KR20230144137A - Apparatus and method for determining stress state of user - Google Patents

Abstract

An apparatus and method for determining a stress state according to an embodiment are disclosed. The stress state determination device includes a biological signal measuring unit including sensors for measuring different biological signals of the user, a biological signal processing unit extracting one or more candidate parameters for each biological signal by processing the different measured biological signals, A biosignal analysis unit that selects target parameters for each biosignal based on one or more extracted candidate parameters, determines the correlation between target parameters for each selected biosignal, and determines the user's stress state based on the determined correlation. and an output unit that outputs the determined stress state of the user.

Description

Apparatus and method for determining stress state {APPARATUS AND METHOD FOR DETERMINING STRESS STATE OF USER}

The following embodiments relate to technology for determining a user's stress state based on biosignals.

Stress is the psychological and physical stimulation and reactions felt by an individual in a difficult environment or situation. The stress response is generated in various internal organs of the autonomic nervous system (heart, lungs, stomach, pupils, etc.) centered on the central nervous system to protect the body and cope with danger, and maintains the physiological balance (homeostasis) achieved within the human body. This can be caused by breaking. Moderate stress is good for health and necessary in life, but excessive or prolonged stress can be the root of all diseases, such as depression and heart disease. Stress leads to the response of various internal organs of the autonomic nervous system and can cause physiological imbalance. Therefore, the importance of monitoring and managing stress is increasing. Since the stress state can change due to various factors and various biological signals can change due to the stress state, research on technology that can accurately monitor the stress state is necessary in order to accurately identify the stress state.

An apparatus for determining a stress state according to an embodiment includes a bio-signal measurement unit including sensors for measuring different bio-signals of a user; a bio-signal processing unit that extracts one or more candidate parameters for each bio-signal by processing the different measured bio-signals; Selecting a target parameter for each biosignal based on the one or more extracted candidate parameters, determining a correlation between target parameters for each biosignal, and determining the user's stress state based on the determined correlation. Biological signal analysis department; and an output unit that outputs the determined stress state of the user.

The biosignal measurement unit may include at least two of an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and a skin conductance sensor.

The biological signal processing unit performs at least one signal preprocessing of noise removal, peak detection, filtering, and Fourier transform on the different measured biological signals, and performs signal preprocessing on each biological signal on which the signal preprocessing has been performed. Candidate parameters can be extracted.

The biological signal analysis unit may select the target parameter from candidate parameters for each biological signal based on at least one of a statistical analysis result and a predefined parameter importance.

The biosignal analysis unit determines the user's current stress level based on the determined correlation value and predefined stress level reference information, and the current stress level may include a plurality of different stress state values. .

The degree of correlation between the selected target parameters for each biological signal may indicate the degree of synchronization between the target parameters over time.

The output unit outputs the user's current stress level, changes over time in the different measured biological signals, changes in values of the target parameters over time, and changes in synchronization patterns between different target parameters over time. At least one more of these can be output.

A method for determining a stress state according to an embodiment includes measuring different biological signals of a user; extracting one or more candidate parameters for each biological signal by processing the different measured biological signals; selecting a target parameter for each biological signal based on the one or more extracted candidate parameters; determining a correlation between target parameters for each selected biological signal; determining the user's stress state based on the determined correlation; and outputting the determined stress state of the user.

The step of measuring the different biological signals includes using at least two of an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and a skin conductance sensor. It may include measuring signals.

The extracting of the candidate parameters may include performing signal preprocessing of at least one of noise removal, peak detection, filtering, and Fourier transform on the different measured biological signals; and extracting candidate parameters for each biological signal on which the signal preprocessing has been performed.

The step of selecting the target parameter may include selecting the target parameter from candidate parameters for each biological signal based on at least one of a statistical analysis result and a predefined parameter importance.

The step of determining the user's stress state includes determining the user's current stress level based on the determined correlation value and predefined stress level reference information, and the current stress level is a plurality of different stress state values. may include.

