KR102314153B1 - Method and Apparatus for Determining Synchronization of Social Relations based on personality - Google Patents

Abstract

Disclosed are a method and apparatus for judging social relationship synchronization based on personal disposition. The disclosed method includes: detecting heart rate data according to an implicit response and location data according to an explicit response from two subjects, determining personality types of the two subjects through a propensity test, extract the variables of , calculate the distance difference between the multiple variables of the two subjects, respectively, evaluate the synchronization level of the variables of the two subjects by the distance difference between the variables of the two subjects, evaluate whether the subjects' personality types are the same, Then, social motivation is evaluated by the level of synchronization of variables and whether the personality types are the same.

Description

Method and Apparatus for Determining Synchronization of Social Relations based on Personality

The present disclosure relates to a method and apparatus for determining whether a social relationship is synchronized based on an individual's disposition, and more particularly, to a method and apparatus for determining synchronization between an individual's behavioral pattern and physiological state in a specific social environment.

Humans, as social beings, have a desire to constantly form relationships with others and to strengthen social cohesion. From an evolutionary point of view, forming such a social relationship would have been more advantageous for survival to form a social group than to live alone as an individual (Silk, 2007). In addition, language and sensibility have been developed to be advantageous for forming such social relationships. Emotions developed before language, and if language is developed for the purpose of accurate information delivery, emotions are a means of communication that can communicate faster than language (Christakis & Fowler, 2009). By developing the ability to recognize the emotions of others and express their emotions in an appropriate response to them, it is possible to distinguish hostile and friendly relationships and to identify dangerous situations more quickly (Salancik & Pfeffer, 1978). Social neuroscience has discovered the existence of a social brain, which has been reported to regulate our emotions and behaviors in order to adapt to the people we interact with (Winkleman & Harmon-Jones, 2006). These emotions and behaviors are controlled automatically by mirror neurons, and emotional transfer that tries to match one's emotions and behaviors with the interacting others occurs (Goleman, 2006). Such emotional transfer occurs more frequently in close relationships (Kimura, Daibo, & Yogo, 2008). Inferred from this, the closer the relationship between individuals within a group, the more likely emotional transfer and motivation will occur. Therefore, interpersonal synchronization can be an important factor in understanding the characteristics of social groups.

In the existing literature, it has been reported that interpersonal synchronization is strongly correlated with social connectivity and plays an important role in group and member interactions (Zumeta, Oriol, Telletxea, Amutio, & Basabe, 2016; Paez, Rime, Basabe, Wlodarczyk, & Zumeta, 2015; Rennung & Goritz, 2016). Interpersonal synchronization is not limited to behavioral synchronization, but includes neurological, physiological, and emotional synchronization (Mazzurega, Pavani, Paladino, & Schubert, 2011; Smith & Semin, 2007; Hatfield, Rapson, & Le, 2011; Levenson & Ruef). , 1997; Nummenmaa et al., 2012). Synchronization with others in direct interaction situations has been experimentally proven, but studies on synchronization in indirect interaction situations are scarce. Individuals are not only directly influenced by interaction with others, but also indirectly influenced by their social context. For example, it can be influenced by content in a virtual space such as SNS, and can be synchronized with others even though there is no direct interaction (Morris, et al., 2011). In addition, it has been reported that interpersonal synchronization is closely related to social proximity or similarity. Previous studies have shown a tendency to help individuals similar to themselves, and it has been reported that the more similar they are to themselves, the better the synchronization (Fessler & Holbrook, 2014; Lumsden, Miles, & Macrae, 2014; Konvalinka et al., 2011).

