CN116115224A - Emotional stability assessment method and device based on analysis of respiratory waveform - Google Patents
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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
Technical Field
The application relates to the technical field of intelligent wearable equipment, for example, to a method and a device for evaluating emotion stability based on analysis of respiratory waveforms.
Background
Emotional stability refers to the condition in which a person's emotional state fluctuates with changes in external or internal conditions. The Eyeson gram emotion stability test is the most used scale evaluation method for emotion stability test, and because the emotion stability is different from person to person, people with more stable emotion can cause slower emotion response, and people with unstable emotion can easily cause emotion response when encountering an emotion event, and life trivial events can also cause strong emotion change. Therefore, when an individual with unstable emotion is encountered, the occurrence of the emotion event is quick and changeable, and the quantitative emotion stability reference value is not available except that the evaluation method has strong subjectivity, complex operation and long time, and the testee is difficult to evaluate the emotion condition by himself.
In the prior art, although some instruments analyze the emotional stability state of a tested person through a certain data acquisition, the measuring method and the measuring instruments can only be used in hospitals due to the fact that the instruments are expensive and the test is complex.
Therefore, there is a lack of a method and apparatus for allowing a subject to easily understand his own emotional state.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
The embodiment of the disclosure provides a method and a device for evaluating emotional stability based on analysis of respiratory waveforms, which can enable a tested person to easily master own emotional stability state in daily life.
In a first aspect, the present application provides a method for evaluating emotional stability based on analysis of respiratory waveforms, which is applied to a portable collection and analysis device in the apparatus for evaluating emotional stability based on analysis of respiratory waveforms, and the method includes:
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;
analyzing the emotion state according to the emotion breathing data;
analyzing the state of the emotion change direction according to the breathing data of the emotion change direction;
analyzing the emotion regulating capacity state according to the breathing data of the emotion regulating capacity;
transmitting an emotion stability assessment data packet to a cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional accommodation ability state;
and sending the emotion stability assessment data packet to a human-computer interaction module.
In a second aspect, the present application provides a method for evaluating emotional stability based on an analysis respiratory waveform, which is applied to a cloud server in the device for evaluating emotional stability based on an analysis respiratory waveform, and the method includes:
establishing a database;
receiving an emotion stability assessment data packet, and storing the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state;
and sending the historical emotion stability assessment data packet.
In a third aspect, the present application provides a method for evaluating emotional stability based on analysis of respiratory waveforms, which is applied to a human-computer interaction module in the device for evaluating emotional stability based on analysis of respiratory waveforms, and the method includes:
receiving an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state;
and receiving an observation data request of a tested person, and displaying a corresponding emotion stability assessment data packet or a corresponding historical emotion stability assessment data packet.
In a fourth aspect, the present application provides an emotional stability assessment device based on analysis of respiratory waveforms, the device comprising: the system comprises portable acquisition and analysis equipment, a cloud server and a man-machine interaction module; the portable acquisition and analysis equipment comprises a breathing parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected with the analysis module, and the analysis module is connected with the cloud server; the man-machine interaction module is respectively connected with the analysis module and the cloud server;
the respiratory parameter acquisition module is used for acquiring respiratory parameters of the tested person;
the analysis module is used for analyzing the breath data of emotion, the breath data of emotion change direction and the breath data of emotion adjustment capacity according to the breath parameters of the tested person; analyzing the emotion state according to the emotion breathing data; analyzing the state of the emotion change direction according to the breathing data of the emotion change direction; analyzing the emotion regulating capacity state according to the breathing data of the emotion regulating capacity; transmitting an emotion stability assessment data packet to a cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional accommodation ability state; transmitting an emotion stability evaluation data packet to a human-computer interaction module;
the cloud server is used for establishing a database; receiving an emotion stability assessment data packet, and storing the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; transmitting a historical emotion stability assessment data packet;
the man-machine interaction module is used for receiving the emotion stability assessment data packet and the historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; and receiving an observation data request of a tested person, and displaying a corresponding emotion stability assessment data packet or a corresponding historical emotion stability assessment data packet.
