CN116115224A - Emotional stability assessment method and device based on analysis of respiratory waveform - Google Patents

Abstract

The application provides a method and a device for evaluating emotion stability based on analysis of respiratory waveforms, which are processed by portable acquisition and analysis equipment, wherein the method comprises the following steps: acquiring respiratory parameters of a tested person; according to the respiratory parameters of the tested person, analyzing respiratory data of emotion, respiratory data of emotion change direction and respiratory data of emotion regulating capacity; further analyzing the emotion state, the emotion change direction state and the emotion regulating capacity state; and sending the message to the cloud server and the man-machine interaction module. The method mainly comprises the steps of acquiring respiratory parameters through portable acquisition and analysis equipment, analyzing an emotion state, an emotion change direction state and an emotion adjustment capacity state, and comprehensively analyzing the emotion stability of a tested person. The embodiment of the disclosure can enable the testee to easily grasp the emotion state of the testee in daily life.

Description

A method and device for evaluating emotional stability based on analyzing respiratory waveform

Technical Field

The present application relates to the technical field of smart wearable devices, for example, to a method and device for evaluating emotional stability based on analyzing respiratory waveforms.

Background Art

Emotional stability refers to the situation where a person's emotional state fluctuates with changes in external or internal conditions. The Eysenck Emotional Stability Test is the most commonly used scale evaluation method for emotional stability tests. Since emotional stability varies from person to person, people with relatively stable emotions have slower emotional reactions, while people with unstable emotions are easily prone to emotional reactions when encountering emotional events, and even trivial things in life can cause strong emotional changes. Therefore, when encountering emotionally unstable individuals, their emotional events occur quickly and changeably. In addition to being highly subjective, cumbersome to operate, and taking a long time, the scale evaluation method makes it difficult for the testee to self-assess his or her emotional state. In addition, each evaluation result is isolated, and there is no quantitative reference value for emotional stability.

In the prior art, although there are some instruments that can analyze the emotional stability of the subject through certain data collection, these measurement methods and instruments can only be used in hospitals because the instruments are expensive and the tests are complicated.

Therefore, the prior art lacks a method and device that can allow the subject to easily understand his or her own emotional state.

Summary of the invention

In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key/critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

The disclosed embodiments provide a method and device for evaluating emotional stability based on analyzing respiratory waveforms, which can enable a subject to easily understand his or her own emotional stability state in daily life.

In a first aspect, the present application provides a method for evaluating emotional stability based on analyzing a respiratory waveform, which is applied to a portable acquisition and analysis device in the emotional stability evaluation device based on analyzing a respiratory waveform, and the method comprises:

Obtaining breathing parameters of the subject;

According to the breathing parameters of the tested person, the breathing data of emotions, the breathing data of the direction of emotion changes and the breathing data of emotion regulation ability are analyzed;

Analyze the emotional state based on the emotional breathing data;

Analyze the state of the emotional change direction based on the breathing data of the emotional change direction;

Analyze the emotion regulation ability status based on the breathing data of emotion regulation ability;

Sending an emotional stability assessment data packet to a cloud server; the emotional stability assessment data packet includes: emotional state, emotional change direction state, and emotional regulation ability state;

Send emotional stability assessment data packets to the human-computer interaction module.

In a second aspect, the present application provides a method for evaluating emotional stability based on analyzing a respiratory waveform, which is applied to a cloud server in the device for evaluating emotional stability based on analyzing a respiratory waveform, and the method comprises:

Establish database;

Receive an emotion stability assessment data packet and store it in a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotion state, an emotion change direction state, and an emotion regulation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion regulation ability state;

Send historical emotional stability assessment data packet.

In a third aspect, the present application provides a method for evaluating emotional stability based on analyzing a respiratory waveform, which is applied to a human-computer interaction module in the device for evaluating emotional stability based on analyzing a respiratory waveform, and the method comprises:

Receive an emotional stability assessment data packet and a historical emotional stability assessment data packet; the emotional stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional regulation ability state; the historical emotional stability assessment data packet includes a historical emotional state, a historical emotional change direction state, and a historical emotional regulation ability state;

Receive a subject's observation data request and display a corresponding emotional stability assessment data packet or a corresponding historical emotional stability assessment data packet.

