CN117540161A - Dizziness grade assessment method, dizziness relieving device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of vehicles, and discloses a dizziness grade assessment method, a dizziness relieving device, equipment and a medium, wherein the dizziness grade assessment method comprises the following steps: acquiring a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, and vehicle data; constructing a first matrix based on a plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one; determining second data related to the dizziness level from the plurality of first data; adjusting the first matrix based on the second data to obtain a second matrix; model training is carried out on the dizziness grade assessment model based on the second matrix; and evaluating the dizziness grade of the user in the vehicle based on the trained dizziness grade evaluation model. The invention can improve the accuracy of the dizziness grade evaluation of personnel in the vehicle, provides a basis for the subsequent establishment of relief measures, and further improves the riding comfort and the driving safety.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a dizziness grade assessment method, a dizziness relieving device, equipment and a medium.
Background
The automatic driving automobile technology is the hot spot and front edge field of the current automobile industry, and has wide application prospect. However, with the development and popularity of autopilot technology, some drivers and occupants may be faced with vertigo problems. Dizziness is a discomfort sensation that not only affects ride comfort, but can also lead to distraction and distraction of the driver, thereby reducing driving safety.
In the related art, the degree of dizziness of a person in a vehicle is generally evaluated by a subjective evaluation method of a user, but this method has a problem of low accuracy in evaluation of the degree of dizziness.
Therefore, there is a need for a dizziness level assessment method that can effectively improve the accuracy of the dizziness level assessment of personnel in a vehicle.
Disclosure of Invention
In view of the above, the invention provides a dizziness level evaluation method, a dizziness relieving device, equipment and a medium, so as to solve the problem of low accuracy of dizziness level evaluation of in-vehicle personnel in the related technology.
In a first aspect, the present invention provides a dizziness level assessment method, including:
acquiring a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, and vehicle data;
Constructing a first matrix based on the plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one;
determining second data related to the dizziness level from the plurality of first data;
adjusting the first matrix based on the second data to obtain a second matrix;
model training is carried out on the dizziness grade assessment model based on the second matrix;
and evaluating the dizziness grade of the user in the vehicle based on the trained dizziness grade evaluation model.
According to the dizziness level assessment method provided by the invention, the second data related to the dizziness level is determined from the plurality of second data, the first matrix is adjusted based on the second data to obtain the second matrix, the dizziness level assessment model is subjected to model training through the second matrix, and the dizziness level of the user in the vehicle is assessed based on the trained dizziness level assessment model, so that the accuracy of the dizziness level assessment of the person in the vehicle can be improved, a basis is provided for the follow-up establishment of relief measures, and the riding comfort and the driving safety are further improved.
In an alternative embodiment, constructing a first matrix based on the plurality of sets of first data and a plurality of dizziness levels corresponding to the plurality of sets of first data one-to-one, includes:
Constructing a sub-matrix based on the plurality of sets of first data; the number of rows of the submatrices represents the serial number of the corresponding group, and the number of columns represents the number of types of the first data;
based on the dizziness level corresponding to each group of first data, adjusting the sub-matrix to obtain a first matrix; wherein the last column of the first matrix represents the dizziness level of each set of first data.
According to the dizziness level assessment method provided by the invention, the submatrices are constructed through the plurality of groups of first data, and the submatrices are adjusted based on the dizziness level corresponding to each group of first data, so that the first matrix is obtained, the dizziness level of the user in the vehicle under different physiological data, behavior data and vehicle data can be accurately judged, and technical support is provided for the follow-up construction of the dizziness level assessment model.
In an alternative embodiment, determining second data related to the dizziness level from the plurality of first data includes:
judging whether each first data has a difference value under different dizziness grades;
and if the first data has a difference value under different dizziness grades and the difference value is larger than a preset threshold value, taking the first data as second data.
According to the dizziness level assessment method provided by the invention, the first characteristics related to the dizziness level can be determined from the plurality of first data and used as the second data by judging whether the difference value exists in each first data under different dizziness levels and judging whether the difference value is larger than the preset threshold, so that the accuracy of the dizziness level assessment can be further improved.
In an alternative embodiment, model training the dizziness level assessment model based on the second matrix comprises:
taking a plurality of second data of each row in the second matrix as independent variables, and taking the dizziness level corresponding to each row as the independent variables, and performing model training on the dizziness level evaluation model;
and taking a level difference value between the self dizziness level fed back by the user and the target dizziness level output by the dizziness level evaluation model as a loss function of model training.
