Motion sickness early warning method and device, vehicle, electronic device and storage medium

Abstract

Embodiments of the present application relate to a car sickness early warning method and device, a vehicle, an electronic device and a storage medium. The method comprises: obtaining a current attitude angle of the vehicle; calculating a current vestibular stimulation intensity based on the attitude angle and a preset car sickness weight coefficient; obtaining a current electrocardiogram of a target user in the vehicle from a preset electrocardiogram acquisition device; determining current heart rate fluctuation data of the target user based on the electrocardiogram; detecting a pupil diameter of the target user to obtain a current pupil diameter of the target user; and using a preset car sickness risk prediction model to predict a car sickness risk of the vestibular stimulation intensity, the heart rate fluctuation data and the pupil diameter to obtain a car sickness risk level. The embodiments of the present application can timely detect the physiological abnormal state of the user in the vehicle, accurately determine the car sickness risk level, and perform targeted detection on a specific user, thereby improving the adaptability of the car sickness early warning and the user, and facilitating the vehicle to perform a targeted response strategy for the degree of car sickness.

Description

Technical Field

[0001] This application relates to the field of intelligent vehicle technology, and in particular to a motion sickness warning method, device, vehicle, electronic device and storage medium. Background Technology

[0002] Motion sickness is a physiological discomfort caused by a conflict between the visual and vestibular systems, and is often closely related to factors such as frequent acceleration and deceleration of the vehicle and insufficient air circulation inside the vehicle. Motion sickness not only affects the driver's concentration but also the health of the passengers.

[0003] With the development of intelligent vehicle technology, methods to address motion sickness have emerged. For example, accelerometers can be used to monitor the vehicle's acceleration and deceleration in real time, issuing motion sickness warnings based on this data. When a passenger is predicted to be at risk of motion sickness, anti-motion sickness strategies can be implemented.

[0004] Existing motion sickness warning methods suffer from limitations in detecting only a single type of signal, making it impossible to comprehensively assess a passenger's risk of motion sickness, resulting in low accuracy. Furthermore, they lack the ability to dynamically adapt to individual passenger physiological characteristics, leading to poor adaptability of the motion sickness warning system. Summary of the Invention

[0005] In view of this, in order to solve some or all of the above-mentioned technical problems, embodiments of this application provide a motion sickness warning method, device, vehicle, electronic device and storage medium.

[0006] In a first aspect, embodiments of this application provide a motion sickness warning method, the method comprising: acquiring the current attitude angle of the vehicle; calculating the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; acquiring the current electrocardiogram signal of a target user inside the vehicle from a preset electrocardiogram signal acquisition device; determining the current heart rate fluctuation data of the target user based on the electrocardiogram signal; detecting the pupil diameter of the target user to obtain the current pupil diameter of the target user; and using a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain a motion sickness risk level.

[0007] In one possible implementation, the motion sickness risk prediction model includes a heart rate impact index prediction sub-model, a comprehensive impact index prediction sub-model, and a motion sickness severity prediction sub-model. Using the pre-defined motion sickness risk prediction model, motion sickness risk is predicted based on vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain a motion sickness risk level. This includes: predicting heart rate impact index data based on heart rate fluctuation data and vestibular stimulation intensity using the heart rate impact index prediction sub-model; predicting comprehensive impact index data based on vestibular stimulation intensity, heart rate impact index data, and pupil diameter using the comprehensive impact index prediction sub-model; predicting motion sickness severity based on the comprehensive impact index data using the motion sickness severity prediction sub-model to obtain a motion sickness severity value; and determining the corresponding motion sickness risk level based on the motion sickness severity value.

[0008] In one possible implementation, based on a heart rate influence index prediction sub-model, heart rate influence index prediction is performed on heart rate fluctuation data and vestibular stimulation intensity to obtain heart rate influence index data, including: acquiring preset vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, and resting heart rate fluctuation data; and using the heart rate influence index prediction sub-model, calculating the vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, resting heart rate fluctuation data, and heart rate fluctuation data to obtain heart rate influence index data.

[0009] In one possible implementation, based on the comprehensive influence index prediction sub-model, the comprehensive influence index prediction is performed on vestibular stimulation intensity, heart rate influence index data, and pupil diameter to obtain comprehensive influence index data. This includes: acquiring preset resting pupil diameter and resting heart rate fluctuation data; and using the comprehensive influence index prediction sub-model to predict the vestibular stimulation intensity, heart rate influence index data, pupil diameter, resting pupil diameter, and resting heart rate fluctuation data to obtain comprehensive influence index data.

[0010] In one possible implementation, based on the motion sickness severity prediction sub-model, the motion sickness severity is predicted from the comprehensive impact index data to obtain the motion sickness severity value, including: obtaining a preset heart rate fluctuation recovery threshold; and using the motion sickness severity prediction sub-model to calculate the motion sickness severity value from the comprehensive impact index data, heart rate impact index data, and heart rate fluctuation recovery threshold.

[0011] In one possible implementation, determining the corresponding motion sickness risk level based on the motion sickness severity value includes: converting the motion sickness severity value using a preset motion sickness severity mapping function to obtain a motion sickness risk index; and determining the motion sickness risk level corresponding to the motion sickness risk index using a preset motion sickness risk level classification strategy.

[0012] In one possible implementation, detecting the pupil diameter of a target user to obtain the target user's current pupil diameter includes: acquiring a facial image of the target user captured by a camera on the vehicle; if an eye image that meets the recognition criteria can be extracted from the facial image, the eye image is recognized to obtain the target user's current pupil diameter; if an eye image that meets the recognition criteria cannot be extracted from the facial image, the pupil diameter is predicted based on a preset pupil diameter prediction sub-model, according to the vestibular stimulation intensity, to obtain the target user's current pupil diameter.

[0013] In one possible implementation, the pupil diameter is predicted based on a preset pupil diameter prediction sub-model to obtain the target user's current pupil diameter by assessing the vestibular stimulation intensity. This includes: acquiring preset resting pupil diameter, pupil dilation gain value, and pupil recovery rate; and using the pupil diameter prediction sub-model to calculate the vestibular stimulation intensity, resting pupil diameter, pupil dilation gain value, and pupil recovery rate to obtain the target user's current pupil diameter.

