CN113283612B - Method, device and storage medium for detecting user dizziness degree in virtual environment - Google Patents
Method, device and storage medium for detecting user dizziness degree in virtual environment Download PDFInfo
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Abstract
The application relates to a method, a device and a storage medium for detecting dizziness degree of a user in a virtual environment, belonging to the technical field of computers, wherein the method comprises the following steps: acquiring movement track information of a user in three-dimensional space coordinates 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 dizziness degree estimation model to obtain a dizziness degree prediction result; the track compression data in the virtual environment is used as input data of the dizziness degree estimation model, a user does not need to wear redundant sensors, the user does not need to leave the virtual environment, and the immersion experience of the user in the virtual environment is not affected. And the system completely depends on basic virtual reality equipment, does not need to additionally configure sensors, simplifies the system structure, and has higher popularization and application potential.
Description
[ field of technology ]
The application relates to a method, a device and a storage medium for detecting dizziness degree of a user in a virtual environment, and belongs to the technical field of computers.
[ background Art ]
With the rapid development of Virtual Reality (VR), VR has been increasingly used. When experiencing virtual reality technology, the illusion of motion in the VR environment often triggers virtual reality vertigo, i.e., no movement in reality where the virtual environment perceives movement due to visual effects.
The recognition and measurement method of dizziness of the virtual environment of the user at the present stage is generally based on filling in subjective questionnaires or subjective scoring of discomfort grades in the virtual environment or outside the virtual environment by the user.
Such methods may, however, leave lost information about the cause of dizziness, time points, etc., or disrupt the immersive experience of the virtual reality.
Another method is to use the physiological index of the user in the game to detect, such as skin conductivity, eye movement information, etc.
However, detecting by the physiological index requires configuring an additional hardware device, resulting in a more complex structure of the VR device.
[ application ]
The application provides a method, a device and a storage medium for detecting the dizziness degree of a user in a virtual environment, which realize the detection of the dizziness of the virtual reality of the user by only relying 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 dizziness degree detection mode is used for losing information such as the reason for enabling relevant dizziness, the time point and the like, or destroying the immersive experience of virtual reality, or needing to additionally configure hardware equipment to achieve dizziness degree detection can be solved. The application provides the following technical scheme:
in a first aspect, a method for detecting dizziness degree of a user in a virtual environment is provided, and the method includes:
acquiring movement track information of a user in three-dimensional space coordinates 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 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.
Optionally, before inputting the compression ratio data and the trajectory compression ratio variation data into a pre-trained dizziness degree estimation model to obtain a dizziness degree prediction result, the method further includes:
after a user enters the virtual environment, acquiring sample movement track information of the user in three-dimensional space coordinates 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 variation data;
acquiring dizziness degree feedback information which is input by the user and corresponds to the sample movement track information;
training a machine learning model which is created in advance by using the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information to obtain the dizziness degree estimation model.
Optionally, the acquiring the dizziness degree feedback information corresponding to the sample movement track information input by the user includes:
displaying a preset questionnaire every preset time length;
and receiving the dizziness degree feedback information filled in the preset questionnaire by the user.
Optionally, the dizziness degree feedback information is represented by a score, the training of a machine learning model created in advance by using the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information to obtain the dizziness degree estimation model includes:
normalizing the dizziness 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 dizziness degree estimation model.
Optionally, the training the pre-created machine learning model using the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information to obtain the dizziness degree estimation model includes:
and carrying out dimension expansion on the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information, and training the machine learning model by using the expanded data to obtain the dizziness degree estimation model.
Optionally, the track compression algorithm is a spatiotemporal algorithm, and the spatiotemporal algorithm compresses the moving track information by using other spatiotemporal information hidden in a time sequence, where the other spatiotemporal information includes: a time-to-distance measurement and a derived speed of the movement track information, the derived speed being a speed value derived from a time stamp and a position of the movement track information.
Optionally, the virtual environment is displayed through a virtual reality VR device, and the movement track information is acquired through the VR device.
