and the data collection and analysis modules must be initialized. Once the user enters the VR scene, the data collection and analysis steps begin running in real time. The sensors continuously collect the user's physiological data and transmit it to the data analysis algorithm for processing.

[0079] Once the data analysis algorithm determines that the user is showing early signs of motion sickness, the visual perception adjustment step is immediately initiated. By capturing and analyzing the user's physiological signals in real time during the virtual reality experience, we can identify and intervene in the early stages of motion sickness. We can promptly formulate intervention methods for users who have subjective feelings of motion sickness, which is conducive to quickly alleviating user discomfort and thus improving user acceptance and satisfaction with virtual reality technology.

[0080] As an optional embodiment, the specific steps of using a data analysis algorithm to perform real-time analysis on the currently collected data to determine whether the user has early data of motion sickness are as follows:

[0081] The training is based on a large amount of labeled user data. It should be noted that the training is based on whether the user experiences motion sickness and the degree of motion sickness.

[0082] The collected real-time multi-dimensional data is used as input; wherein the multi-dimensional data includes but is not limited to features such as head movement, eye movement, and physiological data.

[0083] A decision tree model is constructed to determine whether the current user has early data on motion sickness. It should be noted that, for example, if a certain head movement indicator is too high and the heart rate is also abnormally elevated, the decision tree branch may point to a category with a high probability of motion sickness.

[0084] The decision tree algorithm comprehensively considers the relationships between multiple data features. On the one hand, it mines characteristic patterns under different conditions based on a large amount of training data, thereby more accurately determining whether the user is experiencing early symptoms of motion sickness and reducing false positives.

[0085] On the other hand, more new data features can be easily added. For example, if new physiological monitoring indicators are added in the future, these features can be incorporated into the model through retraining to improve the comprehensiveness and accuracy of the judgment.

[0086] As an optional embodiment, the specific steps of constructing a decision tree model to determine whether the current user has early data of motion sickness are:

[0087] Collect multi-dimensional data from many users; specifically, head movements (such as head rotation angle and frequency), eye movements (such as eye scanning speed and gaze duration), and physiological data (heart rate, blood pressure, etc.). These data are marked with whether motion sickness occurs and the degree of motion sickness to construct the original data set.

[0088] Clean the collected multi-dimensional data and remove data records with obvious errors or too many missing values. Specifically, ensure the quality of the data by eliminating influencing data. At the same time, further normalization or standardization may be required for data with different characteristics. For example, normalize the heart rate data to the interval [0,1] so that the data of each feature are in a similar magnitude range to facilitate subsequent model training and analysis.

[0089] Calculate the correlation between each feature and the motion sickness labeling results. Specifically, for example, by calculating statistical methods such as correlation coefficients, find features that are strongly correlated with the presence and severity of motion sickness. These features are selected to build a decision tree, and redundant features that are not helpful in determining motion sickness are eliminated to simplify the model and improve efficiency.

[0090] Determine which features can be used as input features for the decision tree model. Specifically, for example, if analysis reveals that the angular velocity of head movement, the average scanning period of eye movement, and heart rate are highly correlated with motion sickness, then determine to use these features as input features for building the decision tree model.

[0091] From the identified feature set, select the feature that best distinguishes motion sickness from non-motion sickness as the root node. Specifically, the discriminatory power of each feature can be evaluated by calculating metrics such as information gain, information gain ratio, or the Gini index. Suppose, after calculation, the heart rate feature is found to have the highest discriminatory power in the entire dataset. Then, heart rate is used as the root node, and branches are created based on different heart rate ranges. For example, a branch is created for heart rates less than 60 beats / minute, another for 60-100 beats / minute, and another for greater than 100 beats / minute.

[0092] For each branch divided by the root node, features with strong distinguishing ability are further selected from the remaining features as new internal nodes to further subdivide the branches. Specifically, for example, under the branch with a heart rate greater than 100 beats / minute, the feature of the deflection angular velocity of the head movement is examined again, and the branches are further divided according to its different numerical ranges, such as a deflection angular velocity less than 10° / second as a branch, 10-30° / second as a branch, greater than 30° / second as a branch, etc. This process is repeated continuously to gradually construct the entire decision tree until certain stopping conditions are met (such as reaching the preset tree depth, the number of samples in the node is too small, etc.).

