CN110141259B - Method and device for measuring cognitive load and psychological pressure based on wireless communication - Google Patents
Method and device for measuring cognitive load and psychological pressure based on wireless communication Download PDFInfo
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Abstract
The application provides a method for measuring cognitive load and psychological pressure based on wireless communication, which comprises the following steps: acquiring user channel state information data of a user under wireless communication; carrying out data characteristic preprocessing on the user channel state information data to obtain preprocessed user channel state information data; and predicting to obtain the cognitive load and the psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data. The cognitive load and psychological stress measurement evaluation scheme is non-contact, non-invasive, low-cost and easy to deploy.
Description
Technical Field
The present application relates to the field of information technology, and in particular, to a method and apparatus for measuring cognitive load and psychological stress based on wireless communication.
Background
With the development of information technology, human-oriented intelligent human-computer interaction is very important and has wide application prospects, and the evaluation of cognitive load and psychological stress is very important for the research of user behaviors and the design of human-computer interaction. Cognitive load and psychological stress are loads imposed on the cognitive system-working memory of learners as they feel at the time of information processing. The psychological input refers to the cognitive capacity allocated to the task by the learner, reflects the actual level of cognitive load, and can be used as a measurement index of the cognitive load and the psychological pressure. When the cognitive load or mental input exceeds the learner's cognitive capacity, stress is created and learning processing tasks are hindered. Therefore, the load affects the behavior strategy and the performance result of the learner and can cause the change of the physiological and psychological processes of the learner, so that the measurement of the cognitive load and the psychological stress has important value.
The currently used methods for measuring cognitive load and psychological pressure are mainly divided into subjective methods and objective methods. Subjective methods generally use subjective scoring scales or questionnaires, and objective methods are generally classified into behavioral methods, which generally include behavioral measures and performance measures, and physiological methods. Such as a touch screen based performance measurement, a dual task based performance measurement. Physiological methods are generally based on Electrocardiography (ECG), electroencephalography (EEG), Galvanic Skin Response (GSR) and Photoplethysmogram (PPG).
Chinese patent application No. CN201610165125.9, entitled: a method and a device for measuring cognitive load, psychological pressure and pressure based on peripheral vascular strain provide a method for measuring cognitive load, psychological pressure and pressure based on peripheral vascular strain, and blood vessel volume wave signals are collected through a transmission type or reflection type photoelectric sensor, and are filtered to eliminate noise; performing instantaneous peripheral vascular strain feature extraction; obtaining continuous and stable stroke volume change (SVV) peripheral vascular resistance index (sVRI) output through dynamic sliding window arrangement and data modulation based on statistics; finally, sVRI is used for evaluating cognitive load and psychological stress and stress level, the invention also provides a corresponding measuring device, which is different from the traditional measuring method based on peripheral vascular dilation degree, the sVRI uses the ratio of the amplitudes of adjacent wave bands of PPG signals, and experiments show that the indication has more sensitive and reliable reflecting capacity on the cognitive load and the psychological stress and stress; the cognitive load and psychological pressure and pressure measuring technology based on the peripheral vascular strain has obvious advantages in the aspects of sensitivity, reliability, individual difference and the like when the cognitive load, psychological pressure and other psychological activities of people are measured, can be used for real-time measurement, and is well applied to the universal environment.
However, for the measurement of cognitive load and psychological pressure, the subjective method depends heavily on subjective recall after human tasks, and real-time measurement cannot be achieved, so that the method is difficult to apply to a large number of scenes. However, the cognitive load and psychological stress assessment method based on physiological signals requires that a user wears complicated equipment, has strong invasiveness, is not easy to popularize and use, and is difficult to apply to the field of actual production and life other than clinical practice.
Therefore, how to provide a non-invasive measurement scheme capable of accurately measuring the cognitive load and the psychological stress in real time is a technical problem to be solved in the field.