In a second aspect, there is provided an apparatus for detecting a user's dizziness degree in a virtual environment, the apparatus comprising:
the track acquisition module is used for acquiring movement track information of a user in three-dimensional space coordinates 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 variation data of the moving track information;
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 stores a program that is loaded and executed by the processor to implement the method for detecting the dizziness degree of the user in the virtual environment provided in the first aspect.
In a fourth aspect, there is provided a computer readable storage medium having stored therein a program which when executed by a processor is adapted to carry out the method of detecting a user's dizziness level in a virtual environment provided in the first aspect.
The beneficial effects of the application at least comprise: acquiring movement track information of a user in three-dimensional space coordinates 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 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 a user; the problem that the information such as the reason for the dizziness, the time point and the like is lost, or the immersive experience of virtual reality is destroyed, or hardware equipment is required to be additionally configured to realize the dizziness degree detection in the existing dizziness degree detection mode can be solved; because track compression data in the virtual environment is used as input data of the dizziness degree estimation model, a user does not need to wear redundant sensors, does not need to deviate from the virtual environment, and does not influence 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 sensors, simplifies the system structure, and has higher popularization and application potential.
In addition, the application can adopt a dynamic track compression algorithm, can obtain track compression data by storing user track data in a very small space, and has the characteristic of high efficiency.
The foregoing description is only an overview of the present application, and is intended to provide a better understanding of the present application, as it is embodied in the following description, 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 user dizziness in a virtual environment according to one embodiment of the present application;
FIG. 2 is a flow chart of a training process for a dizziness level estimation model provided by one embodiment of the application;
FIG. 3 is a block diagram of an apparatus for detecting user dizziness in a virtual environment according to one embodiment of the present application;
fig. 4 is a block diagram of an apparatus for detecting a user's dizziness degree in a virtual environment according to another embodiment of the present application.
[ detailed description ] of the application
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
Optionally, the method for detecting the dizziness level of the user in the virtual environment provided by each embodiment is described by taking the VR device as an example, and in actual implementation, the method may also be applied to other devices communicatively connected to the VR device, such as a mobile phone, a computer, a tablet computer, or a server, where the method for detecting the dizziness level of the user in the virtual environment provided by the embodiment is not limited by the type of the device for detecting the dizziness level of the user.
The VR device of the present application has an interaction device for the user to interact with the virtual environment, such as: the interactive device can collect the track of the user during movement, and obtain movement track information.
In other words, the moving track information in the application does not need to additionally configure a sensor in the VR equipment, and the collection of the moving track information can be realized only by using the hardware environment originally provided by the VR equipment.
Fig. 1 is a flowchart of a method for detecting dizziness degree of a user in a virtual environment according to an embodiment of the present application, where the method includes at least the following steps:
step 101, obtaining movement track information of a user in three-dimensional space coordinates of a virtual environment.
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 coordinates, and after the VR equipment is worn by a user, the VR equipment can acquire the motion data of the user and map the motion data into the three-dimensional space coordinates so as to create the effect that the user enters the virtual environment. In the process of displaying the virtual environment for the user, the VR equipment acquires the movement track information of the user in real time. The moving track information comprises a time stamp of each acquisition time and a position coordinate corresponding to the time stamp.
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 spatio-temporal algorithm that compresses moving trajectory information using other spatio-temporal information hidden in the time series, where the other spatio-temporal information includes: the time-to-distance measurement and the derived speed of the movement track information refer to a speed value derived from the time stamp and the position of the movement track information.
The principles of the spatio-temporal algorithm include: the first step, measuring the time ratio distance; and secondly, analyzing the derived speed of the subsequent sections of the track. A large difference between the derived speeds of two subsequent segments is another criterion that can be applied to keep the data points in between. 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 is not limited to the manner in which the time-to-distance measurement is implemented.
The track compression algorithm used in the embodiment can store the user track data only with a small space, has the characteristic of high efficiency, can save the data storage space and improves the data calculation speed.
Wherein the compression ratio data means: for movement track information in the current acquisition time length, the ratio between the compression point and the total sampling point number is reduced. The trajectory compression ratio variation data means: the difference between the compression ratio data corresponding to the current acquisition time length and the compression ratio data corresponding to the last acquisition time length.
Step 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 a user.