The emotion stability evaluation method and device based on analysis of respiratory waveforms provided by the embodiment of the disclosure can realize the following technical effects:
in the embodiment of the disclosure, the breathing parameters of a tested person are obtained through a portable acquisition and analysis device, and an emotion state, an emotion change direction state and an emotion adjustment capacity state are analyzed; then, storing the data in a database of a cloud server; and finally, the man-machine interaction module displays the corresponding emotion state, emotion change direction state and emotion adjustment capacity state according to the observed data request of the tested person. The embodiment of the disclosure can enable the testee to easily grasp the emotion stability state of the testee in daily life.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:
fig. 1 is a schematic diagram of an emotion stability assessment device provided by an embodiment of the present disclosure based on analysis of respiratory waveforms;
FIG. 2 is an interaction diagram of a method for assessing emotional stability based on analysis of respiratory waveforms provided by embodiments of the present disclosure;
FIG. 3 is a flowchart of an emotion stability assessment method based on analysis of respiratory waveforms for use in a portable acquisition analysis device, provided in an embodiment of the present disclosure;
FIG. 4 is a schematic illustration of a respiratory waveform definition provided by an embodiment of the present disclosure;
fig. 5 is a flowchart of an emotion stability evaluation method based on analysis of respiratory waveforms applied to a cloud server according to an embodiment of the present disclosure;
fig. 6 is a flowchart of an emotion stability evaluation method based on analysis of respiratory waveforms applied to a human-computer interaction module according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. The scope of embodiments of the invention encompasses the full ambit of the claims, as well as all available equivalents of the claims. Embodiments may be referred to herein, individually or collectively, by the term "invention" merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. Various embodiments are described herein in a progressive manner, each embodiment focusing on differences from other embodiments, and identical and similar parts between the various embodiments are sufficient to be seen with each other. The method, product and the like disclosed in the examples are relatively simple to describe because they correspond to the method parts disclosed in the examples, and the relevant points are only referred to the description of the method parts.
In addition, the terms "disposed," "connected," "secured" and "affixed" are to be construed broadly. For example, "connected" may be in a fixed connection, a removable connection, or a unitary construction; may be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements, or components. The specific meaning of the above terms in the embodiments of the present disclosure may be understood by those of ordinary skill in the art according to specific circumstances.
Emotional stability refers to the condition in which a person's emotional state fluctuates with changes in external or internal conditions. In the prior art, although some instruments analyze the emotional stability state of a tested person through a certain data acquisition, the measuring method and the measuring instruments can only be used in hospitals due to the fact that the instruments are expensive and the test is complex.
Therefore, there is a lack of a method and apparatus for allowing a subject to easily understand his own emotional state.
As shown in fig. 1, the present application provides an emotion stability assessment device based on analysis of respiratory waveforms, the device comprising: the system comprises portable acquisition and analysis equipment, a cloud server and a man-machine interaction module; the portable acquisition and analysis equipment comprises a breathing parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected with the analysis module, and the analysis module is connected with the cloud server; the man-machine interaction module is respectively connected with the analysis module and the cloud server;
the respiratory parameter acquisition module is used for acquiring respiratory parameters of the tested person;
the analysis module is used for analyzing the breath data of emotion, the breath data of emotion change direction and the breath data of emotion adjustment capacity according to the breath parameters of the tested person; analyzing the emotion state according to the emotion breathing data; analyzing the state of the emotion change direction according to the breathing data of the emotion change direction; analyzing the emotion regulating capacity state according to the breathing data of the emotion regulating capacity; transmitting an emotion stability assessment data packet to a cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional accommodation ability state; transmitting an emotion stability evaluation data packet to a human-computer interaction module; the cloud server is used for establishing a database; receiving an emotion stability assessment data packet, and storing the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; transmitting a historical emotion stability assessment data packet; the man-machine interaction module is used for receiving the emotion stability assessment data packet and the historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; and receiving an observation data request of a tested person, and displaying a corresponding emotion stability assessment data packet or a corresponding historical emotion stability assessment data packet.
It should be appreciated that the emotional stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation state. The emotion state, the emotion change direction state and the emotion adjustment capability state are changed into a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capability state after being stored in the database.
It should be noted that, in the embodiments of the present disclosure, the emotional stability assessment is mainly derived from three parameters, namely, an emotional state, an emotional change direction state, and an emotional adjustment ability state. The three parameters are integrated together to obtain an emotional stability assessment.