In social relationships, individual characteristics are as important as group characteristics. Individual characteristics can be aggregated to indicate group characteristics, and conversely, the context of social relationships can influence individual characteristics. There are many things that can be considered in defining an individual's characteristics, mainly defined by his or her personality. According to Jung's theory of psychological types, personality arises from differences in the way people prefer to perceive and judge (Jung, 2016). Also, this difference in perception and judgment method appears as a difference in behavior. Recognition is the process of recognizing objects such as objects or people, and recognition methods can be divided into two main categories. Sensing is a method of perceiving objects through the five senses, and intuition is a method of indirectly perceiving objects through the unconscious. Judgment is about the method of making a decision about what is perceived through recognition, and it is divided into a thinking type that approaches logically based on objective findings and a feeling type that evaluates based on personal and subjective values. Another difference in perception and judgment is that they are divided into introversion and extraversion depending on whether they are interested in the inner world or the outer world. Introverts focus more on insight into their inner self and pay attention to perceptions and judgments about concepts or ideas. On the other hand, extroverts are more inclined to communicate with the outside world and focus their perceptions and judgments on the outside world. Katharine Cook Briggs developed a personality diagnostic test called the Myers-Briggs Type Indicator (MBTI) by establishing and systematizing Jung's theory that there are differences in introversion and extroversion, and in favored mental processes with respect to judgment and cognition. (Myers, McCaulley, Quenk, & Hammer, 1998). Also, it is divided into Perceiving and Judging according to which of the two is more preferred in recognition and judgment itself. Perception-judgment preference is about attitude toward the external world, and it is divided into Perceiving and Judging according to which of the two is preferred over recognition and judgment itself. The judging type prefers to draw a conclusion or case is resolved, while the perceiving type prefers to consider as many factors as possible, and wants to keep plans and opinions as open as possible until a conclusion is reached (Myer & Myer, 2010). In social relations, there is a close relationship between the characteristics of individuals and groups constituting a group, and both individual and group characteristics must be considered when defining social relations.

Christakis, N. A., and Fowler, J. H., Connected: The surprising power of our social networks and how they shape our lives Little, Brown, 2009. Fessler, D. M., and Holbrook, C., Marching into battle: Synchronized walking diminishes the conceptualized formidability of an antagonist in men. Biology Letters, 10(8), 2014, 10.1098/rsbl.2014.0592. Goleman, D., Emotional intelligence Bantam, 2006. Hatfield, E., Rapson, R. L., and Le, Y. L., Emotional contagion and empathy. The Social Neuroscience of Empathy, 19, 2011. Jung, C., Psychological types Taylor and Francis, 2016. Kimura, M., Daibo, I., and Yogo, M., The study of emotional contagion from the perspective of interpersonal relationships. Social Behavior and Personality: An International Journal, 36(1), 27-42, 2008. Konvalinka, I., Xygalatas, D., Bulbulia, J., Schjodt, U., Jegindo, E. M., Wallot, S., et al., Synchronized arousal between performers and related spectators in a fire-walking ritual. Proceedings of the National Academy of Sciences of the United States of America, 108(20), 8514-8519, 2011. Levenson, R. W., and Ruef, A. M., Physiological aspects of emotional knowledge and rapport. Lumsden, J., Miles, L. K., and Macrae, C. N. (2014). Sync or sink? interpersonal synchrony impacts self-esteem. Frontiers in Psychology, 5, 1064, 1997. Mazzurega, M., Pavani, F., Paladino, M. P., and Schubert, T. W., Self-other bodily merging in the context of synchronous but arbitrary-related multisensory inputs. Experimental Brain Research, 213(2-3), 213-221, 2011. Morris, M. E., Consolvo, S., Munson, S., Patrick, K., Tsai, J., and Kramer, A. D., Facebook for health: Opportunities and challenges for driving behavior change. CHI'11 Extended Abstracts on Human Factors in Computing Systems, pp. 443-446, 2011. Myers, I. B., McCaulley, M. H., Quenk, N. L., and Hammer, A. L., MBTI manual: A guide to the development and use of the myers-briggs type indicator Consulting Psychologists Press Palo Alto, CA, 1998. Myers, I., and Myers, P. (2010). Gifts differing: Understanding personality type Nicholas Brealey Publishing. Nummenmaa, L., Glerean, E., Viinikainen, M., Jaaskelainen, I. P., Hari, R., and Sams, M., Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9599-9604, 2012. Paez, D., Rime, B., Basabe, N., Wlodarczyk, A., and Zumeta, L., Psychosocial effects of perceived emotional synchrony in collective gatherings. Journal of Personality and Social Psychology, 108(5), 711, 2015. Rennung, M., and Goritz, A. S., Prosocial consequences of interpersonal synchrony: A meta-analysis. Zeitschrift Fur Psychologie, 224(3), 168, 2016. Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., et al., Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), e175, 2015. Salancik, G. R., and Pfeffer, J., A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 224-253, 1978. Silk, J. B., Social components of fitness in primate groups. Science (New York, N.Y.), 317 (5843), 1347-1351, 2007. Smith, E. R., and Semin, G. R., Situated social cognition. Current Directions in Psychological Science, 16(3), 132-135, 2007. Winkleman, P., and Harmon-Jones, E., Social neuroscience. New York: Oxford University, 2006. Zumeta, L. N., Oriol, X., Telletxea, S., Amutio, A., and Basabe, N., Collective efficacy in sports and physical activities: Perceived emotional synchrony and shared flow. Frontiers in Psychology, 6, 2016.

