CN113693599A - Psychological evaluation system for human heart recognition - Google Patents

Psychological evaluation system for human heart recognition Download PDF

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CN113693599A
CN113693599A CN202110898347.2A CN202110898347A CN113693599A CN 113693599 A CN113693599 A CN 113693599A CN 202110898347 A CN202110898347 A CN 202110898347A CN 113693599 A CN113693599 A CN 113693599A
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vegetative nerve
nerve signal
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齐中祥
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Womin High New Science & Technology Beijing Co ltd
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Abstract

The invention relates to a psychological evaluation system for human heart recognition, which comprises: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector; the vegetative nerve signal collector is used for collecting vegetative nerve signals of a user in real time; the display is used for displaying the evaluation questions and the mental health evaluation results to the user; the image collector is used for collecting facial images of the user in real time; the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display, and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a psychological health evaluation result and transmitting the psychological health evaluation result to the display. The system evaluates the psychology of the user through the vegetative nerve signals collected by the vegetative nerve signal collector and the images collected by the image collector, and realizes the objective evaluation of the mental health state.

Description

Psychological evaluation system for human heart recognition
Technical Field
The invention relates to the technical field of psychological evaluation, in particular to a psychological evaluation system for human heart recognition.
Background
With the accelerated social rhythm, people are faced with huge psychological pressure, and are easy to have psychological stress reactions such as fatigue, loneliness, anxiety, depression, panic and the like, so that the psychological problem becomes a social problem.
Psychological problems can be transformed into psychologic diseases if the psychological problems are not treated well, and great negative effects are brought to the mind and body of the nation and the work.
Disclosure of Invention
Technical problem to be solved
In view of the above-mentioned shortcomings and drawbacks of the prior art, the present invention provides a psychological assessment system for human heart recognition.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a psychological assessment system for human mind recognition, the system comprising: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector;
the vegetative nerve signal collector is used for collecting vegetative nerve signals of a user in real time;
the display is used for displaying the evaluation questions and the mental health evaluation results to the user;
the image collector is used for collecting facial images of the user in real time;
the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a mental health evaluation result and transmitting the mental health evaluation result to the display.
Optionally, the autonomic nerve signal processor is configured to:
acquiring the corresponding relation between the evaluation questions displayed by the display and the display time;
sequencing the autonomic nervous signals collected by the autonomic nervous signal collector according to a time sequence to form an autonomic nervous sequence number sequence PA(ii) a The vegetative nerve signal set PAEach of which is
Figure BDA0003198866970000021
Comprises 2 dimensions, one dimension being a data value
Figure BDA0003198866970000022
Another dimension is a time value
Figure BDA0003198866970000023
wherein ,PAOf (2) element(s)
Figure BDA0003198866970000024
The vegetative nerve signals collected by the vegetative nerve signal collector correspond to one another, and P isAAny one of the elements of
Figure BDA0003198866970000025
Data value of
Figure BDA0003198866970000026
Figure BDA0003198866970000027
Time value of
Figure BDA0003198866970000028
The acquisition time of A (x), A (x) is the x-th vegetative nerve signal acquired by the vegetative nerve signal acquisition device, and x is the vegetative nerve signal identification acquired by the vegetative nerve signal acquisition device;
extracting facial features of each frame of facial image acquired by the image acquirer, and forming a facial feature set F based on a time sequence, wherein each element in the facial feature set F comprises 2 dimensions, one dimension is a facial feature value of the corresponding facial image, and the other dimension is the acquisition time of the corresponding facial image for the time value;
according to the corresponding relation, PAAnd obtaining a mental health evaluation result by the facial feature set F.
Optionally, the information is obtained according to the corresponding relation and PAThe obtaining of the mental health evaluation result specifically comprises the following steps:
according to vegetative nerve signal set PAForming corresponding vegetative nerve difference set deltaASaid vegetative nerve difference set deltaAEach element of (1)
Figure BDA0003198866970000029
Comprises 2 dimensions, one dimension being a data value
Figure BDA00031988669700000210
Another dimension is a time value
Figure BDA00031988669700000211
wherein ,ΔAOf (2) element(s)
Figure BDA00031988669700000212
The vegetative nerve signals are in one-to-one correspondence with the vegetative nerve signals collected by the vegetative nerve signal collector;
inputting the autonomic nerve signals collected by the autonomic nerve signal collector into a nerve network model, performing feature recognition to obtain the features of the autonomic nerve signal value of each element in the autonomic nerve signals, and further forming a corresponding feature set IAThe set of features IAEach element of
Figure BDA00031988669700000213
Comprises 2 dimensions, one dimension being a data value
Figure BDA0003198866970000031
Another dimension is a time value
Figure BDA0003198866970000032
wherein ,IAOf (2) element(s)
Figure BDA0003198866970000033
Corresponds to the vegetative nerve signals collected by the vegetative nerve signal collector one by one, and
Figure BDA0003198866970000034
data value of
Figure BDA0003198866970000035
Is characterized by the feature of A (x),
Figure BDA0003198866970000036
time value of
Figure BDA0003198866970000037
According to vegetative nerve difference set deltaAAnd feature set IAObtaining the variant sequence CA；
According to the corresponding relation, CAAnd obtaining a mental health evaluation result by the facial feature set F.
