Virtual reality motion sickness subjective feeling intervention method based on perception theory

Abstract

The invention discloses a virtual reality motion sickness subjective feeling intervention method based on a perception theory, and relates to the technical field of virtual reality, and the method comprises the steps: collecting current user body feeling parameter information, the body feeling parameter information comprises but is not limited to head motion data, eyeball motion data and physiological data; performing real-time analysis on the currently acquired data by using a data analysis algorithm, and judging whether the user has early data of motion sickness or not; an early sign of motion sickness of a user is judged through a data analysis algorithm, a visual perception adjustment step is started immediately, and then a physiological signal of the user is captured and analyzed in real time in virtual reality experience, so that the motion sickness can be recognized and intervened in time in the early stage of motion sickness; an intervention method can be timely formulated for a user who generates subjective feeling of motion sickness, and discomfort of the user can be quickly relieved.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and more specifically, to a method for intervening in subjective feelings 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, eyestrain, etc. caused by the incoordination between visual perception and body proprioception and vestibular sense when users use virtual reality (VR) technology, which seriously affects the user's usage time and acceptance of virtual reality equipment.

[0003] At present, the research on virtual reality motion sickness mainly focuses on hardware equipment improvement (such as increasing display frame rate, reducing latency, etc.). Although some results have been achieved, it is still impossible to completely eliminate the occurrence of motion sickness; and for users who have already experienced subjective motion sickness, there is a lack of effective real-time intervention methods. Therefore, it is of great significance to develop a method that can effectively intervene in the subjective feelings of motion sickness during the user experience of virtual reality based on perceptual theory. Summary of the invention

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

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

[0006] Step 101: acquiring 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: 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;

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

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

[0010] Step 105: Continue to 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] Train 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 from 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 portion of the reserved validation data set to see whether its prediction accuracy, recall rate, F1 value and other indicators meet expectations;

[0024] After the multi-dimensional data of the user is collected 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's movement disorder exhibits moderate symptoms;

[0030] If the evaluation data is in the third interval, it is determined to be the third level, indicating that the current user's movement disorder exhibits severe symptoms.

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

[0034] Calculate frequency domain indices 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 electrical 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, where the logical operation formula is:

[0039]

[0040] Among them, T EEG 、T HRV 、T EDA They represent EEG energy, LF / HF ratio of HRV, and threshold 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 conclude;

[0043] Among them, X i 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 V of the current user ys ;

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

[0047] According to formula V new =V ys ×(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 S ys ;

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

[0051] According to formula S new =S ys ×f (V,P) , where S new is the adjusted visual scene parameter.

[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 determining that the motion sickness level is the first level or the second level, preferably adjusting the field of view of the current user;

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

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

[0058] When the movement disorder level is determined to be the third level, the visual field of the current user is adjusted synchronously with the visual scene parameters.

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

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

[0061] After the 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] 1. By using data analysis algorithms to determine when users are experiencing early signs of motion sickness, we can immediately initiate visual perception adjustment steps. 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 also formulate intervention methods for users who have subjective feelings of motion sickness in a timely manner, which is conducive to quickly alleviating user discomfort and thus improving user acceptance and satisfaction with virtual reality technology.

[0066] 2. When the level of motion sickness 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 detail information, while the amount of peripheral vision information is relatively small. By reducing the amount of peripheral vision information, users 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 is mainly focused on hardware equipment improvements (such as increasing display frame rate, reducing latency, etc.). Although some results have been achieved, it is still impossible to completely eliminate the occurrence of motion sickness; and for users who have already experienced subjective motion sickness, there is a lack of effective real-time intervention methods. Therefore, it is of great significance to develop a method that can effectively intervene in the subjective feelings of motion sickness based on perceptual theory during the user experience of virtual reality.

