CN111714143A - Psychological state evaluation method, device, equipment and computer readable storage medium - Google Patents
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Abstract
The invention discloses a psychological state evaluation method, a psychological state evaluation device, a psychological state evaluation equipment and a computer readable storage medium. The method comprises the following steps: acquiring skin conductance data of a target user for each evaluation object in a plurality of evaluation objects; extracting skin conductance data of each evaluation object from skin conductance data of the evaluation object; inputting the electrodermal reaction data into a Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of a target user for an evaluation object; and determining a suspected object which enables the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object. According to the invention, the suspected object which enables the psychological state of the target user to fluctuate is determined through the psychological state evaluation data, the determination mode is objective and scientific, and the problem of low accuracy of manual evaluation is avoided.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a psychological state evaluation method, a psychological state evaluation device, psychological state evaluation equipment and a computer-readable storage medium.
Background
The electrodermal response is always used as the detection basis for psychological evaluation. For example: the basis for lie detection. The electrodermal reaction is the sweating reflex produced under the participation of the central nervous system, and belongs to psychogenic sweating. Psychogenic sweating is sweating caused by mental stress or emotional agitation, and is regulated by the higher cortex of the brain, and therefore, neurogenic sweating is different from hot sweating, which is a process of information processing.
In particular, the magnitude of Skin Conductance (SC) can be measured by applying a constant voltage (current) to the epidermis. In applications, such as in the lie detection field, multiple questions are typically evaluated for a user, each question corresponding to an SC curve. And determining the maximum peak in each SC curve in a visual observation mode of an evaluation person, and estimating the peak area of the maximum peak of each SC curve. For multiple questions, the question corresponding to the SC curve with the largest peak area is determined as the suspected question that maximizes the psychological fluctuation of the user, that is, the user may lie when answering the suspected question.
However, this way of assessing the peak area of the largest peak by visual observation is too subjective. If the psychological assessment relies on subjective evaluation, it is easy to cause the result of the psychological assessment to lack accuracy.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a psychological state evaluation method, a psychological state evaluation device, a psychological state evaluation equipment and a computer readable storage medium, so as to solve the problem that the conventional psychological evaluation depends on subjective evaluation, and the result of the psychological evaluation is easy to lack of accuracy.
In view of the above technical problems, the embodiments of the present invention are solved by the following technical solutions:
the embodiment of the invention provides a psychological state evaluation method, which comprises the following steps: acquiring skin conductance data of a target user for each evaluation object in a plurality of evaluation objects; extracting skin conductance data of the evaluation objects aiming at each evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of the target user aiming at the evaluation object; and determining suspected objects which enable the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
Wherein, in the skin conductance data of the evaluation object, extracting skin galvanic reaction data comprises: fitting the skin conductance data by using a preset exponential moving average algorithm to obtain skin level data; and extracting the skin conductance data according to the skin level data.
Before the picoelectric reaction data is input into a preset Gaussian mixture model, the method further comprises the following steps: and carrying out noise reduction processing on the electrodermal reaction data by adopting a preset filter or a noise reduction algorithm.
Wherein the electrodermal response data comprises a plurality of sampling values; adopt predetermined noise reduction algorithm, to the processing of making an uproar falls in the skin electricity reaction data, include: calculating a mean and a standard deviation of the plurality of sample values in the electrodermal response data; and subtracting the average value from each sampling value in the electrodermal response data, and dividing the average value by the standard deviation to obtain the electrodermal response data subjected to noise reduction treatment.
After the determination of the suspected object causing the target user to have a fluctuation in psychological state in the plurality of evaluation objects, the method further includes: determining integrals of the corresponding electrodermal reaction data of the suspected object and the integrals of the fitting data of the corresponding electrodermal reaction data of the suspected object; wherein the fitting data are obtained by fitting the electrodermal reaction data; and taking the ratio of the integral of the fitting data to the integral of the electrodermal reaction data as the suspicion probability corresponding to the suspicion object.
Wherein the determining, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the plurality of evaluation objects includes: determining the psychological state difference value of each evaluation object relative to other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value with the largest value as a suspected object which enables the target user to have fluctuation in the psychological state among the plurality of evaluation objects.
Wherein the obtaining skin conductance data of the target user for each of the plurality of evaluation subjects comprises: performing a plurality of evaluation operations on the plurality of evaluation objects; in each round of evaluation operation, respectively collecting skin conductance data of the target user for each evaluation object in the plurality of evaluation objects, and sequentially obtaining the skin conductance data of one evaluation object in the plurality of evaluation objects so as to determine psychological state evaluation data of the target user for the currently obtained evaluation object; the determining, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the plurality of evaluation objects includes: after the multiple rounds of evaluation operations are finished, aggregating the psychological state evaluation data corresponding to the same evaluation object in each round of evaluation operation to obtain the psychological state evaluation aggregated data corresponding to the evaluation object; determining psychological state difference values of each evaluation object and other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation aggregate data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value as a suspected object which enables the target user to have psychological state fluctuation.
