KR102223087B1 - Method and Apparatus for determinating Emotion transference - Google Patents

Abstract

A method and apparatus for evaluating emotional transmission using an electrocardiogram are described. Emotional transfer evaluation method: detecting an ECG raw signal (ECG raw singnal) of two subjects in a social interaction relationship by an ECG sensor; Extracting a digitized ECG signal by sampling the circular electrocardiogram signal at a predetermined sampling frequency; Extracting an R-peak to R-peak interval (RRI) from the electrocardiogram signal; Extracting HRP from the RRI; Calculating a correlation coefficient (r ) using the HRP of the two subjects; And evaluating the emotional transfer between the two subjects using the correlation coefficient ( r).

Description

Method and Apparatus for determinating Emotion transference}

The present disclosure describes a method and an apparatus for determining or measuring whether or not an emotional transfer is present, and more particularly, to a method and an apparatus for quantitatively determining and measuring whether or not an emotional transfer or transfer is performed from a leader to a follower.

Emotions are exchanged through verbal and non-verbal information during social interaction. Interpersonal or group interactions with multiple individuals can also affect mood, decision making, behavior, and activity at the group level. This process is called emotional conversation. This emotional transfer has the same meaning as emotion transference and affective mimicry. During interaction, you will subconsciously mimic nonverbal behaviors such as facial expressions and gaze patterns, head movements, gestures, etc. by being synchronized with others. The mood of a follower who imitates nonverbal behavior changes to resemble that of a leader who communicates nonverbal behavior. Previous research on emotional transfer has usually focused on the effects of emotional transfer related to individual and group performance. The mood swings of group members clearly have an impact on task performance. Emotional transfer was greater when people felt happy and participated in group activities.

Despite the accumulated knowledge of the leader's effect on followers and group activity, researchers have yet to find out how to identify leaders during social interactions. In the past experiments, self-evaluation methods were often used to find the direction and degree of emotion transmission. The problem with self-evaluation is that the data collected is data that depends solely on the perception and honesty of people's own feelings. In addition, there is a limitation in that it is not possible to quantitatively evaluate the direction and degree of emotional transmission. In addition, the roles of leaders and followers such as teachers and students, performers and audiences, staff and customers, etc., were predetermined and maintained. These interactions are limited as one-way transfers instead of interactions where everyone has the opportunity to create an emotional transference to others. Who conveyed more emotion in person-to-person interaction, or who interacted most in intra-group relationships would not have been possible without using a self-assessment method within a controlled environment.

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An exemplary embodiment proposes a method and apparatus for measuring a necessary condition of an emotional transition phenomenon, a direction of an emotional transition, and a degree between a positive and a negative transition using HRP synchronization.

Emotional transfer measurement method according to an exemplary embodiment: silver

Detecting an ECG raw singnal of two subjects in a social interaction relationship by an ECG sensor;

Extracting a digitized ECG signal by sampling the circular electrocardiogram signal at a predetermined sampling frequency;

Extracting an R-peak to R-peak interval (RRI) from the electrocardiogram signal;

Extracting HRP from the RRI;

Calculating a correlation coefficient (r ) using the HRP of the two subjects; And

It includes; evaluating the emotional transfer between the two subjects using the correlation coefficient ( r).

According to an exemplary embodiment, the RRI may be extracted in a range of 500 ms-1200 ms.

According to an exemplary embodiment, the evaluation of emotional transfer between two subjects may be performed on negative and positive emotions.

According to an exemplary embodiment, two subjects are divided into a leader and a follower, and the synchronization of the follower's emotion with the leader's emotion may be evaluated.

According to an exemplary embodiment, the electrocardiogram circular signal may be measured with Lead I of a standard limb induction method.

According to an exemplary embodiment, the correlation coefficient is calculated from Pearson's correlation coefficient (r, -1 ≤ r ≤ 1), and the positive correlation coefficient and the negative correlation coefficient are compared to the correlation coefficient threshold value, Metastasis can be evaluated.

According to an exemplary embodiment, the correlation coefficient threshold for comparing the positive correlation coefficient and the negative correlation coefficient is 0.262 (±5%).

Emotional delivery evaluation device using an electrocardiogram performing the method:

An ECG sensor that detects an ECG signal from the subject;

A preprocessor for preprocessing the ECG signal; And

It may include; an analysis unit for analyzing the emotional transition between the two subjects using the signal from the pre-processing unit.