In a fourth aspect, the present application provides an emotional stability assessment device based on analyzing a respiratory waveform, the device comprising: a portable acquisition and analysis device, a cloud server and a human-computer interaction module; the portable acquisition and analysis device comprises a respiratory parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected to the analysis module, and the analysis module is connected to the cloud server; the human-computer interaction module is respectively connected to the analysis module and the cloud server;

A breathing parameter acquisition module is used to obtain the breathing parameters of the subject;

The analysis module is used to analyze the breathing data of emotions, the breathing data of the direction of emotion changes and the breathing data of emotion regulation ability according to the breathing parameters of the subject; analyze the emotional state according to the breathing data of emotions; analyze the state of the direction of emotion changes according to the breathing data of the direction of emotion changes; analyze the state of emotion regulation ability according to the breathing data of emotion regulation ability; send the emotional stability evaluation data packet to the cloud server; the emotional stability evaluation data packet includes: emotional state, emotional change direction state and emotional regulation ability state; send the emotional stability evaluation data packet to the human-computer interaction module;

The cloud server is used to establish a database; receive an emotion stability assessment data packet and store it in the database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotion state, an emotion change direction state, and an emotion regulation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion regulation ability state; send a historical emotion stability assessment data packet;

The human-computer interaction module is used to receive an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: emotion state, emotion change direction state and emotion regulation ability state; the historical emotion stability assessment data packet includes historical emotion state, historical emotion change direction state and historical emotion regulation ability state; receive a subject's observation data request, and display the corresponding emotion stability assessment data packet or the corresponding historical emotion stability assessment data packet.

The present disclosure provides a method and device for evaluating emotional stability based on analyzing respiratory waveforms, which can achieve the following technical effects:

In the disclosed embodiment, the breathing parameters of the subject are mainly obtained through a portable collection and analysis device, and the emotional state, the state of the emotional change direction and the state of the emotional regulation ability are analyzed; then, the data are stored in the database of the cloud server; finally, the human-computer interaction module displays the corresponding emotional state, the state of the emotional change direction and the state of the emotional regulation ability according to the observed data request of the subject. The disclosed embodiment enables the subject to easily grasp his or her own emotional stability state in daily life.

The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

FIG1 is a schematic diagram of an emotional stability assessment device based on analyzing a respiratory waveform provided by an embodiment of the present disclosure;

FIG2 is an interactive diagram of an emotional stability assessment method based on analyzing respiratory waveforms provided by an embodiment of the present disclosure;

3 is a flow chart of an emotional stability assessment method based on analyzing respiratory waveforms applied to a portable data acquisition and analysis device provided by an embodiment of the present disclosure;

FIG4 is a schematic diagram of a respiratory waveform definition provided by an embodiment of the present disclosure;

FIG5 is a flow chart of an emotional stability assessment method based on analyzing respiratory waveforms applied to a cloud server provided by an embodiment of the present disclosure;

FIG6 is a flow chart of an emotional stability assessment method based on analyzing breathing waveforms applied to a human-computer interaction module provided in an embodiment of the present disclosure.

DETAILED DESCRIPTION

In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

The following description and accompanying drawings fully illustrate specific embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The examples represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims. In this article, each embodiment may be represented individually or generally by the term "invention", which is only for convenience, and if more than one invention is disclosed in fact, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, without requiring or implying any actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the presence of other identical elements in the process, method or device including the elements. The various embodiments herein are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, since they correspond to the method part disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

In addition, the terms "disposed", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present disclosure can be understood according to specific circumstances.

Emotional stability refers to the fluctuation of a person's emotional state with changes in external or internal conditions. In the prior art, although there are some instruments that analyze the emotional stability of the subject through certain data collection, these measurement methods and instruments can only be used in hospitals because the instruments are expensive and the tests are complicated.

Therefore, the prior art lacks a method and device that can allow the subject to easily understand his or her own emotional state.

As shown in FIG1 , the present application provides an emotional stability assessment device based on analyzing respiratory waveforms, the device comprising: a portable acquisition and analysis device, a cloud server and a human-computer interaction module; the portable acquisition and analysis device comprises a respiratory parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected to the analysis module, and the analysis module is connected to the cloud server; the human-computer interaction module is respectively connected to the analysis module and the cloud server;

A breathing parameter acquisition module is used to obtain the breathing parameters of the subject;