[0069] like Figure 1 Shown: A method for intervening subjective feelings of virtual reality motion sickness based on perceptual theory, including:

[0070] Step 101: acquiring proprioception parameter information of the current user, the proprioception parameter information including but 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, heart rate, skin conductivity, etc., which are acquired based on special physiological signal acquisition equipment, such as EEG cap, ECG electrode, EDA sensor, etc.;

[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 has early data 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 determine whether the current user has early data of motion sickness through data analysis, for example, the fluctuation amplitude of the electrical activity of the electroencephalogram can evaluate the activity of the autonomic nervous system through changes in the heart rate, and when the human skin resistance value changes, it can be used to detect changes in the human nervous system, so as to determine whether motion sickness occurs. When at least one of the above data fluctuates abnormally, it can be determined as early data 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 and other abnormal conditions, it indicates that there may be a risk of motion sickness.

[0074] Step 103: when early data of motion sickness appears, the level of motion sickness is determined based on the physiological data of motion sickness, and the levels of motion sickness include the first level, the second level and the third level. It should be noted that, in the above data, the physiological data is the main factor reflecting the user's motion sickness symptoms. Therefore, when early data of motion sickness appears, the level of motion sickness is divided by obtaining the abnormal number of physiological data.

[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, and when three or more physiological data abnormalities occur, it is confirmed as the third level.

[0076] Step 104: dynamically adjusting the presentation method of visual information in the virtual reality scene based on the current user's motion sickness level; it should be noted that after determining the level of motion sickness, the presentation method 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 feedback information from users during the intervention process, and further optimize various parameters based on the user feedback information; it should be noted that during the adjustment process, user feedback information 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 the user starts to experience the virtual reality content, first ensure that the sensors of the virtual reality device are working properly and initialize the data collection and analysis module; when the user enters the virtual reality scene, the data collection and analysis steps begin to run in real time; the sensor continuously collects the user's physiological data and transmits 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. Then, 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 also formulate intervention methods for users who have subjective feelings of motion sickness in a timely manner, which is conducive to quickly alleviating user discomfort and thus improving user acceptance and satisfaction with virtual reality technology.

[0080] As an optional embodiment: 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 is 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 suffers from motion sickness and the degree of such 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] Construct a decision tree model to determine whether the current user has early data on motion sickness. It should be noted that, for example, if a certain indicator of head movement is too high and the heart rate also increases abnormally, the branch judgment of the decision tree may point to the category with a high probability of motion sickness.

[0084] The decision tree algorithm can comprehensively consider the relationship between multiple data features. On the one hand, based on a large amount of training data, it can mine feature patterns under different states, so as to more accurately judge whether the user has early symptoms of motion sickness and reduce misjudgment.

[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 by 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, frequency, etc.), eye movements (such as eye scanning speed, gaze duration, etc.), physiological data (heart rate, blood pressure, etc.), and 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 data quality by eliminating influencing data. At the same time, data with different characteristics may need to be further normalized or standardized. For example, normalize heart rate data to the [0,1] interval so that the data of each feature is in a similar magnitude range to facilitate subsequent model training and analysis.

[0089] Calculate the correlation between each feature and the motion sickness labeling result; specifically, for example, by calculating the correlation coefficient and other statistical methods, find out those features that are strongly correlated with the presence and degree of motion sickness, focus on selecting these features for building a decision tree, and eliminate those redundant features that are not very helpful in judging motion sickness, so as to simplify the model and improve efficiency.

[0090] Determine which features can be used as input features of the decision tree model; specifically, for example, after analysis, it is found that the deflection angular velocity of head movement, the average scanning cycle 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] Select a feature that can best distinguish motion sickness from non-motion sickness from the determined feature set as the root node; specifically, the distinguishing ability of each feature can be evaluated by calculating information gain, information gain rate, or Gini index. Assuming that after calculation, it is found that the heart rate feature has the highest distinguishing degree in the entire data set, then the heart rate is used as the root node, and branches are divided according to different ranges of heart rate values, such as a branch for heart rates less than 60 beats / minute, a branch for 60-100 beats / minute, and a branch for more than 100 beats / minute, etc.