[0014] In one possible implementation, after using a preset motion sickness risk prediction model to predict motion sickness risk based on vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain a motion sickness risk level, the method further includes: controlling the vehicle according to a preset vehicle control strategy corresponding to the motion sickness risk level.

[0015] Secondly, embodiments of this application provide a motion sickness warning device, which includes: a first acquisition module for acquiring the current attitude angle of the vehicle; a calculation module for calculating the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; a second acquisition module for acquiring the current electrocardiogram (ECG) signal of a target user inside the vehicle from a preset ECG signal acquisition device; a determination module for determining the current heart rate fluctuation data of the target user based on the ECG signal; a detection module for detecting the pupil diameter of the target user to obtain the current pupil diameter of the target user; and a prediction module for using a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain a motion sickness risk level.

[0016] Thirdly, this application provides a vehicle comprising: a controller, an attitude angle acquisition device, and an electrocardiogram (ECG) signal acquisition device. The attitude angle acquisition device and the ECG signal acquisition device are both connected to the controller, which is used to execute the aforementioned motion sickness warning method.

[0017] Fourthly, embodiments of this application provide an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, wherein when the computer program is executed, it implements the method of any embodiment of the motion sickness warning method of the first aspect of this application.

[0018] Fifthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the method of any embodiment of the motion sickness warning method of the first aspect described above.

[0019] In a sixth aspect, embodiments of this application provide a computer program that includes computer-readable code. When the computer-readable code is run on a device, it causes a processor in the device to implement the method of any embodiment of the motion sickness warning method in the first aspect described above.

[0020] The motion sickness warning method, device, vehicle, electronic device, and storage medium provided in this application embodiment acquire the current attitude angle of the vehicle; calculate the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; acquire the current electrocardiogram (ECG) signal of the target user inside the vehicle from an ECG signal acquisition device; determine the target user's current heart rate fluctuation data based on the ECG signal; acquire eye images of the target user captured by a camera on the vehicle; identify the pupil diameter of the target user by analyzing the eye images; and use a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level. This application embodiment achieves a comprehensive judgment of motion sickness risk using multiple types of sensing signals, enabling timely detection of abnormal physiological states of users inside the vehicle, more accurate and timely judgment of the user's motion sickness risk level, and targeted detection for specific users, improving the adaptability of the motion sickness warning to the user. Furthermore, the motion sickness risk level reflects the degree of motion sickness, which is beneficial for the vehicle to implement targeted response strategies based on the degree of motion sickness. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0024] Figure 1A flowchart illustrating a motion sickness warning method provided in an embodiment of this application;

[0025] Figure 2 A flowchart illustrating the second motion sickness warning method provided in this application embodiment;

[0026] Figure 3 A flowchart illustrating the third motion sickness warning method provided in this application embodiment;

[0027] Figure 4 A flowchart illustrating the fourth motion sickness warning method provided in this application embodiment;

[0028] Figure 5 A flowchart illustrating the fifth motion sickness warning method provided in this application embodiment;

[0029] Figure 6 A flowchart illustrating the sixth motion sickness warning method provided in this application embodiment;

[0030] Figure 7 A flowchart illustrating the seventh motion sickness warning method provided in this application embodiment;

[0031] Figure 8 A flowchart illustrating the eighth motion sickness warning method provided in this application embodiment;

[0032] Figure 9 This is a schematic diagram of the structure of a motion sickness warning device provided in an embodiment of this application;

[0033] Figure 10 A schematic diagram of the structure of a vehicle provided in an embodiment of this application;

[0034] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] Various exemplary embodiments of this application will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this application.

[0036] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of this application are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor do they indicate the logical order between them.

[0037] It should also be understood that in this embodiment, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0038] It should also be understood that any component, data or structure mentioned in the embodiments of this application can generally be understood as one or more unless explicitly defined or given contrary guidance in the context.

[0039] Furthermore, the term "and / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this application generally indicates that the preceding and following related objects have an "or" relationship.

[0040] It should also be understood that the description of the various embodiments in this application emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0041] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0042] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0043] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. To facilitate understanding of the embodiments of this application, the application will be described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0045] To address the technical problems of existing motion sickness warning technologies, such as limited sensor data collection, low detection accuracy, and poor user adaptability, this application provides a motion sickness warning method that employs multimodal signal detection to comprehensively assess the motion sickness risk level for specific users, thereby improving the accuracy and adaptability of motion sickness detection.

[0046] Figure 1This is a flowchart illustrating a motion sickness warning method provided in an embodiment of this application. This method can be applied to a vehicle and executed by the vehicle's controller. It can also be executed by other electronic devices connected to the vehicle, such as one or more electronic devices including smartphones, laptops, desktop computers, portable computers, and servers. Furthermore, the executing entity of this method can be hardware or software. When the executing entity is hardware, it can be one or more of the aforementioned electronic devices. For example, a single electronic device can execute this method, or multiple electronic devices can cooperate with each other to execute this method. When the executing entity is software, this method can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are made here.

[0047] like Figure 1 As shown, the method specifically includes:

[0048] Step 101: Obtain the current attitude angle of the vehicle.

[0049] In some embodiments, the vehicle's attitude angles can be acquired in real time by the vehicle's inertial measurement unit (IMU). Attitude angles include yaw, pitch, and roll. Electronic devices can preprocess and filter the acquired raw attitude angle signals, transform the IMU's local coordinate system (Body Frame) to the vehicle coordinate system (Vehicle Frame) for coordinate alignment, calculate the yaw, pitch, and roll angles using sensor data fusion algorithms, and output them via CAN bus or Ethernet.

[0050] In this embodiment, the data collected by various sensors can first be cleaned and spatiotemporally synchronized. Sensor data may be subject to interference, which can be removed using filtering algorithms (such as Kalman filtering and bandpass filtering). For the synchronization mechanism, since the sampling frequency and timestamps of multi-source sensor data may be inconsistent, a spatiotemporal coordinate transformation is required, ensuring that the timestamp alignment error between each sensor is <1ms.

[0051] Step 102: Calculate the current vestibular stimulation intensity based on the posture angle and the preset motion sickness weighting coefficient.

[0052] In some embodiments, the motion sickness weighting coefficient corresponds to each component of the attitude angle, and the motion sickness weighting coefficient can be obtained in advance through actual vehicle calibration.

