CN113283612A - Method, device and storage medium for detecting dizziness degree of user in virtual environment - Google Patents
Method, device and storage medium for detecting dizziness degree of user in virtual environment Download PDFInfo
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Abstract
The application relates to a method, a device and a storage medium for detecting the vertigo degree of a user in a virtual environment, which belong to the technical field of computers, and the method comprises the following steps: acquiring the movement track information of a user in a three-dimensional space coordinate of a virtual environment; processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variable quantity data of the moving track information; inputting the compression ratio data and the track compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result; the trajectory compression data in the virtual environment is used as the input data of the vertigo degree estimation model, and a user does not need to wear redundant sensors, does not need to be separated from the virtual environment, and does not influence the immersion experience of the user in the virtual environment. And the system completely depends on basic virtual reality equipment, does not need to be additionally provided with a sensor, simplifies the system structure and has higher popularization and application potential.
Description
[ technical field ] A method for producing a semiconductor device
The application relates to a method, a device and a storage medium for detecting the vertigo degree of a user in a virtual environment, belonging to the technical field of computers.
[ background of the invention ]
With the rapid development of Virtual Reality (VR), VR is more and more widely applied. When experiencing virtual reality technology, the illusion of motion in the VR environment often triggers virtual reality dizziness, i.e. no movement in the virtual environment in reality due to the perception of movement by visual effects.
The identification and measurement of dizziness in a user's virtual environment is usually based on the user filling out a subjective questionnaire in or outside the virtual environment or subjectively scoring the discomfort level.
However, such a method may lose information about the cause and time point of vertigo, or destroy the immersive experience of virtual reality.
Another method is to use the physiological indexes of the user in the game to detect, such as skin conductivity, eye movement information and the like.
However, the detection by the physiological index requires additional hardware devices, which makes the structure of the VR device more complicated.
[ summary of the invention ]
The application provides a method, a device and a storage medium for detecting the vertigo degree of a user in a virtual environment, which realize the vertigo detection of the user in the virtual reality by only depending on basic hardware equipment supporting the virtual reality on the premise of not damaging the immersion feeling of the virtual reality of the user; the problem that the existing vertigo degree detection mode causes information related to vertigo, time points and the like to be lost, or immersive experience of virtual reality is damaged, or hardware equipment needs to be additionally configured to realize vertigo degree detection can be solved. The application provides the following technical scheme:
in a first aspect, a method for detecting vertigo degree of a user in a virtual environment is provided, the method comprising:
acquiring the movement track information of a user in a three-dimensional space coordinate of a virtual environment;
processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variation data of the moving track information;
inputting the compression ratio data and the track compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, wherein the vertigo degree prediction result is used for indicating the current vertigo degree of the user.
Optionally, before inputting the compression ratio data and the trajectory compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, the method further includes:
after a user enters the virtual environment, acquiring sample movement track information of the user in a three-dimensional space coordinate of the virtual environment;
processing the sample moving track information based on the track compression algorithm to obtain sample compression ratio data and sample track compression ratio variable quantity data;
acquiring vertigo degree feedback information which is input by the user and corresponds to the sample moving track information;
and training a pre-created machine learning model by using the sample compression ratio data, the sample track compression ratio variation data and the vertigo degree feedback information to obtain the vertigo degree estimation model.
Optionally, the obtaining vertigo degree feedback information corresponding to the sample movement track information, which is input by the user, includes:
displaying a preset questionnaire every other preset time;
and receiving the vertigo degree feedback information filled in the preset questionnaire by the user.
Optionally, the vertigo degree feedback information is represented by a score, and the training of a machine learning model created in advance using the sample compression ratio data, the sample trajectory compression ratio variation data, and the vertigo degree feedback information obtains the vertigo degree estimation model, including:
normalizing the vertigo degree feedback information represented by the score to obtain a normalized sample label;
inputting the sample compression ratio data and the sample track compression ratio variation data into the machine learning model to obtain a prediction result;
comparing the prediction result with the normalized sample label;
and performing iterative training on the machine learning model according to the comparison result to obtain the vertigo degree estimation model.