According to the dizziness level evaluation method provided by the invention, the plurality of second data of each row in the second matrix are used as independent variables, the dizziness level corresponding to each row is used as the independent variables, and the dizziness level evaluation model is subjected to model training, so that the corresponding relation between the plurality of second data of each row and the dizziness level can be accurately judged, the dizziness level of the user to be detected can be determined according to the plurality of second data of the user to be detected, and the basis is provided for formulating subsequent dizziness relief measures.
In an alternative embodiment, the evaluation of the dizziness level of the in-vehicle user based on the trained dizziness level evaluation model comprises:
detecting current physiological data and current behavior data of a user in the vehicle and current vehicle data;
and inputting the current physiological data, the current behavior data and the current vehicle data into the trained dizziness level evaluation model, and outputting the current dizziness level of the user in the vehicle.
In an alternative embodiment, the physiological data includes heart rate data, blood pressure data, and respiratory rate data; the behavioral data includes eye movement data, head pose data, and body pose data; the vehicle data includes acceleration data, angular velocity data, and road surface state data.
In a second aspect, the present invention provides a vertigo relieving method comprising:
and determining corresponding dizziness relieving measures according to the dizziness grade of the user in the vehicle.
In an alternative embodiment, the dizziness level comprises a first dizziness level, a second dizziness level, and a third dizziness level; the second dizziness level is greater than the first dizziness level and less than a third dizziness level; determining a corresponding dizziness relieving measure according to the dizziness grade of the user in the vehicle, comprising:
When the user in the vehicle is at a first dizziness level, sending voice prompt information to the user in the vehicle; the voice prompt information is used for prompting the user in the vehicle to adjust the current state;
when the user in the vehicle is at the second dizziness level, adjusting the seat posture and the window opening proportion of the user in the vehicle and playing music so as to relieve the dizziness of the user in the vehicle;
when the user in the vehicle is at the second dizziness level, the driving mode of the vehicle is adjusted to a comfortable mode so as to reduce preset driving operation; the preset driving operation includes a rapid acceleration operation, a rapid deceleration operation, and a rapid turning operation.
According to the dizziness relieving method provided by the invention, the corresponding dizziness relieving measures are matched according to the dizziness grade of the user in the vehicle, so that the relieving measures which are more in line with the actual dizziness condition of the user in the vehicle can be screened out, the aim of obtaining the optimal dizziness relieving effect is achieved, and the user experience is improved.
In a third aspect, the present invention provides a dizziness level assessment apparatus comprising:
the data acquisition module is used for acquiring a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, and vehicle data;
The first matrix construction module is used for constructing a first matrix based on the plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one;
a second data determining module, configured to determine second data related to the dizziness level from the plurality of first data;
the matrix adjustment module is used for adjusting the first matrix based on the second data to obtain a second matrix;
the model training module is used for carrying out model training on the dizziness grade evaluation model based on the second matrix;
and the dizziness grade evaluation module is used for evaluating the dizziness grade of the user in the vehicle based on the trained dizziness grade evaluation model.
In a fourth aspect, the present invention provides a computer device comprising: the device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions to execute the dizziness grade assessment method of the first aspect or any corresponding embodiment thereof or execute the dizziness relieving method of the second aspect or any corresponding embodiment thereof.
In a fifth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the dizziness level assessment method of the first aspect or any of the embodiments corresponding thereto, or the dizziness relief method of the second aspect or any of the embodiments corresponding thereto.
The invention has the technical effects that:
1. according to the invention, the second data related to the dizziness level is determined from the plurality of second data, the first matrix is adjusted based on the second data to obtain the second matrix, the dizziness level evaluation model is subjected to model training through the second matrix, the dizziness level of the user in the vehicle is evaluated based on the trained dizziness level evaluation model, the accuracy of the dizziness level evaluation of the person in the vehicle can be improved, a basis is provided for the follow-up establishment of relief measures, and the riding comfort and the driving safety are further improved.
2. According to the method, the sub-matrix is constructed through multiple groups of first data, and the sub-matrix is adjusted based on the dizziness grade corresponding to each group of first data, so that the first matrix is obtained, the dizziness grade of a user in a vehicle under different physiological data, behavior data and vehicle data can be accurately judged, and technical support is provided for the follow-up construction of the dizziness grade assessment model.
3. According to the method and the device, whether the difference value exists in each first data under different dizziness levels or not is judged, whether the difference value is larger than the preset threshold value or not is judged, the first characteristic related to the dizziness level can be determined from the plurality of first data, and the first characteristic is used as the second data, so that accuracy of dizziness level assessment can be further improved.
