JP6818878B2 - Biometric devices, biometric systems, and mobile devices - Google Patents

Description

The present invention relates to a biometric authentication device, a biometric authentication system, and a mobile terminal.

In recent years, with the progress of IT technology and the aging society, consumers' health consciousness (disease prevention) has risen, and the healthcare market is expected to expand. Under these circumstances, it is expected that wearable terminals that can constantly monitor the health condition with high accuracy will become widespread.

In addition, the spread of mobile terminals such as mobile phones and smartphones has made it possible to use them for online shopping, online banking, and electronic payment. Along with this, products that apply biometric authentication technologies such as fingerprints, veins, and irises have also appeared in order to prevent unauthorized use of mobile terminals. However, these biometric authentication technologies have been found to be capable of spoofing using gelatin and radish, and security vulnerabilities have been pointed out.

Against this background, authentication technology utilizing electrocardiogram technology is appearing in recent years. While the electrocardiogram technology has the same level of authentication accuracy as fingerprints and veins, it also has the advantage of being able to detect living organisms (confirmation of survival) and making spoofing difficult.

In addition, electrocardiogram technology is an effective index for detecting heart disease, which is one of the three major adult diseases in healthcare applications, and is also an important index for analyzing stress and fatigue, which has been attracting attention in companies in recent years. There is.

In addition, there is an authentication technology using pulse waves as an authentication technology having the same features as the electrocardiogram technology. From the viewpoint of health care, pulse waves can be used as an index of arteriosclerosis in addition to heart disease. However, in diagnosing heart disease, the electrocardiogram is a more accurate and more important index than the pulse wave. Other features are similar to ECG technology.

Although there are individual differences in the electrocardiogram technology, there are variations in the authentication accuracy due to fluctuation factors such as physical condition. Therefore, a mechanism for maintaining a high authentication rate is required regardless of physical condition. In addition, fingerprints and finger veins are basically authenticated by one image capture, but since electrocardiogram is biological information accompanied by periodic time change and authentication takes time, it is necessary to avoid authentication failure as much as possible.

An information terminal device having an authentication function using an electrocardiogram has the technique described in Patent Document 1. In this publication, "an input unit, a physical condition detection unit that detects the physical condition of a subject, a template holding unit that holds a plurality of templates, an operation input to the input unit, and the subject based on the template. The template is different depending on the physical condition. ”

Further, a personal authentication method using a pulse wave, a personal authentication system, and a biometric information measurement system include the techniques described in Patent Document 2. In this publication, "biological information that changes periodically in association with environmental parameters such as physical condition such as exercise amount or body temperature of the person to be identified is stored, and is stored in correspondence with the environmental parameters detected at the time of authentication. By performing authentication using the biometric information provided, it is possible to accurately verify the identity regardless of changes in environmental parameters. "

Japanese Unexamined Patent Publication No. 2013-239125 JP-A-2007-213196

However, Patent Document 1 does not describe a template creation / registration method according to the physical condition. Also, there is no description about the method of detecting physical condition when registering the template.

Further, in Patent Document 2, another sensor (for example, an acceleration sensor) for measuring information other than the biological information to be measured is used to detect an environmental parameter which is a factor of fluctuation in physical condition. Therefore, the number of parts in the device increases. In addition, since the template is registered even when the physical condition fluctuation factor detected by the sensor and the biological information at that time do not necessarily correlate with each other, unnecessary templates increase. Therefore, there is a risk of increasing the acceptance rate of others while improving the personal authentication rate.

In the present invention, a variable pattern indicating a change in physical condition is automatically extracted from biological information measured in advance for healthcare, and a template suitable for registration is selected and registered from the extracted variable pattern to improve authentication accuracy. With the goal.

The biometric authentication device of the present invention has a biometric information measuring unit that measures the user's biometric information, a feature amount extracting unit that extracts the feature amount of the biometric information, and a variation pattern of the biometric information from the biometric information stored in the storage unit. A variable pattern extraction unit that extracts and obtains the feature amount, a template registration determination unit that determines whether or not to register the characteristic amount of the variable pattern of the biological information extracted by the variable pattern extraction unit, the feature amount extraction unit, and the feature amount extraction unit. The template management unit that manages the template that records the feature amount of the biological information extracted from the variable pattern extraction unit is compared with the feature amount extracted by the feature amount extraction unit and the template managed by the template management unit. It is configured as a bio-authentication device characterized by comprising an authentication unit for determining whether or not the user can be authenticated.

The present invention is also understood as a biometric authentication system using the biometric authentication device and a mobile terminal.

According to the present invention, a variable pattern indicating a change in physical condition is automatically extracted from biological information measured in advance for healthcare, and a template suitable for registration is selected and registered from the extracted variable pattern to improve authentication accuracy. can do.

It is a functional block diagram of the biometric authentication apparatus of this Example. It is a hardware block diagram of the biometric authentication apparatus of this Example. It is a processing outline diagram up to the creation of an electrocardiogram template. This is a general flow of electrocardiogram authentication. This is a type of ECG feature. It is a screen IF of the initial registration process of the biometric authentication device of this embodiment. It is the biometric information (electrocardiogram) in the biometric information storage unit. It is the composition of the template (electrocardiogram). It is the structure of the template management table. This is the flow of the initial registration process of the biometric authentication device of this embodiment. It is a screen and registration table for user setting information. This is an example of a risk evaluation table. Additional template Maximum value management table configuration. It is a biometric authentication / biometric information measurement flow of the biometric authentication device of this embodiment. This is an example of a screen IF at the time of electrocardiogram authentication of the biometric authentication device of this embodiment. This is an example of a screen IF of the biometric authentication device of this embodiment for performing electrocardiogram measurement according to a user instruction. This is the flow of the variable pattern extraction process. This is a processing flow for calculating the upper limit of the number of templates that can be added. This is the flow of template registration processing. This is an example of the template registration notification screen IF. It is a hardware configuration of a biometric authentication device to which pulse wave technology is applied. It is a wristband type biometric authentication device using electrocardiogram technology. It is a shirt-type biometric authentication device that uses electrocardiogram technology. It is a smartphone built-in authentication device that uses electrocardiogram technology. It is a ring-type authentication device that uses electrocardiogram technology. It is an earphone type authentication device that uses a pulse wave. It is an ear clip type authentication device that uses a pulse wave. This is an example of a finger guide for a smartphone built-in authentication device that uses pulse wave technology. It is another functional block diagram of the biometric authentication device (registration selection utilizing attribute information). It is a biometric authentication device hardware configuration diagram (attribute data acquisition sensor added). This is a configuration example of the attribute data type table. It is biological information (electrocardiogram) in the biological information storage unit. This is a variable pattern reference table. This is the result of variable pattern extraction. It is a correlation coefficient calculation formula. It is a template management table. This is the flow of pattern type analysis. This is a configuration example of the biometric authentication system of this embodiment. Attribute data type table (additional peripheral information). It is a connection configuration of a biometric authentication device, an external sensor, a smartphone, and a server. This is a processing flow of the attribute information acquisition unit (selecting attribute data to be acquired from a biological information measuring device or a sensor). This is the flow of biometric information fluctuation pattern extraction processing on a server on a smartphone / cloud. This is the processing flow of the attribute information acquisition unit. It is a configuration of a biometric authentication system with a disease diagnosis function. Waveform features by pattern. It is the structure of the disease diagnosis result management table. This is an example of changes in the electrocardiogram at the onset of angina. This is the configuration of the variable pattern management table (on the biometric authentication device side). This is the rescue collaborator registration screen IF. It is a variable pattern management table (inside the Disease Countermeasures Review Department). This is a processing sequence when the onset of myocardial infarction is detected. This is an example of information transmitted from a biometric authentication device when a disease is detected. This is the IF of the diagnosis result notification screen at the onset of arrhythmia. This is an example of a medical service and insurance service cooperation application using the biometric authentication device and system of this embodiment. This is an example of a pension payment service using the biometric authentication device and system of this embodiment.

Hereinafter, examples will be described with reference to the drawings.

First, the electrocardiogram will be briefly described with reference to FIG. The electrocardiogram is a waveform of the electrical activity of the heart recorded from the surface of the body, and expresses the movement of the heart. In the electrocardiogram, the vertical axis is the potential (mV) and the horizontal axis is the time (ms). FIG. 5B shows the basic waveform of the electrocardiogram. Waves such as P wave (541), Q wave (542), R wave (543), S wave (544), T wave (545), and U wave (546) appear in the waveform of the electrocardiogram. A wave that combines a Q wave (542), an R wave (543), and an S wave (544) is called a QRS wave (547). Each wave has a height and a time width.

The basic waveform of the electrocardiogram of FIG. 5 (B) shows one heartbeat (from the beginning of the P wave to the beginning of the next P wave). Therefore, the result of the electrocardiogram measurement of the living body is that the waveform as shown in FIG. 5 (B) is continuously repeated. The straight line connecting the beginning of the P wave to the beginning of the next P wave is called the baseline (548). The waveform that appears above the baseline is called a positive waveform, and the waveform that appears below the baseline is called a negative waveform. Since the R wave is the highest wave among the ECG waveforms, the R wave is a reference point for ECG waveform analysis.

In the waveform analysis of the electrocardiogram in biometric authentication, the waveform is analyzed with reference to the waves of these basic waveforms. The amplitude and time width of these waves may change due to changes in physical condition or the onset of disease. In addition, some waves may disappear.
<Explanation of functional blocks of biometric authentication device (Fig. 1)>

FIG. 1 is a functional block diagram of the biometric authentication device of this embodiment.

The biometric authentication device (101) of this embodiment includes a biometric information measuring unit (102) that measures an electrocardiogram, a biometric information storage unit (103) that stores the measured electrocardiogram in association with a measurement time, and a fixed cycle from biometric information. The biometric information of the minute is read, the feature amount of the waveform is extracted, the mean value or the median value is calculated, and the mean value or the median value of the feature amount of the waveform in a typical or standard range, which is normal for the user, is used. A feature amount extraction unit (104, a case where an average value is obtained for simplification of explanation will be described below) for generating a template (first template), and a feature amount extraction unit (104) are stored in cooperation with each other. The biometric information variation pattern extraction unit (106, hereinafter) that extracts the variation pattern from the biometric information and obtains the feature amount (mean value or median value, which is distinguished from the first template and the second template) of the variation pattern. Then, the case of obtaining the average value for simplification of the explanation will be described), and the template management in which the average value of the feature amounts obtained by the feature amount extraction unit (104) and the biological information fluctuation pattern extraction unit (106) is registered as a template. Unit (105), template registration determination unit (107) that determines whether or not to register the feature amount of the variation pattern of the extracted biometric information, personal authentication unit (115) that authenticates an individual using the biometric information, operated by the user. It is configured to include an operation unit (108) for performing the above, a communication unit (109) for communicating with an external device, and a display unit (110) for displaying various information to the user. Hereinafter, when the term "template" is simply used, the first template and the second template are included.

The biometric authentication device (101) has both a function of authenticating using an electrocardiogram and a function of measuring / recording an electrocardiogram as healthcare information. The function of measuring / recording the electrocardiogram as healthcare information is also realized by the biometric information measuring unit (102) and the biometric information storage unit (103). Further, since the biometric authentication device (101) also serves as a healthcare application, the measurement information is always recorded in the biometric information storage unit (103) even when the biometric information is measured for the purpose of authentication. In addition, when measuring biological information, the measurement time is also recorded so that it can be used for physical condition analysis in healthcare applications.
<Explanation of hardware configuration of biometric authentication device (Fig. 2)>

Next, the hardware configuration of the biometric authentication device (101) described with reference to FIG. 1 will be described with reference to FIG.

The biometric authentication device (101) includes electrodes (plus and minus) (201) for measuring the electrocardiogram potential, an acquisition module (202) for acquiring an electrocardiogram signal from the potential acquired by the electrodes, and the biometric authentication device (101). Central Processing Unit (CPU) (203) that controls all operations related to the functional block of, all programs related to this biometric authentication device (101), various data used in each functional block, and measurement results of biometric information are stored. Storage device (204), Random Access Memory (RAM) (205) that reads the program and various data from the storage device (204) and temporarily stores it, or uses it as a cache or work memory, analog measured by an electrode Analog to Digital Converter (ADC) (206) that converts the potential signal of the above into a digital signal, Timer (207) for measuring the time when measuring biological information and setting a timer, and wired for communicating with an external device. / It is configured to have a wireless communication module (208) and an operation / display display (209) for the user to operate or provide information to the user.

Electrodes (201), acquisition module (202), CPU (203), RAM (205), ADC (206), Timer (207), storage device (204) are in the functional block (116) of the biometric authentication / measurement unit. Correspondingly, the communication module (208) corresponds to the communication unit (109), and the operation / display display (209) corresponds to the operation unit (108) and the display unit (110).
<General authentication processing flow (Figs. 3, 4, 5)>

Next, with reference to FIGS. 4 and 5, the general flow of the authentication process using the electrocardiogram technique is shown. The authentication process mainly consists of a template registration flow and a verification flow. These processes are performed by the biometric authentication device (101). First, the template registration flow will be described.