Alternatively,
for vegetative nerve signal set PALast element in (1)
Figure BDA0003198866970000038
It is related toALast element in (1)
Figure BDA0003198866970000039
Correspond to, and
Figure BDA00031988669700000310
data value of
Figure BDA00031988669700000311
Figure BDA00031988669700000312
Time value of
Figure BDA00031988669700000313
Is composed of
Figure BDA00031988669700000314
Time value of
Figure BDA00031988669700000315
wherein ,xlSet P for vegetative nerve signalsAThe identity of the last element in;
for vegetative nerve signal set PANon-last element of (1)
Figure BDA00031988669700000316
Its one-to-one correspondence ΔAAn element of
Figure BDA00031988669700000317
And is
Figure BDA00031988669700000318
Data value of
Figure BDA00031988669700000319
Figure BDA00031988669700000320
Time value of
Figure BDA00031988669700000321
wherein ,xjSet P for vegetative nerve signalsAIs not the identification of the last element.
Optionally, the sequence of variations CAEach element in (1)
Figure BDA00031988669700000322
Comprises 2 dimensions, one dimension being a data value
Figure BDA00031988669700000323
Another dimension is a time value
Figure BDA00031988669700000324
Wherein the vegetative nerve signal set PAAny one element of
Figure BDA00031988669700000325
One-to-one correspondence change sequence CAAn element of
Figure BDA00031988669700000326
And is
Figure BDA00031988669700000327
Data value of
Figure BDA00031988669700000328
Time value of
Figure BDA00031988669700000329
D1(x) Is based on the vegetative nerve difference set deltaAThe first vegetative nerve parameter, D2(x) Is based on feature set IAAnd obtaining a second autonomic nervous parameter.
Optionally, the
Figure BDA00031988669700000330
Is vegetative nerve difference set deltaAThe maximum of the data values of all elements,
Figure BDA00031988669700000331
is vegetative nerve difference set deltaAThe minimum of the data values of all elements,
Figure BDA00031988669700000332
is set forth as feature set IAThe maximum of the data values of all elements,
Figure BDA00031988669700000333
is set forth as feature set IAThe minimum of the data values of all elements,
Figure BDA00031988669700000334
is set forth as feature set IAMean of data values for all elements.
Optionally, the
Figure BDA0003198866970000041
wherein ,
Figure BDA0003198866970000042
is set forth as feature set IAThe maximum of the data values of all elements,
Figure BDA0003198866970000043
is set forth as feature set IAThe minimum of the data values of all elements;
delta-vegetative nerve difference set deltaATime difference between maximum and minimum of data values of all elements/feature set IAThe time difference between the maximum and minimum of the data values of all elements.
Optionally, the step C is carried out according to the corresponding relationAThe obtaining of the mental health evaluation result by the facial feature set F specifically comprises the following steps:
obtaining CAOf the element is determined
Figure BDA0003198866970000044
According to the corresponding relation, determining the
Figure BDA0003198866970000045
Corresponding evaluation questions are obtained, and further evaluation questions and C are obtainedAThe corresponding relation of the middle elements;
obtaining the corresponding relation between the evaluation question and the elements in the F according to the time values of the elements in the F and the corresponding relation;
for each question, determining C corresponding to each questionAElements forming a corresponding set of elements
Figure BDA0003198866970000046
And determining F element corresponding to the F element to form corresponding element set
Figure BDA0003198866970000047
Based on the standard variation value corresponding to each topic,
Figure BDA0003198866970000048
and
Figure BDA0003198866970000049
and obtaining a psychological health evaluation result.
Optionally, the standard variation value corresponding to each topic based on each topic,
Figure BDA00031988669700000410
and
Figure BDA00031988669700000411
the obtaining of the mental health evaluation result specifically comprises the following steps:
for any subject B, its corresponding CAThe element set is
Figure BDA00031988669700000412
Corresponding F element set is
Figure BDA00031988669700000413
Then calculate
Figure BDA00031988669700000414
Wherein y is
Figure BDA00031988669700000415
Middle element identification, Y identification
Figure BDA00031988669700000416
The identification of the medium-largest element is carried out,
Figure BDA00031988669700000417
is a set
Figure BDA00031988669700000418
The data value of the element with the middle index y,
Figure BDA00031988669700000419
is a set
Figure BDA00031988669700000420
Mean of all the element data values in;
computing
Figure BDA00031988669700000421
Wherein u is
Figure BDA00031988669700000422
Middle element identification, U is
Figure BDA00031988669700000423
The identification of the medium-largest element is carried out,
Figure BDA0003198866970000051
is a set
Figure BDA0003198866970000052
The data value of the element with the middle index u,
Figure BDA0003198866970000053
is a set
Figure BDA0003198866970000054
The average of all the element data values in (a),
Figure BDA0003198866970000055
is a set
Figure BDA0003198866970000056
Maximum value of all element data values in;
according to
Figure BDA0003198866970000057
Determining the score of the question B according to the standard variation value corresponding to the question B;
and determining the psychological health evaluation result according to the scores of all the questions.
