KR20220069428A - Method of measuring bio-metric data using sensors and system performing the same - Google Patents

Abstract

The present invention relates to a respiration signal measurement method using a plurality of sensors to prevent waste of power and a system for executing the same. According to one embodiment of the present invention, a biometric analysis method using a plurality of sensors executed in a respiration signal analysis device comprises the following steps of: inputting a signal corresponding to a predetermined band and specific current into a first sensor; measuring a bioimpedance signal through the first sensor to extract a first respiration signal; measuring signals of at least three axes during respiration by a second sensor to extract a second respiration signal; using the period and size of respiration in at least one of the first respiration signal and the second respiration signal to determine whether respiration is normal and calculate a respiratory rate; determining that the breathing is abnormal when the period and size of the respiration are out of a predetermined range to activate a third sensor; extracting and classifying feature data from the signals measured through the first sensor, the second sensor, and the third sensor as abnormal respiration patterns; and storing or providing the respiratory rate and feature extraction results to another device.

Description

Respiratory signal measurement method using a plurality of sensors and a system for executing the same

The present invention relates to a method for measuring a respiratory signal using a plurality of sensors and a system for executing the same, and more particularly, to a device using a microphone to recognize a cough to prevent power consumption by activating the microphone only in a specific situation It relates to a method for measuring a respiratory signal using a plurality of sensors and a system for implementing the same.

Around the world, 290 to 640,000 people die every year due to influenza, causing social and economic damage. Droplets generated by coughing are the main transmission method of influenza, and it is possible to prevent the spread through cough detection technology. Previous studies on cough detection technology mainly used the method of detecting cough sounds through machine learning methods.

Conventional cough detection technology uses traditional machine learning such as HMM (Hidden Markov Model) and SVM (Support Vector Machine).

In addition, research to apply deep learning technology to cough detection technology is being conducted. Specifically, it replaces the traditional machine learning method through feature extraction and analysis of cough-related data, and learns the features of the analyzed data. For example, when coughing usually occurs, it is possible to determine whether coughing occurs by learning to analyze a sound according to the cough.

Korean Patent Laid-Open No. 10-2020-0072030 relates to a multi-modal cough detection apparatus and method using sound and acceleration data, and discloses a multi-modal cough detection apparatus and method using sound and acceleration data simultaneously.

However, in the case of the conventional invention, there is a problem in that a large amount of calculation and a large amount of current are consumed in the process of processing sound data.

An object of the present invention is to provide a method for measuring a respiratory signal using a plurality of sensors and a system for executing the same, which enable the microphone to be activated only in a specific situation to prevent power consumption in a device using a microphone to recognize a cough.

In addition, an object of the present invention is to provide a method for measuring a respiration signal and an apparatus for executing the same, which can increase storage efficiency during long-term monitoring by storing only important data, not all data received from the sensor.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention not mentioned may be understood by the following description, and will be more clearly understood by the examples of the present invention. It will also be readily apparent that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the appended claims.

A biometric analysis method using a plurality of sensors executed in a respiration signal analysis device for achieving this purpose includes injecting a signal corresponding to a specific band and a specific current to a first sensor, a bioimpedance signal through the first sensor Extracting the first respiration signal by measuring, extracting the second respiration signal by measuring the signal of at least three axes during respiration to the second sensor, respiration from at least one of the first respiration signal and the second respiration signal Determining whether it is normal respiration and calculating the respiration rate using the cycle and size of It includes the steps of extracting feature data from the signals measured by the second sensor and the third sensor and classifying it into an abnormal breathing pattern, and storing the respiration rate and the feature extraction result or providing it to another device.

In addition, the respiratory signal measuring device for achieving this purpose injects a signal corresponding to a specific band and a specific current to the first sensor, and measures the bioimpedance signal through the first sensor to extract the first respiration signal ADC, a second ADC for extracting a second respiration signal by measuring signals on at least three axes during respiration to the second sensor, and the first respiration signal and the second respiration signal using the cycle and size of respiration from at least one of the normal Determining whether it is respiration, calculating the respiration rate, and activating a third sensor by determining if the cycle and size of respiration are out of a specific range as abnormal respiration, and using the first sensor, the second sensor and the third sensor Extract feature data from the measured signal and classify it as an abnormal breathing pattern. and a control unit for storing or providing the respiration rate and the feature extraction result to another device.