The analysis module is used to parse out the breathing data of emotions, the breathing data of the direction of emotion changes and the breathing data of emotion regulation ability according to the breathing parameters of the subject; parse out the emotional state according to the breathing data of emotions; parse out the state of the direction of emotion changes according to the breathing data of the direction of emotion changes; parse out the state of the emotion regulation ability according to the breathing data of the emotion regulation ability; send the emotion stability assessment data packet to the cloud server; the emotion stability assessment data packet includes: the emotion state, the state of the direction of emotion changes and the state of the emotion regulation ability; send the emotion stability assessment data packet to the human-computer interaction module; the cloud server is used to establish a database; receive the emotion stability assessment data packet and store it in the database as a historical emotion stability assessment data packet; the emotion stability assessment data packet The emotional stability assessment data packet includes: emotional state, emotional change direction state and emotional regulation ability state; the historical emotional stability assessment data packet includes: historical emotional state, historical emotional change direction state and historical emotional regulation ability state; the historical emotional stability assessment data packet is sent; a human-computer interaction module is used to receive the emotional stability assessment data packet and the historical emotional stability assessment data packet; the emotional stability assessment data packet includes: emotional state, emotional change direction state and emotional regulation ability state; the historical emotional stability assessment data packet includes: historical emotional state, historical emotional change direction state and historical emotional regulation ability state; receive the observation data request of the subject, and display the corresponding emotional stability assessment data packet or the corresponding historical emotional stability assessment data packet.

It should be understood that the emotional stability assessment data package includes: emotional state, emotional change direction state and emotional regulation ability state. After being stored in the database, the emotional state, emotional change direction state and emotional regulation ability state will become historical emotional state, historical emotional change direction state and historical emotional regulation ability state.

It should be noted that in the disclosed embodiment, the emotional stability assessment is mainly based on three parameters, namely, the emotional state, the emotional change direction state and the emotional regulation ability state. The three parameters are combined together to obtain the emotional stability assessment.

In practical applications, the connection mode between the human-computer interaction module and the connection analysis module can be a Bluetooth wireless connection. The connection mode between the human-computer interaction module and the analysis module can be a Bluetooth wireless connection. It should be understood that the cloud server is connected to the portable collection and analysis device and the human-computer interaction module through the network. The human-computer interaction module can be a numerical control touch screen with input and output functions. The subject can input the subject's observation data request, and the numerical control touch screen then outputs the corresponding data packet.

For example, the subject inputs a command through the human-computer interaction module to observe the emotional stability status in the past month. The human-computer interaction module displays the subject's historical emotional state, historical emotional change direction state, and historical emotional regulation ability state in the past month through the cloud server. The subject understands his or her own emotional stability state by observing the emotional stability assessment data package for this month.

Furthermore, the device also includes a printing module; the printing module is connected to the human-computer interaction module.

The printing module may be a printer, which prints out the content of the data packet according to the data packet requested by the subject's observation data, and displays the content of the data packet more intuitively.

See FIG2 , which is an interactive diagram of an emotional stability assessment method based on analyzing breathing waveforms.

In conjunction with FIG. 3 , an embodiment of the present disclosure provides a method for evaluating emotional stability based on analyzing a respiratory waveform, which is applied to a portable collection and analysis device in the device for evaluating emotional stability based on analyzing a respiratory waveform. The method includes:

S210, the portable collection and analysis device obtains the breathing parameters of the subject.

S220, the portable data collection and analysis device analyzes the breathing data of emotions, the breathing data of the direction of emotion changes, and the breathing data of emotion regulation ability according to the breathing parameters of the subject.

S230, the portable collection and analysis device analyzes the emotional state based on the emotional breathing data.

S240, the portable data collection and analysis device analyzes the state of the emotion change direction according to the breathing data of the emotion change direction.

S250, the portable collection and analysis device analyzes the emotion regulation ability status according to the emotion regulation ability breathing data.

S260, the portable data collection and analysis device sends an emotion stability assessment data packet to the cloud server; the emotion stability assessment data packet includes: emotion state, emotion change direction state and emotion regulation ability state.

S270, the portable data collection and analysis device sends an emotion stability assessment data packet to the human-computer interaction module.

呼吸率RESP、第i个5分钟呼吸周期TT均值

N个

的均值

吸气相T0、吸气相均值

第i个5分钟吸气相TT0均值

N个

的均值

呼气相T1、呼吸呼气相均值

第i个5分钟呼气相TT1均值

N个

的均值

吸气相与呼气相之差△T、吸气相与呼气相之差的均值

第i个5分钟△T均值

N个

的均值

Furthermore, the breathing parameters of the subject include the breathing cycle TT, the breathing cycle mean

Respiratory rate RESP, mean value of TT in the i-th 5-minute respiratory cycle

N

The mean

Inspiratory phase T0, inspiratory phase mean

The mean value of TT0 in the i-th 5-minute inspiratory phase

N

The mean

Expiratory phase T1, breathing expiratory phase mean

The mean value of TT1 in the 5-minute expiratory phase

N

The mean

The difference between the inspiratory phase and the expiratory phase, △T, and the average of the difference between the inspiratory phase and the expiratory phase

The mean value of △T in the i-th 5-minute period

N

The mean

See FIG. 4 , which is a schematic diagram of a respiratory waveform definition in the present application.

