KR102223512B1 - Neckband type healthcare service method and system - Google Patents

Abstract

In the present invention, when a user carries a neckband-type measuring device around his neck and measures the user's own biometric information such as brain waves, pulse waves, and respiration rate and transmits them to the management server, the management server provides the same age group and other healthcare patterns and Disclosed is a neckband-type healthcare service system and method for managing the user's health by comparing and notifying the result to the user. The disclosed neckband type healthcare service system measures the user's EEG signal, pulse wave signal, and respiration rate, and uses the measured EEG signal as a unique ID to transmit pulse wave signal and respiratory rate information to a healthcare server. ; With respect to the EEG signal, pulse wave signal, and respiratory rate information received from the neckband type measuring device, the EEG signal and the respiratory rate information are stored by corresponding to each user information having the EEG signal as a unique ID, and the EEG signal, the pulse wave signal, and the respiration rate A healthcare server for obtaining a healthcare pattern by comparing and analyzing number information with other users by gender, age group, region, season, and month, and transmitting the obtained healthcare pattern to each corresponding user terminal; And a healthcare database (DB) storing the pulse wave signal and respiratory rate information by corresponding to each user information by using the brain wave signal as a unique ID for the brain wave signal, the pulse wave signal, and the respiratory rate information received from the neckband type measuring device. Includes.

Description

Neckband type healthcare service method and system {Neckband type healthcare service method and system}

The present invention relates to a neckband-type healthcare service system and method, and more specifically, a neckband-type measuring device, like a necklace worn around a person's neck, while the user carries a neckband-type measuring instrument around the user's neck, When information is measured and transmitted to the management server, the management server compares the healthcare pattern with other users' healthcare patterns such as the same age group and informs the user of the result so that the user's health can be managed using a neckband type measuring device. It relates to a band-type healthcare service system and method.

As medicine advances, devices for detecting health status by measuring electroencephalogram (EEG) or photo-plethysmography (PPG) signals are being developed.

The EEG meter detects and analyzes the user's EEG in university hospitals and hospitals, and objectively measures the cognitive strength, cognitive speed, concentration, and left/right brain activity detected from the EEG, thereby evaluating essential skills required for learning, and attention deficit excess. To diagnose behavioral disorder (ADHD), attention deficit disorder (ADD), depression, learning disability, anxiety disorder, insomnia, autism, dementia, etc. do.

However, the conventional EEG meter consists of complex measurement equipment, and cannot provide various application services using this, and an application that can manage sleep by analyzing the characteristics of EEG according to the sleep cycle (90 minutes 1 cycle). There is a problem that it cannot provide.

On the other hand, the pulse wave meter is a method of restoring the pulse wave of blood passing through the skin tissue by excitation of a light source on a person's beneath skin and measuring the light source reflected from the skin tissue or transmitted through the skin tissue. Therefore, it can be said that the light source/receiving sensor is a necessary component to construct the optical volume pulse wave. In some cases, the implementation of the light source is omitted and ambient light is used, but there is a problem in that it is difficult to use it in various environments because the signal is greatly affected as the ambient light changes.

Therefore, assuming a bio-signal measurement environment using personalized electronic devices and wearable devices, the weight and volume of the system need to be minimized, and it is required to implement low power while saving power. do.

In addition, there is a need for a technology capable of managing a health state while checking at what stage one's health state is by comparing the measured body information about oneself with other people.

Korean Patent Publication No. 0954817 (Registration Date: April 19, 2010)

An object of the present invention for solving the above-described problems is a management server by measuring biometric information such as the user's own brain waves, pulse waves, and respiratory rate while the user carries a neckband-type measuring device around the neck like a necklace worn around a person's neck. When transmitted to, the management server compares the health care pattern of other users such as the same age group and informs the user of the result, so that the user's health can be managed using a neckband type meter. And to provide a method.

The neckband-type healthcare service system according to the present invention for achieving the above object measures the user's EEG signal, the pulse wave signal, and the respiration rate in a state worn on the user's neck, and the measured EEG signal is a unique ID. A neckband type measuring device for transmitting the pulse wave signal and respiratory rate information to the healthcare server; And with respect to the EEG signal, the pulse wave signal and the respiratory rate information received from the neckband type measuring device, the EEG signal is stored in correspondence with each user information having the EEG signal as a unique ID to store the pulse wave signal and the respiratory rate information, and the EEG signal and the pulse wave signal and It includes a healthcare server that compares and analyzes respiratory rate information with other users by gender, age group, region, season, and month to obtain a healthcare pattern, and transmits the obtained healthcare pattern to each corresponding user terminal.

Here, the neckband type measuring device is formed along an arc of an opened circle, has one or more buttons on its surface, and one or more components are provided inside the neckband body electrically coupled to the one or more buttons ; A first earphone unit electrically connected to one end of the neckband body, including an EEG sensor for measuring a user's EEG, and including a speaker for outputting sound; A second earphone unit electrically connected to the other end of the neckband body, including a pulse wave sensor for measuring a user's pulse wave, and a speaker for outputting sound; And converting and analyzing the EEG signal measured through the first earphone unit into data provided inside the neckband body, converting and analyzing the pulse wave signal measured through the second earphone unit into data, and analyzing the EEG signal. And an analysis engine configured to diagnose a user's emotion and physical state according to a correlation between the pulse wave signal and output a care signal for improving the emotion and physical state.