In practical application, the connection mode between the man-machine interaction module and the connection analysis module can be Bluetooth wireless connection. The connection mode of the man-machine interaction module and the analysis module can be in Bluetooth wireless connection. It is understood that the cloud server is connected with the portable acquisition and analysis equipment and the human-computer interaction module through a network respectively. The man-machine interaction module can be a digital control touch screen and has input and output functions. The tested person can input the tested person observation data request, and the numerical control touch screen further outputs the corresponding data packet.
For example, the testee inputs a command through the man-machine interaction module in order to observe the emotional stability state of the last month. The man-machine interaction module displays the historical emotion state, the historical emotion change direction state and the historical emotion adjustment capacity state of the tested person in the last month through the cloud server. The subject knows his own emotional stability status by observing this month's emotional stability assessment data packet.
Further, the apparatus also includes a printing module; the printing module is connected with the man-machine interaction module.
The print module may be a printer. And printing out the content of the data packet according to the data packet of the observation data request of the tested person, and displaying the content of the data packet intuitively.
Referring to fig. 2, an interaction diagram of a method for assessing emotional stability based on analysis of respiratory waveforms is shown.
Referring to fig. 3, an embodiment of the disclosure provides a method for evaluating emotional stability based on analysis of respiratory waveforms, which is applied to a portable collection and analysis device in the apparatus for evaluating emotional stability based on analysis of respiratory waveforms, and includes:
s210, the portable acquisition and analysis equipment acquires the breathing parameters of the tested person.
S220, the portable collection and analysis equipment analyzes the breath data of emotion, the breath data of emotion change direction and the breath data of emotion adjustment capacity according to the breath parameters of the tested person.
S230, the portable collection and analysis equipment analyzes the emotion state according to the emotion breathing data.
S240, the portable collection and analysis equipment analyzes the emotion change direction state according to the breathing data of the emotion change direction.
S250, the portable collection and analysis equipment analyzes the emotion regulating capacity state according to the breathing data of the emotion regulating capacity.
S260, the portable collection and analysis device sends an emotion stability assessment data packet to the cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional accommodation state.
S270, the portable collection and analysis device sends an emotion stability assessment data packet to the human-computer interaction module.
Further, the respiratory parameters of the tested person comprise respiratory period TT and respiratory period average value
Respiration rate RESP, ith 5min respiration cycle TT mean>
N->
Mean>
Inhalation phase T0, inhalation phase mean +.>
Ith 5min suction gas TT0 mean +.>
N number of
Mean>
Expiratory phase T1, respiratory expiratory phase mean +.>
The ith 5-minute respiratory phase TT1 mean +.>
N->
Mean>
Difference DeltaT between the inspiration phase and the expiration phase, mean value of the difference DeltaT between the inspiration phase and the expiration phase +.>
Ith 5min DeltaT mean +.>
N->
Mean>
Referring to fig. 4, a schematic diagram of a respiratory waveform definition is shown.
It should be appreciated that the breathing cycle TT, i.e. the time from the start of one inhalation to the start of the next inhalation; a gettering phase T0, i.e. a gettering time; the breathing phase is the expiration time.
Further, analyzing a triangular index BRV_TI, a standard deviation SDTT, SDATT, RMSSDTT among all respiratory cycles and a respiratory rate RESP;
SDTT is the standard deviation between all respiratory cycles, calculated according to the following formula (1),
SDATT is to divide all recorded respiratory cycles into a plurality of time periods continuously every 5 minutes according to the recorded time sequence, calculate the average value of the respiratory cycles in every 5 minutes, calculate the standard deviation of the average values, calculate according to the following formula (2),
wherein N is 5min in the evaluation time,
for the ith 5-minute breathing period TT mean, < >>
For N->
Is the average value of (2);
RMSSDTT is the root mean square value of the difference between the adjacent respiratory cycles throughout, calculated as shown in the following equation (3),
brv_ti is the total number of breathing cycle breathing phases T1 divided by the height of the breathing cycle breathing phase T1 histogram;
analyzing the emotional state, comprising: the mood stability constant E1 is calculated according to the formula e1=0.01×brv_ti (sdtt+sdatt+rmssdtt)/RESP.
