Motion sickness prevention and control method and related device

Abstract

The invention discloses a motion sickness prevention and control method and a related device. The method comprises the steps that the motion sickness condition of a user is analyzed based on target data; the motion sickness condition comprises a target motion sickness cause of the user; the target data comprises user data indicating a user state and / or motion data indicating a motion condition of target equipment; the target device is a device which causes the user to generate motion sickness; and recommending a motion sickness prevention and control strategy based on the motion sickness condition. By adopting the embodiment of the invention, motion sickness prevention and control can be effectively realized.

Description

Technical Field

[0001] The present application relates to the field of motion sickness treatment technology, and specifically to a motion sickness prevention and control method and related devices. Background Art

[0002] Motion sickness is a common symptom, which refers to the discomfort such as nausea, vomiting, dizziness, etc. caused by inconsistent information between visual, vestibular and proprioceptive receptors when riding in vehicles, ships, airplanes and other means of transportation. Motion sickness not only affects the comfort and travel experience of passengers, but may also cause safety hazards and health problems. The intelligentization of automobiles makes it more urgent to solve the problem of motion sickness. It will become a more common scenario for drivers and passengers to watch screens, work or entertain in the car, and such scenarios will aggravate the feeling of motion sickness.

[0003] Currently, there are related research and technologies devoted to the anti-motion sickness system of vehicles. However, due to the differences between individual users, the existing implementation schemes do not take into account the impact of individual differences on motion sickness prevention and control, resulting in poor prevention and control effects. Therefore, how to effectively achieve motion sickness prevention and control is a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0004] The present application provides a motion sickness prevention and control method and related devices, which can effectively achieve motion sickness prevention and control.

[0005] In a first aspect, the present application provides a method for preventing and controlling motion sickness, the method comprising:

[0006] analyzing the motion sickness condition of the user based on the target data; the motion sickness condition includes a target motion sickness cause of the user; the target data includes user data indicating a user state, and / or includes motion data indicating a motion condition of a target device; the target device is a device that causes the motion sickness of the user;

[0007] Based on the above motion sickness situations, motion sickness prevention and control strategies are recommended.

[0008] In the above solution, the cause of the user's motion sickness is analyzed based on personalized user data and / or motion data. In a possible implementation, since the user's personal data is taken into consideration, the specific motion conditions of the target device can also be taken into consideration, so that the root cause of the user's motion sickness can be analyzed accurately and specifically. On this basis, the motion sickness prevention and control strategy recommended based on the analyzed cause of motion sickness is highly targeted and accurate, so that motion sickness prevention and control can be effectively achieved.

[0009] In a possible implementation, analyzing the motion sickness condition of the user based on the target data includes:

[0010] Extracting features from the target data to obtain information of a plurality of features; the plurality of features include statistical features of the target data, and each of the plurality of features corresponds to at least one cause of motion sickness;

[0011] The cause of the target motion sickness is analyzed based on the information of the plurality of features.

[0012] In the above scheme, through the correspondence between the characteristics of the target data and the causes of motion sickness, the specific cause of motion sickness can be determined based on the characteristic information extracted from the target data.

[0013] In a possible implementation manner, each of the aforementioned features corresponds to a plurality of preset feature value ranges, each of the aforementioned preset feature value ranges corresponds to a motion sickness degree, and the aforementioned motion sickness degree is a degree of discomfort felt by the aforementioned user due to motion sickness;

[0014] The analyzing the motion sickness condition of the user based on the target data may further include:

[0015] The target motion sickness level of the user is analyzed based on the information of the plurality of features and the plurality of preset feature value ranges corresponding to each of the features.

[0016] In the above scheme, the preset feature range corresponding to the feature of the above target data can characterize the degree of motion sickness. Therefore, by comparing the specific feature information with the preset feature range, the degree of motion sickness of the user can be determined based on the preset feature range that the feature information falls into. In addition, on the basis of determining the cause, the specific degree of motion sickness of the user is further determined, and motion sickness prevention and control strategies are jointly recommended based on the cause of motion sickness and the degree of motion sickness, so that the recommended motion sickness prevention and control strategies can be more targeted and more accurate, thereby effectively achieving motion sickness prevention and control.

[0017] In one possible implementation, the aforementioned target data includes the aforementioned user data, the aforementioned user data includes physiological data indicating the physiological condition of the aforementioned user; the aforementioned multiple features include physiological features extracted based on the aforementioned physiological data; and the preset feature value range corresponding to the aforementioned physiological features is determined based on the historical physiological data of the aforementioned user.

[0018] In the above scheme, the preset feature range corresponding to the physiological feature is determined based on the personalized historical physiological data of the user. Compared with the preset feature range of physiological features of different users using a unified standard, the preset feature range of physiological features in this scheme is highly targeted, which effectively improves the accuracy of judging the cause and / or degree of motion sickness of the user.

[0019] In a possible implementation, the user data further includes sleep data indicating the sleep quality of the user, and the preset characteristic value range corresponding to the physiological characteristic is a characteristic value range corrected based on the sleep data.

[0020] In the above scheme, because sleep conditions affect mental state, there is a certain correlation between the mental state and the occurrence and severity of motion sickness. A good mental state will not cause motion sickness or the symptoms will be mild, while a bad mental state will cause motion sickness or the symptoms will be aggravated. Therefore, modifying the numerical range by sleep data can make the subsequent judgment results more accurate.

[0021] In a possible implementation manner, the historical physiological data of the user is the historical physiological data retained after eliminating the historical physiological data of the user during exercise and sleep.

[0022] In the above scheme, since the user's resting state (except for exercise and sleep) is similar to the state when riding a vehicle, a more accurate numerical range can be obtained by screening the data in the resting state to analyze the preset characteristic range of the aforementioned physiological characteristics.

[0023] In a possible implementation, multiple causes of motion sickness are analyzed based on the information of the multiple features and multiple preset feature value ranges corresponding to each of the features, and the degree of motion sickness corresponding to each of the multiple causes of motion sickness is analyzed;

[0024] The target motion sickness cause and / or the target motion sickness degree are obtained by comprehensively analyzing the multiple motion sickness causes and the motion sickness degrees corresponding to each of the motion sickness causes.

[0025] In the above solution, the final cause and / or degree of motion sickness of the user is obtained by comprehensive analysis of multiple causes and the degrees corresponding to each cause, so as to further improve the accuracy of the analysis.

[0026] In a possible implementation manner, the target data includes the user data, and the user data includes one or more of the following first data: heart rate data, heart cycle data, and sleep data of the user;

[0027] In the case where the cause of the target motion sickness is determined to be great psychological stress based on the analysis of the first data, the recommended motion sickness prevention and control strategy includes using sound soothing to assist in anti-motion sickness operations.

[0028] In the above solution, the cause of motion sickness due to great psychological pressure can be analyzed through the user's heart rate data, heart cycle data, sleep data and other data (for example, after extracting features, the corresponding relationship between the features and the cause of motion sickness is analyzed). After it is determined that the cause of motion sickness is great psychological pressure, sound soothing can be used in a targeted manner to assist in anti-motion sickness operations, so as to effectively prevent and control user motion sickness.

[0029] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes ventilation condition of the target device;

[0030] In the case where the target motion sickness cause is analyzed to be odor allergy based on the aforementioned physiological data and the aforementioned ventilation conditions, the aforementioned recommended motion sickness prevention and control strategy includes adopting fragrance adjustment operation and / or ventilation adjustment operation.

[0031] In the above scheme, the cause of motion sickness due to smell allergy can be analyzed through the user's physiological data and ventilation conditions (for example, after extracting features from the physiological data, the corresponding relationship between the features and the cause of motion sickness and the ventilation conditions are analyzed). After it is determined that the cause of motion sickness is smell allergy, the fragrance adjustment operation and / or ventilation adjustment operation can be used in a targeted manner to effectively prevent and control the user's motion sickness.

[0032] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the usage of the display screen in the target device;

[0033] In the case where the target motion sickness cause is analyzed to be visual confusion based on the aforementioned physiological data and the aforementioned display screen usage, the aforementioned recommended motion sickness prevention and control strategy includes adopting seat adjustment operations and / or the aforementioned display screen direction control operations.

[0034] In the above scheme, the cause of motion sickness due to visual confusion can be analyzed through the user's physiological data and the above-mentioned display screen usage and other data (for example, after extracting features from the physiological data, the corresponding relationship between the features and the cause of motion sickness and the display screen usage is analyzed). After it is determined that the cause of motion sickness is visual confusion, the seat adjustment operation and / or the above-mentioned display screen direction control operation can be used in a targeted manner to effectively prevent and control the user's motion sickness.

[0035] In a possible implementation manner, the aforementioned target data includes the aforementioned motion data, and the aforementioned motion data includes lateral acceleration and / or yaw angular velocity;

[0036] In a case where it is analyzed based on the lateral acceleration and / or yaw angular velocity that the cause of the target motion sickness is the turning of the target device, the recommended motion sickness prevention and control strategy includes an operation of replanning the path.

[0037] In the above solution, the cause of motion sickness caused by the turning of the target device can be analyzed through data such as lateral acceleration and / or yaw angular velocity (for example, after extracting features, the corresponding relationship between the features and the cause of motion sickness is analyzed). After it is determined that the cause of motion sickness is the turning of the target device, the operation of re-planning the path can be adopted in a targeted manner to effectively prevent and control user motion sickness.

[0038] In a possible implementation manner, the target data includes the motion data, and the motion data includes one or more of the following second data: longitudinal jerk, longitudinal acceleration, roll angular velocity, vehicle speed waveform, and acceleration / deceleration duration;

[0039] In a case where it is analyzed based on the second data that the cause of the target motion sickness is the vibration of the target device, the recommended motion sickness prevention and control strategy includes an operation of adjusting the longitudinal motion parameters of the target device.

[0040] In the above scheme, the cause of motion sickness caused by the vibration of the target device can be analyzed through data such as longitudinal jerk, longitudinal acceleration, roll angular velocity, vehicle speed waveform, or acceleration and deceleration duration (for example, after extracting features, the corresponding relationship between the features and the cause of motion sickness is analyzed). After it is determined that the cause of motion sickness is the vibration of the target device, the operation of adjusting the longitudinal motion parameters of the target device can be used in a targeted manner to effectively prevent and control user motion sickness.

[0041] In a possible implementation manner, the aforementioned target data includes the aforementioned motion data, and the aforementioned motion data includes one or more of the following third data: the pitch angular velocity and vertical acceleration of the aforementioned target device;

[0042] In a case where it is analyzed based on the third data that the cause of the target motion sickness is the turbulence of the target device, the recommended motion sickness prevention and control strategy includes a suspension control operation.

[0043] In the above solution, the cause of motion sickness caused by the turbulence of the target device can be analyzed through data such as the pitch angular velocity or vertical acceleration of the target device (for example, after extracting features, the corresponding relationship between the features and the cause of motion sickness is analyzed). After it is determined that the cause of motion sickness is the turbulence of the target device, the suspension control operation can be adjusted in a targeted manner to effectively prevent and control user motion sickness.

[0044] In a second aspect, the present application provides a motion sickness prevention and control device, the device comprising:

[0045] an analyzing unit, configured to analyze the motion sickness condition of the user based on the target data; the motion sickness condition includes a target motion sickness cause of the user; the target data includes user data indicating a user state and / or includes motion data indicating a motion condition of a target device; the target device is a device causing the user to experience motion sickness;

[0046] The recommendation unit is used to recommend a motion sickness prevention and control strategy based on the aforementioned motion sickness situation.

[0047] In a possible implementation manner, the aforementioned analysis unit is specifically used for:

[0048] Extracting features from the target data to obtain information of a plurality of features; the plurality of features include statistical features of the target data, and each of the plurality of features corresponds to at least one cause of motion sickness;

[0049] The cause of the target motion sickness is analyzed based on the information of the plurality of features.