According to an exemplary embodiment, the preprocessor may extract RRI in a range of 500 ms to 1200 ms.

According to an exemplary embodiment, the analysis unit may perform negative emotions and positive emotions when evaluating emotional transfer between two subjects.

According to an exemplary embodiment, the analysis unit may divide the two subjects into a leader and a follower, and may evaluate synchronization of a follower's emotion with the leader's emotion.

According to an exemplary embodiment, the analysis unit may obtain the correlation coefficient r from Pearson's correlation coefficient (r, -1 ≤ r ≤ 1), and use a positive correlation coefficient and a negative correlation coefficient.

According to the present invention, it is possible to compare and evaluate the degree of transfer between positive and negative emotions. In the present invention, measurement of emotional transfer, confirmation of the direction of emotional transfer, and synchronization of the heart rhythm pattern (HRP) are used. According to the experiment of the present invention, while emotion was transferred, the correlation coefficient of negative emotion was significantly higher than that of positive emotion. Through exemplary embodiments, it has become possible to quantitatively confirm the measurement of the transition and its direction. The analysis of emotional transfer is important for gathering information about how our mind works in the process of social interaction and can be applied to a number of techniques and applications. For example, there are various training programs for people with social disabilities, such as social skills training, facial emotion training, and emotion recognition training. In these training programs, you can learn how to monitor whether emotions have been transmitted to the patient and act accordingly.

1 shows an experimental environment according to an exemplary embodiment.
2 shows an experimental procedure according to an exemplary embodiment.
3 illustrates a change or difference in HRP between an imitation task and a self-expression task, according to an exemplary embodiment.
4 shows a result of comparing the correlation coefficient (r ) of HRP between a leader and a follower in an imitation task and a self-expression task according to an exemplary embodiment.
6 shows a comparison result of a correlation coefficient between a synchronized HRP and an HRP reader according to an exemplary embodiment.
7 shows a comparison result of a correlation coefficient for an emotional transfer between positive and negative according to an exemplary embodiment.
8 shows a rule base for evaluating positive emotional transfer according to an exemplary embodiment.
9 shows a result of verifying the rule base of FIG. 8 according to an exemplary embodiment.
Fig. 10 shows a rule base for evaluating negative emotional transfer according to an exemplary embodiment.
Fig. 11 shows a result of verifying the rule base of Fig. 8 according to an exemplary embodiment.

Hereinafter, a method and apparatus for evaluating emotion transmission according to an exemplary embodiment of the present invention will be described in detail with reference to the accompanying drawings.

Heart rhythm pattern (HRP) has a significant correlation with emotional state. It was confirmed that this HRP changes according to the emotional state of a person. HRP of frustrated people showed irregular (negative) patterns, while those who were grateful showed consistent (positive) patterns. Moreover, physiological motivation is closely related to social relationships, group performance, and emotional transfer.

An exemplary embodiment proposes a method and apparatus for objectively and quantitatively recognizing emotional transfer through a pattern of heart response according to a difference in emotional transfer caused in an interactive situation.

In an exemplary embodiment, raw data for evaluating a subject's concentration is extracted using an ECG sensor, and this data is processed by a processing device as an emotion transmission evaluation device.

The processing device or evaluation device includes an image pre-processing unit and an analysis unit, which has an analysis tool or software and a hardware system on which it is executed. Such a processing device may be a computer-based device, a general-purpose computer or a dedicated device including software containing algorithms and hardware capable of running the software.

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

Specifically, the emotion transmission evaluation apparatus includes an ECG sensor for detecting an ECG signal from a subject, and a preprocessor for preprocessing the ECG signal; And it may include; an analysis unit for analyzing the emotional transition between the two subjects by using the signal from the pre-processing unit.

According to an exemplary embodiment, the preprocessor may extract RRI in a range of 500 ms to 1200 ms. In addition, the analysis unit may perform negative sensibility and positive sensibility when evaluating the sensibility transfer between two subjects. In addition, the analysis unit may divide the two subjects into a leader and a follower, and may evaluate the synchronization of the follower's emotion with the leader's emotion. Furthermore, the analysis unit may obtain the correlation coefficient from Pearson's correlation coefficient (r, -1 ≤ r ≤ 1), and use a positive correlation coefficient and a negative correlation coefficient.