It should be understood that the discrete type of the whole respiratory cycle is a key quantity and premise of emotional stability, so that the established data model of the respiratory full waveform can objectively and accurately reflect the class condition E1 of the emotional stability of the subject according to the variability modeling of the respiratory full waveform of the subject, and the data model integrates the relevant parameters of the respiratory cycle such as BRV_ TI, SDTT, SDATT, RMSSDTT, RESP and the like, and can more objectively and accurately reflect the emotional stability of the subject.
E1 is a basic parameter for evaluating the emotion stability based on the whole respiratory cycle, and when the testee E1E [1,15] is in a state of physical and psychological balance, the emotion of the testee is very stable; the emotion of the testee is in a self-regulating state when E1 epsilon (15, 23), the emotion of the testee is in a tension state when E1 epsilon (23,35), the emotion of the testee is in a depression state when E1 epsilon (35,67), the emotion of the testee is in an out-of-control overstress attack state when E1 is more than 67, and the emotion needs to be relieved in time when the emotion is in the self-regulating state.
Further, according to the respiration data of the emotion change direction, analyzing the emotion change direction state includes:
under the condition that E1 is more than 35, according to the breathing data of the emotion change direction, analyzing out an emotion negative trend state;
and under the condition that E1 is less than or equal to 35, analyzing the state of the forward emotion trend according to the respiratory data of the forward emotion trend.
It should be appreciated that E1 is 35, representing the demarcation point for the passive and active states of the subject. In the case of E1 > 35, the emotional state of the subject is biased negative. When E1 is less than or equal to 35, the emotion state of the tested person is deflected to the front.
Further, in the case of E1 > 35, analyzing the respiration data in the emotion change direction, including:
resolving SDTT0, SDATT0, RMSSDTT0 and BRV_TI0;
SDTT0 is the standard deviation of the total respiratory inhalation phase T0 and is calculated according to the following formula (4);
wherein ,
to evaluate the respiratory gas-phase average value in the whole process;
SDATT0 is that all recorded respiratory cycles are inhaled into the gas phase T0, and a time period is set every 5 minutes according to the recorded time sequence; calculating an average value of the inspiratory phase T0 every 5 minutes period; calculating the standard deviation of the average value according to the following formula (5);
wherein N is 5min in the evaluation time,
for the ith 5min suction gas phase TT0 mean, < > x->
N is number of
Is the average value of (2);
RMSSDTT0 is the root mean square value of the difference between the inhalation phases T0 of all the adjacent respiratory cycles, calculated by the following formula (6);
BRVTI0 is the total number of respiratory cycle inspiratory phases T0 divided by the height of the respiratory cycle inspiratory phase T0 histogram;
resolving a negative emotion trend state, comprising:
the negative factor E2 of emotional stability is calculated according to the formula e2=0.1×rmssdtt0×sdtt0/sdatt0+brv_t0.
It will be appreciated that the negative trend of emotion is closely related to the inspiratory phase T0, and that the trend of continued deterioration of emotion is greatly reduced when the T0 variability index is small.
Judging a negative emotion development trend according to the negative emotion stability factor E2, namely when E2 of the tested person is smaller than 90, the emotion stability trend of the tested person develops towards a better direction; when E2 is more than or equal to 90, the emotion stability trend of the tested person is developed towards the negative direction.
Further, under the condition that E1 is less than or equal to 35, analyzing the respiration data in the emotion change direction, wherein the method comprises the following steps:
resolving RMSSDTT1, SDTT1, SDATT1 and BRV_Tt1;
the expiratory phase of the breathing cycle TT is T1, then SDTT1 is the standard deviation of the total breathing expiratory phase T1, calculated according to the following formula (7),
SDATT1 is obtained by dividing all recorded respiratory cycle respiratory phase T1 into a plurality of time periods continuously every 5 minutes according to the recorded time sequence, calculating average value of respiratory cycle T1 every 5 minutes, calculating standard deviation of the plurality of average values, calculating according to the following formula (8),
wherein N is 5min in the evaluation time,
for the ith 5min respiratory phase TT1 mean, -/->
N is number of
Is the average value of (2);
RMSSDTT1 is the root mean square value of the difference between the respiratory phases T1 of all adjacent respiratory cycles, calculated by the following formula (9),
brv_ti is the total number of breathing cycle breathing phases T1 divided by the height of the breathing cycle breathing phase T1 histogram;
analyzing the emotion forward trend state, including:
the emotional stability positive factor E3 is calculated according to the formula e3= (0.1×rmssdtt1×sdtt1/SDATT1×brv_t1)/RESP.