[0050] In a possible implementation manner, each of the aforementioned features corresponds to a plurality of preset feature value ranges, each of the aforementioned preset feature value ranges corresponds to a motion sickness degree, and the aforementioned motion sickness degree is a degree of discomfort felt by the aforementioned user due to motion sickness;

[0051] The aforementioned analysis unit is also specifically used for:

[0052] The target motion sickness level of the user is analyzed based on the information of the plurality of features and the plurality of preset feature value ranges corresponding to each of the features.

[0053] In one possible implementation, the aforementioned target data includes the aforementioned user data, the aforementioned user data includes physiological data indicating the physiological condition of the aforementioned user; the aforementioned multiple features include physiological features extracted based on the aforementioned physiological data; and the preset feature value range corresponding to the aforementioned physiological features is determined based on the historical physiological data of the aforementioned user.

[0054] In a possible implementation, the user data further includes sleep data indicating the sleep quality of the user, and the preset characteristic value range corresponding to the physiological characteristic is a characteristic value range corrected based on the sleep data.

[0055] In a possible implementation manner, the historical physiological data of the user is the historical physiological data retained after eliminating the historical physiological data of the user during exercise and sleep.

[0056] In a possible implementation, multiple causes of motion sickness are analyzed based on the information of the multiple features and multiple preset feature value ranges corresponding to each of the features, and the degree of motion sickness corresponding to each of the multiple causes of motion sickness is analyzed;

[0057] The target motion sickness cause and / or the target motion sickness degree are obtained by comprehensively analyzing the multiple motion sickness causes and the motion sickness degrees corresponding to each of the motion sickness causes.

[0058] In a possible implementation manner, the target data includes the user data, and the user data includes one or more of the following first data: heart rate data, heart cycle data, and sleep data of the user;

[0059] In the case where the cause of the target motion sickness is determined to be great psychological stress based on the analysis of the first data, the recommended motion sickness prevention and control strategy includes using sound soothing to assist in anti-motion sickness operations.

[0060] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes ventilation condition of the target device;

[0061] In the case where the target motion sickness cause is analyzed to be odor allergy based on the aforementioned physiological data and the aforementioned ventilation conditions, the aforementioned recommended motion sickness prevention and control strategy includes adopting fragrance adjustment operation and / or ventilation adjustment operation.

[0062] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the usage of the display screen in the target device;

[0063] In the case where the target motion sickness cause is analyzed to be visual confusion based on the aforementioned physiological data and the aforementioned display screen usage, the aforementioned recommended motion sickness prevention and control strategy includes adopting seat adjustment operations and / or the aforementioned display screen direction control operations.

[0064] In a possible implementation manner, the aforementioned target data includes the aforementioned motion data, and the aforementioned motion data includes lateral acceleration and / or yaw angular velocity;

[0065] In a case where it is analyzed based on the lateral acceleration and / or yaw angular velocity that the cause of the target motion sickness is the turning of the target device, the recommended motion sickness prevention and control strategy includes an operation of replanning the path.

[0066] In a possible implementation manner, the target data includes the motion data, and the motion data includes one or more of the following second data: longitudinal jerk, longitudinal acceleration, roll angular velocity, vehicle speed waveform, and acceleration / deceleration duration;

[0067] In a case where it is analyzed based on the second data that the cause of the target motion sickness is the vibration of the target device, the recommended motion sickness prevention and control strategy includes an operation of adjusting the longitudinal motion parameters of the target device.

[0068] In a possible implementation manner, the aforementioned target data includes the aforementioned motion data, and the aforementioned motion data includes one or more of the following third data: the pitch angular velocity and vertical acceleration of the aforementioned target device;

[0069] In a case where it is analyzed based on the third data that the cause of the target motion sickness is the turbulence of the target device, the recommended motion sickness prevention and control strategy includes a suspension control operation.

[0070] In a third aspect, the present application provides a motion sickness prevention and control device, wherein the device includes a processor, a communication interface and a memory, wherein the communication interface is used to realize the reception and transmission of data, the memory is used to store computer programs or computer instructions, and the processor is used to execute the computer programs or computer instructions stored in the memory, so that the device performs the method as described in any one of the first aspect and its possible implementation methods.

[0071] In a fourth aspect, the present application provides a vehicle, comprising the motion sickness prevention and control device as described in any one of the second aspect or the third aspect above.

[0072] In a fifth aspect, the present application provides a chip, comprising a processor, a communication interface and a memory, wherein the communication interface is used to realize the reception and transmission of data, the memory is used to store computer programs or computer instructions, and the processor is used to execute the computer programs or computer instructions stored in the memory, so that the chip executes a method as described in any one of the first aspect and its possible implementation methods.

[0073] In a sixth aspect, the present application provides a computer-readable storage medium, which stores a computer program or computer instructions, and the aforementioned computer program or computer instructions are executed by a processor to implement a method as described in any one of the above-mentioned first aspect and its possible implementation methods.

[0074] In a seventh aspect, the present application provides a computer program product. When the computer program product is executed by a processor, the method as described in any one of the first aspect and its possible implementation methods will be implemented.

[0075] The solutions provided in the second to seventh aspects are used to implement or cooperate with the corresponding methods provided in the first aspect, and therefore can achieve the same or corresponding beneficial effects as the corresponding methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the three axes of the vehicle;

[0077] Figure 2 and Figure 3 It is a schematic diagram of the application scenario;

[0078] Figures 4 to 6 A schematic diagram of a method flow chart provided in an embodiment of the present application;

[0079] Figures 7 to 9 A schematic diagram of a probability density function provided in an embodiment of the present application;

[0080] Fig.10 A schematic diagram of the logical framework of the method provided in the embodiment of the present application;

[0081] Fig.11 and Fig.12 A schematic diagram of the device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0082] In the embodiment of the present application, "multiple" refers to two or more. In the embodiment of the present application, "and / or" is used to describe the association relationship of the associated objects, indicating three relationships that can exist independently, for example, A and / or B, which can be expressed as: A exists alone, B exists alone, or A and B exist at the same time. The description methods such as "at least one (or at least one) of a1, a2, ... and an" used in the embodiment of the present application include the situation where any one of a1, a2, ... and an exists alone, and also include any combination of any multiple of a1, a2, ... and an, each of which can exist alone; for example, the description method of "at least one of a, b and c" includes the situation where a is alone, b is alone, c is alone, a and b are combined, a and c are combined, b and c are combined, or abc is combined.

[0083] In the various embodiments of the present application, unless otherwise specified or logically conflicting, the terms and / or descriptions between the various embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0084] First, the technical terms involved in the embodiments of the present application are introduced.

[0085] (1) RR interval (RRI): R is a waveform of the electrocardiogram, and the RR interval can be understood as the heartbeat cycle.

[0086] (2) Heart rate variability (HRV): HRV refers to the variation in the differences between successive heartbeats, which contains information about the regulation of the cardiovascular system by neurohumoral factors.

[0087] (3) Power spectral density (PSD): PSD is a measure that describes the power distribution of a signal in the frequency domain and can be calculated using the autocorrelation function of the signal.

[0088] (4) Vehicle three-axis parameters.

[0089] The vehicle's three-axis parameters include the vehicle's angles (or angular velocities) and accelerations in the three-axis directions in three-dimensional space. Specifically, the vehicle's three-axis parameters include the vehicle's longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity (also known as roll angular velocity), pitch angular velocity, and yaw angular velocity. For ease of understanding, see the following example: Figure 1 . Figure 1 In the diagram, the X-axis represents the longitudinal direction, the Y-axis represents the lateral direction, and the Z-axis represents the vertical direction. The longitudinal acceleration is the acceleration in the X-axis direction. The lateral acceleration is the acceleration in the Y-axis direction. The vertical acceleration is the acceleration in the Z-axis direction. The roll angular velocity is the angular velocity around the X-axis. The pitch angular velocity is the angular velocity around the Y-axis. The yaw angular velocity is the angular velocity around the Z-axis. The three-axis parameters of the vehicle can be measured by an inertial measurement unit (IMU). The IMU may include a gyroscope, an accelerometer, a magnetometer, and the like.

[0090] (5) Vehicle bumps: refers to the vibration caused by uneven road surface or other factors during the driving process.

[0091] (6) Vehicle oscillation: refers to the oscillation phenomenon of vehicle motion (displacement, velocity and acceleration).

[0092] In order to effectively prevent and control motion sickness, the embodiments of the present application provide a method and a related device for preventing and controlling motion sickness. The following first introduces possible application scenarios of the embodiments of the present application.

[0093] In one possible implementation, Figure 2 A possible application scenario is shown as an example. Figure 2As shown, in the application scenario of the embodiment of the present application, users, intelligent electronic devices, and vehicles, aircraft or virtual reality (VR) devices may be included. For the convenience of subsequent introduction, vehicles, aircraft or VR devices are collectively referred to as target devices. It will be understood that the introduction of the target device here is only an example and does not constitute a limitation on the embodiment of the present application. In a specific implementation, any device that can cause motion sickness in the user after being used may belong to the target device described in the embodiment of the present application. Exemplarily, if the target device is a vehicle, aircraft or ship, etc., then using the target device refers to driving or riding the vehicle, aircraft or ship. If the target device is a VR device, then using the target device refers to using the VR device to immersively experience the virtual world or play games.

[0094] Exemplarily, the above-mentioned intelligent electronic device may be, for example, a wearable device such as a smart bracelet, a smart watch or smart clothing, etc. It is to be understood that the introduction here is only an example and does not constitute a limitation on the embodiments of the present application.

[0095] The above-mentioned intelligent electronic device and the target device can communicate with each other. For example, the two devices can communicate with each other through wireless communication methods such as Bluetooth communication, NearLink communication, or wireless fidelity (WIFI) communication, or can communicate with each other through wired communication. The embodiments of the present application are not limited to this.

[0096] In a possible implementation, Figure 2 In the scenario shown, the intelligent electronic device can collect user data (see the following introduction for details, which will not be described in detail here). Exemplarily, the intelligent electronic device can also process the collected user data (see the following introduction for specific processing, which will not be described in detail here). Then, the intelligent electronic device can send the collected user data and / or the processed data to the target device. After receiving these data, the target device can recommend the user's motion sickness prevention and control strategy in a targeted manner based on these data.

[0097] Exemplarily, if the target device is a vehicle, the motion sickness prevention and control strategy for the user that is targeted based on these data can be implemented by a control unit in the vehicle. The control unit can be, for example, a domain controller in the vehicle, a vehicle central computer (VCC) or other control modules and any combination thereof. For example, the domain controller can be, for example, a power domain controller, a chassis domain controller, a vehicle domain controller, a cockpit domain controller or an automatic driving domain controller in the vehicle. It is understandable that the introduction of the control unit here is only an example and does not constitute a limitation on the embodiments of the present application. Alternatively, if the target device is a VR device or an aircraft, the motion sickness prevention and control strategy for the user that is targeted based on these data can be executed by a unit having data calculation and processing functions such as a processor in the VR device or an aircraft. For specific implementation, please refer to the following introduction, which will not be described in detail here.

[0098] In another possible implementation, Figure 2 In the scenario shown, the intelligent electronic device can collect user data and recommend a motion sickness prevention and control strategy for the user based on the collected user data. Then, the intelligent electronic device sends the motion sickness prevention and control strategy to the above-mentioned target device for execution, so as to effectively prevent and control the user's motion sickness.

[0099] In another possible implementation, Figure 2 In the scenario shown, the target device can also send the motion data of the target device to the smart electronic device (see the following introduction for details, which will not be described in detail here). Then, the smart electronic device can recommend the user's motion sickness prevention and control strategy in a targeted manner based on the collected user data and the motion data. The motion sickness prevention and control strategy is sent to the target device for execution, so as to effectively prevent and control the user's motion sickness.