Disclosure of Invention
The application aims to provide a method and a device for measuring cognitive load and psychological pressure based on wireless communication, and the technical problem that no non-invasive measuring scheme capable of accurately measuring the cognitive load and the psychological pressure in real time exists in the prior art is solved.
In order to achieve the above object, the present application provides a method for measuring cognitive load and psychological stress based on wireless communication, comprising:
acquiring user channel state information data of a user under wireless communication;
carrying out data characteristic preprocessing on the user channel state information data to obtain preprocessed user channel state information data;
and predicting the cognitive load and the psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data.
Optionally, wherein the data characteristic preprocessing is performed on the user channel state information data to obtain preprocessed user channel state information data, and the preprocessing is performed by:
acquiring user channel state information data of the user in wireless communication, and processing the user channel state information data to obtain stable user channel state information data;
and carrying out normalization processing on the stable user channel state information data by using a normalization model to obtain the preprocessed user channel state information data.
Optionally, the processing the user channel state information data to obtain stable user channel state information data includes:
filtering the user channel state information data, and removing abnormal points caused by noise and measurement errors to obtain filtered user channel state information data;
and compensating packet loss and time delay in the user channel state information data after the filtering by linear interpolation to obtain stable user channel state information data.
Optionally, the filtering process is:
defining a filtering strategy, defining in intervals
The points other than the above are outliers, in which,
for the median of this piece of data,
for the absolute deviation of the median of this piece of data,
indicating the tolerance range of the data from the median;
and filtering the user channel state information data, and removing abnormal points caused by noise and measurement errors to obtain the filtered user channel state information data.
Optionally, the predicting cognitive load and psychological stress of the user according to the neural network of the channel state information and the preprocessed user channel state information data includes:
acquiring channel state information data in wireless communication through a network interface controller;
adjusting the cognitive load and the psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquiring individual channel state information data of training individuals larger than or equal to a training individual number threshold value, corresponding to the cognitive load and the psychological pressure;
preprocessing the individual channel state information data to obtain preprocessed individual channel state information training data;
training the preprocessed individual channel state information training data to a neural network building model to obtain a neural network of channel state information;
and predicting the cognitive load and the psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data.
In another aspect, the present invention further provides a device for measuring cognitive load and psychological stress based on wireless communication, including: the device comprises a user channel state information data receiver, a user channel state information data preprocessor and a cognitive load and psychological stress measurement processor; wherein the content of the first and second substances,
the user channel state information data receiver is connected with the user channel state information data preprocessor and used for acquiring user channel state information data of a user under wireless communication;
the user channel state information data preprocessor is connected with the user channel state information data receiver and the cognitive load and psychological stress measurement processor, and is used for preprocessing the data characteristics of the user channel state information data to obtain preprocessed user channel state information data;
and the cognitive load and psychological stress measurement processor is connected with the user channel state information data preprocessor and used for predicting the cognitive load and the psychological stress of the user according to the neural network of the channel state information and the preprocessed user channel state information data.
Optionally, the user channel state information data preprocessor includes: the device comprises a user channel state information data stabilization processing unit and a user channel state information data normalization processing unit; wherein the content of the first and second substances,
the user channel state information data stabilization processing unit is connected with the user channel state information data receiver and the user channel state information data normalization processing unit, acquires user channel state information data of the user under wireless communication, and processes the user channel state information data to obtain stable user channel state information data;
the user channel state information data normalization processing unit is connected with the user channel state information data stabilization processing unit and the cognitive load and psychological stress measurement processor, and normalization processing is carried out on the stable user channel state information data by using a normalization model to obtain the preprocessed user channel state information data.
