A hyperbolic tangent function-based visual animation anti-dizzy method and device

Abstract

This invention discloses a visual animation anti-nausea method and device based on the hyperbolic tangent function. It establishes a posture motion database by sorting and defining the acquired posture physical motion data according to the time sequence. Visual animation factor vectors are constructed by fusing the hyperbolic tangent function with the posture motion data, thus building an image state vector. The posture motion vector data is input into a dynamic visual anti-nausea model. The response state of the dynamic visual anti-nausea model is controlled according to custom conditions; when the conditions exceed a preset value, the dynamic visual anti-nausea model is activated. The device includes a data acquisition module, a user interaction module, a decision control module, a visual display module, and a storage module. This invention solves the problems of abrupt image jitter and visual fatigue caused by severe shaking in existing visual anti-nausea control technologies, thus achieving anti-nausea. It ensures the smoothness of visual animation, meets the requirements for visual comfort, and thereby achieves anti-nausea.

Description

Technical Field

[0001] This invention relates to the field of vehicle ride comfort control technology, and in particular to a visual animation anti-drowsiness method and device based on hyperbolic tangent function. Background Technology

[0002] Motion sickness is a common physiological reaction when traveling by vehicle. Its root cause lies in the discrepancy between the motion perceived by the vestibular system and the information received by the visual system, leading to sensory conflict. Typical symptoms include dizziness, nausea, cold sweats, and vomiting. In the field of smart glasses, effectively preventing motion sickness by balancing the conflict between the vestibular system and visual perception has become one of the core research directions.

[0003] However, existing motion sickness relief techniques have the following shortcomings:

[0004] 1. Delayed Triggering Logic: Most current intervention methods adopt a passive triggering mode, meaning the system only initiates intervention after the passenger experiences significant discomfort and actively reports it. This intervention mode fails to prevent motion sickness, significantly reducing passenger comfort and offering only mediocre preventative effects.

[0005] 2. Lack of flexible adjustment in intervention intensity: Existing motion sickness intervention programs often adopt a fixed intervention mode. In the visual compensation stage, if the adjustment intensity is too large or the visual image changes abruptly, it may even induce discomfort in passengers due to the inability of the image to respond to the bumpy ride, thus exacerbating the degree of motion sickness.

[0006] 3. Limitations of Wearing: Current visual motion sickness prevention devices generally employ intervention modes that require passengers to pay real-time attention to visual compensation images. Furthermore, the image response is relatively slow and fails to incorporate a real-time monitoring model for low-latency response. This significantly reduces the passenger's travel experience and the effectiveness of the preventative measures. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a visual animation anti-dizziness method and device based on the hyperbolic tangent function, which can ensure the smoothness of visual animation and meet the requirements of visual comfort, thereby achieving anti-dizziness.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: a visual animation anti-drowsiness method based on the hyperbolic tangent function, comprising the following steps:

[0009] S1. Acquire posture and physical motion data in real time through the data acquisition module, sort and define the acquired posture and physical motion data according to the time order of acquisition, and establish a posture and motion database.

[0010] S2. Based on the fusion of hyperbolic tangent function and posture physical motion data, construct visual animation factor vector, combine with the parameters of display module to establish dynamic visual anti-drowsiness model, and construct image state vector;

[0011] S3. Input the posture physical motion data into the dynamic visual anti-drowsiness model and output the changing parameters of the visual animation in real time.

[0012] S4. Control the response state of the dynamic visual anti-drowsiness model according to the custom conditions. When the condition exceeds the preset value, the dynamic visual anti-drowsiness model is turned on.

[0013] In a preferred embodiment, S1 is implemented as follows: A target vehicle is selected, and the acceleration data along the vehicle's x-axis and y-axis are used as the attitude physical motion data; the obtained attitude physical motion data is sorted and defined according to the time sequence of acquisition; different physical quantities at the same moment constitute an attitude physical motion data vector, defined as...

[0014]

[0015] In the formula, , These represent the i-th x-axis acceleration and y-axis acceleration acquired by the data acquisition module, respectively. This represents the i-th attitude physical motion data vector in the attitude motion vector database;

[0016] The aforementioned posture physical motion data includes vehicle and passenger head orientation data.

[0017] In a preferred embodiment, the dynamic visual anti-nausea model is a dynamic visual anti-nausea model constructed based on the fusion of hyperbolic tangent function and posture physical motion data. This dynamic visual anti-nausea model calculates a visual animation factor vector by inputting posture physical motion data, and after adjusting the proportion of the visual animation factor vector, outputs an image state vector in real time. The dynamic visual anti-nausea model has a response state. By inputting custom conditions, it determines whether the custom conditions exceed a preset value. If they do, the dynamic visual anti-nausea model is activated.