An exemplary embodiment is a method that can quantitatively measure individual characteristics and group characteristics by analyzing how individual personality types are related to inter-individual synchronization in order to identify the relationship between group characteristics and individual characteristics within a group and apparatus.

Method for determining synchronization of social relationships according to an exemplary embodiment:

detecting heart rate data according to the implicit response and position data according to the explicit response from at least two subjects;

determining the personality types of the two subjects through a propensity test;

extracting a plurality of parameters from the heartbeat data and location data of the two subjects;

calculating a distance difference between a plurality of variables of the two subjects, respectively;

evaluating the synchronization level of the variables of the two subjects based on the difference in distance between the variables of the two subjects;

assessing whether the subject's personality type is the same; and

and evaluating social motivation based on whether the level of motivation of the variable and the personality type are the same.

According to an exemplary embodiment, the plurality of variables obtained from the heartbeat information: may include a time domain variable and a frequency domain variable.

According to an exemplary embodiment, the parameters of the time domain: BPM (beat per minute), SDNN (Standard deviation of normal to normal), rMSSD (root Mean Square Successive Difference), pNN50 (Percentage of RR intervals that differ more than 50ms).

According to an exemplary embodiment, the frequency domain variable may include the power of a very low frequency (VLF), a low frequency (LF), and a high frequency (HF).

According to an exemplary embodiment, the frequency domain variable may include a dominant frequency and a power of the dominant frequency.

According to an exemplary embodiment, the plurality of variables obtained from the location information are: location variance of the two subjects, entropy obtained from the ratio of time spent in a specific place during a specific period, and total transition time. It may include at least one of a total distance.

According to an exemplary embodiment, the entropy can be calculated by the following equation.

<expression>

In the above equation, P(x) is the probability that event x will occur.

According to an exemplary embodiment, a normal distribution is formed through the average and standard deviation of input data between users of each information, and when the integral value to the right extreme of the normal distribution is equal to or greater than the threshold value, synchronization can be determined.

According to an exemplary embodiment, the method may further include detecting spatial information of a location to which the two subjects belong by using a camera.

According to an exemplary embodiment, the spatial information may include at least one of spatial complexity, color, saturation, brightness, contrast, noise intensity, and frequency component increase of noise.

According to an exemplary embodiment, the spatial complexity may be determined by the sum of the number of vertical and horizontal components in the image obtained from the camera.

According to an exemplary embodiment, the personal disposition may be evaluated by personality type based on introversion-extroversion, intuition-sensing, emotion-thinking, and perception-judgment.

A synchronization evaluation apparatus for performing the method according to an exemplary embodiment includes: a heart rate sensor detecting the heart rate data, a processing unit processing the position data; and a personal terminal connected to the processing device by a network and including a heartbeat sensor and a GPS receiver for detecting the heartbeat data and the location data.

According to an exemplary embodiment, the heart rate sensor may be provided by a wearable device worn on the subject.

According to an exemplary embodiment, the wearable device may transmit heart rate data to the personal terminal through wireless communication with the personal terminal.

According to an exemplary embodiment, the personal terminal may have a built-in camera for capturing an image.

1 shows a schematic configuration of an apparatus for determining synchronization according to an exemplary embodiment.
2 illustrates data extracted from a subject according to an exemplary embodiment.
3 is a graph showing the generation of a rule base for determining data similarity according to an exemplary embodiment.
4 shows the generation of a rule base for multiple types of data according to an exemplary embodiment.
5 illustrates the operation of a synchronization recognition unit that determines whether data synchronization between two subjects and a social connectivity evaluation unit according to the identity of personality types according to individual tendencies, according to an exemplary embodiment.
6 is a two-dimensional graph showing the relationship between a threshold value and entropy according to an exemplary embodiment.
7 shows the group status by social emotion obtained using experimental data according to an exemplary embodiment.

Hereinafter, a method and apparatus for determining synchronization of a social relationship based on a nervous system individual tendency according to an exemplary embodiment will be described in detail with reference to the accompanying drawings.