The degree of correlation between the selected target parameters for each biological signal may indicate the degree of synchronization between the target parameters over time.

The outputting step may include outputting the user's current stress level; and further outputting at least one of changes in the measured different biological signals over time, changes in values of the target parameters over time, and changes in synchronization patterns between different target parameters over time. .

According to one embodiment, the stress state can be monitored based on the degree of synchronization of various biological signals in the individual's body.

According to one embodiment, based on the degree of synchronization of bio-signals in the individual's body, individual differences in bio-signal responses arising from various factors (gender, body age, condition, measurement time, characteristics of individual bio-responses) can be resolved, This can provide personalized stress monitoring.

According to one embodiment, the dynamic causal relationship and state of stress can be confirmed by analyzing the organic connectivity of various biological signals over time.

1 is a diagram illustrating an outline of a stress state determination system according to an embodiment.
FIG. 2 is a diagram illustrating the configuration of an apparatus for determining a stress state according to an embodiment.
FIG. 3 is a diagram illustrating preprocessing of biological signals according to an embodiment.
FIG. 4 is a diagram illustrating a preprocessing process for biosignals related to heart rate according to an embodiment.
Figure 5 is a diagram for explaining a pre-processing process for biological signals related to breathing according to one embodiment.
Figure 6 is a graph showing changes in synchronization patterns between parameters due to changes in a user's stress state according to an embodiment.
Figure 7 is a diagram for explaining the correlation between target parameters according to an embodiment.
Figure 8 is a diagram for explaining a process for determining a stress state according to an embodiment.
FIG. 9 is a diagram illustrating an example of a screen that outputs a stress state according to an embodiment.
Figure 10 is a flowchart for explaining a method for determining a stress state according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be changed and implemented in various forms. Accordingly, the actual implementation form is not limited to the specific disclosed embodiments, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea described in the embodiments.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of the described features, numbers, steps, operations, components, parts, or combinations thereof, and are intended to indicate the presence of one or more other features or numbers, It should be understood that this does not exclude in advance the possibility of the presence or addition of steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art. Terms as defined in commonly used dictionaries should be interpreted as having meanings consistent with the meanings they have in the context of the related technology, and unless clearly defined in this specification, should not be interpreted in an idealized or overly formal sense. No.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted.

1 is a diagram illustrating an outline of a stress state determination system according to an embodiment.

The stress state determination system described in this specification can perform a stress state determination method that monitors the user's stress state based on the correlation between parameters calculated from biological signals. The stress state determination system may determine the user's stress state based on the correlation between parameters based on the characteristic that synchronization is achieved between parameters for each biological signal based on the user's stress state. For example, when the user's stress state changes, the values of biometric signals affected by the autonomic nervous system and parameters calculated from the biosignals may change. The stress state determination system can determine the user's stress state based on at least two biological signals, assuming that the changed parameters will be synchronized with each other.

Referring to FIG. 1 , the stress state determination device 120 may measure different biological signals 110 . The stress state determination device 120 may process the different measured biological signals 110 to extract one or more candidate parameters for each biological signal. The stress state determination device 120 selects a target parameter for each biological signal based on one or more extracted candidate parameters, determines the correlation between the target parameters for each selected biological signal, and determines the user's stress based on the determined correlation. State 130 can be determined.

FIG. 2 is a diagram illustrating the configuration of an apparatus for determining a stress state according to an embodiment.

Referring to FIG. 2 , the stress state determination device 200 may include a biosignal measurement unit 210, a biosignal processing unit 220, a biosignal analysis unit 230, and an output unit 240.

The biosignal measurement unit 210 may include sensors that can measure different biosignals of the user and convert them into data. The biosignal measurement unit 210 measures biosignals such as, for example, an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and a skin conductivity sensor, and converts them into data. It may include at least two of the available sensors. The biosignal measurement unit 210 can measure different biosignals of the user using the sensor that can measure at least two biosignals and convert them into data, as described above.