Wherein the Gaussian mixture model comprises a plurality of Gaussian probability density functions; the types of model parameters of the Gaussian mixture model comprise: a weight coefficient, a mean, and a variance of each of the Gaussian probability density functions in the Gaussian mixture model.
The embodiment of the invention also provides a psychological state evaluation device, which comprises: the system comprises an acquisition module, a judgment module and a processing module, wherein the acquisition module is used for acquiring skin conductance data of a target user aiming at each evaluation object in a plurality of evaluation objects; the evaluation module is used for extracting skin conductance data of each evaluation object from the skin conductance data of the evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of the target user aiming at the evaluation object; and the determining module is used for determining a suspected object which enables the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
The embodiment of the invention also provides psychological state evaluation equipment, which comprises a processor and a memory; the processor is used for executing a psychological state evaluation program stored in the memory so as to realize any one of the psychological state evaluation methods.
An embodiment of the present invention further provides a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement any of the above mental state assessment methods.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, a skin conductance data is extracted to obtain a skin conductance curve capable of reflecting the physiological psychological state of the target user, a Gaussian mixture model is used for fitting the skin conductance data, a model parameter of the Gaussian mixture model is determined by a maximum expectation algorithm, psychological state evaluation data is determined according to the model parameter, and then a suspected object which enables the psychological state of the target user to fluctuate is determined according to the psychological state evaluation data of a plurality of evaluation objects. According to the embodiment of the invention, the suspected object which enables the psychological state of the target user to fluctuate is determined through the psychological state evaluation data, the determination mode is objective and scientific, the problem of low accuracy of manual evaluation is avoided, and the Gaussian mixture model can accurately fit the electrodermal reaction data, so that the fitting data can well reflect the individual psychological state of the target user, and the evaluation object which enables the psychological state of the target user to be abnormal is accurately found out from a plurality of evaluation objects.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a psychological state evaluation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of the steps for determining model parameters, according to one embodiment of the present invention;
fig. 3 is a detailed flowchart of a psychological state evaluation method according to an embodiment of the present invention;
fig. 4 is a structural diagram of a psychological state evaluating device according to an embodiment of the present invention;
fig. 5 is a structural diagram of a psychological state evaluating apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.
According to an embodiment of the present invention, there is provided a psychological state evaluation method. Fig. 1 is a flowchart illustrating a psychological state evaluation method according to an embodiment of the present invention.
Step S110, acquiring skin conductance data of the target user for each of the plurality of evaluation subjects.
The target user refers to a user who receives psychological state evaluation.
The evaluation object refers to a real object and/or a virtual object for evaluating a psychological state of a target object. Real objects include people, things and/or things. The virtual objects include images, audio, and/or text. For example: the evaluation object is a question (matter) asked by an evaluator or a question (text) shown in a screen.
Skin conductance data (SC curve data) refers to: a change in skin conductance of the target user when the target user is evaluated with the evaluation object. Wherein the unit of skin conductance is micro-ohms.
Specifically, a preset constant voltage or a preset constant current may be applied to the epidermis of the target user, and each time an evaluation object is evaluated for the target user, skin conductance data of the target user for the evaluation object may be acquired. The skin conductance data of the target user for the evaluation object can be acquired while acquiring, or the skin conductance data of the target user for the evaluation object can be acquired after the acquisition is finished, or the skin conductance data of the target user for the evaluation object can be acquired when the psychological state evaluation value of the target user for the evaluation object needs to be determined.
The skin conductance data includes a plurality of sample values. The plurality of sampling values are respectively the skin conductance at different times of acquisition. Further, in the process of acquiring skin conductance data, skin conductance (sampling value) is acquired every preset sampling period. The sampling period may be an empirical value or a pass experimental value.
Further, the sampling value in the skin conductance data is collected from the moment when the evaluation object is displayed to the target user, and the sampling value in the skin conductance data is stopped being collected when the display is finished, or the sampling value in the skin conductance data is stopped being collected when the preset time is reached after the evaluation object is displayed. The preset time period may be an empirical value or an experimental value. For example: the preset time period is 5 seconds.
And taking the moment when the real object appears (such as the moment when a question begins to be asked) as the moment when the evaluation object is displayed to the target user, and taking the moment when the real object disappears (such as the moment when the question is asked) as the moment when the evaluation object is displayed. And taking the time when the virtual object starts playing or displaying as the time when the evaluation object is displayed to the target user, and taking the time when the virtual object finishes playing or displaying as the time when the display is finished.
Step S120, extracting skin conductance data of the evaluation object aiming at each evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; and determining the psychological state evaluation data of the target user aiming at the evaluation object according to the model parameters of the Gaussian mixture model.
