JP2022080503A - Wearable multi-functional biological information detector - Google Patents

Abstract

To provide a wearable and extremely compact multi-functional biological information detector capable of detecting a body temperature, a posture, an electrocardiographic signal, saturation of oxygen, and in addition, a heart rate, a blood pressure, a vascular age and the like, and analyzing sleep, metabolic syndrome, stress and the like, and capable of being easily worn to a chest part of a person.SOLUTION: A device main body comprising a display part can be worn to a subject. The device main body comprises: a temperature sensor for measuring an air temperature and a skin temperature; an electro-cardiograph; an air pressure detecting part for detecting an air pressure and air pressure change; a light emission/reception part for two different wavelength regions; and a 9-axis sensor formed of an acceleration sensor, a gyro and a direction sensor. The device main body further comprises an arithmetic processing unit for performing arithmetic processing to temperature data from the temperature sensor, electrocardiographic data from the electro-cardiograph, the air pressure data from the air pressure detecting part, a volume pulse wave from the light emission/reception part, acceleration data from the acceleration sensor, rotation angle speed data from the gyro and direction data from the direction sensor, then displays biological information related to a subject in the display part and outputs the same.SELECTED DRAWING: Figure 7

Description

The present invention is an aggregate of ultra-miniaturized microsensors with high density and ultra-miniaturized light, thin, short and small sensors, and can provide a large amount of biological information indicating the health state, exercise state, etc. of a person (subject). It is related to an ultra-compact multifunctional biological information detection device that can be easily detected and can also be used for sleep analysis, metabolic syndrome analysis, stress analysis, etc., and can be easily worn on the human chest.

Biological information such as body temperature and pulse is used to grasp the health condition and physical abnormality of the subject. The detection of the biological information of the subject is usually performed by the subject himself or a third party such as a nurse by contacting a sensor (biological information sensor) such as a thermometer with the subject's body for a certain period of time. .. Since the time for contacting this biometric information sensor with the body is usually about several minutes, only temporary biometric information can be detected, and phenomena that do not occur frequently are not detected, and the health condition and physical abnormalities of the subject are sufficient. It may not be possible to grasp. For example, since arrhythmia, abnormal heartbeat, etc. do not always appear, it may not be possible to grasp them by short-time contact with the biometric information sensor. In this case, if the biometric information sensor can detect biometric information for a long period of time, this possibility can be reduced.

Further, in preventive medicine, sports medicine, and the like, in order to make an accurate judgment, it is very effective to collect biological information over a long period of time, and there is a demand for a biological information sensor that makes it possible.

Therefore, in order to enable detection of biological information for a long period of time, a method has been proposed in which a small and lightweight biological information sensor is attached to a subject and the data detected by the sensor is transmitted wirelessly.

For example, in Japanese Patent Application Laid-Open No. 10-155479 (Patent Document 1), biological information such as pulse, movement, sound, and body temperature of the human body is measured by a life sensor that can be worn on the human body, and the measured information is installed on the premises. Sending to the life controller. The life controller notifies the caregiver based on the information received from the life sensor. It is also possible to report the past biometric information of the subject stored in the storage means as needed.

Further, in Japanese Patent Application Laid-Open No. 2001-353130 (Patent Document 2), a sensor for acquiring physical information is arranged in a storage case formed in an ear hook type, a form that can be inserted into the ear canal, or a pendant shape. There is. Then, the data detected by the sensor is transmitted wirelessly.

In Patent Document 1 and Patent Document 2, there is only one place where the biometric information sensor comes into contact with the body of the subject, but in Patent No. 3843118 (Patent Document 3) and Patent No. 4589341 (Patent Document 4), By contacting the biometric information sensor with multiple points, more physical abnormalities can be detected.

In Patent Document 3, biometric information sensors are attached to the right and left halves of the body, and abnormality determination of the body is performed based on biometric information wirelessly communicated from the biometric information sensors. Further, the integrated circuit mounted on the biometric information sensor includes a memory, and the data or the like detected by the biometric information sensor can be stored in this memory.