It should be understood that the respiratory cycle TT is the time from the start of one inhalation to the start of the next inhalation; the inhalation phase T0 is the inhalation time; and the respiratory phase is the exhalation time.

Furthermore, the triangle index BRV_TI, the standard deviation SDTT, SDATT, RMSSDTT, and the respiratory rate RESP during all respiratory cycles are analyzed;

SDTT is the standard deviation of all respiratory cycles and is calculated according to the following formula (1):

SDATT is to divide all recorded respiratory cycles into several time periods in 5-minute intervals according to the recorded time sequence. First, calculate the average value of the respiratory cycle in each 5-minute interval, and then calculate the standard deviation of these average values, according to the following formula (2):

为第i个5分钟呼吸周期TT均值，

为N个

的均值；Among them, N is the evaluation time, which is N 5 minutes.

is the mean TT value of the i-th 5-minute respiratory cycle,

For N

The mean of

RMSSDTT is the root mean square value of the difference between adjacent breathing cycles throughout the entire process, calculated according to the following formula (3):

BRV_TI is the total number of T1 in the expiratory phase of the respiratory cycle divided by the height of the T1 histogram in the expiratory phase of the respiratory cycle;

Analyze the emotional state, including: calculate the emotional stability constant E1, according to the formula E1 = 0.01*BRV_TI*(SDTT+SDATT+RMSSDTT)/RESP.

It should be understood that the discrete type of the entire respiratory cycle is the key quantity and prerequisite for emotional stability. Therefore, based on the variability modeling of the subject's full respiratory waveform, the established data model of the full respiratory waveform can objectively and accurately reflect the level of emotional stability of the subject. E1, which integrates relevant parameters of the respiratory cycle such as BRV_TI, SDTT, SDATT, RMSSDTT, RESP, etc., can reflect the emotional stability of the subject more objectively and accurately.

E1 is the basic parameter for evaluating emotional stability based on the entire breathing cycle. When the subject's E1∈[1,15], the subject's emotions are in a state of physical and mental balance, and his emotions are very stable; when E1∈(15,23], the subject's emotions are in a self-regulating state; when E1∈(23,35], the subject's emotions are in a tense state; when E1∈(35,67], the subject's emotions are in a depressed state; when E1＞67, the subject's emotions are in an out-of-control, aggressive state. When in this state, emotional relief should be given in time.

Furthermore, according to the breathing data of the emotion change direction, the emotion change direction state is analyzed, including:

In the case of E1＞35, the negative trend of emotion is analyzed based on the breathing data of the emotion change direction;

When E1≤35, the positive trend state of emotion is analyzed based on the breathing data of the positive trend of emotion.

It should be understood that E1 is 35, which represents the dividing point between the negative state and the positive state of the subject. In the case of E1>35, it means that the emotional state of the subject is biased towards the negative. In the case of E1≤35, it means that the emotional state of the subject is biased towards the positive.

Furthermore, when E1>35, the breathing data of the emotion change direction is analyzed, including:

Parse out SDTT0, SDATT0, RMSSDTT0, BRV_TI0;

SDTT0 is the standard deviation of T0 of the entire respiratory inspiratory phase, calculated as follows:

为测评全程的呼吸吸气相均值；in,

It is the average value of the inspiratory phase of the whole breathing process;

SDATT0 is, the inspiratory phase T0 of all recorded respiratory cycles is divided into a time period of 5 minutes according to the recorded time sequence; the average value of the inspiratory phase T0 in each 5-minute time period is calculated; the standard deviation of the average value is calculated according to the following formula (5);

为第i个5分钟吸气相TT0均值，

为N个

的均值；Among them, N is the evaluation time, which is N 5 minutes.

is the mean value of TT0 in the i-th 5-minute inspiratory phase,

For N

The mean of

RMSSDTT0 is the root mean square value of the difference between the inspiratory phase T0 of all adjacent respiratory cycles, calculated as follows:

BRVTI0 is the total number of inspiratory phase T0 of the respiratory cycle divided by the height of the histogram of inspiratory phase T0 of the respiratory cycle;

Analyze the negative trend of sentiment, including:

Calculate the negative factor of emotional stability E2 according to the formula E2=0.1*RMSSDTT0*SDTT0/SDATT0+BRV_TI0.

It should be understood that the negative development trend of emotions is closely related to the inhalation phase T0. When the T0 variability index is smaller, the tendency of emotions to continue to deteriorate will be greatly reduced.

The negative trend of emotional development is judged according to the negative factor of emotional stability E2, that is, when the subject's E2 is less than 90, the subject's emotional stability trend is developing in a good direction; when the subject's E2 is greater than or equal to 90, the subject's emotional stability trend is developing in a negative direction.