In addition, a biometric analysis method using a plurality of sensors executed in a biometric data analysis apparatus for achieving this object includes extracting first characteristic data by analyzing first biometric data received from a first sensor, the first characteristic determining whether a cough is detected using data; if the determination result indicates that a cough is detected, changing the state of the second sensor from the inactive state to the active state, and after receiving the second biometric data from the second sensor extracting second characteristic data by analyzing second biometric data; determining whether a cough is detected using the first and second characteristic data; and the first characteristic data and and starting or ending recording of the second characteristic data.

In addition, a biometric data measurement system using a plurality of sensors for achieving this purpose maintains an activated state, maintains an inactive state or an activated state according to a first sensor measuring first biometric data and a control command, and maintains an activated state A biometric data measuring device including a second sensor for measuring second biometric data, and analyzing first biometric data received from the first sensor to extract first feature data, and coughing by using the first feature data It is determined whether or not it is detected, and if cough is detected as a result of the determination, the state of the second sensor is changed from the inactive state to the active state, and after receiving the second biometric data from the second sensor, the second biometric data is analyzed. extracting the second characteristic data, determining whether a cough is detected using the first characteristic data and the second characteristic data, and starting or ending recording of the first characteristic data and the second characteristic data according to the determination result A biometric data analysis device is included.

According to the present invention as described above, there is an advantage that power consumption can be prevented by activating the microphone only in a specific situation in a device using a microphone to recognize a cough.

In addition, according to the present invention, there is an advantage that storage efficiency can be increased during long-term monitoring by storing only important data rather than all data received from the sensor.

1 is a network configuration diagram for explaining a respiratory signal measurement system using a plurality of sensors according to the present invention.
Figure 2 is a block diagram for explaining the internal structure of the respiratory signal measuring device according to an embodiment of the present invention.
3 is a flowchart for explaining an embodiment of a method for measuring a respiratory signal according to the present invention.
Figure 4 is a flowchart for explaining another embodiment of the method for measuring a respiratory signal according to the present invention.

The above-described objects, features and advantages will be described below in detail with reference to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the present invention pertains will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to indicate the same or similar components.

A “biometric signal measuring device” according to the present invention is a smartphone, a tablet personal computer, a mobile phone, a video phone, an e-book reader, and a desktop personal computer (desktop personal computer). computer), laptop personal computer, netbook computer, workstation, server, PDA (personal digital assistant), PMP (portable multimedia player), MP3 player, mobile medical device, camera (camera) ), or wearable devices (e.g. smart glasses, head-mounted-device (HMD)), electronic garments, electronic bracelets, electronic necklaces, electronic accessories, electronic tattoos, smart It may include at least one of a mirror or a smart watch.

1 is a network configuration diagram for explaining a respiratory signal measurement system using a plurality of sensors according to the present invention.

Referring to FIG. 1 , the respiratory signal measuring system using a plurality of sensors includes a biosignal measuring device 100 and a respiratory signal analyzing device 200 .

The biosignal measuring device 100 is a device for measuring a user's respiration signal, and may be implemented as a wearable device that can be worn on the user's body. The first sensor 110 , the second sensor 120 and the third sensor 130 are included.

The first sensor 110 and the second sensor 120 maintain an active state, and the third sensor 130 is changed from an inactive state to an active state when the cycle and size of respiration are out of a specific range according to a control command.

The first sensor 110 is located in the neck and chest to measure the impedance change amount based on the change in the volume of the lungs during respiration. The first sensor 110 includes a sensor for measuring bioimpedance and biopotential difference.

A signal corresponding to a specific band and a specific current is injected into the first sensor 110 and a first respiration signal is measured.