[0053] This step can be achieved by pre-setting the calculation method for vestibular stimulation intensity. By using the posture angle and motion sickness weighting coefficient as parameters, the current vestibular stimulation intensity can be calculated.

[0054] For example, the formula for calculating vestibular stimulation intensity is:

[0055]

[0056] Where ε1, ε2, and ε3 are motion sickness weighting coefficients. The yaw rate is angular velocity. The pitch angular velocity, This represents the lateral tilt rate. Typically, ε3 > ε1 > ε2, reflecting that lateral tilt has a significant impact on motion sickness.

[0057] Step 103: Obtain the current electrocardiogram (ECG) signal of the target user inside the vehicle from the preset ECG signal acquisition device.

[0058] In some embodiments, the target user can be a user located anywhere inside the vehicle, and there can be one or more target users. When there are multiple target users, this method is executed once for each target user.

[0059] An electrocardiogram (ECG) signal acquisition device is used to collect the ECG signals of a target user inside the vehicle. This device can be movable or detachable, and is used to collect the target user's ECG signals when it is necessary to assess the target user's risk level of motion sickness.

[0060] Optionally, the ECG signal acquisition device can be installed on the seatbelt corresponding to each seat in the vehicle. For example, the ECG signal acquisition device can be a PVDF physiological index sensor. After the user fastens the seatbelt, the PVDF sensor can be close to the user's heart to collect the user's ECG signal.

[0061] The raw charge signal acquired from the ECG signal acquisition device can be amplified and converted into a voltage signal. After signal denoising, it is sent to the analog-to-digital conversion circuit. The mixed voltage signal containing respiratory and heartbeat signals is then denoised to obtain the ECG signal.

[0062] Step 104: Based on the electrocardiogram signal, determine the target user's current heart rate fluctuation data.

[0063] In some embodiments, heart rate variability data is used to characterize the rate of change in a target user's heart rate. Typically, heart rate variability data can be obtained by calculating SDNN values. The SDNN metric represents the degree of heart rate variability. Because motion sickness triggers a stress response due to the conflict between visual, vestibular, and sensory inputs, leading to sympathetic nerve excitation, increased heart rate, shortened RR interval, and reduced RR interval variability (standard deviation), the SDNN value will significantly decrease. The SDNN value is calculated as follows:

[0064]

[0065] Among them, RR i This represents the i-th RR interval. This represents the average of all RR intervals, where N is the total number of RR intervals. The RR intervals mentioned above represent the time interval between two R-wave peaks (the points with the highest amplitude in the QRS complex) in the acquired electrocardiogram signal, usually measured in milliseconds.

[0066] Step 105: Detect the pupil diameter of the target user to obtain the current pupil diameter of the target user.

[0067] In some embodiments, the electronic device executing this method can acquire real-time images of the target user's eyes, identify the locations of various feature points in the eyes, and measure the pupil diameter in real-time based on the locations of the feature points. Optionally, the electronic device can also determine the pupil diameter corresponding to the vestibular stimulation intensity as the target user's current pupil diameter by using a pre-established correspondence between vestibular stimulation intensity and pupil diameter.

[0068] Step 106: Using a preset motion sickness risk prediction model, predict motion sickness risk based on vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level.

[0069] In some embodiments, the motion sickness risk prediction model is used to represent the correspondence between vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter and motion sickness risk level.

[0070] Motion sickness risk prediction models can take the form of calculation formulas, correspondence tables, neural network models, etc. By inputting the above parameters, the motion sickness risk prediction model can predict each parameter and output the motion sickness risk level.

[0071] For example, the motion sickness risk prediction model can be a table pre-generated based on a large amount of measured data. Based on the currently input vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter, the corresponding motion sickness risk level can be found from the table.

[0072] Motion sickness risk level indicates the severity of motion sickness in the target user; the higher the motion sickness risk level, the more severe the motion sickness in the target user.

[0073] The motion sickness warning method provided in this application involves: acquiring the vehicle's current attitude angle; calculating the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; acquiring the target user's current electrocardiogram (ECG) signal from an ECG signal acquisition device; determining the target user's current heart rate fluctuation data based on the ECG signal; acquiring eye images of the target user captured by a camera on the vehicle; identifying the pupil diameter of the target user from the eye images; and using a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter, thereby obtaining the motion sickness risk level. This application embodiment achieves a comprehensive judgment of motion sickness risk using multiple types of sensor signals, enabling timely detection of abnormal physiological states of users inside the vehicle, more accurate and timely judgment of the user's motion sickness risk level, and targeted detection for specific users, thus improving the adaptability of the motion sickness warning to the user. Furthermore, the motion sickness risk level reflects the degree of motion sickness experienced by the user, which is beneficial for the vehicle to implement targeted response strategies based on the degree of motion sickness.

[0074] In some optional implementations of this embodiment, the motion sickness risk prediction model includes a heart rate influence index prediction sub-model, a comprehensive influence index prediction sub-model, and a motion sickness severity prediction sub-model. Each sub-model can be in various forms such as calculation formulas or tables.

[0075] like Figure 2 As shown, step 106 includes:

[0076] Step 1061: Based on the heart rate influence index prediction sub-model, predict the heart rate influence index based on heart rate fluctuation data and vestibular stimulation intensity to obtain heart rate influence index data.

[0077] The heart rate impact index prediction sub-model represents the correspondence between heart rate fluctuation data and vestibular stimulation intensity with heart rate impact index data. The heart rate impact index data indicates the degree of influence on the aforementioned heart rate fluctuations under the current vestibular stimulation intensity, and thus can represent the degree of influence on motion sickness.

[0078] The predictive sub-model for heart rate-related indicators can be implemented by constructing a calculation formula. Alternatively, it can be achieved by pre-statistically analyzing measured heart rate fluctuation data and vestibular stimulation intensity, labeling the corresponding heart rate-related indicator data, obtaining a corresponding relationship table, and using this relationship table as the predictive sub-model for heart rate-related indicators.

[0079] Step 1062: Based on the comprehensive influence index prediction sub-model, predict the comprehensive influence index data of vestibular stimulation intensity, heart rate influence index data and pupil diameter to obtain comprehensive influence index data.

[0080] The comprehensive impact index prediction sub-model represents the correspondence between vestibular stimulation intensity and the data of the above indicators with the comprehensive impact index data. The comprehensive impact index data represents the degree of comprehensive impact on various dimensions of the user's body under the current vestibular stimulation intensity.