Skin Conductance data includes Skin Conductance Level (SCL) data and Skin Conductance Response (SCR) data. The skin level data is data of a skin level curve, which is a basic trend of physiological activities in a certain state. The electrodermal response data is the data for the electrodermal response curve, which is a transient, relatively rapid fluctuation in the electrodermal level curve. The electrodermal response curve is a physiological state of mind caused by stimulation. In other words, the picolevel curve reflects the state, and the picoresponse curve reflects the immediate response to the stimulation time.
And the Gaussian mixture model is used for fitting the electrodermal reaction data to obtain the fitting data of the electrodermal reaction data. The fitted data may represent a curve trend of the electrodermal response data.
And the maximum expectation algorithm is used for determining the model parameters of the Gaussian mixture model of the input electrodermal reaction data, namely determining the parameters of the fitting data of the electrodermal reaction data.
And the psychological state evaluation data is used for evaluating whether the target user has psychological state fluctuation.
In the embodiment, a skin conductance curve capable of reflecting the physiological psychological state of a target user is extracted from skin conductance data of an evaluation object; fitting the electrodermal reaction curve by using a Gaussian mixture model to obtain fitting data of the electrodermal reaction data; and determining parameters of fitting data of the electrodermal response data according to a maximum expectation algorithm, and further determining psychological state evaluation data of the target user for the evaluation object.
In this embodiment, the fitting data of the electrodermal response data can reflect the curve trend of the electrodermal response data, so that the parameters of the fitting data of the electrodermal response data can be used for measuring whether the target user has psychological state fluctuation, and thus, the model parameters of the gaussian mixture model into which the electrodermal response data has been input can be used as the psychological state evaluation data. The types of model parameters of the gaussian mixture model include, but are not limited to: weight coefficients, mean and variance.
Step S130, determining a suspected object, which causes the target user to have a fluctuation in mental state, among the plurality of evaluation objects according to the mental state evaluation data of the target user for each evaluation object.
The method comprises the following steps of measuring whether a target user has psychological state fluctuation or not by using psychological state evaluation data, specifically, determining the psychological state difference value of each evaluation object relative to other evaluation objects in a plurality of evaluation objects according to the psychological state evaluation data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value with the largest value in the plurality of evaluation objects as a suspected object which enables the target user to have fluctuation in the psychological state.
Further, determining a psychological state difference value of each of the evaluation subjects (target evaluation subjects) with respect to other evaluation subjects of the plurality of evaluation subjects includes: step S1, one of the psychological state evaluation data is sequentially acquired from the psychological state evaluation data of other evaluation objects; step S2, determining the parameter difference data of the psychological state evaluation data of the target evaluation object and the currently acquired psychological state evaluation data; the psychological state evaluation data comprises a plurality of types of parameter values (such as weight coefficients, mean values and variances); the parameter difference data comprises a plurality of types of differences, and each difference is obtained by subtracting the psychological state evaluation data of the target evaluation object from the parameter value of the corresponding type in the currently acquired psychological state evaluation data; step S3, judging whether the psychological state evaluation data of each evaluation object in other evaluation objects is obtained completely, if not, jumping to step S1; if the acquisition is finished, executing step S4; step S4, aggregating the parameter difference data of the psychological state evaluation data of the target evaluation object and the psychological state evaluation data of each of the other evaluation objects to obtain a psychological state difference value of the target evaluation object compared with the other evaluation objects. The polymerization treatment comprises: calculating an average value of the difference values of the corresponding types in the plurality of parameter difference value data, calculating a weighted sum of the average values of the plurality of types of difference values, and taking the weighted sum as a psychological state difference value. The weights corresponding to the different types may be set according to the importance of the types. For example: the parameter difference data of the target evaluation object and the evaluation object A comprises: a weight coefficient A, a mean A and a variance A; the parameter difference data of the target evaluation object and the evaluation object B comprises: a weight coefficient B, a mean B and a variance B; the psychological state difference value of the target evaluation object is first weight × (weight coefficient a + weight coefficient B) ÷ 2+ second weight × (mean a + mean B) ÷ 2+ third weight × (variance a + variance B) ÷ 2. Wherein, the first weight, the second weight and the third weight can be respectively set according to the importance of the weight coefficient, the mean value and the variance.
In this embodiment, a electrodermal response curve that can reflect the physiological psychological state of the target user is extracted from skin conductance data, a gaussian mixture model is used to fit the electrodermal response data, a maximum expectation algorithm is used to determine model parameters of the gaussian mixture model, psychological state evaluation data is determined according to the model parameters, and suspected objects that cause the psychological state of the target user to fluctuate are determined according to the psychological state evaluation data of a plurality of evaluation objects. According to the method, the suspected object which enables the psychological state of the target user to fluctuate is determined through the psychological state evaluation data, the determination mode is objective and scientific, the problem of low accuracy of manual evaluation is solved, the Gaussian mixture model can accurately fit the electrodermal reaction data, the individual psychological state of the target user can be well reflected through the fitting data, and then the evaluation object which enables the target user to have the abnormal psychological state is accurately found out from a plurality of evaluation objects.