The first respiration signal measured by the first sensor 110 includes general data and characteristic data. That is, the first respiration signal measured by the first sensor 110 will generate general data when the user does not cough, and generate characteristic data when the user coughs.

That is, among the first respiration signals measured by the first sensor 110 , a respiration signal less than or equal to a first threshold cough occurrence value is classified as general data, and a respiration signal greater than or equal to the first threshold cough occurrence value is classified as feature data.

Therefore, the biosignal measuring device 100 analyzes the first respiration signal received from the first sensor 110 and determines that the data at the time point is general data when the level of the first respiration signal is less than or equal to the first critical cough occurrence level. Thus, it can be determined that the user is not coughing, and when the value of the first respiratory signal is greater than or equal to the first threshold cough occurrence value, the data at that time is determined as the first characteristic data to determine the state in which the user coughs. it can be

The second sensor 120 is an inertial sensor and includes an acceleration sensor, a gyro sensor, and a geomagnetic sensor. This second sensor 120 measures a second respiration signal (ie, at least three-axis signal) during respiration. The second respiration signal measured by the second sensor 120 will generate general data when the user does not cough, and generate characteristic data when the user coughs.

The third sensor 130 is located around the mouth, neck, and chest to measure an audio signal generated during abnormal respiration. The third sensor 130 includes an audio sensor (microphone) and a bone conduction microphone.

The third sensor 130 maintains an inactive state, but is activated by determining that it is abnormal respiration when the cycle and size of respiration are out of a specific range.

The third sensor 130 does not measure the voice signal when maintaining the inactive state, but measures the voice signal when the cycle and size of respiration are changed to the active state out of a specific range.

That is, the third sensor 130 maintains an activated state only when it is determined that the cycle and size of respiration are out of a specific range based on the first respiration signal and the second respiration signal, and the first respiration signal and the second respiration signal. Based on the respiration signal, if the cycle and size of respiration do not deviate from a specific range, the inactive state is maintained.

As described above, the present invention does not maintain all of the first sensor 110 , the second sensor 120 , and the third sensor 130 in an activated state, and the first sensor 110 and the second sensor 120 . There is an advantage in that power consumption can be prevented by maintaining only the active state and activating the third sensor 130 according to circumstances.

In addition, the biosignal measuring apparatus 100 extracts feature data from the signals measured through the first sensor 110 , the second sensor 120 , and the third sensor 130 and classifies them into abnormal breathing patterns. , the respiration rate and the feature extraction results are stored or provided to the respiratory signal analysis device 200 .

For this, the biosignal measuring apparatus 100 receives the respiration signal (ie, the first respiration signal and the second respiration signal) and then determines whether the user's cough has occurred using the respiration signal.

First, the biosignal measuring device 100 receives the first respiration signal from the first sensor (110). In this case, the first respiration signal measured by the first sensor 110 includes general data generated when the user does not cough and characteristic data generated when the user coughs.

Therefore, the respiratory signal analysis device 200 by analyzing the first respiration signal received from the first sensor 110 to extract the first characteristic data, and analyzing the second respiration signal received from the second sensor 120, The second feature data is extracted.

In one embodiment, the biosignal measuring device 100 pre-processes each of the first respiration signal and the second respiration signal (ie, noise filtering, etc.), and then displays the pre-processed first respiration signal on the graph on the graph It is determined whether each of the displayed first respiration signal and the second respiration signal is greater than or equal to a first threshold cough occurrence value and a second threshold cough occurrence value.

In the above embodiment, the biosignal measuring apparatus 100 is the first respiratory signal displayed on the graph is greater than or equal to the first critical cough occurrence value or the second respiratory signal is greater than or equal to the second critical cough occurrence value, the corresponding Data generated before and after a specific time based on the time point may be extracted as feature data.

This is because, before the respiratory signal (ie, cough) greater than or equal to the first threshold cough occurrence value is generated, a respiratory signal (ie, cough, dry cough, etc.) whose value is less than or equal to the first threshold cough occurrence value may be generated. .