4. According to the invention, the plurality of second data of each row in the second matrix are used as independent variables, the dizziness grade corresponding to each row is used as the independent variables, and the dizziness grade evaluation model is subjected to model training, so that the corresponding relation between the plurality of second data of each row and the dizziness grade can be accurately judged, the dizziness grade of the user to be detected can be determined according to the plurality of second data of the user to be detected, and the basis is provided for formulating subsequent dizziness relieving measures.
5. According to the invention, the corresponding dizziness relieving measures are matched according to the dizziness grade of the user in the vehicle, so that the relieving measures which are more in line with the actual dizziness condition of the user in the vehicle can be screened, the aim of obtaining the optimal dizziness relieving effect is achieved, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a dizziness level assessment method according to an embodiment of the invention;
FIG. 2 is a flow chart of another dizziness level assessment method according to an embodiment of the invention;
fig. 3 is a flow chart of a vertigo relief method according to an embodiment of the present invention;
fig. 4 is a block diagram of a structure of a dizziness level assessment apparatus according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The automatic driving automobile technology is the hot spot and front edge field of the current automobile industry, and has wide application prospect. However, with the development and popularity of autopilot technology, some drivers and occupants may be faced with vertigo problems. Dizziness is a discomfort sensation that not only affects ride comfort, but can also lead to distraction and distraction of the driver, thereby reducing driving safety.
In the related art, the dizziness degree of drivers and passengers is generally evaluated by a subjective evaluation mode of users; however, the influence of subjective factors and objective factors on the dizziness degree in the vehicle is not considered, so that the evaluation accuracy of the existing evaluation mode of the dizziness degree of the driver and the passengers is low.
Therefore, there is a need for a dizziness level evaluation method, which can fully consider the influence of subjective factors and external objective factors on the dizziness level in the vehicle, so as to improve the evaluation accuracy of the dizziness level of drivers and passengers, and is helpful for providing a personalized relief strategy according to the accurate dizziness level; meanwhile, the driving safety is improved.
According to an embodiment of the present invention, there is provided an embodiment of a dizziness level assessment method, it being noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.
In this embodiment, a dizziness level evaluation method is provided, which may be used in a control unit of an in-vehicle dizziness level evaluation device, and fig. 1 is a flowchart of the dizziness level evaluation method according to an embodiment of the present invention, as shown in fig. 1, and the flowchart includes the following steps:
Step S101, a plurality of sets of first data are acquired.
Specifically, physiological data and behavior data of the user in the vehicle, as well as vehicle data of the vehicle itself, may be collected by various data detection devices (e.g., various sensors). The first data includes physiological data and behavioral data associated with the user, as well as vehicle data. The user may be a driver or a passenger in the vehicle; the physiological data includes heart rate data, blood pressure data, and respiratory rate data; the behavioral data includes eye movement data, head pose data, and body pose data; the vehicle data includes acceleration data, angular velocity data, and road surface state data.
Wherein heart rate data can be understood as a number of Beats Per Minute (BPM), which can be detected by a heart rate monitor or electrocardiograph; blood pressure data including systolic and diastolic blood pressure data in millimeters of mercury (mmHg) and being measurable using a sphygmomanometer; respiratory rate data can be understood as breaths per minute (BRPM, breath Rate Per Minute) that can be measured by a respiratory monitor or chest sensor.
The eye movement data includes gaze point data, gaze time data, and gaze path data, which may be measured by an eye tracker; the head pose data includes head angle data and head direction data, which can be measured by a head pose sensor; body posture data includes body tilt data and sitting posture data, which may be measured by a body posture sensor or a seat sensor.
Acceleration data, i.e. meters per second squared (m/s) 2 ) Accelerometer measurements on the vehicle may be used; angular velocity data, i.e., angle per second (deg/s), may be measured using gyroscopes on the vehicle; the road surface condition data, including road surface roughness and irregularities, may be measured using road surface sensors or Inertial Measurement Units (IMUs) on the vehicle.
Specifically, physiological indexes of the driver and the passenger, such as heart rate, blood pressure and eye movement data, can be recorded in real time by using various sensors and devices, such as a heart rate monitor, a blood pressure meter, an eye movement meter and the like; behavior data of the in-vehicle user, such as discomfort feeling, exercise pattern, etc., which will provide detailed information about the in-vehicle user's status and feeling, can be obtained by means of observation and questionnaires, etc.; acceleration, steering, vibration of the vehicle and changes in road conditions and surrounding environment can be monitored in real time by sensors such as accelerometers, gyroscopes, cameras and the like, which will provide information on movement patterns and environmental factors associated with dizziness.