[0081] The comprehensive impact index prediction sub-model can be implemented by constructing a calculation formula. Alternatively, it can be achieved by pre-statistically analyzing the measured vestibular stimulation intensity and various impact index data, labeling the corresponding comprehensive impact index data, obtaining a correspondence table, and using this correspondence table as the comprehensive impact index prediction sub-model.

[0082] Step 1063: Based on the motion sickness severity prediction sub-model, predict the motion sickness severity from the comprehensive influence index data to obtain the motion sickness severity value.

[0083] Specifically, a calculation formula can be set to convert the comprehensive impact index data, and the motion sickness level value can be obtained by calculating the comprehensive impact index data; or the motion sickness level value corresponding to the comprehensive impact index data can be determined by looking up the table through a preset conversion table.

[0084] Step 1064: Determine the corresponding motion sickness risk level based on the motion sickness severity value.

[0085] Specifically, different motion sickness severity values ​​and motion sickness risk levels can be preset, and the corresponding motion sickness risk level can be determined based on the current motion sickness severity value.

[0086] For example, motion sickness risk levels are categorized into mild, moderate, and severe, with three numerical ranges corresponding to each level. See below:

[0087] R = Low risk (mild); 0 <R(t)<a;

[0088] R = Medium risk (moderate); a <R(t)<b;

[0089] R = High risk (severe); b <R(t)<1。

[0090] Where R represents the motion sickness risk level, and R(t) is the motion sickness severity value.

[0091] This embodiment sets up multiple sub-models to calculate motion sickness impact indicators for vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter, thereby integrating measured data from various dimensions to obtain a high-precision motion sickness risk level.

[0092] In some optional implementations of this embodiment, such as Figure 3 As shown, step 1061 includes:

[0093] Step 10611: Obtain preset vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, and resting heart rate fluctuation data.

[0094] Among them, the vestibular inhibition coefficient represents the degree of inhibition of heart rate fluctuation data (such as ECG SDNN values) by vestibular stimulation. The autonomic recovery rate indicates the rate at which the body autonomously recovers to normal heart rate fluctuations. Resting heart rate fluctuation data serves as reference heart rate fluctuation data for users in a normal, healthy state, and can be obtained by calibrating the heart rate fluctuations of individual users.

[0095] For example, under standardized conditions (e.g., every morning for 3-7 consecutive days), repeat the measurement (e.g., measure 5 minutes of resting SDNN once a day). Take the average of the SDNN obtained from multiple measurements (e.g., 3-7 days) as the resting heart rate variability data.

[0096] Step 10612: Using the heart rate influence index prediction sub-model, calculate the vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, resting heart rate fluctuation data, and heart rate fluctuation data to obtain heart rate influence index data.

[0097] This heart rate-related indicator prediction sub-model can be represented by a calculation formula. For example, a differential equation can be established between the heart rate fluctuation data SDNN and the vestibular stimulation intensity S(t), where the stronger the vestibular stimulation, the faster the SDNN value decreases. This differential equation is shown below:

[0098]

[0099] Where, ω SDNN The real-time SDNN value is represented by k1, which is the vestibular inhibition coefficient, for example, 0.1s. -1 k2 represents the autonomic nervous system recovery rate, for example, 0.05s. -1 ;ω base This is the resting SDNN value, for example, 50ms (i.e., resting heart rate fluctuation data).

[0100] By solving the above differential equation, we can obtain the heart rate-related indicators at the current moment. The specific calculation process is as follows.

[0101] Numerical integration is performed using the discrete form of the differential equation (Euler method). The algorithm execution process is as follows:

[0102] ω_SDNN[t]=ω_SDNN[t-1]+Δt*[k1*S[t]*(1-ω_SDNN[t-1] / ω_base)-k2*ω_SDNN[t-1]].

[0103] Where t is the current time, t-1 is the previous time, and Δt is the time interval between the two calculations. ω_SDNN[t] represents the heart rate influence index data, k1 is the vestibular inhibition coefficient, k2 is the autonomic nervous system recovery rate, and ω_base is the resting heart rate fluctuation data.

[0104] This embodiment pre-sets the vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, and resting heart rate fluctuation data, and uses a heart rate influence index prediction sub-model for calculation. This allows the heart rate influence index data to be combined with the body's actual response to vestibular stimulation, enabling the obtained heart rate influence index data to establish a more accurate relationship with the degree of motion sickness and improve the accuracy of motion sickness detection.

[0105] In some optional implementations of this embodiment, such as Figure 4 As shown, step 1062 includes:

[0106] Step 10621: Obtain preset resting pupil diameter and resting heart rate fluctuation data.

[0107] The resting pupil diameter is the pupil diameter of a user under normal and healthy conditions, and its value range is usually 2.5-4mm. The resting heart rate fluctuation data is the reference heart rate fluctuation data of a user under normal and healthy conditions, which can be obtained by calibrating the heart rate fluctuation of a single user.

[0108] Step 10622: Using the comprehensive influence index prediction sub-model, calculate the comprehensive influence index data by analyzing the vestibular stimulation intensity, heart rate influence index data, pupil diameter, resting pupil diameter, and resting heart rate fluctuation data.

[0109] This comprehensive influence index prediction sub-model can be expressed by a calculation formula. For example, an equation of the following form can be defined to represent the degree of motion sickness in relation to electrocardiogram signals and the rate of change in pupil diameter:

[0110]

[0111] Where C(t) represents the comprehensive impact index data, d p (t) represents the current pupil diameter of the target user, d p0 ω is the resting pupil diameter, S(τ) is the vestibular stimulation intensity, and ω is the t-axis. base For resting heart rate fluctuation data (resting SDNN value), ω SDNN (τ) represents the detected heart rate fluctuation data.

[0112] By solving the above formula (4), the comprehensive impact index data at the current moment can be obtained. The specific calculation process is as follows.

[0113] The algorithm using discrete integral form is executed as follows:

[0114] Initialize C = 0;

[0115] Every 0.1 seconds, the integralnd = (d_p[t] / d_p0)*S[t]*(ω_base / ω_SDNN[t]) is updated and calculated.

[0116] C+ = integrand*Δt.