In the template registration flow (401), when the biological information measurement unit (102) measures the electrocardiogram signal (402), the quality check of the signal is performed by the preprocessing (403). Next, the biological information measurement unit (102) extracts the waveform of the electrocardiogram in the electrocardiogram extraction (404). In the electrocardiogram extraction (404), the QRS waveform (547) of the electrocardiogram is extracted, and the P wave (541) and the T wave (545) are extracted. Next, the feature amount extraction unit (104) extracts the feature amount in the waveform in the feature extraction (405). The main features extracted are the spacing, amplitude, and angle in the waveform. The features to be extracted will be described below with reference to the figures.

FIG. 5A shows the intervals between the extracted waveforms. RP (501, interval between P wave peak and R wave peak), RP_L (502, interval between start of P wave and peak of R wave), RT_R (503, interval between peak of R wave and T) shown in the figure. Interval to end of wave), RP_R (504, interval from end of P wave to peak of R wave), RT_L (505, interval from peak of R wave to start of T wave), PR_S (506), ST_S (507), PR_I (508), ST_I (509), RQ (510, interval from Q wave peak to R wave peak), RS (511, interval from R wave peak to S wave peak), In addition to QT (512), add RR_I indicating the interval of R waves.

FIG. 5B shows the types of amplitudes of the extracted waveforms. There are four types, RP_A (520), RQ_A (521), RS_A (522), and RT_A (523), which indicate the amplitude between the peaks of the R wave, the P wave, the Q wave, the S wave, and the T wave.

FIG. 5C shows the types of angles of the extracted waveform. The extracted angles include the angle between P wave-Q wave-R wave (530, AN_Q), the angle between Q wave-R wave-S wave (531, AN_R), and R wave-. There is an angle between the S wave and the T wave (532, AN_S). In the feature extraction (405), the feature amount extraction unit (104) and the biometric information fluctuation pattern extraction unit (106) extract the feature amount of the waveform, calculate the average value of the feature amount, and calculate the average value of the feature amount, and the template management unit (105). ) Registers the result as a template. The operation of the biological information fluctuation pattern extraction unit (106) will be described later with reference to FIG.

The outline of the process up to the template registration will be described with reference to FIG. The feature amount extraction unit (104) detects the R peak (302) from the waveform (301) measured by the biological information measurement unit (102), and divides the waveform into individual waveforms (303). The feature amount extraction unit (104) aligns the time-divided waveforms with the R wave or the like as a reference point (304) (303). After that, the feature amount extraction unit (104) and the biological information fluctuation pattern extraction unit (106) extract the feature amount for each waveform and obtain the average value of the extracted feature amount (305), and the template management unit (105). Registers the result as a template (306, 406). The place where the average value of the feature amount for each waveform is obtained is also performed by the feature extraction (405) and the biological information fluctuation pattern extraction unit (106) in FIG.

Next, the ECG verification flow will be described. In the verification flow, the biological information measurement unit (102) measures the electrocardiogram signal (407) and performs a quality check on the signal by preprocessing (408). Next, the feature amount extraction unit (104) extracts the waveform of the electrocardiogram in the electrocardiogram extraction (409). In electrocardiogram extraction, the QRS complex of the electrocardiogram is extracted, and the P wave and T wave are extracted. Next, the feature amount extraction unit (104) extracts the feature amount in the waveform by the feature extraction (410). The main features extracted are the spacing, amplitude, and angle in the waveform. The processing content of the feature extraction (410) is the same as the content described in the template registration flow.

In general, the number of waveforms used when registering a template is larger than the number of waveforms used during verification. Templates are created using many waveforms to get a more average value. On the other hand, in the verification flow, the number of waveforms used for verification is set as small as possible in order to shorten the verification time. In the collation (411), the personal authentication unit (115) collates the feature amount obtained by the feature extraction with the feature amount registered as a template, and outputs the authentication result. At the time of collation, if the difference from the template value is within the set threshold value, authentication is OK. If it does not fall within the set threshold, authentication fails.
<Correspondence between the authentication flow of this embodiment and the above general authentication flow>

Here, in the biometric authentication device (101), the biometric information measuring unit (102) performs the flow from the measurement (402) of the electrocardiogram signal of FIG. 4 to the preprocessing (403) and the electrocardiogram extraction (404). Further, the flow from the feature amount extraction (405) to the template registration (406) of the template registration flow described with reference to FIG. 4 is a wavy arrow (registration flow) from the biometric information storage unit (103) to the template management unit (105). The flow shown in A) is executed by the feature amount extraction unit (104) and the template registration determination unit (107), respectively.

Similarly, the parts corresponding to the collation (411) from the feature extraction (410) in the verification flow (412) of FIG. 4 are the processing flow shown by the solid line in FIG. 1, respectively, and the feature extraction unit (104) and the feature amount extraction unit (104), respectively. It is executed by the personal authentication unit (115). That is, the feature amount extraction unit (104) acquires the waveform data measured from the biological information storage unit (103), uses the waveform data as an input for the feature amount extraction (104), and uses the feature amount extraction (104) to obtain the feature amount. Ask. The personal authentication unit (115) collates the feature amount extracted by the feature amount extraction (104) with the feature amount (FIG. 8) of the template registered by the template management unit (105), and the difference between the feature amounts is a threshold value. If it is within, authentication is OK, and if it exceeds the threshold value, authentication fails.
<Data structure managed by the biometric information storage unit (Fig. 7)>

Next, the configuration of the biometric information management table (700) stored in the biometric information storage unit (103) will be described with reference to FIG. 7. The biometric information management table (700) is a table in which user's biometric information (for example, electrocardiogram) is recorded, and the electrocardiogram waveform acquired by the biometric information measurement unit (102) is associated with the measurement time at the time of measurement. It has been recorded.

FIG. 7 shows an example of the biological information management table (700). The biological information management table (700) has an item for recording the measurement start time (701) and the measurement completion time (702) of the electrocardiogram and an item for recording the measurement data (703). The measurement data records, for example, the potential every 40 ms. The interval for recording the potential may be any interval required to record a waveform with sufficient accuracy for healthcare and authentication, and is not limited to 40 ms.
<Data structure managed by the template management department (Figs. 8 and 9)>

Next, one or more templates (800) managed by the template management unit (105) and the template management table (900) will be described with reference to FIGS. 8 and 9. Since the biometric authentication device (101) also serves as a healthcare application, the template management unit (105) registers the template (FIG. 8) and manages the template together with the time when the electrocardiogram, which is the original data of the template, is measured. Record on the table (900).

FIG. 8 shows an example of the template (800) stored in the template management unit (105). In the template (800), the average value of the feature amounts is described for each feature (interval 801, amplitude 802, angle 803) in the waveform described in FIGS. 5 (A), (B), and (C). Will be done. The feature variable (805) is the feature amount described in (A) (spacing feature), (B) (amplitude feature), and (C) (angle feature) in FIG. 5, respectively. The unit of interval is "ms", the unit of amplitude is "mV", and the unit of angle is "°" (807).

Since the biometric authentication device (101) is characterized in that a plurality of templates are registered, a number indicating an identifier (804) for identifying the template (800) is also recorded. The data related to the registered template (800) is registered in the template management table (900) described later.

FIG. 9 shows the configuration of the template management table (900). The template management table (900) is a template No. (901) which is an ID of the template (800), a time when the electrocardiogram waveform used when obtaining the template is measured (measurement start time 902 and measurement end time 903), and a collation process. Includes frequency of use (904), which indicates the number of times it has been used.
<Data structure managed by the template registration determination unit (FIG. 11, FIG. 12, FIG. 13)>

Next, the risk evaluation table (1200) and the additional template maximum value management table (1300) managed by the template registration determination unit (107) and stored in the storage device (204) will be described with reference to FIGS. 12 and 13. To do.

FIG. 12 shows an example of the risk evaluation table (1200). The risk evaluation table (1200) is a table for evaluating a user's risk determined based on an electrocardiogram. The risk assessment management table (1200) includes age (1201), height (1202), weight (1203), presence and type of disease (1204), alcohol, cigarettes, caffeine intake (1205), and medication (1205). It includes items to be evaluated by electrocardiogram, such as 1206), exercise (1207), and sleep (1208).

The evaluation of each item of the risk evaluation management table (1200) is performed based on the information set by the user from a series of user information registration screens displayed via the display unit (110) shown in FIG. On a series of registration screens, basic user information (1101) showing the basic characteristics of the user such as age, height, weight, presence / absence and type of disease, intake status of luxury items such as alcohol and cigarettes (1102), medicines, etc. The medication status (1103) and lifestyle information (1104) are input by the user via the operation unit (108).

The series of user information registration screens shown in FIG. 11 is displayed when the user starts using the device, and prompts the user to input information (a series of initial setting procedures will be described later). Here, the result input by the user is shown in the user basic information (1101), the intake status of the favorite item (1102), the medication taking status (1103), and the lifestyle information (1104) shown on the display screen. It is stored by the template registration determination unit (107) in the same configuration as each table.

Here, the evaluation method (1209) for each item of the risk evaluation management table (1200) will be described. In the age item, the risk due to age is evaluated. In general, it is known that arrhythmia increases as the age increases, so the risk can be evaluated for each age. For example, since it is known that arrhythmia occurs more frequently in middle-aged and elderly people, the risk evaluation is +1 for 31 to 50 years old, the risk evaluation +2 for 51 to 70 years old, and the risk evaluation +3 for 71 years old and over (1210).

Regarding height, the risk of heart disease tends to increase when the height is lower than the average height, so the risk is +1 when the height is 10 cm or more lower than the average height (1211).

As for the body weight, the risk is +1 when the body weight is +10 kg or more than the average body weight because the heart is easily burdened when the body weight is higher than the average (1212).

The presence / absence and type of disease section allows you to investigate and assess the risk of diseases that affect the heart. Specifically, the risk is evaluated by examining the presence or absence of heart disease, stroke, kidney disease, diabetes, hypertension, hyperlipidemia, and anemia. The risk was +3 for heart disease, +2 for stroke, +2 for kidney disease, +2 for diabetes, +2 for hypertension, +2 for hyperlipidemia, and +1 for anemia (1213).

In addition, since it is known that luxury items also affect the heart, the intake status of alcohol, cigarettes, and caffeine was examined to evaluate the risk. Specifically, the risk is +1 when alcohol is consumed at least once a day, and the risk is +1 when the daily alcohol intake is 30 cc or more (equivalent to 1 go of sake and 1 large bottle of beer) (1214). ..

For cigarettes, the smoking frequency was examined, and the smoking frequency was set to risk +1 for smoking frequency of 1 to less than 5 times a day and risk +2 for smoking frequency of 5 times or more per day (1215). For caffeine, the risk was +1 when the frequency of caffeine intake was once or more per day (1216).

In addition, we also investigated the medication status and evaluated the risk. Regarding drugs, the risks are evaluated by investigating those that are said to easily affect the heart for each type (1217).

Regarding exercise, it is known that people with low physical activity are more susceptible to ischemic heart disease, so if 20 minutes a day is 20 minutes or less, the risk is +1 (1218).

Regarding sleep, it is known that lack of sleep also affects the heart, so if the average sleep time is 5 hours or less, the risk is +1 (1219).

The above-mentioned risk evaluation table is used for evaluation by gender because the average weight and height differ between men and women. The risk evaluation of each item is an example and is not limited to this example.

Next, using FIG. 13, an example of an additional template maximum value management table (1300) for recording the maximum value of the number of registered templates (800) is shown. In this table, the default value (1301) and the maximum value (1302) of the number of additional templates are stored. The number of templates (Na) that can be added is set to 1 (1303) by default. Therefore, even a person whose risk evaluation result is 0 can newly register one template. In the example of FIG. 13, 5 is set as the maximum value (1304). The template registration determination unit (107) registers the additional template maximum value management table (1300) in the storage device (204).
<Implementation example of the biometric authentication device of this embodiment (FIG. 22)>

Next, an implementation example of the biometric authentication device (101) will be described with reference to FIG. In this embodiment, the example of the biometric authentication device of FIG. 22 will be used for the following flow and screen description.
(A) and (B) of FIG. 22 are mounting examples of a wristband type wearable device. This wearable device has a main body (2201) and a wristband (2202), and the main body portion includes a display (2203) that the user operates or displays information to the user, and a biological information measuring unit (102). An example electrode (one on the front surface of the main body 2204, one on the back surface of FIG. 22B 2205), a button for turning the power on / off (2206), and a button for displaying the authentication status. It is equipped with an LED (2207).
<Initial registration flow of biometric authentication device (registration flow A in FIG. 1, FIG. 10, FIG. 6)>

Next, the registration flow A of FIG. 1 of the biometric authentication device (101) will be described with reference to FIG. In the description, an image of the display screen displayed to the user using FIG. 6 is also used.

The biometric authentication device (101) of FIG. 22 accepts a power ON operation of the power button (2206) of the main body (2201) (step 1001). As a result, the biometric authentication device (101) displays a screen for starting the registration of the template (800) on the display (2203) ((A) in FIG. 6). The screen shows the time required for registration. For example, each time the biometric authentication device (101) executes this process, the process time from displaying the above screen to the completion of registration is measured by a timer (207) and stored in a memory (not shown). Then, the average value of the above processing times in the past may be calculated and displayed. The time (approximate estimated time) stored in the storage device (204) in advance before the product is shipped may be displayed.

Next, the biometric authentication device (101) displays the template registration screen and prompts the user to register the template (800) ((B) in FIG. 6). On the screen, an image of the operation by the user is displayed together with the message prompting the above registration. The biometric authentication device (101) detects that the user has touched the electrode (2204) of the main body, and starts the measurement (step 1002).