It should be appreciated that the forward trend of emotion is closely related to the expiratory phase T1, and that when the T1 variability index is smaller, the forward trend of emotion is weaker.
Judging the forward development trend of the emotion according to the positive factor E3 of the emotion stability, namely when E3 of the tested person is less than 30, the forward trend of the emotion stability of the tested person is weaker; when E3 is more than or equal to 30 and less than 85, the emotion trend of the tested person is in a positive and negative trend sawing state. When E3 is more than or equal to 85, the emotional stability of the tested person is developed towards the positive direction.
Further, resolving respiratory data for mood adjustment capabilities, comprising: resolving the RMSSD delta T, SD delta T, BRV delta T, SDA delta T and RESP;
defining the difference DeltaT between the inspiratory and expiratory phases of the respiratory cycle (TT) then
SD DeltaT is the standard deviation of the difference DeltaT between the respiratory phase and the respiratory phase of the whole respiratory waveform, calculated by the following formula (10),
wherein ,
to evaluate the mean value of the difference between the respiratory waveform inspiratory phase and the respiratory phase in the whole course;
SDA delta T, the difference delta T between the respiratory waveform inhalation phase and the respiratory phase recorded in the whole is a time period every 5 minutes according to the recorded time sequence; calculating an average value of the inspiratory phase Δt every 5 minutes period; calculating the standard deviation of the average value according to the following formula (11);
wherein N is the number of 5min contained in the evaluation time;
is the ith 5-minute DeltaT mean;
For N->
Is the average value of (2);
RMSSD Δt is a root mean square value of a difference Δt between the respiratory waveform inspiratory phase and the respiratory phase adjacent to each other throughout the course, calculated by the following formula (12);
BRV_ DeltaT is the total number of differences DeltaT between respiratory waveform inspiratory phase and respiratory phase divided by the height of the DeltaT histogram of the differences DeltaT between respiratory waveform inspiratory phase and respiratory phase;
resolving an emotion-regulating ability state, comprising:
calculating emotion stability regulation ability E4 according to the formula
E4＝RMSSD△T*(SD△T+BRV_△T+SDA△T)/RESP。
It should be appreciated that the emotion of a person may be adjusted by reversing the inspiration and expiration to achieve a greater ability to adjust emotion when the difference between the inspiratory phase T0 and the expiratory phase T1 is greater.
When E4 is less than 10, the emotion stability adjustment ability of the testee is extremely poor; when E4 is less than or equal to 10 and less than 20, the emotion stability adjusting capability of the testee is poor; when E4 is less than or equal to 20 and less than 28, the emotion stability adjusting capability of the testee is general; when E4 is less than or equal to 28 and less than 40, the emotion stability adjusting capability of the testee is better; when E4 is more than or equal to 40, the emotion stability adjusting capability of the testee is excellent.
Referring to fig. 5, a method for evaluating emotional stability based on analysis of respiratory waveforms is applied to a cloud server in the device for evaluating emotional stability based on analysis of respiratory waveforms, and the method includes:
s310, the cloud server establishes a database.
S320, the cloud server receives the emotion stability assessment data packet and stores the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion adjustment capability state.
S330, the cloud server sends a historical emotion stability assessment data packet.
Referring to fig. 6, a method for evaluating emotional stability based on analysis of respiratory waveforms is applied to a human-computer interaction module in the device for evaluating emotional stability based on analysis of respiratory waveforms, and the method includes:
s410, a man-machine interaction module receives an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet includes a historical emotion state, a historical emotion change direction state, and a historical emotion adjustment capability state.
S420, the man-machine interaction module receives the observed data request of the testee and displays the corresponding emotion stability assessment data packet or the corresponding historical emotion stability assessment data packet.