In addition, the healthcare server, a communication unit for communicating with the neckband type measuring device; A healthcare database storing pulse wave signal and respiratory rate information by corresponding to each user information by using an EEG signal as a unique ID with respect to the brain wave signal, pulse wave signal, and respiratory rate information received from the neckband type measuring device; A pattern analysis unit that compares and analyzes the EEG signal, pulse wave signal, and respiratory rate information with other users by gender, age group, region, season, and month to obtain a health care pattern; And a controller configured to control to obtain a health care pattern for each user through the pattern analysis unit, and to transmit the acquired health care pattern to a corresponding user terminal.

In addition, the neckband type measuring device is provided inside the neckband body, the storage unit for storing as data a sound source signal corresponding to a care signal for improving the emotional and physical condition; A power generating unit provided inside the neckband body, detecting vibrations according to the user's movement, and generating power by generating power using the sensed vibration signals; And a respiration rate measuring unit provided inside the neckband body, detecting a respiration signal for the user's respiration with a gyro sensor, and measuring a respiration rate from the detected respiration signal.

And, the pattern analysis unit, by analyzing the respiratory rate information received from the neckband type measuring device to obtain a breathing pattern including the interval and intensity of the user's inhalation and exhalation, or receive the EEG signal received from the neckband type measuring device. The average heart rate, average heart rate and average by analyzing the pulse wave signal received from the neckband type measuring device or obtaining a two-dimensional space-frequency pattern with event-related information by comparing it with a steady state EEG signal of a previously constructed two-dimensional electrode-frequency power pattern. Information including the result of calculating the pulse wave height, acupuncture elasticity index (EEI), redundant expansion index (DDI), and redundant elasticity index (DEI) is obtained.

On the other hand, the neckband type healthcare service method according to the present invention for achieving the above object, (a) measuring the user's EEG signal, pulse wave signal, and respiration rate while the neckband type meter is worn on the user's neck. The step of doing; (b) converting the measured EEG signal, pulse wave signal, and respiratory rate into data by a neckband-type measuring device and transmitting the EEG signal as body detection information as a unique ID to a healthcare server; (c) storing the pulse wave signal and the respiratory rate information by corresponding to each user information having the brain wave signal as a unique ID with respect to the brain wave signal, the pulse wave signal, and the respiratory rate information received from the neckband type measuring device in the healthcare server. ; (d) comparing and analyzing the EEG signal, pulse wave signal, and respiratory rate information with other users by gender, age group, region, season, and month in a healthcare server; And (e) transmitting the healthcare pattern obtained as a result of comparison and analysis by the healthcare server to each corresponding user terminal.

In addition, in the step (a), the neckband type meter enters the healthcare mode according to the user's selection of the mode button, the first earphone unit measures the user's EEG through the EEG sensor, and the second earphone unit measures the user’s EEG. Through the measurement of the user's pulse wave, the respiratory rate measurement unit detects a breathing signal for the user's breath through a gyro sensor, and measures the breathing rate from the detected breathing signal.

In addition, in the step (a), the neckband type measuring device detects vibration according to the user's movement through a vibration sensor in the power generator provided inside, and generates power using the detected vibration signal to charge the power supply unit. It is done.

In addition, in the step (d), the healthcare server analyzes the respiratory rate information received from the neckband-type meter to obtain a breathing pattern including the interval and intensity of the user's inhalation and exhalation, or the neckband-type meter A two-dimensional space-frequency pattern having event-related information is obtained by comparing the EEG signal received from the EEG signal with a steady state EEG signal of a previously constructed two-dimensional electrode-frequency power pattern.

And, in the step (d), the healthcare server analyzes the pulse wave signal received from the neckband-type measuring device, and analyzes the average heart rate, average pulse wave height, acupuncture elasticity index (EEI), redundant expansion index (DDI), and redundant elasticity index. Information including the result of calculating (DEI) is obtained.

According to the present invention, a device for measuring body information of a user can be implemented in a low-power form that saves power while minimizing weight or volume so that the user can carry it around the neck.

In addition, if the user's biometric information such as EEG, pulse wave, and respiration rate is measured with a neckband type and transmitted to the management server, the management server compares it with the healthcare patterns of other users, such as the same age group, and informs the user of the result. As a result, the user's health can be easily managed with a neckband type.

1 is a block diagram schematically showing the overall configuration of a neckband type healthcare service system according to an embodiment of the present invention.
2 is a view showing the external shape of the neckband type measuring device according to an embodiment of the present invention.
3 is a block diagram schematically showing an example of an internal configuration of a neckband type measuring device according to an embodiment of the present invention.
4 is a block diagram schematically showing a functional block of a healthcare server according to an embodiment of the present invention.
5 is a view showing an overall flow chart for explaining a neckband type healthcare service method according to an embodiment of the present invention.
6 is a view showing an operation flow chart for explaining a healthcare service method of a neckband type measuring device according to an embodiment of the present invention.
7 is a diagram illustrating a waveform of an EEG signal measured through a first earphone unit over time according to an exemplary embodiment of the present invention.
8 is a diagram illustrating a waveform of a pulse signal measured through a second earphone unit according to an embodiment of the present invention.
9 is a diagram showing an example of heart-brain synchronization information according to an embodiment of the present invention.
FIG. 10 is a diagram illustrating a process in which a breathing training system through analysis of a user's breathing pattern according to an embodiment of the present invention measures inhalation and exhalation and compares it with a target value.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art may easily implement the present invention. The present invention may be implemented in various different forms and is not limited to the embodiments described herein.

In order to clearly describe the present invention, parts irrelevant to the description have been omitted, and the same reference numerals are attached to the same or similar components throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

When a part is referred to as being "on" another part, it may be directly on top of another part, or other parts may be involved in between. In contrast, when a part is referred to as being "directly above" another part, no other part is involved in between.

Terms such as first, second and third are used to describe various parts, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one part, component, region, layer or section from another part, component, region, layer or section. Accordingly, a first part, component, region, layer or section described below may be referred to as a second part, component, region, layer or section without departing from the scope of the present invention.