[0092] For each branch divided from the root node, features with strong distinguishing ability are selected from the remaining features as new internal nodes to further subdivide the branches. Specifically, for example, in the branch with a heart rate greater than 100 times / minute, the feature of the angular velocity of the head movement is examined, and the branches are further divided according to its different numerical ranges, such as a branch with a angular velocity less than 10° / second, a branch with 10-30° / second, a branch with a velocity greater than 30° / second, 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, feature selection order, etc., 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 data set to see whether its prediction accuracy, recall rate, F1 value and other indicators meet expectations; if the effect is not good, it can be optimized through pruning and other technologies. For example, pre-pruning is to set some conditions for stopping growth in advance during the decision tree construction process to prevent overfitting; post-pruning is to prune some branch nodes according to certain rules after the decision tree is built, simplify the model, and improve the generalization ability of the model;

[0095] The calculation formula for accuracy is:

[0096]

[0097] 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 parameters of the model (if necessary); by calculating the above indicators, we can understand the performance of the model when processing unknown data;

[0098] 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's parameters or consider using a more complex model to improve performance.

[0099] After the multi-dimensional data of the user is collected in real time, the multi-dimensional data is pre-processed and feature extracted in the same way as the training data;

[0100] 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; specifically, the category corresponding to the leaf node (such as motion sickness present, not present, etc.) is the model's judgment result on the current user status, which is used to determine whether the user currently has early symptoms of motion sickness.

[0101] The decision tree structure presents a tree-like branching form. Each path from the root node to the leaf node can be intuitively interpreted, clearly showing the logical process of making judgments based on different features. For example, it can be directly seen that because the head movement index is too high and the heart rate is abnormally high, it is judged that the possibility of motion sickness is high, which makes it easier for medical staff, researchers, etc. to understand and explain the judgment basis to others.

[0102] Secondly, it can simultaneously accept multi-dimensional data such as head movement, eye movement, physiological data, etc. 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.

[0103] 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:

[0104] The evaluation data of the current early data is obtained; it should be noted that the level of motion sickness is further determined by constructing the evaluation data in advance.

[0105] 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 saliva, drowsiness, etc.

[0106] If the evaluation data is in the second interval, it is determined to be the second level, indicating that the current user's movement disorder shows moderate symptoms; the second level shows moderate symptoms, usually manifested as mild dizziness, headache, slight nausea, slightly pale complexion, increased saliva, drowsiness, etc.

[0107] If the evaluation data is in the third interval, it is determined to be the third level, indicating that the current user's movement disorder shows severe symptoms; the third level shows severe symptoms, usually manifested as severe dizziness, nausea, panic, chest tightness, cold sweat, severe vomiting, cold limbs. Severe cases may experience dehydration, difficulty breathing, slow reaction, near-death feeling, coma, etc. In some cases, patients may also experience cold sweats, cold limbs, or even coma and near-death feeling.

[0108] As an optional embodiment: the specific steps of obtaining the evaluation data of the current early data are:

[0109] Calculate the energy of theta, alpha, beta, and gamma frequency bands in specific brain areas; according to the frequency characteristics of the EEG signal, select the corresponding frequency band for analysis, such as delta wave (0.5-4Hz), theta wave (4-8Hz), alpha wave (8-13Hz), and beta wave (13-30Hz), use Fourier transform or other methods to calculate the power spectrum density of the EEG signal to determine the power of different frequency components; then integrate the power spectrum 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.

[0110] Set the energy threshold of each frequency band. When the actual energy value exceeds the threshold, it is considered that there is a risk of motion sickness.

[0111] Calculate the frequency domain indicators of heart rate variability, including low frequency power (LF) and high frequency power (HF); based on the existing calculation method, the RR interval sequence can be subjected to fast Fourier transform (FFT) or other spectrum estimation methods such as autoregressive model can be used 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, which will not be elaborated here.

[0112] 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.