The biometric authentication device (101) displays a screen indicating that the template is being registered during the measurement, and displays the remaining time of the measurement ((C) in FIG. 6). The biometric authentication device (101) confirms whether the measurement has been performed for a predetermined time (30 seconds in (A) of FIG. 6) (step 1003), and if the measurement has been performed for a predetermined time (step 1003; Yes), the measured electrocardiogram. The feature amount is extracted from the waveform of (step 1004) to obtain the average value.

Next, the biometric authentication device (101) registers the result as the initial template (step 1005). When the template registration is completed, the biometric authentication device (101) displays the registration completion screen ((D) in FIG. 6).

Next, the biometric authentication device (101) displays an authentication screen and prompts the user to perform authentication in order to confirm whether or not authentication can be performed using the registered template ((E) in FIG. 6). )). On the other hand, when the biometric authentication device (101) detects that the user has touched the electrode (2204) of the main body (2201) again, the biometric authentication device (101) authenticates using the detection flow described above. (Step 1006).

Next, the biometric authentication device (101) confirms whether the authentication was successful (step 1007). If the authentication fails as a result of step 1007 (step 1007; No), the biometric authentication device (101) displays the result of the authentication failure and prompts the authentication again (after the display of (F) in FIG. 6 and then in FIG. 6 (E) is displayed). When the authentication fails, the biometric authentication device (101) counts the number of failures and confirms whether the number of authentication failures is N times or less (step 1011).

When the biometric authentication device (101) confirms that the number of failures is N times or less (step 1011; Yes), the biometric authentication device (101) authenticates again (step 1006). When the biometric authentication device (101) confirms that the number of failures is more than N times (step 1011; No), it discards the registered template (step 1012) and ends the process (1010). The allowable number of failures N can be arbitrarily set on the biometric authentication device side.

On the other hand, if the authentication is successful as a result of step 1007 (step 1007; Yes), the biometric authentication device (101) displays the authentication success screen ((G) in FIG. 6), and provides basic information to the user. Is displayed ((H) in FIG. 6), and a setting screen for basic user information and the like is displayed ((I) in FIG. 6: a part of the registration screen, basic user information (1101) in FIG. 11). ). The biometric authentication device (101) receives input of basic information or the like from the user via the operation unit (108) (step 1008).

The biometric authentication device (101) determines whether the input has been completed for all the items (step 1009), and when the registration of the basic information and the like by the user is completed (step 1009; Yes), the series of initial registration processes is completed (step 1009). Step 1010).

The biometric authentication device (101) displays the result on the screen when the registration of the basic information or the like by the user is completed ((J) in FIG. 6).

When the biometric authentication device (101) determines that the input has not been completed for all the items (step 1009; No), the process returns to step 1006.
<Authentication flow of biometric authentication processing and measurement of biometric information (verification flow of FIG. 1, FIG. 14)>

Next, the verification flow of FIG. 1 and the method of measuring biological information will be described with reference to FIG.

The user wears the biometric authentication device (101) of FIG. 22, and the biometric authentication device (101) accepts the power ON operation of the power button (2206) (step 1401).

The biometric authentication device (101) displays the screen shown in FIG. 15 (A) to urge authentication. The biometric authentication device (101) detects that the user has touched the electrode (2204) and starts authentication (step 1402). The biometric authentication device (101) displays a screen notifying that authentication is in progress ((B) in FIG. 15 and also displays the remaining time required for authentication).

The biometric authentication device (101) determines whether or not the personal authentication is successful (step 1403), and if the authentication fails (step 1403; No), the process ends. However, for example, authentication may be retried for a preset allowable number of times. The biometric authentication device (101) of FIG. 22 displays an authentication NG screen ((C) of FIG. 15) when authentication fails.

If the personal authentication is successful (step 1403; Yes), the biometric authentication device (101) starts measuring the biometric information using the biometric information measuring unit (102) (step 1404). The biometric authentication device (101) displays a screen for starting measurement ((E) in FIG. 15), and displays information such as measurement time during measurement ((F) in FIG. 15).

When the biometric authentication device (101) detects that the user has let go of the electrode, the measurement process ends, the measured biometric information is stored (step 1405), and the process ends (step 1406). It is also possible to interrupt the process without performing the measurement when the personal authentication is completed. In that case, the finger is released from the electrode immediately after the screen of this authentication OK ((D) in FIG. 15) is displayed.
<Authentication and biometric information measurement when using timer settings (Fig. 15)>

After performing the authentication flow and the measurement of the biometric information shown in FIG. 14, the biometric authentication device (101) periodically executes the authentication and the measurement by using, for example, the timer (207).

The biometric authentication device (101) may request execution of authentication at regular time intervals according to the required high security level. In the example of the biometric authentication device (101) of FIG. 22, when the preset time is reached, the re-authentication screen is displayed to prompt the re-authentication ((G) of FIG. 15).

Further, the biometric authentication device (101) may prompt the measurement of biometric information at every time set by the timer. The time set by the timer may be set, for example, in a time zone in which there is no measurement data in the biological information storage unit, or in a time zone in which arrhythmia or disease is relatively likely to occur. In the example of the biometric authentication device (101) of FIG. 22, when the preset morning time arrives, the measurement screen is displayed to prompt the measurement ((H) of FIG. 15).
<Authentication by user instruction and measurement of biometric information (Fig. 16)>

Next, an example will be shown in which the user instructs the personal authentication and the biometric information measurement using the biometric authentication device (101) of FIG.

The biometric authentication device (101) can perform re-authentication and measurement of biometric information after performing personal authentication. The state of personal authentication OK may be maintained while the device is attached. For example, in the biometric authentication device (101) of FIG. 22, while the potential from the electrode (2205) on the back side of the main body is detected, it may be determined that the device is being worn. The state of personal authentication OK is discarded when the biometric authentication device (101) is removed and when the power is turned off, and the state of personal authentication OK is maintained until then.

The biometric authentication device (101) can perform authentication and measure biometric information according to the user's instruction when re-authenticating or measuring biometric information even when the authentication OK state continues while the device is attached. Can be.

FIG. 16A shows an example of the main screen when the authentication OK state continues (after the personal authentication, the biometric authentication device is continuously attached). An authentication icon for personal authentication and a measurement icon for electrocardiogram measurement are displayed on the screen. When measuring biometric information in this state, the user can start the measurement of the electrocardiogram by touching the displayed measurement icon (1601) ((B) in FIG. 16). ). Similarly, personal authentication can be performed by touching the authentication icon.

<Variable pattern extraction processing flow of the biological information variation pattern extraction unit (registration flow B in FIG. 1, FIG. 17)>

Next, with reference to FIG. 17, the content of the variable pattern extraction process executed by the biological information variable pattern extraction unit (106), which is a feature of this embodiment, will be described.

The biometric information variation pattern extraction unit (106) of the biometric authentication device (101) starts the variation pattern extraction process when the biometric information for a preset time is stored in the biometric information storage unit (103). To do. It is known that the electrocardiogram may change slowly depending on the time of day during the day's activities. Therefore, it is desirable to start the processing of the biological information fluctuation pattern extraction unit (106) after the biological information for several hours is accumulated in the biological information storage unit (103). However, it does not limit the storage time of biological information as a guideline for starting.

The biological information fluctuation pattern extraction unit (106) reads the measurement start time (see FIG. 7) of the measurement data from the biological information storage unit (103) and sets it in the variable Time (step 1701). In addition, the value of the variable pattern continuous counter N is reset (set to 0) (step 1702).

Next, the biological information fluctuation pattern extraction unit (106) reads out the waveform for a predetermined time (T) from the biological information storage unit (103) and inputs it to the feature amount extraction unit (104) (step 1703). At this time, the predetermined time T is added to the variable Time (step 1704). Here, the number of waveforms read from the biological information storage unit (103) is set to a number at which the feature amount is averaged to some extent. At least a few beats of waveform are required.

The feature amount extraction unit (104) and the biological information fluctuation pattern extraction unit (106) calculate the feature amount of the input waveform for a predetermined time T and obtain an average value (step 1705). The obtained average value is retained until the processing of FIG. 17 is completed.

Next, the biometric information variation pattern extraction unit (106) compared the average value calculated in step 1705 with the template managed by the template management unit (105) (step 1706), and used it at the time of authentication. It is determined whether or not collation is possible within the threshold range (step 1708).

If collation is possible in step 1708 (step 1708; Yes), the process returns to step 1702. If the collation is not possible (step 1708; No), the biometric information variation pattern extraction unit (106) adds 1 to the counter N (step 1709). Next, the biological information fluctuation pattern extraction unit (106) determines whether N is equal to or higher than a preset predetermined value (WN) (step 1710).

The WN value allows the set value to be changed according to the characteristics of the fluctuation pattern of the individual waveform. The setting or change of the above setting value may be input together with the basic information or the like via, for example, the setting screen of the user basic information or the like shown in FIG. 6 (I). If this value is large, a relatively long-lasting variable pattern will be extracted, and if this value is small, a short variable pattern will be extracted. Since the continuity of the variable pattern may vary from person to person, it is desirable that this value can be set flexibly.

When the biological information fluctuation pattern extraction unit (106) determines in step 1710 that the value is equal to or greater than the predetermined value (WN) (step 1710; Yes), the biological information fluctuation pattern extraction unit (106) adds all the feature amounts of the waveform extracted in step 1705. The result is divided by the value of counter N (1711). The result is used as the result of the biological information fluctuation pattern extraction unit (106) and is used as the input value of the template registration determination unit (107). The variable Time is also used as an input value of the template registration determination unit (107). The above result is the fluctuation pattern of the user. The variable pattern is a pattern that indicates a change in the physical condition of the user, and is a pattern with a waveform that does not allow the user to be correctly authenticated. In other words, it is a waveform pattern that is unusual for the user and is out of the typical or standard range. When such a waveform pattern appears, it can be determined that there is a possibility of arrhythmia or heart disease. On the other hand, in the biometric authentication device (101) of the present embodiment, as will be described later, such a variable pattern is further determined. By registering the waveform of, the user's authentication accuracy can be improved.

The method of extracting the fluctuation pattern using the template has been described above. However, the amplitude spectrum of the electrocardiogram waveform frequency-analyzed at the time of generating the template is obtained, and the pattern having an amplitude spectrum different from the amplitude spectrum is extracted as the fluctuation pattern. May be good. That is, biological information in which the amplitude spectrum obtained by frequency analysis of standard biological information and the result of frequency analysis are different may be extracted as a variable pattern.
<Template registration processing flow of the template registration determination unit (FIG. 18, FIG. 19, FIG. 20)>

Next, the template registration determination process executed by the template registration determination unit (107), which is a feature of this embodiment, will be described.

The template registration determination unit (107) determines whether or not to register the variation pattern extracted by the biological information variation pattern extraction unit (106) in the template management unit (105) as a template. At that time, the template registration determination unit (107) evaluates the risk of occurrence of a variable pattern for each user using the risk evaluation table (FIG. 12), and limits the number of templates that can be additionally registered based on the result.

The risk evaluation method executed by the template registration determination unit (107) will be described with reference to FIG.

First, in the initial setting process (step 1008 in FIG. 10) at the start of using the device, the biometric authentication device (101) accepts the initial setting (using the screen in FIG. 11) from the user (step 1801), and the template registration determination unit (step 1801). 107) evaluates the risk for each item set by the user (step 1803) while referring to the risk evaluation table (1200) based on those values (step 1802). Then, the risk evaluation results of each item evaluated in step 1803 are added to calculate the total score (step 1804). The template registration determination unit (107) sets this total point as the upper limit value (Na) of the template to which the total score can be added (step 1805).

Next, with reference to FIG. 19, a template registration process in which the template registration determination unit (107) registers the variation pattern extracted by the biometric information variation pattern extraction unit (106) as an additional template will be described.

When the feature amount and the measurement time are input from the biometric information fluctuation pattern extraction unit (106), the template registration determination unit (107) determines whether the current total number of additional templates N is equal to or greater than the upper limit value Na of the templates that can be added. Determine (step 1901). When the template registration determination unit (107) determines that the value is not Na or higher (step 1901; No), the input feature amount is registered in the template management as a template (step 1902), and the measurement time of the template is also included. And record (step 1905).

Since the measurement time input from the biological information fluctuation pattern extraction unit (106) is the measurement end time, the template registration determination unit (107) registers it in the template management table (900) as the measurement end time. Refer to the variables WN and T in the biological information fluctuation pattern extraction unit, and set the time obtained by subtracting the time of WN * T from the measurement end time as the measurement start time and register it in the template management table (900) of the template management unit.

On the other hand, when the template registration determination unit (107) determines in step 1901 that the current total number of additional templates N is Na or more (step 1901; Yes), the template management table (900) is referred to and used. Select the template with the least frequency (step 1903). Then, the template registration determination unit (107) replaces the template with a new template (step 1904), and also records the measurement time of the new template (step 1905). The method of registering the measurement time in the template management table is the same as above.

When the additional registration of the template is completed, the biometric authentication device (101) displays the result on the display unit (110) and notifies the user that the new template has been added.