[0117] This embodiment combines vestibular stimulation intensity, heart rate influence index data, pupil diameter, resting pupil diameter, and resting heart rate fluctuation data to accurately calculate information that can represent the degree of motion sickness, thereby improving the accuracy of determining the risk level of motion sickness.

[0118] In some optional implementations of this embodiment, such as Figure 5 As shown, step 1063 includes:

[0119] Step 10631: Obtain the preset heart rate fluctuation recovery threshold.

[0120] The heart rate fluctuation recovery threshold represents the threshold for judging whether heart rate fluctuation data recovers from an abnormal state to a normal state. Typically, the heart rate fluctuation recovery threshold is set to 40ms.

[0121] Step 10632: Using the motion sickness severity prediction sub-model, calculate the motion sickness severity value by analyzing the comprehensive influence index data, heart rate influence index data, and heart rate fluctuation recovery threshold.

[0122] This motion sickness severity prediction sub-model can be represented by a calculation formula. For example, a differential equation representing the severity of motion sickness can be defined as follows:

[0123]

[0124] Where N(t) represents the degree of motion sickness at the current moment, ω0 is the SDNN recovery threshold (heart rate fluctuation recovery threshold), ω SDNN (t) represents the detected heart rate fluctuation data.

[0125] By solving the differential equation shown in equation (5), N(t) can be obtained. The specific algorithm execution process is as follows:

[0126] First, calculate Q(t), Q = C * math.exp(-ω_SDNN[t] / ω_0), where ω_0 is the SDNN recovery threshold;

[0127] Then, use the Euler method to update N(t), N = N + Δt*(QN).

[0128] This embodiment calculates comprehensive impact index data, heart rate impact index data, and heart rate fluctuation recovery threshold to compare motion sickness state and resting state. By combining various dimensions of sensory data, the calculated motion sickness severity value is more accurate.

[0129] In some optional implementations of this embodiment, such as Figure 6 As shown, step 1064 includes:

[0130] Step 10641: Using a preset motion sickness severity mapping function, the motion sickness severity value is converted to obtain the motion sickness risk index.

[0131] The motion sickness severity mapping function can convert motion sickness severity values ​​to the [0,1] interval. As an example, the motion sickness severity mapping function is shown in equation (6) below:

[0132]

[0133] Where R(t) is the motion sickness risk index at the current moment, and N0 is the preset motion sickness threshold, such as 5.

[0134] Step 10642: Using a preset motion sickness risk level classification strategy, determine the motion sickness risk level corresponding to the motion sickness risk index.

[0135] Specifically, the full range of the motion sickness risk index can be divided into multiple intervals, with each interval corresponding to a motion sickness risk level.

[0136] For example, motion sickness risk levels are categorized into mild, moderate, and severe, with three numerical ranges corresponding to each level. See below:

[0137] R = Low risk (mild); 0 <R(t)<a;

[0138] R = Medium risk (moderate); a <R(t)<b;

[0139] R = High risk (severe); b <R(t)<1。

[0140] This embodiment maps motion sickness severity values ​​to a motion sickness risk index, which can concentrate the range of the motion sickness risk index within a certain range, thereby facilitating the classification of motion sickness risk levels and improving the efficiency of motion sickness risk level detection.

[0141] In some optional implementations of this embodiment, such as Figure 7 As shown, step 105 includes:

[0142] Step 1051: Obtain the facial image of the target user captured by the camera on the vehicle.

[0143] Step 1052: If an eye image that meets the recognition criteria can be extracted from the face image, the eye image is recognized to obtain the current pupil diameter of the target user.

[0144] Specifically, eye region recognition can be performed on facial images. If the eye region can be recognized and the complete eyeball region can be identified from the eye region, then it is determined that an eye image that meets the recognition criteria has been captured; otherwise, it is determined that an eye image that meets the recognition criteria cannot be captured.

[0145] The electronic device can continue to identify the eye image, determine the feature points of the pupil position, and determine the pupil diameter based on the position of the feature points.

[0146] Step 1053: If an eye image that meets the recognition criteria cannot be extracted from the face image, the pupil diameter is predicted based on the vestibular stimulation intensity using a preset pupil diameter prediction sub-model to obtain the current pupil diameter of the target user.

[0147] The pupil diameter prediction sub-model represents the correspondence between vestibular stimulation intensity and pupil diameter. Changes in pupil diameter can reflect the level of sympathetic nerve excitation; therefore, changes in pupil diameter can indicate the degree of impact on motion sickness.

[0148] The pupil diameter prediction sub-model can be implemented by constructing a calculation formula. Alternatively, it can be achieved by pre-statistically analyzing measured pupil diameters and vestibular stimulation intensities to obtain a correspondence table, which can then be used as the pupil diameter prediction sub-model. Specifically, the pupil diameter corresponding to the current vestibular stimulation intensity can be found in this correspondence table.

[0149] This embodiment can accurately detect the current pupil diameter of the target user by recognizing the eye image. In the case that an eye image that meets the recognition conditions cannot be obtained, the pupil diameter can be predicted quickly by performing pupil diameter prediction, thereby avoiding the reduced accuracy of motion sickness risk level detection caused by the direct absence of the pupil and improving the stability of motion sickness risk level detection.

[0150] In some optional implementations of this embodiment, such as Figure 8 As shown, step 1053 includes:

[0151] Step 10531: Obtain the preset resting pupil diameter, pupil dilation gain value, and pupil recovery rate.

[0152] The resting pupil diameter is the pupil diameter of a user under normal, healthy conditions, typically ranging from 2.5 to 4 mm. The pupil dilation gain is the adjustment amount of pupil dilation under vestibular stimulation intensity, typically ranging from 0.2 to 0.4 mm / s. The pupil recovery rate is the rate at which the pupil diameter returns to its normal state under normal conditions, typically ranging from 0.1 to 0.2 s. -1 .

[0153] Step 10532: Using the pupil diameter prediction sub-model, calculate the vestibular stimulation intensity, resting pupil diameter, pupil dilation gain value, and pupil recovery rate to obtain the current pupil diameter of the target user.