In Patent Document 4, biometric information sensors are attached to at least one set of upper and lower body, upper and lower limbs, abdominal surface and back, relatively upper and lower parts of the body, and front and back surfaces of a part of the body. , The body abnormality is determined based on the biological information wirelessly communicated from the biological information sensor. Further, at least one of the plurality of biometric information sensors is provided with a memory and can store the detected biometric information.

However, in Patent Documents 1 and 2, the biometric information sensor is small and lightweight, and it is possible to detect biometric information for a long time. However, since the data output from the biometric information sensor is performed by wireless communication, the biometric information sensor is output by wireless communication. In an environment where wireless communication is difficult, such as underwater, or in an environment where it is difficult to install a device that receives wireless communication, such as in a crowded train, biometric information may not be acquired.

In Patent Documents 3 and 4, the biometric information detected by the biometric information sensor can be stored in the memory equipped in the biometric information sensor in addition to the transmission to the outside by wireless communication, so that even in the above environment. , Biological information can be acquired from the data stored in the memory. However, in this case, a separate operation of retrieving the data stored in the memory is required.

Japanese Unexamined Patent Publication No. 10-155479 Japanese Unexamined Patent Publication No. 2001-353130 Japanese Patent No. 38431188 Japanese Patent No. 4589341 Japanese Patent No. 6429149

A biological information detection device that solves the above-mentioned problems has been proposed in Japanese Patent No. 6429149, but the biological information includes body temperature, posture, electrocardiographic signal, and oxygen saturation in blood, and health management. , Diagnosis, physical condition measurement, etc. are not sufficient as biological information. Conventional sensors are large in size, and it is difficult to measure long-term contact with the body when moving.

The present invention has been made due to the above-mentioned circumstances, and an object of the present invention is a light, thin, short and small sensor that has never existed in the past. , It is possible to detect heart rate, blood pressure, blood vessel age, etc. in addition to oxygen saturation, sleep analysis, metabo (metabolic syndrome) analysis, stress analysis, etc., and it can be easily attached to the chest of a person (subject). The purpose is to provide an ultra-compact multifunctional biometric information detection device.

The present invention relates to a wearable multifunctional biological information detection device, and the above object of the present invention is a structure in which a device main body provided with a display unit can be worn by a subject, and the device main body measures air pressure and skin temperature. It is equipped with a temperature sensor, an electrocardiograph, a barometric pressure detector that detects barometric pressure and changes in barometric pressure, a light emitting / receiving unit in two different wavelength ranges, and a 9-axis sensor consisting of an acceleration sensor, a gyro, and an azimuth meter. Temperature data from the temperature sensor, electrocardiographic data from the electrocardiograph, barometric pressure data from the barometric pressure detection unit, volumetric pulse wave from the light emitting / receiving unit, acceleration data from the acceleration sensor, rotation from the gyro. This is achieved by providing an arithmetic processing unit that arithmetically processes the angular velocity data and the azimuth data from the azimuth meter and displays and outputs biological information about the subject on the display unit.

Above all, a board mounting technology for mounting many sensors compactly on one board has been developed, and it has become light, thin, short and small.

According to the multifunctional biological information detection device of the present invention, it can be easily and stably attached to the chest of a subject, and it is ultra-compact with high density by light, thin, short and small sensors, and a large amount of biological information for health, exercise, etc. Can be detected. In addition, it has the feature that sleep analysis, metabolic syndrome (metabolic syndrome) analysis, stress analysis, etc. can be easily performed.