Furthermore, when E1≤35, the breathing data of the direction of emotional change is analyzed, including:

Parse out RMSSDTT1, SDTT1, SDATT1, BRV_TI1;

The exhalation phase of the respiratory cycle TT is T1, and SDTT1 is the standard deviation of the exhalation phase T1 of all breaths, calculated according to the following formula (7):

SDATT1 is, all recorded respiratory cycle expiratory phase T1, according to the recorded time sequence, divided into several time periods every 5 minutes, first calculate the average value of respiratory cycle T1 in each 5-minute time period, and then calculate the standard deviation of these several average values, according to the following formula (8):

为第i个5分钟呼气相TT1均值，

为N个

的均值；Among them, N is the evaluation time, which is N 5 minutes.

is the mean value of TT1 in the ith 5-minute expiratory phase,

For N

The mean of

RMSSDTT1 is the root mean square value of the difference between the expiratory phase T1 of the entire adjacent respiratory cycle, calculated according to the following formula (9):

BRV_TI is the total number of T1 in the expiratory phase of the respiratory cycle divided by the height of the T1 histogram in the expiratory phase of the respiratory cycle;

Analyze the positive trend of sentiment, including:

The emotional stability positive factor E3 is calculated according to the formula E3 = (0.1*RMSSDTT1*SDTT1/SDATT1*BRV_TI1)/RESP.

It should be understood that the positive development trend of emotions is closely related to the expiratory phase T1. When the T1 variability index is smaller, the positive development trend of emotions is weaker.

The positive trend of emotional development is judged according to the positive factor of emotional stability E3, that is, when the subject's E3 is less than 30, the positive trend of emotional stability of the subject is weak; when 30≤E3＜85, the subject's emotional trend is in a positive and negative trend tug-of-war state. When E3≥85, the subject's emotional stability is developing in a positive direction.

Further, the breathing data of emotion regulation ability is parsed, including: RMSSD△T, SD△T, BRV_△T, SDA△T and RESP are parsed;

Define the difference between the inspiratory phase and the expiratory phase of the respiratory cycle (TT) as △T, then

SD△T is the standard deviation of the difference △T between the inspiratory phase and the expiratory phase of all respiratory waveforms, which is calculated according to the following formula (10):

为测评全程的呼吸波形吸气相与呼气相之差的均值；in,

It is the average value of the difference between the inspiratory phase and the expiratory phase of the respiratory waveform during the whole process of evaluation;

SDA△T, the difference △T between the inspiratory phase and the expiratory phase of all recorded respiratory waveforms, is recorded in chronological order, with each 5 minutes as a time period; the average value of the inspiratory phase △T in each 5-minute time period is calculated; the standard deviation of the average value is calculated according to the following formula (11);

为第i个5分钟△T均值；

为N个

的均值；Among them, N is the number of evaluation time including 5 minutes;

is the mean value of △T in the i-th 5 minutes;

For N

The mean of

RMSSD△T is the root mean square value of the difference △T between the inspiratory phase and the expiratory phase of the entire adjacent respiratory waveform, calculated according to the following formula (12);

BRV_△T is the total number of △T differences between the inspiratory phase and the expiratory phase of the respiratory waveform divided by the height of the △T difference histogram between the inspiratory phase and the expiratory phase of the respiratory waveform;

Analyze the state of emotion regulation ability, including:

Calculate the emotional stability regulation ability E4 according to the formula

E4＝RMSSD△T*(SD△T+BRV_△T+SDA△T)/RESP.

It should be understood that a person's emotions can be adjusted through the reverse regulation of inhalation and exhalation. When the difference between the inhalation phase T0 and the exhalation phase T1 is large, the person's emotion regulation ability is stronger.

When the subject said E4＜10, his/her ability to regulate emotional stability is extremely poor; when the subject said 10≤E4＜20, his/her ability to regulate emotional stability is relatively poor; when the subject said 20≤E4＜28, his/her ability to regulate emotional stability is average; when the subject said 28≤E4＜40, his/her ability to regulate emotional stability is relatively good; when the subject said E4≥40, his/her ability to regulate emotional stability is extremely good.

As shown in FIG5 , a method for evaluating emotional stability based on analyzing respiratory waveforms is applied to a cloud server in the device for evaluating emotional stability based on analyzing respiratory waveforms. The method includes:

S310, the cloud server establishes a database.

S320, the cloud server receives an emotion stability assessment data packet and stores it in a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: emotion state, emotion change direction state and emotion regulation ability state; the historical emotion stability assessment data packet includes historical emotion state, historical emotion change direction state and historical emotion regulation ability state.

S330, the cloud server sends a historical emotion stability assessment data packet.