[0113] Calculating the rate of change of the electrical skin signal; specifically, calculating the rate of change of the electrical skin signal (EDA) is a prior art and is widely used in psychology, physiology and medical research, and will not be elaborated in detail.

[0114] Set a threshold for the rate of change of the skin electrical signal. When the rate of change exceeds the threshold, it is considered that there is a risk of motion sickness.

[0115] According to the threshold values ​​of the above indicators, a logical operation formula is used to determine the level of motion sickness, where the logical operation formula is:

[0116]

[0117] Among them, T EEG 、T HRV 、T EDA They represent the thresholds of EEG power, LF / HF ratio of HRV, and EDA change rate, respectively;

[0118] It should be understood that, assuming we have the following data:

[0119] T EEG =0.5 (EEG energy threshold);

[0120] T HRV =1.0 (LF / HF ratio threshold of HRV);

[0121] T EDA =0.03 (EDA change rate threshold);

[0122] And the following data:

[0123] EEG energy: theta band energy is 0.6, alpha band energy is 0.8;

[0124] HRV: LF is 120, HF is 80, and the LF / HF ratio is 1.5;

[0125] EDA: skin electrical signal change rate is 0.04;

[0126] According to the logical formula:

[0127] E band =max(0.6,0.8)=0.8;

[0128] LF / HF=1.5;

[0129] EDA Rate = 0.04;

[0130] Based on the above, we can conclude that E band≥ T EDA And EDA Rate ≥ T EDA , but E band< 2T EDA And EDA Rate < 2T EDA ,From this, we can judge that the current user's motion sickness level is a mild symptom.

[0131] This logical formula provides a method for evaluating the level of motion sickness based on physiological signal thresholds, which can be flexibly adjusted according to the physiological response thresholds of different individuals, thus facilitating the evaluation of the level of motion sickness.

[0132] 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:

[0133] According to the formula to conclude;

[0134] Among them, X i is the power spectral density value in the frequency band, n is the number of samples;

[0135] 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 states, beta waves are related to alertness and concentration, and gamma waves are related to higher cognitive functions;

[0136] 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:

[0137] Among them, X i is the signal value, f is the fuzzy factor, and N is the signal length;

[0138] The complexity and regularity of time series data are measured by fuzzy entropy. Fuzzy entropy is a method to quantify the complexity of time series, which is highly robust to noise and changes;

[0139] Fuzzy entropy can be used to analyze the dynamic characteristics of EEG signals. For example, the fuzzy entropy in the δ frequency band can reflect the complexity of EEG activity during sleep.

[0140] As an optional embodiment, a specific method of dynamically adjusting the presentation mode of visual information in a virtual reality scene based on the current user's motion sickness level includes the following steps:

[0141] Get the original field of view V of the current user ys ; It should be noted that the original visual field can be obtained based on the device used. For example, through professional equipment such as a perimeter, the actual visual field of the user in the VR environment can be measured. This may involve presenting specific stimuli in the VR environment and recording the area where the user can perceive these stimuli, etc.

[0142] 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, the central corneal refractive power is weakened, and the peripheral corneal refractive power is increased, thereby affecting the peripheral retinal defocus value;

[0143] Use retinoscopy, subjective optometry, infrared computer automatic ophthalmometer, aberrometer and other methods to test peripheral refraction to obtain retinal peripheral defocus value, etc.;

[0144] According to formula V new =V ys ×(1-R) ​​to get the adjusted field of view.

[0145] It should be noted that according to research, the sensory conflict theory believes that the mismatch between vestibular-visual-proprioceptive afferent signals is an important cause of motion sickness. By reducing the information in the peripheral area of ​​the retina, the conflict between visual-vestibular-proprioceptive afferent signals can be effectively reduced, thereby effectively reducing visual-induced motion sickness;

[0146] Therefore, when users experience early signs of motion sickness, reducing information in the peripheral area of ​​the retina can effectively reduce the degree of conflict between visual-vestibular-proprioceptive afferent signals, thereby effectively reducing visually induced motion sickness.