Optionally, wherein the user channel state information data stabilization processing unit includes: a filtering processing unit for the user channel state information data and a compensation processing unit for the user channel state information data; wherein the content of the first and second substances,
the user channel state information data filtering processing unit is connected with the user channel state information data receiver and the user channel state information data compensation processing unit, and is used for filtering the user channel state information data, removing abnormal points caused by noise and measurement errors and obtaining filtered user channel state information data;
the user channel state information data compensation processing unit is connected with the user channel state information data filtering processing unit and the user channel state information data normalization processing unit, and compensates packet loss and time delay in the user channel state information data after being filtered through linear interpolation to obtain stable user channel state information data.
Optionally, the filtering processing unit for user channel state information data includes: a filtering processing strategy definition processing unit and a user channel state information data filtering execution processing unit;
the filtering processing strategy definition processing unit is connected with the user channel state information data receiver and the user channel state information data filtering execution processing unit, defines the filtering processing strategy and is defined in the interval
The points other than the above are outliers, in which,
for the median of this piece of data,
for the absolute deviation of the median of this piece of data,
indicating the tolerance range of the data from the median;
and the user channel state information data filtering execution processing unit is connected with the filtering processing strategy definition processing unit and the user channel state information data compensation processing unit, and is used for filtering the user channel state information data, removing abnormal points caused by noise and measurement errors and obtaining the filtered user channel state information data.
Optionally, wherein the cognitive load and psychological stress measurement processor comprises: the system comprises a wireless communication channel state information data acquisition unit, an individual channel state information training data preprocessing unit, a cognitive load and psychological stress neural network building processing unit and a cognitive load and psychological stress measuring processing unit; wherein the content of the first and second substances,
the wireless communication channel state information data acquisition unit is connected with the individual channel state information training data acquisition unit and acquires channel state information data in wireless communication through a network interface controller;
the individual channel state information training data acquisition unit is connected with the wireless communication channel state information data acquisition unit and the individual channel state information training data preprocessing unit, adjusts cognitive load and psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquires individual channel state information data corresponding to training individuals with training individual number threshold values larger than or equal to the cognitive load and psychological pressure;
the individual channel state information training data preprocessing unit is connected with the individual channel state information training data acquisition unit and the cognitive load and psychological pressure neural network building processing unit, and is used for preprocessing the individual channel state information data to obtain preprocessed individual channel state information training data;
the cognitive load and psychological pressure neural network building processing unit is connected with the individual channel state information training data preprocessing unit, the cognitive load and psychological pressure measuring processing unit and the user channel state information data preprocessor, and the neural network building model is trained by the preprocessed individual channel state information training data to obtain a neural network of channel state information;
and the cognitive load and psychological pressure measurement processing unit is connected with the cognitive load and psychological pressure neural network construction processing unit, and the cognitive load and psychological pressure of the user are obtained through prediction according to the neural network of the channel state information and the preprocessed user channel state information data.
The method and the device for measuring cognitive load and psychological pressure based on wireless communication have the following beneficial effects:
(1) compared with the prior art, the cognitive load and psychological pressure measurement evaluation scheme based on wireless communication, such as Wi-Fi, is provided, due to the universality of wireless communication equipment, the cognitive load and psychological pressure measurement evaluation scheme which is non-contact, non-invasive, low in cost and easy to deploy is realized, and the method and the device are easy to popularize and use.
