Display adjustment method and apparatus, and electronic device

Abstract

This application provides a display adjustment method, apparatus, and electronic device. The method includes: acquiring a first physiological characteristic time-series signal of a user using a virtual reality device via a photoelectric sensor array; identifying whether the user is at risk of motion sickness based on the first physiological characteristic time-series signal; in response to the presence of motion sickness risk, generating a first field-of-view adjustment parameter based on abnormal information in the first physiological characteristic time-series signal, wherein the degree of abnormality corresponding to the abnormal information is positively correlated with the first field-of-view adjustment parameter; adjusting the display screen of the virtual reality device based on the first field-of-view adjustment parameter, and simultaneously overlaying a grid display image onto the adjusted display screen. This application solves the technical problem of the inability to adjust the display screen in a timely manner, improving the timeliness and accuracy of display adjustment.

Description

Technical Field

[0001] This application relates to the field of display technology, and more specifically, to a display adjustment method, apparatus, and electronic device. Background Technology

[0002] Virtual Reality (VR) technology, through its highly immersive visual and interactive experiences, is widely used in entertainment, education, healthcare, and industrial simulation. However, one of its core bottlenecks is Visual-Induced Motion Sickness (VIMS)—when a user visually perceives rapid movement, but the vestibular system does not detect corresponding body movement, the brain experiences sensory conflict, triggering discomfort such as dizziness, nausea, and sweating.

[0003] To alleviate VIMS, the relevant technologies mainly adopt the user-initiated feedback intervention method. This method relies on the user triggering a slowdown, black screen, or pause by pressing buttons on the controller or using voice commands after feeling discomfort. This method has a serious time lag. By the time the user perceives dizziness, the physiological stress response has already been initiated, and the discomfort signals from the nervous system are irreversible, making timely intervention impossible.

[0004] Therefore, the display adjustment methods in related technologies have a technical problem: they rely on subjective user feedback, which makes it impossible to adjust the display screen in a timely manner. Summary of the Invention

[0005] This application provides a display adjustment method, apparatus, and electronic device to at least solve the technical problem in related technologies where display adjustment methods cannot adjust the display screen in a timely manner due to reliance on user subjective feedback.

[0006] According to one aspect of the embodiments of this application, a display adjustment method is provided, applied to a virtual reality device, the virtual reality device including a photoelectric sensor array, the method comprising: acquiring a first physiological characteristic time-series signal of a user using the virtual reality device through the photoelectric sensor array; identifying whether the user has a risk of motion sickness based on the first physiological characteristic time-series signal; in response to the existence of the risk of motion sickness, generating a first field of view adjustment parameter based on abnormal information in the first physiological characteristic time-series signal, wherein the first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the display screen, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter; adjusting the display screen of the virtual reality device based on the first field of view adjustment parameter, and synchronously overlaying a grid display image on the adjusted display screen.

[0007] According to another aspect of the embodiments of this application, a display adjustment device is also provided, applied to a virtual reality device, the virtual reality device including a photoelectric sensor array, the device including: a acquisition unit, configured to acquire a first physiological characteristic time-series signal of a user when using the virtual reality device through the photoelectric sensor array; an identification unit, configured to identify whether the user has a risk of motion sickness based on the first physiological characteristic time-series signal; a generation unit, configured to generate a first field of view adjustment parameter based on abnormal information in the first physiological characteristic time-series signal in response to the existence of the motion sickness risk, wherein the first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the display screen, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter; and an adjustment unit, configured to adjust the display screen of the virtual reality device based on the first field of view adjustment parameter, and synchronously overlay a grid display image on the adjusted display screen.

[0008] In an exemplary embodiment, the first physiological feature timing signal is a pulse wave timing signal, and the identification unit includes:

[0009] The first processing module is used to preprocess the pulse wave timing signal to obtain the preprocessed pulse wave timing signal.

[0010] The first determining module is used to determine a set of specified features corresponding to the preprocessed pulse wave timing signal based on the preprocessed pulse wave timing signal, wherein the set of specified features includes at least one of the following: heart rate features, pulse amplitude features, and signal waveform features.

[0011] The first identification module is used to identify whether the user is at risk of motion sickness based on the set of specified features.

[0012] In one exemplary embodiment, the first determining module includes at least one of the following:

[0013] The first determining submodule is used to determine a set of peak points in the preprocessed pulse wave time sequence signal; calculate the instantaneous heart rate between each adjacent peak based on the time interval between each adjacent peak in the set of peak points; calculate a set of average heart rate values ​​based on the instantaneous heart rate between each adjacent peak, and determine the set of average heart rate values ​​as the heart rate feature;

[0014] The second determining submodule is used to perform envelope extraction and transformation processing on the preprocessed pulse wave time series signal to extract the amplitude envelope of the preprocessed pulse wave time series signal and determine the amplitude envelope of the preprocessed pulse wave time series signal as the pulse amplitude feature.

[0015] The third determining submodule is used to differentiate the preprocessed pulse wave time sequence signal to obtain the derivative waveform corresponding to the pulse wave time sequence signal; and to determine the peak feature in the derivative waveform corresponding to the pulse wave time sequence signal as the signal waveform feature.

[0016] In an exemplary embodiment, the first identification module includes: a first processing submodule, configured to: perform feature fusion processing on the set of specified features to obtain fused features corresponding to the pulse wave time sequence signal;

[0017] The second processing submodule is used to input the fused features into a pre-trained motion sickness prediction model and output the motion sickness risk probability corresponding to the pulse wave time sequence signal.

[0018] The third processing submodule is used to determine that the user has a risk of motion sickness when the probability of motion sickness risk is greater than or equal to a specified probability threshold. The specified probability threshold is obtained by adjusting a preset probability threshold based on feedback information when the user detects the risk of motion sickness while using the virtual reality device.

[0019] In an exemplary embodiment, the first physiological feature timing signal is a pulse wave timing signal, and the identification unit includes:

[0020] The second processing module is used to preprocess the pulse wave timing signal to obtain the preprocessed pulse wave timing signal.

[0021] The second determining module is used to determine a set of signal information in the preprocessed pulse wave time sequence signal, wherein the set of signal information includes statistical values ​​of heart rate variability, statistical values ​​of instantaneous heart rate, and pulse amplitude;

[0022] The first acquisition module is used to acquire a set of specified thresholds, wherein the set of specified thresholds includes an instantaneous heart rate threshold, a pulse amplitude threshold, and a heart rate variability threshold. The set of specified thresholds is determined based on the initial pulse wave timing signal recorded by the user during a specified time period when the virtual reality device is turned on. The start time of the specified time period is the startup time of the virtual reality device.

[0023] The first calculation module is used to calculate the heart rate rise rate based on the statistical value of the instantaneous heart rate and the instantaneous heart rate threshold; to calculate the pulse amplitude decay rate based on the pulse amplitude and the pulse amplitude threshold; and to calculate a specified heart rate variability threshold based on the heart rate variability threshold and a preset coefficient.

[0024] The third determining module is used to determine that the user has the risk of motion sickness if at least one of the following conditions is met: the heart rate rise rate is greater than or equal to a preset heart rate change rate threshold; the pulse amplitude decay rate is greater than or equal to a preset pulse amplitude decay rate threshold; and the statistical value of the heart rate variability is less than or equal to a specified heart rate variability threshold.

[0025] In one exemplary embodiment, the second determining module includes:

[0026] The fourth processing submodule is used to determine a set of peak points in the preprocessed pulse wave time sequence signal; calculate the instantaneous heart rate between each adjacent peak based on the time interval between each adjacent peak in the set of peak points; and calculate the statistical value of the instantaneous heart rate based on the instantaneous heart rate between each adjacent peak.

[0027] The fifth processing submodule is used to perform envelope extraction and transformation processing on the preprocessed pulse wave time sequence signal to determine the pulse amplitude of the preprocessed pulse wave time sequence signal.

[0028] The sixth processing submodule is used to determine the standard deviation of heart rate change over a preset number of heartbeat cycles based on a set of peak points in the preprocessed pulse wave time sequence signal, and to determine the standard deviation of heart rate change over the preset number of heartbeat cycles as the statistical value of heart rate variability, wherein a heartbeat cycle is determined based on a pair of adjacent peak points in a set of peak points in the pulse wave time sequence signal.

[0029] In one exemplary embodiment, the abnormal information includes the heart rate rise rate and the pulse amplitude decay rate;

[0030] The generation unit includes: a fourth determining module, configured to, in response to the existence of the motion sickness risk, determine the weighted sum of the heart rate rise rate and the pulse amplitude decay rate as the first field of view adjustment parameter, wherein the heart rate rise rate is positively correlated with the first field of view adjustment parameter, and the pulse amplitude decay rate is positively correlated with the first field of view adjustment parameter.