The steps for determining mental state assessment data are further described below. Performing the steps shown in FIG. 2 for skin conductance data of each of a plurality of evaluation subjects
Fig. 2 is a flowchart of the determination step of mental state assessment data according to an embodiment of the present invention.
Step S210, extracting skin conductance data of the evaluation object.
The skin conductance data can be timely reflected by the skin conductance level data, so that the signal distortion of the skin conductance level data can be avoided while the skin conductance response data is extracted. Therefore, in the embodiment, a fitting algorithm that configures a larger weight for the near-end data and a smaller weight for the far-end data in any two data of the skin conductance data is adopted to fit the skin conductance data, so that the influence of the near-end data on the fitting result of the current data is strengthened, the influence of the far-end data on the fitting result of the current data is weakened, and the influence of the far-end data farther from the current data on the fitting result of the current data is smaller.
The near-end data refers to the sample values in the skin conductance data that are near the current time.
The remote data refers to sampling values in the galvanic conductance data which are far away from the current time.
For such a requirement, the embodiment may use a preset Exponential moving average (EMA for short) algorithm to fit the skin conductance data to obtain the skin level data; and extracting the skin conductance data according to the skin level data.
The EMA algorithm, also called a weight moving average algorithm, is an average algorithm that gives higher weight to recent data (near-end data), that is, when fitting a plurality of sample values in skin conductance data, the data weight of the sample value near the current time in the skin conductance data is made greater than the data weight of the sample value far from the current time.
Furthermore, fitting a plurality of sampling values in the skin conductance data by utilizing an EMA algorithm to obtain skin level data; and removing the skin level data obtained by fitting from the skin conductance data to obtain the skin conductance response data. In this embodiment, sampling values in the skin conductance data of the target user for the evaluation object may be acquired while acquiring and fitting the sampling values, that is: and in the process of acquiring the sampling value in the skin conductance data, fitting the sampling value by utilizing an EMA algorithm in real time every time one sampling value is acquired.
For example, the EMA algorithm may fit the skin conductance data using equation (1) below and may remove the skin conductance level data from the skin conductance data using equation (2) below:
wherein t represents time t, vtRepresents the value at time t, v, in the picolevel data(t-1)Represents the value theta at the t-1 moment in the picolevel datatRepresenting the sampled value at time t in the skin conductance data, β representing the preset weight corresponding to time t-1, mtAnd the value of t moment in the electrodermal response data is represented, wherein β is an empirical value or an experimental value, and β is less than 1.
According to the calculation formula of the picolevel data, v istβ× v is contained in the formula(t-1)，v(t-1)β× v is contained in the formula(t-2)，v(t-2)β× v is contained in the formula(t-3)And so on; then, if v is calculated in a recursive mannertThen v istWill contain β× v(t-1)Or β2×v(t-2)Or β3×v(t-3)Since β is less than 1 and β is finger-shapedNumber varies, so distance vtThe farther the t time is, the smaller the data weight of the value is, and the distance vtThe closer the time t is, the higher the data weight of the value is.
Because the skin conductance data is a trend line of the skin conductance data, the skin conductance data can be obtained after the skin conductance data is removed, namely: the galvanic skin response data includes a portion of the sampled values in the skin conductance data.
Step S220, inputting the electrodeionization data into a preset gaussian mixture model, and determining model parameters of the gaussian mixture model into which the electrodeionization data has been input by using a preset maximum Expectation-maximization (EM).
The method comprises the steps of inputting a plurality of sampling values in the electrodermal reaction data into a Gaussian mixture model, fitting the plurality of sampling values of the electrodermal reaction data by using the Gaussian mixture model to obtain Gaussian mixture distribution curves (fitting data of the electrodermal reaction data) corresponding to the plurality of sampling values, and determining model parameters of the Gaussian mixture distribution curves by using a maximum expectation algorithm.
In the present embodiment, the gaussian mixture model includes a plurality of gaussian probability density functions; types of model parameters of the gaussian mixture model include, but are not limited to: a weight coefficient, a mean, and a variance of each of the gaussian probability density functions in a gaussian mixture model.
In this embodiment, a preset filter or a noise reduction algorithm is adopted to perform noise reduction processing on the electrodermal response data. Further, the skin conductance data comprises a plurality of sampling values, namely a part of the sampling values after the skin conductance data is removed; adopt predetermined noise reduction algorithm, to the processing of making an uproar falls in the skin electricity reaction data, include: calculating a mean and a standard deviation of the plurality of sample values in the electrodermal response data; and subtracting the average value from each sampling value in the electrodermal response data, and dividing the average value by the standard deviation to obtain the electrodermal response data subjected to noise reduction treatment.