In another embodiment, the biosignal measuring device 100 pre-processes each of the first respiration signal and the second respiration signal (ie, noise filtering, etc.), and then displays the pre-processed first respiration signal and the second respiration signal on the graph Even if the values of the first respiratory signal and the second respiratory signal displayed on the graph are below the first critical cough occurrence value and the second critical cough occurrence value, the data at that time point is extracted as feature data according to the difference value up to a specific value can do.

In the above embodiment, the biosignal measuring device 100 is up to the first threshold cough occurrence value even if the respective values of the first respiratory signal and the second respiratory signal are less than or equal to the first threshold cough occurrence value and the second threshold cough occurrence value. If the difference value is less than or equal to a specific value, data at that time point may be extracted as feature data.

In addition, in the above embodiment, the biosignal measuring apparatus 100 determines that the difference value up to the first critical cough occurrence value is greater than or equal to a specific value even if the respective values of the first respiration signal and the second respiration signal are less than or equal to the first threshold cough occurrence value If this is the case, the data at that point in time may not be extracted as feature data.

This is because, in the case of a respiratory signal (ie, residual acupuncture, dry cough, etc.) that is less than the first critical cough occurrence value, the difference value up to the first critical cough occurrence value may be greater than or equal to a specific value.

Thereafter, the biosignal measuring apparatus 100 determines whether normal respiration is normal using the first characteristic data and the second characteristic data, calculates the respiration rate, and determines that the respiration is abnormal if the cycle and size of respiration are out of a specific range. to activate the third sensor 130 .

Then, the biosignal measuring apparatus 100 extracts feature data from the signals measured through the first sensor 110 , the second sensor 120 , and the third sensor 130 and classifies it into an abnormal breathing pattern. After that, the respiratory rate and feature extraction results are stored or provided to the biosignal analysis device 200 .

Figure 2 is a block diagram for explaining the internal structure of the respiratory signal measuring device according to an embodiment of the present invention.

Referring to FIG. 2 , the biosignal measuring apparatus 100 includes a first sensor 110 , a second sensor 120 , a third sensor 130 , a first ADC 140 , a second ADC 150 , and a second 3 ADC 160 and a control unit 170 are included.

A signal corresponding to a specific band and a specific current is injected into the first sensor 110, and a first respiration signal (ie, bioimpedance signal) is measured.

That is, among the first respiration signals measured by the first sensor 110 , a respiration signal less than or equal to a first threshold cough occurrence value is classified as general data, and a respiration signal greater than or equal to the first threshold cough occurrence value is classified as feature data.

The second sensor 120 is an inertial sensor and includes an acceleration sensor, a gyro sensor, and a geomagnetic sensor. This second sensor 120 measures a second respiration signal (ie, at least three-axis signal) during respiration. The second respiration signal measured by the second sensor 120 will generate general data when the user does not cough, and generate characteristic data when the user coughs.

The third sensor 130 is located around the mouth, neck, and chest to measure an audio signal generated during abnormal respiration. The third sensor 130 includes an audio sensor (microphone) and a bone conduction microphone.

The third sensor 130 maintains the inactive state, but is activated by determining that it is abnormal respiration when the cycle and size of respiration are out of a specific range under the control of the controller 170 .

The third sensor 130 does not measure the voice signal when maintaining the inactive state, but measures the voice signal when the cycle and size of respiration are changed to the active state out of a specific range.

That is, the third sensor 130 maintains an activated state only when it is determined that the cycle and size of respiration are out of a specific range based on the first respiration signal and the second respiration signal, and the first respiration signal and the second respiration signal. Based on the respiration signal, if the cycle and size of respiration do not deviate from a specific range, the inactive state is maintained.

As described above, the present invention does not maintain all of the first sensor 110 , the second sensor 120 , and the third sensor 130 in an activated state, and the first sensor 110 and the second sensor 120 . There is an advantage in that power consumption can be prevented by maintaining only the active state and activating the third sensor 130 according to circumstances.