The following experiment was conducted for the objective evaluation of the emotion transmission evaluation method according to the present invention and a device applying the same.

<Participants, Participants>

64 college students (32 women) aged 20-29 years (M±SD = 25.2±4.3) participated in the experiment. All subjects had no history of heart-related disease or family history, and voluntarily participated in the experiment. Each subject was paid a subject fee of $105.26. Prior to the experiment, consent for the experiment was obtained, and from 12 hours before the experiment, alcohol, cigarettes, and caffeine intake were prohibited, and they were asked to take adequate rest. All protocols used in this study were certified by the Sangmyung University Bioethics Review Committee.

<Experimental procedure>

Fig. 1 shows an experiment environment according to an exemplary embodiment, and Fig. 2 shows an experiment procedure according to an exemplary embodiment.

Subjects were randomly assigned leader and follower roles. As shown in Fig. 1, the leader and the follower communicated with facial expressions of happiness and sadness presented through a display or face-to-face. The leader and follower sat down comfortably in their chairs, face to face. A TV display showing facial expression images was located behind them.

Referring to Fig. 2, the experimental task consisted of an introduction, practice, and a main experiment session. In the main experimental session, an imitation task that mimics the other's surface and a self-expression task that makes expressions on their own are performed. During the 30 second introductory session, facial expressions of happiness or sadness as defined by Ekman were described. In the 50 second practice session, I practiced making facial expressions. The main experimental session was divided into imitation and self-expression tasks to induce emotional transfer. In the imitation task, the leader made a happy or sad face according to the expression presented through the display and expressed it to the follower. The follower imitated the leader's facial expressions. In the self-expression task, which makes facial expressions, the role of the leader is the same, but the follower imitated the facial expressions presented on the screen. Each task in the main experimental session lasted 50 seconds.

All experiments consisted of 4 trials as follows.

Trial 1: positive imitation (happiness)

Trial 2: negative imitation (sad)

Trial 3: Positive Self-Expression (Happiness)

Trial 4: negative self-marking (sadness).

The order of the trials as described above was randomly determined, and the time interval between trials was set to 10 minutes to minimize the effect of previous stimulation. The facial expressions of happiness and sadness were based on the facial expressions of Ekman's 6 basic emotions. During the experiment, the ECG sensor was used to measure the ECG signals of the leader and follower.

<Data Acquisition, Signal Processing, and Analysis>

Data collection and processing were performed as follows. Electrocardiogram (ECG) was measured by the Lead I method of standard limb guidance. The circular signal or data obtained in this way is pre-processed by a pre-processing part. For example, MP 100 power supply (Biopac System Inc., USA) and ECG 100C amplifier, NI-DAQ-Pad9205 ( National Instrument Inc., USA), etc. may be used. A circular signal from the sensor is amplified through a preprocessing unit including these elements, and a digitized ECG signal can be obtained with a sampling frequency of 500 Hz.

For the ECG signal obtained through the pre-processing process, the R peak was calculated through the QRS detection algorithm, and RRI (R-peak to R-peak interval) was calculated.

The RRI, which is the interval between the R peaks, was calculated from signals with normal intervals in the range between 500ms and 1200ms. BPM (beat per minute) used as a parameter for confirming the heart rhythm pattern (HRP) was calculated through the RRI reciprocal. Signal processing was performed through labVIEW 2015 (National Instruments Inc.). In this study, the existence of emotional transfer was defined as two tasks: emotional communication with another person (imitation task) or face image on the display (self-expression task).

The correlation coefficient (r ) of HRP between the leader and follower was calculated during the practice and main experimental sessions (imitative task and self-expression task). The change in the correlation coefficient between the practice and the main experiment session was compared between the imitation task and the self-expression task. Also, in this case, the difference in the correlation coefficient between positive and negative emotions was compared. Finally, in order to confirm the direction of the emotional transfer between the leader and the follower, the average HRP of the leaders and followers was summed and the synchronized HRP was calculated as the data during the imitation task. During practice, the HRP correlation coefficient of the leader and followers was calculated.