[0093] Using the labeled training data set, a decision tree model is generated according to the above construction process. Specifically, by continuously adjusting the node division conditions and feature selection order, the decision tree can better fit the occurrence pattern of motion sickness in the training data.

[0094] Evaluate the trained decision tree model based on a reserved validation dataset to see whether its prediction accuracy, recall rate, F1 value, and other indicators meet expectations. If the results are not good, optimize it through pruning and other techniques. For example, pre-pruning is to set some stop-growth conditions in advance during the decision tree construction process to prevent overfitting; post-pruning is to prune some branches and leaves according to certain rules after the decision tree is built to simplify the model and improve its generalization ability.

[0095] The calculation formula for accuracy is: ;

[0096] ;

[0097] ;

[0098] ;

[0099] When evaluating the model, we will divide the reserved validation dataset into two parts: one part is used to test the performance of the model, and the other part is used to adjust the model parameters (if necessary). By calculating the above indicators, we can understand the performance of the model when processing unknown data.

[0100] If the model's prediction accuracy, recall rate, and F1 value meet expectations, then we can consider the model's performance to be satisfactory. If it does not meet expectations, we may need to readjust the model parameters or consider using a more complex model to improve performance.

[0101] After collecting the user's multi-dimensional data in real time, the multi-dimensional data is pre-processed and feature extracted in the same way as the training data;

[0102] The processed data starts from the root node of the decision tree and is mapped to different branch paths according to the numerical values ​​of the corresponding features, ultimately reaching the leaf nodes. Specifically, the category corresponding to the leaf node (such as motion sickness present or not present) is the model's judgment result on the current user status, which is used to determine whether the user is currently experiencing early signs of motion sickness.

[0103] The decision tree structure presents a tree-like branching pattern, allowing intuitive interpretation of each path from the root node to the leaf nodes, clearly demonstrating the logical process of making judgments based on different characteristics. For example, it can be directly seen that the high probability of motion sickness is due to excessive head movement indicators and abnormally elevated heart rate, making it easier for medical staff, researchers, and others to understand and explain the judgment basis to others.

[0104] Secondly, it can simultaneously accept multi-dimensional data such as head movement, eye movement, and physiological data as input features, integrate information from different aspects to comprehensively judge the early manifestations of motion sickness, make full use of various relevant data to comprehensively consider the user's actual situation, and avoid misjudgment caused by relying solely on a single data dimension.

[0105] As an optional embodiment: when early data of motion sickness appears, the specific steps of determining the level of motion sickness based on physiological data of motion sickness include:

[0106] The evaluation data of the current early data is obtained; it should be noted that the level of motion sickness is further determined by pre-building the evaluation data.

[0107] If the evaluation data is in the first interval, it is determined to be the first level, indicating that the current user's motion sickness manifests as mild symptoms; the first level manifests as mild symptoms, usually manifested as mild dizziness, headache, slight nausea, slightly pale complexion, increased drooling, drowsiness, etc.

[0108] If the evaluation data is in the second interval, it is determined to be the second level, indicating that the current user has moderate symptoms of motion sickness; the second level shows moderate symptoms, usually manifested as mild dizziness, headache, slight nausea, slightly pale complexion, increased saliva, drowsiness, etc.

[0109] If the assessment data falls within the third range, it is determined to be Level 3, indicating that the user is experiencing severe motion sickness. Level 3 symptoms typically include severe dizziness, nausea, palpitations, chest tightness, cold sweats, severe vomiting, and cold extremities. Severe cases may include dehydration, difficulty breathing, slowed reaction time, a sense of impending death, or even coma. In some cases, the patient may also experience cold sweats, cold extremities, or even coma and a sense of impending death.

[0110] As an optional embodiment, the specific steps of obtaining the evaluation data of the current early data are:

[0111] Calculate the energy of the theta, alpha, beta, and gamma frequency bands in specific brain regions. Based on the frequency characteristics of the EEG signal, select the appropriate frequency band for analysis, such as delta waves (0.5-4Hz), theta waves (4-8Hz), alpha waves (8-13Hz), and beta waves (13-30Hz). Use Fourier transform or other methods to calculate the power spectral density of the EEG signal to determine the power of different frequency components. Then, integrate the power spectral density of the selected frequency band to obtain the total energy of the frequency band. This can be achieved by using numerical integration methods such as Simpson's rule to improve the accuracy of the calculation.