[0018] In a preferred embodiment, the visual animation factor vector includes two vectors affecting animation displacement and one vector affecting animation size. The animation displacement and size are calculated using a hyperbolic tangent function. The size of the animation image gradually decreases as the animation image moves. The animation image changes from its maximum size to zero in one cycle, and this cycle repeats continuously. When the size of the animation image is zero, the animation image of the current cycle disappears, and the animation image of the next cycle begins to appear. The specific calculation steps are as follows:

[0019] Let time u be the time when the animation of the previous cycle disappears, and time i be the time within the current cycle. Let the current cycle be the nth cycle.

[0020] The first two terms of the visual animation factor vector are the animation displacement factors along the x-axis and y-axis, respectively. These are obtained by combining the data input from the pose motion database with the hyperbolic tangent function, and their calculation formula is as follows:

[0021]

[0022]

[0023] In the formula, Let x and y represent the x-axis animation displacement factor and y-axis animation displacement factor at time i, respectively. These represent the horizontal scaling factors of the x-axis animation displacement factor and the y-axis animation displacement factor, respectively. , These represent the x-axis acceleration and y-axis acceleration acquired by the data acquisition module at the i-th moment, respectively.

[0024] From time u to time i, the amount of motion of the animated image is obtained by integrating the acceleration along the x-axis and y-axis over all times during this time interval according to the complex trapezoidal rule. The calculation formula is as follows:

[0025]

[0026]

[0027] in, This indicates the amount of movement of the animated image along the x-axis. This represents the x-axis acceleration acquired by the data acquisition module at the u-th time. This represents the x-axis acceleration acquired by the data acquisition module at time u+1. This represents the x-axis acceleration acquired by the data acquisition module at the (u+2)th time. This represents the x-axis acceleration acquired by the data acquisition module at the (i-1)th time step. This indicates the amount of movement of the animated image along the y-axis. This represents the y-axis acceleration acquired by the data acquisition module at the u-th time. This represents the y-axis acceleration acquired by the data acquisition module at time u+1. This represents the y-axis acceleration acquired by the data acquisition module at the (u+2)th time. This represents the y-axis acceleration acquired by the data acquisition module at time i-1; the integral method for calculating the motion of the animation image includes the complex trapezoidal rule.

[0028] The third term of the visual animation factor vector is the animation size factor, which is obtained by combining the animation image movement along all x and y axes from time u to time i within the current period with the hyperbolic tangent function. Its calculation formula is as follows:

[0029]

[0030] In the formula, This represents the animation size factor at time i. The horizontal scaling factor represents the animation size factor, and C represents the animation size vanishing constant.

[0031] The animation size factor changes the overall size of the animated image, while the ratio of the lengths in each direction of the animated image remains unchanged before and after the change; the animation size factor uses, but is not limited to, the distance between the animation center and the image edge as a reference value; the horizontal scaling factor... The animation size vanishing constant C is used to adjust the smoothness of animation image changes and to adjust the initial size of the animation image and the time required for the animation image to vanish, while satisfying the following conditions: ;

[0032] when When the animation disappears, the current cycle ends, the animation for the next cycle begins, and the next cycle starts.

[0033] The animation displacement factor and animation size factor are integrated into a visual animation factor vector, and its calculation formula is as follows:

[0034]

[0035] In the formula, This represents the visual animation factor vector at time i.

[0036] In a preferred embodiment, the animated image shape includes circles, squares, triangles, and irregular shapes; the animated image color includes a single color or a random combination of colors.

[0037] In a preferred embodiment, the visual animation factors include three items: animation displacement, animation size, and animation color changes.

[0038] In a preferred embodiment, the image state vector is constructed by fusing and analyzing the visual animation factor vector and the display parameters of the visual display module to represent the actual visual animation state in the visual display module. The calculation formula is as follows:

[0039]

[0040] In the formula, Indicates the visual proportion coefficient. This represents the visual animation factor vector at time i. Let represent the image state vector at time i.

[0041] In a preferred embodiment, the image state vector is the visual animation change parameter described in S3; the visual scaling coefficient The image is determined based on the display resolution of the visual display module; the display parameters of the visual display module include display resolution, including image quality, color, and pixel pitch parameters; the image state vector includes visual animation displacement, visual animation size, visual animation color, and visual animation density.