The present invention evaluates the synchronization of vital signs and spatial behavioral patterns in physical real space or virtual space based on cardiovascular response in a specific environment based on personal disposition or personality. The spatial behavior pattern includes a behavior pattern in a real space based on GPS and a content use pattern in the Internet, ie, a behavior pattern in a virtual space.

In the exemplary embodiment, as shown in FIG. 1 , a GPS receiver (sensor) and a built-in smart phone and a portable terminal were used, and as a pulse wave sensor, a wearable device worn on a body part such as a wrist was used. The wearable device is paired with the terminal by a communication method such as Bluetooth. In order to integrate and analyze the individual data collected through the terminal, a processing device existing on the network (WAN/LAN) was used. The processing device has a tool or software for analysis and a hardware system on which it is executed. The processing device may be a computer-based device, a general-purpose computer including software containing an algorithm, and hardware capable of running the software, or a dedicated device.

The processing result from the processing device as described above may be displayed by the display device, and may further include a general external interface device, for example, a keyboard, a mouse, etc. as input means.

<Experiment method>

Through an experiment according to the present invention, it was attempted to determine how differences in individual personality characteristics are related to inter-individual synchronization. Interpersonal synchronization was subdivided into not only emotional coincidence but also cardiovascular response coincidence, behavioral pattern coincidence, spatial information coincidence, and web use pattern coincidence to consider the types of synchronization. In addition, we tried to consider synchronization in a spatially separated state, that is, remote synchronization, rather than directly facing and interacting with each other. To this end, a field study was conducted, and as shown in FIG. 2 , the user's pulse wave-based heart information (heart data), location-based behavior pattern information (location data), camera and microphone-based surrounding environment information (spatial information), and web Based content usage pattern information (content data) was collected.

For location-based behavior patterns, behavior patterns that vary depending on location, such as activity radius, activity amount, and regularity of life, were considered based on previous studies that life patterns are related to depression (Saeb et al., 2015). In a laboratory-based environment in which face-to-face interaction occurs, behavior patterns can be observed by whether or not they imitate each other's behavior. As an alternative to quantitatively compare behavioral patterns, location-based behavioral patterns were observed.

<Subjects and Experimental Process>

79 undergraduate and graduate students without cardiovascular disease were recruited as experimental participants (subjects). The average age of the participants was 23.25 years (±3.18 years), 35 males and 44 females. The experiment procedure and information to be collected were explained in detail to all participants, and the experiment was conducted after obtaining consent from the participants who read the description. The cost of participation in the experiment was paid after the experiment was completed in consideration of the number of days and time of the experiment. This experiment was approved by the Bioethics Review Committee of Sangmyung University.

The experiment was conducted for about 5 hours from 1:00 PM to 6:00 PM every day for a total of 15 days. The experiment participants wore a wrist-type wearable device for measuring pulse wave (PPG) and always carried a smartphone. Experimental participants were asked to take pictures of their surroundings with a smartphone camera every hour, while living their daily lives as they normally would.

<Collection and processing of data>

The user's GPS location data and ambient noise were collected in real time through a smartphone, and a frontal image was collected every hour. In addition, as another type of spatial information, which is location information in virtual space, when a subject uses web content with a smartphone, the URL and time (use time, use time, etc.) of the accessed web page are collected. The pulse wave data measured for a total of 60 seconds through the wearable device was inputted from the smartphone through Bluetooth communication and transmitted to the server. Of course, this communication method does not limit the technical scope of the present invention, and communication methods other than Bluetooth, WAN, and LAN may be employed. The primary data of the collected pulse wave, location, image, sound, and web page access record were pre-processed and processed into secondary data of implicit response, explicit response, spatial information, and web content usage pattern information.

The implicit response is variously expressed as 21 cardiac response parameters extracted through HRV (Heart Rate Variability) analysis from pulse wave data, and these 21 variables are represented by 4 time domain variables and 17 Frequency domain variables were included. In order to extract the variables related to the above-mentioned implicit response, RRI (R-peak to R-peak Interval) was detected from the pulse wave signal through a QRS detection algorithm, and SDNN (standard deviation normal to normal) and SDNN (standard deviation normal to normal) using RRI were detected. Time domain data such as pNN50 (Percentage of RR intervals that differ more than 50ms) and rMSSD (Root Mean Square of Successive Differences) were calculated. Then, a heart rate variability (HRV) spectrum for extracting an indicator (data) in the frequency domain was extracted through fast Fourier transform (FFT) analysis by sampling the RRI at 2 Hz.