[0154] This pupil diameter prediction sub-model can be represented by a calculation formula. For example, pupil diameter reflects the level of sympathetic nerve excitation. When there is rapid acceleration or braking, vestibular stimulation causes pupil dilation. The dynamic response formula for pupil diameter is as follows:

[0155]

[0156] Where, d p0 γ is the resting pupil diameter, for example, 3 mm; γ is the pupil dilation gain, for example, 0.3 mm·s; λ is the pupil recovery rate, for example, 0.15 s. -1 .

[0157] By solving the above formula for the dynamic response of the pupil diameter, the pupil diameter d at the current moment can be obtained. p (t). The pupil diameter can be calculated using a discrete integral form (exponentially weighted moving average) algorithm. The specific algorithm execution process is as follows:

[0158] Initialize d_p_integral = 0;

[0159] Update d_p_integral = d_p_integral*math.exp(-λ*Δt) + γ*S[t]*Δt every 0.1 seconds;

[0160] d_p[t] = d_p0 + d_p_integra.

[0161] Where d_p_integral is the integral part of equation (7).

[0162] This embodiment calculates the vestibular stimulation intensity, resting pupil diameter, pupil dilation gain, and pupil recovery rate, effectively utilizing the relationship between vestibular stimulation intensity and pupil diameter to accurately predict pupil diameter. This improves the accuracy of motion sickness risk detection when eye images cannot be used for pupil diameter recognition.

[0163] Based on the above embodiments, Table 1 below shows the changes of each parameter and the motion sickness risk index as time t changes.

[0164] Table 1. Changes in motion sickness risk index and other parameters over time.

[0165]

[0166] As shown in Table 1 above, overall, vestibular stimulation leads to a rapid increase in the risk of motion sickness. SDNN Decrease (cardiac depression) and d p The increase in C(t) (pupil dilation) leads to a rapid increase in N(t), which in turn drives up N(t).

[0167] In some optional implementations of this embodiment, after step 106, the method further includes:

[0168] The vehicle is controlled accordingly based on the preset vehicle control strategy corresponding to the motion sickness risk level.

[0169] As an example, when the risk level of motion sickness is low, the control strategy is dynamic monitoring, such as reducing the time interval between continuous detection of the risk level of motion sickness.

[0170] When the risk level of motion sickness is at the medium level, the windows automatically lower to allow ventilation, and the air purification system in the vehicle releases a mixture of menthol and eucalyptus oil to suppress the vomiting center of people with motion sickness through olfactory stimulation; the audio control unit starts the soothing mode, playing alpha wave white noise to reduce the power of beta waves in the brain.

[0171] When the risk level of motion sickness is high, the air purification system starts to adjust the fragrance dosage, the windows of the whole vehicle are opened for ventilation, the audio control unit starts the soothing mode, plays alpha wave white noise (8-12Hz) and reduces the power of brainwave beta waves (12-27Hz), the chassis control unit activates the air suspension and starts pitch angle suppression, and the seat control unit activates the seat pressure sensor to simulate road vibration and enhance proprioceptive input.

[0172] This embodiment sets corresponding control strategies for different motion sickness risk levels, enabling the various functional modules within the vehicle to work together to provide users with dynamic monitoring and intelligent analysis. This helps to achieve human-vehicle interaction and improve the effectiveness of motion sickness prevention.

[0173] Figure 9 This is a schematic diagram of a motion sickness warning device provided in an embodiment of this application. Specifically, it includes:

[0174] The first acquisition module 901 is used to acquire the current attitude angle of the vehicle;

[0175] The calculation module 902 is used to calculate the current vestibular stimulation intensity based on the posture angle and the preset motion sickness weight coefficient.

[0176] The second acquisition module 903 is used to acquire the current electrocardiogram signal of the target user inside the vehicle from a preset electrocardiogram signal acquisition device.

[0177] The determination module 904 is used to determine the current heart rate fluctuation data of the target user based on the electrocardiogram signal;

[0178] The detection module 905 is used to detect the pupil diameter of the target user and obtain the current pupil diameter of the target user;

[0179] The prediction module 906 is used to predict the risk of motion sickness based on vestibular stimulation intensity, heart rate fluctuation data and pupil diameter using a preset motion sickness risk prediction model, and obtain the motion sickness risk level.

[0180] In one possible implementation, the motion sickness risk prediction model includes a heart rate impact index prediction sub-model, a comprehensive impact index prediction sub-model, and a motion sickness severity prediction sub-model. The prediction module includes: a first prediction unit, used to predict heart rate impact index data based on heart rate fluctuation data and vestibular stimulation intensity using the heart rate impact index prediction sub-model; a second prediction unit, used to predict comprehensive impact index data based on vestibular stimulation intensity, heart rate impact index data, and pupil diameter using the comprehensive impact index prediction sub-model; a third prediction unit, used to predict motion sickness severity based on the comprehensive impact index data using the motion sickness severity prediction sub-model; and a determination unit, used to determine the corresponding motion sickness risk level based on the motion sickness severity value.

[0181] In one possible implementation, the first prediction unit includes: a first acquisition subunit, used to acquire preset vestibular inhibition coefficient, autonomic nerve recovery rate, and resting heart rate fluctuation data; and a first calculation subunit, used to use a heart rate influence index prediction sub-model to calculate the vestibular inhibition coefficient, autonomic nerve recovery rate, resting heart rate fluctuation data, and heart rate fluctuation data to obtain heart rate influence index data.

[0182] In one possible implementation, the second prediction unit includes: a second acquisition subunit for acquiring preset resting pupil diameter and resting heart rate fluctuation data; and a second calculation subunit for using a comprehensive influence index prediction submodel to calculate the vestibular stimulation intensity, heart rate influence index data, pupil diameter, resting pupil diameter, and resting heart rate fluctuation data to obtain comprehensive influence index data.

[0183] In one possible implementation, the third prediction unit includes: a third acquisition subunit for acquiring a preset heart rate fluctuation recovery threshold; and a third calculation subunit for using a motion sickness severity prediction sub-model to calculate the motion sickness severity value based on the comprehensive influence index data, heart rate influence index data, and heart rate fluctuation recovery threshold.

[0184] In one possible implementation, the determining unit includes: a conversion subunit, used to convert the motion sickness severity value using a preset motion sickness severity mapping function to obtain a motion sickness risk index;

[0185] The sub-unit is defined to determine the motion sickness risk level corresponding to the motion sickness risk index using a preset motion sickness risk level classification strategy.