Optionally, the said according to
Figure BDA0003198866970000058
Determining the score of the topic B according to the standard change corresponding to the topic B specifically includes:
Figure BDA0003198866970000059
(III) advantageous effects
The psychological evaluation system for human mind recognition of the present invention comprises: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector; the autonomic nerve signal collector is used for collecting autonomic nerve signals of a user in real time; the display is used for displaying the evaluation questions and the mental health evaluation results to the user; the image collector is used for collecting facial images of a user in real time; the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a psychological health evaluation result and transmitting the psychological health evaluation result to the display. The system evaluates the psychology of the user through the vegetative nerve signals collected by the vegetative nerve signal collector and the images collected by the image collector, and realizes the objective evaluation of the mental health state.
Drawings
Fig. 1 is a schematic structural diagram of a psychological assessment system for human mind recognition according to an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
With the accelerated social rhythm, people are faced with huge psychological pressure, and are easy to have psychological stress reactions such as fatigue, loneliness, anxiety, depression, panic and the like, so that the psychological problem becomes a social problem. Psychological problems can be transformed into psychologic diseases if the psychological problems are not treated well, and great negative effects are brought to the mind and body of the nation and the work.
The invention provides a psychological evaluation system for human heart recognition, which comprises: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector; the autonomic nerve signal collector is used for collecting autonomic nerve signals of a user in real time; the display is used for displaying the evaluation questions and the mental health evaluation results to the user; the image collector is used for collecting facial images of a user in real time; the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a psychological health evaluation result and transmitting the psychological health evaluation result to the display. The system evaluates the psychology of the user through the vegetative nerve signals collected by the vegetative nerve signal collector and the images collected by the image collector, and realizes the objective evaluation of the mental health state.
In particular implementations, a psychometric questionnaire may be provided to a user, and the user is psychometric by a psychometric system for human recognition as shown in fig. 1 in answering the questionnaire.
Referring to fig. 1, the psychological evaluation system for human mind recognition provided in this embodiment includes: the device comprises a vegetative nerve signal collector, a vegetative nerve signal processor, a display and an image collector.
Wherein, the autonomic nerve signal processor is respectively connected with the autonomic nerve signal collector, the image collector and the display.
1. Vegetative nerve signal collector
And the vegetative nerve signal collector is used for collecting vegetative nerve signals of the user in real time.
2. Image collector
And the image collector is used for collecting the facial image of the user in real time.
3. Display device
And the display is used for displaying the evaluation questions and the mental health evaluation results to the user.
4. Vegetative nerve signal processor
And the vegetative nerve signal processor is used for processing the image acquired by the image acquisition device and the vegetative nerve signal acquired by the vegetative nerve signal acquisition device according to the evaluation questions displayed by the display to obtain a psychological health evaluation result and transmitting the psychological health evaluation result to the display.
During specific implementation, the evaluation questions are displayed to a user by the display, the vegetative nerve signal collector collects vegetative nerve signals of the user in real time in the answering process of the user, the image collector collects facial images of the user in real time, the vegetative nerve signals collected by the vegetative nerve signal collector are transmitted to the vegetative nerve signal processor, and the image collector also transmits the collected facial images to the vegetative nerve signal processor. And the vegetative nerve signal processor processes the image acquired by the image acquisition device and the vegetative nerve signal acquired by the vegetative nerve signal acquisition device according to the evaluation questions displayed by the display to obtain a psychological health evaluation result, transmits the psychological evaluation result to the display, and displays the evaluation result by the display.
The vegetative nerve signal processor processes the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display, and the process of obtaining the psychological health evaluation result is as follows:
the autonomic nerve signal processor firstly acquires the corresponding relation between the evaluation questions displayed by the display and the display time, and simultaneously, the autonomic nerve signal processor sorts the autonomic nerve signals acquired by the autonomic nerve signal acquisition device according to the time sequence to form an autonomic nerve sequence number sequence PAAnd the mind signal processor extracts the facial features of each frame of facial image collected by the image collector, forms a facial feature set F based on time sequence, and finally forms a facial feature set F according to the corresponding relation and PAAnd obtaining a mental health evaluation result by the facial feature set F.
Wherein, the vegetative nerve signal set P obtained by the vegetative nerve signal processorAEach of the elements
Figure BDA0003198866970000081
Comprises 2 dimensions, one dimension being a data value
Figure BDA0003198866970000082
Another dimension is a time value
Figure BDA0003198866970000083
PAOf (2) element(s)
Figure BDA0003198866970000084
The vegetative nerve signals collected by the vegetative nerve signal collector correspond to one another, and P isAAny one of the elements of
Figure BDA0003198866970000085
Data value of
Figure BDA0003198866970000086
Figure BDA0003198866970000087
Time value of
Figure BDA0003198866970000088
The acquisition time is A (x), A (x) is the x-th vegetative nerve signal acquired by the vegetative nerve signal acquirer, and x is the vegetative nerve signal identification acquired by the vegetative nerve signal acquirer.