Step S230, determining psychological state evaluation data of the target user for the evaluation object according to the model parameters of the gaussian mixture model.
In this embodiment, the weight coefficient, the mean, and the variance of each gaussian probability density function in the gaussian mixture model are used as the psychological state evaluation data of the target user for the evaluation target.
According to the embodiment of the invention, the Gaussian mixture model is used for fitting the electrodermal response data, the maximum expectation algorithm is used for determining the model parameters of the Gaussian mixture model, namely the parameters of the fitting data of the electrodermal response data, whether the psychological state fluctuation of a target user for an object to be evaluated occurs or not is measured through the parameters of the fitting data of the electrodermal response data, the psychological state evaluation value is determined through an objective calculation mode, and the problem of low accuracy caused by manual judgment is avoided.
In the embodiment of the invention, the psychological state evaluation value is determined by utilizing a Gaussian mixture model, and the Gaussian mixture model is based on an individual comparison method, and internal difference analysis is carried out on the picoelectric reaction data, so that the psychological fluctuation range condition of the picoelectric reaction data can be determined.
According to the embodiment of the invention, the EMA algorithm is used for fitting the skin conductance level data, the trend of the skin conductance data can be totally reflected, the change of the skin conductance data is timely responded according to the algorithm characteristics that the weight of the near-end data is larger and the weight of the far-end data is smaller, the sampling value in the skin conductance data is increased, the value fitted by the EMA is also increased in time, the sampling value in the skin conductance data is reduced, and the value fitted by the EMA is also reduced in time, so that the problem of signal distortion of the skin conductance data easily caused when the skin conductance data is extracted by using a median subtraction method or an average subtraction method can be avoided. Further, the median or mean subtraction is to subtract the median or mean of the skin conductance data in a moving window to obtain the electrodermal response data. In the process, if the size of the moving window is too small, the fluctuation of the picoelectric reaction data is not obvious enough; if the moving window is too large, then the median or mean of the moving window may still rise as the peaks in the skin conductance data begin to recover, i.e., as the skin conductance data is falling, so that the electrodermal response data will become very negative in absolute value, making it difficult for the second peak in the electrodermal bimodal response to show, resulting in signal distortion.
According to the embodiment of the invention, after the skin conductance data is fitted by using the EMA algorithm, the skin conductance data is subtracted from the skin conductance data to obtain the skin conductance response data, and the skin conductance response data can clearly see the skin conductance response condition triggered by each event.
The application of the psychological state evaluation method of the present invention to the lie detection field will be further explained below. Of course, it should be understood by those skilled in the art that the psychological state evaluation method of the present invention is not limited to application in the lie detection field.
Fig. 3 is a detailed flowchart of a mental state assessment method according to an embodiment of the present invention.
Step S310, respectively acquiring skin conductance data of the target user for each of the plurality of evaluation subjects.
For example: the method comprises the steps that audio data corresponding to an evaluation question are collected by a microphone in sequence, the audio data serve as an evaluation object, and certainly, manual questioning can also be carried out by an evaluator; applying a constant voltage to the skin of the target user; playing the collected audio data by using a loudspeaker in sequence, enabling the target user to answer the evaluation question corresponding to the audio data every time one piece of audio data is played, and collecting skin conductance data of the target user aiming at the audio data, wherein the steps are as follows: collecting audio data in a first room where an evaluator is located, playing the audio data in a second room where a target object is located, and collecting skin conductance data of a target user aiming at the audio data; and for each piece of audio data, starting to acquire the skin conductance data of the target user from the playing of the audio data, and ending to acquire the skin conductance data of the target user from the completion of the playing when the preset time length is reached. Finally, skin conductance data corresponding to each audio data can be obtained.
Step S320, sequentially selecting one of the plurality of evaluation objects.
Step S330, acquiring skin conductance data of the target user aiming at the currently selected evaluation object.
And step S340, extracting the skin conductance data of the currently selected evaluation object.
And step S350, inputting the electrodermal reaction data into a Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a maximum expectation algorithm.
And step S360, determining the psychological state evaluation data of the target user aiming at the currently selected evaluation object according to the model parameters of the Gaussian mixture model.
Step S370, determining whether all the evaluation subjects among the plurality of evaluation subjects perform an overcentre state evaluation; if yes, go to step S380; if not, step S330 is performed.
Step S380, determining a psychological state difference value of each of the evaluation objects relative to other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
In step S390, among the plurality of evaluation objects, the evaluation object corresponding to the mental state difference value with the largest value is determined as a suspected object that causes the mental state fluctuation of the target user.