The dizziness degree estimation model is obtained by training a machine learning model by using training data, and the training process refers to fig. 2, at this time, compression ratio data and track compression ratio variation data are input into a pre-trained dizziness degree estimation model, and before the dizziness degree prediction result is obtained, the method further comprises the following steps:
and step 21, after the user enters the virtual environment, acquiring sample movement track information of the user in three-dimensional space coordinates of the virtual environment.
The detailed description of this step is shown in step 101, and this embodiment is not repeated here.
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 variation data.
The details of this step are shown in step 102, and this embodiment is not described here again.
Step 23, the dizziness degree feedback information corresponding to the sample movement track information input by the user is obtained.
Specifically, a preset questionnaire is displayed every preset time period; and receiving dizziness degree feedback information filled in a preset questionnaire by the user. Such as: the VR device may also be of other durations when actually implemented, and in this embodiment, the preset questionnaire in the virtual environment is displayed once every 1 minute (the value of the preset duration is not limited), so as to collect feedback information of the dizziness degree of the user, and further obtain the change data of the dizziness degree.
And step 24, training a machine learning model which is created in advance by using the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information to obtain a dizziness degree estimation model.
In one example, the dizziness degree feedback information is represented by a score, and a machine learning model created in advance is trained by using sample compression ratio data, sample track compression ratio variation data and the dizziness degree feedback information, so as to obtain a dizziness degree estimation model, which includes: normalizing the dizziness 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 a 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 a dizziness degree estimation model.
Optionally, the data for training the machine learning model is expanded in dimension according to the specific situations of different users, the training data with more or less dimensions is used for training the machine learning model, and the data is sent to the machine learning model in the subsequent process by using the same type of data structure. Specifically, the dimension expansion is performed on the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information, and the machine learning model is trained by using the expanded data to obtain the dizziness degree estimation model.
In this embodiment, after the training of the machine learning model is completed, the rapid questionnaire system in the virtual environment is turned off to stop displaying the preset questionnaire, and at the same time, the track compression program is maintained. And the user enters the VR scene to perform normal activities, and meanwhile, compression ratio data and track compression ratio variation data are acquired.
Then, the obtained compression ratio data and the trace compression ratio variation data are input into the dizziness degree estimation model every unit time, so that a dizziness degree prediction result output by the dizziness degree estimation model is obtained, and the dizziness degree variation in unit time can be obtained. And obtaining the dizziness degree data of the user at the current time point through the dizziness degree change amount data, so as to detect the dizziness degree of the user.
In summary, in the method for detecting the dizziness degree of the user in the virtual environment provided by the embodiment, the movement track information of the user in the three-dimensional space coordinates 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 variation data of the moving track information; 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 a user; the problem that the information such as the reason for the dizziness, the time point and the like is lost, or the immersive experience of virtual reality is destroyed, or hardware equipment is required to be additionally configured to realize the dizziness degree detection in the existing dizziness degree detection mode can be solved; because track compression data in the virtual environment is used as input data of the dizziness degree estimation model, a user does not need to wear redundant sensors, does not need to deviate from the virtual environment, and does not influence 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 sensors, simplifies the system structure, and has higher popularization and application potential.
In addition, the application can adopt a dynamic track compression algorithm, can obtain track compression data by storing user track data in a very small space, and has the characteristic of high efficiency.
Fig. 3 is a block diagram of an apparatus for detecting a user's dizziness degree 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 stun estimation module 330.
A track acquisition module 310, configured to acquire movement track information of a user in three-dimensional space coordinates of a virtual environment;
the track compression module 320 is configured to process the movement track information based on a track compression algorithm, so as to obtain compression ratio data and track compression ratio variation data of the movement track information;
the dizziness estimation module 330 is configured to input the compression ratio data and the trajectory compression ratio variation data into a pre-trained dizziness degree estimation model, to obtain a dizziness degree prediction result, where the dizziness degree prediction result is used to indicate the current dizziness degree of the user.
For relevant details reference is made to the method embodiments described above.