[0042] In a preferred embodiment, the custom conditions described in S4 include automatic or manual conditions for attitude quantity, auditory quantity, olfactory quantity, in-vehicle and out-of-vehicle environment quantity, and passenger perception quantity. When the custom conditions exceed preset values, the dynamic visual model is activated.

[0043] This invention also provides a visual animation anti-drowsiness device based on the hyperbolic tangent function, used to execute the aforementioned visual animation anti-drowsiness method based on the hyperbolic tangent function, comprising:

[0044] Data acquisition module: used to collect attitude and physical motion state data in real time;

[0045] User interaction module: Provides passengers with visual image styles to choose from, including color and density, and collects passenger feedback data;

[0046] Decision control module: used to run the dynamic visual anti-drowsiness model;

[0047] Visual display module: Displays the anti-drowsiness visual animation output by the dynamic visual anti-drowsiness model;

[0048] Storage module: Used to store model parameters and historical data combining posture motion data with the model.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) The visual animation anti-motion sickness method proposed in this invention achieves smooth and natural visual animation effects by constructing animation factors based on the hyperbolic tangent function, effectively avoiding visual discomfort caused to passengers by abrupt features such as violent shaking and rapid switching of the screen, and improving the success rate of preventing and relieving motion sickness.

[0051] (2) The visual animation anti-dizziness method based on hyperbolic tangent function proposed in this invention is applicable to a wide range of visual display modules, is simple, fast and flexible, and can adjust the corresponding parameters according to different visual display modules to ensure the quality requirements of animation effects. Attached Figure Description

[0052] Figure 1 A flowchart of a visual animation anti-dizziness method based on the hyperbolic tangent function provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram illustrating a visual animation anti-dizziness method based on the hyperbolic tangent function provided in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the device frame for a visual animation anti-dizziness method based on the hyperbolic tangent function provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram illustrating the unidirectional displacement change in animation under special circumstances, with and without the algorithm of this invention. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0057] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0058] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0059] A visual animation anti-drowsiness method based on hyperbolic tangent function, such as... Figure 1 , Figure 2 , Figure 3 As shown, the main steps include:

[0060] S1. Acquire posture and physical motion data in real time through the data acquisition module, sort and define the acquired posture and physical motion data according to the time sequence of data acquisition, and establish a posture and motion database.

[0061] Acquiring attitude physical motion data includes: real-time acquisition of the vehicle's acceleration in the forward and backward direction (x-axis direction) and the left and right direction (y-axis direction);

[0062] This example uses a data acquisition rate of 1 second to acquire 25 sets of acceleration data in the x-axis and y-axis directions.

[0063] S2. Based on the fusion of hyperbolic tangent function and posture motion data, construct visual animation factor vector, and combine with the parameters of display module to establish dynamic visual anti-dizziness model and construct image state vector.

[0064] In this example, the visual animation factors include three items: x-axis animation displacement, y-axis animation displacement, and animation size.

[0065] Calculate the first two terms of the visual animation factor vector at time i, namely the x-axis animation displacement factor and the y-axis animation factor. In this example, , The calculation formula is as follows:

[0066]

[0067]

[0068] Let time u be the time when the animation of the previous cycle disappears, and time i be the time within the current cycle. Let the current cycle be the nth cycle.

[0069] From time u to time i, the amount of motion of the animated image is obtained by integrating the acceleration along the x-axis and y-axis over all times during this time interval according to the complex trapezoidal rule. The calculation formula is as follows:

[0070]

[0071]

[0072] The third term of the visual animation factor vector, namely the animation size factor at time i, is calculated by combining the animation image movement along all x and y axes from time u to time i within the current period with the hyperbolic tangent function. A numerically positive animation size vanishing constant C is given to adjust the initial size of the animation image and the time required for the animation image to vanish. In this example... , The calculation formula is as follows:

[0073]

[0074] The animation displacement factor and animation size factor are integrated into a visual animation factor vector, and its calculation formula is as follows:

[0075]

[0076] In the formula, Represents the visual animation factor vector at time i;

[0077] when When the animation ends, all animated images in the current cycle disappear, the current cycle ends, the animation of the next cycle appears, and the next cycle begins.

[0078] S3. Input the posture motion vector data into the dynamic visual anti-drowsiness model and output the changing parameters of the visual animation in real time.