FIG. 20 shows an example of a template registration notification screen that notifies the contents after the completion of template additional registration. On the notification screen, notification of additional registration, registration date and time, total number of registered templates, maximum number of templates that can be registered, and addition of templates enable authentication according to physical condition changes from the beginning of use. It will be displayed that the convenience has been improved.
<Hardware configuration when using pulse waves (Fig. 21)>

Although the first embodiment has been described based on the electrocardiogram technique, the same can be applied to the pulse wave. In the case of pulse wave, since the measurement principle is different, the configuration of the hardware for implementing pulse wave authentication is shown in FIG. In the case of pulse wave, volumetric pulse wave is measured and the measurement result is used for authentication. The volume pulse wave is a measurement of the volume of a blood vessel using an LED (2101) and a photodiode (PD, 2102). Using LEDs and PDs instead of electrodes, the acquisition module (202) acquires pulse wave signals from the volumetric pulse waves measured by the LEDs and PDs. Other than that, the hardware configuration is the same as the hardware configuration when using the electrocardiogram.
<Appearance of biometric authentication device (Figs. 23-28)>

Although the mounting example of the biometric authentication device (101) is shown in FIG. 22, not only the wristband type wearable biometric authentication device but also other mounting examples are shown.

FIG. 23 is an example of a shirt-type biometric authentication device (101). A cloth-type electrode that adheres to the body is sewn into the shirt body. The cloth-shaped electrodes (2301, 2302) are arranged so as to be mounted above and below the shirt body portion corresponding to the position near the heart. Wiring is connected to the electrodes and is connected to the main body device (2303). In the main body device (2303), the difference in potential measured by the positive and negative electrodes is calculated, and the result is transmitted to the smartphone (2304) using the wireless device provided in the main body device (2303). ..

The smartphone (2304) can authenticate the person and measure the biological information on the smartphone side based on the potential difference information transmitted from the main body using the built-in wireless device. A functional block other than the biological information measuring unit (102) shown in FIG. 1 is configured on the smartphone side. In the shirt-type biometric authentication device, both electrodes are always in close contact with the body, so that continuous personal authentication and electrocardiogram measurement are possible. In FIG. 23, a smartphone has been described as an example, but the present invention is not limited to this, and various terminals having a wireless communication function can be used.

FIG. 24 is an example in which the biometric authentication device (101) is mounted on a smartphone. Electrodes, which are biometric information measuring units (102), are arranged on the front surface ((A) of FIG. 24) and the back surface ((B) of FIG. 24) of the smartphone (2401, 2402, respectively). The usage is shown in FIG. 24 (C). The electrode (2402) on the back surface is placed on the palm of one hand and brought into contact with the other hand, and the electrode (2401) arranged on the surface of the smartphone is brought into contact with the other hand. In the example of (C), the thumb is brought into contact with the electrode (2401), and the positional relationship of the fingers when the user browses with one hand (the relationship in which the thumb is closer to the user's hand than the other fingers) is taken into consideration. (2401) was arranged so as to be below the browsing surface (the user's hand side). In this way, if one of the electrodes is arranged on the same surface as the browsing surface operated by the user, the smartphone can be browsed even during the electrocardiogram measurement. In FIG. 24, a smartphone has been described as an example, but the present invention is not limited to this, and various terminals having a wireless communication function can be used. As described above, in this example, the biometric information authentication unit that authenticates the user using the biometric information having periodic fluctuations and the contact with the human body to detect the biometric information used for the user authentication are detected. In a smartphone provided with a biometric information measuring unit which is a biometric information detecting unit, the biometric information detecting unit is provided on a browsing surface side which is an operation surface side operated by a user and a back side which is a back surface with respect to the operation surface. (The same applies to the configuration in FIG. 28).

FIG. 25 shows an example in which the biometric authentication device (101) is mounted on a ring-type device. It is a set (paired) of two rings (2501 and 2502), which are a first biometric authentication device and a second biometric authentication device that can be attached to and detached from the fingers of both hands of the user. Both rings are provided with electrodes (2503, 2504) that are biometric information measuring units. The measured potential information is transmitted from the wireless device provided in one of the rings. As a result, the potential difference between the left and right hands can be obtained from each other within the main body (2505, 2506) of the ring on the transmitted side. For example, when the main body (2505) receives the potential information measured by the main body (2506), the main body (2505) calculates the potential difference between the received information and the potential information measured from the electrode (2503) provided on the ring (2501). ..

Regarding functions other than the above, the same functions as those of the wristband type biometric authentication device of FIG. 22 are installed (however, functions other than the communication function may be integrated in one ring). The merit of the ring-type biometric authentication device is that it is possible to continuously authenticate the person and measure the electrocardiogram while wearing the ring on the left and right hands. FIG. 25 also shows an example when the ring is attached (in this example, one is attached to each of the left and right fingers to measure the electrocardiogram).

26, 27, and 28 show an implementation example when the pulse wave technology is adopted. The pulse wave (volume pulse wave) is measured by LED and PD. To measure the pulse wave, the LED and PD need only be brought into contact with a part of the body. However, since it is easily affected by noise due to external light, it is desirable to measure in a place that is relatively close to the body.

FIG. 26 shows an example in which a biometric authentication device using a pulse wave is mounted on an earphone. The LED (2602) and PD (2603) are built into the earphone (2601). The earphone (2601) is connected to the main body (2604). The pulse wave signal measured by the earphone (2601) is processed by the main body (2604). The main body (2604) includes a power button (2605), a display display (2606), and a status display LED (2607) as shown in FIG. 22. In FIG. 26, the earphone (2601) and the main body (2604) are connected by a wired cable, but they may be connected wirelessly.

FIG. 27 shows an example in which a biometric authentication device using a pulse wave is mounted on an ear clip. The ear clip (2701) has a built-in LED (2702) and PD (2703). The ear clip (2701) is connected to the main body (2704). The pulse wave signal measured by the ear clip (2701) is processed by the main body (2704). The main body (2704) includes a power button (2705), a display display (2706), and a status display LED (2707) as shown in FIG. In FIG. 27, the ear clip main body is connected by a wired cable, but it may be connected wirelessly.

FIG. 28 shows an example in which a biometric authentication device using a pulse wave is mounted on a smartphone. LED (2801) and PD (2802) are mounted on the back of the smartphone. Since the pulse wave measurement by the LED (2801) is easily affected by the external light, it is necessary to bring the body (finger) into close contact with the LED (2801) and the PD (2802) as much as possible. For this reason, a guide (2803) for fixing the finger was installed at the place where the LED (2801) and PD (2802) on the back of the main body were installed. That is, on the back surface of the main body, LEDs (2801) and PD (2802) are located at positions corresponding to guides for holding a smartphone (for example, a finger-shaped groove. In FIG. 28, a groove shaped like the index finger, middle finger, and ring finger). Is provided. With this guide, the middle finger comes into close contact with the LED (2801) and PD (2802) portions. Guides for the right and left hands are required according to the user's dominant hand. The guide may be of a type that can be attached to and detached from the main body, such as a cover case, without being provided on the back surface of the main body.

As described above, in the biometric authentication device of the present embodiment, in order to prevent the authentication rate from decreasing according to the physical condition, the waveform of the biometric authentication device (106) greatly fluctuates from the biometric information measured for healthcare. By extracting the time fluctuation pattern and newly adding the feature amount of the pattern as a template, it is possible to maintain a high authentication rate regardless of the fluctuation of the physical condition.

At that time, in order to prevent the acceptance rate of others from being increased by mistakenly accepting another person as the person by increasing the number of templates to be registered, the template registration determination unit (107) can additionally register the template. I try to limit the number.

In this embodiment, an embodiment different from that of the first embodiment is shown. In the first embodiment, the number of templates that can be added by the template registration determination unit (107) is limited based on the evaluation result by the risk evaluation management table, but in the present embodiment, another method is provided.

That is, an attribute information measuring unit is newly provided in the biometric authentication device (101) shown in FIG. 1 of the first embodiment, and the attribute data measured by the attribute information measuring unit and the biometric information fluctuation pattern in the template registration determination unit (107). The characteristics of the biovariable pattern extracted by the template registration determination unit (107) are obtained when the correlation with the characteristic amount of the variable pattern extracted by the extraction unit (106) is obtained and it is determined that a correlation is observed between them. Allows the amount (mean or median) to be registered as a template. Other functions are the same as those in the first embodiment.

FIG. 29 is a functional block diagram of the biometric authentication device (2901) of this embodiment.

The biometric authentication device (2901) of this embodiment includes a biometric information measurement unit (2902), a biometric information storage unit (2903), a feature amount extraction unit (2904), a template management unit (2906), and a biometric information fluctuation pattern extraction unit (2906). 2907), template registration determination unit (2908), attribute information measurement unit (2909) for acquiring attribute information at the time of biometric information measurement, personal authentication unit (2913), operation unit (2910), communication unit (2911), display unit. (2912). Since the functions of the above parts are basically the same as those of the first embodiment, the common parts will be omitted, and the different parts will be mainly described below.

FIG. 30 shows the hardware configuration of the biometric authentication device (2901) described in the functional block of FIG. 29.

The difference from the hardware configuration of the first embodiment (FIG. 2) is that a body temperature / acceleration sensor (3001) and a GPS (3002) are newly added in order to realize the attribute information measuring unit (2909). The speaker (3003) is not indispensable.

The processing program and various tables (FIGS. 31, 32, 33) described in this embodiment are stored in the storage device. The processing program or table is temporarily read into RAM (205) and executed. Further, the data and the table (FIG. 34) generated in the middle of the process are stored in the RAM (205). Further, the added template and the template management table are stored in the storage device (204). The biometric information authentication device (2901) shown in FIG. 29 can also be mounted in the form of FIGS. 22 to 28, as in the case of the first embodiment.

The parts different from the first embodiment will be described below. FIG. 31 shows the type (3101) of the attribute data measured by the attribute information measuring unit (2909) newly added in this embodiment.

The attribute data is data other than the electrocardiogram measured during the electrocardiogram measurement, and is related to the user to be measured. For example, it includes body temperature (3103), body water ratio (3104), and body fat percentage (3105) with respect to the user attribute (3102). It also includes a wake-up time (3107), a meal time (3108), a bedtime (3109), and an exercise time (3110) with respect to the user's habit attribute (3106).

Next, the configuration of the biometric information management table (3200) managed by the biometric information information storage unit (2903) will be described with reference to FIG. 32.

In the biometric information management table (3200), in addition to the biometric information measurement time (start time 3201 and end time 3202) and measurement data (3203) similar to those in the first embodiment, attribute data (3204) at the time of biometric information measurement is recorded. And remember. In the example of FIG. 32, the user's biological information (body water ratio 3205, body fat percentage 3206) and activity event (3207) are recorded during the biological information measurement. The activity event includes various events performed when the user leads a daily life, such as getting up and eating.

Next, an example of the variable pattern extraction table (3300) is shown with reference to FIG. 33. In the variable pattern extraction table (3300), the types of variable patterns (3301) and the waveform characteristics of the variable patterns (3302) are described.

For example, pattern A is characterized by an RR interval (interval between R waves and R waves) of 0.6 seconds or less (3303). Pattern B has an RR interval of 1.0 second or more (3304), and pattern C has no P-wave identification (3305). As described above, the variable pattern extraction table (3300) defines the extraction conditions for the variable pattern. FIG. 33 describes these three types of patterns, but is not limited thereto.

The template registration determination unit (2908) refers to the waveform characteristics (3302) of the variation pattern extraction table (3300) of FIG. 33, analyzes the feature amount input from the biological information variation pattern extraction unit (2907), and determines which Determine if it corresponds to the pattern. FIG. 34 shows a table for registering the results of pattern determination (hereinafter referred to as pattern determination result table). This table shows features (3401), measurement time (3407), attribute information (3402), variation pattern (3403), analysis status (3404, either unanalyzed or analyzed), and correlation analysis results (3406, It has an item for recording the correlated attribute data type). This table is stored and held in the storage device (204) by the template registration determination unit (2908).

When the template registration determination unit (2908) performs pattern determination of the feature amount input from the biometric information variation pattern extraction unit (2907), the determination result and the measurement input to the bioinformation variation pattern extraction unit (2907) are performed. The time and attribute information are recorded in the table of FIG. The value of the analysis status (3404) is unanalyzed as an initial value.

As described in Example 1, the feature amount is a record of the average value of the features of the interval, amplitude, and angle in the electrocardiogram waveform. In Example 1, all of the 20 feature quantities (501-512, 806, 520-523, 531-533) described are recorded (feature variables in [] of 3401). In the item of the feature amount in the table of FIG. 34, a numerical value is entered for each feature amount, but since it is difficult to understand in the explanation, a character string is used for convenience.

The template registration determination unit (2908) analyzes the correlation between the feature amount and the attribute data (body temperature, body water ratio, body fat percentage in FIG. 34) of the attribute information (3402).

Next, using FIG. 36, a configuration example of a template management table (3600) stored and managed by the template management unit (2906) in the storage device (204) is shown.

The template management table (3600) includes items of template No. (3601), measurement time (measurement start time 3602, measurement end time 3603), waveform characteristics (3604), attribute information (3605), and frequency of use (3606). .. Items other than the waveform feature (3604) and attribute information (3605) are the same items as in the first embodiment. For the attribute information data (attribute data) to be registered, the average value for each pattern is registered. In the item of attribute information (3605), refer to the correlation result (3406) of FIG. 34 and record the type of attribute data in which the correlation with the feature amount was recognized (3607 had a correlation with body temperature). This is an example shown).

Next, with reference to FIG. 37, the content of the process of analyzing the correlation between the attribute data and the feature amount in the template registration determination unit (2908), which is a feature of this embodiment, will be described.