[0031] In an exemplary embodiment, the apparatus further includes: an execution unit, configured to acquire a second physiological feature timing signal after adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter for a preset duration, wherein the second physiological feature timing signal is a physiological feature timing signal acquired when adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter; a first adjustment unit, configured to, in response to a difference between the second physiological feature timing signal and the first physiological feature timing signal being greater than or equal to a preset difference threshold, decrease the first field-of-view adjustment parameter to obtain a second field-of-view adjustment parameter, adjust the display screen of the virtual reality device using the second field-of-view adjustment parameter, and simultaneously overlay a grid display image on the adjusted display screen; and a second adjustment unit, configured to, in response to a difference between the second physiological feature timing signal and the first physiological feature timing signal being less than the preset difference threshold, increase the first field-of-view adjustment parameter to obtain a third field-of-view adjustment parameter, adjust the display screen of the virtual reality device using the third field-of-view adjustment parameter, and simultaneously overlay a grid display image on the adjusted display screen.

[0032] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0033] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0034] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0035] This application utilizes a photoelectric sensor array to collect a user's first physiological characteristic time-series signal while using a virtual reality device to identify whether the user is at risk of motion sickness. In response to the presence of motion sickness risk, a first field-of-view adjustment parameter is generated based on abnormal information in the first physiological characteristic time-series signal. This first field-of-view adjustment parameter instructs the virtual reality device to adjust the field-of-view contraction rate of the displayed image, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field-of-view adjustment parameter. Based on the first field-of-view adjustment parameter, the display image of the virtual reality device is adjusted, and a grid display image is simultaneously overlaid on the adjusted display image. Because a photoelectric sensor array is integrated into the virtual reality device, the user's physiological state can be monitored, eliminating reliance on subjective user feedback. This solves the technical problem in related technologies where display adjustment methods, due to reliance on subjective user feedback, cannot adjust the display image in a timely manner. Furthermore, the first field-of-view adjustment parameter generated based on abnormal information in the first physiological characteristic time-series signal adjusts the display image, enabling adjustments based on the user's real-time abnormal conditions, thus improving the timeliness and accuracy of display adjustments. Attached Figure Description

[0036] Figure 1 This is a schematic diagram illustrating an application scenario of a display adjustment method according to an embodiment of this application;

[0037] Figure 2 This is a flowchart illustrating an optional display adjustment method according to an embodiment of this application;

[0038] Figure 3 This is a schematic diagram of an optional display adjustment method according to an embodiment of this application;

[0039] Figure 4 This is a schematic diagram of another optional display adjustment method according to an embodiment of this application;

[0040] Figure 5 This is a schematic diagram of another optional display adjustment method according to an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of an optional motion sickness prediction model according to an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of another optional display adjustment method according to an embodiment of this application;

[0043] Figure 8 This is a schematic diagram of another optional display adjustment method according to an embodiment of this application;

[0044] Figure 9This is a structural block diagram of an optional display adjustment device according to an embodiment of this application;

[0045] Figure 10 This is a computer system architecture block diagram of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] According to one aspect of the embodiments of this application, a display adjustment method is provided. Optionally, in this embodiment, the above-described display adjustment method may be applied to, but is not limited to, [examples of other methods]. Figure 1 The hardware environment shown includes a virtual reality device 102 and a server 104. The server 104 can be connected to the virtual reality device 102 via a network and can be used to provide services (e.g., application services, etc.) to the virtual reality device 102 or clients installed on the virtual reality device 102. A database can be set up on or independently of the server 104 to provide data storage services for the server 104.

[0049] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI), Bluetooth. Server 104 may be, but is not limited to, a cloud server, server cluster, or other server type.

[0050] The display adjustment method of this application embodiment can be executed by the virtual reality device 102, or it can be jointly executed by the server 104 and the virtual reality device 102. Alternatively, the virtual reality device 102 can execute the display adjustment method of this application embodiment by a client installed on it.

[0051] Taking the display adjustment method in this embodiment as an example, which is performed by the virtual reality device 102, the virtual reality device 102 includes an array of photoelectric sensors. Figure 2 This is a flowchart illustrating an optional display adjustment method according to an embodiment of this application, such as... Figure 2 As shown, the process of this method may include the following steps:

[0052] Step S202: Collect the first physiological characteristic time-series signal of the user when using the virtual reality device through the photoelectric sensor array;

[0053] Step S204: Based on the time-series signal of the first physiological characteristic, identify whether the user is at risk of motion sickness;

[0054] Step S206: In response to the risk of motion sickness, a first field of view adjustment parameter is generated based on the abnormal information in the first physiological characteristic time-series signal. The first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the displayed image. The degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter.

[0055] Step S208: Adjust the display screen of the virtual reality device based on the first field of view adjustment parameters, and simultaneously overlay a grid display image on the adjusted display screen.

[0056] The display adjustment method in this embodiment can be applied to the field of display technology and virtual reality (VR) devices, and is especially suitable for scenarios that require long-term immersive experience, such as VR games, flight simulation training, virtual clinic rehabilitation, immersive education, etc.

[0057] Virtual Reality (VR) technology, through its highly immersive visual and interactive experiences, is widely used in entertainment, education, healthcare, and industrial simulation. However, one of its core bottlenecks is Visual-Induced Motion Sickness (VIMS). When a user visually perceives rapid movement, but the vestibular system does not detect corresponding body movement, the brain experiences sensory conflict, triggering discomfort such as dizziness, nausea, and sweating.

[0058] To alleviate VIMS, the relevant technologies mainly adopt the user-initiated feedback intervention method. This method relies on the user triggering a slowdown, black screen, or pause by pressing buttons on the controller or using voice commands after feeling discomfort. This method has a serious time lag. By the time the user perceives dizziness, the physiological stress response has already been initiated, and the discomfort signals from the nervous system are irreversible, making timely intervention impossible.

[0059] Therefore, the display adjustment methods in related technologies have a technical problem: they rely on subjective user feedback, which makes it impossible to adjust the display screen in a timely manner.

[0060] To at least partially solve the aforementioned technical problems, in this embodiment, a photoelectric sensor array is used to collect the first physiological characteristic time-series signal of the user when using the virtual reality device to identify whether the user is at risk of motion sickness. In response to the presence of motion sickness risk, a first field-of-view adjustment parameter is generated based on abnormal information in the first physiological characteristic time-series signal. This first field-of-view adjustment parameter instructs the virtual reality device to adjust the field-of-view contraction rate of the displayed image, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field-of-view adjustment parameter. Based on the first field-of-view adjustment parameter, the display image of the virtual reality device is adjusted, and a grid display image is simultaneously overlaid on the adjusted display image. Because a photoelectric sensor array is integrated into the virtual reality device, the physiological state of the user can be monitored, eliminating reliance on subjective user feedback. This solves the technical problem in related technologies where display adjustment methods cannot adjust the display image in a timely manner due to reliance on subjective user feedback. Furthermore, the first field-of-view adjustment parameter generated based on abnormal information in the first physiological characteristic time-series signal adjusts the display image, enabling adjustments based on the user's real-time abnormal conditions, thus improving the timeliness and accuracy of display adjustments.

[0061] It should be noted that the photoelectric sensor array can refer to a sensing module consisting of multiple near-infrared light sources and photodetectors integrated inside a VR headset, close to a designated area of ​​the user. The designated area could be the temple area. Optionally, the photoelectric sensor array can be used for non-invasive acquisition of photovolume change (PPG) signals from subcutaneous veins (such as the superficial temporal vein).

[0062] The first physiological characteristic time-series signal refers to physiological signal data continuously collected by a photoelectric sensor array and arranged in a time sequence. This data may include, but is not limited to, multi-dimensional time-series features such as pulse wave amplitude, peak interval (reflecting heart rate), and second derivative waveform (reflecting vascular tension). Optionally, the first physiological characteristic time-series signal can be collected according to a preset sampling frequency. The preset sampling frequency can be set based on actual needs or adjusted based on fluctuations in relevant information within the first physiological characteristic time-series signal. For example, the preset sampling frequency can be no less than 50Hz, and the sampling window length no less than 5 seconds. When the relevant information in the first physiological characteristic time-series signal fluctuates significantly, the preset sampling frequency can be decreased; when the relevant information in the first physiological characteristic time-series signal fluctuates less, the preset sampling frequency can be increased.

[0063] Motion sickness risk can refer to the early autonomic nervous system disorder caused by visual-vestibular conflict in the VR environment. It may not have reached the level of subjective discomfort, but it may have manifested as abnormal heart rate, decreased pulse wave amplitude, and other abnormalities.

[0064] The first field of view adjustment parameter refers to a numerical parameter used to control the dynamic contraction rate of the field of view (FoV) of the VR display screen. A larger value indicates a faster contraction of the field of view. This parameter is automatically calculated by the system based on the degree of physiological abnormality and is not a preset fixed value. Optionally, the first field of view adjustment parameter is used to instruct the virtual reality device to adjust the contraction rate of the display screen's field of view. The degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter. When adjusting the field of view adjustment parameter, a low-brightness, static grid texture (corresponding to a grid display image) can be superimposed on the edge area of ​​the adjusted display screen as a visual reference to help the brain rebuild spatial stability without interfering with the core visual content.