More specifically, each set of first data may be understood as physiological data, behavioral data, and data of the vehicle at a certain instant or within a certain period of time, for example: group 1: physiological data 1-behavioral data 1-vehicle data 1; group 2: physiological data 2-behavioral data 2-vehicle data 2.
In some alternative embodiments, after acquiring the plurality of sets of first data, the method further comprises:
the physiological data, the behavior data and the vehicle data acquired by the plurality of sensors are preprocessed, namely, the acquired data are subjected to characteristic index extraction, such as maximum value, minimum value, standard deviation, mean value and the like, the data in a fixed time period (such as 2 min) are intercepted, characteristic value calculation and normalization processing are performed, and the processed data can be used for a subsequent step S102.
Step S102, a first matrix is constructed based on the plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one.
Specifically, the dizziness level may be classified according to the serious dizziness condition of the user, and the specific classification is not specifically limited, and may be set according to the actual condition, for example, the dizziness level 1, the dizziness level 2, the dizziness level 3, the dizziness level 4, and the dizziness level 5; wherein, dizziness level 5 represents very dizziness, dizziness level 4 represents comparative dizziness, dizziness level 3 represents general dizziness, dizziness level 2 represents comparative dizziness, and dizziness level 1 represents non-dizziness.
The dizziness level may be predetermined, for example: pre-selecting a plurality of users, respectively carrying out a dizziness test on each user, carrying out real-time monitoring on physiological data and behavior data of each user and vehicle self data in the dizziness test process, judging the dizziness grade of each user by observing or questionnaire investigation after the dizziness test, and establishing a relation between the physiological data and the behavior data of the user and the vehicle data and the dizziness grade obtained by judgment, thereby obtaining the following corresponding relation: group 1: physiological data 1-behavioral data 1-vehicle data 1-dizziness level 1; group 2: physiological data 2-behavioral data 2-vehicle data 2-dizziness level 2.
A first matrix can be constructed according to the obtained corresponding relations; each row of the first matrix represents physiological data and behavioral data of the corresponding user, as well as vehicle data; each column represents a type of measurement data (e.g., physiological data, behavioral data, or vehicle data); the last column of each row represents the dizziness level corresponding to the measurement data of the corresponding row.
The first matrix is as follows:
wherein, the first matrix is based on the submatrix, and the dizziness level of the data of the group is set in the last column of each group, for example, C1 represents the dizziness level corresponding to the M1 group, and C2 represents the dizziness level corresponding to the M2 group; n represents the number of kinds of data in the first matrix.
Step S103, determining second data related to the dizziness level from the plurality of first data.
Specifically, the first data refers to physiological data and behavioral data of the user, as well as vehicle own data; while the second data refers to physiological data related to motion sickness, behavioral data and vehicle itself data, for example, physiological data may include various kinds, in which nausea, vomiting, dizziness, palpitation, general weakness and pale complexion are all related to dizziness, and thus can be regarded as the second data. The behavior data comprise a plurality of types, wherein the gaze point of the eye is on the mobile phone, and the dizziness can be caused by the overlarge head deflection angle and the overlarge body inclination rate, so that the gaze point of the eye can be used as second data; the vehicle own data includes various kinds of data in which rapid acceleration, rapid deceleration, road surface roughness is excessively high, and the like can be used as the second data.
Step S104, adjusting the first matrix based on the second data, to obtain a second matrix.
Specifically, the first data which is irrelevant to dizziness in the first matrix can be filtered through the second data, so that a new matrix is obtained, the correlation between the new matrix and the dizziness is obviously higher than that of the first matrix, and the new matrix can be used as the second matrix.
The second matrix is shown below:
wherein, the first matrix is based on the submatrix, and the dizziness level of the data of the group is set in the last column of each group, for example, C1 represents the dizziness level corresponding to the M1 group, and C2 represents the dizziness level corresponding to the M2 group; m represents the number of kinds of data in the second matrix, and m is smaller than n.
Step S105, performing model training on the dizziness level assessment model based on the second matrix.