It is a perspective view which shows the appearance example of the main body of the multifunctional biological information detection apparatus of this invention. It is a bottom view of the main body of the multifunctional biological information detection apparatus of this invention. It is a side view and a plan view which show the structural example of an electrode piece. It is a bottom view with the electrode piece attached. It is a perspective view of the state which attached the electrode piece. It is a schematic diagram for demonstrating the attachment and the coordinate (xyz) example of the multifunction body information detection apparatus main body. It is a block diagram which shows the structural example of the main body of the multifunctional biological information detection apparatus of this invention. It is a figure which shows the connection relationship between a multifunctional biometric information detection apparatus main body and a personal computer (PC). It is a block diagram which shows the other structural example of the multifunctional biological information detection apparatus main body of this invention. It is a block diagram which shows the structural example of an electrocardiograph. It is a block diagram which shows the structural example of the atmospheric pressure detection part. It is a block diagram which shows the structural example of a light emitting and receiving part. It is a block diagram which shows the structural example of a 9-axis sensor. It is a block diagram which shows the structural example of a control calculation part. It is a waveform diagram which shows an example of electrocardiographic data. It is a waveform diagram which shows the normal example and the abnormal example of the electrocardiographic data waveform. It is a waveform diagram which shows the waveform example of a volume pulse wave, a velocity pulse wave, and an acceleration pulse wave. It is a waveform diagram which shows the waveform example of the acceleration pulse wave corresponding to the age. It is a characteristic diagram which shows the difference example of the absorption rate with respect to hemoglobin of infrared light and visible light. It is a waveform diagram which shows the state of calculation of a pulse transit time. It is a block diagram which shows the structural example of the breathing detection part. It is a block diagram which shows the structural example of a sleep analysis part. It is a characteristic diagram which shows the relationship between METs and motion acceleration. It is a time chart for explaining sleep analysis.

The present invention is a collection of ultra-miniaturized microsensors with high density and ultra-miniaturized light, thin, short and small sensors, such as body temperature, posture, electrocardiographic signal, oxygen saturation, heart rate, and blood pressure. It is an ultra-compact multifunctional biological information detection device that can detect blood vessel age, sleep analysis, metabolic syndrome (commonly known as metabo) analysis, stress analysis, etc., and can be easily worn on the human chest.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

FIG. 1 shows an example of the appearance of a wearable and ultra-compact multifunctional biometric information detection device main body 100 according to the present invention. A display unit 110 that displays detected sensor data, notification information, time, etc., a button switch 101 that turns the power on / off, and mode switches 103A and 103B for changing and switching the display mode on the display unit 110. A charging lamp 104 indicating that the built-in battery is being charged and operating, and a capacity lamp 105 that lights up to notify when the battery capacity becomes a predetermined value or less are provided. The reason why the two mode switches 103A and 103B are provided is that many measurement modes are displayed by the combination of ON / OFF. Further, a recess is provided at the end of the upper surface, and a light emitting / receiving unit 150 for measuring the blood vessel age and oxygen saturation is provided by holding the finger of the person to be inspected over the recess. A USB terminal 102 for charging the built-in battery 106 and transmitting the detected data and information by a lead wire or the like is provided on the side surface.

FIG. 2 shows the bottom surface of the multifunctional biological information detection device main body 100, which is provided with a temperature sensor 107 for detecting the air temperature and the skin temperature of the subject, and two circular conductive materials 108A and 108B are on the bottom surface of the main body. It is embedded on both sides, and recesses 109A and 109B, which are recesses for attaching and detaching the electrode pieces 200A and 200B described later, are provided in the central portion of the conductive materials 108A and 108B.

Further, the electrode pieces 200A and 200B have the same configuration, and a configuration example of the electrode piece 200A is shown in FIG. FIG. 3A is a side view, and FIG. 3B is a plan view. The electrode piece 200A is made of a flexible long-shaped synthetic resin or the like, and a disk-shaped conductive engaging member 201A that contacts and engages with the conductive material 108A is provided at one end of one surface. The upper surface of the engaging member 201A is provided with a columnar convex portion 202A that fits into the concave portion 109A of the conductive material 108A. The concave portion 109A and the convex portion 202A can be easily attached and detached. At the other end of the other surface of the electrode piece 200A, a disk-shaped electrode material 203A that contacts or presses against the subject's skin is provided, and the engaging member 201A and the electrode material 203A are conductive leads. It is electrically connected by the wire 204A, and the potential measured by the electrode material 203A passes through the lead wire 204A → the engaging member 201A (convex portion 202A) → the conductive material 108A (concave portion 109A), and is a multifunctional biometric information detection device. It is input to the data processing unit 170 in the main body 100, and then input to the arithmetic processing unit 300 including a CPU (Central Processing Unit) or the like described later.