[0065] Optionally, the virtual reality device can use an internally integrated photoelectric sensor array (such as a 760nm and 850nm dual-wavelength near-infrared LED combined with a photodiode) to continuously emit light towards the superficial temporal vein region and receive reflected light signals. The changes in the intensity of the reflected light are converted into continuous electrical signals, which are then amplified and filtered to form a high-precision, low-noise physiological time-series signal, namely the first physiological characteristic time-series signal.

[0066] Optionally, the photoelectric sensor array contains multiple sensing channels, and low-quality channels can be dynamically shielded through hardware-level channel optimization and algorithm confidence evaluation, retaining only channels with a signal-to-noise ratio higher than a preset threshold for signal processing.

[0067] Optionally, the risk of motion sickness in the user can be identified based on the time-series signal of the first physiological characteristic. Optionally, a preset data processing algorithm can be used to preprocess the time-series signal of the first physiological characteristic to obtain a preprocessed time-series signal of the first physiological characteristic, thereby identifying the risk of motion sickness in the user based on the preprocessed time-series signal of the first physiological characteristic.

[0068] Optionally, a personalized threshold can be used to identify whether a user is at risk of motion sickness. Alternatively, a neural network model can be used to identify this risk. The personalized threshold can be derived based on the user's historical usage data.

[0069] Optionally, after determining that the user is at risk of motion sickness, a continuous field of view contraction rate parameter, i.e., the first field of view adjustment parameter, can be calculated based on the abnormal information in the time-series signal of the first physiological characteristic. The abnormal information will indicate the degree of abnormality. The higher the degree of abnormality, the faster the contraction, so that the more severe the condition, the stronger the corresponding intervention intensity.

[0070] Optionally, when the first physiological characteristic time-series signal includes multiple abnormal information, the first field-of-view adjustment parameter can be determined based on the degree of abnormality corresponding to the largest abnormal information among the multiple abnormal information. Alternatively, the first field-of-view adjustment parameter can be determined by weighted summation of the multiple abnormal information. Furthermore, when adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter, a static mesh texture with lower brightness is simultaneously superimposed on the edge area of ​​the screen as a visual anchor point. This mesh only appears after the field of view shrinks and does not affect the central content, ensuring a balance between immersion and stability.

[0071] Optionally, the brightness and density of the grid-based real-image can be positively correlated with the first field-of-view adjustment parameter, and after the physiological signal returns to normal, the full field-of-view display can be restored at a slower rate to avoid secondary dizziness.

[0072] The embodiments provided in this application collect first physiological characteristic time-series signals of a user when using a virtual reality device using a photoelectric sensor array to identify whether the user is at risk of motion sickness. In response to the presence of motion sickness risk, a first field-of-view adjustment parameter is generated based on abnormal information in the first physiological characteristic time-series signal. This first field-of-view adjustment parameter instructs the virtual reality device on the rate at which the field-of-view shrinkage of the displayed image is adjusted, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field-of-view adjustment parameter. Based on the first field-of-view adjustment parameter, the display image of the virtual reality device is adjusted, and a grid display image is simultaneously overlaid on the adjusted display image. Because a photoelectric sensor array is integrated into the virtual reality device, the physiological state of the user can be monitored, eliminating reliance on subjective user feedback. This solves the technical problem in related technologies where display adjustment methods cannot adjust the display image in a timely manner due to reliance on subjective user feedback. Furthermore, the first field-of-view adjustment parameter generated based on abnormal information in the first physiological characteristic time-series signal adjusts the display image, enabling adjustments based on the user's real-time abnormal conditions, thus improving the timeliness and accuracy of display adjustments.

[0073] In an exemplary embodiment, the first physiological characteristic time-series signal is a pulse wave time-series signal. Identifying whether a user is at risk of motion sickness based on the first physiological characteristic time-series signal includes: preprocessing the pulse wave time-series signal to obtain a preprocessed pulse wave time-series signal; determining a set of specified features corresponding to the preprocessed pulse wave time-series signal based on the preprocessed pulse wave time-series signal, wherein the set of specified features includes at least one of the following: heart rate features, pulse amplitude features, and signal waveform features; and identifying whether a user is at risk of motion sickness based on the set of specified features.

[0074] It should be noted that the pulse wave timing signal can refer to the raw electrical signal collected by a photoelectric sensor array that reflects the periodic changes in subcutaneous vascular volume. Its waveform includes information about blood flow fluctuations caused by heartbeats and is typically a non-stationary, noisy analog signal. When the first physiological characteristic timing signal is the pulse wave timing signal, the corresponding photoelectric sensor array in the virtual reality device can be the surface projection area of ​​the frontal branch of the superficial temporal vein, such as... Figure 3 As shown, specifically, it can be located on the inner side of the head-mounted display pad, where it fits snugly against the temple area when the user wears the virtual reality device. This location is situated above the brow ridge and in front of the tragus on the side of the skull, ensuring that the sensor array stably covers the blood flow path of the superficial temporal vein during normal wear. Of course, as... Figure 3As shown in the second figure, a heat sink can be placed around the photoelectric sensor array to ensure its stable operation over long periods, prevent thermal noise from interfering with signal acquisition, reduce sensor noise, and thus ensure the accuracy and reliability of PPG signal acquisition during prolonged wear. To further improve signal monitoring accuracy, such as... Figure 4 As shown, in a VR device, a near-infrared LED (760nm / 850nm) can be deployed in the photoelectric sensor array, and the outer part can be a black anti-light strip. This structure can achieve optical path isolation, revealing how the photoelectric sensor array can accurately align with the main trunk of the superficial temporal vein in the temple area to achieve non-invasive, high signal-to-noise ratio venous pulsation signal capture.

[0075] Optionally, the pulse wave time-series signal can be preprocessed, such as through denoising and stabilization, to eliminate artifacts caused by head micro-movements, respiratory interventions, changes in ambient light, or poor sensor contact. The preprocessing process may include: using bandpass filtering (e.g., 0.5 to 5 Hz) to preserve the dominant pulse frequency component; applying adaptive sliding window median filtering to suppress sudden motion artifacts; and then eliminating low-frequency drift through baseline drift correction. The processed signal possesses a high signal-to-noise ratio and a stable waveform, providing reliable input for subsequent feature extraction.

[0076] After preprocessing the pulse wave time sequence signal, a set of specified features can be determined based on the preprocessed pulse wave time sequence signal. These specified features include at least one of the following: heart rate features, pulse amplitude features, and signal waveform features. Specifically, heart rate features can refer to the heart rate and its changing trend reflected by the time interval (RR interval) between adjacent peaks of the pulse wave; pulse amplitude features can refer to the amplitude variation between the peaks and troughs of the pulse wave, reflecting the intensity of peripheral vascular pulsation and closely related to sympathetic nerve tone; and signal waveform features can refer to the morphological parameters of the pulse wave, such as the peak value of the second derivative and the rise / fall slope, used to characterize changes in vascular elasticity and blood flow resistance.

[0077] Optionally, at least one of the above-mentioned physiological features can be automatically extracted from the preprocessed pulse wave signal, and its statistical value within a sliding time window (such as 5 or 10 seconds) can be calculated to determine the statistical value as a specified feature.

[0078] Optionally, the risk of motion sickness in users can be identified using a set of specified features and personalized thresholds corresponding to each of these features. Alternatively, the risk can be identified using a set of specified features and a pre-trained neural network model. The personalized thresholds can be derived based on the user's historical usage. In identifying the risk of motion sickness using a set of specified features and a pre-trained neural network model, the specified features can be fused, allowing for the identification of the risk based on the fused features and the pre-trained neural network model.

[0079] In this embodiment, by preprocessing the time-series signal of the first physiological feature, noise interference and baseline drift are eliminated. Then, at least one of the heart rate feature, pulse amplitude feature and signal waveform feature is extracted from the preprocessed pulse wave signal as a structured analysis dimension, so that the originally ambiguous physiological signal response is transformed into a quantifiable combination of physiological indicators, thereby significantly improving the accuracy of identifying early risks of motion sickness.

[0080] In an exemplary embodiment, in order to more accurately identify whether the user is at risk of motion sickness, one or more of the heart rate characteristics, pulse amplitude characteristics, and signal waveform characteristics can be calculated based on the preprocessed pulse wave time sequence signal.

[0081] Correspondingly, based on the preprocessed pulse wave time series signal, a set of specified features corresponding to the pulse wave time series signal is determined, including at least one of the following: determining a set of peak points in the preprocessed pulse wave time series signal; calculating the instantaneous heart rate between each adjacent peak based on the time interval between each adjacent peak in the set of peak points; calculating a set of average heart rate values ​​based on the instantaneous heart rate between each adjacent peak, and determining the set of average heart rate values ​​as heart rate features; performing envelope extraction transformation processing on the preprocessed pulse wave time series signal to extract the amplitude envelope of the preprocessed pulse wave time series signal, and determining the amplitude envelope of the preprocessed pulse wave time series signal as pulse amplitude features; differentiating the preprocessed pulse wave time series signal to obtain the derivative waveform corresponding to the pulse wave time series signal; and determining the peak features in the derivative waveform corresponding to the pulse wave time series signal as signal waveform features.