As shown in FIG6 , a method for evaluating emotional stability based on analyzing a breathing waveform is applied to a human-computer interaction module in the device for evaluating emotional stability based on analyzing a breathing waveform. The method includes:

S410, the human-computer interaction module receives an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: emotion state, emotion change direction state and emotion regulation ability state; the historical emotion stability assessment data packet includes historical emotion state, historical emotion change direction state and historical emotion regulation ability state.

S420, the human-computer interaction module receives the observation data request of the subject and displays the corresponding emotional stability assessment data packet or the corresponding historical emotional stability assessment data packet.

A specific embodiment may be to use a portable collection and analysis device to obtain the breathing parameters of the subject. According to the above breathing parameters, the breathing data of emotions, the breathing data of the direction of emotion changes, and the breathing data of emotion regulation ability are analyzed. SDTT, BRV_TI, SDATT, RMSSDTT, SDTT0, BRV_TI0, SDATT0, RMSSDTT0, SDTT1, BRV_TI1, SDATT1, RMSSDTT1, SD△T, BRV_△T, SDA△T, RMSSD△T are calculated, and the specific contents are shown in Table 1.

Table 1

date on Monday Tuesday Wednesday Thursday Friday Saturday Sunday RESP 18 twenty one twenty four 17 20 16 20 SDTT 1768 1686 1710 1652 1756 1657 1664 SDATT 523 489 475 519 521 502 518 RMSSDTT 1059 640 1356 589 1084 773 1006 BRV_TI 16 12 14 16 17 13 16 SDTT0 975 925 933 917 986 945 894 SDATT0 514 479 479 512 515 474 508 RMSSDTT0 429 262 405 277 482 307 440 BRV_TI0 11 9 9 10 11 8 9 SDTT1 1039 964 997 972 1019 947 944 SDATT1 564 567 543 577 551 576 521 RMSSDTT1 455 251 553 283 532 330 445 BRV_TI1 12 10 8 10 13 11 12 SD△T 51 57 51 65 58 48 40 SDA△T twenty three 27 twenty three 32 30 16 16 RMSSD△T twenty two 29 twenty three twenty three 25 20 16 BRV_△T 11 11 10 11 11 10 8 E1 30 16 twenty one 26 29 twenty four 26 E2 none none none none none none none E3 56 20 34 28 64 37 48 E4 104 131 81 146 124 93 51

According to Table 1, the corresponding E1, E2 or E3, E4 are calculated to judge the state of the subject, and then the emotional state, emotional change direction state and emotional regulation ability state are obtained. Since in Table 1, E1≤35, the calculated emotional change direction state is the emotional positive trend state, that is, E3.

The portable collection and analysis device sends an emotion stability assessment data packet to the cloud server and sends an emotion stability assessment data packet to the human-computer interaction module.

The cloud server stores the emotional stability assessment data package in the database. The human-computer interaction module obtains the subject's request to view the above one-week data and displays the subject's emotional state, emotional change direction state and emotional regulation ability state for one week.

The above description and accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and/or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and/or comprising refer to the presence of stated features, wholes, steps, operations, elements, and/or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and/or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the above-described devices, devices and units may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

The flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, may be implemented by dedicated hardware-based devices that perform the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims (10)

1. A method for evaluating emotional stability based on analyzing breathing waveforms, characterized in that it is applied to a portable acquisition and analysis device in the emotional stability evaluation device based on analyzing breathing waveforms, and the method comprises: Obtaining breathing parameters of the subject; According to the breathing parameters of the tested person, the breathing data of emotions, the breathing data of the direction of emotion changes and the breathing data of emotion regulation ability are analyzed; Analyze the emotional state based on the emotional breathing data; Analyze the state of the emotional change direction based on the breathing data of the emotional change direction; Analyze the emotion regulation ability status based on the breathing data of emotion regulation ability; Sending an emotion stability assessment data packet to a cloud server; the emotion stability assessment data packet includes: an emotion state, an emotion change direction state, and an emotion change direction state; Send emotional stability assessment data packets to the human-computer interaction module. 2. The method according to claim 1, characterized in that the respiratory parameters of the subject include respiratory cycle TT, respiratory cycle mean

Respiratory rate RESP, mean value of TT in the i-th 5-minute respiratory cycle

N

The mean

Inspiratory phase T0, inspiratory phase mean

The mean value of TT0 in the i-th 5-minute inspiratory phase

N

The mean

Expiratory phase T1, breathing expiratory phase mean

The mean value of TT1 in the 5-minute expiratory phase

N

The mean

The difference between the inspiratory phase and the expiratory phase, △T, and the average of the difference between the inspiratory phase and the expiratory phase