<Statistical analysis>

The difference in correlation between before and after the task in the emotional (non)transfer condition was evaluated by Analysis of covariance (ANCOVA). The one-way analysis of covariance compared dependent variables between groups after the task based on the covariate before the task. An independent t-test was used to confirm the statistical significance of the difference in correlation between leader and follower, including positive and negative. In addition to statistical significance, the effective size was calculated through the eta-squared value (η2) and Cohen's d. Standard values of 0.01 (0.20), 0.06 (0.50), and 0.14 (0.80) for the effective size in Cohen's d are generally small and medium, large, respectively. Pearson's correlation coefficient (r, -1 ≤ r ≤ 1) based on the normality test was used for correlation analysis, as the correlation coefficient approaches a value of -1, there is a strong negative correlation. And a strong positive correlation is indicated when it approaches a value of 1. All statistical analyzes were performed through SPSS 17 (SPSS, Inc., Chicago, IL) software.

<Emotional Contagion and non-Contagion>

3 illustrates the change or difference in HRP between subject 7's imitation task and self-expression task. In Figure 3, (A) HRP before performing a positive pre-self-expression task, (B) is a negative pre-self-expression task (negative pre-self-expression task), (C) positive Positive pre-imitation task, (D) is negative pre-imitation task, (A') is positive post-self-expression task, (B ') is the negative post-self-expression task, (C') is the positive post-imitation task, and (D') is the HRP results after the negative imitation task. see.

The correlation coefficient (r ) between the leader and the follower before creating positive and negative self-expression before the task (emotional vision state) was -0.4436 and -0.3404, respectively. After the task (sensibility transition state), correlation coefficient results of -0.1424 and -0.0905 were shown, and there was no statistically significant difference. However, the correlation coefficient between the leader and the follower before the positive and negative imitation task (emotional vision) was 0.2455 and 0.0415, respectively. In the post-task (emotional transition state), the correlation coefficients were 0.5518 and 0.7614, and were significantly increased compared to before the task.

Fig. 4 shows the results of comparing the correlation coefficient (r ) of HRP between the leader and the follower in the imitation task and the self-expression task, and the left and right shows the comparison results in the positive and negative states, respectively.

Referring to FIG. 4, after the imitation task and the self-expression task, the correlation coefficient of HRP between the leader and the follower significantly increased in both the positive and negative emotional conditions in the imitation task.

Positive

F (1, 62) = 195.609

p = 0.000

η ² = 0.762

Negative:

F (1, 62) = 295.002

p = 0.000

η ² = 0.829

However, before the imitation and self-expression task, the correlation coefficient of HRP between leader and follower in both positive and negative emotional conditions did not show statistically significant results.

Positive

F (1, 27) = 2.709

p = 0.105

η ² = 0.043

Negative

F (1, 27) = 1.176

p = 0.282

η ² = 0.019

Above, the test statistic F value is expressed as the ratio of the mean squared error (MSE) to the mean squared error (MSR).

Table 1 shows the results of comparing the correlation coefficient (r ) of HRP between the leader and the follower in detail in the imitation task and the self-expression task.

<Direction of Emotional Contagion>

Figure 5 is an example of comparing the HRP of the synchronized leader and follower through the emotional transfer between the leader and the follower for subject 7, (A) a comparison of positively synchronized HRP, (B) a comparison of negatively synchronized HRP , (A') shows a comparison of HRP of positively synchronized leaders and followers, and (B') shows a comparison of HRP of negatively synchronized leaders and followers.

The synchronized HRP analyzed the HRP correlation between the leader and followers before synchronization, and the correlation coefficient between the HRP and the leader's HRP before synchronization was 0.319, and the correlation coefficient of the follower's HRP was -0.265. In the negative condition, the correlation coefficient between the synchronized HRP and the HRP of the leader before synchronization was 0.399, and the correlation coefficient of the HRP of the follower before synchronization was -0.027.

6 shows the comparison result of the correlation coefficient between the synchronized HRP and the HRP reader.

In the statistical analysis, the correlation coefficient between the synchronized HRP and the HRP with the leader showed statistically higher significance results in both positive and negative conditions than the synchronized HRP and HRP with the follower, as shown in FIG. 6.

Positive

t (62) = -9.589

p = 0.000

Cohen's d = 2.432

Negative

t (62) = -14.692

p = 0.000

Cohen's d = 0.627.