The evaluation object with the largest psychological state evaluation value means that the target user has the largest psychological fluctuation degree aiming at the evaluation object. When the user lies, the psychology is easy to fluctuate, and the greater the psychological fluctuation is, the greater the suspicion of lying is, so that the evaluation object with the largest psychological state evaluation value is determined as the suspicion object in the embodiment.
After the evaluation object with the largest psychological state evaluation value is determined as the suspected object, the suspicion probability of the suspected object can be determined. Specifically, determining integrals of the corresponding electrodermal reaction data of the suspected object and integrals of the fitting data of the corresponding electrodermal reaction data of the suspected object; and taking the ratio of the integral of the fitting data and the integral of the electrodermal reaction data as the suspicion probability corresponding to the suspicion object.
The target user may be configured to obtain the skin conductance data of the suspected object, and the skin conductance data may be extracted from the skin conductance data of the suspected object.
The fitting data are obtained by fitting the electrodermal reaction data. Furthermore, the electrodermal reaction data can be input into a preset Gaussian mixture model, and the electrodermal reaction data are fitted by using the Gaussian mixture model to obtain fitting data of the electrodermal reaction data.
In order to increase the accuracy of the lie detection result, multiple rounds of evaluation operations may be performed on the multiple evaluation objects. Specifically, in each round of evaluation operation, skin conductance data of a target user for each of the plurality of evaluation subjects is respectively acquired, and skin conductance data of one of the plurality of evaluation subjects is sequentially acquired, so as to determine psychological state evaluation data of the target user for the currently acquired evaluation subject, that is: extracting skin conductance data of each evaluation object from the skin conductance data of the evaluation object; inputting the electrodermal reaction data into a Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a maximum expectation algorithm; and determining the psychological state evaluation data of the target user aiming at the evaluation object according to the model parameters of the Gaussian mixture model. After the execution of multiple rounds of evaluation operations is finished, aggregating the psychological state evaluation data corresponding to the same evaluation object in each round of evaluation operation to obtain the psychological state evaluation aggregate data corresponding to the evaluation object; determining psychological state difference values of each evaluation object and other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation aggregate data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value as a suspected object which enables the target user to have psychological state fluctuation.
Aggregating the psychological state evaluation data corresponding to the same evaluation object in each round of evaluation operation, wherein the process comprises the following steps: and obtaining psychological state evaluation aggregate data comprising average values corresponding to a plurality of types (namely, average values of parameter values of a plurality of types) according to the average values of the parameter values of the corresponding types in the psychological state evaluation data respectively corresponding to the same evaluation object in a plurality of rounds of evaluation operations.
For example: the Gaussian mixture model comprises a Gaussian probability density function 1, a Gaussian probability density function 2 and a Gaussian probability density function 3; the model parameter types of the gaussian mixture model include: weight coefficient pi, mean mu and variance sigma2(ii) a Three evaluation operations are performed on 5 questions, and the psychological state evaluation aggregate data corresponding to each question is shown in table 1:
TABLE 1
Wherein,
the mean value of the weight coefficients of the gaussian probability density function 1 in the three evaluation operations is represented;
represents the average of the mean of the gaussian probability density function 1 in three evaluation runs;
represents the mean of the variances of gaussian probability density function 1 in three evaluation runs;
the mean value of the weight coefficients of the gaussian probability density function 2 in the three evaluation operations is represented;
represents the average of the mean of the gaussian probability density function 2 in three evaluation runs;
mean value representing the variance of the gaussian probability density function 3 in three evaluation runs;
the average value of the weight coefficients of the Gaussian probability density function 3 in three evaluation operations is represented;
represents the average of the mean of the gaussian probability density function 3 in three evaluation runs;
represents the mean of the variances of the gaussian probability density function 2 in three evaluation runs.
Determining the mental state difference value between each evaluation object and other evaluation objects in the plurality of evaluation objects according to the mental state evaluation aggregate data respectively corresponding to the plurality of evaluation objects, comprising: step S1, one of the psychological state evaluation aggregate data is sequentially acquired from the psychological state evaluation aggregate data of other evaluation objects; step S2, determining the parameter difference data of the psychological state evaluation aggregate data of the target evaluation object and the currently acquired psychological state evaluation aggregate data; the psychological state evaluation data comprises parameter average values of a plurality of types; the parameter difference data comprises a plurality of differences, and each difference is obtained by subtracting the psychological state evaluation aggregate data of the target evaluation object from the parameter average value of the corresponding type in the currently acquired psychological state evaluation aggregate data; step S3, judging whether the psychological state evaluation aggregate data of each evaluation object in other evaluation objects is obtained completely, if not, jumping to step S1; if the acquisition is finished, executing step S4; step S4, aggregating the parameter difference data of the aggregate psychological state evaluation data of the target evaluation object and the aggregate psychological state evaluation data of each of the other evaluation objects to obtain a psychological state difference value of the target evaluation object compared with the other evaluation objects. The polymerization treatment comprises: calculating an average value of the difference values of the corresponding types in the plurality of parameter difference value data, calculating a weighted sum of the average values of the plurality of types of difference values, and taking the weighted sum as a psychological state difference value.