One specific embodiment may be to acquire the breathing parameters of the subject using a portable acquisition and analysis device. And analyzing the breath data of emotion, the breath data of emotion change direction and the breath data of emotion regulating capacity according to the breath parameters. SDTT, BRV_ TI, SDATT, RMSSDTT, SDTT0, BRV_T0, SDATT0, RMSSDTT0, SDTT1, BRV_T1, SDATT1, RMSSDTT1, SD delta T, BRV delta T, SDA delta T, RMSSD delta T are calculated, and the specific contents are shown in Table 1.
TABLE 1
| Date of day | Monday | Zhoudi (Zhoudi) | Wednesday | Zhou four | Friday (friday) | Saturday (Saturday) | (Sunday) |
| RESP | 18 | 21 | 24 | 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 | 23 | 27 | 23 | 32 | 30 | 16 | 16 |
| RMSSD△T | 22 | 29 | 23 | 23 | 25 | 20 | 16 |
| BRV_△T | 11 | 11 | 10 | 11 | 11 | 10 | 8 |
| E1 | 30 | 16 | 21 | 26 | 29 | 24 | 26 |
| E2 | Without any means for | Without any means for | Without any means for | Without any means for | Without any means for | Without any means for | Without any means for |
| E3 | 56 | 20 | 34 | 28 | 64 | 37 | 48 |
| E4 | 104 | 131 | 81 | 146 | 124 | 93 | 51 |
And calculating corresponding E1, E2 or E3 and E4 according to the table 1, judging the state of the tested person, and further obtaining the emotion state, the emotion change direction state and the emotion regulating capacity state. In Table 1, E1 is less than or equal to 35, so the calculated emotional change direction states are all emotional forward trend states, namely E3.
The portable collection and analysis device sends the emotion stability assessment data packet to the cloud server, and sends the emotion stability assessment data packet to the human-computer interaction module.
The cloud server stores the emotion stability assessment data packet in a database. And the man-machine interaction module acquires the request of the testee for watching the data of one week and displays the emotion state, emotion change direction state and emotion adjustment capacity state of the testee for one week.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in this application, the terms "comprises," "comprising," and/or "includes," and variations thereof, mean that the stated features, integers, steps, operations, elements, and/or components are present, but that the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, the apparatus and the units described above may refer to corresponding procedures in the foregoing method embodiments, which are not described in detail herein.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based apparatus which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for evaluating emotional stability based on analysis of respiratory waveforms, which is applied to a portable collection and analysis device in an apparatus for evaluating emotional stability based on analysis of respiratory waveforms, the method comprising:
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;
analyzing the emotion state according to the emotion breathing data;
analyzing the state of the emotion change direction according to the breathing data of the emotion change direction;
analyzing the emotion regulating capacity state according to the breathing data of the emotion regulating capacity;
transmitting an emotion stability assessment data packet to a cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional change direction state;
and sending the emotion stability assessment data packet to a human-computer interaction module.
2. The method of claim 1, wherein the respiratory parameter of the subject comprises respiratory cycle TT, respiratory cycle average
Respiration rate RESP, ith 5min respiration cycle TT mean>
N->
Mean>
Inhalation phase T0, inhalation phase mean +.>
Ith 5 th minuteZhong Xi gas phase TT0 mean->
N->
Mean>
Expiratory phase T1, respiratory expiratory phase mean +.>
The ith 5-minute respiratory phase TT1 mean +.>
N->
Mean>
Difference DeltaT between the inspiration phase and the expiration phase, mean value of the difference DeltaT between the inspiration phase and the expiration phase +.>
Ith 5min DeltaT mean +.>
N->
Mean>
3. The method of claim 2, wherein parsing the emotional respiratory data comprises:
analyzing a triangular index BRV_TI and a standard deviation SDTT, SDATT, RMSSDTT among all respiratory periods;
SDTT is the standard deviation between all respiratory cycles, calculated according to the following formula (1),
SDATT is to divide all recorded respiratory cycles into a plurality of time periods continuously every 5 minutes according to the recorded time sequence, calculate the average value of the respiratory cycles in every 5 minutes, calculate the standard deviation of the average values, calculate according to the following formula (2),
wherein N is 5min in the evaluation time,
for the ith 5-minute breathing period TT mean, < >>
For N->
Is the average value of (2);
RMSSDTT is the root mean square value of the difference between the adjacent respiratory cycles throughout, calculated as shown in the following equation (3),
brv_ti is the total number of breathing cycle breathing phases T1 divided by the height of the breathing cycle breathing phase T1 histogram;
analyzing the emotional state, comprising: calculating an emotional stability constant E1 according to the formula e1=0.01×brv_ti (sdtt+sdatt+rmssdtt)/RESP;
when the testee E1 epsilon [1,15], the emotion of the testee is in a state of physical and psychological balance; and when E1E (15, 23), the emotion of the evaluator is in a self-regulating state, when E1E (23,35), the emotion of the testee is in a tension state, when E1E (35,67), the emotion of the testee is in a depression state, and when E1 is more than 67, the emotion of the testee is in an out-of-control overstress attack state.