The template registration determination unit (2908) refers to the variation pattern extraction table (3300), identifies the type of the variation pattern of the feature amount extracted by the biological information variation pattern extraction unit (2907), and determines the result in the pattern determination result table (3300). It is stored in FIG. 34) (step 3701).

Next, the template registration determination unit (2908) obtains the total number of variable pattern types from the pattern determination result table (3400) of FIG. 34, and substitutes the value into T. Further, the template registration determination unit (2908) provides a pattern counter value N and sets the value to 0 (step 3702).

Next, the template registration determination unit (2908) selects a pattern of unanalyzed data from the pattern determination result table (3400) of FIG. 34, and adds 1 to the pattern counter N (step 3703). Then, one piece of attribute information data that has not been correlated is selected and the correlation with the pattern feature amount is analyzed (step 3704).

For example, the template registration determination unit (2908) uses the correlation equation shown in FIG. 35 to combine body temperature, which is one of the attribute information (3402) (attribute data) of FIG. 34, with the constituent elements of the feature amount (3401). Determine if there is a correlation.

The presence or absence of correlation is determined for each of the components of the feature amount. The correlation equation of FIG. 35 is an equation for observing the correlation between two variables X (for example, feature quantity RP) and Y (for example, body temperature T of attribute data). Using the value of r (3501), which is the result of this value, it is confirmed whether or not there is a correlation between the attribute data and the components of the feature amount (step 3705). For example, when the absolute value of r is larger than 0.4 (r> 0.4 is a range of values generally judged to be "correlated" or more), it can be determined that there is a correlation. The value of r for determining the presence or absence of correlation was set to r> 0.4, but the value is not limited to this. It may be possible to set it arbitrarily on the biometric information authentication device side.

In the example of FIG. 34, the template registration determination unit (2908) correlates the RP variable, which is one of the feature quantities, with the body temperature T variable of the attribute data, and calculates the value of r (3501). For example, the value of the variable RP in FIG. 34 is the variable X (X0 = RP1, X1 = RPN) in FIG. 35, and the value of the variable T in FIG. 34 is the variable Y (Y0 = T1, Y1 = TN) in FIG. In this case, N = 2) in FIG. 35, the correlation coefficient r is calculated, and whether or not there is a correlation is analyzed.

In step 3705, when the template registration determination unit (2908) determines that there is no correlation between the component of the selected feature amount and the attribute data (step 3705; No), another feature amount (component) and the attribute data The correlation with is analyzed (step 3706).

As a result of the analysis in step 3706, the template registration determination unit (2908) determines whether or not there is a correlation between the attribute data and another feature amount (component) (step 3707).

In step 3707, when the template registration determination unit (2908) determines that there is no correlation (step 3707; No), it determines whether or not there is a component that has not been analyzed (step 3708).

When the template registration determination unit (2908) determines in step 3708 that there is a component that has not been analyzed (step 3708; Yes), the process returns to step 3706. When there is no unanalyzed configuration (step 3708; No), it is determined whether or not there is an attribute that has not been further analyzed (step 3709). When the template registration determination unit (2908) determines that there is an attribute that has not been analyzed (step S3709; Yes), returns to step 3704 and determines that there is no attribute that has not been analyzed (step S3709; No). , Step 3713.

On the other hand, in step 3707, when it is determined that there is a correlation between another feature amount (component) and the attribute data (step 3707; Yes), the template registration determination unit (2908) determines the pattern in FIG. The type of attribute data for which the correlation was found is stored in the correlation result (3406) of the result table (3400) (step 3710).

The template registration determination unit (2908) inputs the average value of the feature amount of the corresponding pattern to be registered as a template into the template management unit (2906), and the template management unit (2906) registers it as a template (step 3711). The template registration determination unit (2908) inputs the measurement time, attribute information, waveform characteristics (3302), and correlation result (3406) of the corresponding pattern into the template management unit (2906), and the newly added template No. (3601) ), The template management unit (2906) registers in the template management table (3600) of FIG. 36 (step 3712). The template registration determination unit (2908) determines whether or not the correlation analysis has been performed for all patterns (T <= N) (step 3713), and when it is determined that the correlation analysis has been performed for all patterns (step 3713; Yes). , End the process. When the template registration determination unit (2908) determines that the correlation analysis has not been performed for all patterns (step 3713; No), the process returns to step 3703. In the flow of FIG. 37, the correlation analysis was performed on all the components of the feature amount and the attribute data, but since the amount of calculation is large, even if the feature amount showing the feature for each pattern is narrowed down and the correlation analysis is performed. Good.

In this embodiment, the configuration and function of the biometric authentication system including the biometric authentication apparatus described in the first and second embodiments will be described.

The difference between Example 1 and Example 2 is that the biometric information fluctuation pattern extraction unit is mounted on a resource-rich smartphone or a server on the cloud, and the biometric authentication device cooperates with those smartphones or servers to change the biometric information. The pattern is being extracted. The smartphone or server is composed of a general computer equipped with a storage device such as a CPU or HDD as hardware.

Further, another difference from the first embodiment and the second embodiment is that the biometric authentication device is newly provided with an attribute information acquisition unit. In the second embodiment, the attribute data was acquired by the attribute information measurement unit (2909), but in the third embodiment, from an external sensor, a biometric information measurement device, a smartphone, or a server connected through the communication unit of the biometric authentication device. Acquires peripheral data at the time of biometric information measurement and attribute data indicating information about the user. Other functions are the same as those of the first and second embodiments.
The differences from the first and second embodiments will be described below.
<Structure of biometric authentication system (Fig. 38, Fig. 40)>

FIG. 38 shows the biometric authentication system of this embodiment. The biometric authentication system of this embodiment includes a biometric authentication device (3801), a smartphone paired with a biometric information device (3830), a server in which the biometric authentication device is registered (3840), and an external sensor (3850) that can be connected to a network. ) And a biometric information measuring device (3860). Here, pairing is device pairing in communication, and means both pairing on an application (the same applies hereinafter).

The biometric authentication device (3801) is a sensor (3802) provided in a biometric information measurement unit (3802), a biometric information storage unit (3803), a feature amount extraction unit (3804), a template management unit (3805), and a smartphone (3830). Attribute information acquisition unit (3807) that acquires attribute information from 3833), an external sensor (3850) connected to the network, or biometric information measurement device (3860), and a measurement environment recorded as attribute information obtained during biometric information measurement. The template registration determination unit (3808) and the personal authentication unit (3815), which examine the correlation between the attribute information such as, and the feature amount of the extracted variable pattern, and register the characteristic amount of the variable pattern having the correlation as a template (Fig. 8). ), An operation unit (3809), a communication unit (3810), and a display unit (3811). Since the functions of the above parts are basically the same as those of the first and second embodiments, the common parts will be omitted, and the different parts will be mainly described below.

The smartphone (3830) extracts a variation pattern of biometric information from the biometric information recorded in the biometric information storage unit (3831) and the biometric information storage unit (3831) that store the biometric information measured by the biometric authentication device (3801). It is provided with a biometric information variation pattern extraction unit (3832) and a sensor (3833) for obtaining the feature amount.

Similarly, the server (3840) also uses the biometric information recorded in the biometric information storage unit (3841) and the biometric information storage unit (3841) that store the biometric information measured by the biometric authentication device (3801). It is provided with a biometric information variation pattern extraction unit (3842) and a sensor (3843) for extracting the variation pattern of the above and obtaining the feature amount thereof.

The difference between Example 1 and Example 2 is that the biometric information fluctuation pattern extraction unit (3832, 3842) is mounted on a resource-rich smartphone (3830) or a server (3840) on the cloud, and the biometric authentication device (3801). And the smartphone (3830) and the server (3840) are being operated in cooperation with each other. The biometric information fluctuation pattern uploads the biometric information measured in the biometric authentication device (3801) from the biometric authentication device (3801) to a smartphone (3830) or a server (3840) for analysis.

In Example 1 and Example 2, the template (FIG. 8) in the biometric authentication device (101, 2901) was utilized when extracting the variable pattern. In the third embodiment, the template (FIG. 8) may be similarly copied to the smartphone (3830) or the server (3840), and the variable pattern may be extracted by utilizing the template. However, if the template (FIG. 8) cannot be taken out of the biometric authentication device (3801), the amplitude spectrum of a standard electrocardiogram waveform is first obtained, and a pattern having an amplitude spectrum different from the amplitude spectrum is extracted as a variable pattern. You may.

Further, another difference between the first embodiment and the second embodiment is that the biometric authentication device (3801) is provided with an attribute information acquisition unit (3807). In the second embodiment, the attribute information measurement unit (2909) acquired the attribute data, but in the biometric information authentication device (3801), an external sensor (3850) or biometric information measurement connected through the communication unit (3810). From the device (3860), the smartphone (3830), and the server (3840), peripheral data at the time of measuring biometric information of the biometric authentication device (3801) and attribute data indicating information about the user of the biometric authentication device (3801) are acquired. When the external sensor (3850) does not exist, the same information may be acquired from the sensor (3833) or the sensor (3843).

The attribute data may be acquired by providing an attribute information measuring unit (2909) inside the biometric authentication device (3801) as in the second embodiment.

The biometric authentication device (3801) of FIG. 38 is paired with the smartphone (3830) on the application. In addition, the biometric authentication device (3801) is registered in advance in the server (3840) on the linked cloud through a Web screen or the like displayed on the display unit of the smartphone (3830). This registration can be performed, for example, by utilizing the Fast IDentity Online (FIDO) authentication platform.

FIG. 40 shows a communication method between the biometric authentication device (3801) and an external device such as a sensor (3850) in the biometric authentication system of this embodiment. An example is shown when Bluetooth / WiFi (both are registered trademarks; the same applies hereinafter) for wireless communication is used as the communication means. The biometric authentication device (3801) in the biometric authentication system of this embodiment can communicate with a smartphone (3830) using Bluetooth / WiFi. In addition, the biometric authentication device (3801) can communicate with a communicable biometric information measuring device (3860) and an external sensor (3850) using Bluetooth. In addition, it is possible to connect to the IoT gateway (4001) or the WiFi access point (4002) to connect to the Internet (4003) and cooperate with the cloud server (3840) on the Internet (4003). In FIG. 40, an example in which Bluetooth / WiFi is used as the communication means has been described, but the biometric information device (3801) includes an external sensor (3850), a biometric information measuring device (3860), a smartphone (3830), and a server. The means for connecting to (3840) is not limited to these communication means.
<Attribute data type table managed by the attribute information acquisition unit (Fig. 39)>

FIG. 39 shows an example of the data type (3901) measured by the attribute information acquisition unit (3807). Since data can be acquired from an external sensor (3850), a biological information measuring device (3860), a smartphone (3830), and a server (3840), it is more than measured by the attribute information measuring unit (2909) of the second embodiment. Kind of data can be obtained. As for the data of the user attribute (3902), it is possible to acquire data measured at the same time by another biometric information measuring device (3860) connected to the network. Regarding the data of the peripheral information (3903), for example, the weather (3904), the atmospheric pressure (3905), the humidity (3906), the illuminance (3907), and the noise amount (3908) by wireless communication via the external sensor (3850). ) Can be obtained.
<Processing flow of attribute information acquisition unit (Fig. 43)>

The processing of the attribute information acquisition unit (3807) will be described with reference to FIG. 43. When the biometric authentication device (3801) starts measuring biometric information, it determines whether or not there is a sensor or device that can be connected through the communication unit (3810) (step 4301). When the biometric authentication device (3801) determines that there is no possible device (step 4301; No), it waits for a certain period of time (step 4302), determines whether there is a sensor or device that can be reconnected (step 4301), When it is determined that there is a connectable device (step 4301; Yes), the device is connected using Bluetooth or WiFi (step 4303). The biometric authentication device (3801) stores attribute data indicating information about the user of the biometric information measuring device (3860) in the template registration determination unit (3808) (step 4304). In the first embodiment, the attribute data is input by the user, but some data (for example, body weight) can be acquired from an external device via wireless communication. Next, the biometric authentication device (3801) acquires the data detected by the sensors (3850, 3843, 3833) and stores the data together with the biometric information measurement data in the biometric information storage unit (3803) (step 4305).

Returning to FIG. 43, the biometric authentication device (3801) determines whether or not to continue the biometric information measurement (step 4306), and if it is determined that the biometric information measurement is not continued (step 4306), the process ends. On the other hand, when the biometric authentication device (3801) determines that it will continue (step 4306; Yes), determines whether there is a connectable sensor or device (step 4307), and determines that there is no connectable sensor or device. (Step 4307), the process ends. On the other hand, when the biometric authentication device (3801) determines that there is a sensor or device that can be connected (step 4307; Yes), the process returns to step 4305.

If the sensors and devices that can be connected to the IoT gateway (4001) or the types of data that can be acquired from them are recorded and the information can be acquired from the connected biometric authentication device (3801), they will be used. The user or the biometric authentication device (3801) may actively collect the data by utilizing the data of the above.
<Processing flow of the attribute information acquisition unit (selecting the biological information measuring device sensor) (Fig. 41)>

The processing when there are many biometric information measuring devices and sensors that can be connected to the biometric authentication device of this embodiment and the information of all the biometric information measuring devices and sensors cannot be acquired will be described.