[0100] In one possible implementation, Figure 3 Another possible application scenario is exemplified. In this scenario, in addition to the above-mentioned users, intelligent electronic devices and target devices, third-party devices may also be included. The third-party device may be, for example, a user terminal device such as a mobile phone or a tablet computer. Alternatively, the third-party device may also be a cloud server, etc. The third-party device may communicate with the above-mentioned intelligent electronic device and the target device. Exemplarily, the intelligent electronic device may send the collected user data and / or the data obtained after processing the user data to the third-party device. Optionally, the target device may also send the motion data of the target device to the third-party device. Then, the third-party device may recommend a motion sickness prevention and control strategy for the user in a targeted manner based on the received data. And the motion sickness prevention and control strategy is sent to the above-mentioned target device for execution, so as to effectively prevent and control the user's motion sickness.

[0101] It is understandable that the above Figure 2 and Figure 3 The application scenarios shown are only examples and do not constitute a limitation on the embodiments of the present application. In a specific implementation, the application scenarios of the embodiments of the present application include scenarios for implementing motion sickness prevention and control strategy recommendations based on the above user data and / or motion data of the target device.

[0102] For example, see Figure 4 . Figure 4 The flowchart of the motion sickness prevention and control method provided in the embodiment of the present application is exemplarily shown. Figure 2 and Figure 3 As described in the application scenario, the method can be executed by the target device (or the control unit or processor in the target device), the intelligent electronic device or the third-party device. Alternatively, the method can also be executed by a combination of any two or three of the three devices. For the convenience of the following introduction, the execution subject of the method is collectively referred to as an execution device. The following mainly takes the target device as a vehicle as an example to exemplify the implementation process of the method.

[0103] like Figure 4 As shown, the motion sickness prevention and control method provided in the embodiment of the present application may include but is not limited to the following steps.

[0104] S401. Analyze the motion sickness condition of the user based on target data; the motion sickness condition includes a target motion sickness cause of the user; the target data includes user data indicating a user state, and / or includes motion data indicating a motion condition of a target device; the target device is a device that causes the user to experience motion sickness.

[0105] Exemplarily, the user data may include physiological data and / or sleep data of the user. The sleep data may indicate the sleep quality of the user (or may characterize the mental state of the user). The physiological data may include, for example, some or all of the following: the user's heart rate, heart rate cycle (RRI), blood oxygen, body temperature, skin temperature or blood pressure, etc. For the convenience of subsequent introduction, the user data included in the above target data is referred to as target user data. The physiological data included in the target user data is referred to as target physiological data. The sleep data included in the target user data is referred to as target sleep data.

[0106] Exemplarily, if the target device is a vehicle, the motion data of the target device may include part or all of the three-axis parameters of the vehicle. For example, it may include part or all of the vehicle's roll angle (also known as roll angle), pitch angle, yaw angle, longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity, pitch angular velocity, and yaw angular velocity. The motion data may also include vehicle speed, etc. For the convenience of subsequent introduction, the motion data included in the target data is referred to as target motion data.

[0107] In a possible implementation, the target sleep data may be, for example, a user's target sleep score (referred to as the target sleep score) or a sleep state level, etc. The present application embodiment takes the target sleep score as an example. The target sleep score may be calculated based on the user's sleep condition before using the target device. Figure 2 or Figure 3 In the application scenario shown, the user's sleep condition can be collected by the above-mentioned intelligent electronic device. Exemplarily, the user's target sleep score can be calculated by the intelligent electronic device based on the collected sleep condition. Or, exemplarily, the intelligent electronic device can send the collected sleep condition to the above-mentioned target device or the above-mentioned third-party device, and the target device or the third-party device calculates the user's target sleep score based on the collected sleep condition.

[0108] The present application embodiment does not limit the calculation method of the target sleep score. Figure 5 A method for calculating a user's target sleep score is described below. Take the target device as a vehicle as an example. Figure 5 As shown, the method for calculating the user's target sleep score may include, but is not limited to, the following steps.

[0109] 501. Detect whether a communication connection is established between the intelligent electronic device and the vehicle.

[0110] For example, the user carries an intelligent electronic device. The vehicle can check whether the user is close to the vehicle by detecting whether the intelligent electronic device has established a communication connection with the vehicle. The communication connection can be, for example, a wireless or wired communication connection. For details, see the aforementioned Figure 2 The relevant introduction is not repeated here.

[0111] If the intelligent electronic device establishes a communication connection with the vehicle, the user is close to the vehicle. Conversely, if the intelligent electronic device does not establish a communication connection with the vehicle, the user is far away from the vehicle.

[0112] 502. Detect whether the user is on the bus.

[0113] If the above-mentioned intelligent electronic device establishes a communication connection with the vehicle, the vehicle can further detect whether the user has boarded the vehicle. For example, it can be determined whether the user has boarded the vehicle by detecting whether the vehicle is started, the weight pressure of the seat, the camera in the cabin taking pictures and analyzing, or detecting the distance between the intelligent electronic device and the vehicle. The embodiment of the present application does not limit the specific method of determining whether the user has boarded the vehicle.

[0114] 503. Obtain the user's sleeping status.

[0115] If the user has boarded the vehicle, the user's sleep status can be sent to the vehicle through the intelligent electronic device. The vehicle receives the user's sleep status. Exemplarily, the sleep status may include part or all of the following: the user's sleep time on the day, the user's historical sleep time set, the user's sleep time and wake time on the day, the user's historical sleep time set and wake time set, the user's deep sleep time on the day, the user's historical deep sleep time set, the user's sleep time and wake time during a nap on the day, the user's historical sleep time and wake time set during a nap, etc.

[0116] 504. Calculate the user's sleep distribution.

[0117] After obtaining the sleep condition of the above-mentioned user, the vehicle can calculate the sleep distribution of the user based on the historical sleep data in the sleep condition. The historical sleep data may include the historical sleep time set of the above-mentioned user, the historical sleep time set and awake time set of the user, the historical deep sleep duration set of the user, the sleep time and awake time set of the user during the historical nap, etc. The sleep distribution may include, for example, the distribution of the user's deep sleep duration, the distribution of the user's sleep duration, the distribution of the user's sleep time, and the distribution of the user's lunch break duration. Exemplarily, these distribution conditions can be obtained by calculating the data distribution law and distribution parameters using the chi-square test or the single-sample KS test method, or by fitting the data to find the best approximation probability density function, etc. The specific calculation process is not repeated here.

[0118] 505. Estimate the difference between the sleep condition of the day and the sleep distribution obtained by the above calculation.

[0119] After obtaining the sleep distribution of the user, the sleep condition of the user on that day obtained in step 503 may be compared with the sleep distribution to determine the difference.

[0120] The user's sleep condition of the day may include, for example, the user's sleep duration of the day, the user's sleep time and wakefulness time of the day, the user's deep sleep duration of the day, the user's sleep time and wakefulness time during the nap of the day, etc. Then, the difference between the user's deep sleep duration of the day and the distribution of the user's deep sleep duration can be calculated. Calculate the difference between the user's sleep duration of the day and the distribution of the user's sleep duration. Calculate the difference between the user's sleep time of the day and the distribution of the user's sleep time. Calculate the difference between the user's nap time of the day and the distribution of the user's lunch break time. Exemplarily, the kernel function estimation method can be used to test the significance of the difference between the sleep condition of the day and the user's sleep distribution, and comprehensively evaluate the difference between the sleep condition of the day and the sleep distribution. The specific calculation process is not repeated here.

[0121] 506. Calculate the user's target sleep score based on the above differences.

[0122] After obtaining the above differences, the user's target sleep score can be determined based on the differences. Alternatively, the current mental state can be evaluated by combining the current time t and the user's motion data, and the current target sleep score W(t) can be calculated to represent the mental state at time t. The specific calculation process is not described here.

[0123] Exemplarily, if the execution device is the smart electronic device or third-party device, after the vehicle calculates the target sleep score of the user, the target sleep score can be sent to the smart electronic device or third-party device for subsequent processing. Similarly, if the user's target sleep score is calculated by a smart electronic device, a vehicle or a third-party device, and the execution device is not a device that calculates the target sleep score, the device that calculates the target sleep score can send the target sleep score to the execution device for subsequent processing. This will not be repeated here.

[0124] Exemplarily, the cause of motion sickness of the user is the reason that causes the user to experience motion sickness. Exemplarily, the cause of motion sickness may include one or more of the following: great psychological pressure, lack of sleep, odor allergy, visual confusion, target device steering (such as vehicle steering, etc.), target device vibration (such as vehicle vibration, etc.) or target device bumping (such as vehicle bumping, etc.), etc. It can be understood that the causes of motion sickness listed here are only examples, and other causes of motion sickness may also be included in a specific implementation, which are not listed one by one. Exemplarily, in a specific implementation, the above-mentioned target motion sickness cause may include one or more of these causes.

[0125] In another possible implementation, the above-mentioned user's motion sickness condition may also include the user's target motion sickness degree. The motion sickness degree is the degree to which the user feels uncomfortable due to motion sickness. Exemplarily, the motion sickness degree may be divided into, for example, no motion sickness, mild motion sickness, moderate motion sickness, severe motion sickness, and the like. Alternatively, the motion sickness degree may be distinguished by level. For example, it may be divided into level 0, level 1, level 2, and level 3. Level 0, level 1, level 2, and level 3 may represent no motion sickness, mild motion sickness, moderate motion sickness, and severe motion sickness, respectively. Alternatively, the motion sickness degree may be distinguished by scoring. For example, it may be divided into four scoring ranges of 0-25 points, 26-50 points, 51-75 points, and 76-100 points. The four scoring ranges represent no motion sickness, mild motion sickness, moderate motion sickness, and severe motion sickness, respectively. It is to be understood that the division and representation of the motion sickness degree here are only examples and do not constitute a limitation to the embodiments of the present application. For example, in a specific implementation, the target motion sickness level may include one or more of these motion sickness levels. The motion sickness level included in the target motion sickness level may correspond to the target motion sickness cause, and details are provided below, which will not be described in detail here.

[0126] Based on the above description, the user's motion sickness condition can be obtained based on the above target data analysis. Exemplarily, in a possible implementation, the user's motion sickness cause can be analyzed through the analysis results of data from different sources (or the features of data from different sources), or different analysis results of data from the same source (or the features of data from the same source). That is, there is a corresponding relationship between the motion sickness cause and the features of the data. Each feature can correspond to at least one motion sickness cause. In addition, in a possible implementation, each feature corresponds to a plurality of preset feature value ranges. Each of the preset feature value ranges corresponds to a degree of motion sickness. Therefore, there is also a corresponding relationship between the motion sickness cause and the degree of motion sickness. For example, taking a feature as an example, a certain motion sickness cause can be determined through the feature. In addition, the degree of discomfort caused by the motion sickness cause can be determined through the feature and the corresponding preset feature value range. If the above target motion sickness cause includes multiple causes, the degree of motion sickness corresponding to each cause can be the same or different.

[0127] It is understandable that, since the above-mentioned target data includes the above-mentioned target user data and / or target motion data, other auxiliary data may also be included. The other auxiliary data may include, for example, the usage of the mobile phone or the car screen, the ventilation of the car window or the air conditioner, etc. Due to the large number of data types, there may be multiple causes of motion sickness obtained based on the analysis of multiple data. However, in most cases, there is still a certain tendency for the main cause of motion sickness of the user. This tendency can be determined based on a comprehensive analysis of the multiple causes of motion sickness obtained by analysis and their corresponding degrees of motion sickness, and then the cause of motion sickness that mainly causes discomfort to the user is finally determined, that is, the above-mentioned target cause of motion sickness is determined. This will be further introduced below. In the process described below, for the sake of clarity, when only one feature is used as an example, it is assumed that only the analysis result of the one feature is considered, and the analysis results of other features are temporarily not considered. However, in specific implementations, the analysis results of multiple features are generally considered comprehensively.

[0128] The following describes, by way of example, the process of analyzing the target motion sickness cause and / or the target motion sickness degree.

[0129] In the first case, in a possible implementation, the user's motion sickness condition can be analyzed based on the above target user data.

[0130] Based on the above introduction, it can be known that the above target user data includes target physiological data and / or target sleep data. The target sleep data can be, for example, the above Figure 5 Or the target sleep score calculated in other possible ways, etc. Exemplarily, the sleep data itself is a feature, and no further feature extraction is required.