[0079] This example uses augmented reality (AR) glasses for its visual display module. Based on the fusion analysis of the visual animation factor vector and the AR glasses' display parameters, an image state vector is constructed to represent the actual visual animation state within the AR glasses. The image state vector in this example mainly includes visual animation displacement and visual animation size, calculated using the following formulas:

[0080]

[0081] In the formula, Indicates the visual proportion coefficient. This represents the visual animation factor vector at time i. This represents the image state vector at time i.

[0082] In this example, the main display parameters of the AR glasses include display resolution, which is 1080p. The animated image shape is circular, and the color and density of the animated image can be changed according to the user's needs.

[0083] like Figure 4 As shown, taking an acceleration that increases proportionally with a step size of 0.5, starting from 0 and gradually increasing, as an example, the visual proportionality coefficient is taken. With a value of 1.5, the motion blur prevention method based on the hyperbolic tangent function proposed in this invention and the dot animation displacement effect without this method were plotted at 10-20 time points. When this method was not used, the displacement change and acceleration of the animation image remained proportional, and both methods only considered unidirectional displacement, not multi-directional animation displacement or changes in the size of the animation dots. Figure 4 As can be seen, the visual animation anti-dizziness method based on hyperbolic tangent function proposed in this invention can effectively prevent abrupt phenomena such as violent shaking and rapid switching of the screen, which can lead to visual fatigue and poor anti-dizziness effect for viewers, and can ensure the smoothness of visual animation.

[0084] S4. Control the response state of the dynamic visual anti-drowsiness model according to the custom conditions. When the condition exceeds the preset value, the dynamic visual anti-drowsiness model is turned on.

[0085] In this example, the custom conditions are mainly based on vehicle motion state variables, which are used to control the response state of the dynamic vision model. If the acceleration of the vehicle's x-axis and y-axis is compared with a given threshold after certain calculation transformation, and if it exceeds the given threshold, the dynamic vision model is activated and responds.

[0086] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A visual animation anti-drowsiness method based on hyperbolic tangent function, characterized in that: Includes the following steps: S1. Acquire posture and physical motion data in real time through the data acquisition module, sort and define the acquired posture and physical motion data according to the time order of acquisition, and establish a posture and motion database. S2. Based on the fusion of hyperbolic tangent function and posture physical motion data, construct visual animation factor vector, combine with the parameters of display module to establish dynamic visual anti-drowsiness model, and construct image state vector; S3. Input the posture physical motion data into the dynamic visual anti-drowsiness model and output the changing parameters of the visual animation in real time. S4. Control the response state of the dynamic visual anti-drowsiness model according to the custom conditions. When the condition exceeds the preset value, the dynamic visual anti-drowsiness model is turned on.

2. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 1, characterized in that, The implementation of S1 is as follows: A target vehicle is selected, and the acceleration data along the vehicle's x-axis and y-axis are used as the attitude physical motion data; the obtained attitude physical motion data is sorted and defined according to the time sequence of acquisition; different physical quantities at the same moment constitute an attitude physical motion data vector, defined as... In the formula, , These represent the i-th x-axis acceleration and y-axis acceleration acquired by the data acquisition module, respectively. This represents the i-th attitude physical motion data vector in the attitude motion vector database; The aforementioned posture physical motion data includes vehicle and passenger head orientation data.

3. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 1, characterized in that, The dynamic visual anti-drowsiness model is a dynamic visual anti-drowsiness model constructed based on the fusion of hyperbolic tangent function and posture physical motion data. This dynamic visual anti-drowsiness model calculates a visual animation factor vector by inputting posture physical motion data, and after adjusting the proportion of the visual animation factor vector, outputs an image state vector in real time. The dynamic visual anti-drowsiness model has a response state. By inputting custom conditions, it determines whether the custom conditions exceed a preset value. If they do, the dynamic visual anti-drowsiness model is activated.

4. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 3, characterized in that, The visual animation factor vector includes two vectors affecting animation displacement and one vector affecting animation size. The animation displacement and size are calculated using the hyperbolic tangent function. The size of the animation image gradually decreases as the animation image moves. A cycle occurs when the animation image size changes from its maximum to zero. This cycle repeats continuously. When the animation image size reaches zero, the animation image of the current cycle disappears, and the animation image of the next cycle begins to appear. The specific calculation steps are as follows: Let time u be the time when the animation of the previous cycle disappears, and time i be the time within the current cycle. Let the current cycle be the nth cycle. The first two terms of the visual animation factor vector are the animation displacement factors along the x-axis and y-axis, respectively. These are obtained by combining the data input from the pose motion database with the hyperbolic tangent function, and their calculation formula is as follows: In the formula, Let x and y represent the x-axis animation displacement factor and y-axis animation displacement factor at time i, respectively. These represent the horizontal scaling factors of the x-axis animation displacement factor and the y-axis animation displacement factor, respectively. , These represent the x-axis acceleration and y-axis acceleration acquired by the data acquisition module at the i-th moment, respectively. From time u to time i, the amount of motion of the animated image is obtained by integrating the acceleration along the x-axis and y-axis over all times during this time interval according to the complex trapezoidal rule. The calculation formula is as follows: in, This indicates the amount of movement of the animated image along the x-axis. This represents the x-axis acceleration acquired by the data acquisition module at the u-th time. This represents the x-axis acceleration acquired by the data acquisition module at time u+1. This represents the x-axis acceleration acquired by the data acquisition module at the (u+2)th time. This represents the x-axis acceleration acquired by the data acquisition module at the (i-1)th time step. This indicates the amount of movement of the animated image along the y-axis. This represents the y-axis acceleration acquired by the data acquisition module at the u-th time. This represents the y-axis acceleration acquired by the data acquisition module at time u+1. This represents the y-axis acceleration acquired by the data acquisition module at the (u+2)th time. This represents the y-axis acceleration acquired by the data acquisition module at time i-1; the integral method for calculating the motion of the animation image includes the complex trapezoidal rule. The third term of the visual animation factor vector is the animation size factor, which is obtained by combining the animation image movement along all x and y axes from time u to time i within the current period with the hyperbolic tangent function. Its calculation formula is as follows: In the formula, This represents the animation size factor at time i. The horizontal scaling factor represents the animation size factor, and C represents the animation size vanishing constant. The animation size factor changes the overall size of the animated image, while the ratio of the lengths in each direction of the animated image remains unchanged before and after the change; the animation size factor uses, but is not limited to, the distance between the animation center and the image edge as a reference value; the horizontal scaling factor... The animation size vanishing constant C is used to adjust the smoothness of animation image changes and to adjust the initial size of the animation image and the time required for the animation image to vanish, while satisfying the following conditions: ; when When the animation disappears, the current cycle ends, the animation for the next cycle begins, and the next cycle starts. The animation displacement factor and animation size factor are integrated into a visual animation factor vector, and its calculation formula is as follows: In the formula, This represents the visual animation factor vector at time i.

5. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 3, characterized in that, The animated image shapes include circles, squares, triangles, and irregular shapes; the animated image colors include a single color or a random combination of colors.

6. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 3, characterized in that, The visual animation factors include three items: animation displacement, animation size, and animation color changes.

7. The visual animation anti-drowsiness method based on the hyperbolic tangent function according to claim 3, characterized in that, The image state vector is constructed by fusing and analyzing the visual animation factor vector and the display parameters of the visual display module. This image state vector represents the actual visual animation state within the visual display module, and its calculation formula is as follows: In the formula, Indicates the visual proportion coefficient. This represents the visual animation factor vector at time i. Let represent the image state vector at time i.

8. A visual animation anti-drowsiness method based on hyperbolic tangent function as described in claim 7, characterized in that, The image state vector is the visual animation change parameter mentioned in S3; the visual scaling coefficient The image is determined based on the display resolution of the visual display module; the display parameters of the visual display module include display resolution, including image quality, color, and pixel pitch parameters; the image state vector includes visual animation displacement, visual animation size, visual animation color, and visual animation density.

9. A visual animation anti-drowsiness method based on hyperbolic tangent function as described in claim 1, characterized in that, The custom conditions described in S4 include automatic or manual conditions for attitude, hearing, smell, in-vehicle and out-of-vehicle environment, and passenger perception. When the custom conditions exceed the preset values, the dynamic visual model is activated.

10. A visual animation anti-drowsiness device based on hyperbolic tangent function, used to perform the visual animation anti-drowsiness method based on hyperbolic tangent function according to any one of claims 1-9, characterized in that: include: Data acquisition module: used to collect attitude and physical motion state data in real time; User interaction module: Provides passengers with visual image styles to choose from, including color and density, and collects passenger feedback data; Decision control module: used to run the dynamic visual anti-drowsiness model; Visual display module: Displays the anti-drowsiness visual animation output by the dynamic visual anti-drowsiness model; Storage module: Used to store model parameters and historical data combining posture motion data with the model.

Images