[0082] It should be noted that in the preprocessed pulse wave time-series signal, each peak point in a set of peak points can refer to a local maximum point caused by periodic blood flow impact, with each point representing the peak vascular volume corresponding to one cardiac contraction. Instantaneous heart rate can be calculated as the time interval (RR interval) between two adjacent peak points, converted into heart rate per minute (bpm), reflecting the frequency of a single beat. The average heart rate value can refer to the average value obtained by statistically averaging multiple instantaneous heart rates within a preset sliding time window (e.g., 5 to 10 seconds), used to characterize the dynamic trend of heart rate over a period of time, rather than instantaneous fluctuations.

[0083] Optionally, a set of continuous and stable peak points can be identified in the preprocessed pulse wave time series signal using a local maximum detection algorithm (such as differential thresholding and minimum interval constraint). The instantaneous heart rate between each pair of adjacent peak points in the set is calculated to obtain the instantaneous heart rate between each pair of adjacent peaks. Then, within a preset sliding window (e.g., a certain number of heartbeat cycles), the average of multiple instantaneous heart rates is processed to generate a smooth average heart rate sequence (corresponding to a set of average heart rate values). Specifically, the calculation process of instantaneous heart rate and average heart rate values ​​can refer to the following formulas (1) and (2):

[0084] (1)

[0085] (2)

[0086] in, The RR interval refers to the time between the k-th peak and the (k-1)-th peak, where k represents the chronological order of the peaks in the signal. It can represent a heartbeat cycle. Instantaneous heart rate, Average real-time heart rate within a sliding window, where N represents the number of heartbeat cycles contained within the sliding window. The range is from 1 to N, representing the i-th historical heartbeat cycle in the current window. This refers to the average heart rate cycle.

[0087] Optionally, the amplitude envelope of the preprocessed pulse wave time series signal can refer to the "outer contour" of the preprocessed pulse wave time series signal, reflecting the maximum amplitude of the change in vascular volume during each heartbeat. Optionally, the envelope extraction and transformation processing can employ Hilbert transform, wavelet transform, adaptive filtering, etc., to convert the real signal into an analytic signal, extract its instantaneous amplitude, thereby removing high-frequency oscillations and retaining low-frequency energy trends.

[0088] Specifically, the calculation process of the amplitude envelope of the preprocessed pulse wave time sequence signal can be referred to the following formula (3):

[0089] (3)

[0090] in, This could refer to the preprocessed pulse wave timing signal. This refers to the original pulse wave timing signal. This refers to the Hilbert transform. It refers to the instantaneous intensity of the instantaneous amplitude envelope pulse wave time-series signal at time t.

[0091] Optionally, the preprocessed pulse wave time-series signal is differentiated to obtain the derivative waveform corresponding to the pulse wave time-series signal; the peak characteristics in the derivative waveform corresponding to the pulse wave time-series signal are determined as signal waveform characteristics. Among them, the derivative waveform can refer to the curve obtained after numerical differentiation (such as second derivative) of the pulse wave signal, which reflects the changes in blood flow acceleration and vascular wall elasticity and is sensitive to vascular tension. The peak characteristics can refer to the amplitude or time position of the local maximum value in the derivative waveform, which is used to quantify the steepness of the waveform. Specifically, the functional expression of the derivative waveform corresponding to the pulse wave time-series signal can refer to the following formula (4):

[0092] (4)

[0093] The process of determining the peak characteristics in the derivative waveform corresponding to the pulse wave time sequence signal can refer to the following formula (5):

[0094] (5)

[0095] Optionally, a set of specified features may also include heart rate variability features. The generation method of heart rate variability features can refer to the calculation process of heart rate variability, which will not be elaborated here.

[0096] In this embodiment, heart rate, pulse amplitude, and signal waveform features are extracted from the pulse wave time-series signal collected by the photoelectric sensor array. This enables comprehensive comparison and abnormal coupling analysis based on multidimensional physiological parameters, thereby improving the accuracy and robustness of motion sickness risk identification.

[0097] In an exemplary embodiment, in order to improve the adaptability to the differences in sensitivity to motion sickness among different users, a threshold can be determined by the subjective feedback information of the users during actual use, thereby improving the adaptability to the differences in sensitivity to motion sickness among different users.

[0098] Correspondingly, based on a set of specified features, the method identifies whether a user is at risk of motion sickness, including: performing feature fusion processing on a set of specified features to obtain fused features corresponding to the pulse wave time-series signal; inputting the fused features into a pre-trained motion sickness prediction model to output the motion sickness risk probability corresponding to the pulse wave time-series signal; and determining that the user is at risk of motion sickness if the motion sickness risk probability is greater than or equal to a specified probability threshold, wherein the specified probability threshold is obtained by adjusting a preset probability threshold based on feedback information from the user when detecting a motion sickness risk while using the virtual reality device.

[0099] It should be noted that feature fusion processing can include feature concatenation or weighted summation. A set of specified features can be fused through mathematical transformations or neural network mapping to create a unified, highly discriminative feature vector—the fused feature—which enhances the recognition capabilities of subsequent models.

[0100] A pre-trained motion sickness prediction model can refer to a lightweight temporal classification model (such as a three-layer dilated causal convolutional network) trained on large-scale real user data. Its goal is to map a set of fused features to a probability value between 0 and 1, representing "the possibility of motion sickness precursors at the current moment." The model outputs a risk probability, which reflects the confidence level in the physiological state at that moment.

[0101] Optionally, a set of specified features may also include heart rate variability features. The generation method of heart rate variability features can refer to the calculation process of heart rate variability, which will not be elaborated here.

[0102] Optionally, when a set of specified features includes heart rate variability features, the corresponding fusion feature can be the result of fusing heart rate features, pulse amplitude features, signal waveform features, and heart rate variability features.

[0103] Optionally, the process of generating fused features can refer to Figure 5 Specifically, after the user puts on the VR headset, the system uses a dual-band (760nm / 850nm) near-infrared light source array and a multi-channel photoelectric sensor (corresponding to the photoelectric sensor array) integrated inside the headset pad facing the frontal branch of the superficial temporal vein to collect raw photoplethysmography (PPG) signals (corresponding to the first physiological characteristic time-series signal) in real time. The system then assesses the signal quality. Signals that pass the quality assessment undergo preprocessing (such as filtering and baseline shift removal). Multidimensional features are extracted from the preprocessed signals to obtain multidimensional features (corresponding to a set of specified features). These multidimensional features are then fused to obtain fused features. If the signal quality assessment differs, the system adjusts the light intensity of the photoelectric sensor array or reminds the user to adjust the VR headset settings.

[0104] It should be noted that signal quality can be judged through hardware channel optimization and algorithm confidence assessment to filter out high signal-to-noise ratio data.

[0105] Optionally, such as Figure 6 As shown, the pre-trained motion sickness prediction model can include a first convolutional layer, a second convolutional layer, a third convolutional layer, a fully connected layer, and an activation function layer. Its input is a multi-dimensional temporal feature matrix (corresponding to fused features). The first convolutional layer can use a 1×1 convolutional kernel and increase the number of channels (e.g., 16 dimensions), and its output is a high-dimensional spatiotemporal feature. The first convolutional layer maps the original low-dimensional features to a higher-dimensional semantic space, enhancing representation capabilities. The second convolutional layer, which can be called an intermediate feature extraction layer, compresses the output of the first convolutional layer in the temporal dimension using a preset stride (e.g., a stride of 2 to reduce computational burden). The third convolutional layer further convolves the compressed features from the second convolutional layer to obtain high-dimensional abstract features. The fully connected layer performs pooling on the high-level abstract features for compression, then maps them to 2 dimensions. After Softmax activation, it outputs a single motion sickness risk probability p∈[0,1], achieving end-to-end mapping from multi-dimensional temporal features to clinical risk.

[0106] Optionally, the pre-trained motion sickness prediction model can employ a lightweight temporal convolutional network (TCN) framework. The training of the pre-trained motion sickness prediction model can be performed in the cloud, where a server communicating with the virtual reality device is used. During training, 10,000 PPG temporal samples can be used (e.g., positive samples: 10 seconds before motion sickness, label 1; negative samples: calm period, label 0, positive-to-negative ratio 1:3), with a 3×1000 multidimensional physiological feature matrix as input. A weighted cross-entropy loss function (positive sample weight 3), Adam optimizer (lr=0.001), batch size 32, and training for N (e.g., 50) epochs with early stopping enabled to prevent overfitting can be used. Furthermore, five-fold cross-validation can be employed for validation.

[0107] Optionally, to meet the personalized needs of different users, a specified probability threshold can be obtained by adjusting a preset probability threshold based on feedback information from users when they detect a risk of motion sickness while using virtual reality devices. The preset probability threshold can refer to an empirical value obtained based on a large number of trials. Feedback information regarding a risk of motion sickness can refer to the user's actual behavioral information after the system determines a "risk of motion sickness," such as user-initiated ratings, behavioral records (e.g., number of pauses), or automatic system inferences (e.g., increased head shaking).