(2) According to the method and the device for measuring the cognitive load and the psychological pressure based on the wireless communication, the cognitive load and the psychological pressure of a human body are evaluated through wireless signals (Wi-Fi), a large amount of Wi-Fi data related to the cognitive load and the psychological pressure of the human body can be obtained and stored in advance to carry out model training, an accurate rule model of the cognitive load and the psychological pressure is obtained, the channel state information of an individual user is utilized to obtain the conditions of the cognitive load and the psychological pressure of the human body, real-time evaluation of the measurement of the cognitive load and the psychological pressure of the human body is realized, and the intelligence and the rapidity of the measurement of the cognitive load and the psychological pressure are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic flowchart illustrating a method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
fig. 2 is a schematic flowchart illustrating a second method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a filtering process performed on channel state information data in the method for measuring cognitive load and psychological stress based on wireless communication according to the embodiment of the present invention in fig. 2;
fig. 4 is a schematic flowchart illustrating a third method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
fig. 5 is a schematic flowchart illustrating a fourth method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
fig. 6 is a schematic flowchart illustrating a fifth method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
fig. 7 is a schematic flowchart of a method for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an apparatus for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a second apparatus for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the invention;
FIG. 10 is a schematic structural diagram of a third apparatus for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the invention;
FIG. 11 is a diagram illustrating a fourth apparatus for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the invention;
fig. 12 is a schematic structural diagram of a fifth apparatus for measuring cognitive load and psychological stress based on wireless communication according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, fig. 1 is a schematic flow chart of a method for measuring cognitive load and psychological stress based on wireless communication in this embodiment, which can accurately measure cognitive load and psychological stress in a non-invasive manner in real time, and specifically includes the steps of:
Optionally, the wireless communication in this embodiment may be Wi-Fi, and the cognitive load and the psychological pressure of the human body are distinguished based on Channel State Information (CSI) data extracted by the Wi-Fi device, and a Network Interface Controller (NIC) driver is modified to obtain the CSI data of the Wi-Fi.
And 102, adjusting the cognitive load and the psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquiring individual channel state information data of training individuals with the number greater than or equal to a training individual number threshold value, corresponding to the cognitive load and the psychological pressure.
103, processing the individual channel state information data to obtain stable individual channel state information training data; and carrying out normalization processing on the stable individual channel state information training data by using a normalization model to obtain normalized individual channel state information training data.
And 104, training the normalized individual channel state information training data on a neural network building model to obtain the neural network of the channel state information.
105, acquiring user channel state information data of a user in wireless communication, and processing the user channel state information data to obtain stable user channel state information data; and carrying out normalization processing on the stable user channel state information data by using a normalization model to obtain normalized user channel state information data.
And 106, inputting the normalized user channel state information data into a neural network of the channel state information to obtain the cognitive load and the psychological pressure of the user.
After the model training is finished, preprocessing is carried out on CSI data of any user through Wi-Fi, the trained neural network is input, the cognitive load and psychological pressure conditions of the user are obtained, and due to the fact that the preprocessing calculation amount is small, real-time processing can be achieved. The cognitive load and the psychological pressure of the human body are evaluated through Wi-Fi wireless signals, a large amount of Wi-Fi data related to the cognitive load and the psychological pressure of the human body are required to be obtained in advance, model training is carried out, and real-time evaluation of the cognitive load and the psychological pressure of the human body is achieved.
In some optional embodiments, as shown in fig. 2 and 3, fig. 2 is a schematic flowchart of a second method for measuring cognitive load and psychological stress based on wireless communication in this embodiment; fig. 3 is a schematic diagram illustrating a filtering process performed on channel state information data in the method for measuring cognitive load and psychological stress based on wireless communication in fig. 2. Different from fig. 1, the processing of the individual channel state information data to obtain stable individual channel state information training data is as follows:
Optionally, a Hampel filter is used on the data to remove outliers due to noise and measurement errors.
In some optional embodiments, as shown in fig. 4, a schematic flowchart of a third method for measuring cognitive load and psychological stress based on wireless communication in this embodiment is shown, and different from fig. 2, the filtering process is:
In some optional embodiments, as shown in fig. 5, a schematic flowchart of a fourth method for measuring cognitive load and psychological stress based on wireless communication in this embodiment is shown, and different from fig. 1, the normalization process is:
and step 501, calling a preset Z-Score normalization model.
And 502, normalizing the channel state information data by using a Z-Score normalization model.