[0112] Set the energy threshold for each frequency band. When the actual energy value exceeds the threshold, it is considered that there is a risk of motion sickness.

[0113] Calculate frequency domain indicators of heart rate variability, including low-frequency power LF and high-frequency power HF; perform fast Fourier transform (FFT) on the RR interval sequence based on existing calculation methods or use other spectrum estimation methods such as autoregressive models to obtain the spectrum of the heart rate signal. The above is one of many steps. The frequency domain indicator of heart rate variability (HRV) is an existing technology, which is evaluated by analyzing the power distribution of the heart rate signal in different frequency ranges. It will not be elaborated here.

[0114] A threshold value of the LF / HF ratio is set. When the ratio exceeds the threshold value, it is considered that there is a risk of motion sickness.

[0115] Calculating the rate of change of the skin electrical signal; specifically, calculating the rate of change of the skin electrical signal (EDA) is an existing technology and is widely used in psychology, physiology and medical research, so it will not be elaborated on in detail.

[0116] Set a threshold for the rate of change of the skin electrode signal. When the rate of change exceeds the threshold, it is considered that there is a risk of motion sickness.

[0117] According to the threshold values ​​of the above indicators, a logical operation formula is used to determine the level of motion sickness, wherein the logical operation formula is:

[0118]

[0119] in, 、 、 They represent the threshold values ​​of EEG power, LF / HF ratio of HRV, and EDA change rate, respectively;

[0120] It should be understood that, assuming we have the following data:

[0121] = 0.5 (EEG energy threshold);

[0122] =1.0 (LF / HF ratio threshold of HRV);

[0123] =0.03 (EDA change rate threshold);

[0124] And the following data:

[0125] EEG energy: theta band energy is 0.6, alpha band energy is 0.8;

[0126] HRV: LF 120, HF 80, LF / HF ratio 1.5;

[0127] EDA: skin electrical signal change rate is 0.04;

[0128] According to the logical formula:

[0129] =max(0.6,0.8)=0.8;

[0130] =1.5;

[0131] =0.04;

[0132] Based on the above, we can conclude that and ,but and ,From this, we can judge that the current user's motion sickness level is mild.

[0133] This logical formula provides a motion sickness level assessment method based on physiological signal thresholds, which can be flexibly adjusted according to the physiological response thresholds of different individuals, thereby facilitating the assessment of motion sickness levels.

[0134] As an optional embodiment, the method for calculating the energy of the θ, α, β, and γ frequency bands in a specific brain region is as follows:

[0135] According to the formula to obtain;

[0136] in, is the power spectral density value in the frequency band, n is the number of samples;

[0137] By comparing the energy of different frequency bands, the brain's activation state can be analyzed, such as theta waves are related to memory and learning processes, alpha waves are related to relaxation, beta waves are related to alertness and concentration, and gamma waves are related to higher cognitive functions;

[0138] It should be noted that when calculating the energy of a specific brain area, the fuzzy entropy of the δ frequency band also needs to be calculated. The calculation method of the fuzzy entropy is: ;

[0139] in, is the signal value, is the fuzzy factor, N is the signal length;

[0140] Fuzzy entropy is used to measure the complexity and regularity of time series data. Fuzzy entropy is a method to quantify the complexity of time series, which is highly robust to noise and changes.

[0141] Fuzzy entropy can be used to analyze the dynamic characteristics of EEG signals. For example, the fuzzy entropy of the δ frequency band can reflect the complexity of EEG activity during sleep.

[0142] As an optional embodiment, a specific method of dynamically adjusting the presentation of visual information in a virtual reality scene based on the current user's motion sickness level includes the following steps:

[0143] Get the original field of view of the current user It should be noted that the original visual field can be obtained based on the device used. For example, professional equipment such as a perimeter can be used to measure the user's actual visual field in a VR environment. This may involve presenting specific stimuli in the VR environment and recording the area in which the user can perceive these stimuli.

[0144] The ratio R of the information of the peripheral retinal area that is reduced is obtained. It should be noted that the corneal refractive power is changed by methods such as orthokeratology lenses and multifocal defocus soft lenses, which weakens the central corneal refractive power and increases the peripheral corneal refractive power, thereby affecting the peripheral retinal defocus value.