It should be noted that: when the device for detecting the dizziness degree of the user in the virtual environment provided in the above embodiment detects the dizziness degree of the user in the virtual environment, only the division of the functional modules is used for illustration, in practical application, the allocation of the functions may be completed by different functional modules according to needs, that is, the internal structure of the device for detecting the dizziness 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 dizziness degree of the user in the virtual environment provided by the above embodiment belongs to the same concept as the method embodiment for detecting the dizziness degree of the user in the virtual environment, and the specific implementation process of the device is detailed in the method embodiment, which is not described herein.
Fig. 4 is a block diagram of an apparatus for detecting a user's dizziness degree 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 DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 401 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 401 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 401 may also 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 of detecting a user's dizziness level in a virtual environment provided by an embodiment of the method of the present application.
In some embodiments, the apparatus may further optionally include: a peripheral interface and at least one peripheral. The processor 401, memory 402, and peripheral interfaces may be connected by buses or signal lines. The individual peripheral devices may be connected to the peripheral device interface via buses, signal lines or circuit boards. Illustratively, peripheral devices include, but are not limited to: radio frequency circuitry, touch display screens, audio circuitry, and power supplies, among others.
Of course, the device for detecting the dizziness degree of the user may also include fewer or more components, which is not limited in this embodiment.
Optionally, the present application further provides a computer readable storage medium, where a program is stored, where the program is loaded and executed by a processor to implement a method for detecting a dizziness degree of a user in a virtual environment according to the above method embodiment.
Optionally, the present application further provides a computer product, where the computer product includes a computer readable storage medium, where a program is stored, where the program is loaded and executed by a processor to implement a method for detecting a dizziness degree of a user in a virtual environment according to the above method embodiment.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for detecting a user's dizziness level in a virtual environment, the method comprising:
acquiring movement track information of a user in three-dimensional space coordinates 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 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.
2. The method according to claim 1, wherein the inputting the compression ratio data and the trajectory compression ratio variation data into a pre-trained dizziness degree estimation model, before obtaining the predicted result of the dizziness degree, further comprises:
after a user enters the virtual environment, acquiring sample movement track information of the user in three-dimensional space coordinates 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 variation data;
acquiring dizziness degree feedback information which is input by the user and corresponds to the sample movement track information;
training a machine learning model which is created in advance by using the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information to obtain the dizziness degree estimation model.
3. The method according to claim 2, wherein the acquiring the dizziness degree feedback information corresponding to the sample movement trajectory information input by the user includes:
displaying a preset questionnaire every preset time length;
and receiving the dizziness degree feedback information filled in the preset questionnaire by the user.
4. The method according to claim 3, wherein the dizziness degree feedback information is represented by a score, the training of a machine learning model created in advance using the sample compression ratio data, sample trajectory compression ratio variation data, and the dizziness degree feedback information to obtain the dizziness degree estimation model includes:
normalizing the dizziness 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 dizziness degree estimation model.
5. The method of claim 2, wherein training a pre-created machine learning model using the sample compression ratio data, sample trajectory compression ratio variation data, and the dizziness degree feedback information to obtain the dizziness degree estimation model comprises:
and carrying out dimension expansion on the sample compression ratio data, the sample track compression ratio variation data and the dizziness degree feedback information, and training the machine learning model by using the expanded data to obtain the dizziness degree estimation model.
6. The method of claim 1, wherein the trajectory compression algorithm is a spatio-temporal algorithm that compresses the moving trajectory information using other spatio-temporal information hidden in a time sequence, wherein the other spatio-temporal information includes: a time-to-distance measurement and a derived speed of the movement track information, the derived speed being a speed value derived from a time stamp and a position of the movement track information.
7. The method of claim 1, wherein the virtual environment is displayed by a virtual reality VR device and the movement track information is collected by the VR device.
8. An apparatus for detecting a user's dizziness level in a virtual environment, the apparatus comprising:
the track acquisition module is used for acquiring movement track information of a user in three-dimensional space coordinates 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 variation data of the moving track information;
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 dizziness degree of a user in a virtual environment, wherein the apparatus comprises a processor and a memory; stored in the memory is a program that is loaded and executed by the processor to implement the method of detecting a user's dizziness degree 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 therein a program which, when executed by a processor, is adapted to carry out a method of detecting a user's dizziness level in a virtual environment according to any one of claims 1 to 7.
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