The biosignal processing unit 220 may extract one or more candidate parameters for each biosignal by processing different measured biosignals. The biological signal processing unit 220 may perform at least one signal preprocessing among noise removal, peak detection, filtering, and Fourier transformation on different measured biological signals. Additionally, the biological signal processing unit 220 may perform signal preprocessing of at least one of band-pass filtering, fast Fourier transform, notch filtering, outlier removal, low-frequency filtering, and moving window processing on different biological signals. The biological signal processing unit 220 may extract candidate parameters for each biological signal for which signal preprocessing has been performed. For example, the biological signal processing unit 220 may segment data of the biological signal through a moving window, move the moving window, and extract candidate parameters from the biological signal. Candidate parameters may include parameters that can be selected as target parameters.

The biological signal analysis unit 230 may select a target parameter for each biological signal based on one or more extracted candidate parameters. The biological signal analysis unit 230 may select a target parameter from candidate parameters for each biological signal based on at least one of a statistical analysis result and a predefined parameter importance. At this time, parameters determined to be statistically significant may be determined in advance, and a ranking based on performance for each parameter may be ranked in advance based on predefined parameter importance.

Ranking based on parameter performance is based on the results of statistical significance analysis between parameters and permutation feature importance analysis, which randomly/repeatedly measures the stress state caused by meditation and the importance of parameters using a tree-based model. This can be decided. Ranking is not only a method of analyzing permutation feature importance, but also principal component analysis (PCA), support vector machine (SVM), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), decision tree classifier (DTC), and RFC. It can also be determined through at least one of random forest classifier, repeated measure anova, and post-hoc statistical analysis methods.

The biological signal analysis unit 230 determines each biological signal based on the statistical analysis results, predefined parameter importance, parameters previously determined to be statistically significant, and the average rank of predefined parameter rankings based on the predefined parameter importance. A target parameter can be selected from candidate parameters.

The biological signal analysis unit 230 may determine the degree of correlation between target parameters for each selected biological signal and determine the user's stress state based on the determined degree of correlation. Here, the degree of correlation between the target parameters for each biological signal selected may indicate the degree of synchronization between the target parameters over time. In one embodiment, the causal relationship between the biological signals and the user's stress state may be predefined by analyzing the degree of synchronization (or organic connectivity) of various biological signals over time. For example, the stress state when the user's stress is gradually relieving and the different biosignals collected over time in this case, or the stress state when the user's stress is gradually worsening and the different biosignals collected over time in this case. Based on different biosignals, a causal relationship between different biosignals and the user's stress state may be defined in advance.

The degree of correlation and synchronization between target parameters for each biological signal can be explained in more detail in FIGS. 7 and 8.

The biosignal analysis unit 230 may determine the user's current stress level based on the determined correlation value and predefined stress level reference information. The current stress level may include multiple different stress state values. For example, the current stress level may include stress state values including at least one of none, mild, moderate, severe, and very severe.

The output unit 240 may output the determined user's stress state. The output unit 240 may output the user's current stress level. In addition, the output unit 240 may further output at least one of changes in different measured biological signals over time, changes in values of target parameters over time, and changes in synchronization patterns between different target parameters over time. there is. The output unit may include, for example, a display. The screen output by the output unit 240 can be described in more detail in FIG. 9.

FIG. 3 is a diagram illustrating preprocessing of biological signals according to an embodiment.

Referring to FIG. 3, reference number 310 may be a graph representing a raw signal of an electrocardiogram (ECG), that is, an unprocessed signal. Reference number 320 may be a graph representing a raw signal of respiration (RSP). The raw signal graph 310 of the ECG may be changed to the graph indicated by reference numeral 330 after preprocessing is performed. The respiration raw signal graph 320 may be changed as shown in the graph 340 after preprocessing is performed. The stress state determination device can obtain the graph of reference numeral 330 by performing Fast Fourier Transform (FFT) on the raw signal graph 310 of the electrocardiogram. Additionally, the stress state determination device may obtain a graph indicated by reference numeral 340 by performing peak detection on the raw signal graph 320 of respiration.