FIG. 41 describes an example in which attribute data to be stored is selected according to the user's internal response. The reason for paying attention to the activity content is that the factors that affect the living body change greatly depending on the activity content. For example, factors affecting the living body differ during meals, commuting, and sleep. If you are eating, the type and amount of food you eat is likely to have a significant impact on your body. Also, if you are commuting, there is a high possibility that the degree of congestion in transportation and the components in the air inside the vehicle will have an effect. Also, if you are sleeping, the room temperature, noise, illuminance, etc. are likely to have an effect. Therefore, if the number of attribute data that can be acquired / stored at one time is limited, the attribute data to be acquired should be changed according to the activity content of the user.

That is, the biometric authentication device of this embodiment detects the user's activity by wirelessly communicating with the activity meter (step 4101), determines whether or not the activity is awake (step 4102), and the activity is performed. When it is determined to wake up (step 4102; Yes), for example, biometric information such as body temperature, pulse, respiration, and blood pressure and environmental attribute data at the time of wakeup such as illuminance and outside temperature are preferentially read from the activity meter. Recording is performed in the storage device (204) in the biometric authentication device (step 4103).

Further, when the biometric authentication device determines that the activity is not awakening (step 4102; No), further determines whether or not the activity is a meal (step 4104), and determines that the activity is a meal (step 4104). Yes), for example, the attribute data relating to the oral intake is preferentially read from the activity meter and recorded in the storage device (204) in the biometric authentication device (step 4105).

Further, when the biometric authentication device determines that it is not a meal (step 4104; No), determines whether or not it is commuting / moving (step 4106), and determines that it is commuting / moving (step 4106). Yes), for example, the attribute data related to the environment such as the degree of congestion, the outside air temperature, and the chemical composition in the air is preferentially read from the activity meter and recorded in the storage device (204) in the biometric authentication device (step 4107).

Further, when the biometric authentication device determines that it is not commuting / moving (step 4106; No), it determines whether or not it is sleeping (step 4108), and it determines that it is sleeping (step 4108;). Yes), for example, prioritize reading biometric information such as body temperature, pulse, respiration, and blood pressure from the activity meter and attribute data related to the environment such as illuminance, noise, and outside temperature, and record it in the storage device (204) in the biometric authentication device. (Step 4109). As described above, the biometric authentication device of this embodiment selects and records the type of data to be acquired from the connectable biometric information measuring devices and sensors according to the activity content of the user. Although FIG. 41 shows a method of selecting attribute data for waking up, eating, commuting / moving, and sleeping, the activities handled by the biometric authentication device are not limited to these. When there are many types of attribute data in the selected biometric information and environmental information, the attribute data in which a large change is recognized (the amount of change judged to be a large change is defined in advance) should be acquired.

The activity may be detected by the activity meter as described above, or may be detected by referring to the user's schedule or the like recorded on a smartphone or the like. Activities with little movement, such as eating, can be detected by other biological information (heart rate variability, mouth movement, etc.).

In FIG. 41, the attribute data is selected according to the activity content of the user, but as another method, the type of the attribute data to be acquired may be changed according to the time zone of the day. It is generally known that living organisms have a circadian rhythm. The living body changes in a cycle of about 24 hours and has a constant rhythm of change. It is known that there is a circadian rhythm at the onset time of myocardial infarction and arrhythmia. For this reason, during times when diseases and arrhythmias are likely to occur due to circadian rhythms (mainly in the morning and evening), information about the living body and attribute data that affect circadian rhythms (for example, the illuminance of light when waking up, outside) You may try to get the temperature etc.).

In the above, the method of selecting the attribute data is determined based on the general tendency, but the attribute data to be acquired may be selected depending on the constitution of each user and the morbidity of the disease. For example, in the case of a user suffering from angina, since it is known that exercise causes a change in heart activity, attribute data regarding the amount of heart activity is acquired. In addition, users with respiratory diseases mainly acquire attribute data related to components in the air and humidity. People with high blood pressure mainly acquire attribute data such as salt concentration and fat content of ingested food. Further, if the genetic constitution is known in advance by genetic analysis or the like, the attribute data to be acquired may be selected by using the result.

If the type of attribute data that has a large influence on each user is known from the analysis result of big data related to the user, the result may be used for selection. In addition, users who are susceptible to seasonal environmental factors such as pollinosis may change the type of attribute data acquired for each season. In addition, in the case of elderly people, water management is important for maintaining their physical condition, and dehydration has a large effect on the heart, so attribute data related to water intake should be obtained with priority. May be good. In addition, when the time difference or environment changes due to travel or the like, the change may be detected by GPS or the like, and attribute data (illuminance, etc.) that affects jet lag may be selected.

In the above, the method of selecting different types of attribute data has been described, but a biometric information measuring device or sensor that can acquire the same type of attribute data, or a biometric information measuring device or sensor that acquires data when there are many sensors is selected. The method will be described.

Attribute data related to living organisms is acquired from biological information measuring devices and sensors that can acquire the latest data of time. Attribute data related to oral ingestion is acquired from biometric information measuring devices and sensors that can acquire the latest data in time. What is taken into the body through the respiratory organs is obtained from the closest biological information measuring device or sensor in space. Attribute data related to the surrounding environment is acquired from biometric information measuring devices and sensors that are closest in spatial distance.

As the user moves in space, the sensors that can acquire ambient environment data change significantly. In this case, in particular, the data of the sensor that is close to the user in space and can detect a large change is preferentially acquired. In particular, changes in outside air temperature are known to place a heavy burden on the heart, so temperature changes should be obtained accurately.

The above has explained the method of selecting attribute data to be acquired focusing on the content that has a physical effect, but it is also necessary to consider psychological factors. Therefore, for example, the type of attribute data to be selected may be changed in response to a life event such as going on to school, getting a job, or getting married that greatly changes the life. Life events are recorded by the user. Before and after an event that changes your life significantly, you will get priority on attribute data related to your lifestyle. In the case of an event such as moving where the surrounding environment (temperature and precipitation) changes significantly, attribute data related to the surrounding environment is preferentially acquired.

In addition, when living alone due to bereavement of a family member, the amount of communication with people is important for maintaining psychological health, so the number of people who come into contact with each other and conversation time per day. Priority is given to the attribute data related to such. Regarding psychological factors, if the type of attribute data that has a large influence on each user is known from the analysis result of big data related to the user, the attribute data may be selected by utilizing the result.

<Biometric information fluctuation pattern extraction processing flow between smartphone / server biometric authentication device (Fig. 42)>

Finally, with reference to FIG. 42, the biometric information variation pattern extraction processing flow between the smartphone (3830) and the server (3840) and the biometric authentication device (3801) will be described.

FIG. 42 shows the processing flow of the biometric authentication device (3801), the smartphone (3830), and the cloud server (3840) in the biometric authentication system of this embodiment. The process is started at an arbitrary timing after the completion of personal authentication in the biometric authentication device (3801). The biometric authentication device (3801) in the biometric authentication system of this embodiment starts communication by WiFi (step 4201). Next, the biometric authentication device (3801) connects to a smartphone (3830) or a WiFi access point (step 4202). When the connection is completed, the biometric authentication device (3801) transmits the biometric information in the biometric information storage unit (3803) to the smartphone (3830) or the server (3840) on the cloud (step 4203). The data structure in the biological information storage unit (3803) is as shown in FIG.

The server (3840) on the smartphone (3830) cloud analyzes the fluctuation pattern (step 4204). The biometric authentication device (3801) receives the variation pattern analysis result from the smartphone (3830) or the server (3840) on the cloud (step 4205). The biometric authentication device (3801) determines whether or not the reception is completed (step 4206), and if it is determined that the reception is completed (step 4206; Yes), the process ends. On the other hand, when the biometric authentication device (3801) determines that the process has not been completed (step 4206; No), the biometric authentication device (3801) returns to step 4205 and continues to output the variable pattern analysis result from the smartphone (3830) or the server (3840) on the cloud. Receive.

In the above, the communication between the biometric authentication device and the smartphone is WiFi, but Bluetooth may be used.

In FIG. 38, an example in which the template registration determination unit (3808) is provided in the biometric authentication device (3801) is shown, but the template registration determination unit (3808) is provided in the smartphone (3830) or the server (3840) to register the template. You may be able to do it. Further, the biological information on the smartphone (3830) may be transmitted on the server (3840) after analysis. Then, new biometric information may be overwritten in the area vacated after transmission to the server (3840). If the biometric information is not stored after the analysis, the biometric information after the analysis on the smartphone (3830) or the server (3840) may be overwritten with new biometric information.

In this embodiment, the configuration and function of the medical service to which the biometric authentication devices and systems of the first, second, and third embodiments are applied will be described.

The difference from Examples 1 to 3 is that the biometric authentication device (4401) is provided with a predicted waveform pattern management unit (4411) and a disease diagnosis unit (4409), and can detect heart disease and arrhythmia. .. In addition, the feature amount extraction unit (4404) newly has a function of extracting the amplitudes of Q wave, ST, and T wave on a baseline basis, and those feature amounts are included in the template and the predicted waveform pattern. In addition, the predicted waveform pattern is generated from the template for initial registration and the characteristics of each variable pattern.

The difference from the third embodiment is that the linked smartphone (4430) and the server (4440) on the cloud are further equipped with disease countermeasure examination units (4433, 4443) so that countermeasures can be examined at the onset of the disease. Further, the smartphone (4430) and the server (4440) linked with the biometric authentication device (4401) can be linked with the server (4450) of another linking organization to support the user.
<Configuration of biometric authentication system (Fig. 44)>

FIG. 44 shows another embodiment of the biometric authentication system of this embodiment. The biometric authentication system of this embodiment includes a biometric authentication device (4401), a smartphone (4430) paired with a biometric information device (4401), a server (4440) in which the biometric authentication device (4401) is registered, and a biometric authentication device. (4401) is configured to include a registered server (4440), a linked server (4450) linked with a smartphone (4430), a sensor group (4470), and a biometric information measuring device group (4480).

The biometric authentication device (4401) includes a biometric information measurement unit (4402), a biometric information storage unit (4403), a feature amount extraction unit (4404), a template management unit (4406), an attribute information acquisition unit (4407), and a template registration. Judgment unit (4408), personal authentication unit (4421), disease diagnosis unit (4409) that diagnoses the disease by analyzing the measurement result of biometric information, characteristic waveform at the time of disease onset from the user's initial registration template (FIG. 45) ), A predictive waveform pattern management unit (4411), an operation unit (4412), a communication unit (4413), and a display unit (4414) that predict and manage them. Since the functions of the above parts are basically the same as those of the first to third embodiments, the common parts will be omitted, and the different parts will be mainly described below.

The smartphone extracts a variation pattern of biometric information from the biometric information recorded in the biometric information storage unit (4431) and the biometric information storage unit (4431) that stores the biometric information measured by the biometric authentication device, and determines the characteristic amount thereof. It is provided with a desired biometric information fluctuation pattern extraction unit (4432), a disease countermeasure examination unit (4433) for examining countermeasures at the onset of a disease, and a sensor (4434).

Similarly, the server (4440) also stores the biometric information measured by the biometric authentication device (4401) from the biometric information recorded in the biometric information storage unit (4441) and the biometric information storage unit (4441). It is provided with a biometric information variation pattern extraction unit (4442), a disease countermeasure study unit (4443), and a sensor (4444) for extracting the variation pattern of the above and obtaining the characteristic amount thereof.

The data structure (FIG. 32) of the biometric information storage unit (4403, 4431, 4441) and the structure of the template management table in the biometric information authentication device (4401) are the same as those of the second embodiment (FIG. 36). The biometric authentication device (4401) is registered in advance in the linked server (4440). In addition, the smartphone (4430) paired with the biometric authentication device (4401) is also registered in the linked server (4440). The paired smartphone (4430) is also registered in the collaborative institution server (4450).

If a serious illness develops, the help of someone close to you may be needed before ambulance help arrives. Therefore, the server (4440) provides rescuer information (for example, user information such as name, address, and telephone number) for identifying not only the user but also a person (4460) who can request rescue cooperation of the user. Register in advance in.
<Rescue cooperator registration screen (Fig. 49)>

FIG. 49 is an example of a screen for registering a rescue collaborator (4460). The screen is input from an input device such as a keyboard of the server (4440) and displayed on a display device such as a display of the server (4440). In this example, the case where the rescue cooperator (4460) is registered from the server (4440) is described, but it is input from the mobile terminal such as a smartphone or the input device of the PC possessed by the rescue cooperator (4460). It may be registered in the server (4440) via the network. As shown in FIG. 49, the screen is provided with a registration field for rescue cooperators, and user information about the rescue cooperators such as name, age, and contact information is displayed. In this way, the contact information is mainly registered from the screen so that the user can request rescue in case of emergency.
<Table for managing the characteristics of variable patterns and angina diagnostic indicators (Fig. 48, Fig. 47)>

The biometric authentication device (4401) stores a table (FIG. 48) for managing the characteristics of the variable pattern in the storage device (204) in the predicted waveform pattern management unit (4411). The characteristics of this variable pattern are mainly the characteristics of the disease and patterns such as arrhythmia that require attention.

From the characteristics of the waveform change of the electrocardiogram, there is an index that can be used as a guide for diagnosing the type of heart disease or arrhythmia. For example, in angina, a change occurs between S wave and T wave (hereinafter referred to as ST). (A) and (B) of FIG. 47 show changes in the waveform of the electrocardiogram at the onset of angina. The ST is usually at the baseline (4703) position, but when angina develops, this part descends and becomes negative (ST horizontal 4701, ST descend 4702). The above-mentioned features are described in a table (FIG. 48) stored in the predicted waveform pattern management unit (4411).