The terminology used herein is only for referring to specific embodiments and is not intended to limit the present invention. Singular forms as used herein also include plural forms unless the phrases clearly indicate the opposite. As used in the specification, the meaning of "comprising" specifies a specific characteristic, region, integer, step, action, element and/or component, and the presence of another characteristic, region, integer, step, action, element and/or component, or It does not exclude additions.

Terms indicating a relative space such as "below" and "above" may be used to more easily describe the relationship of one part to another part shown in the drawings. These terms are intended to include other meanings or operations of the device in use together with their intended meaning in the drawings. For example, if the device in the drawings is turned over, certain parts described as being "below" other parts are described as being "above" other parts. Thus, the exemplary term “down” includes both up and down directions. The device can be rotated by 90 degrees or other angles, and terms that refer to relative space are interpreted accordingly.

Although not defined differently, all terms including technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms defined in a commonly used dictionary are additionally interpreted as having a meaning consistent with the related technical literature and the presently disclosed content, and are not interpreted in an ideal or very formal meaning unless defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art may easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein.

1 is a block diagram schematically showing the overall configuration of a neckband type healthcare service system according to an embodiment of the present invention.

Referring to FIG. 1, a neckband type healthcare service system 100 according to an embodiment of the present invention includes a neckband type measuring device 110 to 114, a healthcare server 120, and a healthcare database 130. do.

The neckband type measuring device 110 is formed in a shape that can be worn on the user's neck, and operates in a healthcare mode or a sound output mode according to the user's selection.

Here, in the healthcare mode, the user's EEG signal, pulse wave signal, and respiration rate are measured while worn on the user's neck, and the pulse wave signal and respiratory rate information are stored in the healthcare server 120 by using the EEG signal as a unique ID. This is the mode of operation to transmit to.

In addition, the sound output mode is an operation mode in which an acoustic signal is output as an audible sound through the left earphone and the right earphone electrically connected to the neckband type measuring device 110.

The healthcare server 120 stores the pulse wave signal and respiratory rate information by corresponding to each user information by using the brain wave signal as a unique ID for the brain wave signal, the pulse wave signal, and the respiratory rate information received from the neckband type measuring device 110. And, by comparing and analyzing the EEG signal, pulse wave signal, and respiratory rate information with other users by gender, age group, region, season, and month, a health care pattern is obtained, and the acquired health care pattern is transmitted to each corresponding user terminal. Will be given.

The healthcare database 130 stores EEG signals, pulse wave signals, and respiratory rate information transmitted from each neckband type measuring device 110 to 114 in correspondence with each user's unique ID, or stores EEG signals and pulse wave signals for each user and It stores health care patterns obtained by comparing and analyzing respiratory rate information by gender, age group, region, season, and month.

FIG. 2 is a view showing an external shape of a neckband-type measuring device according to an embodiment of the present invention, and FIG. 3 is a schematic diagram showing an example of an internal configuration of a neckband-type measuring device according to an embodiment of the present invention.

2 and 3, the neckband type measuring device 110 according to the present invention includes a neckband body 210, a first earphone part 260 and a second earphone electrically connected to the neckband body 210. It includes an earphone unit 270.

Here, an EEG sensor for measuring a user's EEG in the vicinity of the neckband body 210 and a pulse wave sensor for measuring a user's pulse wave in the vicinity of the neckband body 210 may be further included.

In the exemplary embodiment of the present invention, an EEG sensor is mounted on the first earphone unit 260 and a pulse wave sensor is mounted on the second earphone unit 270 as an example. However, the present invention is not limited thereto, and the EEG sensor may be mounted inside the neckband body 210, may be mounted on the second earphone unit 270, and the pulse wave sensor is also mounted inside the neckband body 210. Or it may be mounted on the first earphone unit 260.

The neckband body 210 is formed along an arc of an opened circle, and one or more buttons 212 to 220 are provided on the surface, and one or more buttons 212 to 220 are provided with one or more components therein. Is electrically coupled with

The first earphone unit 260 is electrically connected to one end of the neckband body 210, includes an EEG sensor for measuring a user's EEG, and a first speaker for outputting sound.

The second earphone unit 270 is electrically connected to the other end of the neckband body 210, includes a pulse wave sensor for measuring a user's pulse wave, and a second speaker for outputting sound.

Here, the one or more buttons 212 to 220 provided on the outer surface of the neckband body 210 include a play button 212 for playing, stopping, or pausing a sound source; A call button 214 for the neckband body to operate in a call mode; A volume button 216 for adjusting the volume of sound output through the first earphone unit 260 or the second earphone unit 270; A selection button 218 for selecting a next song or a previous song for the sound source; It includes a power button 220 for instructing to supply power required for operation.

On the other hand, the analysis engine unit 230 not shown in FIG. 2 is provided inside the neckband body 210, converts the EEG signal measured through the first earphone unit 260 into data and analyzes it, and the second A care signal that converts and analyzes the pulse wave signal measured through the earphone unit 270 into data, diagnoses the user's emotion and physical condition according to the correlation between the EEG signal and the pulse wave signal, and improves the emotional and physical condition Is controlled to output.

The storage unit 310 is provided inside the neckband body 210 and stores as data a sound source signal corresponding to a care signal that improves emotions and physical conditions.

The respiratory rate measurement unit 320 is provided inside the neckband body 210, detects a breathing signal for the user's breathing with a gyro sensor, and measures the breathing rate from the detected breathing signal.

The power generation unit 330 is provided inside the neckband body 210, detects vibration according to the user's movement, and generates power using the detected vibration signal.