The stress state determination device of the present specification is described focusing on an embodiment in which the biosignals are electrocardiogram and respiration, but is not limited to this and is based on mechanisms within the human body among other autonomic nervous system biosignals (pupil response, blood sugar response, hormonal response, etc.) The explanation may be based on at least one or in addition to at least one of these.

FIG. 4 is a diagram illustrating a preprocessing process for biosignals related to heart rate according to an embodiment.

The following processes may be explicitly described as being performed by the stress state determination device, but may also be performed by the stress state determination device or a biosignal processing unit included in the stress state determination device.

Referring to FIG. 4, in step 410, the stress state determination device may measure the electrocardiogram by measuring biosignals related to the heartbeat. In step 420, the stress state determination device may remove noise from the measured electrocardiogram. Additionally, the stress state determination device can apply a moving window technique or a sliding window technique of 180 seconds, 120 seconds, and 60 seconds to the ECG from which noise has been removed. In step 430, the stress state determination device may apply a peak detection algorithm to the ECG.

The stress state determination device may calculate RRI (R-R Interval) from the electrocardiogram. Later, in step 450, the stress state determination device may calculate parameters in the time domain. Parameters calculated in the time domain include, for example, mean RRI, standard deviation of normal to normal interval (SDNN), and the square root of the mean of the square of the difference between adjacent RR intervals. -mean-square of successive differences; RMSSD), pNN20(percentage of successive RR interval differences whose absolute value exceeded 20 ms), pNN50(percentage of successive RR interval differences whose absolute value exceeded 50 ms), range RRI(range RRI), SD1 (standard deviation), SD2, SD2/SD1, CVSD (coefficient of variation of successive differences), CVRRI (coefficient of variation RRI), median RRI (median RRI), mean heart rate (HR), maximum HR (max) HR), minimum HR (min HR), sd HR, triangular index, etc. may be included.

Parameters calculated in the time domain can be calculated as follows:

Additionally, the stress state determination device may perform interpolation and re-sampling after calculating the RRI from the electrocardiogram. Additionally, the stress state determination device may perform fast Fourier transform (Hanning window). Afterwards, in step 440, the stress state determination device may calculate parameters in the frequency domain. Parameters calculated in the frequency domain include, for example, very low frequency (VLF), which refers to the variance of HRV at very low frequencies (equivalent to power), and the variation of HRV at low frequencies (equivalent to power). ), at least among LF (low frequency), HF (high frequency), LF/HF, HFnu (High frequency normalized units), and LFnu (Low frequency normalized units), indicating changes in HRV at high frequencies (same as power) It can contain one.

Parameters calculated in the frequency domain can be calculated as follows:

For example, VLF may be 0.003 to 0.04 Hertz (Hz) as an initial value, LF may be 0.04 to 0.15 Hertz (Hz) as an initial value, and HF may be 0.15 to 0.4 Hertz (Hz) as an initial value. .

plot)에 의한 CSI(cardiosympathetic index), CVI(cardiac vagal index), 수정된 CSI(modified CSI), SD1, SD2, SD2/SD1 및 엔트로피(Entropy)에 의한 샘플 엔트로피(SampEn)을 포함할 수 있다.Additionally, the stress state determination device may calculate the additional index HRV (Heart Rate Variability) after calculating the RRI from the electrocardiogram. Additional index HRV is obtained using a Poincaré plot.

It may include cardiosympathetic index (CSI), cardiac vagal index (CVI), modified CSI (modified CSI), SD1, SD2, SD2/SD1, and sample entropy (SampEn) by entropy.

The additional index HRV can be calculated as follows:

Figure 5 is a diagram for explaining a pre-processing process for biological signals related to breathing according to one embodiment.

The following processes may be explicitly described as being performed by the stress state determination device, but may also be performed by the stress state determination device or a biosignal processing unit included in the stress state determination device.