Optionally, the training a pre-created machine learning model using the sample compression ratio data, the sample trajectory compression ratio variation data, and the vertigo degree feedback information to obtain the vertigo degree estimation model includes:
and carrying out dimension expansion on the sample compression ratio data, the sample track compression ratio variation data and the vertigo degree feedback information, and training the machine learning model by using the expanded data to obtain the vertigo degree estimation model.
Optionally, the trajectory compression algorithm is a spatio-temporal algorithm, and the spatio-temporal algorithm compresses the mobile trajectory information by using other spatio-temporal information hidden in a time sequence, where the other spatio-temporal information includes: a time-versus-distance measurement and a derived velocity of the movement trace information, the derived velocity being a velocity value derived from a timestamp and a position of the movement trace information.
Optionally, the virtual environment is displayed through virtual reality VR equipment, and the movement track information is acquired through the VR equipment.
In a second aspect, there is provided an apparatus for detecting vertigo degree of a user in a virtual environment, the apparatus comprising:
the track acquisition module is used for acquiring the moving track information of the user in the three-dimensional space coordinate of the virtual environment;
the track compression module is used for processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variable quantity data of the moving track information;
and the dizziness estimation module is used for inputting the compression ratio data and the track compression ratio variation data into a pre-trained dizziness degree estimation model to obtain a dizziness degree prediction result, and the dizziness degree prediction result is used for indicating the current dizziness degree of the user.
In a third aspect, an electronic device is provided, the device comprising a processor and a memory; the memory has stored therein a program that is loaded and executed by the processor to implement the method for detecting vertigo of a user in a virtual environment provided by the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, in which a program is stored, and the program is used to implement the method for detecting the vertigo degree of the user in the virtual environment provided by the first aspect.
The beneficial effects of this application include at least: acquiring the movement track information of a user in a three-dimensional space coordinate of a virtual environment; processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variable quantity data of the moving track information; inputting the compression ratio data and the track compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, wherein the vertigo degree prediction result is used for indicating the current vertigo degree of a user; the problems that the information about dizziness reasons, time points and the like is lost, or the immersive experience of virtual reality is damaged, or hardware equipment needs to be additionally configured to realize dizziness degree detection in the conventional dizziness degree detection mode can be solved; because the trajectory compression data in the virtual environment is used as the input data of the vertigo degree estimation model, a user does not need to wear redundant sensors, does not need to be separated from the virtual environment, and does not influence the immersion experience of the user in the virtual environment. In addition, the method completely depends on basic virtual reality equipment, does not need to additionally configure a sensor, simplifies the system structure and has higher popularization and application potential.
In addition, the invention can adopt a dynamic track compression algorithm, only uses little space to store the user track data, can obtain the track compression data, and has the characteristic of high efficiency.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
[ description of the drawings ]
FIG. 1 is a flow chart of a method for detecting vertigo of a user in a virtual environment according to an embodiment of the present application;
fig. 2 is a flowchart of a training process of a vertigo degree estimation model provided in an embodiment of the present application;
FIG. 3 is a block diagram of an apparatus for detecting vertigo of a user in a virtual environment according to an embodiment of the present application;
fig. 4 is a block diagram of an apparatus for detecting vertigo degree of a user in a virtual environment according to another embodiment of the present application.
[ detailed description ] embodiments
The following detailed description of embodiments of the present application will be made with reference to the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
Optionally, the method for detecting the vertigo degree of the user in the virtual environment provided by each embodiment is described as an example, and in practical implementation, the method may also be applied to other devices connected to the VR device in communication, such as a mobile phone, a computer, a tablet computer, or a server.
VR equipment has the interactive equipment that supplies the user to interact with virtual environment in this application, if: the interaction device can acquire the track of the user when the user moves to obtain the information of the moving track.
In other words, the moving track information in the application does not need to be additionally provided with a sensor in the VR equipment, and the moving track information can be acquired only by using the original hardware environment of the VR equipment.
Fig. 1 is a flowchart of a method for detecting vertigo degree of a user in a virtual environment according to an embodiment of the present application, the method at least includes the following steps:
In this embodiment, the virtual environment is displayed through the VR device, and the movement track information is acquired through the VR device.