The power supply unit 340 is provided inside the neckband body 210 and supplies power required for operation of one or more buttons 212 to 220 and components provided inside the neckband body 210.

The connector 350 transmits/receives data to and from an external device in a USB type, or receives power from an external device in a USB type.

The communication unit 360 is provided inside the neckband body 210, and the analysis engine unit 230 diagnoses the user's emotion and physical state according to the correlation between the EEG signal and the pulse wave signal, and outputs the emotion and physical state. It transmits a care signal that improves to an external device through short-range communication. In this case, as short range communication technology, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and the like may be used.

In addition, although not shown in FIG. 2, on the outer surface of the neckband body 210, a flow preventing portion for preventing the neckband body from moving or flowing according to the user's movement may be formed in a protrusion or convex shape.

4 is a block diagram schematically showing a functional block of a healthcare server according to an embodiment of the present invention.

Referring to FIG. 4, the healthcare server 120 according to an embodiment of the present invention includes a communication unit 410, a healthcare database 130, a pattern analysis unit 420, and a control unit 430.

The communication unit 410 communicates with the neckband type measuring device 110 through a wired or wireless communication network.

The healthcare database 130 stores the pulse wave signal and respiratory rate information by corresponding to each user information by using the brain wave signal as a unique ID for the brain wave signal, the pulse wave signal, and the respiratory rate information received from the neckband type measuring device.

The pattern analysis unit 420 obtains a healthcare pattern by comparing and analyzing the EEG signal, pulse wave signal, and respiratory rate information with other users by gender, age group, region, season, and month.

In addition, the pattern analysis unit 420 may analyze the respiratory rate information received from the neckband type measuring device to obtain a breathing pattern including the interval and intensity of the user's inhalation and exhalation.

In addition, the pattern analysis unit 420 compares the EEG signal received from the neckband-type measuring device with a steady state EEG signal of a pre-built two-dimensional electrode-frequency power pattern, or event-related (de) synchronization (event-related (de) ) Synchronization: ERD/ERS) It is possible to obtain a two-dimensional space-frequency ERD/ERS pattern having information.

In addition, the pattern analysis unit 420 analyzes the pulse wave signal received from the neckband-type measuring device and calculates the average heart rate, average pulse wave height, acupuncture elasticity index (EEI), redundant expansion index (DDI), and redundant elasticity index (DEI). Pulse wave information including one result value may be obtained.

Accordingly, the healthcare database 130 stores the healthcare pattern obtained through the pattern analysis unit 420 in correspondence with each user ID, or the breathing pattern obtained through the pattern analysis unit 420 or the EEG pattern at rest. , 2D space-frequency ERD/ERS pattern, and pulse wave information are stored.

The control unit 430 controls to obtain a health care pattern for each user through the pattern analysis unit, and controls to transmit the acquired health care pattern to a corresponding user terminal.

5 is a view showing an overall flow chart for explaining a neckband type healthcare service method according to an embodiment of the present invention.

Referring to FIG. 5, the neckband-type healthcare service system 100 according to the present invention includes a user's EEG signal when the neckband-type measuring device 110 is worn on the user's neck and in a healthcare mode. And measure the pulse wave signal and respiration rate (S510).

Subsequently, the neckband type measuring device 110 converts the EEG signal, the pulse wave signal, and the respiratory rate into data and transmits the EEG signal to the healthcare server 120 as body detection information having a unique ID (S520).

Subsequently, the healthcare server 120 corresponds to user information having an EEG signal as a unique ID with respect to the EEG signal, pulse wave signal, and respiratory rate information received from the neckband type measuring device, so that the body detection information is stored in the healthcare database 130. It is stored in (S530).

Subsequently, the healthcare server 120 compares and analyzes the EEG signal, pulse wave signal, and respiratory rate information with other users by gender, age group, region, season, and month (S540).

Here, the healthcare server 120 may analyze the respiratory rate information through the pattern analysis unit 420 to obtain a breathing pattern including the interval and intensity of the user's inhalation and exhalation as shown in FIG. 10. FIG. 10 is a diagram illustrating a process in which a breathing training system through analysis of a user's breathing pattern according to an embodiment of the present invention measures inhalation and exhalation and compares it with a target value. As shown in Fig. 10, the preset time and interval of inhalation and exhalation indicated by dotted lines (respiratory training target pattern, e.g., inhalation and exhalation in a ratio of 1:2) are measured in real time. And compared with the exhaled pattern, it is possible to analyze the degree of pattern matching.

In addition, the healthcare server 120 compares the user's EEG signal with a steady state EEG signal of a pre-built two-dimensional electrode-frequency power pattern, or event-related (de)synchronization: ERD/ ERS) it is possible to obtain a two-dimensional space-frequency ERD/ERS pattern having information. In addition, the healthcare server 120 analyzes the pulse wave signal received from the neckband type measuring device and calculates the average heart rate, the average pulse wave height, the acupuncture point elasticity index (EEI), the redundant expansion index (DDI), and the redundant elasticity index (DEI). Pulse wave information including one result value may be obtained.

Then, the healthcare server 120 transmits the healthcare pattern obtained as a result of the comparison and analysis to each corresponding user terminal (S550).

Therefore, the user wearing the neckband type measuring device 110 on the neck checks the health care pattern received by the user terminal such as a smartphone that he carries, and by gender and age group, by region, by season, by month, You can check your health status through the data analyzed and compared with them.

6 is a view showing an operation flow chart for explaining a healthcare service method of a neckband type measuring device according to an embodiment of the present invention.

6, in the neckband-type wearable healthcare system 100 according to the present invention, first, the neckband body 210 enters a healthcare mode according to a selection operation of a mode button according to a user's manipulation (S610). ).