For example, the autonomic nerve signal collector collects 1-5 ms autonomic nerve signals S of the user AAAs shown in Table 1, the autonomic nerve signal processor will then assign SAGenerating a set of autonomic nervous signals PA，PAThe method comprises 5 elements, each element comprises 2 dimensions, the first dimension is the corresponding value of A (x), and the second dimension is the acquisition time of the value.
TABLE 1
Autonomic nerve signal identification 0 1 2 3 4
Vegetative nerve signal value A(0) A(1) A(2) A(3) A(4)
Vegetative nerve signal acquisition time 1 2 3 4 5
E.g. PAThe 5 elements of (c) are:
Figure BDA0003198866970000089
wherein ,
Figure BDA00031988669700000810
data value of
Figure BDA00031988669700000811
Is a (0) of a,
Figure BDA00031988669700000812
time value of
Figure BDA00031988669700000813
Is 1.
Figure BDA00031988669700000814
Data value of
Figure BDA00031988669700000815
Is a compound of formula (A) 1,
Figure BDA00031988669700000816
time value of
Figure BDA00031988669700000817
Is 2.
Figure BDA00031988669700000818
Data value of
Figure BDA00031988669700000819
Is a compound of formula (2),
Figure BDA00031988669700000820
time value of
Figure BDA00031988669700000821
Is 3.
Figure BDA00031988669700000822
Data value of
Figure BDA00031988669700000823
Is a compound of formula (A) 3,
Figure BDA00031988669700000824
time value of
Figure BDA00031988669700000825
Is 4.
Figure BDA00031988669700000826
Data value of
Figure BDA00031988669700000827
Is A (4),
Figure BDA00031988669700000828
time value of
Figure BDA00031988669700000829
Is 5.
It should be noted that, the identification of the autonomic nervous signals from 0 and the identification of the elements in the P set from 0 in table 1 are only examples, and in practical applications, the identification may be started from 1, or may be started from any identification point, which is not limited in this embodiment.
Similarly, the identifiers appearing in the present embodiment are all started from 0 as an example, and the identifiers may be started from other identifiers in actual application.
In addition, each element in the facial feature set F includes 2 dimensions, one dimension is a facial feature value of its corresponding facial image, and the other dimension is a time value of the acquisition time of its corresponding facial image.
In addition, the vegetative nerve signal collector is used for collecting the vegetative nerve signals according to the corresponding relation PAAnd the specific process of obtaining the mental health evaluation result by the facial feature set F is as follows:
1) the vegetative nerve signal collector collects P according to the vegetative nerve signalAForming corresponding vegetative nerve difference set deltaAVegetative nerve difference set deltaAEach element of (1)
Figure BDA0003198866970000091
Comprises 2 dimensions, one dimension being a data value
Figure BDA0003198866970000092
Another dimension is a time value
Figure BDA0003198866970000093
wherein ,ΔAOf (2) element(s)
Figure BDA0003198866970000094
Vegetative nerve signal collected by vegetative nerve signal collectorAnd correspond to each other.
In particular, the method comprises the following steps of,
for vegetative nerve signal set PALast element in (1)
Figure BDA0003198866970000095
It is related toALast element in (1)
Figure BDA0003198866970000096
Correspond to, and
Figure BDA0003198866970000097
data value of
Figure BDA0003198866970000098
Figure BDA0003198866970000099
Time value of
Figure BDA00031988669700000910
Is composed of
Figure BDA00031988669700000911
Time value of
Figure BDA00031988669700000912
wherein ,xlSet P for vegetative nerve signalsAThe identity of the last element in (a).
For vegetative nerve signal set PANon-last element of (1)
Figure BDA00031988669700000913
Its one-to-one correspondence ΔAAn element of
Figure BDA00031988669700000914
And is
Figure BDA00031988669700000915
Data value of
Figure BDA00031988669700000916
Figure BDA00031988669700000917
Time value of
Figure BDA00031988669700000918
wherein ,xjSet P for vegetative nerve signalsAIs not the identification of the last element.