The virtual environment displayed by the VR equipment is established based on the three-dimensional space coordinate, after the user wears the VR equipment, the VR equipment can acquire the motion data of the user and map the motion data to the three-dimensional space coordinate, so that the effect that the user enters the virtual environment is created. In the process of displaying the virtual environment for the user, the VR equipment collects the movement track information of the user in real time. The moving track information comprises a timestamp of each acquisition moment and a position coordinate corresponding to the timestamp.
And 102, processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variation data of the moving track information.
In one example, the trajectory compression algorithm is a spatiotemporal algorithm that compresses the mobile trajectory information using other spatiotemporal information hidden in the time series, wherein the other spatiotemporal information includes: the time-specific distance measurement and a derived velocity of the movement trace information, the derived velocity being a velocity value derived from a time stamp and a position of the movement trace information.
The principles of spatiotemporal algorithms include: firstly, measuring time ratio and distance; and the second step analyzes the derived speed of the subsequent section of the track. A large difference between the derived velocities of two subsequent segments is another criterion that may be applied to keep the data point in the middle. To this end, the spatio-temporal algorithm also sets a speed difference threshold, indicating above which speed difference we will always keep data points.
The time-to-distance measurement may be implemented by a top-down and open window algorithm, and the embodiment does not limit the manner of the time-to-distance measurement.
The track compression algorithm used in the embodiment can store the user track data only by using a small amount of space, has the characteristic of high efficiency, can save the data storage space, and improves the data calculation speed.
Wherein the compression ratio data is: and for the moving track information in the current acquisition time length, the ratio between the compression point and the total sampling point number. The track compression ratio variation data means: the difference between the compression ratio data corresponding to the current acquisition duration and the compression ratio data corresponding to the previous acquisition duration.
And 103, inputting the compression ratio data and the track compression ratio variation data into a pre-trained dizziness degree estimation model to obtain a dizziness degree prediction result, wherein the dizziness degree prediction result is used for indicating the current dizziness degree of the user.
The vertigo degree estimation model is obtained by training a machine learning model by using training data, the training process refers to fig. 2, at this time, compression ratio data and track compression ratio variation data are input into a vertigo degree estimation model trained in advance, and before obtaining a vertigo degree prediction result, the vertigo degree estimation model further comprises the following steps:
and step 21, after the user enters the virtual environment, acquiring sample movement track information of the user in a three-dimensional space coordinate of the virtual environment.
The related description of this step is detailed in step 101, and this embodiment is not described herein again.
And step 22, processing the sample moving track information based on a track compression algorithm to obtain sample compression ratio data and sample track compression ratio variable quantity data.
The details of this step are shown in step 102, and this embodiment is not described herein again.
And step 23, acquiring vertigo degree feedback information corresponding to the sample moving track information and input by the user.
Specifically, displaying a preset questionnaire every preset time length; and receiving vertigo degree feedback information filled in a preset questionnaire by a user. Such as: the VR device displays the preset questionnaire in the virtual environment once every 1 minute (in actual implementation, other durations are also available, and the present embodiment does not limit the value of the preset duration) for collecting vertigo degree feedback information of the user, and further acquiring vertigo degree change amount data.
And 24, training a pre-established machine learning model by using the sample compression ratio data, the sample track compression ratio variation data and the vertigo degree feedback information to obtain a vertigo degree estimation model.
In one example, the vertigo degree feedback information is represented by a score, and a machine learning model created in advance is trained by using the sample compression ratio data, the sample trajectory compression ratio variation data and the vertigo degree feedback information to obtain an vertigo degree estimation model, including: normalizing the vertigo degree feedback information represented by the score to obtain a normalized sample label; inputting the sample compression ratio data and the sample track compression ratio variable quantity data into a machine learning model to obtain a prediction result; comparing the prediction result with the normalized sample label; and carrying out iterative training on the machine learning model according to the comparison result to obtain the vertigo degree estimation model.