[0186] In one possible implementation, the detection module includes: an acquisition unit for acquiring a facial image of the target user captured by a camera on the vehicle; a recognition unit for recognizing an eye image that meets the recognition criteria from the facial image to obtain the current pupil diameter of the target user; and a fourth prediction unit for predicting the pupil diameter based on a preset pupil diameter prediction sub-model based on the vestibular stimulation intensity if an eye image that meets the recognition criteria cannot be extracted from the facial image to obtain the current pupil diameter of the target user.

[0187] In one possible implementation, the fourth prediction unit includes: a fourth acquisition subunit, used to acquire preset resting pupil diameter, pupil dilation gain value and pupil recovery rate; and a fourth calculation subunit, used to calculate the vestibular stimulation intensity, resting pupil diameter, pupil dilation gain value and pupil recovery rate using a pupil diameter prediction sub-model to obtain the current pupil diameter of the target user.

[0188] In one possible implementation, the device further includes a control module for controlling the vehicle according to a preset vehicle control strategy corresponding to the motion sickness risk level.

[0189] The motion sickness warning device provided in this embodiment can be as follows: Figure 9 The motion sickness warning device shown can execute all the steps of the above motion sickness warning methods, thereby achieving the technical effects of the above motion sickness warning methods. Please refer to the relevant descriptions above for details. For the sake of brevity, it will not be elaborated here.

[0190] Figure 10 This is a schematic diagram of the structure of a vehicle 1000 provided in an embodiment of this application. The vehicle 1000 includes: a controller 1001, an attitude angle acquisition device 1002, and an electrocardiogram (ECG) signal acquisition device 1003. The attitude angle acquisition device and the ECG signal acquisition device are both connected to the controller, which is used to execute the motion sickness warning method described in the above embodiments.

[0191] After the controller executes the motion sickness warning method, it can execute the corresponding vehicle control strategy according to the motion sickness risk level, and adjust the corresponding functional modules in the vehicle to deal with the motion sickness state of the target user.

[0192] Optionally, the vehicle can also be equipped with a camera connected to a controller. The camera can capture images of the eyes of the people inside the vehicle, and the pupil diameter of the user can be identified based on the eye images.

[0193] The vehicle provided in this embodiment, by applying the aforementioned motion sickness warning method, achieves a comprehensive assessment of motion sickness risk using multiple types of sensor signals. This allows for timely detection of abnormal physiological states of users inside the vehicle, more accurate and timely determination of the user's motion sickness risk level, and targeted detection for specific users, improving the adaptability of the motion sickness warning to the user. Furthermore, the motion sickness risk level reflects the degree of motion sickness experienced by the user, facilitating the vehicle to implement targeted response strategies based on the severity of motion sickness.

[0194] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 11 The illustrated electronic device 1100 includes at least one processor 1101, a memory 1102, at least one network interface 1104, and other user interfaces 1103. The various components in the electronic device 1100 are coupled together via a bus system 1105. It is understood that the bus system 1105 is used to implement communication between these components. In addition to a data bus, the bus system 1105 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 11 The general labeled all buses as Bus System 1105.

[0195] The user interface 1103 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0196] It is understood that the memory 1102 in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1102 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0197] In some implementations, memory 1102 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 11021 and application program 11022.

[0198] The operating system 11021 includes various system programs, such as a framework layer, a core library layer, and a driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 11022 includes various applications, such as a media player and a browser, used to implement various application functions. Programs implementing the methods of the embodiments of this application can be included in the application program 11022.

[0199] In this embodiment, by calling the program or instructions stored in memory 1102, specifically the program or instructions stored in application program 11022, processor 1101 executes the method steps provided in each method embodiment, including, for example:

[0200] The system acquires the vehicle's current attitude angle; calculates the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; acquires the target user's current electrocardiogram (ECG) signal from a preset ECG signal acquisition device; determines the target user's current heart rate fluctuation data based on the ECG signal; detects the target user's pupil diameter to obtain the target user's current pupil diameter; and uses a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level.

[0201] The methods disclosed in the embodiments of this application can be applied to or implemented by processor 1101. Processor 1101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 1101 or by instructions in the form of software. The processor 1101 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 1102. Processor 1101 reads the information in memory 1102 and, in conjunction with its hardware, completes the steps of the above method.

[0202] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described above in this application, or combinations thereof.

[0203] For software implementation, the techniques described herein can be implemented by units that perform the functions described above. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0204] The electronic device provided in this embodiment may be as follows: Figure 11 The electronic device shown can execute all the steps of the motion sickness warning methods described above, thereby achieving the technical effects of the motion sickness warning methods described above. For details, please refer to the relevant descriptions above. For the sake of brevity, it will not be elaborated here.

[0205] This application also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; it may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; and it may also include combinations of the above types of memory.

[0206] One or more programs in the storage medium can be executed by one or more processors to implement the motion sickness warning method executed on the electronic device side.

[0207] The processor described above is used to execute a program stored in memory to implement the following steps of a motion sickness warning method executed on the electronic device side:

[0208] The system acquires the vehicle's current attitude angle; calculates the current vestibular stimulation intensity based on the attitude angle and a preset motion sickness weighting coefficient; acquires the target user's current electrocardiogram (ECG) signal from a preset ECG signal acquisition device; determines the target user's current heart rate fluctuation data based on the ECG signal; detects the target user's pupil diameter to obtain the target user's current pupil diameter; and uses a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level.