For example, if the signal processor obtains the set of autonomic nerve signals as
Figure BDA00031988669700000919
The vegetative nerve difference set Δ also has 5 elements, which are:
Figure BDA00031988669700000920
wherein ,
Figure BDA00031988669700000921
data value of
Figure BDA00031988669700000922
Figure BDA00031988669700000923
Time value of
Figure BDA00031988669700000924
Figure BDA00031988669700000925
Data value of
Figure BDA00031988669700000926
Figure BDA00031988669700000927
Time value of
Figure BDA00031988669700000928
Figure BDA00031988669700000929
Figure BDA00031988669700000930
Data value of
Figure BDA00031988669700000931
Figure BDA00031988669700000932
Time value of
Figure BDA00031988669700000933
Figure BDA00031988669700000934
Data value of
Figure BDA00031988669700000935
Figure BDA00031988669700000936
Time value of
Figure BDA0003198866970000101
Figure BDA0003198866970000102
Data value of
Figure BDA0003198866970000103
Figure BDA0003198866970000104
Time value of
Figure BDA0003198866970000105
2) The vegetative nerve signal collector inputs the vegetative nerve signals collected by the vegetative nerve signal collector into the nerve network model for feature recognition to obtain the feature of the vegetative nerve signal value of each element in the vegetative nerve signals, and then a corresponding feature set I is formedASet of features IAEach element of
Figure BDA0003198866970000106
Comprises 2 dimensions, one dimension being a data value
Figure BDA0003198866970000107
Another dimension is a time value
Figure BDA0003198866970000108
wherein ,IAOf (2) element(s)
Figure BDA0003198866970000109
Corresponds to the vegetative nerve signals collected by the vegetative nerve signal collector one by one, and
Figure BDA00031988669700001010
data value of
Figure BDA00031988669700001011
Is characterized by the feature of A (x),
Figure BDA00031988669700001012
time value of
Figure BDA00031988669700001013
Figure BDA00031988669700001014
For example, the autonomic nerve Signal SAThe corresponding feature set I also includes 5 elements, which are:
Figure BDA00031988669700001015
wherein ,
Figure BDA00031988669700001016
data value of
Figure BDA00031988669700001017
Is the characteristic value of A (0),
Figure BDA00031988669700001018
time value of
Figure BDA00031988669700001019
Figure BDA00031988669700001020
Data value of
Figure BDA00031988669700001021
Is the characteristic value of A (1),
Figure BDA00031988669700001022
time value of
Figure BDA00031988669700001023
Figure BDA00031988669700001024
Data value of
Figure BDA00031988669700001025
Is the characteristic value of A (2),
Figure BDA00031988669700001026
time value of
Figure BDA00031988669700001027
Figure BDA00031988669700001028
Data value of
Figure BDA00031988669700001029
Is the characteristic value of A (3),
Figure BDA00031988669700001030
time value of
Figure BDA00031988669700001031
Figure BDA00031988669700001032
Data value of
Figure BDA00031988669700001033
Is the characteristic value of A (4),
Figure BDA00031988669700001034
time value of
Figure BDA00031988669700001035
3) The vegetative nerve signal collector collects delta according to vegetative nerve differenceAAnd feature set IAObtaining the variant sequence CA。
Variant sequence CAEach element in (1)
Figure BDA00031988669700001036
Comprises 2 dimensions, one dimension being a data value
Figure BDA00031988669700001037
Another dimension is a time value
Figure BDA00031988669700001038
Wherein, the vegetative nerve signal set PAAny one element of
Figure BDA00031988669700001039
One-to-one correspondence change sequence CAAn element of
Figure BDA00031988669700001040
And is
Figure BDA00031988669700001041
Data value of
Figure BDA00031988669700001042
Figure BDA00031988669700001043
Time value of
Figure BDA00031988669700001044
D1(x) Is based on the vegetative nerve difference set deltaAThe first vegetative nerve parameter, D2(x) Is based on feature set IAAnd obtaining a second autonomic nervous parameter.
Figure BDA0003198866970000111
Is vegetative nerve difference set deltaAThe maximum of the data values of all elements,
Figure BDA0003198866970000112
is vegetative nerve difference set deltaAThe minimum of the data values of all elements,
Figure BDA0003198866970000113
is set forth as feature set IAThe maximum of the data values of all elements,
Figure BDA0003198866970000114
is set forth as feature set IAThe minimum of the data values of all elements,
Figure BDA0003198866970000115
is set forth as feature set IAMean of data values for all elements.
Figure BDA0003198866970000116
wherein ,
Figure BDA0003198866970000117
is set forth as feature set IAThe maximum of the data values of all elements,
Figure BDA0003198866970000118
is set forth as feature set IAThe minimum of the data values of all elements.
Delta-vegetative nerve difference set deltaATime difference between maximum and minimum of data values of all elements/feature set IAThe time difference between the maximum and minimum of the data values of all elements.
Also as an example, change set CAThe number of the elements in (1) is also 5, respectively
Figure BDA0003198866970000119
Figure BDA00031988669700001110
Figure BDA00031988669700001111
Data value of
Figure BDA00031988669700001112
Figure BDA00031988669700001113
Time value of
Figure BDA00031988669700001114
wherein ,
Figure BDA00031988669700001115
Figure BDA00031988669700001116
Figure BDA00031988669700001117
Figure BDA00031988669700001118
data value of
Figure BDA00031988669700001119
Figure BDA00031988669700001120
Figure BDA00031988669700001121
Time value of
Figure BDA00031988669700001122
wherein ,
Figure BDA00031988669700001123
Figure BDA00031988669700001124
Figure BDA00031988669700001125
Figure BDA00031988669700001126
data value of
Figure BDA00031988669700001127
Figure BDA00031988669700001128
Time value of
Figure BDA00031988669700001129
wherein ,
Figure BDA00031988669700001130
Figure BDA00031988669700001131
Figure BDA00031988669700001132
data value of
Figure BDA00031988669700001133
Figure BDA00031988669700001134
Time value of
Figure BDA00031988669700001135
Figure BDA0003198866970000121
wherein ,
Figure BDA0003198866970000122
Figure BDA0003198866970000123
Figure BDA0003198866970000124
Figure BDA0003198866970000125
data value of
Figure BDA0003198866970000126
Figure BDA0003198866970000127
Time value of
Figure BDA0003198866970000128
wherein ,
Figure BDA0003198866970000129
Figure BDA00031988669700001210
Figure BDA00031988669700001211
by doing so, an autonomic nerve signal S can be obtainedACorresponding to a vegetative nerve signal set PAAn vegetative nerve difference set ΔAA feature set IAAnd a variant sequence CA。
PA、ΔA、IA、CANumber of middle element and SAThe number of middle signals is the same, and PA、ΔA、IA、CAAll elements in the same time value correspond to SAThe same signal in (1). Passing time valueCan find SAIn which each signal is in PA、ΔA、IA、CATo the corresponding elements in (1). That is, by the time value t, and t is at PA、ΔA、IA、CAThe element in (1) can know SAA signal value, a difference value, a characteristic value, and a variance. By tracking the above situation, the real response of the autonomic nerve signals of the user to the inner heart can be obtained.