<Emotional Transfer: Positive and Negative >

Examples of the correlation coefficient between positive and negative for subject 7 are as shown in Fig. 3 (C, C', D, and D'). The correlation coefficient after the task increased compared to before the task in both the positive and negative conditions. However, the correlation coefficient of the negative condition (0.0415-0.7614) increased compared to the positive condition (0.2455-0.5518) after the emotional transfer task. As shown in Figure 7, the correlation coefficient of the negative condition showed a statistically significant difference from the positive condition ( t (62) = -2.508, p = 0.015, with a medium effect size (Cohen's d = 3.736)). Detailed results are shown in Table 1 above.

The purpose of the experiment according to an exemplary embodiment is to measure emotional transfer, determine its direction in an expressive binary interaction, and identify the difference between positive and negative emotions from emotional transfer.

In this experiment, an experiment was conducted on the happy and sad expressions of two people who caused emotional transfer. Emotional transfer and vision transfer were evaluated by imitation and self-expression work. The imitation task was to express facial expressions face to face, while expressing self-expression based on facial expressions.

Overall, three important facts were confirmed through this experiment for the present invention.

First, Emotional Contagion significantly increased the correlation coefficient of HRP between the two people in both positive and negative emotions, but emotional vision transfer did not. HRP has a significant correlation with the emotional state, and HRP synchronization between two people indicates that the emotions between the leader and the follower are synchronized.

In previous studies, it was reported that physiological synchronicity was associated with emotional transfer. Jaimovich's study revealed that the patterns of GSR and HRV of musical performers and listeners became similar. In addition, the physiological connection between the two persons during interactive interactions was investigated as an electrodermal response (ED). Emotionally stimulating subjects used a 9-point Likert scale, with high collision situations and low levels. In the experiment of the present invention, the degree of emotional motivation that can be measured emotionally was found in the ED compared to the situation in which there were few or no conflicts among participants in severe conflict situations. To represent the HRP correlation between the two people was used.

Second, after the HRP of the leader and the follower were synchronized, the direction of emotion transmission was confirmed. In this experiment, the synchronous HRP was analyzed using the average value between the HRP of the leader and the follower. The correlation coefficient between the leader (before emotional transfer) and synchronized HRP (after emotional transfer) was significantly higher than that of the follower. This phenomenon means that there was no significant difference in the leader's HRP before and after the sentiment transfer, even though the follower's HRP significantly changed before and after the sentiment transfer. Therefore, the follower's HRP was shifted to match the leader's HRP through the emotional transfer activity. In this experiment, emotional transfer is defined as the synchronized HRP between the leader and the follower. The direction of emotional transfer was quantitatively detected by comparing HRP before and after synchronization of each person.

Finally, the intensity of the positive and negative emotional transfer was compared. During the emotional transition period, the correlation coefficient of negative emotions was significantly higher than that of positive emotions. It was found that increasing the correlation coefficient between the two signals increases the intensity of the emotional transition. Therefore, this experiment found that the effect of transfer on negative emotions is higher than the effect on positive emotions. In the experiment of emotional transfer, which was investigated through the deliberate expression of positive, negative, and neutral emotions, it was found that the expression of negative emotions had a greater influence on other people than the positive emotions. The subjects responded with higher intensity to negative expressions such as extreme demand expressions compared to positive emotion expressions.

8 shows a rule base for positive emotion and a distribution of correlation coefficients of subjects acquired in an experiment according to an exemplary embodiment. The threshold value of the correlation coefficient r determined according to the experiment according to the present embodiment is 0.262. Referring to FIG. 8, the X-axis represents subjects (32 samples and 64 subjects, respectively), and the Y-axis represents the correlation coefficient of each sample. As shown in Fig. 8, the correlation coefficients of most subjects plotted on the dotted line in the horizontal direction of the correlation coefficient of 0.262 (Y value) showed a positive emotional transition, but a slight negative result was found in the vicinity of 0.262. Here, a subject with a correlation coefficient greater than the threshold of 0.262 exhibits a positive emotional transition, and below that indicates a negative emotional transition.

9 shows the result of verifying the rule base shown in FIG. 8. The rule base of Fig. 9 is a result of verification with new data of 20 samples and 40 people, respectively. As a result of verifying with the threshold value r=0.262 set as the rule base, it was confirmed that the emotional transition was accurately judged (accuracy of 90%) in 36 samples out of 40 samples.