In some optional embodiments, as shown in fig. 6, a schematic flowchart of a fifth method for measuring cognitive load and psychological stress based on wireless communication in this embodiment is shown, and different from fig. 1, the cognitive load and psychological stress are adjusted by a preset cognitive load and psychological stress strategy, and the method includes:
The corresponding relations between the cognitive load and the psychological pressure under different environmental conditions and the channel state information of wireless communication may be different, and the cognitive load and the psychological pressure under different environmental conditions are measured by calling the corresponding relation between the cognitive load and the psychological pressure strategy under the current real-time environment through the preset corresponding relation between the cognitive load and the psychological pressure under different environmental conditions (such as geographical position, weather conditions and the like) and the channel state information of wireless communication, so that the accuracy of cognitive load and psychological pressure measurement is improved.
In some optional embodiments, as shown in fig. 7, a flowchart of a method for measuring cognitive load and psychological stress based on wireless communication is shown in this embodiment, and the method includes the following steps:
and 701, acquiring Wi-Fi data of a large number of different individuals under different cognitive loads and psychological pressures.
And step 702, abnormal value processing.
And step 707, acquiring real-time Wi-Fi data.
And 709, obtaining the current cognitive load and psychological pressure condition of the human body.
Further, in the embodiment of the application, the cognitive load and the psychological pressure of the user are obtained through prediction, specifically, the method includes the steps that N preprocessed user channel state information data before the current time are selected to generate a prediction result, the prediction result is normalized to a standard range through a normalization algorithm, and the normalized result is used as an actual cognitive load and psychological pressure measurement value at the current time; wherein N is a positive integer;
optionally, normalizing to within the standard range may be normalizing the measurement to within 0-1 as the final measurement.
In some alternative embodiments, as shown in fig. 8, a schematic structural diagram of an apparatus 800 for measuring cognitive load and psychological stress based on wireless communication according to this embodiment is used to implement the method for measuring cognitive load and psychological stress based on wireless communication described above. Specifically, the apparatus includes: a channel state information data receiver 801, a user channel state information data preprocessor 802, and a cognitive load and psychological stress measurement processor 803.
The user channel state information data receiver 801 is connected to the user channel state information data preprocessor 802, and acquires user channel state information data of a user in wireless communication.
The user channel state information data preprocessor 802 is connected with the user channel state information data receiver 801 and the cognitive load and psychological stress measurement processor 803, and performs data characteristic preprocessing on the user channel state information data to obtain preprocessed user channel state information data.
The cognitive load and psychological stress measurement processor 803 is connected to the user channel state information data preprocessor 802, and predicts the cognitive load and psychological stress of the user according to the neural network of the user channel state information and the preprocessed user channel state information data.
In some optional embodiments, as shown in fig. 9, which is a schematic structural diagram of a second apparatus 900 for measuring cognitive load and psychological stress based on wireless communication in this embodiment, different from fig. 8, a user channel state information data preprocessor 802 includes: a user channel state information data stabilization processing unit 821 and a user channel state information data normalization processing unit 822.
The user channel state information data stabilization processing unit 821 is connected to the user channel state information data receiver 801 and the user channel state information data normalization processing unit 822, and obtains user channel state information data of the user in wireless communication, and processes the user channel state information data to obtain stable user channel state information data.
The user channel state information data normalization processing unit 822 is connected with the user channel state information data stabilization processing unit 821 and the cognitive load and psychological stress measurement processor 803, and performs normalization processing on the stable user channel state information data by using a normalization model to obtain preprocessed user channel state information data.
Optionally, the user channel state information data normalization processing unit 822 invokes a preset Z-Score normalization model; and carrying out normalization processing on the user channel state information data by using a Z-Score normalization model.
In some optional embodiments, as shown in fig. 10, which is a schematic structural diagram of a third apparatus 1000 for measuring cognitive load and psychological stress based on wireless communication in this embodiment, different from fig. 9, the user channel state information data stabilization processing unit 821 includes: a filtering unit 1001 for user channel state information data and a compensation processing unit 1002 for user channel state information data.
The filtering unit 1001 is connected to the receiver 801 and the compensation processing unit 1002, and performs filtering processing on the user channel state information data to remove abnormal points caused by noise and measurement errors, so as to obtain filtered user channel state information data.