Specifically, the dizziness level evaluation model may be a deep convolutional neural network, and in the process of performing model training on the dizziness level evaluation model based on the second matrix, the second data of each line in the second matrix may be used as an independent variable, and the dizziness level corresponding to each line is used as an independent variable to perform model training on the dizziness level evaluation model, which specifically includes the following steps:
firstly, taking a plurality of second data of each row in a second matrix and corresponding dizziness grades as a data set, dividing the data set into a training set and a test set, taking 80% of data as the training set and 20% of data as the test set, so as to perform model training and evaluation;
Second, the dizziness level assessment model is trained using the training set data, and weights and parameters are adjusted by a back propagation algorithm to minimize the loss function. When training the dizziness level assessment model, the difference between the predicted value and the actual value of the feedback of the user on the dizziness level of the user under the current internal and external driving state is defined as the loss function of the machine learning model, and the final target of the training of the dizziness recognition model of the driver is to minimize the loss function
Finally, a prediction result of the dizziness degree of the user is obtained through the trained dizziness grade evaluation model, the prediction result is used as a reference to activate parameters of the dizziness relieving measure, the user satisfaction degree is collected after the intervention measure is relieved, and the intervention time point is continuously and iteratively relieved by the method, so that the optimal user dizziness relieving satisfaction degree is obtained.
And step S106, estimating the dizziness grade of the user in the vehicle based on the trained dizziness grade estimation model.
Specifically, physiological data, behavioral data and vehicle data of a user to be detected in the vehicle at the current time or at the current moment can be input into a trained dizziness level evaluation model, and the dizziness level of the user to be detected is output. The dizziness level evaluation model may be a deep learning model.
In this embodiment, a dizziness level evaluation method is provided, which may be used in a control unit of an in-vehicle dizziness level evaluation device, and fig. 2 is a flowchart of the dizziness level evaluation method according to an embodiment of the present invention, as shown in fig. 2, and the flowchart includes the following steps:
step S201, obtaining a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, as well as vehicle data.
Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, constructing a first matrix based on the multiple sets of first data and multiple dizziness levels corresponding to the multiple sets of first data one-to-one.
Step S202 further includes steps S2021 to S2022, which are specifically as follows:
step S2021, constructing a submatrix based on the plurality of sets of first data; the number of rows of the submatrices represents the serial number of the corresponding group, and the number of columns represents the number of types of the first data.
Specifically, the submatrices are as follows:
wherein the number of rows of the submatrices represents the serial number of the corresponding group, for example, M1 represents group 1; each group comprising physiological data, behavioral data, and vehicle data of the user; the column number indicates the number of types of the first data, for example, N1 indicates any one of physiological data, or indicates any one of behavior data, or indicates any one of vehicle data types;
Step S2021, adjusting the sub-matrix based on the dizziness level corresponding to each group of first data, to obtain a first matrix; wherein the last column of the first matrix represents the dizziness level of each set of first data.
Specifically, the first matrix is as follows:
the first matrix is that the dizziness level of the data of the group is set in the last column of each group on the basis of the submatrix, for example, C1 represents the corresponding dizziness level of the group M1, and C2 represents the corresponding dizziness level of the group M2.
Step S203, determining second data related to the dizziness level from the plurality of first data.
Step S203 further includes steps S2031 to S2032, specifically as follows:
step S2031, determining whether each first data has a difference value at different dizziness levels.
Specifically, first data of a user at a first dizziness level and first data of the user at a second dizziness level are respectively determined; wherein the first dizziness level may be a dizziness level 1 (i.e. no dizziness state) and the second dizziness level may be any one of a dizziness level 2, a dizziness level 3, a dizziness level 4. When a difference exists between the first data at the first dizziness level and the first data at the second dizziness level, the first data is initially determined to be relevant to the dizziness. The first data may be any one of a plurality of physiological data, any one of a plurality of behavior data, or any one of a plurality of vehicle data.
Step S2032, if the first data has a difference value at different dizziness levels, and the difference value is greater than a preset threshold, takes the first data as second data.
Specifically, when there is a difference between the first data at the first dizziness level and the first data at the second dizziness level, and the difference is greater than a corresponding preset threshold, the first data may be used as the second data. The above steps are described using heart rate as the first data:
and comparing the heart rate value of the user at the dizziness level 1 with the heart rate value of the user at the dizziness level 2, and if a difference exists and the difference is larger than a first preset threshold value, taking the heart rate as second data, wherein the heart rate is an important factor affecting the dizziness.
And comparing the heart rate value of the user at the dizziness level 1 with the heart rate value of the user at the dizziness level 3, and taking the heart rate as second data if a difference exists and the difference is larger than a second preset threshold value.
And comparing the heart rate value of the user at the dizziness level 1 with the heart rate value of the user at the dizziness level 4, and taking the heart rate as second data if a difference exists and is larger than a third preset threshold value.
The first preset threshold is smaller than the second preset threshold, the second preset threshold is smaller than the third preset threshold, and the first preset threshold, the second preset threshold and the third preset threshold can be set according to actual conditions, and are not particularly limited herein.
And step S204, adjusting the first matrix based on the second data to obtain a second matrix.
Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S205, performing model training on the dizziness level assessment model based on the second matrix.
Please refer to step S105 in the embodiment shown in fig. 1 in detail, which is not described herein.
And S206, estimating the dizziness grade of the user in the vehicle based on the trained dizziness grade estimation model.
Step S206 further includes steps S2061 to S2062, specifically as follows:
in step S2061, current physiological data and current behavior data of the in-vehicle user, and current vehicle data are detected.
In particular, the current physiological data may represent physiological data for a current period of time, such as: the average heart rate over 5 minutes may also represent physiological data at the current time, such as the blood pressure value at the current time. The current behavior data and the current vehicle data may represent behavior data and vehicle data for a current period of time, or behavior data and vehicle data for a current time.
Step S2062, inputting the current physiological data, the current behavior data and the current vehicle data into the trained dizziness level evaluation model, and outputting the current dizziness level of the in-vehicle user.
Specifically, the trained dizziness level evaluation model can determine the current dizziness level corresponding to the current physiological data, the current behavior data and the current vehicle data through the corresponding relation between the physiological data, the behavior data and the vehicle data and the dizziness level.
In this embodiment, there is provided a dizziness relieving method including the steps of:
step S301, corresponding dizziness relieving measures are determined according to the dizziness grade of the user in the vehicle.
Specifically, the in-vehicle user may be a driver or a passenger in the vehicle; the dizziness level of the user in the vehicle can be determined according to the steps S101 to S106; the dizziness relieving measures are used for relieving users in the vehicle in a dizziness state so as to reduce uncomfortable feeling of the users; the dizziness relieving measures can be matched according to the dizziness grade, and different dizziness grades can be understood to correspond to different dizziness relieving measures.
As shown in fig. 3, the step S301 includes the steps of:
Step S3011, when the in-vehicle user is at a first dizziness level, sending out voice prompt information to the in-vehicle user; the voice prompt information is used for prompting the user in the vehicle to adjust the current state.
Specifically, the dizziness level includes a first dizziness level, a second dizziness level, and a third dizziness level; the second dizziness level is greater than the first dizziness level and less than the third dizziness level, i.e., the first dizziness level < the second dizziness level < the third dizziness level. When the dizziness degree of the user in the vehicle is lower, namely, the user in the vehicle is informed of transferring the attention through a voice reminding mode, the current state is adjusted, for example, the user in the vehicle is reminded of not playing a mobile phone or adjusting the sitting posture through voice, and the dizziness degree is relieved by adopting a mode that eyes look outside windows or are closed.
And step S3012, when the user in the vehicle is at the second dizziness level, adjusting the seat posture and the window opening proportion of the user in the vehicle and playing music so as to relieve the dizziness of the user in the vehicle.
Specifically, when the degree of dizziness of the user in the vehicle is general, i.e. at the second dizziness level, the following measures may be taken, i.e.: the discomfort caused by dizziness is relieved by playing music (such as light and comfortable or dynamic music), releasing fragrance (such as peppermint and orange flavor) beneficial to refreshing, opening windows for ventilation (such as opening windows for ventilation by 20% -40%), adjusting the posture of a seat (such as adjusting the angles of a backrest, a waist support and a leg support), and the like.
Step S3013, when the user in the vehicle is at the second dizziness level, adjusting the driving mode of the vehicle to a comfortable mode so as to reduce the preset driving operation; the preset driving operation includes a rapid acceleration operation, a rapid deceleration operation, and a rapid turning operation.
Specifically, when the dizziness degree of the user in the vehicle is higher, that is, the user is at the second dizziness level, an intervention operation is required to be performed on the driving mode of the vehicle, that is, the driving mode of the vehicle is adjusted to be a comfortable mode, so that operations that sudden acceleration, sudden deceleration, sudden turning and the like may cause dizziness are reduced, the dizziness level of the user in the vehicle is relieved, or the driver is reminded of stopping and resting in a voice prompt mode.
In some alternative embodiments, the method further comprises:
after the dizziness relieving measures are implemented, inquiring satisfaction degree of the relieving measures to the users in the vehicle through a preset mode; the satisfaction is used to optimize the vertigo relief measures.
Specifically, the preset mode can be a mode of pushing a message or questionnaire by using a central control screen popup window, car machine voice or APP; the satisfaction reasons or dissatisfaction reasons of the users in the vehicle can be inquired in the process of inquiring the satisfaction degree of the relieving measures, and the dizziness relieving is expected to be carried out in what mode, so that the mode and the time of the dizziness relieving are iterated and optimized continuously according to the feedback of the users, and the optimal dizziness relieving effect is obtained.