[0209] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0210] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0211] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0212] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A motion sickness warning method, characterized in that, The method includes: Obtain the vehicle's current attitude angles; Based on the posture angle and the preset motion sickness weighting coefficient, the current vestibular stimulation intensity is calculated. The current electrocardiogram (ECG) signal of the target user inside the vehicle is obtained from a pre-set ECG signal acquisition device. Based on the electrocardiogram signal, the current heart rate fluctuation data of the target user is determined; The pupil diameter of the target user is detected to obtain the current pupil diameter of the target user; Using a preset motion sickness risk prediction model, motion sickness risk is predicted based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level. The motion sickness risk prediction model includes a heart rate impact index prediction sub-model, a comprehensive impact index prediction sub-model, and a motion sickness severity prediction sub-model. The method utilizes a preset motion sickness risk prediction model to predict motion sickness risk based on vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter, thereby obtaining a motion sickness risk level, including: Based on the heart rate influence index prediction sub-model, the heart rate influence index is predicted for the heart rate fluctuation data and the vestibular stimulation intensity to obtain heart rate influence index data. Obtain preset resting pupil diameter and resting heart rate fluctuation data; Using the comprehensive influence index prediction sub-model, the vestibular stimulation intensity, the heart rate influence index data, the pupil diameter, the resting pupil diameter, and the resting heart rate fluctuation data are predicted to obtain the comprehensive influence index data; Based on the motion sickness severity prediction sub-model, the motion sickness severity is predicted from the comprehensive impact index data to obtain the motion sickness severity value. Based on the motion sickness severity value, the corresponding motion sickness risk level is determined; The comprehensive impact index prediction sub-model is expressed by a calculation formula, defined as an equation of the following form representing the degree of motion sickness and the rate of change of electrocardiogram signal and pupil diameter: in, This represents the comprehensive impact indicator data. The target user's current pupil diameter, The resting pupil diameter Vestibular stimulation intensity, This is data on resting heart rate fluctuations (resting SDNN values). This is for detecting heart rate fluctuation data.

2. The method according to claim 1, characterized in that, The heart rate influence index prediction sub-model, based on the heart rate fluctuation data and the vestibular stimulation intensity, predicts the heart rate influence index to obtain heart rate influence index data, including: Acquire preset data on vestibular inhibition coefficient, autonomic nervous system recovery rate, and resting heart rate fluctuation; Using the heart rate influence index prediction sub-model, the vestibular stimulation inhibition coefficient, autonomic nerve recovery rate, resting heart rate fluctuation data, and heart rate fluctuation data are calculated to obtain the heart rate influence index data.

3. The method according to claim 1, characterized in that, The motion sickness severity prediction sub-model, based on the comprehensive impact index data, predicts the motion sickness severity to obtain a motion sickness severity value, including: Obtain the preset heart rate fluctuation recovery threshold; Using the motion sickness severity prediction sub-model, the motion sickness severity value is obtained by calculating the comprehensive impact index data, the heart rate impact index data, and the heart rate fluctuation recovery threshold.

4. The method according to any one of claims 2 or 3, characterized in that, The process of determining the corresponding motion sickness risk level based on the motion sickness severity value includes: Using a preset motion sickness severity mapping function, the motion sickness severity value is converted to obtain a motion sickness risk index; Using a preset motion sickness risk level classification strategy, the motion sickness risk level corresponding to the motion sickness risk index is determined.

5. The method according to claim 1, characterized in that, The step of detecting the pupil diameter of the target user to obtain the current pupil diameter of the target user includes: Acquire a facial image of the target user captured by a camera on the vehicle; If an eye image that meets the recognition criteria can be extracted from the face image, the eye image can be recognized to obtain the current pupil diameter of the target user; If an eye image that meets the recognition criteria cannot be extracted from the face image, the pupil diameter is predicted based on the vestibular stimulation intensity using a preset pupil diameter prediction sub-model to obtain the current pupil diameter of the target user.

6. The method according to claim 5, characterized in that, The pre-defined pupil diameter prediction sub-model predicts the pupil diameter based on the vestibular stimulation intensity to obtain the current pupil diameter of the target user, including: Obtain preset resting pupil diameter, pupil dilation gain value, and pupil recovery rate; Using the pupil diameter prediction sub-model, the vestibular stimulation intensity, the resting pupil diameter, the pupil dilation gain value, and the pupil recovery rate are calculated to obtain the current pupil diameter of the target user.

7. The method according to claim 1, characterized in that, After using a preset motion sickness risk prediction model to predict the motion sickness risk based on the vestibular stimulation intensity, heart rate fluctuation data, and pupil diameter to obtain the motion sickness risk level, the method further includes: The vehicle is controlled according to a preset vehicle control strategy corresponding to the motion sickness risk level.

8. A motion sickness warning device, characterized in that, The device includes: The first acquisition module is used to acquire the current attitude angle of the vehicle; The calculation module is used to calculate the current vestibular stimulation intensity based on the posture angle and a preset motion sickness weighting coefficient. The second acquisition module is used to acquire the current electrocardiogram (ECG) signal of the target user inside the vehicle from a preset ECG signal acquisition device. The determination module is used to determine the current heart rate fluctuation data of the target user based on the electrocardiogram signal; The detection module is used to detect the pupil diameter of the target user and obtain the current pupil diameter of the target user; The prediction module is used to predict the risk of motion sickness based on the vestibular stimulation intensity, heart rate fluctuation data and pupil diameter using a preset motion sickness risk prediction model, and to obtain the motion sickness risk level. The motion sickness risk prediction model includes a heart rate impact index prediction sub-model, a comprehensive impact index prediction sub-model, and a motion sickness severity prediction sub-model. The prediction module includes: The first prediction unit is used to predict the heart rate influence index based on the heart rate influence index prediction sub-model, and obtain the heart rate influence index data. The second acquisition subunit is used to acquire preset resting pupil diameter and resting heart rate fluctuation data; The second calculation subunit is used to predict the vestibular stimulation intensity, the heart rate influence index data, the pupil diameter, the resting pupil diameter, and the resting heart rate fluctuation data using the comprehensive influence index prediction submodel, so as to obtain the comprehensive influence index data. The third prediction unit is used to predict the degree of motion sickness based on the motion sickness degree prediction sub-model and obtain the motion sickness degree value. The determining module is also used to determine the corresponding motion sickness risk level based on the motion sickness severity value; The comprehensive impact index prediction sub-model is expressed by a calculation formula, defined as an equation of the following form representing the degree of motion sickness and the rate of change of electrocardiogram signal and pupil diameter: in, This represents the comprehensive impact indicator data. The target user's current pupil diameter, The resting pupil diameter Vestibular stimulation intensity, This is data on resting heart rate fluctuations (resting SDNN values). This is for detecting heart rate fluctuation data.

9. A vehicle, characterized in that, The vehicle includes: a controller, an attitude angle acquisition device, and an electrocardiogram (ECG) signal acquisition device. The attitude angle acquisition device and the ECG signal acquisition device are both connected to the controller. The controller is used to execute the motion sickness warning method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, it implements the motion sickness warning method according to any one of claims 1-7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the motion sickness warning method according to any one of claims 1-7.

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