[0131] Exemplarily, for the feature of sleep data, the corresponding cause of motion sickness may be, for example, lack of sleep or high psychological pressure. Sleep data also corresponds to multiple preset feature value ranges. For example, taking the sleep score as an example, assuming that the sleep score is a percentage system, then the multiple preset feature value ranges corresponding to the sleep data include: 0-59 points, 60-79 points, and 80-100 points. Among them, between 0-59 points indicates poor sleep quality, bad mental state, and the corresponding degree of severe motion sickness. If the above target sleep score falls within the range of 0-59 points, it indicates that the user may suffer from severe motion sickness due to lack of sleep or high psychological pressure. Between 60-79 points, it indicates that the sleep quality is average, the mental state is average, and the corresponding degree of mild motion sickness. If the above target sleep score falls within the range of 60-79 points, it indicates that the user may suffer from mild motion sickness due to lack of sleep or high psychological pressure. Between 80-100 points indicates good sleep quality, good mental state, and no corresponding motion sickness. If the target sleep score falls within the range of 80-100 points, it indicates that the user may not suffer from motion sickness. It is understood that the description here is only an example and does not constitute a limitation on the embodiments of the present application. In the specific implementation, other preset characteristic value ranges can be divided as needed, and the embodiments of the present application do not limit this.

[0132] Exemplarily, based on the previous introduction, physiological data may include a variety of data such as the user's heart rate, RRI, blood oxygen, body temperature, skin temperature or blood pressure. In order to better implement data analysis, these physiological data may be feature extracted to obtain corresponding physiological features. Each physiological feature corresponds to a plurality of preset feature value ranges. For ease of distinction, the preset feature value range corresponding to the physiological feature is referred to as a preset physiological feature value range. That is, each physiological feature corresponds to a plurality of preset physiological feature value ranges.

[0133] For example, the preset physiological characteristic value range may be pre-calibrated. Alternatively, the preset physiological characteristic value range may be determined based on the historical physiological data of the user. Figure 6 The implementation process of determining the preset physiological characteristic value range based on the historical physiological data of the user is exemplarily introduced. The implementation process includes but is not limited to the following steps.

[0134] 601. Obtain historical physiological data of the user.

[0135] For example, the above Figure 2 or Figure 3 Taking the scenario shown as an example, the historical physiological data of the user can be collected through the intelligent electronic device.

[0136] In a possible implementation, the historical physiological data of the user may be, for example, the historical physiological data retained after the historical physiological data of the user during exercise and sleep are removed. The retained historical physiological data may be the historical physiological data of the user in a resting state. Since the resting state is similar to the state during riding, targeted screening of the resting state data may make the subsequently determined value range more accurate.

[0137] Exemplarily, the above-obtained historical physiological data of the user may be, for example, historical physiological data acquired during multiple first preset time periods when the user is in a resting state. For example, a set of historical physiological data of the user is acquired during each first preset time period. Then, multiple sets of historical physiological data of the user may be acquired. The first preset time period may be set according to actual application needs, and the embodiments of the present application do not limit this.

[0138] 602. Extract features from the user's historical physiological data to obtain physiological features.

[0139] Exemplarily, after obtaining multiple copies of user historical physiological data, the physiological characteristics of each copy of historical physiological data can be extracted. The physiological characteristics may include statistical characteristics of the historical physiological data within a first preset time period. For ease of understanding, the following exemplary introduction.

[0140] The above-mentioned acquisition of a user's historical physiological data may include part or all of the user's historical heart rate, historical RRI, historical blood oxygen, historical body temperature, historical skin temperature, historical blood pressure and other data in the above-mentioned first preset time period. Take the historical heart rate as an example. Then, the statistical characteristics of the historical heart rate include one or more of the mean, change rate, extreme value and standard deviation of the historical heart rate within the first preset time period. The statistical characteristics of other physiological data are similar and will not be repeated.

[0141] In a possible implementation, if the above-mentioned historical physiological data includes historical RRI. Then, the above-mentioned physiological characteristics may also include the time domain characteristics and frequency domain characteristics of the heart rate variability HRV calculated based on the historical RRI. The time domain characteristics of the HRV may, for example, include the root mean square of successive differences (RMSSD) of adjacent RR interval differences, and / or include the percentage of successive normal-to-normal intervals that differ by more than 50ms (pNN50) in the total number of all normal heartbeat intervals. The frequency domain characteristics of the HRV may, for example, include high-frequency energy (HF power), low-frequency energy (LF power), and the energy ratio of low frequency to high frequency (LF / HF) that quantifies the law of change in the RRI time series by calculating the power spectral density PSD.

[0142] In a possible implementation, after obtaining the time domain features and frequency domain features of the HRV, the statistical features of the time domain features and frequency domain features of the HRV may be further calculated, which will not be described in detail here.

[0143] Based on the above description, a physiological feature can be extracted from each of the above multiple copies of user historical physiological data. For example, assuming that each copy of historical physiological data includes heart rate, and assuming that the extracted physiological feature includes the mean of the corresponding physiological data. Then, a heart rate mean can be extracted from each copy of historical physiological data. Then multiple copies of historical physiological data can extract multiple heart rate means. The same is true for other physiological data, which will not be repeated.

[0144] 603. Calculate corresponding probability distribution according to the extracted physiological characteristics.

[0145] After extracting multiple features of each physiological data based on multiple copies of historical physiological data of the user, the probability distribution of each feature can be calculated based on the multiple features of each physiological data. Exemplarily, the data distribution law and distribution parameters can be calculated by using the chi-square test or the single-sample KS test method, or the data can be fitted to obtain the best approximation probability density function, etc. For ease of understanding, take the probability density function fitting as an example. For example, taking the multiple heart rate means extracted above as an example, the probability density function can be fitted to the multiple heart rate means, and the probability density function obtained by fitting can characterize the probability distribution of the physiological feature of the heart rate mean. For example, you can refer to the example Figure 7, which is a schematic diagram of the probability density function obtained by fitting. The horizontal axis of the probability density function represents the value of the physiological characteristic of the mean heart rate. The integral of the probability density function is 1. It can be understood that Figure 7 The above is only an example and does not constitute a limitation to the embodiments of the present application. The probability distribution of other physiological characteristics is similar and will not be described in detail here.

[0146] 604. Determine a preset physiological characteristic value range based on the calculated probability distribution and a preset probability threshold.

[0147] In a possible implementation, for ease of understanding, the above-mentioned heart rate mean physiological characteristic is still taken as an example. The probability distribution of the heart rate mean physiological characteristic is, for example, as shown in the above Figure 7 The preset probability threshold of the physiological characteristic of the heart rate mean may be one or more. The preset probability threshold may be obtained, for example, through expert experience or data training, and the present application embodiment does not limit this. For example, assuming that the preset probability threshold of the physiological characteristic of the heart rate mean is one, three heart rate mean ranges can be determined based on the preset probability threshold. For ease of understanding, please refer to the example Figure 8 .

[0148] exist Figure 8 It can be seen that the probability between the heart rate mean 1 and the heart rate mean 2 is the above-mentioned preset probability threshold. That is, based on the preset probability threshold, the heart rate mean range 1 between the heart rate mean 1 and the heart rate mean 2, the heart rate mean range 2 less than the heart rate mean 1, and the heart rate mean range 3 greater than the heart rate mean 2 can be determined. If the user's heart rate mean within the above-mentioned first preset time length falls within the heart rate mean range 1, it means that the user's heart rate is normal. On the contrary, if the user's heart rate mean within the above-mentioned first preset time length falls within the heart rate mean range 2 or the heart rate mean range 3, it means that the user's heart rate is abnormal and physiological discomfort (i.e., motion sickness) occurs.

[0149] In another possible example, assuming that there are two preset probability thresholds for the physiological characteristic of the heart rate mean (represented by preset probability threshold 1 and preset probability threshold 2), five heart rate mean ranges can be determined based on the two preset probability thresholds. Fig. 9 .

[0150] exist Fig. 9It can be seen that the probability between the heart rate mean 1 and the heart rate mean 2 is the above-mentioned preset probability threshold 1. The probability between the heart rate mean 3 and the heart rate mean 4 is the above-mentioned preset probability threshold 2. That is, based on the preset probability threshold 1 and the preset probability threshold 2, it is possible to determine the heart rate mean range 1 between the heart rate mean 1 and the heart rate mean 2, the heart rate mean range 2 less than the heart rate mean 1 and greater than the heart rate mean 3, the heart rate mean range 3 greater than the heart rate mean 2 and less than the heart rate mean 4, the heart rate mean range 4 less than the heart rate mean 3, and the heart rate mean range 5 greater than the heart rate mean 4. If the user's heart rate mean within the above-mentioned first preset time length falls within the heart rate mean range 1, it means that the user's heart rate is normal. If the user's heart rate mean within the above-mentioned first preset time length falls within the heart rate mean range 2 or the heart rate mean range 3, it means that the user's heart rate is slightly abnormal and mild physiological discomfort occurs (i.e. mild motion sickness occurs). If the user's heart rate average within the first preset time period falls within heart rate average range 4 or heart rate average range 5, it means that the user's heart rate is seriously abnormal and severe physiological discomfort occurs (i.e., severe motion sickness occurs).

[0151] It is understandable that the above Figure 8 and Fig. 9 The above is only an example and does not constitute a limitation to the embodiments of the present application. In a specific implementation, the preset probability threshold of the physiological characteristic of the heart rate mean value can be more, and more heart rate mean value ranges can be determined. The embodiments of the present application will not be described one by one.

[0152] Based on the above introduction on determining the preset physiological characteristic value range corresponding to the physiological characteristic of the heart rate mean, the corresponding preset physiological characteristic value ranges can be determined for other physiological characteristics in the same way, which will not be elaborated here.

[0153] 605. Modify the preset physiological characteristic value range based on the target sleep score.

[0154] In a specific implementation, the target sleep score may be, for example, the aforementioned Figure 5 Or the user's target sleep score calculated in other possible implementations. Based on the previous introduction, it can be seen that the user's target sleep score represents the user's mental state. The quality of the user's mental state is related to the user's motion sickness and the degree of motion sickness. For example, the higher the user's target sleep score, the better the mental state, the lower the probability of motion sickness or the milder the degree of motion sickness. The lower the user's target sleep score, the worse the mental state, the higher the probability of motion sickness or the more severe the degree of motion sickness. Therefore, in order to more accurately determine the cause of the user's motion sickness and / or the degree of motion sickness, the preset physiological characteristic value range determined above can be corrected by the user's target sleep score calculated above.

[0155] Exemplarily, the rule for correcting the above-determined preset physiological characteristic value range by the user's target sleep score is as follows: the higher the user's target sleep score, the larger the preset physiological characteristic value range used to characterize the user's normal physiology (i.e., no motion sickness). That is, the greater the probability of no motion sickness. Conversely, the lower the user's target sleep score, the larger the preset physiological characteristic value range used to characterize the user's physiological discomfort (i.e., motion sickness). That is, the greater the probability of motion sickness. Alternatively, the higher the user's target sleep score, the larger the preset physiological characteristic value range used to characterize the user's mild physiological discomfort compared to the preset physiological characteristic value range used to characterize the user's severe physiological discomfort. Conversely, the lower the user's target sleep score, the larger the preset physiological characteristic value range used to characterize the user's severe physiological discomfort compared to the preset physiological characteristic value range used to characterize the user's mild physiological discomfort. It can be understood that the description here is only an example. Under the principle that the higher the user's target sleep score, the better the mental state, and the less likely it is to produce motion sickness, more correction rules can be extended, which are not listed here one by one.