The first ADC 140 injects a signal corresponding to a specific band and a specific current to the first sensor 110 , and measures a bioimpedance signal through the first sensor 110 to extract a first respiration signal.

The second ADC 160 extracts the second respiration signal by measuring the signal of at least three axes during respiration to the second sensor 120 .

The third ADC 160 converts the audio signal of the third sensor 130 from an analog signal to a digital signal.

The control unit 170 extracts feature data from the signals measured through the first sensor 110, the second sensor 120, and the third sensor 130 and classifies it into an abnormal breathing pattern, and then the respiration rate and the The feature extraction result is stored or provided to the respiratory signal analysis device 200 .

For this, the control unit 170 receives the respiration signal (ie, the first respiration signal and the second respiration signal), and then determines whether the user's cough has occurred using the respiration signal.

First, the control unit 170 receives the first respiration signal from the first ADC (140). In this case, the first respiration signal received from the first ADC 140 includes general data generated when the user does not cough and characteristic data generated when the user coughs.

Accordingly, the controller 170 analyzes the first respiration signal received from the first sensor 110 to extract the first characteristic data, and analyzes the second respiration signal received from the second sensor 120 to analyze the second characteristic extract data.

In one embodiment, the controller 170 pre-processes each of the first respiration signal and the second respiration signal (ie, noise filtering, etc.), and then displays the pre-processed first respiration signal on the graph to display the first respiration signal displayed on the graph It is determined whether each of the respiration signal and the second respiration signal is equal to or greater than the first threshold cough occurrence value and the second threshold cough occurrence value.

In the above embodiment, if the value of the first respiratory signal displayed on the graph is greater than or equal to the first critical cough occurrence value or the value of the second respiratory signal is greater than or equal to the second critical cough occurrence value, the control unit 170 is based on the corresponding time point In this way, data generated before and after a specific time can be extracted as feature data.

This is because, before the respiratory signal (ie, cough) greater than or equal to the first threshold cough occurrence value is generated, a respiratory signal (ie, cough, dry cough, etc.) whose value is less than or equal to the first threshold cough occurrence value may be generated. .

In another embodiment, the controller 170 pre-processes each of the first respiration signal and the second respiration signal (ie, noise filtering, etc.), and then displays the pre-processed first respiration signal and the second respiration signal on the graph to graph Even if the values of the first respiratory signal and the second respiratory signal displayed on the above are equal to or less than the first threshold cough occurrence value and the second threshold cough occurrence value, the data at the corresponding time point can be extracted as feature data according to the difference value up to a specific value .

In the above embodiment, the controller 170 determines that the difference value up to the first critical cough occurrence value is lower than the first threshold cough occurrence value and the second threshold cough occurrence value even if the respective values of the first respiratory signal and the second respiratory signal are less than or equal to the first threshold cough occurrence value and the second threshold cough occurrence value If it is less than a specific value, the data at that point in time can be extracted as feature data.

In addition, in the above embodiment, the control unit 170, even if the respective values of the first respiratory signal and the second respiratory signal are less than or equal to the first threshold cough occurrence value, if the difference value to the first critical cough occurrence value is greater than or equal to a specific value, the corresponding time point may not be extracted as feature data.

This is because, in the case of a respiratory signal (ie, residual acupuncture, dry cough, etc.) that is less than the first critical cough occurrence value, the difference value up to the first critical cough occurrence value may be greater than or equal to a specific value.

After that, the control unit 170 determines whether normal breathing is normal using the first characteristic data and the second characteristic data, calculates the respiratory rate, and determines that the breathing is abnormal if the cycle and size of the breathing are out of a specific range, and the third The sensor 130 is activated.

Then, the biosignal measuring apparatus 100 extracts feature data from the signals measured through the first sensor 110 , the second sensor 120 , and the third sensor 130 and classifies it into an abnormal breathing pattern. After that, the respiratory rate and feature extraction results are stored.

3 is a flowchart for explaining an embodiment of a method for measuring a respiratory signal according to the present invention.