[0108] In one example, a new user is using VR for the first time. During a roller coaster scene, the model output is 0.72, triggering a field-of-view contraction. If the user stops or provides a rating afterward (e.g., feeling slightly dizzy but tolerable), the threshold will be increased, such as from 0.72 to 0.73. In the next identical or similar scene, the threshold will be adaptively adjusted based on the user's feedback to determine the user's personalized threshold.

[0109] In this embodiment, the pulse wave time-series signal of the user is collected by a photoelectric sensor array and a set of specified features are extracted. These features are then fused to generate fused features, which are then input into a pre-trained motion sickness prediction model. The output probability value serves as a quantitative assessment result of motion sickness risk, thus overcoming the limitations of traditional single-feature threshold judgment, which is sensitive to individual physiological fluctuations and prone to misjudgment or omission. Furthermore, the judgment threshold is dynamically adjusted based on the subjective feedback information of the user during actual use, enabling it to adapt to the differences in the sensitivity of different users to motion sickness.

[0110] In an exemplary embodiment, in order to improve the adaptability to the differences in sensitivity to motion sickness among different users, in this embodiment, relevant information and timing signals of the first physiological characteristics when the user starts using the virtual display device can be processed to determine whether the user is at risk of motion sickness.

[0111] Correspondingly, the first physiological characteristic time-series signal is the pulse wave time-series signal. Based on the first physiological characteristic time-series signal, identifying whether the user is at risk of motion sickness includes: preprocessing the pulse wave time-series signal to obtain a preprocessed pulse wave time-series signal; determining a set of signal information in the preprocessed pulse wave time-series signal, wherein the set of signal information includes the statistical value of heart rate variability, the statistical value of instantaneous heart rate, and pulse amplitude; obtaining a set of specified thresholds, wherein the set of specified thresholds includes an instantaneous heart rate threshold, a pulse amplitude threshold, and a heart rate variability threshold, the set of specified thresholds being determined based on the initial pulse wave time-series signal recorded by the user during a specified time period when the virtual reality device is turned on, the start time of the specified time period being the startup time of the virtual reality device; calculating the heart rate rise rate based on the statistical value of instantaneous heart rate and the instantaneous heart rate threshold; calculating the pulse amplitude decay rate based on the pulse amplitude and the pulse amplitude threshold; and calculating a specified heart rate variability threshold based on the heart rate variability threshold and a preset coefficient.

[0112] A user is deemed at risk of motion sickness if at least one of the following conditions is met: heart rate rise rate is greater than a preset heart rate variability threshold; pulse amplitude decay rate is greater than a preset pulse amplitude decay rate threshold; or the statistical value of heart rate variability is less than or equal to a specified heart rate variability threshold.

[0113] It should be noted that instantaneous heart rate refers to the number of heartbeats per minute converted from the time interval between adjacent pulse peaks; pulse amplitude is the peak height of the pulse wave envelope, reflecting the intensity of blood perfusion; heart rate variability (HRV) is the standard deviation of the RR interval, reflecting the activity of autonomic nervous system regulation. Optionally, within a specified sliding window, all local maxima can be detected as heartbeat moments, and the instantaneous heart rate can be calculated from the interval between adjacent peaks; the envelope amplitude can be extracted using Hilbert transform; and the standard deviation of the RR interval within the window can be calculated as HRV to determine the statistical values ​​of heart rate variability and instantaneous heart rate.

[0114] A set of specified thresholds includes instantaneous heart rate threshold, pulse amplitude threshold, and heart rate variability threshold. These thresholds are determined based on the initial pulse wave timing signal recorded by the user during a specified time period after activating the virtual reality device. The start time of the specified time period is the startup time of the virtual reality device. Here, the specified thresholds are user-personalized baseline values, not fixed universal values, used to measure the degree of individual physiological deviation. Optionally, the specified time period can be the first 30 seconds after VR startup (the user's sedentary adaptation period), during which PPG signals are continuously collected, and the statistical values ​​(such as the mean or median) of the three indicators during this period are calculated as the baseline.

[0115] Optionally, both the rise rate and decay rate are relative percentage changes, reflecting the degree to which the current value deviates from the individual's baseline. The specified heart rate variability threshold can be the product of the heart rate variability threshold and a preset coefficient.

[0116] To determine whether a user is at risk of motion sickness, this can be done based on at least one of the following conditions: the rate of increase in heart rate is greater than a preset threshold for heart rate variability; the rate of decrease in pulse amplitude is greater than a preset threshold for pulse amplitude decrease; and the rate of decrease in heart rate variability is greater than a preset threshold for decrease. Specifically, refer to the following formulas (6) to (8):

[0117] (6)

[0118] (7)

[0119] (8)

[0120] in, As the first condition, As the second condition, As the third condition, This is a statistical value of instantaneous heart rate. Instantaneous heart rate threshold, To preset a threshold for heart rate variability, such as 10%, This refers to the amplitude of the pulse. The pulse amplitude threshold, To preset the pulse amplitude decay rate threshold, This represents the statistical value of heart rate variability. This is a preset coefficient, such as 0.7, which means a reduction of 30%. This is the threshold for heart rate variability.

[0121] Of course, the risk of motion sickness is determined to be present in the user only if all of the following conditions are met. Specifically, as shown in the following formula (9):

[0122] (9)

[0123] In this embodiment, by limiting the first physiological characteristic time-series signal to a pulse wave time-series signal, and extracting multi-dimensional physiological characteristic information including statistical values ​​of heart rate variability, statistical values ​​of instantaneous heart rate, and pulse amplitude, and combining it with the initial pulse wave time-series signal, individualized instantaneous heart rate threshold, pulse amplitude threshold, and heart rate variability threshold are dynamically generated. Then, based on the above thresholds, the heart rate rise rate, pulse amplitude decay rate, and heart rate variability reduction rate are calculated respectively. When any ratio exceeds the corresponding preset threshold, it is determined that the user has a risk of motion sickness, thereby achieving adaptive improvement of the sensitivity differences of different users to motion sickness.

[0124] In an exemplary embodiment, determining a set of signal information in a preprocessed pulse wave time-series signal includes: determining a set of peak points in the preprocessed pulse wave time-series signal; calculating the instantaneous heart rate between each adjacent peak point based on the time interval between each adjacent peak point in the set of peak points; calculating a statistical value of the instantaneous heart rate based on the instantaneous heart rate between each adjacent peak point; performing envelope extraction and transformation processing on the preprocessed pulse wave time-series signal to determine the pulse amplitude of the preprocessed pulse wave time-series signal; determining the standard deviation of heart rate change over a preset number of heartbeat cycles based on a set of peak points in the preprocessed pulse wave time-series signal, and determining the standard deviation of heart rate change over a preset number of heartbeat cycles as a statistical value of heart rate variability, wherein a heartbeat cycle is determined based on a pair of adjacent peak points in a set of peak points in the pulse wave time-series signal.

[0125] It should be noted that instantaneous heart rate is the reciprocal of the time interval (RR interval) between two adjacent peak points, converted to a value in "beats / minute", reflecting the instantaneous heart rate. The corresponding calculation process can be referred to the above formula (1). The statistical value of instantaneous heart rate can refer to the representative value (such as the moving average, median, etc.) of the instantaneous heart rate sequence aggregated within a specific time window, used to characterize the recent heart rate trend. For example, in the descent section of a VR roller coaster, the system continuously records 11 instantaneous heart rate values ​​(88, 92, 95, 97, 99, 101, 98, 96, 93, 90, 89 bpm) within 10 seconds, calculates its average value as 94.5 bpm, and uses it as the representative value of the current heart rate level during this period to compare the increase with the user's baseline (such as 72 bpm).

[0126] Heart rate variability (HRV) is the statistical dispersion of the degree of fluctuation between multiple heartbeat cycles (RR intervals). It is obtained by determining the standard deviation of heart rate changes over a preset number of heartbeat cycles using a set of peak points in a preprocessed pulse wave time-series signal. In one example, during a VR immersive flight experience, the most recent 10 RR intervals (in seconds) were recorded as: 0.62, 0.59, 0.61, 0.60, 0.63, 0.58, 0.60, 0.64, 0.57, and 0.61. The standard deviation was calculated to be 0.022 seconds (22 ms), which was used as the current HRV value to determine whether "heart rhythm disorder," a precursor to motion sickness, was present.

[0127] Optionally, the calculation process for the standard deviation of heart rate variation over a preset number of heartbeat cycles can refer to the following formula (10):

[0128] (10)

[0129] in, Standard deviation For peak quantity, Let i be the instantaneous heart rate. This represents the average heart rate.

[0130] This embodiment significantly improves the accuracy and robustness of motion sickness risk identification by accurately calculating three key physiological parameters: instantaneous heart rate, pulse amplitude, and heart rate variability.