[0156] Exemplarily, based on the above-mentioned revised rules, the above-mentioned preset probability threshold can be first corrected based on the above-mentioned target sleep score, and then the above-mentioned preset physiological characteristic value range can be re-determined based on the revised probability threshold. For specific implementation, please refer to the relevant introduction of the aforementioned step 604, which will not be repeated here. The re-determined preset physiological characteristic value range is the revised preset physiological characteristic value range. Or, exemplarily, based on the above-mentioned revised rules, the preset physiological characteristic value range determined in the above step 604 can be directly corrected based on the above-mentioned target sleep score to obtain the revised preset physiological characteristic value range. It can be understood that this is only an example and does not constitute a limitation to the embodiments of the present application. For ease of understanding, for example, refer to the above Figure 8 Assuming that the target sleep score is high, for example, higher than a preset threshold, it can be indicated that the user's mental state is good. Therefore, the heart rate average range 1 indicating that the user's heart rate is normal can be expanded. For example, the expanded range is Figure 8 The range between the two straight lines in the figure. That is, the expanded heart rate mean range 1 is the corrected heart rate mean range 1. The specific amount of expansion can be set according to actual application requirements, and the embodiment of the present application does not limit this. Since the heart rate mean range 1 is expanded, the heart rate mean range 2 and the heart rate mean range 3 are reduced. That is, the reduced heart rate mean range 2 and the heart rate mean range 3 are the corrected heart rate mean range 2 and the corrected heart rate mean range 3.

[0157] In another possible implementation, the above steps 603 to 605 can be replaced by the following implementation. For ease of understanding, the above-mentioned heart rate mean physiological feature is still taken as an example. Based on the description of the above step 602, it can be seen that multiple heart rate means can be extracted based on multiple historical physiological data. Then, the mean of the multiple heart rate means (referred to as the target heart rate mean) is calculated. Then, the preset heart rate mean deviation value is obtained. The preset heart rate deviation value can be one or more. The preset heart rate mean deviation value is a value that deviates from the target heart rate mean. Therefore, based on the preset heart rate deviation value and the target heart rate mean, multiple heart rate mean ranges can be determined. Other physiological characteristics can similarly determine the corresponding preset physiological characteristic value ranges, which will not be repeated here. Similarly, the determined preset physiological characteristic value range can also be corrected based on the user target sleep score obtained above. For specific implementation, please refer to the relevant introduction of the above-mentioned step 605, which will not be repeated here.

[0158] In the above Figure 8 or Fig. 9 It can be seen from the related examples that the determined heart rate mean range can correspond to whether motion sickness occurs and / or the degree of motion sickness. In addition, the cause of motion sickness corresponding to the physiological characteristic of the heart rate mean is, for example, great psychological pressure. The preset physiological characteristic value ranges corresponding to other physiological characteristics can similarly characterize whether motion sickness occurs and / or the degree of motion sickness, which are not listed here one by one. In addition, other physiological characteristics can also correspond to specific causes of motion sickness. For example, see Table 1 for examples.

[0159] Table 1

[0160]

[0161] In the above Table 1, physiological characteristics and sleep scores are collectively referred to as user data characteristics. As shown in Table 1, the causes of motion sickness are related to user data characteristics, and some causes of motion sickness are related to the above-mentioned other auxiliary data in addition to user data characteristics. For example, if the user is using a display device such as a mobile phone or a car screen, the possibility of motion sickness caused by visual confusion increases. For another example, if the car window is not ventilated or the air conditioner is turned off, the possibility of motion sickness caused by odor allergy increases. Therefore, if motion sickness is analyzed by a certain user data feature (optionally, it can also be combined with the above-mentioned corresponding other auxiliary data), the corresponding cause of motion sickness can be determined by looking up the table. For example, if the user's RRI standard deviation, RMSSD, pNN50 and other user data characteristics of the user, as well as the use of display devices such as mobile phones or car screens indicate that the user has motion sickness, the cause of motion sickness includes the above-mentioned visual confusion. The same is true for others, which will not be repeated. In addition, based on the previous introduction, it can be seen that in the process of analyzing the generation of motion sickness, the degree of motion sickness can also be determined by analyzing which preset physiological characteristic value range the user's physiological characteristics fall into. For example, please refer to the relevant introduction of the above step 604, which will not be repeated here.

[0162] In addition, in a possible implementation, it can be seen in Table 1 above that the user data features corresponding to the two causes of motion sickness, visual confusion and odor allergy, both include features such as RRI standard deviation, RMSSD or pNN50. For example, although the features corresponding to these two causes may be the same, the preset physiological feature value ranges corresponding to different causes under the same features may be different. In this way, different causes can be distinguished. For example, compared with odor allergy, the heartbeat interval tends to be disordered when motion sickness caused by visual confusion occurs, and corresponding indicators such as RRI standard deviation, RMSSD, pNN50, etc. will show an upward trend. That is, if the value of RRI standard deviation, RMSSD or pNN50 is large, it falls within the preset physiological feature value range with a large value. It can be determined that the motion sickness is caused by visual confusion. Alternatively, a machine learning method can be used to train the preset physiological feature value ranges corresponding to visual confusion and odor allergy, respectively, to distinguish different causes of the disease.

[0163] It is understandable that the above Table 1 is only an example and does not constitute a limitation on the embodiments of the present application. In a specific implementation, there may be more physiological characteristics and more causes of motion sickness, which are not listed one by one in the embodiments of the present application.

[0164] In another possible implementation, the above-mentioned preset physiological characteristic range can be replaced by the preset numerical range corresponding to the user data itself. That is, there is no need to extract features from the user data. Instead, the probability distribution of data such as historical heart rate, historical RRI, historical blood oxygen, historical body temperature, historical skin temperature and historical blood pressure in the above-mentioned first preset time is directly calculated, and then the preset physiological characteristic numerical range is determined based on the calculated probability distribution and the preset probability threshold. The preset physiological characteristic numerical range determined is then corrected by the user's target sleep score. The specific implementation can be the same as the implementation of the aforementioned steps 603 to 605, which will not be repeated. Similarly, these corrected preset physiological characteristic numerical ranges can also characterize whether motion sickness occurs and / or the degree of motion sickness that occurs. In addition, it can also correspond to the specific cause of motion sickness. It will not be repeated. The following introduction mainly takes the above-mentioned feature extraction implementation method as an example.

[0165] Based on the above introduction, the correspondence between the cause of motion sickness and the user data features is known (e.g., Table 1 above). Then, the cause of motion sickness and / or the degree of motion sickness of the user can be determined based on the target user data and the correspondence. Exemplarily, the target sleep data included in the target user data (e.g., the target sleep score calculated above) can be directly searched for the correspondence to determine the corresponding cause of motion sickness.

[0166] Exemplarily, the target physiological data in the target user data may be, for example, the user physiological data collected in real time by the intelligent electronic device within a first preset time period during the user's use of the target device. After acquiring the target physiological data, feature extraction may be performed on the target physiological data to obtain target physiological feature information. The specific feature extraction implementation may refer to the relevant introduction of step 602 above, which will not be described here.

[0167] Exemplarily, the target physiological characteristic information may include information of n physiological characteristics, where n is an integer greater than 1. Each of the n physiological characteristics corresponds to a plurality of preset physiological characteristic value ranges. The preset physiological characteristic value range may be, for example, the preset physiological characteristic value range corrected in step 605. Exemplarily, the number of preset physiological characteristic value ranges corresponding to different physiological characteristics may be the same or different.

[0168] After the information of the n physiological characteristics is extracted, the information of each physiological characteristic in the n physiological characteristics can be matched with the corresponding multiple preset physiological characteristic value ranges to determine in which preset physiological characteristic value range the information of each physiological characteristic falls. Then, the specific cause of motion sickness and / or the degree of motion sickness are determined. For ease of understanding, the following example is given.

[0169] For example, assuming that the target physiological characteristic information includes two physiological characteristic information: target heart rate mean and target RMSSD. Assume that the preset physiological characteristic value range corresponding to the physiological characteristic of heart rate mean includes the above Figure 8 The corrected heart rate mean range 1, the corrected heart rate mean range 2 and the corrected heart rate mean range 3 are shown. It is assumed that the preset physiological characteristic value range corresponding to the physiological characteristic of RMSSD includes five ranges from the corrected RMSSD range 1 to the corrected RMSSD range 5. The corrected RMSSD range 1 corresponds to no motion sickness, that is, the user's RMSSD falls within the corrected RMSSD range 1, indicating that the user does not have motion sickness. The corrected RMSSD range 2 and the corrected RMSSD range 3 correspond to mild motion sickness, that is, the user's RMSSD falls within the two ranges, indicating that the user has mild motion sickness. The corrected RMSSD range 4 and the corrected RMSSD range 5 correspond to severe motion sickness, that is, the user's RMSSD falls within the two ranges, indicating that the user has severe motion sickness.

[0170] Based on the above description, illustratively, if the above target heart rate mean falls within the above corrected heart rate mean range 2 or the corrected heart rate mean range 3, it indicates that the user may suffer from motion sickness. By looking up the table (for example, looking up the above Table 1), it can be determined that the cause of the user's motion sickness includes great psychological pressure. If the above target RMSSD falls within the corrected RMSSD range 1, it indicates that the user does not suffer from motion sickness. Combining the two analysis results, it can be determined that the user suffers from motion sickness, and the cause of the user's motion sickness (i.e., the above target motion sickness cause) is great psychological pressure.

[0171] Alternatively, based on the above description, illustratively, if the above target heart rate mean falls within the above corrected heart rate mean range 2 or the corrected heart rate mean range 3, it indicates that the user may suffer from motion sickness. After looking up the table (for example, looking up the above Table 1), it can be determined that the cause of the user's motion sickness includes great psychological pressure. If the above target RMSSD falls within the corrected RMSSD range 4, it indicates that the user suffers from severe motion sickness. After looking up the table (for example, looking up the above Table 1), it can be determined that the cause of the user's motion sickness includes visual confusion or odor allergy. In order to further determine whether it is visual confusion or odor allergy, it can be judged in combination with the use of display devices such as the above mobile phone or car screen, and / or the ventilation conditions of the car windows or air conditioners. Please refer to the previous introduction for details, which will not be repeated here. It is assumed here that it is visual confusion. Then, based on the two analysis results, it can be determined that the user suffers from motion sickness, and the causes of the user's motion sickness (i.e., the above target motion sickness causes) are two: great psychological pressure and visual confusion. For the target motion sickness degree that is finally determined, illustratively, the most severe degree corresponding to the two motion sickness causes can be used as the motion sickness degree that is finally determined. Based on this, it can be determined that the target motion sickness degree is severe motion sickness. Or, illustratively, it can be determined by comprehensively considering the motion sickness degrees corresponding to the two motion sickness causes. For example, take the middle degree, or calculate the motion sickness degree by weight, etc. The embodiments of the present application are not limited to this.

[0172] It is understandable that the above examples are not intended to limit the embodiments of the present application. In a specific implementation, there may be a variety of possible analysis situations, which are not listed one by one in the embodiments of the present application.

[0173] In the second case, in a possible implementation, the user's motion sickness condition can be analyzed based on the above target motion data.

[0174] For example, based on the foregoing introduction, the target motion data may include part or all of the motion data of the target device (a vehicle is taken as an example below), such as the roll angle, pitch angle, yaw angle, vehicle speed, longitudinal acceleration, lateral acceleration, vertical acceleration, roll angular velocity, pitch angular velocity, and yaw angular velocity. In order to better implement data analysis, feature extraction may be performed on these motion data to obtain corresponding motion features.

[0175] Exemplarily, in a specific implementation, the target motion data may be, for example, motion data collected in real time by the vehicle within a second preset duration during the process of the user using the vehicle. The second preset duration may be set according to actual application needs, and the embodiment of the present application does not limit this. After acquiring the target motion data, feature extraction may be performed on the target motion data to obtain target motion feature information. The target motion feature information may include information on m motion features, where m is an integer greater than 1.

[0176] Exemplarily, the m motion features may include statistical features of the target motion data within a second preset time period. For example, taking longitudinal acceleration as an example, the statistical features of the longitudinal acceleration include one or more of the mean, extreme value, and standard deviation of the longitudinal acceleration obtained within the second preset time period. The statistical features of other motion data are similar and will not be described in detail.