At this time, after power is supplied to each component from the power supply unit 340 provided inside the neckband body 210, the health care mode may be entered according to a selection operation of the mode button.

In addition, the neckband body 210 detects vibration according to the user's movement through a vibration sensor in the power generation unit 330 provided therein, and generates power using the detected vibration signal to the power supply unit 340. Can be charged.

In addition, the neckband body 210 transmits/receives data to/from an external device in a USB type through a connector 350 provided externally, or receives power in a USB type from the outside of the connector 350 and charges the power supply unit 340 can do.

Subsequently, the first earphone unit 260 measures the user's EEG signal as shown in FIG. 7 through the EEG sensor (S620).

EEG is an electric current generated according to the brain's activity or a waveform that is derived and amplified and recorded. 7 is a diagram illustrating a waveform of an EEG signal measured through a first earphone unit over time according to an exemplary embodiment of the present invention. For example, the EEG information may include the amplitude of the EEG waveform and the frequency information of the EEG waveform.

Here, the first earphone unit 260 may acquire a plurality of brainwave information. The plurality of EEG information may mean a plurality of EEG information measured at different locations on the scalp in the same time period. Alternatively, it may mean a plurality of EEG information measured at different time periods at the same location on the scalp. Here, the same time zone means that the start time and the end time of the measurement are the same, and the different time zone means that at least one of the start time and the end time of the measurement are different.

When the EEG information acquired at the first location in the same time period is defined as first EEG information, the EEG information acquired at a second location different from the first location may be defined as second EEG information (see FIG. 7 ). ).

Alternatively, when the EEG information acquired in the first time zone at the same location is defined as first EEG information, the EEG information acquired in a second time zone different from the first time zone may be defined as second EEG information (Fig. 7).

Subsequently, the second earphone unit 270 measures the user's pulse wave signal as shown in FIG. 8 through the pulse wave sensor (S630).

Pulse is a periodic wave generated when blood from the heart touches the wall of an artery by the heartbeat. 8 is a diagram illustrating a waveform of a pulse signal measured through a second earphone unit according to an embodiment of the present invention. As illustrated in FIG. 8, the pulse rate information may include waveform information about a wave. For example, the pulse information may include information about a pulse rate, an amplitude of a pulse wave, a period of a pulse wave, a change in a pulse wave period, a frequency of a pulse wave, a rate at which the amplitude of the pulse changes, and the like.

Here, since the pulse signal is included in the cardiac information regarding cardiac activity, the pulse signal may be expressed as cardiac information.

Subsequently, the respiration rate measurement unit 320 detects a respiration signal for the user's respiration through a gyro sensor, and measures the respiration rate from the detected respiration signal (S640).

Subsequently, the analysis engine unit 230 converts the measured EEG signal and pulse wave signal into data and analyzes it (S650).

That is, the analysis engine unit 230 amplifies the acquired brain wave, removes the unwanted wave component from the amplified signal, and converts the signal from which the unwanted wave is removed into a digital signal. In addition, the analysis engine unit 230 analyzes the brain waves by performing a Fourier transform on the amplified brain waves and calculating an output value for each frequency of the brain waves.

The analysis engine unit 230 may obtain an EEG harmonic by signal processing the acquired EEG. The EEG harmonic is an index indicating the degree to which the frequency spectrums of a plurality of EEG are matched. For example, the EEG harmonic may mean a correlation between frequency information between a plurality of EEG waveforms. The phase relationship may indicate a phase relationship between a plurality of EEG waveforms, and the degree of symmetry may indicate a degree of symmetry between the plurality of EEG waveforms.

The types of brain waves generated in the human brain are largely divided into gamma waves, alpha waves, beta waves, theta waves, delta waves, ILF (Infra Low fluctuation), and DC according to the frequency band.

The gamma wave may have a frequency of 30 Hz or more. Alpha waves are brain waves that occur when humans close their eyes and relax their body and can have a frequency between 8 Hz and 12 Hz. Beta waves are most brain waves that occur when consciousness is awake and can have a frequency between 13 Hz and 32 Hz. Theta wave (Theta wave) occurs in the state of shallow sleep and may have a lower frequency (4 Hz ~ 8 Hz) than the alpha wave, and is generated at the boundary between perception and dream. Delta wave (Delta wave) can have a frequency lower than 4 Hz lower than that of theta wave, and is the most frequently measured brain wave in a sleeping or unconscious state. SCP (Slow cortical potential) can have a frequency of less than 1 Hz.

There may be various ways to obtain the EEG harmonics.

For example, the analysis engine unit 230 may convert first EEG information into a frequency domain and convert second EEG information into a frequency domain.

In this case, the analysis engine unit 230 may determine that the similarity is higher as the number of frequency bands in the same range between the first EEG information and the second EEG information is higher, and the first EEG information and the second EEG information are in the same range. It can be determined that the lower the frequency band, the lower the similarity. In addition, EEG harmonics can be calculated using a maximum likelihood method or a cross-correlation method.

For example, the EEG harmonic may be obtained by Equation 1 below.

Equation 1 below is applied when the EEG information acquired at the first location in the same time zone is defined as the first EEG information, and the EEG information acquired at the second location different from the first location is defined as the second EEG information. I can.

Here, it is assumed that EEG information is acquired from two channels (x, y).

Here, |CrossPowerSpectrum| is the mutual power spectrum density between channel x and channel y, and PowerSpectrum(X) and PowerSpectrum(Y) mean the magnetic power spectrum density of channel x and channel y.