FIG. 48 shows the configuration of the table stored in the storage device (204) by the predicted waveform pattern management unit (4411). This table shows the types of variable patterns (4801), variable pattern features and index features (4802), disease names that can be diagnosed based on the variable pattern features (4805), and the urgency of the disease (4803). And the item that recorded the countermeasure (4804). The countermeasure (4804) described here differs depending on the service content of the service organization (4450) that cooperates with the biometric authentication device (4401).
<Explanation of features for disease diagnosis and features used in authentication, and composition of waveform features by template and pattern (Fig. 45)>

The feature amount used for the disease index and the feature amount used for the certification do not always match. For example, in FIG. 5B, four feature quantities of amplitude used at the time of authentication, RP_A (520), RQ_A (521), RS_A (522), and RT_A (523), have been described. , These indexes do not include the features indicating the amplitude from the baseline (548) for the Q wave (542), the S wave (544), and the T wave (545). Therefore, in the biometric authentication device (4401) shown in FIG. 44, the amplitudes of the Q wave, ST, and T wave based on the baseline (548) are newly added to Q_A (524), ST_A (525), and T_A (526), respectively. In addition to the feature amount to be analyzed, it is added to a part of the feature amount (4802) which is an index in the table (FIG. 48) of the predicted waveform pattern management unit (4411). Since there is no peak for ST_A (525), the average amplitude of ST is recorded. Further, since the amplitude is viewed on a baseline basis, the fluctuation is positive above the baseline (548) and negative below the baseline (548). Since the baseline-based waveform feature analysis function is not included in the biometric authentication devices of Examples 1 to 3, the feature extraction unit (4404) has a baseline-based Q wave, ST, and T wave. It is necessary to newly provide a function to extract the amplitude of.

FIG. 45 shows the configuration of the template and the waveform feature amount for each pattern. In the biometric authentication device (4401), in addition to the feature amount for authentication (4500) in Examples 1 to 3, an analysis index (4501) for disease / arrhythmia is newly added. Baseline-based Q wave, S wave, and T wave amplitude Q_A (4502), ST_A (4503), and T_A (4504) are added. However, the analytical index (4501) for disease / arrhythmia is not limited to these three.

The predicted waveform pattern management unit (4411) refers to the table (FIG. 48) and uses the template for initial registration (registered in step 1005, identified as ID = 000 in FIG. 45), and the waveform fluctuates due to a disease or arrhythmia. A pattern-specific waveform feature amount (FIG. 45) is generated, which predicts the waveform feature amount at the time of. This process is performed immediately after the initial registration process shown in FIG. 10 is completed. However, when a new feature pattern is recognized, the server (4440) adds a new to the variable pattern management table (FIGS. 48 and 50) of the smartphone (4430), the server (4440), and the biometric authentication device (4401). You can register various patterns. Further, in response to this, the biometric authentication device (4401) may be able to newly generate and register a predicted waveform pattern corresponding to the pattern.

FIG. 45 is also an example of the feature amount of pattern A (4801). Since pattern A (4801) is characterized in that ST_A is 0.1 or less, the ST_A value is set to -0.1 or less in the waveform feature amount of pattern A (4505). Other features are the same as the features of the initial registration template (registered in step 1005, identified as ID = 000 in FIG. 45). Although the other features are the same in pattern A, the features affected by the pattern are different, and the changes in all patterns are not limited to specific features as in pattern A.
<Table for registering disease diagnosis processing and diagnosis results (Fig. 46)>

The biometric authentication device (4401) measures the electrocardiogram with the biometric information measuring unit (4402), and stores the result together with the measurement time and attribute information in the biometric information storage unit (4403). Immediately after, the feature amount extraction unit (4404) reads out the electrocardiogram data (3203), the measurement time (3201, 3202), and the attribute information (3204) stored in the information storage unit (4403), and then calculates the feature amount. , The result, the measurement time, and the attribute information are input to the disease diagnosis unit (4409).

Next, the disease diagnosis unit (4409) compares the input feature amount with the pattern-specific waveform feature amount (described in the structure of FIG. 45) of the predicted waveform pattern management unit (4411). The disease diagnosis unit (4409) determines whether or not the feature amount as an index meets the set conditions, and the other feature amounts are within the set threshold value as in the case of authentication. Determine if it fits. If the disease diagnosis unit (4409) matches the set conditions and the threshold range, it determines that the pattern has been detected, and determines that the disease characterized by the pattern has been detected. Then, the result is recorded in a table (FIG. 46) including a registration number, a disease name, and an onset time.

FIG. 46 shows the structure of the table. A unique number is assigned to the registration number (4601) as a result of pattern detection, and the number is registered. In the disease name (4602), a disease name characterized by the detected pattern is registered (the relationship between the pattern and the disease is determined with reference to Table FIG. 48). In the occurrence time (4603, 4604), the measurement time (measurement start time and measurement end time) input to the disease diagnosis unit (4409) together with the feature amount is described.
<Example of processing after disease detection and information transmitted by the server of the service provider (Fig. 52)>

Next, the processing after the disease is detected by the disease diagnosis unit (4409) will be described. When the biometric authentication device (4401) detects the onset of a disease, it transmits information on the diagnosis result, information on the user's situation, and information on the surrounding situation to a server (4440) registered in advance.

FIG. 52 shows an example of information to be transmitted to the server. FIG. 52 (A) is an example of information regarding the diagnosis result. It relates to the detected disease name (5201) and variable pattern (5202), the urgency of the disease (5203), and the time of occurrence (5204). The detected disease name (5201) and urgency (5203) are acquired by the disease diagnosis unit (4409) with reference to a table (FIG. 48) in the prediction waveform management unit. The time of occurrence (5204) is obtained from the disease diagnosis unit (4409).

FIG. 52B is an example of information regarding the user situation. Consciousness level (5205), age / gender (5206), vital information (blood pressure 5207, pulse 5208, respiratory rate 5209, body temperature 5216), situation at onset (5210 exercise, etc.), presence / absence of rescue cooperator registration (5211) , GPS information (5215) and the like.

The consciousness level (5205) may be determined based on the content of the diagnosed disease, or may be determined based on the attribute data acquired by the attribute information acquisition unit (4407) in the biometric authentication device (4401). As the age and gender (5206), those registered by the user at the start of using the device (stored in the template registration determination unit 4408) are used. Vital information (blood pressure 5207, pulse 5208, respiratory rate 5209, body temperature 5216), onset status (5210), GPS information (5215) are acquired from attribute information input to the disease diagnosis unit (4409). As the information regarding the presence / absence of rescue cooperator registration (5211), the information first registered by the user is used (contents of FIG. 49, stored in the biological information storage unit 4403).

FIG. 52 (C) is an example of peripheral information at the onset of the disease. Also for this, the attribute information input in the disease diagnosis unit (4409) is used.
<Functions of the Disease Response Examination Department and internally managed tables (Fig. 50)>

Next, the functions of the disease response study units (4433, 4443) provided on the smartphone (4430) or the server (4440) will be described. The disease response study unit (4433, 4443) sent information on the diagnosis result ((A) in FIG. 52), information on the user status ((B) in FIG. 52), and peripheral information transmitted from the biometric authentication device (4401). Consider countermeasures based on information such as (52 (C)). The specific processing will be described later.

FIG. 50 shows the configuration of a table stored and managed in a storage device (204) by a disease response study unit (4433, 4443) on a smartphone (4430) and a server (4440). A table similar to that of the biometric authentication device (4401) is stored in the table. This makes it possible to perform a follow-up test on the sent diagnostic result (however, in order to share the biometric information for the follow-up test, it is necessary to separately send the biometric information from the biometric authentication device 4401).

In the biometric authentication device (4401), the countermeasure (4804) is determined in the table (FIG. 48) in the predicted waveform pattern management unit (4411), but the biometric authentication device (4401) alone can handle the countermeasure (4401). It has been described. On the other hand, in the table of the disease response study department (4433, 4443) of the smartphone (4430) and the server (4440), countermeasures are set in consideration of the collaborative organization (4450) and the relief cooperator (4460). In the disease response study departments (4433, 4443) of the smartphone (4430) and the server (4440), information on the user status at the time of onset (contents of (B) in FIG. 52) and the status of the collaborative organization (4450) (congestion degree, etc.) ) To determine the final measures.
<Processing sequence when the onset of myocardial infarction is detected (Fig. 51)>

FIG. 51 shows a processing sequence at the time of detecting the onset of myocardial infarction in the biometric authentication system of this example. The components are a biometric authentication device (4401), a smartphone (4430), a server (4440, a service provider), a collaborative institution server (4450, a collaborative service institution), and a rescue collaborator (4460). The relief cooperator (4460) has a mobile terminal such as a smartphone capable of communicating with the server (4440).

The biometrics device (4401) detects the onset of myocardial infarction (5101). Upon receiving the result, the alarm (speaker, 3003) in the device is turned ON (5102).

Next, information on the diagnosis result ((A) in FIG. 52), information on the user situation ((B) in FIG. 52), and peripheral information at the onset of the disease ((C) in FIG. 52) are provided on the smartphone (4430) and the server. (4440, service provider) (5103, 5104). All the processes in the biometric authentication device (4401) up to this point do not need to be operated by the user and are all performed automatically.

The smartphone (4430) transmits the information received from the biometric authentication device (4401) as it is to the registration destination server (4440) (5105). The reason for transmitting information from both the biometric authentication device (4401) and the smartphone (4430) to the server (4440) is to reduce the risk due to the interruption of the communication line.

The server (4440, service provider) and the disease countermeasure study department (4433, 4443) of the smartphone (4430) that received the information received information on the diagnosis result ((A) in FIG. 52) and information on the user status (FIG. 52). (B)), output the examination information considering the countermeasures in response to the peripheral information at the time of the onset of the disease ((C) in FIG. 52), and request the cooperation agency server (4450, cooperation service organization) to activate the ambulance. (5106, 5107).

For example, the disease countermeasure study department (4433, 4443) reads the disease name (for example, angina) with reference to the information on the diagnosis result, and further sets it as a medical item handled by a pre-registered medical institution. It is determined whether or not the disease name is included, and if it is determined that the disease name is included, the contact information (for example, e-mail address) of the medical institution is notified of the request to activate the ambulance, and the disease name is included. Information on the above diagnosis result, information on the user situation, and peripheral information at the time of onset of the disease are attached to the notification. In the case of the most urgent disease such as myocardial infarction, an ambulance may be activated from both the smartphone (4430) and the server (4440) of the service providing organization. In this way, each disease countermeasure study unit rescues the user to a linked server that communicates with an external server based on the detection result that is the diagnosis result of the user's heart disease diagnosed by the disease diagnosis unit of the biometric authentication device. Notify the request for cooperation.

In addition, the disease countermeasure study unit (4433, 4443) transmits a support request notification to the mobile terminal (for example, an e-mail address) of the rescue cooperator of the user who has been registered in advance (5108). At the same time, the disease countermeasure study department (4433, 4443) notifies the mobile terminal of the rescue collaborator of the Automated External Defibrillator (AED) information (5109). For example, the disease countermeasure study unit (4433, 4443) transmits information indicating the location of the nearest AED to the mobile terminal. The nearest AED installation location may be determined, for example, by comparing the GPS information included in the information regarding the user situation with the map of the AED installation location registered in advance.

Upon receiving the above notification, the mobile terminal of the relief cooperator (4460) notifies (reports) the response status to the request to the server (4440, service provider) (5110). The above correspondence status is input from the input unit (for example, a touch panel) of the mobile terminal by the relief cooperator. The response status includes, for example, information indicating the actual relief contents of the relief cooperator, such as the date and time of relief and what kind of relief was provided. In the case of a serious case such as myocardial infarction, it is difficult for the person to handle it, so no direct instruction is given to the user.
<Example of information sent to the user by the server of the service provider (Fig. 53)>

The disease countermeasure study unit (4443) of the server (4440, service providing organization) transmits the diagnosis result including the medical institution information, the cause analysis result, and the countermeasure information to the user's smartphone (4430) as a result of the above study. FIG. 53 shows an example of medical institution information, cause analysis result, and countermeasure information sent to the user by the server (4440, service provider).

The server (4430, service provider) notifies the attention level of the detected disease or arrhythmia (5301), and provides information on the medical institution capable of diagnosing or treating the detected disease or arrhythmia (5302). This information receives GPS information (5215) in the user status ((B) of FIG. 52) transmitted from the user, and for example, the disease countermeasure study unit (4443) is the nearest location that is highly convenient for the user. Information on medical institutions may be provided. In the cause analysis (5303), the result of analyzing the detected disease and the cause of the arrhythmia is displayed. As in the third embodiment, the analysis result transmits the biometric information measured from the biometric authentication device (4401) to the smartphone (4430) or the server (4440), and shares the data with the linked server (4450). It may be analyzed.

In the cause analysis result (5303) of FIG. 53, fatigue and stress are also analyzed as causes. The heart is beating under the influence of both sympathetic and parasympathetic nerves. Therefore, the balance between the sympathetic nerve and the parasympathetic nerve can be analyzed by analyzing the measurement data of the electrocardiogram, and the stress and the degree of fatigue can be analyzed depending on the state of the balance.