10 shows a rule base for negative emotions and a distribution of correlation coefficients of subjects acquired in an experiment according to an exemplary embodiment. The threshold value of the correlation coefficient r for evaluating the negative emotional transition determined according to the experiment according to the present embodiment is 0.262. Referring to FIG. 10, the X-axis represents subjects (32 samples and 64 subjects, respectively), and the Y-axis represents the correlation coefficient of each sample. As shown in Fig. 10, the correlation coefficients of most subjects plotted on the dotted line in the horizontal direction of the correlation coefficient of 0.262 (Y value) showed negative emotional transition, but a few positive results were found in the vicinity of 0.262. Here, a subject with a correlation coefficient greater than the threshold of 0.262 indicates a negative emotional transition, and below that indicates a positive emotional transition.

11 shows the result of verifying the rule base shown in FIG. 10. The rule base of Fig. 11 is a result of verification with 20 samples and 40 new data, respectively. As a result of verifying with the threshold r=0.262 set as the rule base, it was confirmed that the emotional transition was accurately determined (95% accuracy) in 38 samples out of 40 samples. In view of these results, the central value of the threshold value for evaluating the positive and negative sentiment is 0.262, and the error can be seen as about 5%. Accordingly, the correlation coefficient threshold value defined in the exemplary embodiment is 0.262, and the tolerance may be defined as ±5%.

According to the emotional transfer evaluation method proposed through exemplary embodiments, the emotional transfer and its direction can be quantitatively measured. To measure sentiment transfer through the identification of the leader, you can guide people to adjust their behavior to improve sentiment. A better understanding of emotional transmission and the identification of leaders in interactions can improve the analysis of everyday social interactions such as work environments, social gatherings and others.

The analysis of emotional transfer is important for gathering information about how our mind works in the process of social interaction and can be applied to a number of techniques and applications. For example, there are various training programs for people with social disabilities, such as social skills training, facial emotion training, and emotion recognition training. In these training programs, you can learn how to monitor whether emotions have been transmitted to the patient and act accordingly. This leader's identification can also be used to evaluate work settings. Considering emotions along with rationality is by no means important in a business strategy. These physiological assessments can help track employee leadership, compelling skills, and customer service skills. In addition, by applying this method in the education field, it is possible to monitor participation, empathy, class attitude and participation in online and offline domains. This experiment identified only two emotions, happiness and sadness. However, since the heart rhythm reflects various emotional states, the emotional transfer measurement method according to an exemplary embodiment has a wide expandability.

The present invention has been described with reference to the embodiments shown in the drawings, but these are merely exemplary, and those of ordinary skill in the art will appreciate that various modifications and other equivalent embodiments are possible therefrom. Therefore, the true scope of protection of the present invention should be determined only by the appended claims.

Claims (13)

Detecting an ECG raw signal (ECG raw singnal) of two subjects in a social interaction relationship by an ECG sensor;
Extracting a digitized ECG signal by sampling the circular electrocardiogram signals of the two subjects at a predetermined sampling frequency;
Extracting an R-peak to R-peak interval (RRI) from each of the electrocardiogram signals of the two subjects;
Extracting a heart rhythm pattern (HRP) from each of the RRIs of the two subjects;
Calculating Pearson's correlation coefficient (r) using the HRP of the two subjects;
Comparing the Pearson correlation coefficient with a predetermined threshold to determine whether or not the two subjects are synchronized; And
Electrocardiogram comprising: when emotional synchronization between two subjects occurs, obtaining a correlation coefficient between HRP before and after synchronization of each of the two subjects and comparing the correlation coefficient between HRP of the two subjects to determine a direction of emotional transfer between the two subjects; How to judge the sentiment to be used. The method of claim 1,
The RRI is extracted in a range of 500 ms-1200 ms, a method of determining emotional transmission using an electrocardiogram. The method of claim 1,
The evaluation of emotional transfer between two subjects is a method of determining emotional transfer using an electrocardiogram in which negative and positive emotions are evaluated. delete delete delete The method according to any one of claims 1 to 3,
The threshold value is 0.262 (±5%), the emotion transmission determination method. In the device for performing the method of any one of claims 1 to 3 or 7,
An ECG sensor that detects an ECG signal from the subject;
A preprocessor for preprocessing the ECG signal; And
Containing, an emotional transmission determination device using an electrocardiogram; an analysis unit for analyzing the emotional transition between two subjects by using a signal from the preprocessor. delete delete delete delete delete

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