From the HRV spectrum, VLF (very low frequency, 0.0033-0.04 Hz), LF (low frequency, 0.04-0.15 Hz) and HF (high frequency, 0.15-0.4 Hz) band power (P _VLF , P _LF , P _HF ) were extracted, respectively, and from them, as a ratio of _{the power value (P TF} ) of the entire frequency band (TF, 0.0033 to 0.4 Hz), which is a frequency domain indicator, _{VLF%(P VLF} /P _TF ), LF%(LF/TF) and was obtain the ratio (VLF / HF, LF / HF ) of P _VLF and _LF P for P _HF), by taking the natural log P from the _{VLF, LF} P, P _HF was calculated lnVLF, lnLF, lnHF. LabVIEW 2015 (National Instrument Inc., USA) was used for this process. This analysis process is performed by a computer-based analysis device running analysis software.

The explicit response refers to six life pattern variables extracted by processing location data, and the calculation formula for each variable and its meaning is as follows.

Location variance (LV)

The amount of change in the subject's position using the position (GPS) information is calculated by the following equation.

NC <Number of clusters >

It is the number of clusters in the subject's activity area classified by machine learning algorithms such as K-Means, and by clustering (clustering) latitude and longitude with the K-Means algorithm, the K value is increased until the maximum distance between clusters is 500m. , the corresponding K value is used as NC (Number of Cluster).

ENT <Entropy>

It means the uncertainty of belonging to the corresponding cluster in the activity domain. The higher the entropy value, the unclear which cluster it will belong to. Conversely, the lower the entropy value, the clearer it will belong to.

p: a specific place during a specific period

i, the percentage of time spent during a specific time period over a specific time period

CM <Circadian movement>

It indicates the regularity of moving pattern in daily life, and the higher the number, the more it indicates that the subject moves according to a certain pattern, and the calculation formula is as follows.

E _lat : Average of power values in a preset frequency band according to latitude

E _long : average of power values in a preset frequency band according to hardness,

Psd: the sum of the power values in the preset frequency band ( fi )

i _{1 :} Maximum frequency in preset frequency band

i ₂ : Minimum frequency in preset frequency band

TT <Transition time>

It represents the total movement time of the subject, and is the ratio of the time the subject moved in a non-stationary status. This TT has a value obtained by dividing the number of position samples during the movement time by the total number of position samples.

Total distance <Total distance>

The distance between locations (samples) visited by the subject is accumulated and is the distance in Km calculated by the following equation.

As shown in Table 2 below, spatial information is information about the surrounding environment analyzed from image and sound data. Temporal complexity, spatial complexity (Vertical Edge, Horizontal Edge), color components (Hue, Saturation) , Intensity, Contrast). 9 variables of noise (Sound Amplitude, Sound Frequency) were included. For web content usage pattern information, duration and frequency of use were calculated from web page access records.

Recognition of interpersonal synchronization

Interpersonal synchronization was judged by comparing the similarity of variables between the individuals in the implicit response, the explicit response, spatial information, and web content usage pattern information.

Variable similarity was calculated as the distance between variables, and the distance between variables was calculated using the distance calculation formula. Since the resolution of each variable is different, the distance formula was applied by dividing the Euclidean method and the DTW (Dynamic Time Warping) method according to the resolution of the variable. For variables with high resolution, the DTW distance calculation formula was used, and for variables with low resolution, the Euclidean distance calculation formula was used.

The shorter the distance between variables, the higher the inter-individual variable similarity was, and the inter-variable similarity was expressed as a normalized value between "0" and "1". Interpersonal synchronization was recognized in units of 1 hour within the entire experimental time.

<Measurement of personality>

An individual's personality type was measured individually through the Myers-Briggs Type Indicator (MBTI) test. MBTI is a personality type test developed by Myers and Briggs, and as shown in Table 3, introversion and extroversion, intuition and sensing, perceiving and judging, It judges 16 personality types using 4 indicators of feeling and thinking. In this experiment, in the index of emotion-thinking, all subjects were reported to be emotional types, so only the indexes of introversion-extroversion, intuition-sensing, and perception-judgment were considered. As a result of the personality type test of 79 subjects, 46 (58%) of introverts, 33 (42%) of extroverts, 26 (33%) of intuition, 53 (67%) of sensing, 45 of perceiving types ( 57%), and the judgment type was 34 (43%).