The general method is as follows. It can be analyzed by taking the distribution of the interval between the R peaks of the electrocardiogram R peak (hereinafter referred to as RRI, 4506) and obtaining the power spectral density for each frequency component of the distribution graph. The integral value of the density of the low frequency component 0.05-0.15 Hz power spectrum reflects the activity of both sympathetic and parasympathetic nerves. On the other hand, the integral value of the density of the power spectrum of 0.15-0.45 Hz is an index of the function of the parasympathetic nerve. The RRI coefficient of variation (standard deviation of RRI divided by the mean of RRI) is an index of autonomic nerve activity. By analyzing the above-mentioned indexes, the degree of stress and fatigue can be analyzed by analyzing the activity of the autonomic nerve and the activity of the sympathetic nerve / parasympathetic nerve.

The above analysis can be carried out by utilizing a cooperating institution (4450) and the analysis result can be utilized, or can be carried out by providing an analysis function inside the biometric authentication device (4401). It is also possible to install and implement the function on a linked smartphone (4430) or server (4440). Returning to FIG. 53, as for the content of the countermeasure, the result of the cause analysis (5303) is received, and the countermeasure (advice) for solving the result is described (5304). These measures may be registered in advance in association with the above analysis results.

Example 5 shows an example of an application utilizing the biometric authentication apparatus and system of Examples 1 to 4.
<Example of application to medical services and insurance services (Fig. 54)>

FIG. 54 shows an example of an application in which a user of the biometric authentication device of this embodiment subscribes to both a medical service and an insurance service.

The user (5401) registers the biometric authentication device of this embodiment and the terminal (for example, the e-mail address of a mobile terminal such as a smartphone) notified from the medical service in the server of the subscribed medical service. Next, the user (5401) uses the registered biometric authentication device to transmit the biometric information measured after the person's authentication to the server of the medical service institution (5402). The server of the medical service institution (5402) analyzes the signs of the disease by referring to the biometric information received from the registered biometric authentication device. If signs of illness are detected, the results will be notified to the above contact information, and information on medical institutions will be provided to encourage hospital visits.

The user (5401) executes a payment process for paying a service usage fee for this service by operating the terminal registered in advance or the like. As for the payment method, an application can be installed in advance and various conventionally known payment methods can be used. On the other hand, the user (5401) also subscribes to the insurance service institution (5403) affiliated with the medical service institution (5402), and operates the terminal to execute the settlement process for paying the insurance premium. The server of the insurance service institution (5403) receives information indicating the health management status of the insured from the above-mentioned server of the medical service institution (5402) with the permission of the user (5401), and receives the contents. Check. When the server of the insurance service organization (5403) confirms the health management status of the insured, it executes a process of cashing back a part of the paid insurance premiums and transmits the result to the terminal. The reason for carrying out such processing is that it is advantageous for the insured to use the services of affiliated medical institutions to manage their health, which has the advantage of reducing the risk of contracting the disease.
<Example of application to pension payment service (Fig. 55)>

FIG. 55 shows an example of an application when the biometric authentication device of this embodiment is applied to pension payment. The Japan Pension Service (5502) will provide the pension recipient (5501) with the biometric authentication device of this embodiment (at the start of receiving the pension).

The pensioner (5501) measures the electrocardiogram using the biometric authentication device of this embodiment and registers the template. The biometric authentication device in which the template is registered is registered in advance on the server of the Japan Pension Service (5502) (it can also be registered using a paired smartphone).

The pensioner (5501) uses the biometric authentication device of this embodiment to authenticate the person in the month of receiving the pension (because the biometric authentication device is registered in advance on the server of the Japan Pension Organization, the person is authenticated. Results can be sent).

The server of the Japan Pension Service (5502) determines that if the person of the pensioner (5501) is authenticated, the pension payment can be permitted, and the pension is paid to the pre-registered pensioner (5501) financial institution (5503). Executes the process of transferring. It is also possible to confirm the existence by the above authentication.

The pension recipient (5501) authenticates himself / herself at the financial institution (5503) that has registered the pension transfer. For example, if the cooperation registration with the biometric authentication device of this embodiment is performed in advance on the management server of the ATM of the bank, the biometric authentication device of this embodiment that has been authenticated (and the authentication is OK). You can receive the pension from the pension transfer account of the pensioner (5501) simply by holding the device over the biometric authentication terminal of the ATM.

The biometric authentication device of this embodiment can perform personal authentication and confirm the survival of the user. Therefore, it is possible to prevent the illegal receipt of the pension after the death of the pensioner.
<Other application examples of this embodiment>

Other applications of the biometric authentication device of this embodiment include payment, entry / exit control, access control to information devices and data, car door and engine keys, public institution driver authentication and health care, and single-person use. It is a watching service.

As described above, according to the biometric authentication device, the biometric authentication system, and the mobile terminal shown in each of the above embodiments, a template according to the physical condition can be automatically created / registered. At that time, it can be carried out without adding new parts to the device. In addition, the personal authentication rate can be improved without depending on the physical condition, and the risk of an increase in the acceptance rate of others due to the registration of unnecessary templates can be reduced.

In the processing performed by each device such as the biometric authentication device, the smartphone, and the server shown in each of the above embodiments, the arithmetic unit such as the CPU (203) actually stores the memory such as the storage device (204). The functions of the above-mentioned parts are realized by reading the program stored in the above and loading it on a main storage device (not shown) and executing the program. The above program is provided as a file in an installable format or an executable format, which is recorded on a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory) or DVD (Digital Versatile Disc). It may be configured to be stored on another computer connected to a network such as the Internet and provided or distributed by downloading via the network.

101 Biometric authentication device 102 Biometric information measurement unit 103 Biometric information storage unit 104 Feature quantity extraction unit 105 Template management unit 106 Biometric information fluctuation pattern extraction unit 107 Template registration judgment unit 115 Personal authentication unit

Claims (11)

A biometric information measurement unit that measures the user's biometric information,
A feature amount extraction unit that extracts the feature amount of the biological information,
A variable pattern extraction unit that extracts fluctuation patterns of biological information from biological information stored in the storage unit and obtains its features,
A template registration determination unit that determines whether or not to register the feature amount of the variation pattern of the biological information extracted by the variation pattern extraction unit, and
A template management unit that manages a template that records the feature amount of the biological information extracted from the feature amount extraction unit and the variable pattern extraction unit, and a template management unit.
It is provided with an authentication unit that collates the feature amount extracted by the feature amount extraction unit with the template managed by the template management unit and determines whether or not the user can be authenticated .
The variable pattern extraction unit extracts a pattern of biological information different from the amplitude spectrum obtained by frequency analysis of standard biological information as the variable pattern.
A biometric authentication device characterized by this. A biometric information measurement unit that measures the user's biometric information,
A feature amount extraction unit that extracts the feature amount of the biological information,
A variable pattern extraction unit that extracts fluctuation patterns of biological information from biological information stored in the storage unit and obtains its features,
Based on the maximum number of templates, a template registration determination unit that determines whether or not to register the feature amount of the variation pattern of the biological information extracted by the variation pattern extraction unit, and
A template management unit that manages a template that records the feature amount of the biological information extracted from the feature amount extraction unit and the variable pattern extraction unit, and a template management unit.
It is provided with an authentication unit that collates the feature amount extracted by the feature amount extraction unit with the template managed by the template management unit and determines whether or not the user can be authenticated.
The authentication unit authenticates the user using the initial template managed by the template management unit.
The template registration determination unit includes basic user information input from the operation unit indicating the characteristics of the authenticated user, preference information of the user, drug administration information of the user, and lifestyle information of the user. The risk of occurrence of the fluctuation pattern is analyzed based on the registration information of the user including and.
Raw material authentication apparatus characterized by. The biological information measuring unit measures the biological information of the user from the registration information of the user is input through the operation unit,
The biometric authentication device according to claim 2 . The template registration determination unit determines the maximum number of templates based on the result of the analysis, and displays the result on the display unit.
The biometric authentication device according to claim 2 . A biometric information measurement unit that measures the user's biometric information,
A feature amount extraction unit that extracts the feature amount of the biological information,
A variable pattern extraction unit that extracts fluctuation patterns of biological information from biological information stored in the storage unit and obtains its features,
A template registration determination unit that determines whether or not to register the feature amount of the variation pattern of the biological information extracted by the variation pattern extraction unit, and
A template management unit that manages a template that records the feature amount of the biological information extracted from the feature amount extraction unit and the variable pattern extraction unit, and a template management unit.
An authentication unit that collates the feature amount extracted by the feature amount extraction unit with a template managed by the template management unit and determines whether or not the user can be authenticated.
It is provided with an attribute information measuring unit for measuring the attribute information of the user when the biological information is measured.
The template registration determination unit obtained a correlation between the attribute information measured by the attribute information measurement unit and the feature amount of the variation pattern extracted by the variation pattern extraction unit, and determined that a correlation was found between the two. In the case, the feature amount of the extracted variable pattern is registered.
A biometric authentication device characterized by this. The template management unit stores in the storage unit the attribute information that is determined by the template registration determination unit to have a correlation between the two.
The biometric authentication device according to claim 5. The biometric authentication device uses the user's electrocardiogram as the biometric information to obtain the biometric information measuring unit, the feature amount extraction unit, the variable pattern extraction unit, the template management unit, the template registration determination unit, and the authentication unit. Execute the process,
The biometric authentication device according to any one of claims 1 to 6 , characterized in that. The biometric information measuring unit is provided on the front surface and the back surface of the mobile terminal operated by the user.
The biometric authentication device according to any one of claims 1 to 6 , characterized in that. The authentication unit holds the authentication result of the user until the biometric authentication device is removed from the user.
The biometric authentication device according to any one of claims 1 to 6 , characterized in that. A biometric authentication system in which a biometric authentication device and a mobile terminal communicate with each other to authenticate a user.
The biometric authentication device is
A biometric information measurement unit that measures the user's biometric information,
A feature amount extraction unit that extracts the feature amount of the biological information,
An attribute information acquisition unit that acquires attribute information about the user from a sensor provided in the mobile terminal, an external sensor connected to the network, or a biometric information measuring device.
A template registration determination unit that determines whether or not to register a template, which is a feature amount of the variation pattern of the biological information extracted by the variation pattern extraction unit,
A template management unit that manages a template that records the feature amount of the biological information extracted from the feature amount extraction unit and the variable pattern extraction unit, and a template management unit.
An authentication unit that collates the feature amount extracted by the feature amount extraction unit with a template managed by the template management unit and determines whether or not the user can be authenticated.
A device communication unit that communicates with the mobile terminal is provided.
The mobile terminal
The variable pattern extraction unit that extracts the variable pattern of biological information from the biological information stored in the storage unit and obtains the feature amount thereof, and the variable pattern extraction unit.
A terminal communication unit that communicates with the biometric authentication device is provided.
The attribute information acquisition unit of the biometric authentication device acquires the attribute information from an external sensor or biometric information measuring device connected to the network, and obtains the attribute information.
The template registration determination unit of the biometric authentication device examines the correlation between the attribute information obtained at the time of measuring the biometric information and the extracted feature amount of the variable pattern, and determines the feature amount of the variable pattern having the correlation. Store as a template,
A biometric authentication system characterized by this. A biometric authentication system in which a biometric authentication device and a mobile terminal communicate with each other to authenticate a user.
The biometric authentication device is
A biometric information measurement unit that measures the user's biometric information,
A feature amount extraction unit that extracts the feature amount of the biological information,
An attribute information acquisition unit that acquires attribute information about the user from a sensor provided in the mobile terminal, an external sensor connected to the network, or a biometric information measuring device.
A template registration determination unit that determines whether or not to register a template, which is a feature amount of the variation pattern of the biological information extracted by the variation pattern extraction unit,
A template management unit that manages a template that records the feature amount of the biological information extracted from the feature amount extraction unit and the variable pattern extraction unit, and a template management unit.
An authentication unit that collates the feature amount extracted by the feature amount extraction unit with a template managed by the template management unit and determines whether or not the user can be authenticated.
A device communication unit that communicates with the mobile terminal is provided.
The mobile terminal
The variable pattern extraction unit that extracts the variable pattern of biological information from the biological information stored in the storage unit and obtains the feature amount thereof, and the variable pattern extraction unit.
A terminal communication unit that communicates with the biometric authentication device is provided.
The biometric authentication system
An external device including a server variation pattern extraction unit that extracts a variation pattern of biometric information from the biometric information stored in the storage unit and obtains a feature amount thereof, a server communication unit that communicates with the biometric authentication device, and the sensor. Have a server
Instead of the variable pattern extraction unit of the mobile terminal, the server variable pattern extraction unit of the external server extracts the variable pattern, and the template management unit of the biometric authentication device extracts the extracted template of the variable pattern. Manage and
The biometric authentication device is
A disease diagnosis unit that diagnoses the user's disease by analyzing the measurement result of the biological information, and
A predictive waveform pattern management unit that predicts a characteristic waveform at the onset of a disease from a template stored in the storage unit is provided.
The mobile terminal and the external server
It is equipped with a disease countermeasures study unit, which examines countermeasures when the user develops a disease.
Each of the disease countermeasure study units requests the user's rescue cooperation from the cooperation server that communicates with the external server based on the detection result of the user's heart disease diagnosed by the disease diagnosis unit of the biometric authentication device. Notify,
Raw material authentication system characterized by.

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