The embodiment of the invention also provides a psychological state evaluation device. Fig. 4 is a block diagram of a psychological state evaluation device according to an embodiment of the present invention.
Psychological state evaluation device includes: the system comprises an acquisition module 410, an evaluation module 420 and a determination module 430.
An obtaining module 410, configured to obtain skin conductance data of a target user for each of a plurality of evaluation subjects.
The evaluation module 420 is configured to, for each evaluation object, extract electrodermal response data from skin conductance data of the evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; and determining the psychological state evaluation data of the target user aiming at the evaluation object according to the model parameters of the Gaussian mixture model.
A determining module 430, configured to determine, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the multiple evaluation objects.
The functions of the apparatus according to the embodiments of the present invention have been described in the above method embodiments, so that reference may be made to the related descriptions in the foregoing embodiments for details which are not described in the present embodiment, and further details are not described herein.
The present embodiment provides a psychological state evaluation apparatus. Fig. 5 is a block diagram of a psychological state evaluating apparatus according to an embodiment of the present invention.
In this embodiment, the psychological state evaluation device includes, but is not limited to: processor 510, memory 520.
The processor 510 is configured to execute a mental state evaluation program stored in the memory 520 to implement the mental state evaluation method described above.
Specifically, the processor 510 is configured to execute the mental state evaluation program stored in the memory 520 to implement the following steps: acquiring skin conductance data of a target user for each evaluation object in a plurality of evaluation objects; extracting skin conductance data of the evaluation objects aiming at each evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of the target user aiming at the evaluation object; and determining suspected objects which enable the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
Wherein, in the skin conductance data of the evaluation object, extracting skin galvanic reaction data comprises: fitting the skin conductance data by using a preset exponential moving average algorithm to obtain skin level data; and extracting the skin conductance data according to the skin level data.
Before the picoelectric reaction data is input into a preset Gaussian mixture model, the method further comprises the following steps: and carrying out noise reduction processing on the electrodermal reaction data by adopting a preset filter or a noise reduction algorithm.
Wherein the electrodermal response data comprises a plurality of sampling values; adopt predetermined noise reduction algorithm, to the processing of making an uproar falls in the skin electricity reaction data, include: calculating a mean and a standard deviation of the plurality of sample values in the electrodermal response data; and subtracting the average value from each sampling value in the electrodermal response data, and dividing the average value by the standard deviation to obtain the electrodermal response data subjected to noise reduction treatment.
After the determination of the suspected object causing the target user to have a fluctuation in psychological state in the plurality of evaluation objects, the method further includes: determining integrals of the corresponding electrodermal reaction data of the suspected object and the integrals of the fitting data of the corresponding electrodermal reaction data of the suspected object; wherein the fitting data are obtained by fitting the electrodermal reaction data; and taking the ratio of the integral of the fitting data to the integral of the electrodermal reaction data as the suspicion probability corresponding to the suspicion object.
Wherein the determining, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the plurality of evaluation objects includes: determining the psychological state difference value of each evaluation object relative to other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value with the largest value as a suspected object which enables the target user to have fluctuation in the psychological state among the plurality of evaluation objects.
Wherein the obtaining skin conductance data of the target user for each of the plurality of evaluation subjects comprises: performing a plurality of evaluation operations on the plurality of evaluation objects; in each round of evaluation operation, respectively collecting skin conductance data of the target user for each evaluation object in the plurality of evaluation objects, and sequentially obtaining the skin conductance data of one evaluation object in the plurality of evaluation objects so as to determine psychological state evaluation data of the target user for the currently obtained evaluation object; the determining, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the plurality of evaluation objects includes: after the multiple rounds of evaluation operations are finished, aggregating the psychological state evaluation data corresponding to the same evaluation object in each round of evaluation operation to obtain the psychological state evaluation aggregated data corresponding to the evaluation object; determining psychological state difference values of each evaluation object and other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation aggregate data corresponding to the plurality of evaluation objects respectively; and determining the evaluation object corresponding to the psychological state difference value as a suspected object which enables the target user to have psychological state fluctuation.
Wherein the Gaussian mixture model comprises a plurality of Gaussian probability density functions; the types of model parameters of the Gaussian mixture model comprise: a weight coefficient, a mean, and a variance of each of the Gaussian probability density functions in the Gaussian mixture model.