[0145] Use retinoscopy, subjective refraction, infrared computer automatic ophthalmometer, aberrometer and other methods to test peripheral refraction to obtain retinal peripheral defocus value, etc.

[0146] According to the formula × (1-R) ​​to get the adjusted field of view.

[0147] It should be noted that according to research, sensory conflict theory posits that a mismatch between vestibular, visual, and proprioceptive afferent signals is a key cause of motion sickness. Reducing information from the peripheral retinal area can effectively reduce the conflict between visual, vestibular, and proprioceptive afferent signals, thereby effectively alleviating visually induced motion sickness.

[0148] Therefore, when users experience early signs of motion sickness, reducing information in the peripheral areas of the retina can effectively reduce the degree of conflict between visual-vestibular-proprioceptive afferent signals, thereby effectively reducing visually induced motion sickness.

[0149] As an optional embodiment, the specific method of dynamically adjusting the presentation of visual information in the virtual reality scene based on the current user's motion sickness level further includes the following steps:

[0150] Get the original visual scene parameters ; Capture visual scenes in the real world through high-precision cameras and sensors, and then extract scene parameters through image processing technology;

[0151] Apply computer vision algorithms, such as scene recognition and object detection, to analyze and understand visual scenes in the real world, thereby obtaining scene parameters, etc.

[0152] The visual scene parameters are adjusted according to the current user's vestibular perception V and proprioception P, and expressed as ;

[0153] According to the formula = × ,in, is the adjusted visual scene parameter;

[0154] It should be noted that when adjusting the field of view fails to achieve the expected effect, the visual clues in the VR environment involved in sensory matching can be adjusted by adjusting parameters such as the movement speed of the visual scene and the change in viewing angle to match the user's inner ear perception, thereby further adjusting the patient's motion sickness. Adjusting the match between visual and vestibular sensations is helpful in reducing the symptoms of motion sickness.

[0155] It should be noted that according to sensory conflict theory, the conflict between visual, vestibular, and proprioceptive inputs must first be reduced. This can be achieved by reducing the amount of information in the peripheral retinal area, such as by adjusting the field of view in a VR environment to reduce the amount of peripheral vision information.

[0156] Secondly, after reducing sensory conflicts, the next step is to adjust the parameters of perceptual matching to match the user's inner ear perception and proprioception. This involves adjusting parameters such as the movement speed of the visual scene and the change of perspective.

[0157] In summary, reducing the conflict between visual, vestibular, and proprioceptive afferent signals is the basis for adjusting the perceptual matching parameters, and the adjustment of the perceptual matching parameters is to further optimize the user experience.

[0158] As an optional embodiment, the order of presenting visual information in the virtual reality scene is dynamically adjusted based on the current user's motion sickness level:

[0159] When the motion sickness level is determined to be the first or second level, priority is given to adjusting the current user's field of view. It should be noted that when the motion sickness level is determined to be the first or second level, the symptoms of motion sickness are alleviated by adjusting the field of view. By reducing the amount of peripheral vision information, the user's comfort in the VR environment can be improved, and the discomfort caused by visual information overload can be reduced, thereby improving the overall user experience. The foveal field of view (i.e., the central area of ​​the field of view) has higher visual resolution and richer detailed information, while the amount of peripheral vision information is relatively small. By reducing the amount of peripheral vision information, the user can focus more on important information in the center of the field of view, thereby improving visual efficiency.

[0160] After obtaining the adjusted field of view, determine whether the current user's motion sickness is alleviated; after adjusting the field of view, it is necessary to determine in real time whether the current user's motion sickness is alleviated to facilitate subsequent processing.

[0161] When the motion sickness symptoms persist, the visual scene parameters are adjusted; specifically, when the motion sickness symptoms persist, the visual scene parameters are optimized and adjusted again based on the field of view adjustment to reduce the user's discomfort.

[0162] After obtaining the adjusted visual scene parameters, it is determined whether the current user's motion sickness is alleviated; based on the above, it is determined again whether the motion sickness symptoms are alleviated.

[0163] When motion sickness symptoms persist, the user's field of view and visual scene parameters are adjusted synchronously.

[0164] If adjusting vision or motion perception alone fails to reduce motion sickness, a dual adjustment strategy, adjusting both vision and motion perception simultaneously, can be implemented to seek more effective relief.