Where |CrossPowerSpectrum| ² is C _n ² =(a _n u _n +b _n v _n ) ² + (a _n v _n -b _n u _n ) ² =|cospectrum| ² + |quadspectrum| ^{It is 2} . a _n and b _n are Fourier cosine coefficients and sine coefficients of the brainwave signal x(t) obtained from the channel (x), respectively.

In addition, u _n and v _n are Fourier cosine coefficients and sine coefficients of the EEG signal y(t) obtained in a channel (y) different from the channel (x), respectively. It can be expressed as PowerSpectrum(X) = a _n ² + b _n ² , and PowerSpectrum(Y) = u _n ² + v _n ² .

According to Equation 1 as described above, the analysis engine unit 230 may obtain an EEG harmonic degree based on EEG information. EEG harmonics may have a value between 0 and 1.

The closer the EEG harmonic value is to 1, the more similar or coincident the frequency spectrums of the two EEG waves. The EEG coherence is an average of a value calculated by Equation 1 for a predetermined measurement time in all channel combinations that can be derived from a plurality of channels. EEG harmonics can be measured for each frequency band. For example, the EEG harmonic is an average of a value calculated by Equation 1 for a predetermined measurement time in a combination of channels derived from two channels.

Meanwhile, the phase relationship may be obtained by Equation 2 below.

The phase relationship is an average of the values calculated by Equation 2 for a predetermined measurement time in all channel combinations that can be derived from a plurality of channels. The phase diagram can be measured for each frequency band. For example, the phase diagram is an average of a value calculated by Equation 2 for a predetermined measurement time in a combination of channels derived from two channels.

In addition, the degree of symmetry (Amplitude asymmetry) may be obtained by Equation 3 below.

The degree of symmetry is an average of a value calculated by Equation 3 for a predetermined measurement time in all channel combinations that can be derived from a plurality of channels. The degree of symmetry can be measured for each frequency band. For example, the Amplitude Asymmetry is an average of a value calculated by Equation 3 for a predetermined measurement time in a combination of channels derived from two channels.

In addition, power is calculated through FFT (4 sec Epoch, Hanning Window, Overlapping 50%) after removing noise from the measured EEG, and means a value averaged over a predetermined measurement time. Power may be calculated in each of a plurality of channels (eg, 19 channels). Power can be measured for each frequency band (DC, ILF, delta, theta, alpha, beta, gamma, etc.).

Meanwhile, the analysis engine unit 230 may remove a noise signal from the pulse signal as illustrated in FIG. 8, convert it into a digital signal, analyze it, and obtain a cardiac harmonic degree as a result of the analysis.

Cardiac harmony is an index that can indicate the rate of change in the pulse wave. A method of acquiring a cardiac harmonic degree based on pulse rate information, referring to FIG. 8, is a first waveform in a predetermined section (eg, a first section) and a predetermined section that does not completely coincide with the first section (e.g., The similarity of the second waveform in the second section) may be measured.

There are many ways to achieve cardiac harmony.

For example, the analysis engine unit 230 may convert a first waveform into a frequency domain (first pulse information) and convert a second waveform into a frequency domain (second pulse information).

At this time, the analysis engine unit 230 may determine that the similarity is higher as the number of frequency bands in the same range between the first pulse information and the second pulse information is higher, and the first pulse information and the second pulse information are in the same range. It can be determined that the lower the frequency band, the lower the similarity. In addition, cardiac harmony can be calculated using a maximum likelihood method or a cross-correlation method.

The cardiac harmony may be obtained by Equation 4 below.

Here, the power at the center of the peak means the power in a predetermined band (eg, 0.03 Hz) centering on a frequency having the largest power in the power spectrum of the pulse information (eg, 0.04 to 0.4 Hz).

A predetermined band centered on a frequency having the largest power in the power spectrum may be appropriately set as necessary. For example, the frequency of the power at the center of the peak may be used as 0.08 to 0.15 Hz or 0.04 to 0.26 Hz.

In addition, the power of the entire band means the total power of the power spectrum of the pulse information.

According to Equation 4 as described above, the analysis engine unit 230 may obtain a cardiac harmonic degree based on the pulse rate information. Cardiac harmony can have a value between 0 and 1. The closer the cardiac harmony is to 1, the more regular the heartbeat changes over time.

Subsequently, the analysis engine unit 230 diagnoses the user's emotional and physical state according to the measured respiration rate and the correlation between the EEG signal and the pulse wave signal (S660).

That is, the analysis engine unit 230 uses the cardiac-brain synchronization information based on the cardiac harmony degree and the EEG harmony degree, as shown in FIG. 9, according to the correlation between the cardiac harmony degree and the EEG harmony degree. Is to diagnose. 9 is a diagram showing an example of heart-brain synchronization information according to an embodiment of the present invention. In FIG. 9, (a) shows the cardiac harmony degree in a predetermined time period, (b) shows the EEG harmony degree in a predetermined time period, and (c) is the cardiac harmony degree in (a), It shows the heart-brain tuning information based on the EEG harmonics in (b). As shown in (c), the x-axis of the graph may indicate a cardiac harmony degree, and the y-axis of the graph may indicate an EEG harmonic degree.

That is, through the heart-brain synchronization information, a correlation between the cardiac harmony degree and the EEG harmony degree can be grasped. For example, it may be expressed as a linear function model by regression analysis between cardiac harmonics and EEG harmonics. That is, the cardiac harmony degree and the brain wave harmony degree may be expressed by Equation 5 below.

Here, y denotes an EEG harmonic degree, and x may denote a heart harmonic degree. a and b may be arbitrary rational numbers to satisfy the relational expression.