The embodiment of the invention also provides a computer readable storage medium. The computer-readable storage medium herein stores one or more programs. Among other things, computer-readable storage media may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
When one or more programs in the computer-readable storage medium are executable by one or more processors, the mental state assessment method described above is implemented. Since the psychological state evaluation method has been described in detail in the above embodiments, it is not described herein in detail.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (11)
1. A mental state assessment method, comprising:
acquiring skin conductance data of a target user for each evaluation object in a plurality of evaluation objects;
extracting skin conductance data of the evaluation objects aiming at each evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of the target user aiming at the evaluation object;
and determining suspected objects which enable the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
2. The method according to claim 1, wherein the extracting of electrodermal response data in the skin conductance data of the subject comprises:
fitting the skin conductance data by using a preset exponential moving average algorithm to obtain skin level data;
and extracting the skin conductance data according to the skin level data.
3. The method of claim 1, prior to said inputting said electrodermal response data into a pre-set gaussian mixture model, further comprising:
and carrying out noise reduction processing on the electrodermal reaction data by adopting a preset filter or a noise reduction algorithm.
4. The method of claim 3,
the electrodermal response data comprises a plurality of sampling values;
adopt predetermined noise reduction algorithm, to the processing of making an uproar falls in the skin electricity reaction data, include:
calculating a mean and a standard deviation of the plurality of sample values in the electrodermal response data;
and subtracting the average value from each sampling value in the electrodermal response data, and dividing the average value by the standard deviation to obtain the electrodermal response data subjected to noise reduction treatment.
5. The method according to claim 1, wherein after the determining a suspected object causing the target user to have a fluctuating psychological state from among the plurality of evaluation objects, the method further comprises:
determining integrals of the corresponding electrodermal reaction data of the suspected object and the integrals of the fitting data of the corresponding electrodermal reaction data of the suspected object; wherein the fitting data are obtained by fitting the electrodermal reaction data;
and taking the ratio of the integral of the fitting data to the integral of the electrodermal reaction data as the suspicion probability corresponding to the suspicion object.
6. The method according to claim 1, wherein the determining, according to the mental state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuation in mental state from among the plurality of evaluation objects comprises:
determining the psychological state difference value of each evaluation object relative to other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation data corresponding to the plurality of evaluation objects respectively;
and determining the evaluation object corresponding to the psychological state difference value with the largest value as a suspected object which enables the target user to have fluctuation in the psychological state among the plurality of evaluation objects.
7. The method of claim 1,
the acquiring skin conductance data of a target user for each evaluation object in a plurality of evaluation objects comprises:
performing a plurality of evaluation operations on the plurality of evaluation objects;
in each round of evaluation operation, respectively collecting skin conductance data of the target user for each evaluation object in the plurality of evaluation objects, and sequentially obtaining the skin conductance data of one evaluation object in the plurality of evaluation objects so as to determine psychological state evaluation data of the target user for the currently obtained evaluation object;
the determining, according to the psychological state evaluation data of the target user for each evaluation object, a suspected object that causes the target user to have a fluctuating psychological state in the plurality of evaluation objects includes:
after the multiple rounds of evaluation operations are finished, aggregating the psychological state evaluation data corresponding to the same evaluation object in each round of evaluation operation to obtain the psychological state evaluation aggregated data corresponding to the evaluation object;
determining psychological state difference values of each evaluation object and other evaluation objects in the plurality of evaluation objects according to the psychological state evaluation aggregate data corresponding to the plurality of evaluation objects respectively;
and determining the evaluation object corresponding to the psychological state difference value as a suspected object which enables the target user to have psychological state fluctuation.
8. The method according to any one of claims 1 to 7,
the Gaussian mixture model comprises a plurality of Gaussian probability density functions;
the types of model parameters of the Gaussian mixture model comprise: a weight coefficient, a mean, and a variance of each of the Gaussian probability density functions in the Gaussian mixture model.
9. A psychological state evaluation device, comprising:
the system comprises an acquisition module, a judgment module and a processing module, wherein the acquisition module is used for acquiring skin conductance data of a target user aiming at each evaluation object in a plurality of evaluation objects;
the evaluation module is used for extracting skin conductance data of each evaluation object from the skin conductance data of the evaluation object; inputting the electrodermal reaction data into a preset Gaussian mixture model, and determining model parameters of the Gaussian mixture model into which the electrodermal reaction data are input by using a preset maximum expectation algorithm; according to the model parameters of the Gaussian mixture model, determining psychological state evaluation data of the target user aiming at the evaluation object;
and the determining module is used for determining a suspected object which enables the target user to have psychological state fluctuation in the plurality of evaluation objects according to the psychological state evaluation data of the target user for each evaluation object.
10. A psychological state assessment device, characterized in that the psychological state assessment device comprises a processor, a memory; the processor is used for executing a psychological state evaluation program stored in the memory so as to realize the psychological state evaluation method according to any one of claims 1 to 8.
11. A computer-readable storage medium storing one or more programs which are executable by one or more processors to implement the mental state assessment method according to any one of claims 1 to 8.
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