Analysis and results <Analysis and results>

A cross-analysis was performed to check whether there was a difference in synchronization according to personality type. The total number of samples was 5925 as the product of the number of subjects (79 persons), the duration of the experiment (5 hours), and the number of days of the experiment (15 days). Personality types were analyzed by dividing them into 8 personality types: introvert-extrovert, intuition-sensing, perception-judgment index, and ENFJ, ENFP, ESFJ, ESFP, INFJ, INFP, ISFJ, ISFP (Tables 4 to 7). ).

As a result of cross-analysis, in the introvert-extroversion index, the introverts had a higher rate of synchronization than the extroverts, but there was no statistically significant difference (p=.878). In the case of intuition-sensory index, the rate of synchronization was higher in the sensory type (42.9%) than the intuition type (36.1%) (p<.001). In the case of recognition-judgment index, the rate of synchronization was higher in the case of the judgment type (43.1%) than the case of the recognition type (38.7%) (p<.001). There was also a significant difference in synchronization among the 8 personality types (p<.001). ESFJ personality type showed the highest synchronization rate (44.1%), and INFP showed the lowest synchronization rate (32.9%).

Table 4 below is a cross-table matrix for comparing synchronization according to Introversion-Extraversion.

Table 5 below is a cross-table matrix for comparing intuition-emotional synchronization.

Table 6 below compares the synchronization of recognition and judgment according to individual disposition excluding emotion-thinking.

Table 7 below is a cross-table matrix for comparing the synchronization according to individual disposition excluding emotion-thinking.

Table 8 compares frequency synchronization according to intuition-sensory synchronization variables.

As a result of checking the frequency of each type of synchronization variable for each personality indicator, the sensory type was more synchronized with implicit and explicit responses than the intuitive type, and the synchronization frequency of spatial information and contents was low.

Table 9 compares the frequency synchronization according to the recognition-judgment synchronization variables. The judgment type had higher synchronization in spatial information and content than the cognitive type, and the implicit and explicit responses had a lower synchronization frequency.

Table 10 compares the synchronization frequencies according to the synchronization variables of sensory-thinking.

Among the eight personality types, INFJ had the highest frequency of synchronization between implicit response and content use pattern, and ESFJ for explicit response and ENFP for spatial information showed the highest frequency. On the other hand, ENFPs had the lowest synchronization frequency between implicit responses and content usage patterns, and INFJs had the lowest synchronization frequencies between explicit responses and spatial information.

Data processing and application <processing and application>

A synchronization evaluation system according to the present invention includes an input device for collecting input data as shown in FIG. 1 ; In addition, a processing device that processes data, generates a rule base (threshold), and evaluates synchronization using the rule base is provided.

The rule base is built based on user-to-user distance data of four types of information: heart, location, space, and web content. The input data includes heart data, location data, spatial data, and content data, as shown in FIG. 2 mentioned above, and each is obtained from at least two persons for evaluation of social motivation. Whether to synchronize between individuals is determined by the rule base.

The rule base (threshold value, Vt) can be created for each data by recording heart data, location data, spatial data, and content data among the input data, and can be set based on the data numerical distribution of the total number of people (N) recorded. can As shown in FIG. 3 , the threshold value Vt refers to the accuracy indicating how many errors are allowed among the total number of recorded persons (N persons).

The distance between the input data of each piece of information is calculated using a dynamic time warping technique. A normal distribution is formed through the average and standard deviation of the input data between users of each information, which is possible by the central limit theorem (formation of the rule base). Here, if the integral value up to the right extreme of the normal distribution is greater than or equal to the threshold, it is determined that synchronization is achieved.

In this embodiment, the threshold is set to 87% (0.87), but the user can adjust the accuracy according to the purpose. This threshold (Vt) serves as a criterion for evaluating social motivation, and a data similarity value (Vc) compared to this threshold is calculated from data obtained for each individual.

In selecting the threshold (Vt), which is the rule base, the social emotional entropy I(x) of users who create a synchronization group is calculated based on information theory, and the threshold value with the lowest entropy value is finally selected. 6 is a two-dimensional graph showing the relationship between the threshold value and entropy, and FIG. 7 is a graph showing the group status by social emotion obtained using experimental data.

In the above equation, P(x) is the probability that event x will occur.

The synchronization levels are determined as follows.