In some alternative embodiments, the method further comprises:
and continuously monitoring physiological data and vehicle data of the user in the vehicle, and timely adjusting the relieving measures according to the real-time physiological data and the real-time vehicle data of the user in the vehicle.
Specifically, through the mode of the real-time monitoring and the real-time adjustment relief measures, the real-time dizziness state of the user in the vehicle can be more attached, so that the best relief measures are adjusted, and the user in the vehicle is helped to relieve the dizziness.
The dizziness grade assessment method and the dizziness relieving method provided by the invention adopt proper relieving measures according to the dizziness grade obtained by assessment. Based on the dizziness degree and real-time monitoring data of the driver, the running mode of the vehicle can be automatically adjusted, such as acceleration and steering smoothness are adjusted, and vibration is reduced. In addition, the invention provides personalized feedback information for passengers through a display screen or an acoustic prompt mode, and guides the passengers to adjust the posture or to disperse the attention. Through real-time monitoring and feedback, the relief measures can be continuously optimized to realize the best dizziness relief effect. By the method for evaluating and relieving the dizziness problem, riding comfort can be improved, distraction and inattention of a driver can be reduced, and driving safety can be further improved.
The embodiment also provides a dizziness level evaluation device, which is used for realizing the embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a dizziness level evaluation device, as shown in fig. 4, including:
a data acquisition module 401, configured to acquire a plurality of sets of first data; the first data includes physiological data and behavioral data associated with the user, as well as vehicle data.
The first matrix construction module 402 is configured to construct a first matrix based on the multiple sets of first data and multiple dizziness levels corresponding to the multiple sets of first data one to one.
A second data determining module 403, configured to determine second data related to the dizziness level from the plurality of first data.
And a matrix adjustment module 404, configured to adjust the first matrix based on the second data, so as to obtain a second matrix.
And the model training module 405 is configured to perform model training on the dizziness level assessment model based on the second matrix.
And the dizziness level evaluation module 406 is configured to evaluate the dizziness level of the in-vehicle user based on the trained dizziness level evaluation model.
In some alternative embodiments, the first matrix construction module 402 includes:
a sub-matrix construction unit for constructing a sub-matrix based on the plurality of sets of first data; the number of rows of the submatrices represents the serial number of the corresponding group, and the number of columns represents the number of types of the first data.
The sub-matrix adjusting unit is used for adjusting the sub-matrix based on the dizziness level corresponding to each group of first data to obtain a first matrix; wherein the last column of the first matrix represents the dizziness level of each set of first data.
In some alternative embodiments, the second data determination module 403 includes:
and the difference value judging unit is used for judging whether the difference value exists in each first data under different dizziness grades.
And the second data determining unit is used for taking the first data as second data if the first data has difference values under different dizziness levels and the difference values are larger than a preset threshold value.
In some alternative embodiments, the model training module 405 includes:
the model training unit is used for carrying out model training on the dizziness level evaluation model by taking a plurality of second data of each row in the second matrix as independent variables and the dizziness level corresponding to each row as independent variables;
and taking a level difference value between the self dizziness level fed back by the user and the target dizziness level output by the dizziness level evaluation model as a loss function of model training.
In some alternative embodiments, dizziness level assessment module 406 includes:
the data detection unit is used for detecting current physiological data and current behavior data of the user in the vehicle and current vehicle data.
The current dizziness level output unit is used for inputting the current physiological data, the current behavior data and the current vehicle data into the trained dizziness level evaluation model and outputting the current dizziness level of the user in the vehicle.
In some alternative embodiments, the physiological data includes heart rate data, blood pressure data, and respiratory rate data; the behavioral data includes eye movement data, head pose data, and body pose data; the vehicle data includes acceleration data, angular velocity data, and road surface state data.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The present embodiment provides a dizziness relief apparatus, including:
the dizziness relieving measure determining module is used for determining corresponding dizziness relieving measures according to the dizziness grade of the user in the vehicle.
In some alternative embodiments, the vertigo relief measure determination module includes:
the first dizziness relieving measure determining unit is used for sending out voice prompt information to the in-vehicle user when the in-vehicle user is at a first dizziness level; the voice prompt information is used for prompting the user in the vehicle to adjust the current state.
And the second dizziness relieving measure determining unit is used for adjusting the seat posture and the window opening proportion of the in-vehicle user and playing music when the in-vehicle user is at a second dizziness level so as to relieve the dizziness of the in-vehicle user.