[0131] In one exemplary embodiment, the abnormal information includes the rate of increase in heart rate and the rate of decrease in pulse amplitude; in response to the presence of motion sickness risk, a first field of view adjustment parameter is generated based on the abnormal information in the first physiological characteristic time-series signal, including:

[0132] In response to the risk of motion sickness, the weighted sum of the heart rate rise rate and the pulse amplitude decay rate is determined as the first field of view adjustment parameter, wherein the heart rate rise rate and the pulse amplitude decay rate are positively correlated with the first field of view adjustment parameter.

[0133] It should be noted that abnormal information may include heart rate rise rate and pulse amplitude decay rate. The heart rate rise rate can be the percentage increase of the current average instantaneous heart rate relative to the user's individual baseline (corresponding to the instantaneous heart rate threshold). The specific calculation formula can be found in the above formula (6). The pulse amplitude decay rate is the percentage decrease of the current pulse wave envelope amplitude relative to the individual baseline (corresponding to the pulse amplitude threshold). The specific calculation formula can be found in the above formula (7).

[0134] Optionally, the weighted sum of the heart rate rise rate and the pulse amplitude decay rate is determined as the first field of view adjustment parameter. Specifically, the calculation process of the corresponding first field of view adjustment parameter can be referred to the following formula (11):

[0135] (11)

[0136] in, Adjust parameters for the first field of view. The magnitude of the increase in heart rate. This represents the pulse amplitude decay rate. , , A set of weighting coefficients.

[0137] Optionally, after confirming the risk of motion sickness, a preset weighting coefficient is immediately invoked to weight and sum the heart rate rise rate and amplitude decay rate, and output a continuous value between 0 and 100 as the target force of field of view contraction, i.e. the first field of view adjustment parameter, to avoid miscontrol or delay caused by relying on a single indicator.

[0138] Optionally, the rate of increase of heart rate is positively correlated with the first field of view adjustment parameter, and the rate of decrease of pulse amplitude is positively correlated with the first field of view adjustment parameter. In this embodiment, the greater the rate of increase of heart rate, the greater the corresponding first field of view adjustment parameter, and the greater the rate of decrease of pulse amplitude, the greater the corresponding first field of view adjustment parameter.

[0139] In this embodiment, by using the heart rate rise rate and pulse amplitude decay rate as indicators of motion sickness risk and performing a weighted summation to obtain the first field of view adjustment parameter, the field of view contraction rate can dynamically and accurately respond to the combined physiological abnormality of heart rate acceleration and pulse amplitude reduction. This ensures that the intervention intensity of field of view adjustment is synchronously matched with the evolution trend of the user's actual motion sickness risk, effectively avoiding insufficient or excessive intervention caused by the lag in response of a single physiological indicator or the neglect of multidimensional synergistic abnormalities.

[0140] In an exemplary embodiment, the method further includes: after adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter for a preset duration, acquiring a second physiological feature timing signal, wherein the second physiological feature timing signal is a physiological feature timing signal acquired when adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter; in response to the degree of difference between the second physiological feature timing signal and the first physiological feature timing signal being greater than or equal to a preset difference threshold, decreasing the first field-of-view adjustment parameter to obtain a second field-of-view adjustment parameter, using the second field-of-view adjustment parameter to adjust the display screen of the virtual reality device, and simultaneously overlaying a grid display image on the adjusted display screen; in response to the degree of difference between the second physiological feature timing signal and the first physiological feature timing signal being less than a preset difference threshold, increasing the first field-of-view adjustment parameter to obtain a third field-of-view adjustment parameter, using the third field-of-view adjustment parameter to adjust the display screen of the virtual reality device, and simultaneously overlaying a grid display image on the adjusted display screen.

[0141] It should be noted that the preset duration can be a pre-set intervention observation window (e.g., 5 seconds) used to assess whether the intervention is effective. The size of the preset duration can be set based on the intervention intensity indicated by the first field of view adjustment parameter. For example, the preset duration is positively correlated with the size of the first field of view adjustment parameter.

[0142] The second physiological characteristic time-series signal can refer to the new PPG signal collected at the same sensor location after the first intervention, reflecting the physiological response of the user to the intervention.

[0143] The degree of difference between the second physiological characteristic time series signal and the first physiological characteristic time series signal can refer to the magnitude of change in key indicators (such as heart rate and amplitude) of the two sets of physiological signals. It can be calculated by weighted sum of standardized differences (such as the difference in heart rate change rate and the difference in amplitude change rate). The preset difference threshold can be a preset physiological improvement threshold (such as a total difference ≥15% is considered effective) to determine whether the intervention is effective.

[0144] Optionally, in response to the degree of difference between the second physiological characteristic time-series signal and the first physiological characteristic time-series signal being greater than or equal to a preset difference threshold, the first field of view adjustment parameter is reduced to obtain the second field of view adjustment parameter, which means reducing the intensity of intervention. For example, restoring the FOV from 85° to 90° indicates that "it has been effective and can be relaxed appropriately".

[0145] Optionally, if the degree of difference between the second physiological feature time-series signal and the first physiological feature time-series signal is less than a preset difference threshold, it means that the intervention intensity needs to be increased, that is, the first field of view adjustment parameter is increased to obtain the third field of view adjustment parameter.

[0146] After generating field-of-view adjustment parameters based on the first physiological characteristic time-series signal and completing the display adjustment and grid overlay, the second physiological characteristic time-series signal reflecting the intervention effect is collected after a preset time. The difference between the second and the initial signals is used for dynamic feedback judgment: when the difference is greater than or equal to a preset threshold, it is determined that the intervention has not effectively alleviated the risk of motion sickness, so the original adjustment parameters are reduced to enhance the field-of-view contraction rate and increase the intervention intensity; when the difference is less than the preset threshold, it is determined that the intervention has produced a therapeutic effect or there is a risk of over-inhibition, so the original adjustment parameters are increased to slow down the contraction rate and avoid causing secondary discomfort. This achieves closed-loop adaptive adjustment based on the trend of physiological state changes. After each parameter adjustment, the grid display image is simultaneously overlaid to assist visual stabilization. Ultimately, the intervention strategy is optimized in real time, accurately, and dynamically before the user subjectively perceives dizziness. This effectively solves the problem that traditional single interventions are prone to being insufficient or excessive, leading to a deterioration in the experience, and significantly improves the accuracy and comfort of motion sickness suppression.

[0147] In this embodiment, when the degree of difference is greater than or equal to a preset threshold, it is determined that the intervention measures have not effectively alleviated the risk of motion sickness. Therefore, the original adjustment parameter is reduced to enhance the field of view contraction rate and increase the intensity of the intervention. When the degree of difference is less than the preset threshold, it is determined that the intervention has produced therapeutic effects or there is a risk of excessive inhibition. Therefore, the original adjustment parameter is increased to slow down the contraction rate and avoid causing secondary discomfort, thereby achieving closed-loop adaptive adjustment based on the trend of physiological state changes.

[0148] The display adjustment method in the embodiments of this application will be explained below with reference to optional examples. In one example, such as Figure 7As shown, the fused features are input into a lightweight temporal convolutional network (TCN) (corresponding to a pre-trained motion sickness prediction model) deployed on the embedded processor of the head-mounted display. The output is a motion sickness precursor probability (corresponding to motion sickness risk probability) between 0 and 1. This probability is compared with a personalized baseline threshold (corresponding to a specified probability threshold). If the probability exceeds this threshold, an intervention is deemed necessary, indicating a risk of motion sickness for the user. Abnormal parameters such as heart rate rise rate and pulse amplitude decay rate are extracted from the first physiological feature temporal signal, and an initial intervention intensity (first field of view adjustment parameter) is calculated using a linear weighting method. This initial intensity serves as a continuous control signal for the intervention intensity, achieving precise conversion from complex physiological pattern recognition to executable intervention commands, ensuring accurate judgment and rapid response. When the motion sickness precursor probability is less than the personalized threshold, the user's physiological baseline data is updated and monitored via a photoelectric sensor array.

[0149] In another example, such as Figure 8 As shown, upon receiving the intervention command (including the command to adjust the first field of view parameter), the system performs intervention processing based on the command (such as gradually shrinking the display field of view at a step rate and simultaneously overlaying static mesh textures at the edge of the screen to provide visual anchors). After the intervention lasts for a preset duration, the system collects the user's PPG signal (corresponding to the second physiological characteristic time-series signal) again and compares the difference with the physiological characteristics before the intervention (the first physiological characteristic time-series signal). If the physiological indicators are significantly relieved (such as a decrease in heart rate or an increase in amplitude exceeding a preset threshold), the intervention intensity is appropriately reduced and the field of view is slowly restored (corresponding to reducing the first field of view parameter). Adjust the parameters to obtain the second field of view adjustment parameter, where the first field of view adjustment parameter can be a preset multiple (e.g., 3) of the second field of view adjustment parameter, to avoid secondary dizziness, and record the intervention data (e.g., trigger threshold, intervention intensity, response time, recovery curve) to update the personalized threshold of the user; if it is not relieved, increase the contraction intensity (correspondingly increase the first field of view adjustment parameter to obtain the third field of view adjustment parameter, where the second field of view adjustment parameter can be a preset multiple (e.g., 1.5) of the first field of view adjustment parameter), and continuously monitor the physiological characteristic time sequence signal during VR use.