- Level 1: heart, location, space, web content (group 4) → very weak synchronization

- Level 2: heart-location, heart-space, heart-web content, location-space, location-web content, spatial-web content (group 6) → weak synchronization

- 3rd level: heart-location-space, heart-location-web content, heart-space-web content, location-space-web content (group 4) → strong synchronization

- 4th level: heart-location-space-web content (14 groups) → very strong synchronization

Thresholds Vt1, Vt2, Vt3, and Vt4 for each data for determining data synchronization are calculated by the above-described method using respective data obtained from a plurality of individuals (N persons) as shown in FIG. 5 . In this embodiment, Vt1 denotes a threshold value of heart data, Vt2 denotes a threshold value of position data, Vt3 denotes a threshold value of spatial data, and Vt4 denotes a threshold value of content data.

Comparison values Vc1, Vc2, Vc3, and Vc4 corresponding to the respective thresholds are calculated by receiving data from two persons to be compared. In this embodiment, Vct1 denotes a comparison value of heart data, Vc2 denotes a comparison value of position data, Vc3 denotes a comparison value of spatial data, and Vc4 denotes a comparison value of content data.

As shown in FIG. 6 , by comparing the threshold value of the two data entered as input data and the data defined in the rule base as described above, it is determined whether the new input data exceeds the threshold value, and the synchronization level is evaluated accordingly. The social connectivity evaluation compares the input data with the synchronization level output from the data synchronization evaluation above and the personality type of each individual, and determines whether social connectivity exists based on this.

Specifically, the data synchronization level and social connectivity are evaluated as follows.

Output data includes two types of data: synchronization level (output from data synchronization recognition unit) and social connection (output from social connection evaluation unit). .

Here, the level of synchronization refers to the similarity of data between two individuals, and the social connection refers to whether or not there is a connection between the two individuals. In summary, the present invention is a system that creates a social connection between two individuals based on individual data and personality type and defines the meaning of the connection between two individuals. Judgment by this system is made as follows.

Judgment 1: Does the similarity value (Vc1) between the heart data of Person A and Person B exceed the threshold value (Vt1) of the rule base?

Judgment 2: Does the similarity value (Vc2) between the location data of person A and person B exceed the threshold value (Vt2) of the rule base?

Judgment 3: Does the similarity value (Vc3) between the spatial data of person A and person B exceed the threshold value (Vt3) of the rule base?

Judgment 4: Does the similarity (Vc4) between the content data of person A and person B exceed the threshold value (Vt4) of the rule base?

Judgment 5: Are the personality types of Person A and Person B the same?

Output 1: Synchronization level (0 level: unsynchronized, 1 level: weak synchronization Levels 2-4: strong synchronization)

Output 2: Whether or not socially connected (O: connected, X: not connected)

Table 11 below shows the determination results according to an embodiment.

Conclusion and discussion <Conclusion and discussion>

As a result of cross-analysis to analyze the difference in synchronization according to personality types, the synchronization rate of personality types including Sensing and Judging was higher than that of other personality types. The sensory type had a high synchronization rate especially in the implicit and explicit responses, and the judgment type had a high synchronization rate in spatial information and content usage patterns.

Sensing types are usually sensitive to observation and like to imitate the actions of others. They also tend to focus on and enjoy the present pleasure. Considering these personality traits, it is thought that the reason that the sensory type has a high rate of motivation in implicit and explicit responses is because they tend to imitate other people's actions and focus on their own emotions. The autonomic nervous system response may have played an important role in the connection with others as well as the personality traits of responding well to pleasure and recognizing the world using the five senses. In addition, the high rate of synchronization in the behavioral pattern, the explicit response, is thought to indicate that the personality trait that tries to imitate other people's behavior patterns also appeared in the synchronization pattern.

The judging type is decisive and wants anything to be resolved and decided as quickly as possible. They value plans and standards, and prefer to stick to conventions. They try to avoid unnecessary and undesirable experiences and rely on judgment. He has a strong ability to control himself and has a clear sense of purpose. Perceiving types, on the other hand, are curious and value the moment, and can easily adapt to accidental or unexpected events. They value experiences, constantly want new experiences, and feel great pleasure in new experiences. Leave the opportunity for revision as long as possible before making a decision. Flexible and generous Compared to the cognitive type, the judgment type is more self-disciplined and less tolerant of others. It is presumed to have shown

The experiment for the present invention quantitatively measured and analyzed the context in which a person is connected when they are synchronized with others according to the personality type. This is significant as a basic research for personal data-based connection between individuals in research fields such as social network service (SNS), social life logging (SLL), and recommendation systems. In this experiment, the emotion-thinking index was not considered among the four indicators of the MBTI test used as a personality type test. Therefore, if we further study the difference between the emotional type and the thinking type and consider all the relevance between each index, it will be possible to identify the relationship of motivation between individuals according to more systematic personality characteristics.