The user channel state information data compensation processing unit 1002 is connected to the user channel state information data filtering processing unit 1001 and the user channel state information data normalization processing unit 822, and compensates for packet loss and time delay in the filtered user channel state information data through linear interpolation to obtain stable user channel state information data.
In some optional embodiments, as shown in fig. 11, which is a schematic structural diagram of a fourth apparatus 1100 for measuring cognitive load and psychological stress based on wireless communication in this embodiment, different from fig. 10, the user channel state information data filtering unit 1001 includes: a filtering strategy definition processing unit 1011 and a filtering execution processing unit 1012 for the user channel state information data.
A filtering strategy definition unit 1011 connected to the CSI data receiver 801 and the CSI data filtering execution unit 1012 for defining filtering strategies defined in intervals
The points other than the above are outliers, in which,
for the median of this piece of data,
for the absolute deviation of the median of this piece of data,
indicating the tolerance range of the data from the median.
The filtering execution processing unit 1012 of the user channel state information data is connected to the filtering processing strategy definition processing unit 1011 and the user channel state information data compensation processing unit 1022, and performs filtering processing on the user channel state information data to remove abnormal points caused by noise and measurement errors, so as to obtain the filtered user channel state information data.
In some optional embodiments, as shown in fig. 12, which is a schematic structural diagram of a fifth apparatus 1200 for measuring cognitive load and psychological stress based on wireless communication in this embodiment, different from fig. 8, the cognitive load and psychological stress measuring processor 803 includes: a wireless communication channel state information data acquisition unit 831, an individual channel state information training data acquisition unit 832, an individual channel state information training data preprocessing unit 833, a cognitive load and psychological stress neural network building processing unit 834 and a cognitive load and psychological stress measurement processing unit 835.
The wireless communication channel state information data acquisition unit 831 is connected to the individual channel state information training data acquisition unit 832, processes the network interface controller, and acquires the channel state information data in the wireless communication through the network interface controller.
An individual channel state information training data acquisition unit 832, which is connected with the wireless communication channel state information data acquisition unit 831 and the individual channel state information training data preprocessing unit 833, adjusts the cognitive load and the psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquires individual channel state information data corresponding to training individuals with the training individual number threshold value or more under the cognitive load and the psychological pressure.
The individual channel state information training data preprocessing unit 833 is connected with the individual channel state information training data acquisition unit 832 and the cognitive load and psychological stress neural network building processing unit 834, and is used for preprocessing the individual channel state information data to obtain preprocessed individual channel state information training data.
The cognitive load and psychological stress neural network building processing unit 834 is connected with the individual channel state information training data preprocessing unit 833, the cognitive load and psychological stress measurement processing unit 835 and the user channel state information data preprocessor 802, and trains the neural network building model by the preprocessed individual channel state information training data to obtain the neural network of the channel state information.
And the cognitive load and psychological pressure measurement processing unit 835 is connected with the cognitive load and psychological pressure neural network building processing unit 834, and predicts and obtains the cognitive load and the psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data.