Optionally, the data used for training the machine learning model is subjected to dimension expansion according to specific conditions of different users, training of the machine learning model is performed by using training data with more or less dimensions, and the data is sent to the machine learning model by using the same type of data structure in a subsequent process. Specifically, the data of the sample compression ratio, the data of the sample track compression ratio variation and the feedback information of the vertigo degree are subjected to dimensionality expansion, and the expanded data are used for training a machine learning model to obtain an vertigo degree estimation model.
In this embodiment, after the training of the machine learning model is completed, the fast questionnaire system in the virtual environment is closed to stop displaying the preset questionnaire, and the trajectory compression program is retained. And the user enters a VR scene to carry out normal activities, and simultaneously acquires compression ratio data and track compression ratio variation data.
Then, the vertigo degree estimation model is input into the obtained compression ratio data and track compression ratio variation data every unit time, and the vertigo degree prediction result output by the vertigo degree estimation model is obtained, that is, the vertigo degree variation per unit time can be obtained. And obtaining the vertigo degree data of the user at the current time point through the vertigo degree change quantity data so as to detect the vertigo degree of the user.
In summary, in the method for detecting the vertigo degree of the user in the virtual environment provided by this embodiment, the movement track information of the user in the three-dimensional space coordinate of the virtual environment is obtained; processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variable quantity data of the moving track information; inputting the compression ratio data and the track compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, wherein the vertigo degree prediction result is used for indicating the current vertigo degree of a user; the problems that the information about dizziness reasons, time points and the like is lost, or the immersive experience of virtual reality is damaged, or hardware equipment needs to be additionally configured to realize dizziness degree detection in the conventional dizziness degree detection mode can be solved; because the trajectory compression data in the virtual environment is used as the input data of the vertigo degree estimation model, a user does not need to wear redundant sensors, does not need to be separated from the virtual environment, and does not influence the immersion experience of the user in the virtual environment. In addition, the method completely depends on basic virtual reality equipment, does not need to additionally configure a sensor, simplifies the system structure and has higher popularization and application potential.
In addition, the invention can adopt a dynamic track compression algorithm, only uses little space to store the user track data, can obtain the track compression data, and has the characteristic of high efficiency.
Fig. 3 is a block diagram of an apparatus for detecting vertigo degree of a user in a virtual environment according to an embodiment of the present application. The device at least comprises the following modules: a trajectory acquisition module 310, a trajectory compression module 320, and a vertigo estimation module 330.
A track obtaining module 310, configured to obtain movement track information of a user in a three-dimensional space coordinate of a virtual environment;
the track compression module 320 is configured to process the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variation data of the moving track information;
a vertigo estimating module 330, configured to input the compression ratio data and the trajectory compression ratio variation data into a vertigo degree estimating model trained in advance, to obtain a vertigo degree predicting result, where the vertigo degree predicting result is used to indicate a current vertigo degree of the user.
For relevant details reference is made to the above-described method embodiments.
It should be noted that: in the above embodiment, when the device for detecting the vertigo degree of the user in the virtual environment detects the vertigo degree of the user in the virtual environment, only the division of the functional modules is taken as an example, in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the device for detecting the vertigo degree of the user in the virtual environment is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for detecting the vertigo degree of the user in the virtual environment and the method embodiment for detecting the vertigo degree of the user in the virtual environment provided by the embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment and are not described herein again.
Fig. 4 is a block diagram of an apparatus for detecting vertigo degree of a user in a virtual environment according to an embodiment of the present application. The apparatus comprises at least a processor 401 and a memory 402.
Processor 401 may include one or more processing cores such as: 4 core processors, 8 core processors, etc. The processor 401 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, the processor 401 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one instruction for execution by processor 401 to implement a method for detecting a level of vertigo to a user in a virtual environment as provided by method embodiments herein.
In some embodiments, the apparatus may further include: a peripheral interface and at least one peripheral. The processor 401, memory 402 and peripheral interface may be connected by bus or signal lines. Each peripheral may be connected to the peripheral interface via a bus, signal line, or circuit board. Illustratively, peripheral devices include, but are not limited to: radio frequency circuit, touch display screen, audio circuit, power supply, etc.
Of course, the lower device for detecting the vertigo degree of the user may further include fewer or more components, which is not limited in this embodiment.