Although the present invention has been described with reference to the embodiments shown in the drawings, these are merely exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Accordingly, the true protection scope of the present invention should be defined only by the appended claims.

Claims (16)

detecting, respectively, heart rate data according to an implicit response and position data according to an explicit response from at least two subjects carrying a personal terminal equipped with a heart rate sensor and a GPS receiver using a heart rate sensor and a GPS receiver; and
Evaluating the social motivation of the two subjects by a processing device that processes the heart rate data and the location data transmitted from the personal terminal;
In the step of evaluating the social motivation of the two subjects by the processing device, a plurality of parameters are extracted from the heart rate data and the location data of the two subjects, and a distance difference between the plurality of variables of the two subjects is calculated, respectively, , the synchronization level of the variables of the two subjects is calculated by the distance difference between the variables of the two subjects, whether the personality type of the subject is the same is evaluated by the synchronization level, and the synchronization level of the variables and the personality type A method for judging the synchronization of social relationships based on individual tendencies, in which social motivation is evaluated by whether or not they are identical. According to claim 1,
The plurality of variables obtained from the heart rate data includes a time domain variable and a frequency domain variable, the method for determining synchronization of a social relationship based on an individual tendency. 3. The method of claim 2,
The parameter of the time domain is at least one of beat per minute (BPM), standard deviation of normal to normal (SDNN), root mean square successive difference (rMSSD), and percentage of RR intervals that differ more than 50ms (pNN50). A method for judging the synchronization of social relationships based on personal and personal inclinations. 3. The method of claim 2,
The frequency domain variable includes the power of a very low frequency (VLF), a low frequency (LF) and a high frequency (HF), a method for determining synchronization of a social relationship based on an individual tendency. 5. The method of claim 4,
The frequency domain variable includes a dominant frequency and the power of the dominant frequency, a method for determining synchronization of a social relationship based on an individual tendency. According to claim 1,
The plurality of variables obtained from the location data includes the location variance of the two subjects, entropy obtained from the ratio of time spent in a specific place during a specific period, total transition time, and total distance. A method for determining the synchronization of a social relationship based on an individual tendency, including at least one of. 7. The method of claim 6,
The entropy is calculated by the following equation, a method for determining the synchronization of a social relationship based on an individual tendency.
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In the above equation, P(x) is the probability that event x will occur. 8. The method of claim 7,
A normal distribution is formed through the mean and standard deviation of the input data between the two subjects, and when the integral value to the right extreme of the normal distribution is greater than or equal to a predetermined threshold value, synchronization is determined. Synchronization of social relationships based on individual tendencies How to judge. According to claim 1,
The method for determining synchronization of a social relationship based on an individual tendency, further comprising the step of further detecting spatial information of a location to which the two subjects belong by using a camera. 10. The method of claim 9,
Wherein the spatial information includes at least one of spatial complexity, color, saturation, brightness, contrast, noise intensity, and frequency component increase of noise. 11. The method of claim 10,
The spatial complexity is determined by the sum of the number of vertical and horizontal components in the image obtained from the camera, the method for determining the synchronization of a social relationship based on an individual tendency. According to claim 1,
The personal disposition is evaluated by personality type based on introversion-extroversion, intuition-sensing, emotion-thinking, and recognition-judgment, a method for judging the synchronization of social relationships based on personal disposition. 13. A device for carrying out the method according to any one of claims 1 to 12, comprising:
a processing device for processing the heart rate data and location data; and a personal terminal connected to the processing device by a network, the personal terminal including a heartbeat sensor and a GPS receiver for detecting the heartbeat data and the location data. 14. The method of claim 13,
The heart rate sensor is provided by a wearable device worn on the subject, and the apparatus for determining synchronization of social relationships based on individual tendencies. 15. The method of claim 14,
and the wearable device transmits heart rate data to the personal terminal through wireless communication with the personal terminal. 14. The method of claim 13,
The personal terminal has a built-in camera for capturing an image, a device for determining synchronization of social relationships based on personal preferences.

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