In some optional embodiments, the apparatus for measuring cognitive load and psychological stress based on wireless communication may further include: the cognitive load and psychological stress strategy adjusting processor is connected with the individual channel state information data acquisition processor and presets the corresponding relation between the environmental parameters and the cognitive load and the psychological stress strategy; acquiring real-time environment parameters of the current cognitive load and psychological pressure, acquiring a cognitive load and psychological pressure strategy in the current real-time environment according to the real-time environment parameters, and adjusting the cognitive load and the psychological pressure in the training and sampling process according to the cognitive load and psychological pressure strategy in the current real-time environment.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (5)
1. A method for measuring cognitive load and psychological stress based on wireless communication, comprising:
acquiring user channel state information data of a user under wireless communication;
carrying out data characteristic preprocessing on the user channel state information data to obtain preprocessed user channel state information data;
predicting to obtain the cognitive load and psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data;
the predicting the cognitive load and the psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data specifically comprises the following steps:
acquiring channel state information data in wireless communication through a network interface controller;
adjusting the cognitive load and the psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquiring individual channel state information data of training individuals larger than or equal to a training individual number threshold value, corresponding to the cognitive load and the psychological pressure;
preprocessing the individual channel state information data to obtain preprocessed individual channel state information training data;
training the preprocessed individual channel state information training data to a neural network building model to obtain a neural network of channel state information;
predicting to obtain the cognitive load and psychological pressure of the user according to the neural network of the channel state information and the preprocessed user channel state information data;
the method for adjusting cognitive load and psychological stress by measuring preset cognitive load and psychological stress specifically comprises the following steps:
presetting a corresponding relation between the environmental parameters and the cognitive load and the psychological stress strategy;
acquiring real-time environment parameters of current cognitive load and psychological pressure, acquiring a cognitive load and psychological pressure strategy in the current real-time environment according to the real-time environment parameters, and adjusting the cognitive load and the psychological pressure in the training and sampling process according to the cognitive load and psychological pressure strategy in the current real-time environment;
performing data characteristic preprocessing on the user channel state information data to obtain preprocessed user channel state information data, wherein the data characteristic preprocessing comprises the following steps:
acquiring user channel state information data of the user in wireless communication, and processing the user channel state information data to obtain stable user channel state information data;
carrying out normalization processing on the stable user channel state information data by using a normalization model to obtain the preprocessed user channel state information data;
processing the user channel state information data to obtain stable user channel state information data, wherein the stable user channel state information data comprises the following steps:
filtering the user channel state information data, and removing abnormal points caused by noise and measurement errors to obtain filtered user channel state information data;
compensating for packet loss and time delay in the filtered user channel state information data through linear interpolation to obtain stable user channel state information data;
the filtering process comprises the following steps:
defining a filtering strategy, defining in intervals
The points other than the above are outliers, in which,
for the median of this piece of data,
for the absolute deviation of the median of this piece of data,
indicating the tolerance range of the data from the median;
and filtering the user channel state information data, and removing abnormal points caused by noise and measurement errors to obtain the filtered user channel state information data.
2. The method according to claim 1, wherein the cognitive load and the psychological stress of the user are predicted, and further comprising selecting a plurality of preprocessed user channel state information data before the current time to generate a prediction result, normalizing the prediction result to a standard range by using a normalization algorithm, and using the normalized result as an actual cognitive load and psychological stress measurement value at the current time.
3. An apparatus for measuring cognitive load and psychological stress based on wireless communication, using the method for measuring cognitive load and psychological stress based on wireless communication according to any one of claims 1 or 2, comprising: the device comprises a user channel state information data receiver, a user channel state information data preprocessor and a cognitive load and psychological stress measurement processor; wherein the content of the first and second substances,
the user channel state information data receiver is connected with the user channel state information data preprocessor and used for acquiring user channel state information data of a user under wireless communication;
the user channel state information data preprocessor is connected with the user channel state information data receiver and the cognitive load and psychological stress measurement processor, and is used for preprocessing the data characteristics of the user channel state information data to obtain preprocessed user channel state information data;
the cognitive load and psychological stress measuring processor is connected with the user channel state information data preprocessor and used for predicting to obtain the cognitive load and psychological stress of the user according to the neural network of the user channel state information and the preprocessed user channel state information data;