The more convergence is possible with a regression equation such as Equation 5, the greater the heart-brain coordination, and it can be determined as a healthy state. Here, a state in which complete convergence is possible with a regression equation such as Equation 5 can be defined as a state with the highest proximity, and a state in which convergence is impossible with a regression equation such as Equation 5 can be defined as a state with the lowest proximity. have. That is, the degree of convergence to the regression equation shown in Equation 5 is defined as proximity.

According to an embodiment of the present invention, the analysis engine unit 230 may determine the proximity to the linear function model based on the cardiac harmonic degree and the EEG harmonic degree, and through this, it is possible to determine the heart-brain synchronization.

Cardiac harmony and phase relationship, cardiac harmony and symmetry, or cardiac harmony and power can be determined by determining the correlation by applying the same method to determine the cardiac-brain synchronization.

The cardiac-brain synchronization information can be an indicator of health status by considering the autonomic nerve and the central nerve at the same time. This is because cardiac harmony can be a health indicator for the autonomic nervous system, and EEG can be a health indicator for the central nervous system. to be. For example, the analysis engine unit 130 may grasp heart-brain synchronization information based on proximity and determine a health state.

On the other hand, the higher the degree of cardiac harmony, the healthier it can be determined. In addition, even if the degree of cardiac harmony is increased, a state in which the degree of EEG does not increase may be determined as an unhealthy condition. In other words, it is possible to determine the health status by grasping the correlation between the cardiac harmony degree and the EEG harmony degree, and displaying the cardiac-brain synchronization information in the form of a graph.

Subsequently, the analysis engine unit 230 controls to output a care signal that improves emotions and physical states (S670).

That is, the analysis engine unit 230 reads the sound source data corresponding to the care signal from the storage unit 140 that stores the sound source signal corresponding to the care signal for improving the emotion and the physical state as data, and the first earphone unit It can be controlled to be output to 260 and the second earphone unit 270.

For example, the analysis engine unit 230 may output a sound source signal that enhances the alpha wave or a sound source signal capable of improving a sad or depressed state to the first earphone unit 260 and the second earphone unit 270. To control.

In addition, the analysis engine unit 230 may transmit a care signal for improving an emotion and a physical condition to an external device through a communication unit through short-range communication.

Therefore, the user hears a sound source signal suitable for his or her physical condition or a sound source signal that can improve a sad or depressed state through the first earphone unit 260 and the second earphone unit 270, thereby reducing the emotional and physical state. It can be improved.

Meanwhile, although not shown in the drawing, the healthcare server 120 according to the present invention learns and models the relationship between cardiac harmony and EEG harmony, and can predict other data by inputting only one data through the modeled model. A learning device or a learning server may be separately provided.

In other words, the learning device (server) accumulates and stores the measured cardiac harmonics and EEG harmonics measured every day, then aggregates them on a weekly, monthly, and yearly basis, and matches the cardiac harmonic and EEG harmonics with week or month to learn data. Can be stored in the database.

Subsequently, the learning device (server) may classify into a modeling data group for generating a model by modeling the training data, and a verification data group for verifying the generated model.

In addition, the learning device (server) may learn cardiac harmonics and EEG harmonics on a weekly or monthly basis for the modeling data group, and calculate an estimated function equation (H(x)) representing the relationship between the cardiac harmonics and the EEG harmonics. I can.

Then, the learning device (server) calculates the EEG harmonics for each user and the distance values ([H(x)-y(x)]2) between estimated data corresponding to the graph of the estimation function equation (H(x)). A functional equation that has an EEG harmonic and a cardiac harmonic that is the smallest among the calculated distance values and is closest to the graph of the estimated function equation by summing all the calculated distance values and dividing it by the number of data (m) according to the number of users. The slope (a) value and the intercept (b) value of can be calculated.

Subsequently, the learning device (server) determines the selection function equation (y=ax+b) by substituting the calculated slope (a) value and the intercept (b) value into the estimation function equation, and determines the EEG harmonic degree according to the cardiac harmony degree. You can create a selection model for calculation.

And, the learning device (server) calculates the EEG harmonic degree (y) for each user by substituting each cardiac harmony degree (x) calculated for each user (r) into the selection model, respectively, and calculates the EEG harmonic degree (y) for each user. ) By comparing and listing, and according to the results of the listing, the week or the month in which the EEG harmonic degree (y) is the most highest can be selected.

In addition, the learning device (server) calculates each cardiac harmony degree (x) for each user for the verification data group, and substitutes each calculated cardiac harmony degree (x) into the selection model, respectively, and the EEG harmonic degree (y ) Is calculated, and for each cardiac harmonic degree (x) and brain wave harmonic degree (y) calculated for each user, the distance values with the graph of the estimation function are calculated, and the distance average value for the calculated distance values is calculated. Calculate and compare the calculated distance average value with the minimum distance value, and based on the comparison result, the selected model can be verified according to whether the difference between the calculated distance average value and the minimum distance value is within the error range. have.

And, the learning device (server) reclassifies the classified modeling data group into three fields: weekly, monthly, and yearly according to the date classification, and for each reclassified modeling data group, the cardiac harmony (x ), by learning the EEG harmonic degree (y), an estimated functional equation (H(x)) representing the relationship between the cardiac harmonic degree (x) and the EEG harmonic degree (y) can be calculated for each of the three fields.

Then, the learning device (server) is the distance value between the EEG harmonic data (y) data for each user and the estimated data corresponding to the graph of the estimation function equation (H(x)) for each of the three fields ([H(x)- y(x)]2) is calculated, and the calculated distance values are summed and divided by the number of data (m) according to the number of users (r). The slope (a) value and the intercept (b) value of the functional equation having the adjacent cardiac harmonic degree (x) and EEG harmonic degree (y) can be calculated according to three fields, respectively.