[0147] As an optional embodiment, 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:

[0148] Get the original visual scene parameters S ys ; Capture visual scenes in the real world through high-precision cameras and sensors, and then extract scene parameters through image processing technology;

[0149] 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 and so on.

[0150] The visual scene parameters are adjusted according to the vestibular perception (V) and proprioception (P) of the current user and are expressed as f (V,P) ;

[0151] According to formula S new =S ys ×f (V,P) , where S new is the adjusted visual scene parameter;

[0152] It should be noted that when the expected effect cannot be achieved by adjusting the field of view, the visual clues in the VR environment involved in the 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.

[0153] It should be noted that according to the sensory conflict theory, the conflict between visual-vestibular-proprioceptive afferent signals needs to be reduced first. This can be achieved by reducing the information in the peripheral area of ​​the retina, such as reducing the amount of peripheral vision information by adjusting the field of view in the VR environment;

[0154] Secondly, after reducing sensory conflicts, the next step is to adjust the parameters of sensory 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 in perspective;

[0155] In summary, reducing the conflict between visual-vestibular-proprioceptive afferent signals is the basis for adjusting the perceptual matching parameters, and the adjustment of perceptual matching parameters is to further optimize the user experience.

[0156] As an optional embodiment: 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:

[0157] When the motion sickness level is determined to be the first level or the second level, it is preferred to adjust the field of view of the current user; it should be noted that when the motion sickness level is determined to be the first level or the 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 detail information, while the amount of peripheral vision information is relatively small; by reducing the amount of peripheral vision information, users can focus more on important information in the center of the field of view, thereby improving visual efficiency.

[0158] 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.

[0159] When the motion sickness symptoms persist, the visual scene parameters are selected to be adjusted; specifically, when the motion sickness symptoms persist, the visual scene parameters are optimized and adjusted again on the basis of the field of view adjustment to reduce the user's discomfort.

[0160] After obtaining the adjusted visual scene parameters, it is determined whether the motion sickness phenomenon of the current user is alleviated; based on the above, it is determined again whether the symptoms of motion sickness are alleviated.

[0161] When the motion sickness symptoms persist, the user's field of view is adjusted synchronously with the visual scene parameters;

[0162] If adjusting vision or motion perception alone fails to reduce motion sickness, a dual adjustment strategy can be implemented, that is, adjusting vision and motion perception at the same time to seek a more effective relief method.

[0163] When the movement disorder level is determined to be the third level, the visual field of the current user is adjusted simultaneously with the visual scene parameters;

[0164] It should be noted that when motion sickness reaches the third level, the user's reaction is relatively large, so an overall optimization method is adopted to adjust these two key factors at the same time in the hope of achieving a better relief effect.

[0165] As an optional embodiment: continuously collect feedback information from users during the intervention process, and further optimize various parameters based on the feedback information from users. The specific steps are as follows:

[0166] After adjusting the field of view of the current user or adjusting the visual scene parameters, evaluate the changes in motion sickness symptoms after various field of view adjustments; it should be noted that when adjusting related parameters individually or simultaneously, the changes in the motion sickness symptoms of the current user after adjusting the corresponding parameters.

[0167] 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, retrieve the adjustment status of the adjustment parameters to determine which changes in parameters lead to the worsening of the motion sickness symptoms.

[0168] 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 synchronously after determination, for example, adjustment of the field of view leads to aggravated dizziness.

[0169] Further optimize and adjust the parameter factors that have a negative impact on motion sickness symptoms; after determining the impact of parameter adjustment, further optimize the parameter factors that have a negative impact. For example, if the adjusted field of view aggravates the symptoms of 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 certain period of time before adjusting the field of view to avoid aggravating motion sickness due to adjusting the field of view too quickly;

[0170] For another example, the adjusted field of view aggravates the symptoms of motion sickness, while the visual scene parameters will not affect the aggravation of motion sickness. The visual scene parameters can be appropriately adjusted, while the adjustment range of the field of view is reduced, and so on.