wherein the cognitive load and psychological stress measurement processor comprises: the system comprises a wireless communication channel state information data acquisition unit, an individual channel state information training data preprocessing unit, a cognitive load and psychological stress neural network building processing unit, a cognitive load and psychological stress measuring processing unit and a cognitive load and psychological stress strategy adjusting processing unit;
the wireless communication channel state information data acquisition unit is connected with the individual channel state information training data acquisition unit and acquires channel state information data in wireless communication through a network interface controller;
the individual channel state information training data acquisition unit is connected with the wireless communication channel state information data acquisition unit and the individual channel state information training data preprocessing unit, adjusts cognitive load and psychological pressure according to a preset cognitive load and psychological pressure strategy, and acquires individual channel state information data corresponding to training individuals with training individual number threshold values larger than or equal to the cognitive load and psychological pressure;
the individual channel state information training data preprocessing unit is connected with the individual channel state information training data acquisition unit and the cognitive load and psychological pressure neural network building processing unit, and is used for preprocessing the individual channel state information data to obtain preprocessed individual channel state information training data;
the cognitive load and psychological pressure neural network building processing unit is connected with the individual channel state information training data preprocessing unit, the cognitive load and psychological pressure measuring processing unit and the user channel state information data preprocessor, and the neural network building model is trained by the preprocessed individual channel state information training data to obtain a neural network of channel state information;
the cognitive load and psychological stress measurement processing unit is connected with the cognitive load and psychological stress neural network construction processing unit, and the cognitive load and psychological stress of the user are obtained through prediction according to the neural network of the channel state information and the preprocessed user channel state information data;
the cognitive load and psychological stress strategy adjusting and processing unit is connected with the individual channel state information training data acquisition unit and presets the corresponding relation between the environmental parameters and the cognitive load and the psychological stress strategy; acquiring real-time environment parameters of the current cognitive load and psychological pressure, acquiring a cognitive load and psychological pressure strategy in the current real-time environment according to the real-time environment parameters, and adjusting the cognitive load and the psychological pressure in the training and sampling process according to the cognitive load and psychological pressure strategy in the current real-time environment.
4. The apparatus for measuring cognitive load and psychological stress according to claim 3, wherein the user channel state information data preprocessor comprises: the device comprises a user channel state information data stabilization processing unit and a user channel state information data normalization processing unit; wherein the content of the first and second substances,
the user channel state information data stabilization processing unit is connected with the user channel state information data receiver and the user channel state information data normalization processing unit, acquires user channel state information data of the user under wireless communication, and processes the user channel state information data to obtain stable user channel state information data;
the user channel state information data normalization processing unit is connected with the user channel state information data stabilization processing unit and the cognitive load and psychological stress measurement processor, and normalization processing is carried out on the stable user channel state information data by using a normalization model to obtain the preprocessed user channel state information data.
5. The apparatus for measuring cognitive load and psychological stress based on wireless communication according to claim 4, wherein the user channel state information data stabilization processing unit comprises: a filtering processing unit for the user channel state information data and a compensation processing unit for the user channel state information data; wherein the content of the first and second substances,
the user channel state information data filtering processing unit is connected with the user channel state information data receiver and the user channel state information data compensation processing unit, and is used for filtering the user channel state information data, removing abnormal points caused by noise and measurement errors and obtaining filtered user channel state information data;
the user channel state information data compensation processing unit is connected with the user channel state information data filtering processing unit and the user channel state information data normalization processing unit, and compensates packet loss and time delay in the user channel state information data after being filtered through linear interpolation to obtain stable user channel state information data;
the filtering processing unit for the user channel state information data comprises: a filtering processing strategy definition processing unit and a user channel state information data filtering execution processing unit; wherein the content of the first and second substances,
the filtering processing strategy definition processing unit is connected with the user channel state information data receiver and the user channel state information data filtering execution processing unit, defines the filtering processing strategy and is defined in the interval
The points other than the above are outliers, in which,
for the median of this piece of data,
for the absolute deviation of the median of this piece of data,
indicating the tolerance range of the data from the median;
and the user channel state information data filtering execution processing unit is connected with the filtering processing strategy definition processing unit and the user channel state information data compensation processing unit, and is used for filtering the user channel state information data, removing abnormal points caused by noise and measurement errors and obtaining the filtered user channel state information data.
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