[0165] When the motion sickness level is determined to be the third level, the current user's field of view and the visual scene parameters are adjusted simultaneously;

[0166] It should be noted that when motion sickness reaches the third level, the user's reaction is greater, so a holistic optimization approach is adopted to adjust these two key factors at the same time in the hope of achieving a better relief effect.

[0167] As an optional embodiment: continuously collecting user feedback information during the intervention process, and further optimizing various parameters based on the user feedback information. The specific steps are as follows:

[0168] After adjusting the current user's field of view or visual scene parameters, evaluate how motion sickness symptoms change after various field of view adjustments. It should be noted that when adjusting related parameters individually or simultaneously, the changes in the current user's motion sickness symptoms after adjusting the corresponding parameters.

[0169] After the motion sickness symptoms worsen, determine which changes in visual field parameters lead to the worsening of the motion sickness symptoms; specifically, when the motion sickness symptoms worsen, adjust the adjustment parameters to determine which parameter changes lead to the worsening of the motion sickness symptoms.

[0170] Analyze the specific effects of different parameter adjustments on motion sickness symptoms. Specifically, after determining which parameter changes lead to aggravated motion sickness symptoms, record the effects simultaneously after determination, for example, whether the adjustment of the field of view leads to aggravated dizziness.

[0171] Further optimize and adjust parameters that negatively affect motion sickness. After determining the impact of parameter adjustments, further optimize those parameters that negatively affect motion sickness. For example, if the adjusted field of view worsens motion sickness, the adjusted field of view can be appropriately reduced, or the field of view can be adjusted to a specified range first, allowing the user to adapt for a period of time before adjusting the field of view again to avoid worsening motion sickness due to adjusting the field of view too quickly.

[0172] For another example, the adjusted field of view aggravates the symptoms of motion sickness, while the visual scene parameters do not affect the aggravation of motion sickness. The visual scene parameters can be appropriately adjusted, while the adjustment amplitude of the field of view is reduced, and so on.

[0173] How it works: Before a user begins experiencing VR content, the device's sensors are first ensured to function properly, and the data collection and analysis modules are initialized. Once the user enters the VR scene, the data collection and analysis steps begin in real time. The sensors continuously collect the user's physiological data and transmit it to the data analysis algorithm for processing.

[0174] Once the data analysis algorithm determines that the user is showing early signs of motion sickness, the visual perception adjustment step is immediately initiated. By capturing and analyzing the user's physiological signals in real time during the virtual reality experience, we can identify and intervene in the early stages of motion sickness. We can promptly formulate intervention methods for users who have subjective feelings of motion sickness, which is conducive to quickly alleviating user discomfort and thus improving user acceptance and satisfaction with virtual reality technology.

[0175] The above are only preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be pointed out that for ordinary technical personnel in this technical field, certain improvements and modifications that do not depart from the principles of the present invention should also be considered as the scope of protection of this template.

Claims

1. A method for intervening in subjective feelings of virtual reality motion sickness based on perceptual theory, characterized in that: include: Step 101: Acquire proprioception parameter information of the current user, wherein the proprioception parameter information includes but is not limited to head movement data, eye movement data, and physiological data; Step 102: Analyze the currently collected data in real time using a data analysis algorithm to determine whether the user has early data of motion sickness; Step 103: When early-stage motion sickness data appears, determining a motion sickness level based on physiological data of the motion sickness, wherein the motion sickness level includes a first level, a second level, and a third level; Step 104: Dynamically adjust the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level; Step 105: Continuously collect user feedback during the intervention process, and further optimize various parameters based on the user feedback; The specific method of dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level includes the following steps: Get the original field of view of the current user ; The ratio R of the reduced retinal peripheral area information is obtained; According to the formula × (1-R) ​​to get the adjusted field of view; The specific method of dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level also includes the following steps: Get the original visual scene parameters ; Adjust the visual scene parameters according to the current user's vestibular perception V and proprioception P, and express ; According to the formula = × ,in, is the adjusted visual scene parameter; The order of dynamically adjusting the presentation of visual information in the virtual reality scene based on the current user's motion sickness level is: When the motion sickness level is determined to be level 1 or level 2, the user's field of view is preferably adjusted; After obtaining the adjusted field of view, determine whether the current user's motion sickness is alleviated; When motion sickness symptoms persist, choose to adjust visual scene parameters; After obtaining the adjusted visual scene parameters, determine whether the current user's motion sickness is alleviated; When motion sickness symptoms persist, the user's field of view and visual scene parameters are adjusted synchronously. When the motion sickness level is determined to be the third level, the field of view of the current user and the visual scene parameters are adjusted synchronously.