[0177] In a possible implementation, the longitudinal acceleration can also be derived to obtain the longitudinal jerk. Then, the statistical characteristics of the longitudinal jerk can also be calculated. Then the m motion characteristics mentioned above can include the longitudinal jerk and the statistical characteristics of the longitudinal jerk. The same is true for other motion data such as lateral acceleration and vertical acceleration, which will not be described in detail.

[0178] In a possible implementation, acceleration waveform analysis can also be performed based on vehicle speed. The acceleration waveform analysis includes one or more of the following: detecting whether the vehicle speed waveform tends to be trapezoidal or triangular, detecting the duration of acceleration and deceleration, detecting the average value of the maximum speed area, detecting the duration of the maximum value area, fitting the slope of the vehicle speed increase or decrease, etc. Then the above-mentioned m motion features may include one or more features of the vehicle speed waveform, the duration of acceleration and deceleration, the average value of the maximum speed area, the duration of the maximum value area, the slope of the vehicle speed increase or decrease, etc. Exemplarily, in addition to the above-mentioned vehicle speed waveform, each motion feature corresponds to a plurality of preset feature value ranges. For the sake of distinction, the preset feature value range corresponding to the motion feature is referred to as the preset motion feature value range. That is, each motion feature corresponds to a plurality of preset motion feature value ranges. Exemplarily, the above-mentioned preset motion feature value range may be pre-calibrated. In a possible implementation, the preset motion feature value range may also be regularly revised as the above-mentioned vehicle is used, and the specific implementation of the revision is not limited in the embodiment of the present application.

[0179] Based on the above introduction, each of the above motion features can correspond to at least one cause of motion sickness. Each preset motion feature value range corresponds to a degree of motion sickness. For ease of understanding, the following examples are given. For example, take the standard deviation of the above longitudinal acceleration as an example. The cause of motion sickness corresponding to the standard deviation of the longitudinal acceleration is vehicle oscillation. Then, assume that the multiple preset motion feature value ranges corresponding to the motion feature of the standard deviation of the longitudinal acceleration include: (0, 0.4), [0.4, 0.7), [0.7, 1). Exemplarily, other ranges except these three ranges are invalidated. Among them, between (0, 0.4) indicates that the vehicle oscillation is small, corresponding to no motion sickness. Between [0.4, 0.7) indicates that the vehicle oscillation is more serious, corresponding to a mild degree of motion sickness. Between [0.7, 1) indicates that the vehicle oscillation is serious, corresponding to a severe degree of motion sickness. It can be understood that this is only an example and does not constitute a limitation on the embodiments of the present application. In the specific implementation, other preset motion feature value ranges can also be divided as needed, and the embodiments of the present application do not limit this. The preset motion feature value ranges corresponding to other motion features can also indicate whether motion sickness occurs and / or the degree of motion sickness, which are not listed here one by one. In addition, other motion features can also correspond to specific causes of motion sickness. For example, see Table 2 for example.

[0180] Table 2

[0181]

[0182]

[0183] As shown in Table 2, the causes of motion sickness correspond to the motion characteristics. For example, by calculating the statistical characteristics based on the motion data such as pitch angular velocity and / or vertical acceleration, it can be analyzed that the causes of the user's motion sickness include vehicle bumps. The same is true for other reasons, which will not be repeated here. In addition, based on the previous introduction, it can be known that in the process of analyzing the generation of motion sickness, the degree of motion sickness can also be determined by analyzing within which preset motion characteristic value range the user's motion characteristics fall, which will not be repeated here. It can be understood that what is shown in Table 2 is only an example and does not constitute a limitation to the embodiments of the present application. In a specific implementation, there may be more motion characteristics and more causes of motion sickness, which are not listed one by one in the embodiments of the present application.

[0184] Based on the correspondence between the causes of motion sickness and the motion features (e.g., Table 2 above), after the information of the m motion features is extracted, the information of each motion feature in the m motion features can be matched with the corresponding multiple preset motion feature value ranges. To determine in which preset motion feature value range the information of each motion feature falls. Then, the specific cause of motion sickness and / or the degree of motion sickness is determined. For ease of understanding, the following example is given.

[0185] For example, take the standard deviation of the longitudinal acceleration as an example. Assume that the target motion feature information includes the target standard deviation of the longitudinal acceleration. The multiple preset motion feature value ranges corresponding to the motion feature of the standard deviation of the longitudinal acceleration are as described above, specifically including: (0, 0.4), [0.4, 0.7), [0.7, 1). Then, illustratively, if the target standard deviation falls within (0, 0.4), it indicates that the user may not have motion sickness. If the target standard deviation falls within [0.4, 0.7), it indicates that the user may have mild motion sickness. If the target standard deviation falls within [0.7, 1), it indicates that the user may have severe motion sickness. After looking up the table (for example, looking up the above Table 2), it can be determined that the cause of the user's motion sickness includes vehicle oscillation. It can be understood that the examples given here are not sufficient to limit the embodiments of the present application. In a specific implementation, there may be a variety of possible analysis situations, such as comprehensive analysis of multiple features, etc., which are not listed one by one in the embodiments of the present application.

[0186] In the third case, in a possible implementation, the user's motion sickness condition can be analyzed based on the target user data and the target motion data. For example, in this case, a comprehensive analysis is actually performed in combination with the first case and the second case. The analysis principle can refer to the relevant introduction in the first case and the second case. For ease of understanding, the following example is given.

[0187] For example, assuming that the above target user data includes RRI, the extracted user data features include, for example, the RRI standard deviation, and the specific RRI standard deviation feature value is referred to as the target RRI standard deviation. Assume that the above target motion data includes longitudinal acceleration. The extracted motion features include, for example, longitudinal jerk (the specific extracted feature value is referred to as the target longitudinal jerk) and longitudinal acceleration standard deviation (the specific extracted feature value is referred to as the target longitudinal acceleration standard deviation).

[0188] The RRI standard deviation is a physiological characteristic. Assume that the cause of motion sickness corresponding to the RRI standard deviation is visual confusion. Assume that the preset physiological characteristic value range corresponding to the above-mentioned RRI standard deviation physiological characteristic includes three ranges: RRI standard deviation range 1 to RRI standard deviation range 3. These preset physiological characteristic value ranges can be, for example, ranges corrected by the above-mentioned target sleep score. RRI standard deviation range 1 corresponds to no motion sickness. RRI standard deviation range 2 corresponds to mild motion sickness. RRI standard deviation range 3 corresponds to severe motion sickness.

[0189] The cause of motion sickness corresponding to the longitudinal jerk is vehicle oscillation. Assume that the preset motion characteristic value range corresponding to the longitudinal jerk motion characteristic includes three ranges from longitudinal jerk range 1 to longitudinal jerk range 3. Longitudinal jerk range 1 corresponds to no motion sickness. Longitudinal jerk range 2 corresponds to mild motion sickness. Longitudinal jerk range 3 corresponds to severe motion sickness.

[0190] The cause of motion sickness corresponding to the longitudinal acceleration standard deviation is vehicle oscillation. Assume that the preset motion feature value range corresponding to the longitudinal acceleration standard deviation motion feature includes three ranges from longitudinal acceleration standard deviation range 1 to longitudinal acceleration standard deviation range 3. Longitudinal acceleration standard deviation range 1 corresponds to no motion sickness. Longitudinal acceleration standard deviation range 2 corresponds to mild motion sickness. Longitudinal acceleration standard deviation range 3 corresponds to severe motion sickness.

[0191] In a possible example, by comparing the above-mentioned target RRI standard deviation, target longitudinal jerk and target longitudinal acceleration standard deviation with their respective corresponding preset characteristic value ranges, any possible analysis result shown in Table 3 below may be obtained.

[0192] Table 3

[0193]

[0194]

[0195]

[0196] The analysis results in the above Table 3 are only examples. In specific implementations, there may be other analysis results. For example, take the case where the above target RRI standard deviation range falls into the RRI standard deviation range 2, the target longitudinal jerk falls into the longitudinal jerk range 2, and the target longitudinal acceleration standard deviation falls into the longitudinal acceleration standard deviation range 3. In this case, the analysis results shown in the above Table 3 are severe motion sickness caused by a combination of visual confusion and vehicle oscillation. This is because based on the target RRI standard deviation range 2 falling into the RRI standard deviation range 2, it can be analyzed that mild motion sickness is caused by visual confusion. Based on the target longitudinal jerk falling into the longitudinal jerk range 2, it can be analyzed that mild motion sickness is caused by vehicle oscillation. Based on the target longitudinal acceleration standard deviation falling into the longitudinal acceleration standard deviation range 3, it can be analyzed that severe motion sickness is caused by vehicle oscillation. If the degree of motion sickness uses the severity as the final degree of motion sickness (i.e. the above target degree of motion sickness), then it can be determined that the target motion sickness is severe motion sickness. Both causes of motion sickness exist, so it can be determined that the above-mentioned target motion sickness causes include the visual confusion and vehicle vibration.

[0197] In another possible implementation, if the degree of motion sickness is based on the number of occurrences, it can be determined that the target motion sickness degree is mild motion sickness. In this case, the analysis result obtained is that the mild motion sickness is caused by the combination of visual confusion and vehicle vibration.

[0198] In another possible implementation, if the cause of motion sickness is based on the cause of more severe motion sickness, it can be determined that the target cause of motion sickness is vehicle vibration, and the target degree of motion sickness is severe motion sickness. In this case, the analysis result obtained is that severe motion sickness is caused by the comprehensive vehicle vibration.

[0199] Based on the above introduction, the other analysis results in Table 3 are similar. Different motion sickness causes and / or motion sickness degree evaluation rules can determine different analysis results. The evaluation rules can be determined according to actual application requirements. The description of the embodiments of the present application is only an example and does not constitute a limitation on the embodiments of the present application.

[0200] S402. Recommend motion sickness prevention and control strategies based on the motion sickness situation.

[0201] For example, in a specific implementation, the target motion sickness cause of the user is analyzed, and a target motion sickness prevention and control operation can be recommended based on the target motion sickness cause. For example, there is a preset correspondence between the motion sickness prevention and control operation and the motion sickness cause. After the motion sickness cause is determined, the corresponding motion sickness prevention and control operation can be found through the preset correspondence. For example, see Table 4.

[0202] Table 4

[0203]

[0204] Table 4 above illustrates the correspondence between motion sickness prevention and control operations and the causes of motion sickness. For example, if the cause of motion sickness is excessive psychological pressure, the user's discomfort can be alleviated by sound soothing and auxiliary anti-sickness operations. The same is true for other reasons, which will not be repeated. It can be understood that what is shown in Table 4 above is only an example and does not constitute a limitation on the embodiments of the present application. In a specific implementation, the motion sickness prevention and control operations corresponding to the causes of motion sickness are not limited to the operations listed in Table 4 above, and other operations can also be performed. The embodiments of the present application do not limit the specific motion sickness prevention and control operations.

[0205] In a possible implementation, parameters for motion sickness prevention and control operations or adjustment levels for motion sickness prevention and control operations, etc. can also be recommended based on the target motion sickness level obtained by the above analysis. Exemplarily, the adjustment level can also correspond to the parameters actually adjusted. Exemplarily, the lighter the degree of motion sickness, the smaller the change in the recommended parameters for adjusting the motion sickness prevention and control operation compared to the parameters before adjustment (or the lower the recommended adjustment level). Conversely, the more severe the degree of motion sickness, the greater the change in the recommended parameters for adjusting the motion sickness prevention and control operation compared to the parameters before adjustment (or the higher the recommended adjustment level). Specific adjustment parameters or adjustment levels can be set according to actual application requirements, and the embodiments of the present application are not limited to this.