Referring to FIG. 3 , the biosignal measuring apparatus 100 analyzes the first respiration signal received from the first sensor to extract the first characteristic data (step S310). In this case, the first respiration signal measured by the first sensor will generate general data when the user does not cough, and generate characteristic data when the user coughs.

Accordingly, the respiratory signal analysis apparatus 200 analyzes the first respiratory signal received from the first sensor, and when the value of the first respiratory signal is less than or equal to the first critical cough occurrence value, the user determines the data at the time as general data. It can be determined that the user is not coughing, and when the value of the first respiratory signal is equal to or greater than the first threshold cough occurrence value, the data at the time point can be determined as the first characteristic data to determine that the user is coughing. .

The biosignal measuring apparatus 100 determines whether a cough is detected using the first characteristic data (step S315).

When a cough is detected as a result of the determination, the biosignal measuring apparatus 100 activates the second sensor (step S325).

After receiving the second respiration signal from the second sensor, the biosignal measuring device 100 analyzes the second respiration signal to extract second characteristic data (step S330). At this time, the second respiration signal measured by the second sensor will generate general data when the user does not cough, and generate characteristic data when the user coughs.

The biosignal measuring apparatus 100 determines whether a cough is detected by using the first characteristic data and the second characteristic data (step S330).

The biosignal measuring apparatus 100 starts or ends the recording of the first characteristic data and the second characteristic data according to the determination result (step S335).

In an embodiment of step S340, when it is determined that cough is detected using the first characteristic data and the second characteristic data, the biosignal measuring apparatus 100 stores the first characteristic data and the second characteristic data into an internal memory. and, when coughing is not detected using the first characteristic data and the second characteristic data, the first characteristic data and the second characteristic data are not stored in the internal memory.

Figure 4 is a flowchart for explaining another embodiment of the method for measuring a respiratory signal according to the present invention.

4, the respiratory signal measuring device 100 injects a signal corresponding to a specific band and a specific current to the first sensor (step S410).

Respiratory signal measuring device 100 extracts the first respiration signal by measuring the bioimpedance signal through the first sensor (step S420).

Respiratory signal measuring device 100 extracts the second respiration signal by measuring the signal of at least three axes during respiration to the second sensor (step S430).

Respiratory signal measuring device 100 determines whether normal respiration by using the period and size of respiration in at least one of the first respiration signal and the second respiration signal and calculates the respiration rate (step S440).

Respiratory signal measuring device 100 activates the third sensor by determining that the cycle and size of respiration is out of a specific range as abnormal respiration (step S450).

Respiratory signal measuring apparatus 100 extracts feature data from the signals measured through the first sensor, the second sensor, and the third sensor and classifies it as an abnormal breathing pattern (step S460).

Respiratory signal measuring device 100 stores the respiration rate and the feature extraction result or provides to another device (step S470).

Although it has been described with reference to the limited embodiments and drawings, the present invention is not limited to the above embodiments, and various modifications and variations are possible from these descriptions by those of ordinary skill in the art to which the present invention pertains. Accordingly, the spirit of the present invention should be understood only by the claims described below, and all equivalents or equivalent modifications thereof will fall within the scope of the spirit of the present invention.

100: biosignal measuring device;
110: a first sensor;
120: a second sensor;
130: a third sensor;
140: first ADC,
150: second ADC;
160: a third ADC;
170: control unit;
200: respiratory signal analysis device

Claims (10)