Referring to FIG. 5, in step 510, the stress state determination device may measure a respiratory signal, which is a biological signal from the lungs. Additionally, in step 530, the stress state determination device may measure a signal from the electrocardiogram. The stress state determination device may remove noise from the respiratory signal in step 520 and remove noise from the electrocardiogram in step 540. The stress state determination device can detect window-based signals by applying a moving window technique to noise-removed respiratory signals and electrocardiograms. Additionally, the stress state determination device can apply a peak detection algorithm to the electrocardiogram to which the moving window technique is applied. In step 550, the stress state determination device may detect a respiratory signal from the pre-processed ECG by applying an ECG derived respiration (EDR) technique to the pre-processed ECG.

In step 560, the stress state determination device may apply a peak detection algorithm to the preprocessed respiration signal and the respiration signal derived from the electrocardiogram. In step 570, the stress state determination device may calculate parameters for the breathing signal.

Parameters for the respiratory signal can be calculated using the following equations.

Figure 6 is a graph showing changes in synchronization patterns between parameters due to changes in a user's stress state according to an embodiment.

Referring to Figure 6, parameters may include SDNN and mean RSP. Graph (a) may include parameter values of SDNN and mean RSP in condition 0. Graph (b) may include parameter values of SDNN and mean RSP in state 1. Graph (c) may include parameter values of SDNN and mean RSP in state 2. State 0 corresponds to the resting state, state 1 corresponds to the initial state of meditation, which is the state 0 to 10 minutes after starting meditation, and state 2 corresponds to the late meditation state, which is the state 10 to 20 minutes after starting meditation. We can respond. Based on the ECG and respiration signals measured over time, SDNN can be calculated from the ECG, and the mean RSP can be calculated from the respiration signal.

That is, the method for determining the stress state described in this specification is based on the above graph, when the user's stress is alleviated, the correlation (r) between the heart rate signal parameter (SDNN) and the respiratory signal parameter (mean RSP) is the state. It can be calculated as -0.170 in 0, 0.781 in state 1, and 0.919 in state 2. As the correlation between states 0 and 1 increases, a pattern in which the degree of synchronization between parameters increases over time can be confirmed. The correlation between the parameters of the heart rate signal and the parameters of the breathing signal may show a pattern of increasing positive correlation when the user responds in a subjective questionnaire that stress is alleviated, and this pattern is consistent with the stress condition described herein. It can be a premise for a decision method. Since a pattern has emerged in which the degree of synchronization increases in a state in which the user's stress is alleviated, this pattern can be a premise when the method for determining the stress state described herein is performed on the user. When the stress state determination device determines the user's stress state, if the correlation between the target parameters for each biosignal of the user shows a pattern of increasing positive correlation, the stress state determination device determines whether the user's stress state is being alleviated. It can be decided that Conversely, when the user's stress intensifies due to a stressful stimulus being applied to the user, the homeostasis of the autonomic nervous system may be broken, resulting in a pattern of decreased synchronization. This pattern can also serve as a premise for the stress state determination method described herein.

The correlations in FIG. 6 were calculated based on the parameters of biological signals calculated in a situation where the user is relieving stress while meditating, but the parameters are not limited to this and can be calculated even in a situation where the user's stress is intensified by a stress stimulus. You can. In situations where the user's stress intensifies, the calculated correlations may show a different pattern from the correlations in FIG. 6.

Figure 7 is a diagram for explaining the correlation between target parameters according to an embodiment.

Referring to FIG. 7 , the table of FIG. 7 may include parameters calculated based on biosignals related to breathing and biosignals related to the heart. For the explanation of FIG. 7 , an embodiment corresponding to parameters calculated based on biosignals related to breathing and biosignals related to the heart will be described. However, parameters calculated based on other biosignals depending on the embodiment are shown in the table of FIG. 7 may be included in

State 0 corresponds to the resting state, state 1 corresponds to the initial state of meditation, which is the state 0 to 10 minutes after starting meditation, and state 2 corresponds to the late meditation state, which is the state 10 to 20 minutes after starting meditation. We can respond. Figure 7 may show the correlation between parameters calculated in each state. Referring to Figure 7, for example, the correlation between mean RSP and SDNN in state 0 may be 0.084, the correlation between mean RSP and SDNN in state 1 may be 0.333, and the correlation between mean RSP and SDNN in state 2 may be 0.333. The degree may be 0.445. The correlation between each parameter may show a pattern of increasing positive or negative correlation from state 0 to state 2. This pattern can be the premise of the stress state determination method described herein.