[0150] This optional example effectively solves the technical problem in related technologies where VR motion sickness relief solutions rely on post-event feedback, static intervention, or external devices, and cannot actively suppress dizziness before the user experiences subjective discomfort. By fusing multi-dimensional physiological features through a lightweight temporal convolutional network, it achieves millisecond-level early warning of motion sickness precursors, overcoming the limitations of traditional single threshold methods that are prone to missed or false alarms. Furthermore, based on real-time re-import and differential assessment of physiological signals after intervention, it dynamically adjusts the field of view contraction intensity, ensuring precise matching between the intervention intensity and individual physiological response, avoiding excessive or insufficient intervention, significantly improving comfort and immersion, and requiring no manual user intervention or additional hardware.

[0151] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0152] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0153] According to another aspect of the embodiments of this application, a display adjustment device is also provided. This display adjustment device can be used to implement the display adjustment method provided in the above embodiments and is applied to a virtual reality device. The virtual reality device includes a photoelectric sensor array, which has already been described and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0154] Figure 9 This is a structural block diagram of an optional display adjustment device according to an embodiment of this application, such as... Figure 9 As shown, the display adjustment device includes:

[0155] The acquisition unit 902 is used to acquire the first physiological characteristic time-series signal of the user when using the virtual reality device through a photoelectric sensor array;

[0156] The identification unit 904 is used to identify whether the user is at risk of motion sickness based on the timing signal of the first physiological characteristic.

[0157] The generation unit 906 is used to generate a first field of view adjustment parameter in response to the risk of motion sickness, based on abnormal information in the first physiological characteristic time-series signal. The first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the displayed image. The degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter.

[0158] The adjustment unit 908 is used to adjust the display screen of the virtual reality device based on the first field of view adjustment parameters, and simultaneously overlay a grid display image on the adjusted display screen.

[0159] It should be noted that the acquisition unit 902 in this embodiment can be used to perform the above step S202, the identification unit 904 in this embodiment can be used to perform the above step S204, the generation unit 906 in this embodiment can be used to perform the above step S206, and the adjustment unit 908 in this embodiment can be used to perform the above step S208.

[0160] The embodiments provided in this application collect first physiological characteristic time-series signals of a user when using a virtual reality device using a photoelectric sensor array to identify whether the user is at risk of motion sickness. In response to the presence of motion sickness risk, a first field-of-view adjustment parameter is generated based on abnormal information in the first physiological characteristic time-series signal. This first field-of-view adjustment parameter instructs the virtual reality device on the rate at which the field-of-view shrinkage of the displayed image is adjusted, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field-of-view adjustment parameter. Based on the first field-of-view adjustment parameter, the display image of the virtual reality device is adjusted, and a grid display image is simultaneously overlaid on the adjusted display image. Because a photoelectric sensor array is integrated into the virtual reality device, the physiological state of the user can be monitored, eliminating reliance on subjective user feedback. This solves the technical problem in related technologies where display adjustment methods cannot adjust the display image in a timely manner due to reliance on subjective user feedback. Furthermore, the first field-of-view adjustment parameter generated based on abnormal information in the first physiological characteristic time-series signal adjusts the display image, enabling adjustments based on the user's real-time abnormal conditions, thus improving the timeliness and accuracy of display adjustments.

[0161] In an exemplary embodiment, the first physiological characteristic timing signal is a pulse wave timing signal, and the identification unit includes:

[0162] The first processing module is used to preprocess the pulse wave timing signal to obtain the preprocessed pulse wave timing signal.

[0163] The first determining module is used to determine a set of specified features corresponding to the preprocessed pulse wave time-series signal based on the preprocessed pulse wave time-series signal, wherein the set of specified features includes at least one of the following: heart rate features, pulse amplitude features, and signal waveform features.

[0164] The first identification module is used to identify whether the user is at risk of motion sickness based on a set of specified features.

[0165] In one exemplary embodiment, the first determining module includes at least one of the following:

[0166] The first determining submodule is used to determine a set of peak points in the preprocessed pulse wave time sequence signal; calculate the instantaneous heart rate between each adjacent peak based on the time interval between each adjacent peak in the set of peak points; calculate a set of average heart rate values ​​based on the instantaneous heart rate between each adjacent peak, and determine the set of average heart rate values ​​as heart rate features;

[0167] The second determining submodule is used to perform envelope extraction and transformation processing on the preprocessed pulse wave time series signal to extract the amplitude envelope of the preprocessed pulse wave time series signal and determine the amplitude envelope of the preprocessed pulse wave time series signal as the pulse amplitude feature.

[0168] The third determination submodule is used to differentiate the preprocessed pulse wave time sequence signal to obtain the derivative waveform corresponding to the pulse wave time sequence signal; and to determine the peak characteristics in the derivative waveform corresponding to the pulse wave time sequence signal as the signal waveform characteristics.

[0169] In an exemplary embodiment, the first identification module includes: a first processing submodule, configured to: perform feature fusion processing on a set of specified features to obtain fused features corresponding to the pulse wave time sequence signal;

[0170] The second processing submodule is used to input the fused features into the pre-trained motion sickness prediction model and output the motion sickness risk probability corresponding to the pulse wave time sequence signal.

[0171] The third processing submodule is used to determine that the user has a risk of motion sickness when the probability of motion sickness risk is greater than or equal to a specified probability threshold. The specified probability threshold is obtained by adjusting the preset probability threshold based on the feedback information when the user detects a risk of motion sickness while using the virtual reality device.

[0172] In an exemplary embodiment, the first physiological characteristic timing signal is a pulse wave timing signal, and the identification unit includes:

[0173] The second processing module is used to preprocess the pulse wave timing signal to obtain the preprocessed pulse wave timing signal.

[0174] The second determining module is used to determine a set of signal information in the preprocessed pulse wave time sequence signal, wherein the set of signal information includes the statistical value of heart rate variability, the statistical value of instantaneous heart rate, and pulse amplitude;

[0175] The first acquisition module is used to acquire a set of specified thresholds, wherein the set of specified thresholds includes instantaneous heart rate threshold, pulse amplitude threshold and heart rate variability threshold. The set of specified thresholds is determined based on the initial pulse wave timing signal recorded by the user during a specified time period when the virtual reality device is turned on. The start time of the specified time period is the start time of the virtual reality device.

[0176] The first calculation module is used to calculate the heart rate rise rate based on the statistical value of instantaneous heart rate and the instantaneous heart rate threshold; to calculate the pulse amplitude decay rate based on the pulse amplitude and the pulse amplitude threshold; and to calculate a specified heart rate variability threshold based on the heart rate variability threshold and a preset coefficient.

[0177] The third determination module is used to determine that the user is at risk of motion sickness if at least one of the following conditions is met: the heart rate rise rate is greater than or equal to a preset heart rate change rate threshold; the pulse amplitude decay rate is greater than or equal to a preset pulse amplitude decay rate threshold; and the statistical value of heart rate variability is less than or equal to a specified heart rate variability threshold.

[0178] In one exemplary embodiment, the second determining module includes:

[0179] The fourth processing submodule is used to determine a set of peak points in the preprocessed pulse wave time sequence signal; calculate the instantaneous heart rate between each adjacent peak based on the time interval between each adjacent peak in the set of peak points; and calculate the statistical value of the instantaneous heart rate based on the instantaneous heart rate between each adjacent peak.

[0180] The fifth processing submodule is used to perform envelope extraction and transformation processing on the preprocessed pulse wave time series signal to determine the pulse amplitude of the preprocessed pulse wave time series signal.

[0181] The sixth processing submodule is used to determine the standard deviation of heart rate change over a preset number of heartbeat cycles based on a set of peak points in the preprocessed pulse wave time sequence signal, and to determine the standard deviation of heart rate change over a preset number of heartbeat cycles as the statistical value of heart rate variability. Here, a heartbeat cycle is determined based on a pair of adjacent peak points in a set of peak points in the pulse wave time sequence signal.

[0182] In one exemplary embodiment, the abnormal information includes the rate of increase in heart rate and the rate of decrease in pulse amplitude;

[0183] The generation unit includes: a fourth determining module, used to determine the weighted sum of the heart rate rise rate and the pulse amplitude decay rate as the first field of view adjustment parameter in response to the risk of motion sickness, wherein the heart rate rise rate is positively correlated with the first field of view adjustment parameter and the pulse amplitude decay rate is positively correlated with the first field of view adjustment parameter.