[0206] In order to more intuitively reflect the logical framework of the motion sickness prevention and control method provided by the embodiment of the present application as a whole. For example, in a possible implementation, it can be exemplified by referring to Fig.10 . Fig.10 A schematic diagram of the logical framework is shown as an example. Fig.10 Take the vehicle as an example. In this logical framework, it can be seen that after user data such as user sleep data and user physiological data, and vehicle motion data such as vehicle three-axis angle, vehicle three-axis angular velocity and vehicle three-axis acceleration are input into the execution device. The execution device can calculate and output a motion sickness prevention and control strategy based on the input data. The motion sickness prevention and control strategy is input into the vehicle control unit. The vehicle control unit can control components such as the vehicle's motion control system, navigation module, air conditioning and body system. Exemplarily, the motion control system can be used to adjust the vehicle's motion parameters and perform corresponding suspension control. The navigation module can make corresponding path planning adjustments. The air conditioner can adjust the direction of the air outlet and the amount of air to achieve ventilation. The body system can adjust related components such as sound, fragrance, seats, and screens. The vehicle control unit can control these components to perform corresponding motion sickness prevention and control operations based on the received motion sickness prevention and control strategy. For example, if the motion sickness prevention and control operation is a ventilation adjustment operation, the vehicle control unit can control the air conditioner to perform the ventilation adjustment operation. Other motion sickness prevention and control operations are each implemented by the corresponding components, which will not be repeated here. It can be understood that the above Fig.10 What is shown is only an example and does not constitute a limitation to the embodiments of the present application.

[0207] In a possible implementation, the feature extraction operation of the physiological data and / or motion data can be implemented by a pre-trained first machine learning algorithm model. The input of the first machine learning algorithm model is the physiological data and / or motion data, and the output is the corresponding physiological characteristics and / or motion characteristics.

[0208] In a possible implementation, the operation of analyzing the cause of motion sickness and / or the degree of motion sickness based on the extracted features can be implemented by a pre-trained second machine learning algorithm model. The input of the second machine learning algorithm model is physiological characteristics and / or motion characteristics. Optionally, the input may also include other auxiliary data such as the usage of the mobile phone or the car screen, the ventilation of the car windows or the air conditioner, etc. The output is the corresponding cause of motion sickness and / or the degree of motion sickness.

[0209] In a possible implementation, the operation of recommending a motion sickness prevention and control strategy based on the motion sickness cause and / or the motion sickness degree can be implemented by a pre-trained third machine learning algorithm model. The input of the third machine learning algorithm model is the motion sickness cause and / or the motion sickness degree, and the output is the corresponding motion sickness prevention and control strategy.

[0210] In another possible implementation, a fourth machine learning algorithm model can be trained. The input of the fourth machine learning algorithm model is the above-mentioned physiological data and / or motion data. Optionally, the input may also include other auxiliary data such as the usage of the above-mentioned mobile phone or vehicle screen, the ventilation conditions of the windows or air conditioners, etc. The output is the corresponding motion sickness prevention and control strategy. That is, the fourth machine learning algorithm model directly establishes a mapping relationship between physiological data and / or motion data and motion sickness prevention and control strategies, omitting the intermediate feature extraction and motion sickness cause analysis steps.

[0211] Exemplarily, any model from the first to fourth machine learning algorithm models can be implemented by using decision tree methods such as CAST, random forest, XGBoost, etc. Alternatively, other machine learning algorithms, such as various deep neural networks, can be selected to implement. This embodiment of the application is not limited to this.

[0212] In the above-described embodiments, the target device is mainly introduced as a vehicle. In a possible implementation, the target device may be, for example, an airplane. Passengers on an airplane may also experience motion sickness, especially during takeoff, landing, turning, or encountering air currents. Therefore, the execution device may analyze the specific cause of motion sickness and / or the degree of motion sickness based on the user's physiological data, sleep data, and the motion parameters of the airplane. Then, based on the cause of motion sickness and / or the degree of motion sickness, a motion sickness prevention and control strategy is recommended. Exemplarily, the motion parameters of the airplane may include three-axis parameters (for example, the three-axis parameters of the vehicle may be referred to), attitude angle, flight altitude, speed, and other data. The cause of motion sickness may also include, for example, turbulence, oscillation, steering, visual confusion, odor allergy, lack of sleep, or high psychological pressure, etc. The recommended motion sickness prevention and control strategy may, for example, include adjusting the inclination angle of the seat, the height of the headrest, the air flow rate, or the brightness of the light, or may also refer to the motion sickness prevention and control strategy of the aforementioned vehicle, which will not be described in detail here. Regarding the implementation of the method for preventing and controlling motion sickness for users on an airplane, reference may be made to the implementation process of the method for preventing and controlling motion sickness for users on the aforementioned vehicle, which will not be described in detail here.

[0213] In another possible implementation, the target device may be, for example, a VR device. It may also cause some players to experience symptoms such as dizziness, nausea, and headaches, which is called VR disease, similar to motion sickness. Therefore, the execution device may analyze the specific cause of motion sickness and / or the degree of motion sickness based on the user's physiological data, sleep data, and the game scene of the VR device. Then, based on the cause of motion sickness and / or the degree of motion sickness, a motion sickness prevention and control strategy is recommended. The cause of motion sickness may also include, for example, bumps (bumps that occur in the game scene), oscillations (oscillations that occur in the game scene), steering (steering that occurs in the game scene), visual confusion, lack of sleep, or high psychological pressure, etc. The recommended motion sickness prevention and control strategy may include, for example, adjusting the speed, viewing angle, or brightness of the game screen, or may refer to the motion sickness prevention and control strategy of the aforementioned vehicle, which will not be repeated here. The implementation of the motion sickness prevention and control method for users on VR devices may refer to the implementation process of the motion sickness prevention and control method for users on the aforementioned vehicle, which will not be repeated here.

[0214] In summary, in the embodiment of the present application, sleep data is used to evaluate the user's current sleep status, historical and real-time physiological information is used to evaluate the user's health status, and vehicle driving data is used to evaluate the vehicle driving status. Based on the above evaluation results, a comprehensive judgment is made using a data fusion method to obtain a description of the cause tendency and / or degree of motion sickness, which reduces the impact of individual differences in motion sickness and improves the precision and accuracy of motion sickness detection. In addition, according to the cause tendency and / or degree of motion sickness, different motion sickness prevention and control strategies are tendentiously recommended to improve the pertinence and effectiveness of motion sickness prevention and control strategies and reduce the negative impact of anti-sickness measures on users.

[0215] The above mainly introduces the motion sickness prevention and control method provided by the embodiment of the present application. It can be understood that in order to realize the corresponding functions mentioned above, the above-mentioned execution device includes hardware structures and / or software modules corresponding to the execution of each function. In combination with the units and steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0216] The embodiment of the present application can divide the functional modules of the above-mentioned execution device according to the above-mentioned method example. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0217] In the case of dividing each functional module according to each function, the embodiment of the present application also provides a motion sickness prevention and control device for implementing any of the above methods. For example, a motion sickness prevention and control device is provided, including a unit (or means) for implementing each step in any of the above methods.

[0218] For example, see Fig.11 , which is a structural diagram of a motion sickness prevention and control device 1100 provided in an embodiment of the present application. The motion sickness prevention and control device 1100 may be an execution device for implementing any embodiment of the motion sickness prevention and control method described above. The motion sickness prevention and control device 1100 may include an analysis unit 1101 and a recommendation unit 1102. Among them:

[0219] The analyzing unit 1101 is configured to analyze the motion sickness condition of the user based on the target data; the motion sickness condition includes a target motion sickness cause of the user; the target data includes user data indicating a user state, and / or includes motion data indicating a motion condition of a target device; the target device is a device causing the user to experience motion sickness;

[0220] The recommendation unit 1102 is used to recommend a motion sickness prevention and control strategy based on the motion sickness situation.

[0221] In a possible implementation manner, the analysis unit 1101 is specifically used for:

[0222] Extracting features from the target data to obtain information of a plurality of features; the plurality of features include statistical features of the target data, and each of the plurality of features corresponds to at least one cause of motion sickness;

[0223] The cause of the target's motion sickness is analyzed based on information of the multiple features.

[0224] In a possible implementation manner, each feature corresponds to a plurality of preset feature value ranges, each of the preset feature value ranges corresponds to a motion sickness degree, and the motion sickness degree is a degree of discomfort felt by the user due to motion sickness;

[0225] The analysis unit 1101 is also specifically used for:

[0226] The target motion sickness level of the user is analyzed based on the information of the multiple features and the multiple preset feature value ranges corresponding to each feature.

[0227] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the multiple features include physiological features extracted based on the physiological data; and the preset feature value range corresponding to the physiological feature is determined based on the historical physiological data of the user.

[0228] In a possible implementation, the user data further includes sleep data indicating the sleep quality of the user, and the preset characteristic value range corresponding to the physiological characteristic is a characteristic value range corrected based on the sleep data.

[0229] In a possible implementation manner, the historical physiological data of the user is the historical physiological data retained after excluding the historical physiological data of the user during exercise and sleep.

[0230] In a possible implementation, multiple motion sickness causes are analyzed based on the information of the multiple features and multiple preset feature value ranges corresponding to each feature, and the motion sickness degree corresponding to each of the multiple motion sickness causes is analyzed;

[0231] The target motion sickness cause and / or the target motion sickness degree is obtained by comprehensively analyzing the multiple motion sickness causes and the motion sickness degrees corresponding to each motion sickness cause.

[0232] In a possible implementation manner, the target data includes the user data, and the user data includes one or more of the following first data: heart rate data, heart cycle data, and sleep data of the user;

[0233] When it is analyzed based on the first data that the cause of the target's motion sickness is great psychological pressure, the recommended motion sickness prevention and control strategy includes using sound soothing to assist in anti-motion sickness operation.

[0234] In a possible implementation, the target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the ventilation condition of the target device;

[0235] When it is analyzed based on the physiological data and the ventilation condition that the cause of the target motion sickness is odor allergy, the recommended motion sickness prevention and control strategy includes adopting fragrance adjustment operation and / or ventilation adjustment operation.

[0236] In a possible implementation, the target data includes the user data, and the user data includes physiological data indicating the physiological condition of the user; the target data also includes the usage of the display screen in the target device;

[0237] When it is analyzed based on the physiological data and the usage of the display screen that the cause of the target motion sickness is visual confusion, the recommended motion sickness prevention and control strategy includes adopting seat adjustment operation and / or display screen direction control operation.

[0238] In a possible implementation manner, the target data includes the motion data, and the motion data includes lateral acceleration and / or yaw angular velocity;

[0239] In a case where it is analyzed based on the lateral acceleration and / or the yaw angular velocity that the cause of the target motion sickness is the turning of the target device, the recommended motion sickness prevention and control strategy includes an operation of replanning the path.

[0240] In a possible implementation manner, the target data includes the motion data, and the motion data includes one or more of the following second data: longitudinal jerk, longitudinal acceleration, rollover angular velocity, vehicle speed waveform, and acceleration / deceleration duration;

[0241] In a case where it is analyzed based on the second data that the cause of the target motion sickness is the vibration of the target device, the recommended motion sickness prevention and control strategy includes an operation of adjusting the longitudinal motion parameters of the target device.

[0242] In a possible implementation manner, the target data includes the motion data, and the motion data includes one or more of the following third data: a pitch angular velocity and a vertical acceleration of the target device;

[0243] In a case where it is analyzed based on the third data that the target motion sickness cause is the turbulence of the target device, the recommended motion sickness prevention and control strategy includes a suspension control operation.

[0244] Figure 4 The specific operation and beneficial effects of each unit in the motion sickness prevention and control device 1100 can be found in the above Figure 4 The corresponding descriptions in possible embodiments thereof will not be repeated here.

[0245] For example, see Fig.12 , which is a structural schematic diagram of a possible physical entity of the motion sickness prevention and control device provided in this application. Fig.12 The motion sickness prevention and control device 1200 shown may be an execution device for implementing any embodiment of the motion sickness prevention and control method. The motion sickness prevention and control device 1200 includes: a processor 1201, a memory 1202, and a communication interface 1203. The processor 1201, the communication interface 1203, and the memory 1202 may be connected to each other or connected to each other through a bus 1204.

[0246] Exemplarily, the memory 1202 is used to store computer programs and data of the motion sickness prevention and control device 1200. The memory 1202 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM) or portable read only memory (compact disc read-only memory, CD-ROM), etc.

[0247] The software or program code required for all or part of the functions of the motion sickness prevention and control device in the above method embodiment may be stored in the memory 1202 .