The same applies to the electrode piece 200B, and the periphery of the electrode materials 203A and 203B has a known suction cup structure or the like, and can be easily attached to the skin (chest) of the subject. Further, the shapes of the electrode pieces 200A and 200B can be changed in various ways, and do not have to be rectangular.

The two electrode pieces 200A and 200B having such a configuration are formed by fitting the concave portions 109A and 109B of the conductive materials 108A and 108B and the convex portions 202A and 202B of the engaging members 201A and 201B, respectively. It can be attached to the functional biological information detection device main body 100, and when the electrode pieces 200A and 200B are attached, the structure is as shown in the bottom view of FIG. 4 and the perspective view of FIG.

In this state, the suction cups at both ends of the electrode pieces 200A and 200B are applied to the chest of the subject, and the multifunctional biological information detection device main body 100 is attached to the chest of the subject as shown in FIG. At that time, in the standing state, the vertical direction is the y-axis, the left-right direction is the x-axis, and the front-back direction is the z-axis, but the xyz-axis can be set in any direction.

As shown in FIG. 4, the bottom surface of the biological information detection device main body 100 has a structure in which two electrode materials 203A and 203B forming the electrocardiograph 130 are arranged, and many are interposed via the electrode pieces 200A and 200B. When the functional biological information detection device main body 100 is attached to the chest, the electrode materials 203A and 203B are in contact with or pressed against the skin of the chest. Since the suction cup can be attached by simply touching the chest of the subject or by lightly pressing (pressing), it is easy for children, infants, and the like.

The electrode materials 203A and 203B are electrodes of an electrocardiograph, and in this example, the potentials of the subject are measured at two locations, so that the interval d is 80 [mm] or more. Is desirable. Further, the mounting member is not limited to the suction cup structure, and other known means can be used. Further, the concave-convex relationship between the concave portions 109A and 109B of the conductive materials 108A and 108B and the convex portions 202A and 202B of the electrode pieces 200A and 200B may be reversed, respectively.

FIG. 7 shows an example of the internal configuration of the multifunctional biological information detection device main body 100, and the multifunctional biological information detection device main body 100 has the following light, thin, short and small multifunction sensors for detecting biological information. It has a data processing unit 170 that processes noise processing, timing, and the like of data and information from multifunction sensors. The data and information processed by the data processing unit 170 are directly stored in the USB terminal 102 or in the memory 180, and then transmitted to the arithmetic processing unit 300 via the USB terminal 102 for arithmetic processing, and the arithmetic processing is performed. Biological information related to exercise and the like is output via the output unit 300A.

The arithmetic processing unit 300 and the output unit 300A can be configured by a personal computer (PC) 400, and FIG. 8 shows a connection relationship between the multifunctional biological information detection device main body 100 and the personal computer 400. In this example, the multifunctional biometric information detection device main body 100 and the personal computer 400 are connected by a wired lead wire 402, and the USB terminal 401 attached to the lead wire 402 is connected to the USB terminal 102 of the multifunctional biometric information detection device main body 100. Insert into and connect.

It is also possible that the multifunctional biological information detection device main body 100 includes a calculation processing unit 300, and a configuration example in this case is shown in FIG. That is, the data and information processed by the data processing unit 170, such as noise processing, are input to the built-in arithmetic processing unit 300, and the data and information processed by the arithmetic processing unit 300 are stored in the memory 180. , It is output to the outside by wire or wirelessly via the transmission unit 102A.