4) The vegetative nerve signal collector is used for collecting the vegetative nerve signals according to the corresponding relation CAAnd obtaining a mental health evaluation result by the facial feature set F.
For the same time t, the autonomic nerve signal processor can determine the evaluation questions displayed by the display at the time, and the autonomic nerve signal processor can also obtain the autonomic nerve signals of the user at the time, and the signal values, the difference values, the characteristic values and the change conditions corresponding to the autonomic nerve signals. The autonomic nerve signal processor will also get an image of the user's face at that moment.
However, the user continues for a period of time when answering each evaluation question, the total time of each question is displayed on the display as the answering time of each question, and a series of corresponding facial images, a series of corresponding autonomic nervous signals, and corresponding signal values, difference values, characteristic values and change conditions can be obtained according to the answering time of each question. The autonomic nervous signal processor can evaluate the psychology of the user in answering each question according to the series of parameters with the same time attribute (i.e. t).
In particular, the method comprises the following steps of,
(1) obtaining CAOf the element is determined
Figure BDA0003198866970000131
(2) According to the corresponding relationship, determining the
Figure BDA0003198866970000132
Corresponding evaluation questions are obtained, and further evaluation questions and C are obtainedACorresponding relation of middle elementIs described.
(3) And obtaining the corresponding relation between the evaluation questions and the elements in the F according to the time values and the corresponding relation of the elements in the F.
(4) For each question, determining C corresponding to each questionAElements forming a corresponding set of elements
Figure BDA0003198866970000133
And determining F element corresponding to the F element to form corresponding element set
Figure BDA0003198866970000134
Executing each question to correspond to one element set
Figure BDA0003198866970000135
And a set of elements
Figure BDA0003198866970000136
Figure BDA0003198866970000137
The change situation of the autonomic nervous signals of the user answering each question is characterized,
Figure BDA0003198866970000138
facial images of the user answering each question are characterized.
(5) Based on the standard variation value corresponding to each topic,
Figure BDA0003198866970000139
and
Figure BDA00031988669700001310
and obtaining a psychological health evaluation result.
For example, for any topic B, its corresponding CAThe element set is
Figure BDA00031988669700001311
Corresponding F element set is
Figure BDA00031988669700001312
Then calculate
Figure BDA00031988669700001313
And
Figure BDA00031988669700001314
according to
Figure BDA00031988669700001315
The standard variation value corresponding to topic B determines the score of topic B. And determining the psychological health evaluation result according to the scores of all the questions.
Wherein y is
Figure BDA00031988669700001316
Middle element identification, Y identification
Figure BDA00031988669700001317
The identification of the medium-largest element is carried out,
Figure BDA00031988669700001318
is a set
Figure BDA00031988669700001319
The data value of the element with the middle index y,
Figure BDA00031988669700001320
is a set
Figure BDA00031988669700001321
Average of all element data values in (a).
u is
Figure BDA00031988669700001322
Middle element identification, U is
Figure BDA00031988669700001323
The identification of the medium-largest element is carried out,
Figure BDA00031988669700001324
is a set
Figure BDA00031988669700001325
The data value of the element with the middle index u,
Figure BDA00031988669700001326
is a set
Figure BDA00031988669700001327
The average of all the element data values in (a),
Figure BDA00031988669700001328
is a set
Figure BDA00031988669700001329
The maximum value of all element data values in.
Figure BDA0003198866970000141
The standard variation value is a standard determined when evaluating the subject design.
In addition, the existing process can be adopted according to the scores of all the questions and the process of determining the mental health evaluation result, for example, the scores of all the questions are added, and according to which score segment the added result belongs to, the mental health evaluation result is the result corresponding to the score segment.
The autonomic nerve signals are governed and adjusted by cerebral cortex and hypothalamus, but are not controlled by the will of people, that is, the user can control the emotion, expression, heartbeat, blood and the like, avoid showing the real psychology of the user and cause error of the evaluation result, but cannot control the autonomic nerve signals, so that the truest reaction of the user can be obtained through analyzing the autonomic nerve signals, the final evaluation result is not controlled by people to generate errors, and the accuracy and the objectivity of the evaluation result obtained by the system are improved.