4. A method according to claim 3, wherein resolving the mood change direction state from the mood change direction respiration data comprises:
under the condition that E1 is more than 35, according to the breathing data of the emotion change direction, analyzing out an emotion negative trend state;
and under the condition that E1 is less than or equal to 35, analyzing the state of the forward emotion trend according to the respiratory data of the forward emotion trend.
5. The method of claim 4, wherein parsing out breath data for a direction of mood changes comprises:
resolving SDTT0, SDATT0, RMSSDTT0 and BRV_TI0;
SDTT0 is the standard deviation of the total respiratory inhalation phase T0 and is calculated according to the following formula (4);
wherein ,
to evaluate the respiratory gas-phase average value in the whole process;
SDATT0 is that all recorded respiratory cycles are inhaled into the gas phase T0, and a time period is set every 5 minutes according to the recorded time sequence; calculating an average value of the inspiratory phase T0 every 5 minutes period; calculating the standard deviation of the average value according to the following formula (5);
wherein N is 5min in the evaluation time,
for the ith 5min suction gas phase TT0 mean, < > x->
For N->
Is the average value of (2);
RMSSDTT0 is the root mean square value of the difference between the inhalation phases T0 of all the adjacent respiratory cycles, calculated by the following formula (6);
BRVTI0 is the total number of respiratory cycle inspiratory phases T0 divided by the height of the respiratory cycle inspiratory phase T0 histogram;
resolving a negative emotion trend state, comprising:
calculating the negative factor E2 of the emotional stability according to the formula
E2＝0.1*RMSSDTT0*SDTT0/SDATT0+BRV_TI0；
When E2 of the testee is smaller than 90, the emotion stability trend of the testee is developed towards a better direction; when E2 is more than or equal to 90, the emotion stability trend of the tested person is developed towards the negative direction.
6. The method of claim 4, wherein parsing out breath data for a direction of mood changes comprises:
resolving RMSSDTT1, SDTT1, SDATT1 and BRV_Tt1;
the expiratory phase of the respiratory cycle TT is T1, then
SDTT1 is the standard deviation of the total respiratory phase T1, calculated according to equation (7),
SDATT1 is obtained by dividing all recorded respiratory cycle respiratory phase T1 into a plurality of time periods continuously every 5 minutes according to the recorded time sequence, calculating average value of respiratory cycle T1 every 5 minutes, calculating standard deviation of the plurality of average values, calculating according to the following formula (8),
wherein N is 5min in the evaluation time,
for the ith 5min respiratory phase TT1 mean, -/->
For N->
Is the average value of (2);
RMSSDTT1 is the root mean square value of the difference between the respiratory phases T1 of all adjacent respiratory cycles, calculated by the following formula (9),
brv_ti is the total number of breathing cycle breathing phases T1 divided by the height of the breathing cycle breathing phase T1 histogram;
analyzing the emotion forward trend state, including:
calculating emotional stability positive factor E3 according to the formula
E3＝(0.1*RMSSDTT1*SDTT1/SDATT1*BRV_TI1)/RESP；
When E2 of the testee is smaller than 90, the emotion stability trend of the testee is developed towards a better direction; when E2 is more than or equal to 90, the emotion stability trend of the tested person is developed towards the negative direction.