A third dizziness relief measure determination unit configured to adjust a driving mode of the vehicle to a comfort mode to reduce a preset driving operation when the in-vehicle user is at a second dizziness level; the preset driving operation includes a rapid acceleration operation, a rapid deceleration operation, and a rapid turning operation.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides computer equipment, which is provided with the dizziness grade assessment device shown in the figure 4.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 5, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (11)
1. A method of vertigo grade assessment, the method comprising:
acquiring a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, and vehicle data;
constructing a first matrix based on the plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one;
determining second data related to the dizziness level from the plurality of first data;
adjusting the first matrix based on the second data to obtain a second matrix;
model training is carried out on the dizziness grade assessment model based on the second matrix;
and evaluating the dizziness grade of the user in the vehicle based on the trained dizziness grade evaluation model.
2. The method of claim 1, wherein constructing a first matrix based on the plurality of sets of first data and a plurality of dizziness levels corresponding one-to-one to the plurality of sets of first data comprises:
Constructing a sub-matrix based on the plurality of sets of first data; the number of rows of the submatrices represents the serial number of the corresponding group, and the number of columns represents the number of types of the first data;
based on the dizziness level corresponding to each group of first data, adjusting the sub-matrix to obtain a first matrix; wherein the last column of the first matrix represents the dizziness level of each set of first data.
3. The method according to claim 1 or 2, wherein determining second data related to a dizziness level from the plurality of first data comprises:
judging whether each first data has a difference value under different dizziness grades;
and if the first data has a difference value under different dizziness grades and the difference value is larger than a preset threshold value, taking the first data as second data.
4. The method according to claim 1 or 2, characterized in that model training of the vertigo grade assessment model based on the second matrix comprises:
taking a plurality of second data of each row in the second matrix as independent variables, and taking the dizziness level corresponding to each row as the independent variables, and performing model training on the dizziness level evaluation model;
And taking a level difference value between the self dizziness level fed back by the user and the target dizziness level output by the dizziness level evaluation model as a loss function of model training.
5. The method according to claim 1 or 2, wherein evaluating the dizziness level of the in-vehicle user based on the trained dizziness level evaluation model comprises:
detecting current physiological data and current behavior data of a user in the vehicle and current vehicle data;
and inputting the current physiological data, the current behavior data and the current vehicle data into the trained dizziness level evaluation model, and outputting the current dizziness level of the user in the vehicle.
6. The method of claim 1 or 2, wherein the physiological data comprises heart rate data, blood pressure data, and respiratory rate data; the behavioral data includes eye movement data, head pose data, and body pose data; the vehicle data includes acceleration data, angular velocity data, and road surface state data.
7. A dizziness relief method, characterized by comprising the dizziness level assessment method according to any one of claims 1 to 6; the dizziness relieving method comprises the following steps:
And determining corresponding dizziness relieving measures according to the dizziness grade of the user in the vehicle.
8. The method of claim 7, wherein the dizziness level comprises a first dizziness level, a second dizziness level, and a third dizziness level; determining a corresponding dizziness relieving measure according to the dizziness grade of the user in the vehicle, comprising:
when the user in the vehicle is at a first dizziness level, sending voice prompt information to the user in the vehicle; the voice prompt information is used for prompting the user in the vehicle to adjust the current state;
when the user in the vehicle is at the second dizziness level, adjusting the seat posture and the window opening proportion of the user in the vehicle and playing music so as to relieve the dizziness of the user in the vehicle;
when the user in the vehicle is at the second dizziness level, the driving mode of the vehicle is adjusted to a comfortable mode so as to reduce preset driving operation; the preset driving operation includes a rapid acceleration operation, a rapid deceleration operation, and a rapid turning operation.
9. A vertigo grade assessment device, the device comprising:
the data acquisition module is used for acquiring a plurality of groups of first data; the first data includes physiological data and behavioral data associated with the user, and vehicle data;
The first matrix construction module is used for constructing a first matrix based on the plurality of groups of first data and a plurality of dizziness levels corresponding to the plurality of groups of first data one by one;
a second data determining module, configured to determine second data related to the dizziness level from the plurality of first data;
the matrix adjustment module is used for adjusting the first matrix based on the second data to obtain a second matrix;
the model training module is used for carrying out model training on the dizziness grade evaluation model based on the second matrix;
and the dizziness grade evaluation module is used for evaluating the dizziness grade of the user in the vehicle based on the trained dizziness grade evaluation model.
10. A computer device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the dizziness level assessment method of any of claims 1 to 6, or the dizziness mitigation method of any of claims 7 to 8.
11. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the dizziness level assessment method according to any one of claims 1 to 6 or the dizziness relief method according to any one of claims 7 to 8.
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