The embodiment relates to a psychological evaluation system for human heart recognition, which comprises: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector; the autonomic nerve signal collector is used for collecting autonomic nerve signals of a user in real time; the display is used for displaying the evaluation questions and the mental health evaluation results to the user; the image collector is used for collecting facial images of a user in real time; the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a psychological health evaluation result and transmitting the psychological health evaluation result to the display. The system evaluates the psychology of the user through the vegetative nerve signals collected by the vegetative nerve signal collector and the images collected by the image collector, and realizes the objective evaluation of the mental health state.
In order to better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While preferred embodiments of the present invention 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, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.

Claims (10)

1. A psychological assessment system for human mind recognition, said system comprising: the system comprises an autonomic nerve signal collector, an autonomic nerve signal processor, a display and an image collector;
the vegetative nerve signal collector is used for collecting vegetative nerve signals of a user in real time;
the display is used for displaying the evaluation questions and the mental health evaluation results to the user;
the image collector is used for collecting facial images of the user in real time;
the vegetative nerve signal processor is respectively connected with the vegetative nerve signal collector, the image collector and the display and is used for processing the image collected by the image collector and the vegetative nerve signal collected by the vegetative nerve signal collector according to the evaluation questions displayed by the display to obtain a mental health evaluation result and transmitting the mental health evaluation result to the display.
2. The system of claim 1, wherein the autonomic nerve signal processor is configured to:
acquiring the corresponding relation between the evaluation questions displayed by the display and the display time;
sequencing the autonomic nervous signals collected by the autonomic nervous signal collector according to a time sequence to form an autonomic nervous sequence number sequence PA(ii) a The vegetative nerve signal set PAEach of which is
Figure FDA0003198866960000011
Comprises 2 dimensions, one dimension being a data value
Figure FDA0003198866960000012
Another dimension is a time value
Figure FDA0003198866960000013
wherein ,PAOf (2) element(s)
Figure FDA0003198866960000014
The vegetative nerve signals collected by the vegetative nerve signal collector correspond to one another, and P isAAny one of the elements of
Figure FDA0003198866960000015
Data value of
Figure FDA0003198866960000016
Figure FDA0003198866960000017
Time value of
Figure FDA0003198866960000018
The acquisition time of A (x), A (x) is the x-th vegetative nerve signal acquired by the vegetative nerve signal acquisition device, and x is the vegetative nerve signal identification acquired by the vegetative nerve signal acquisition device;
extracting facial features of each frame of facial image acquired by the image acquirer, and forming a facial feature set F based on a time sequence, wherein each element in the facial feature set F comprises 2 dimensions, one dimension is a facial feature value of the corresponding facial image, and the other dimension is the acquisition time of the corresponding facial image for the time value;
according to the corresponding relation, PAAnd obtaining a mental health evaluation result by the facial feature set F.
3. The system of claim 2, wherein said mapping is based on said correspondence and PAThe obtaining of the mental health evaluation result specifically comprises the following steps:
according to vegetative nerve signal set PAForming corresponding vegetative nerve difference set deltaASaid vegetative nerve difference set deltaAEach element of (1)
Figure FDA0003198866960000021
Comprises 2 dimensions, one dimension being a data value
Figure FDA0003198866960000022
Another dimension is a time value
Figure FDA0003198866960000023
wherein ,ΔAOf (2) element(s)
Figure FDA0003198866960000024
The vegetative nerve signals are in one-to-one correspondence with the vegetative nerve signals collected by the vegetative nerve signal collector;
inputting the autonomic nerve signals collected by the autonomic nerve signal collector into a nerve network model, performing feature recognition to obtain the features of the autonomic nerve signal value of each element in the autonomic nerve signals, and further forming a corresponding feature set IAThe set of features IAEach element of
Figure FDA0003198866960000025
Comprises 2 dimensions, one dimension being a data value
Figure FDA0003198866960000026
Another dimension is a time value
Figure FDA0003198866960000027
wherein ,IAOf (2) element(s)
Figure FDA0003198866960000028
Corresponds to the vegetative nerve signals collected by the vegetative nerve signal collector one by one, and
Figure FDA0003198866960000029
data value of
Figure FDA00031988669600000210
Is characterized by the feature of A (x),
Figure FDA00031988669600000211
time value of
Figure FDA00031988669600000212
According to vegetative nerve difference set deltaAAnd feature set IAObtaining the variant sequence CA；
According to the corresponding relation, CAAnd obtaining a mental health evaluation result by the facial feature set F.