The mean value of △T in the i-th 5-minute period

N

The mean

3. The method according to claim 2, characterized in that parsing the emotional breathing data comprises: Analyze the triangle index BRV_TI, standard deviation SDTT, SDATT, and RMSSDTT during all respiratory cycles; SDTT is the standard deviation of all respiratory cycles and is calculated according to the following formula (1):

SDATT is to divide all recorded respiratory cycles into several time periods in 5-minute intervals according to the recorded time sequence. First, calculate the average value of the respiratory cycle in each 5-minute interval, and then calculate the standard deviation of these average values, according to the following formula (2):

Among them, N is the evaluation time, which is N 5 minutes.

is the mean TT value of the i-th 5-minute respiratory cycle,

For N

The mean of RMSSDTT is the root mean square value of the difference between adjacent breathing cycles throughout the entire process, calculated according to the following formula (3):

BRV_TI is the total number of T1 in the expiratory phase of the respiratory cycle divided by the height of the T1 histogram in the expiratory phase of the respiratory cycle; Analyze the emotional state, including: calculate the emotional stability constant E1, according to the formula E1 = 0.01 * BRV_TI * (SDTT + SDATT + RMSSDTT) / RESP; When the subject’s E1∈[1,15], the subject’s emotions are in a state of physical and mental balance; when E1∈(15,23], the subject’s emotions are in a state of self-regulation; when E1∈(23,35], the subject’s emotions are in a state of tension; when E1∈(35,67], the subject’s emotions are in a state of depression; when E1＞67, the subject’s emotions are in a state of out-of-control and aggressive attack. 4. The method according to claim 3, characterized in that the emotion change direction state is analyzed according to the breathing data of the emotion change direction, comprising: In the case of E1＞35, the negative trend of emotion is analyzed based on the breathing data of the emotion change direction; When E1≤35, the positive trend state of emotion is analyzed based on the breathing data of the positive trend of emotion. 5. The method according to claim 4, characterized in that parsing the breathing data of the direction of emotion change comprises: Parse out SDTT0, SDATT0, RMSSDTT0, BRV_TI0; SDTT0 is the standard deviation of T0 of the entire respiratory inspiratory phase, calculated as follows:

in,

It is the average value of the inspiratory phase of the whole breathing process; SDATT0 is, the inspiratory phase T0 of all recorded respiratory cycles is divided into a time period of 5 minutes according to the recorded time sequence; the average value of the inspiratory phase T0 in each 5-minute time period is calculated; the standard deviation of the average value is calculated according to the following formula (5);

Among them, N is the evaluation time, which is N 5 minutes.

is the mean value of TT0 in the i-th 5-minute inspiratory phase,

For N

The mean of RMSSDTT0 is the root mean square value of the difference between the inspiratory phase T0 of all adjacent respiratory cycles, calculated as follows:

BRVTI0 is the total number of inspiratory phase T0 of the respiratory cycle divided by the height of the histogram of inspiratory phase T0 of the respiratory cycle; Analyze the negative trend of sentiment, including: Calculate the negative factor of emotional stability E2 according to the formula E2＝0.1*RMSSDTT0*SDTT0/SDATT0+BRV_TI0; When the subject's E2 is less than 90, the subject's emotional stability is developing in a positive direction; when the subject's E2 is greater than or equal to 90, the subject's emotional stability is developing in a negative direction. 6. The method according to claim 4, characterized in that parsing the breathing data of the direction of emotional change includes: Parse out RMSSDTT1, SDTT1, SDATT1, BRV_TI1; The expiratory phase of the respiratory cycle TT is T1, then SDTT1 is the standard deviation of T1 in the expiratory phase of all breaths, calculated according to the following formula (7):

SDATT1 is, all recorded respiratory cycle expiratory phase T1, according to the recorded time sequence, divided into several time periods every 5 minutes, first calculate the average value of respiratory cycle T1 in each 5-minute time period, and then calculate the standard deviation of these several average values, according to the following formula (8):

Among them, N is the evaluation time, which is N 5 minutes.

is the mean value of TT1 in the ith 5-minute expiratory phase,

For N

The mean of RMSSDTT1 is the root mean square value of the difference between the expiratory phase T1 of the entire adjacent respiratory cycle, calculated according to the following formula (9):