2. The method for intervening in subjective feelings of virtual reality motion sickness based on perceptual theory according to claim 1, characterized in that: The specific steps of using the data analysis algorithm to perform real-time analysis on the currently collected data to determine whether the user has early data of motion sickness are as follows: Training based on a large amount of labeled user data; The collected real-time multi-dimensional data is used as input; Build a decision tree model to determine whether the current user has early data on motion sickness.

3. The method for intervening in subjective feelings of virtual reality motion sickness based on perceptual theory according to claim 2, characterized in that: The specific steps of constructing the decision tree model to determine whether the current user has early data of motion sickness are: Collect multi-dimensional data from many users; Clean the collected multi-dimensional data and remove data records with obvious errors or too many missing values; Calculate the correlation between each feature and the motion sickness annotation results; Determine which features can be used as input features for the decision tree model; Select a feature that best distinguishes motion sickness from non-motion sickness from the determined feature set as the root node; For each branch divided by the root node, continue to select features with strong distinguishing ability from the remaining features as new internal nodes to further subdivide the branches; Using the labeled training data set, a decision tree model is generated according to the above construction process; Evaluate the trained decision tree model based on a reserved validation dataset to see whether its prediction accuracy, recall rate, and F1 value indicators meet expectations; After collecting the user's multi-dimensional data in real time, the multi-dimensional data is pre-processed and feature extracted in the same way as the training data; The processed data starts from the root node of the decision tree, corresponds to different branch paths according to the values ​​of the corresponding features, and finally reaches the leaf node.

4. The method for intervening in subjective feelings of virtual reality motion sickness based on perceptual theory according to claim 3, characterized in that: When early data on motion sickness are available, the specific steps for determining the level of motion sickness based on the physiological data of motion sickness include: Obtain evaluation data for current early data; If the evaluation data is in the first interval, it is determined to be the first level, indicating that the current user's motion sickness is mild. If the evaluation data is in the second interval, it is determined to be the second level, indicating that the current user has moderate symptoms of motion sickness; If the evaluation data is in the third interval, it is determined to be the third level, indicating that the current user has severe symptoms of motion sickness.

5. The method for intervening in subjective feeling of virtual reality motion sickness based on perceptual theory according to claim 4, characterized in that: The specific steps of obtaining the evaluation data of the current early data are: Calculate the energy of theta, alpha, beta, and gamma frequency bands in specific brain areas; Set the energy threshold for each frequency band. When the actual energy value exceeds the threshold, it is considered that there is a risk of motion sickness. Calculate the frequency domain indicators of heart rate variability, including low-frequency power LF and high-frequency power HF; A threshold value of the LF / HF ratio is set. When the ratio exceeds the threshold value, it is considered that there is a risk of motion sickness. Calculate the rate of change of skin electrical signals; Set a threshold for the rate of change of the skin electrode signal. When the rate of change exceeds the threshold, it is considered that there is a risk of motion sickness. According to the threshold values ​​of the above indicators, a logical operation formula is used to determine the level of motion sickness, wherein the logical operation formula is: ； in, 、 、 They represent the EEG energy, the LF / HF ratio of HRV, and the threshold value of EDA change rate, respectively.

6. The method for intervening in subjective feeling of virtual reality motion sickness based on perceptual theory according to claim 5, characterized in that: The method for calculating the energy of the θ, α, β, and γ frequency bands in a specific brain region is: According to the formula to obtain; in, is the power spectral density value in the frequency band, and n is the number of samples.

7. The method for intervening in subjective feeling of virtual reality motion sickness based on perceptual theory according to claim 1, characterized in that: The specific steps of continuously collecting user feedback information during the intervention process and further optimizing various parameters based on the user feedback information are as follows: After adjusting the current user's field of view or adjusting visual scene parameters, evaluate the changes in motion sickness symptoms after various field of view adjustments; After motion sickness symptoms worsen, determine which changes in visual field parameters lead to the worsening of motion sickness symptoms; Analyze the specific effects of different parameter adjustments on motion sickness symptoms; Parameter factors that have a negative impact on motion sickness symptoms are further optimized and adjusted.

Images