Figure 8 is a diagram for explaining a process for determining a stress state according to an embodiment.

Referring to FIG. 8, in step 810, N sensors can each collect biological signals. Here, N may be a natural number of 2 or more. The stress state determination device can measure different biological signals by having N sensors each collecting biological signals. The stress state determination device may extract one or more candidate parameters for each biological signal by processing the different biological signals measured in step 820. In step 820, the stress state determination device may select a target parameter for each biological signal based on one or more extracted candidate parameters. Parameters shaded in the figure corresponding to step 820 may be target parameters. The stress state determination device may select a target parameter based on a predetermined statistical analysis result, the magnitude or significance of the variation of the parameter, the importance of the parameter, and the ranking of the parameter.

In step 830, the stress state determination device may calculate the value of each target parameter for each time. Referring to FIG. 8, it can be seen that the values of each target parameter become almost similar over time. The stress state determination device may determine the degree of correlation between target parameters for each biological signal based on the values of each target parameter.

The stress state determination device may determine the user's stress state based on the correlation between target parameters in step 840. Additionally, the stress state determination device can determine the causal relationship between the correlation and the stress state.

FIG. 9 is a diagram illustrating an example of a screen that outputs a stress state according to an embodiment.

The stress state determining device may output the determined stress state of the user. Referring to FIG. 9 , the screen may include graphs 910 corresponding to raw signals of different biological signals. In the embodiment of Figure 9, graphs 910 of log signals corresponding to heart rate and respiration signals may be included on the screen. Additionally, the screen of FIG. 9 may include a graph 920 to indicate the synchronization of parameters calculated from heart rate and respiration parameters. Reference number 930 is a predefined synchronization pattern and may be graphs showing a synchronization pattern of parameters corresponding to recently measured biological signals and a causal relationship between stress states and biological signals over time.

Additionally, the screen of FIG. 9 may include a visualized user's current stress level 940. The current stress level may include multiple different stress state values. A plurality of different stress state values may include, for example, none, mild, moderate, severe, and very severe. The stress state determination device may determine the user's stress state based on the correlation between target parameters for each biological signal. The stress state can be expressed as a current stress level and output as reference number 940.

Figure 10 is a flowchart for explaining a method for determining a stress state according to an embodiment.

Referring to FIG. 10, in step 1010, the stress state determination device may measure different biological signals of the user. The stress state determination device uses at least two of the following sensors that can measure and convert biosignals into data, such as an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and skin conductance. This allows different biological signals to be measured.

In step 1020, the stress state determination device may extract one or more candidate parameters for each biological signal by processing different measured biological signals. The stress state determination device performs at least one signal preprocessing of noise removal, peak detection, filtering, and Fourier transformation on different measured biological signals, and selects candidate parameters for each biological signal on which signal preprocessing has been performed. It can be extracted.

In step 1030, the stress state determination device may select a target parameter for each biological signal based on one or more extracted candidate parameters. The stress state determination device may select a target parameter from candidate parameters for each biological signal based on at least one of statistical analysis results and predefined parameter importance.

In step 1040, the stress state determination device may determine the degree of correlation between target parameters for each selected biological signal. Here, the degree of correlation between the target parameters for each biological signal selected may indicate the degree of synchronization between the target parameters over time.

In step 1050, the stress state determination device may determine the user's stress state based on the determined correlation. The stress state determination device may determine the user's current stress level based on the determined correlation value and predefined stress level reference information. Here, the current stress level may include a plurality of different stress state values.