7. The method of claim 2, wherein parsing out breath data for mood adjustment capabilities comprises:
resolving the RMSSD delta T, SD delta T, BRV delta T and SDA delta T;
defining the difference DeltaT between the inspiratory and expiratory phases of the respiratory cycle (TT) then
SD DeltaT is the standard deviation of the difference DeltaT between the respiratory phase and the respiratory phase of the whole respiratory waveform, calculated by the following formula (10),
wherein ,
to evaluate the mean value of the difference between the respiratory waveform inspiratory phase and the respiratory phase in the whole course;
SDA delta T, the difference delta T between the respiratory waveform inhalation phase and the respiratory phase recorded in the whole is a time period every 5 minutes according to the recorded time sequence; calculating an average value of the inspiratory phase Δt every 5 minutes period; calculating the standard deviation of the average value according to the following formula (11);
wherein N is the number of 5min contained in the evaluation time;
is the ith 5-minute DeltaT mean;
For N->
Is the average value of (2);
RMSSD Δt is a root mean square value of a difference Δt between the respiratory waveform inspiratory phase and the respiratory phase adjacent to each other throughout the course, calculated by the following formula (12);
BRV_ DeltaT is the total number of differences DeltaT between respiratory waveform inspiratory phase and respiratory phase divided by the height of the DeltaT histogram of the differences DeltaT between respiratory waveform inspiratory phase and respiratory phase;
resolving an emotion-regulating ability state, comprising:
calculating emotion stability regulation ability E4 according to the formula
E4＝RMSSD△T*(SD△T+BRV_△T+SDA△T)/RESP；
When E4 is less than 10, the emotion stability adjustment ability of the testee is extremely poor; when E4 is less than or equal to 10 and less than 20, the emotion stability adjusting capability of the testee is poor; when E4 is less than or equal to 20 and less than 28, the emotion stability adjusting capability of the testee is general; when E4 is less than or equal to 28 and less than 40, the emotion stability adjusting capability of the testee is better; when E4 is more than or equal to 40, the emotion stability adjusting capability of the testee is excellent.
8. An emotion stability assessment method based on analysis of respiratory waveforms is characterized by being applied to a cloud server in the emotion stability assessment device based on analysis of respiratory waveforms, and the method comprises the following steps:
establishing a database;
receiving an emotion stability assessment data packet, and storing the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state;
and sending the historical emotion stability assessment data packet.
9. An emotion stability assessment method based on analysis of respiratory waveforms is characterized by being applied to a human-computer interaction module in the emotion stability assessment device based on analysis of respiratory waveforms, and the method comprises the following steps:
receiving an emotion stability assessment data packet and a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state;
and receiving an observation data request of a tested person, and displaying a corresponding emotion stability assessment data packet or a corresponding historical emotion stability assessment data packet.
10. An emotional stability assessment device based on analysis of respiratory waveforms, the device comprising: the system comprises portable acquisition and analysis equipment, a cloud server and a man-machine interaction module; the portable acquisition and analysis equipment comprises a breathing parameter acquisition module and an analysis module; the respiratory parameter acquisition module is connected with the analysis module, and the analysis module is connected with the cloud server; the man-machine interaction module is respectively connected with the analysis module and the cloud server;
the respiratory parameter acquisition module is used for acquiring respiratory parameters of the tested person;
the analysis module is used for analyzing the breath data of emotion, the breath data of emotion change direction and the breath data of emotion adjustment capacity according to the breath parameters of the tested person; analyzing the emotion state according to the emotion breathing data; analyzing the state of the emotion change direction according to the breathing data of the emotion change direction; analyzing the emotion regulating capacity state according to the breathing data of the emotion regulating capacity; transmitting an emotion stability assessment data packet to a cloud server; a mood stability assessment data package comprising: an emotional state, an emotional change direction state, and an emotional accommodation ability state; transmitting an emotion stability evaluation data packet to a human-computer interaction module;
the cloud server is used for establishing a database; receiving an emotion stability assessment data packet, and storing the emotion stability assessment data packet into a database as a historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; transmitting a historical emotion stability assessment data packet;
the man-machine interaction module is used for receiving the emotion stability assessment data packet and the historical emotion stability assessment data packet; the emotion stability assessment data packet includes: an emotional state, an emotional change direction state, and an emotional accommodation ability state; the historical emotion stability assessment data packet comprises a historical emotion state, a historical emotion change direction state and a historical emotion adjustment capacity state; and receiving an observation data request of a tested person, and displaying a corresponding emotion stability assessment data packet or a corresponding historical emotion stability assessment data packet.
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