And, the learning device (server) determines the selection function equation (y=ax+b) by substituting the slope (a) value and the intercept (b) value calculated according to the three fields into the estimation function equation for each of the three fields, respectively. , A selection model that calculates the EEG harmony according to the cardiac harmony can be generated for each of the three fields according to weekly, monthly, and yearly.

As described above, according to the present invention, when a user carries a neckband type measuring device around his neck like a necklace worn around a person's neck, the user's own brain waves, pulse waves, and respiration rate are measured and transmitted to the management server. , The management server compares the health care pattern of other users such as the same age group and informs the user of the result, thereby realizing a neckband type healthcare service system and method that enables the user's health to be managed using a neckband type meter. I can.

Those skilled in the art to which the present invention pertains, since the present invention may be implemented in other specific forms without changing the technical spirit or essential features thereof, the embodiments described above are illustrative in all respects and should be understood as non-limiting. Only do it. The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. .

100: Neckband type healthcare service system
110 ~ 114: neckband type measuring device 120: healthcare server
130: healthcare DB 210: neckband body
212: play button 214: call button
216: volume button 218: selection button
220: power button 260: first earphone unit
270: second earphone unit 310: storage unit
320: respiration rate measurement unit 330: power generation unit
340: power supply unit 350: connector
360: communication unit 410: communication unit
420: pattern analysis unit 430: control unit

Claims (8)

A neckband type measuring device configured to be worn on a user's neck, comprising: a neckband body formed along an arc of an opened circle;
An EEG sensor for measuring EEG of the user;
A pulse wave sensor for measuring a pulse wave of the user;
The EEG signal measured through the EEG sensor is converted into data and analyzed, the pulse wave signal measured through the pulse wave sensor is converted into data and analyzed, and the user's emotions and the user's emotions are analyzed according to the correlation between the EEG signal and the pulse wave signal. An analysis engine unit that diagnoses a physical condition and controls to output a care signal that improves an emotion and a physical condition,
The analysis engine unit,
Analyzing the EEG signal to obtain an EEG harmonic degree from the EEG signal,
Analyzing the pulse wave signal to obtain a cardiac harmonic degree from the pulse wave signal,
Acquiring cardiac-brain tuning information indicating a correlation between the EEG harmonic and the cardiac harmonic using the EEG harmonic degree and the cardiac harmonic degree,
Neckband type measuring device for diagnosing the health status of the user from the heart-brain tuning information. The method of claim 1,
The neckband body,
One or more buttons are provided on the surface, and one or more components are provided therein to be electrically coupled to the one or more buttons,
The neckband type measuring device,
A first earphone unit electrically connected to one end of the neckband body and including a first speaker for outputting sound; And
A neckband type measuring device further comprising a second earphone unit electrically connected to the other end of the neckband body and having a second speaker for outputting sound. The method of claim 1, wherein the neckband type measuring device,
A storage unit for storing a sound source signal corresponding to a care signal for improving the emotional and physical condition as data;
A power generation unit that detects vibration according to the user's movement and generates power by generating power using the detected vibration signal; And
A respiration rate measurement unit for detecting a respiration signal for the user's respiration with a gyro sensor and measuring a respiration rate from the detected respiration signal;
The neckband type measuring device further comprising a. A communication unit for receiving the brain wave signal, the pulse wave signal, and the respiratory rate information from a measuring device for measuring the user's brain wave signal, pulse wave signal, and respiratory rate information;
A healthcare database storing pulse wave signal and respiratory rate information by corresponding to each user information by using the brain wave signal as a unique ID for the received brain wave signal, pulse wave signal, and respiratory rate information;
A pattern analysis unit that compares and analyzes the user's brain wave signal, pulse wave signal, and respiratory rate information with other users to obtain a health care pattern; And
And a control unit for controlling to obtain a health care pattern for each user through the pattern analysis unit, and for controlling the acquired health care pattern to be transmitted to each user terminal,
The pattern analysis unit,
By analyzing the received respiratory rate information, a breathing pattern including the interval and intensity of the user's inhalation and exhalation is obtained,
Acquiring a two-dimensional space-frequency pattern having event-related information from the EEG signal,
By analyzing the pulse wave signal, pulse wave information including the result of calculating the average heart rate, the average pulse wave height, the acupuncture point elasticity index (EEI), the redundant expansion index (DDI), and the redundant elasticity index (DEI) is obtained,
Obtaining the healthcare pattern including the breathing pattern, the two-dimensional space-frequency pattern and the pulse wave information,
Healthcare server. delete In the healthcare service method performed by the healthcare server,
Receiving body sensing information including a user's brain wave signal, pulse wave signal, and respiratory rate;
Storing the pulse wave signal and the respiratory rate information by corresponding to each user information having the brain wave signal as a unique ID with respect to the received brain wave signal, pulse wave signal, and respiratory rate information;
Comparing and analyzing the user's EEG signal, pulse wave signal, and respiratory rate information with other users and obtaining a healthcare pattern; And
Including the step of transmitting the obtained healthcare pattern to each corresponding user terminal,
The step of obtaining the healthcare pattern,
Analyzing the respiratory rate information to obtain a breathing pattern including the interval and intensity of the user's inhalation and exhalation;
Acquiring a two-dimensional space-frequency pattern having event-related information from the brainwave signal;
Analyzing the pulse wave signal to obtain pulse wave information including a result of calculating an average heart rate, an average pulse wave height, an acupuncture point elasticity index (EEI), a redundant expansion index (DDI), and a redundant elasticity index (DEI); And
Comprising the step of obtaining the healthcare pattern including the breathing pattern, the two-dimensional space-frequency pattern and the pulse wave information, healthcare service method.
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