In step 1060, the stress state determination device may output the determined stress state of the user. The stress state determination device may output the user's current stress level. In addition, the stress state determination device may further output at least one of changes in different measured biological signals over time, changes in values of target parameters over time, and changes in synchronization patterns between different target parameters over time. .

The hardware devices described above may be configured to operate as one or multiple software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on this. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

120, 200: Stress state determination device 210: Biological signal measurement unit
220: Biological signal processing unit 230: Biological signal analysis unit
240: output unit

Claims (14)

In the stress state determination device,
a bio-signal measurement unit including sensors for measuring different bio-signals of a user;
a bio-signal processing unit that extracts one or more candidate parameters for each bio-signal by processing the different measured bio-signals;
Selecting a target parameter for each biosignal based on the one or more extracted candidate parameters, determining a correlation between target parameters for each biosignal, and determining the user's stress state based on the determined correlation. Biological signal analysis department; and
An output unit that outputs the determined stress state of the user
Including,
Stress state determination device. According to paragraph 1,
The biological signal measuring unit,
Containing at least two of an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and a skin conductance sensor,
Stress state determination device. According to paragraph 1,
The biological signal processing unit,
Performing at least one signal preprocessing of noise removal, peak detection, filtering, and Fourier transformation on the different measured biological signals,
Extracting candidate parameters for each biological signal on which the signal preprocessing was performed,
Stress state determination device. According to paragraph 1,
The biosignal analysis unit,
Selecting the target parameter from candidate parameters for each biological signal based on at least one of statistical analysis results and predefined parameter importance,
Stress state determination device. According to paragraph 1,
The biosignal analysis unit,
Determine the user's current stress level based on the determined correlation value and predefined stress level reference information,
The current stress level is,
Containing a plurality of different stress state values,
Stress state determination device. According to paragraph 1,
The correlation between the target parameters for each selected biological signal is,
Indicating the degree of synchronization between target parameters over time,
Stress state determination device. According to paragraph 1,
The output unit,
Output the user's current stress level,
Further outputting at least one of changes in the measured different biological signals over time, changes in values of the target parameters over time, and changes in synchronization patterns between different target parameters over time.
Stress state determination device. In the method of determining the stress state,
Measuring different biosignals of the user;
extracting one or more candidate parameters for each biological signal by processing the different measured biological signals;
selecting a target parameter for each biological signal based on the one or more extracted candidate parameters;
determining a correlation between target parameters for each selected biological signal;
determining the user's stress state based on the determined correlation; and
Outputting the determined stress state of the user
Including,
How to determine your stress state. According to clause 8,
The step of measuring the different biological signals is,
Measuring the different bio-signals using at least two of an electrocardiogram sensor, a respiration sensor, a pupil measurement sensor, a blood sugar measurement sensor, a hormone measurement sensor, a blood pressure sensor, a temperature sensor, and a skin conductance sensor.
Including,
How to determine your stress state. According to clause 8,
The step of extracting the candidate parameters is,
performing at least one signal preprocessing of noise removal, peak detection, filtering, and Fourier transform on the measured different biological signals; and
Extracting candidate parameters for each biological signal on which the signal preprocessing was performed
Including,
How to determine your stress state. According to clause 8,
The step of selecting the target parameter is,
Selecting the target parameter from candidate parameters for each biological signal based on at least one of statistical analysis results and predefined parameter importance.
Including,
How to determine your stress state. According to clause 8,
The step of determining the user's stress state is,
Determine the user's current stress level based on the determined correlation value and predefined stress level reference information,
The current stress level is,
Containing a plurality of different stress state values,
How to determine your stress state. According to clause 8,
The correlation between the target parameters for each selected biological signal is,
Indicating the degree of synchronization between target parameters over time,
How to determine your stress state. According to clause 8,
The output step is,
outputting the user's current stress level; and
Further outputting at least one of changes over time in the measured different biological signals, changes in values of the target parameters over time, and changes in synchronization patterns between different target parameters over time.
Including,
How to determine your stress state.

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