4. The system of claim 3, wherein:
for vegetative nerve signal set PALast element in (1)
Figure FDA00031988669600000213
It is related toALast element in (1)
Figure FDA00031988669600000214
Correspond to, and
Figure FDA00031988669600000215
data value of
Figure FDA00031988669600000216
Figure FDA00031988669600000217
Time value of
Figure FDA00031988669600000218
Is composed of
Figure FDA00031988669600000219
Time value of
Figure FDA00031988669600000220
wherein ,xlSet P for vegetative nerve signalsAThe identity of the last element in;
for vegetative nerve signal set PANon-last element of (1)
Figure FDA00031988669600000221
Its one-to-one correspondence ΔAAn element of
Figure FDA00031988669600000222
And is
Figure FDA00031988669600000223
Data value of
Figure FDA00031988669600000224
Figure FDA00031988669600000225
Time value of
Figure FDA00031988669600000226
wherein ,xjSet P for vegetative nerve signalsAIs not the identification of the last element.
5. The system of claim 4, wherein the sequence of changes CAEach element in (1)
Figure FDA0003198866960000031
Comprises 2 dimensions, one dimension being a data value
Figure FDA0003198866960000032
Another dimension is a time value
Figure FDA0003198866960000033
Wherein the vegetative nerve signal set PAAny one element of
Figure FDA0003198866960000034
One-to-one correspondence change sequence CAAn element of
Figure FDA0003198866960000035
And is
Figure FDA0003198866960000036
Data value of
Figure FDA0003198866960000037
Figure FDA0003198866960000038
Time value of
Figure FDA0003198866960000039
D1(x) Is based on the vegetative nerve difference set deltaAThe first vegetative nerve parameter, D2(x) Is based on feature set IAAnd obtaining a second autonomic nervous parameter.
6. The system of claim 5, wherein the system is configured to perform
Figure FDA00031988669600000310
Figure FDA00031988669600000311
Figure FDA00031988669600000312
Is vegetative nerve difference set deltaAThe maximum of the data values of all elements,
Figure FDA00031988669600000313
is vegetative nerve difference set deltaAThe minimum of the data values of all elements,
Figure FDA00031988669600000314
is set forth as feature set IAThe maximum of the data values of all elements,
Figure FDA00031988669600000315
is set forth as feature set IAThe minimum of the data values of all elements,
Figure FDA00031988669600000316
is set forth as feature set IAMean of data values for all elements.
7. The system of claim 6, wherein the system is configured to perform
Figure FDA00031988669600000317
Figure FDA00031988669600000318
wherein ,
Figure FDA00031988669600000319
is set forth as feature set IAThe maximum of the data values of all elements,
Figure FDA00031988669600000320
is set forth as feature set IAThe minimum of the data values of all elements;
delta-vegetative nerve difference set deltaATime difference between maximum and minimum of data values of all elements/feature set IAThe time difference between the maximum and minimum of the data values of all elements.
8. The system of claim 7, wherein the first and second sensors are arranged in a single package,wherein C is the correspondenceAThe obtaining of the mental health evaluation result by the facial feature set F specifically comprises the following steps:
obtaining CAOf the element is determined
Figure FDA00031988669600000321
According to the corresponding relation, determining the
Figure FDA00031988669600000322
Corresponding evaluation questions are obtained, and further evaluation questions and C are obtainedAThe corresponding relation of the middle elements;
obtaining the corresponding relation between the evaluation question and the elements in the F according to the time values of the elements in the F and the corresponding relation;
for each question, determining C corresponding to each questionAElements forming a corresponding set of elements
Figure FDA0003198866960000041
And determining F element corresponding to the F element to form corresponding element set
Figure FDA0003198866960000042
Based on the standard variation value corresponding to each topic,
Figure FDA0003198866960000043
and
Figure FDA0003198866960000044
and obtaining a psychological health evaluation result.
9. The system according to claim 8, wherein the standard variation value is based on a correspondence of each topic,
Figure FDA0003198866960000045
and
Figure FDA0003198866960000046
the obtaining of the mental health evaluation result specifically comprises the following steps:
for any subject B, its corresponding CAThe element set is
Figure FDA0003198866960000047
Corresponding F element set is
Figure FDA0003198866960000048
Then calculate
Figure FDA0003198866960000049
Wherein y is
Figure FDA00031988669600000410
Middle element identification, Y identification
Figure FDA00031988669600000411
The identification of the medium-largest element is carried out,
Figure FDA00031988669600000412
is a set
Figure FDA00031988669600000413
The data value of the element with the middle index y,
Figure FDA00031988669600000414
is a set
Figure FDA00031988669600000415
Mean of all the element data values in;
computing
Figure FDA00031988669600000416
Wherein u is
Figure FDA00031988669600000417
Middle element identification, U is
Figure FDA00031988669600000418
The identification of the medium-largest element is carried out,
Figure FDA00031988669600000419
is a set
Figure FDA00031988669600000420
The data value of the element with the middle index u,
Figure FDA00031988669600000421
is a set
Figure FDA00031988669600000422
The average of all the element data values in (a),
Figure FDA00031988669600000423
is a set
Figure FDA00031988669600000424
Maximum value of all element data values in;
according to
Figure FDA00031988669600000425
Determining the score of the question B according to the standard variation value corresponding to the question B;
and determining the psychological health evaluation result according to the scores of all the questions.
10. The system of claim 9, wherein the basis is
Figure FDA00031988669600000426
Determining the score of the topic B according to the standard change corresponding to the topic B specifically includes:
Figure FDA00031988669600000427
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