[0171] Working principle: Before the user starts to experience the VR content, first ensure that the VR device's sensors are working properly and initialize the data collection and analysis module; when the user enters the VR scene, the data collection and analysis steps start to run in real time; the sensor continuously collects the user's physiological data and transmits it to the data analysis algorithm for processing;

[0172] Once the data analysis algorithm determines that the user is showing early signs of motion sickness, the visual perception adjustment step is immediately initiated. Then, 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 also formulate intervention methods for users who have subjective feelings of motion sickness in a timely manner, which is conducive to quickly alleviating user discomfort and thus improving user acceptance and satisfaction with virtual reality technology.

[0173] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A method for intervening subjective feelings of virtual reality motion sickness based on perceptual theory, characterized in that: include: Step 101: acquiring 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: 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; Step 103: when early data of motion sickness appears, determining the level of motion sickness based on physiological data of motion sickness, wherein the level of motion sickness includes a first level, a second level, and a third level; Step 104: dynamically adjusting the presentation mode of visual information in the virtual reality scene based on the current user's motion sickness level; Step 105: Continue to collect user feedback information during the intervention process, and further optimize various parameters based on the user feedback information.

2. The method for intervening 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: Train 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 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 from 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 portion of the reserved validation data set to see whether its prediction accuracy, recall rate, F1 value and other indicators meet expectations; After the multi-dimensional data of the user is collected 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 subjective feelings of virtual reality motion sickness based on the perceptual theory according to claim 3, characterized in that: When early data of motion sickness appears, specific steps for determining the level of motion sickness based on 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's movement disorder exhibits moderate symptoms; If the evaluation data is in the third interval, it is determined to be the third level, indicating that the current user's movement disorder exhibits severe symptoms.

5. The method for intervening subjective feelings of motion sickness in virtual reality 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 of each frequency band. When the actual energy value exceeds the threshold, it is considered that there is a risk of motion sickness. Calculate frequency domain indices 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 electrical 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, where the logical operation formula is: Among them, T EEG , T HRV , T EDA They represent EEG energy, LF / HF ratio of HRV, and threshold of EDA change rate, respectively.

6. The method for intervening subjective feelings of virtual reality motion sickness based on the 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 conclude; Among them, X i is the power spectral density value in the frequency band, and n is the number of samples.

7. The method for intervening subjective feelings of motion sickness in virtual reality based on perceptual theory according to claim 1, characterized in that: 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 V of the current user ys ; The ratio R of the information obtained in the peripheral retinal area that is reduced; According to formula V new =V ys ×(1-R) ​​to get the adjusted field of view.

8. The method for intervening subjective feelings of virtual reality motion sickness based on the perceptual theory according to claim 7, characterized in that: 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 S ys ; Adjust the visual scene parameters according to the current user's vestibular perception (V) and proprioception (P), and express it as f(v,p); According to formula S new =S ys ×f (V,P) , where S new is the adjusted visual scene parameter.

9. The method for intervening subjective feelings of virtual reality motion sickness based on the theory of perception according to claim 8, characterized in that: 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 determining that the motion sickness level is the first level or the second level, preferably adjusting the field of view of the current user; After obtaining the adjusted field of view, determine whether the motion sickness of the current user is alleviated; When motion sickness symptoms persist, choose to adjust visual scene parameters; After obtaining the adjusted visual scene parameters, determine whether the motion sickness phenomenon of the current user is alleviated; When the motion sickness symptoms persist, the user's field of view is adjusted synchronously with the visual scene parameters; When the movement disorder level is determined to be the third level, the visual field of the current user is adjusted synchronously with the visual scene parameters.

10. The method for intervening subjective feelings of virtual reality motion sickness based on the perceptual theory according to claim 9, characterized in that: The specific 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: After adjusting the current user's field of view or adjusting the visual scene parameters, evaluate the changes in motion sickness symptoms after various field of view adjustments; After the 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