Optionally, the present application further provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the method for detecting the vertigo degree of the user in the virtual environment according to the above-mentioned method embodiment.
Optionally, the present application further provides a computer product, which includes a computer-readable storage medium, where a program is stored in the computer-readable storage medium, and the program is loaded and executed by a processor to implement the method for detecting vertigo degree of a user in a virtual environment according to the above-mentioned method embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for detecting vertigo degree of a user in a virtual environment, the method comprising:
acquiring the movement track information of a user in a three-dimensional space coordinate of a virtual environment;
processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variation data of the moving track information;
inputting the compression ratio data and the track compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, wherein the vertigo degree prediction result is used for indicating the current vertigo degree of the user.
2. The method according to claim 1, wherein before inputting the compression ratio data and the trajectory compression ratio variation data into a pre-trained vertigo degree estimation model to obtain a vertigo degree prediction result, the method further comprises:
after a user enters the virtual environment, acquiring sample movement track information of the user in a three-dimensional space coordinate of the virtual environment;
processing the sample moving track information based on the track compression algorithm to obtain sample compression ratio data and sample track compression ratio variable quantity data;
acquiring vertigo degree feedback information which is input by the user and corresponds to the sample moving track information;
and training a pre-created machine learning model by using the sample compression ratio data, the sample track compression ratio variation data and the vertigo degree feedback information to obtain the vertigo degree estimation model.
3. The method according to claim 2, wherein the obtaining of the vertigo degree feedback information corresponding to the sample movement track information input by the user comprises:
displaying a preset questionnaire every other preset time;
and receiving the vertigo degree feedback information filled in the preset questionnaire by the user.
4. The method according to claim 3, wherein the vertigo degree feedback information is represented by a score, and the vertigo degree estimation model is obtained by training a machine learning model created in advance using the sample compression ratio data, the sample trajectory compression ratio variation data, and the vertigo degree feedback information, including:
normalizing the vertigo degree feedback information represented by the score to obtain a normalized sample label;
inputting the sample compression ratio data and the sample track compression ratio variation data into the machine learning model to obtain a prediction result;
comparing the prediction result with the normalized sample label;
and performing iterative training on the machine learning model according to the comparison result to obtain the vertigo degree estimation model.
5. The method of claim 2, wherein the training a pre-created machine learning model using the sample compression ratio data, sample trajectory compression ratio variation data, and the vertigo degree feedback information to obtain the vertigo degree estimation model comprises:
and carrying out dimension expansion on the sample compression ratio data, the sample track compression ratio variation data and the vertigo degree feedback information, and training the machine learning model by using the expanded data to obtain the vertigo degree estimation model.
6. The method of claim 1, wherein the trajectory compression algorithm is a spatiotemporal algorithm that compresses the mobile trajectory information using other spatiotemporal information hidden in a time series, wherein the other spatiotemporal information comprises: a time-versus-distance measurement and a derived velocity of the movement trace information, the derived velocity being a velocity value derived from a timestamp and a position of the movement trace information.
7. The method of claim 1, wherein the virtual environment is displayed by a Virtual Reality (VR) device and the movement trajectory information is collected by the VR device.
8. An apparatus for detecting vertigo degree of a user in a virtual environment, the apparatus comprising:
the track acquisition module is used for acquiring the moving track information of the user in the three-dimensional space coordinate of the virtual environment;
the track compression module is used for processing the moving track information based on a track compression algorithm to obtain compression ratio data and track compression ratio variable quantity data of the moving track information;
and the dizziness estimation module is used for inputting the compression ratio data and the track compression ratio variation data into a pre-trained dizziness degree estimation model to obtain a dizziness degree prediction result, and the dizziness degree prediction result is used for indicating the current dizziness degree of the user.
9. An apparatus for detecting a user's vertigo degree in a virtual environment, the apparatus comprising a processor and a memory; the memory stores a program that is loaded and executed by the processor to implement the method for detecting vertigo degree of a user in a virtual environment according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a program which, when being executed by a processor, is adapted to carry out a method of detecting vertigo of a user in a virtual environment according to any one of claims 1 to 7.
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