[0184] In an exemplary embodiment, the apparatus further includes: an execution unit, configured to acquire a second physiological feature timing signal after a preset duration of adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter, wherein the second physiological feature timing signal is a physiological feature timing signal acquired when adjusting the display screen of the virtual reality device using the first field-of-view adjustment parameter; a first adjustment unit, configured to, in response to a difference between the second physiological feature timing signal and the first physiological feature timing signal being greater than or equal to a preset difference threshold, decrease the first field-of-view adjustment parameter to obtain a second field-of-view adjustment parameter, adjust the display screen of the virtual reality device using the second field-of-view adjustment parameter, and simultaneously overlay a grid display image on the adjusted display screen; and a second adjustment unit, configured to, in response to a difference between the second physiological feature timing signal and the first physiological feature timing signal being less than a preset difference threshold, increase the first field-of-view adjustment parameter to obtain a third field-of-view adjustment parameter, adjust the display screen of the virtual reality device using the third field-of-view adjustment parameter, and simultaneously overlay a grid display image on the adjusted display screen.

[0185] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0186] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0187] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0188] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0189] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0190] According to another aspect of the embodiments of this application, a computer program product is also provided, comprising a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0191] Figure 10 A schematic block diagram of a computer system architecture for implementing embodiments of the present application is shown. Figure 10 As shown, the computer system 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in ROM 1002 or programs loaded into RAM 1003 from storage section 1008. Random access memory 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0192] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a local area network card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. Drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0193] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions defined in the system of this application.

[0194] It should be noted that, Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0195] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0196] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A display adjustment method, characterized in that, Applied to a virtual reality device, the virtual reality device including an array of photoelectric sensors, the method includes: The photoelectric sensor array is used to collect the temporal signal of the first physiological characteristic of the user when using the virtual reality device; Based on the first physiological characteristic time-series signal, identify whether the user is at risk of motion sickness; In response to the existence of the motion sickness risk, a first field of view adjustment parameter is generated based on the abnormal information in the first physiological characteristic time-series signal. The first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the displayed image. The degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter. Based on the first field of view adjustment parameter, the display screen of the virtual reality device is adjusted, and a grid display image is simultaneously superimposed on the adjusted display screen.

2. The method according to claim 1, characterized in that, The first physiological characteristic time-series signal is a pulse wave time-series signal. The step of identifying whether the user is at risk of motion sickness based on the first physiological characteristic time-series signal includes: The pulse wave timing signal is preprocessed to obtain the preprocessed pulse wave timing signal. Based on the preprocessed pulse wave time sequence signal, a set of specified features corresponding to the pulse wave time sequence signal is determined, wherein the set of specified features includes at least one of the following: heart rate features, pulse amplitude features, and signal waveform features. Based on the specified set of characteristics, it is determined whether the user is at risk of motion sickness.

3. The method according to claim 2, characterized in that, The determination of a set of specified features corresponding to the preprocessed pulse wave time-series signal includes at least one of the following: Identify a set of peak points in the preprocessed pulse wave time sequence signal; calculate the instantaneous heart rate between each adjacent peak point based on the time interval between each adjacent peak point in the set of peak points; Based on the instantaneous heart rate between each adjacent peak, a set of average heart rate values ​​is calculated, and the set of average heart rate values ​​is determined as the heart rate feature; The preprocessed pulse wave time series signal is subjected to envelope extraction and transformation processing to extract the amplitude envelope of the preprocessed pulse wave time series signal, and the amplitude envelope of the preprocessed pulse wave time series signal is determined as the pulse amplitude feature. The preprocessed pulse wave time sequence signal is differentiated to obtain the derivative waveform corresponding to the pulse wave time sequence signal; the peak feature in the derivative waveform corresponding to the pulse wave time sequence signal is determined as the signal waveform feature.

4. The method according to claim 2, characterized in that, The process of identifying whether the user is at risk of motion sickness based on the specified set of features includes: The specified set of features is subjected to feature fusion processing to obtain the fused features corresponding to the pulse wave time sequence signal; The fused features are input into a pre-trained motion sickness prediction model, and the motion sickness risk probability corresponding to the pulse wave time sequence signal is output. If the probability of motion sickness risk is greater than or equal to a specified probability threshold, it is determined that the user has the risk of motion sickness. The specified probability threshold is obtained by adjusting a preset probability threshold based on feedback information from the user when the user detects the risk of motion sickness while using the virtual reality device.

5. The method according to claim 1, characterized in that, The first physiological characteristic time-series signal is a pulse wave time-series signal. The step of identifying whether the user is at risk of motion sickness based on the first physiological characteristic time-series signal includes: The pulse wave timing signal is preprocessed to obtain the preprocessed pulse wave timing signal. A set of signal information is determined in the preprocessed pulse wave time sequence signal, wherein the set of signal information includes the statistical value of heart rate variability, the statistical value of instantaneous heart rate, and pulse amplitude; A set of specified thresholds is obtained, wherein the set of specified thresholds includes an instantaneous heart rate threshold, a pulse amplitude threshold, and a heart rate variability threshold. The set of specified thresholds is determined based on the initial pulse wave timing signal recorded by the user during a specified time period when the virtual reality device is turned on. The start time of the specified time period is the startup time of the virtual reality device. Based on the statistical value of the instantaneous heart rate and the instantaneous heart rate threshold, the heart rate rise rate is calculated; based on the pulse amplitude and the pulse amplitude threshold, the pulse amplitude decay rate is calculated; and based on the heart rate variability threshold and a preset coefficient, a specified heart rate variability threshold is calculated. The user is determined to be at risk of motion sickness if at least one of the following conditions is met: The heart rate rise rate is greater than or equal to a preset heart rate change rate threshold; The pulse amplitude attenuation rate is greater than or equal to a preset pulse amplitude attenuation rate threshold; The statistical value of the heart rate variability is less than or equal to the specified heart rate variability threshold.

6. The method according to claim 5, characterized in that, The determination of a set of signal information in the preprocessed pulse wave timing signal includes: A set of peak points is determined in the preprocessed pulse wave time sequence signal; based on the time interval between each adjacent peak in the set of peak points, the instantaneous heart rate between each adjacent peak is calculated; based on the instantaneous heart rate between each adjacent peak, the statistical value of the instantaneous heart rate is calculated; The preprocessed pulse wave time series signal is subjected to envelope extraction and transformation processing to determine the pulse amplitude of the preprocessed pulse wave time series signal; Based on a set of peak points in the preprocessed pulse wave time sequence signal, the standard deviation of heart rate change over a preset number of heartbeat cycles is determined, and the standard deviation of heart rate change over the preset number of heartbeat cycles is determined as the statistical value of heart rate variability. Here, a heartbeat cycle is determined based on a pair of adjacent peak points in a set of peak points in the pulse wave time sequence signal.

7. The method according to claim 5, characterized in that, The abnormal information includes the heart rate rise rate and the pulse amplitude decay rate; In response to the presence of the motion sickness risk, the generation of a first field-of-view adjustment parameter based on abnormal information in the first physiological characteristic time-series signal includes: In response to the risk of motion sickness, the weighted sum of the heart rate rise rate and the pulse amplitude decay rate is determined as the first field of view adjustment parameter, wherein the heart rate rise rate is positively correlated with the first field of view adjustment parameter, and the pulse amplitude decay rate is positively correlated with the first field of view adjustment parameter.

8. The method according to claim 7, characterized in that, The method further includes: After a preset time period of adjusting the display screen of the virtual reality device using the first field of view adjustment parameter, a second physiological characteristic time sequence signal is collected, wherein the second physiological characteristic time sequence signal is the physiological characteristic time sequence signal collected when adjusting the display screen of the virtual reality device using the first field of view adjustment parameter; In response to the fact that the degree of difference between the second physiological feature time-series signal and the first physiological feature time-series signal is greater than or equal to a preset difference threshold, the first field of view adjustment parameter is reduced to obtain the second field of view adjustment parameter. The display screen of the virtual reality device is adjusted using the second field of view adjustment parameter, and a grid display image is simultaneously superimposed on the adjusted display screen. In response to the fact that the difference between the second physiological feature time-series signal and the first physiological feature time-series signal is less than the preset difference threshold, the first field of view adjustment parameter is increased to obtain the third field of view adjustment parameter. The third field of view adjustment parameter is used to adjust the display screen of the virtual reality device, and a grid display image is simultaneously superimposed on the adjusted display screen.

9. A display adjustment device, characterized in that, Applied to virtual reality devices, the virtual reality device including an array of photoelectric sensors, the device includes: The acquisition unit is used to acquire the first physiological characteristic time-series signal of the user when using the virtual reality device through the photoelectric sensor array; The identification unit is used to identify whether the user is at risk of motion sickness based on the first physiological characteristic time-series signal; A generation unit is configured to, in response to the existence of the motion sickness risk, generate a first field of view adjustment parameter based on abnormal information in the first physiological characteristic time-series signal, wherein the first field of view adjustment parameter is used to instruct the virtual reality device to adjust the field of view contraction rate of the displayed image, and the degree of abnormality corresponding to the abnormal information is positively correlated with the first field of view adjustment parameter; The adjustment unit is used to adjust the display screen of the virtual reality device based on the first field of view adjustment parameter, and simultaneously overlay a grid display image on the adjusted display screen.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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