[0248] In one possible implementation, if the software or program code required for some functions is stored in the memory 1202, the processor 1201, in addition to calling the program code in the memory 1202 to implement some functions, can also cooperate with other components (such as the communication interface 1203) to complete other functions described in the method embodiment (such as the function of receiving or sending data).

[0249] There may be multiple communication interfaces 1203 for supporting the motion sickness prevention and control device 1200 to communicate, such as receiving or sending data or messages.

[0250] Exemplarily, the processor 1201 may be a processor, which is a circuit with data processing capability. In one implementation, the processor may be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which may be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by a processor as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration may be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. Alternatively, the processor 1201 can be a combination of at least two of these processor forms, etc.

[0251] The processor 1201 can be used to read the program stored in the memory 1202 and execute the above Figure 4 and the operations performed by the execution device in its possible embodiments.

[0252] Fig.12 The specific operation and beneficial effects of each unit in the motion sickness prevention and control device 1200 can be referred to above. Figure 4 The corresponding descriptions in possible embodiments thereof will not be repeated here.

[0253] The present application also provides a vehicle, which comprises the Figure 4 or Figure 5 The motion sickness prevention and control device.

[0254] The present application also provides a chip, the aforementioned chip includes a processor, a communication interface and a memory, wherein the aforementioned communication interface is used to realize the reception and transmission of data, the aforementioned memory is used to store computer programs or computer instructions, and the aforementioned processor is used to execute the computer programs or computer instructions stored in the aforementioned memory, so that the aforementioned chip executes the aforementioned Figure 4 And a method implemented by an execution device in any of its possible implementation modes.

[0255] The present application also provides a computer-readable storage medium, which stores a computer program or computer instructions, which is executed by a processor to implement the above Figure 4 And a method implemented by an execution device in any of its possible implementation modes.

[0256] The present application also provides a computer program product. When the computer program product is read and executed by a computer, the above Figure 4 The method implemented by the execution device in any of its possible implementation modes will be executed.

[0257] In this application, the terms "first", "second", etc. are used to distinguish between identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there a limitation on the quantity and execution order. It should also be understood that although the following description uses the terms first, second, etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another element.

[0258] It should also be understood that in the various embodiments of the present application, the size of the serial number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0259] It should also be understood that the term “comprise” (also known as “includes,” “including,” “comprises” and / or “comprising”) when used in this specification specifies the presence of stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0260] It should also be understood that the references to "one embodiment", "an embodiment", or "a possible implementation" throughout the specification mean that specific features, structures, or characteristics related to the embodiment or implementation are included in at least one embodiment of the present application. Therefore, the references to "in one embodiment" or "in an embodiment", or "a possible implementation" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for preventing and controlling motion sickness, characterized in that: The method comprises: analyzing the motion sickness condition of the user based on the target data; the motion sickness condition includes the target motion sickness cause of the user; the target data includes user data indicating the user state, and / or includes motion data indicating the motion condition of a target device; the target device is a device that causes the user to experience motion sickness; Motion sickness prevention and control strategies are recommended based on the described motion sickness situations.

2. The method according to claim 1, characterized in that Analyzing the motion sickness condition of the user based on the target data includes: Extracting features from the target data to obtain information of a plurality of features; the plurality of features include statistical features of the target data, and each of the plurality of features corresponds to at least one cause of motion sickness; The target motion sickness cause is analyzed based on information of the plurality of features.

3. The method according to claim 2, characterized in that Each of the characteristics corresponds to a plurality of preset characteristic value ranges, each of the preset characteristic value ranges corresponds to a motion sickness degree, and the motion sickness degree is the degree of discomfort felt by the user due to motion sickness; Analyzing the motion sickness condition of the user based on the target data further includes: The target motion sickness level of the user is analyzed based on the information of the multiple features and the multiple preset feature value ranges corresponding to each feature.

4. The method according to claim 3, characterized in that The target data includes the user data, and the user data includes physiological data indicating the physiological condition of the user; the multiple features include physiological features extracted based on the physiological data; and the preset feature value range corresponding to the physiological feature is determined based on the historical physiological data of the user.

5. The method according to claim 4, characterized in that The user data also includes sleep data indicating the sleep quality of the user, and the preset characteristic value range corresponding to the physiological characteristic is the characteristic value range corrected based on the sleep data.

6. The method according to claim 4 or 5, characterized in that: The historical physiological data of the user is the historical physiological data retained after the historical physiological data of the user during exercise and sleep are eliminated.

7. The method according to any one of claims 3 to 6, characterized in that: Analyzing a plurality of motion sickness causes based on the information of the plurality of features and a plurality of preset feature value ranges corresponding to each feature, and analyzing the motion sickness degree corresponding to each of the plurality of motion sickness causes; The target motion sickness cause and / or the target motion sickness degree is obtained by comprehensively analyzing the multiple motion sickness causes and the motion sickness degrees corresponding to each motion sickness cause.

8. The method according to any one of claims 1 to 7, characterized in that: The target data includes the user data, and the user data includes one or more of the following first data: heart rate data, heart cycle data, and sleep data of the user; When it is analyzed based on the first data that the cause of the target motion sickness is great psychological stress, the recommended motion sickness prevention and control strategy includes using sound soothing to assist in motion sickness prevention operations.

9. The method according to any one of claims 1 to 8, characterized in that: The target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the ventilation condition of the target device; In a case where it is analyzed based on the physiological data and the ventilation conditions that the target motion sickness cause is odor allergy, the recommended motion sickness prevention and control strategy includes adopting a fragrance adjustment operation and / or a ventilation adjustment operation.

10. The method according to any one of claims 1 to 9, characterized in that: The target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the display screen usage in the target device; When it is analyzed based on the physiological data and the display screen usage that the target motion sickness cause is visual confusion, the recommended motion sickness prevention and control strategy includes adopting a seat adjustment operation and / or a display screen direction control operation.

11. The method according to any one of claims 1 to 10, characterized in that: The target data includes the motion data, and the motion data includes lateral acceleration and / or yaw rate; In a case where it is analyzed based on the lateral acceleration and / or the yaw angular velocity that the cause of the target motion sickness is the turning of the target device, the recommended motion sickness prevention and control strategy includes an operation of replanning a path.

12. The method according to any one of claims 1 to 11, characterized in that: The target data includes the motion data, and the motion data includes one or more of the following second data: longitudinal jerk, longitudinal acceleration, rollover angular velocity, vehicle speed waveform, and acceleration / deceleration duration; In a case where it is analyzed based on the second data that the cause of the target motion sickness is the vibration of the target device, the recommended motion sickness prevention and control strategy includes an operation of adjusting a longitudinal motion parameter of the target device.

13. The method according to any one of claims 1 to 12, characterized in that: The target data includes the motion data, and the motion data includes one or more of the following third data: the pitch angular velocity and the vertical acceleration of the target device; In a case where it is analyzed based on the third data that the cause of the target motion sickness is the turbulence of the target device, the recommended motion sickness prevention and control strategy includes a suspension control operation.

14. A motion sickness prevention and control device, characterized in that: The device comprises: an analyzing unit, configured to analyze the motion sickness condition of the user based on target data; the motion sickness condition includes a target motion sickness cause of the user; the target data includes user data indicating a user state, and / or includes motion data indicating a motion condition of a target device; the target device is a device causing the user to experience motion sickness; A recommendation unit is used to recommend a motion sickness prevention and control strategy based on the motion sickness situation.

15. The device according to claim 14, characterized in that The analysis unit is specifically used for: Extracting features from the target data to obtain information of a plurality of features; the plurality of features include statistical features of the target data, and each of the plurality of features corresponds to at least one cause of motion sickness; The target motion sickness cause is analyzed based on information of the plurality of features.

16. The device according to claim 15, characterized in that Each of the characteristics corresponds to a plurality of preset characteristic value ranges, each of the preset characteristic value ranges corresponds to a motion sickness degree, and the motion sickness degree is the degree of discomfort felt by the user due to motion sickness; The analysis unit is also specifically used for: The target motion sickness level of the user is analyzed based on the information of the multiple features and the multiple preset feature value ranges corresponding to each feature.

17. The device according to claim 16, characterized in that The target data includes the user data, and the user data includes physiological data indicating the physiological condition of the user; the multiple features include physiological features extracted based on the physiological data; and the preset feature value range corresponding to the physiological feature is determined based on the historical physiological data of the user.

18. The device according to claim 17, characterized in that The user data also includes sleep data indicating the sleep quality of the user, and the preset characteristic value range corresponding to the physiological characteristic is the characteristic value range corrected based on the sleep data.

19. The device according to claim 17 or 18, characterized in that The historical physiological data of the user is the historical physiological data retained after the historical physiological data of the user during exercise and sleep are eliminated.

20. The device according to any one of claims 16 to 19, characterized in that Analyzing a plurality of motion sickness causes based on the information of the plurality of features and a plurality of preset feature value ranges corresponding to each feature, and analyzing the motion sickness degree corresponding to each of the plurality of motion sickness causes; The target motion sickness cause and / or the target motion sickness degree is obtained by comprehensively analyzing the multiple motion sickness causes and the motion sickness degrees corresponding to each motion sickness cause.

21. The device according to any one of claims 14 to 20, characterized in that The target data includes the user data, and the user data includes one or more of the following first data: heart rate data, heart cycle data, and sleep data of the user; When it is analyzed based on the first data that the cause of the target motion sickness is great psychological stress, the recommended motion sickness prevention and control strategy includes using sound soothing to assist in motion sickness prevention operations.

22. The device according to any one of claims 14 to 21, characterized in that The target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the ventilation condition of the target device; In a case where it is analyzed based on the physiological data and the ventilation conditions that the target motion sickness cause is odor allergy, the recommended motion sickness prevention and control strategy includes adopting a fragrance adjustment operation and / or a ventilation adjustment operation.

23. The device according to any one of claims 14 to 22, characterized in that The target data includes the user data, the user data includes physiological data indicating the physiological condition of the user; the target data also includes the display screen usage in the target device; When it is analyzed based on the physiological data and the display screen usage that the target motion sickness cause is visual confusion, the recommended motion sickness prevention and control strategy includes adopting a seat adjustment operation and / or a display screen direction control operation.

24. The device according to any one of claims 14 to 23, characterized in that The target data includes the motion data, and the motion data includes lateral acceleration and / or yaw rate; In a case where it is analyzed based on the lateral acceleration and / or the yaw angular velocity that the cause of the target motion sickness is the turning of the target device, the recommended motion sickness prevention and control strategy includes an operation of replanning a path.

25. The device according to any one of claims 14 to 24, characterized in that The target data includes the motion data, and the motion data includes one or more of the following second data: longitudinal jerk, longitudinal acceleration, rollover angular velocity, vehicle speed waveform, and acceleration / deceleration duration; In a case where it is analyzed based on the second data that the cause of the target motion sickness is the vibration of the target device, the recommended motion sickness prevention and control strategy includes an operation of adjusting a longitudinal motion parameter of the target device.

26. The device according to any one of claims 14 to 25, characterized in that The target data includes the motion data, and the motion data includes one or more of the following third data: the pitch angular velocity and the vertical acceleration of the target device; In a case where it is analyzed based on the third data that the cause of the target motion sickness is the turbulence of the target device, the recommended motion sickness prevention and control strategy includes a suspension control operation.

27. A motion sickness prevention and control device, characterized in that: The device includes a processor, a communication interface and a memory, wherein the communication interface is used to realize the reception and transmission of data, the memory is used to store computer programs or computer instructions, and the processor is used to execute the computer programs or computer instructions stored in the memory, so that the device executes the method according to any one of claims 1 to 13.

28. A vehicle, characterized in that: Comprising the motion sickness prevention and control device as described in claim 27.

29. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program or computer instructions, and the computer program or computer instructions are executed by a processor to implement the method according to any one of claims 1 to 13.

30. A computer program product, characterized in that When the computer program product is executed by a processor, the method described in any one of claims 1 to 13 will be implemented.

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