BRV_TI is the total number of T1 in the expiratory phase of the respiratory cycle divided by the height of the T1 histogram in the expiratory phase of the respiratory cycle; Analyze the positive trend of sentiment, including: Calculate the emotional stability positive factor E3, according to the formula E3＝(0.1*RMSSDTT1*SDTT1/SDATT1*BRV_TI1)/RESP; When the subject's E2 is less than 90, the subject's emotional stability is developing in a positive direction; when the subject's E2 is greater than or equal to 90, the subject's emotional stability is developing in a negative direction. 7. The method according to claim 2, characterized in that parsing the breathing data of emotion regulation ability comprises: Analyze RMSSD△T, SD△T, BRV_△T and SDA△T; Define the difference between the inspiratory phase and the expiratory phase of the respiratory cycle (TT) as △T, then

SD△T is the standard deviation of the difference △T between the inspiratory phase and the expiratory phase of all respiratory waveforms, which is calculated according to the following formula (10):

in,

It is the average value of the difference between the inspiratory phase and the expiratory phase of the respiratory waveform during the whole process of evaluation; SDA△T, the difference △T between the inspiratory phase and the expiratory phase of all recorded respiratory waveforms, is recorded in chronological order, with each 5 minutes as a time period; the average value of the inspiratory phase △T in each 5-minute time period is calculated; the standard deviation of the average value is calculated according to the following formula (11);

Among them, N is the number of evaluation time including 5 minutes;

is the mean value of △T in the i-th 5 minutes;

For N

The mean of RMSSD△T is the root mean square value of the difference △T between the inspiratory phase and the expiratory phase of the entire adjacent respiratory waveform, calculated according to the following formula (12);

BRV_△T is the total number of △T differences between the inspiratory phase and the expiratory phase of the respiratory waveform divided by the height of the △T difference histogram between the inspiratory phase and the expiratory phase of the respiratory waveform; Analyze the state of emotion regulation ability, including: Calculate the emotional stability regulation ability E4 according to the formula E4＝RMSSD△T*(SD△T+BRV_△T+SDA△T)/RESP; When the subject said E4＜10, his/her ability to regulate emotional stability is extremely poor; when the subject said 10≤E4＜20, his/her ability to regulate emotional stability is relatively poor; when the subject said 20≤E4＜28, his/her ability to regulate emotional stability is average; when the subject said 28≤E4＜40, his/her ability to regulate emotional stability is relatively good; when the subject said E4≥40, his/her ability to regulate emotional stability is extremely good. 8. A method for evaluating emotional stability based on analyzing respiratory waveforms, characterized in that it is applied to a cloud server in the device for evaluating emotional stability based on analyzing respiratory waveforms, and the method comprises: Establish database; Receive an emotion stability assessment data packet and store it in a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotion state, an emotion change direction state, and an emotion regulation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion regulation ability state; Send historical emotional stability assessment data packet. 9. A method for evaluating emotional stability based on analyzing breathing waveforms, characterized in that it is applied to a human-computer interaction module in the device for evaluating emotional stability based on analyzing breathing waveforms, and the method comprises: Receive an emotional stability assessment data packet and a historical emotional stability assessment data packet; the emotional stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional regulation ability state; the historical emotional stability assessment data packet includes a historical emotional state, a historical emotional change direction state, and a historical emotional regulation ability state; Receive a subject's observation data request and display a corresponding emotional stability assessment data packet or a corresponding historical emotional stability assessment data packet. 10. An emotional stability assessment device based on analyzing respiratory waveforms, characterized in that the device comprises: a portable acquisition and analysis device, a cloud server and a human-computer interaction module; the portable acquisition and analysis device comprises a respiratory parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected to the analysis module, and the analysis module is connected to the cloud server; the human-computer interaction module is respectively connected to the analysis module and the cloud server; A breathing parameter acquisition module is used to obtain the breathing parameters of the subject; The analysis module is used to analyze the breathing data of emotions, the breathing data of the direction of emotion changes and the breathing data of emotion regulation ability according to the breathing parameters of the subject; analyze the emotional state according to the breathing data of emotions; analyze the state of the direction of emotion changes according to the breathing data of the direction of emotion changes; analyze the state of emotion regulation ability according to the breathing data of emotion regulation ability; send the emotional stability evaluation data packet to the cloud server; the emotional stability evaluation data packet includes: emotional state, emotional change direction state and emotional regulation ability state; send the emotional stability evaluation data packet to the human-computer interaction module; The cloud server is used to establish a database; receive an emotion stability assessment data packet and store it in the database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotion state, an emotion change direction state, and an emotion regulation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion regulation ability state; send a historical emotion stability assessment data packet; The human-computer interaction module is used to receive an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: emotion state, emotion change direction state and emotion regulation ability state; the historical emotion stability assessment data packet includes historical emotion state, historical emotion change direction state and historical emotion regulation ability state; receive a subject's observation data request, and display the corresponding emotion stability assessment data packet or the corresponding historical emotion stability assessment data packet.

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