A biosignal measuring method using a plurality of sensors executed in a biosignal measuring device, comprising:
injecting a signal corresponding to a specific band and a specific current into the first sensor;
extracting a first respiration signal by measuring a bioimpedance signal through the first sensor;
extracting a second respiration signal by measuring a signal of at least three axes during respiration to a second sensor;
determining whether normal respiration is normal using the cycle and size of respiration in at least one of the first respiration signal and the second respiration signal and calculating the respiration rate;
activating a third sensor by determining that the respiration cycle and size are out of a specific range as abnormal respiration;
extracting feature data from signals measured through the first sensor, the second sensor, and the third sensor and classifying it into an abnormal breathing pattern; and
Storing the respiration rate and the feature extraction result or comprising the step of providing to another device
Respiratory signal measurement method using a plurality of sensors.
According to claim 1,
If the cycle and size of the respiration are out of a specific range, determining that it is abnormal respiration and activating the third sensor
Activating a third sensor by determining any one abnormal respiration among tachypnea, hyperventilation, apnea, dry cough, wet cough, sneezing, wheezing, and dyspnea according to the cycle and size of the respiration
Respiratory signal measurement method using a plurality of sensors.
According to claim 1,
the first sensor
It is implemented as a sensor that measures bioimpedance and biopotential difference,
It is located in the neck and chest, characterized in that it measures the amount of impedance change based on the change in the volume of the lungs during respiration.
Respiratory signal measurement method using a plurality of sensors.
According to claim 1,
the third sensor
It is located in the periphery of the mouth, neck and chest, characterized in that it measures the audio signal generated during abnormal breathing.
Respiratory signal measurement method using a plurality of sensors.

In the respiratory signal measuring device,
a first ADC for injecting a signal corresponding to a specific band and a specific current into the first sensor, and extracting a first respiration signal by measuring a bioimpedance signal through the first sensor;
A second ADC for extracting a second respiration signal by measuring the signal of at least three axes during respiration to the second sensor; and
Using the cycle and size of respiration in at least one of the first respiration signal and the second respiration signal, it is determined whether it is normal respiration and the respiration rate is calculated, and when the respiration cycle and size are out of a specific range, it is determined as abnormal respiration to activate the third sensor, extract feature data from signals measured through the first sensor, the second sensor, and the third sensor, and classify it as an abnormal breathing pattern. Storing the respiration rate and the feature extraction result or comprising a control unit for providing to another device
Respiratory signal measuring device using a plurality of sensors.
6. The method of claim 5,
the control unit
Characterized in that the third sensor is activated by determining any one abnormal respiration among tachypnea, hyperventilation, apnea, dry cough, wet cough, sneezing, wheezing, and dyspnea according to the cycle and size of the respiration
Respiratory signal measuring device using a plurality of sensors.
6. The method of claim 5,
the first sensor
It is implemented as a sensor that measures bioimpedance and biopotential difference,
It is located in the neck and chest, characterized in that it measures the amount of impedance change based on the change in the volume of the lungs during respiration.
Respiratory signal measuring device using a plurality of sensors.
6. The method of claim 5,
the third sensor
It is located in the periphery of the mouth, neck and chest, characterized in that it measures the audio signal generated during abnormal breathing.
Respiratory signal measuring device using a plurality of sensors.
In the biometric analysis method using a plurality of sensors executed in the biometric data analysis device,
extracting first characteristic data by analyzing the first biometric data received from the first sensor;
determining whether a cough is detected using the first characteristic data;
When coughing is detected as a result of the determination, the state of the second sensor is changed from the inactive state to the active state, and after receiving the second biometric data from the second sensor, the second biometric data is analyzed to extract second characteristic data. step;
determining whether a cough is detected using the first characteristic data and the second characteristic data;
and starting or ending recording of the first characteristic data and the second characteristic data according to the determination result.
A method of measuring biometric data using a plurality of sensors.
Biometric data measurement including a first sensor that maintains an activated state and measures first biometric data, and a second sensor that maintains an inactive state or an active state according to a control command, and measures second biometric data when the active state is maintained Device; and
The first biometric data received from the first sensor is analyzed to extract first characteristic data, and it is determined whether a cough is detected using the first characteristic data, and if the determination result is that cough is detected, the state of the second sensor is changed from an inactive state to an active state, and after receiving the second biometric data from the second sensor, the second biometric data is analyzed to extract second feature data, and the first feature data and the second feature data are used to determine whether a cough is detected, and a biometric data analysis device for starting or ending recording of the first characteristic data and the second characteristic data according to the determination result
A biometric data measurement system using a plurality of sensors.

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