In either case where the arithmetic processing device 300 is outside the multifunctional biological information detection device main body 100 or when the multifunctional biological information detection device main body 100 incorporates the arithmetic processing device 300, the multifunctional biological information detection device main body 100 , As multifunctional sensors, an electrocardiogram based on a temperature sensor 107 that detects the air pressure and the skin (skin) temperature of the subject and outputs temperature data Th, and two electrode materials 203A and 203B that come into contact with the skin of the subject. An electrocardiograph 130 that outputs data EC, a barometric pressure detection unit 140 that detects barometric pressure (PR) and its rate of change (PRR), and light emission and light reception to a subject's finger (blood vessel) with different wavelengths (for example, infrared light). A light emitting / receiving unit 150 that outputs a photoplethysmogram RCA and RCB corresponding to the amount of light received (absorption rate), and an acceleration (αx,) of movement (movement) accompanying the subject's behavior or movement. It is equipped with a 9-axis sensor 160 that detects the rotation angle velocity (θrx, θry, θrz) and geomagnetism and measures the orientation in the absolute direction (DRx, DRy, DRz). All of the above sensors are ultra-compact and can be made denser.

The temperature sensor 106 measures the temperature and also measures the skin temperature (skin temperature) of the subject, and the measured temperature data Th is input to the data processing unit 170.

The electrocardiograph 130 has a configuration as shown in FIG. 10, measures the potential with the two electrode materials 203A and 203B, and inputs the subject's electrocardiographic data EC calculated from the potential difference to the data processing unit 170. Further, as shown in FIG. 10, the electrocardiograph 130 is composed of a potential difference calculation unit 131 that obtains the difference between the potential e1 of the electrode material 203A and the potential e2 of the electrode material 203B by the following equation 1 and outputs the electrocardiographic data EC. The calculated electrocardiographic data EC is input to and input to the data processing unit 170.
(Number 1)
EC = e1-e2

When the number of electrodes is 3 in order to improve the accuracy (the third electrode potential is set to e3), the potential difference calculation unit 131 calculates the potential difference according to the following number 2 or number 3 and is based on the potential difference. The calculated electrocardiographic data EC is output. In this case, the number of electrode pieces is three.
(Number 2)
EC = {(e1-e2) + (e1-e3)} / 2
(Number 3)
EC = e1- (e2 + e3) / 2

An example of the configuration of the atmospheric pressure detection unit 140 is shown in FIG. 11, which includes a barometer 141 for measuring the atmospheric pressure at the current position and a change rate calculation unit 142 for calculating the change rate of the atmospheric pressure. The barometer 141 detects and outputs the barometric pressure PR, and the change rate calculation unit 142 calculates and outputs the barometric pressure change rate PRR based on the barometric pressure PR. The barometric pressure PR is also used for altitude measurement, and the barometric pressure change rate PRR is used for grasping the situation such as a slope during exercise.

An example of the configuration of the light emitting / receiving unit 150 is shown in FIG. 12, in which a pair of light emitting / receiving elements 151A and 151B are connected to the battery 106, and the light emitting / receiving elements 151A and 151B have the same element configuration but different wavelength regions. is doing. That is, the light emitting / receiving element 151A is a region of infrared light, and the light emitting / receiving element 151B is a region of visible light. As for the configuration, the light emitting and receiving elements 151A and 151B are composed of light emitting elements LEDs1A and LED1B such as LEDs and light receiving elements PD1A and PD1B such as photodiodes, respectively, and the light receiving elements PD1A and PD1B have current sensors 152A and 152B, respectively. Is connected. The light emitting element LED1A emits light in the infrared light region to irradiate the inspected object such as a finger, and the light emitting element LED1B emits light in the visible light region to irradiate the inspected object such as a finger (for example, 1 minute). ).

In such a configuration, the infrared light and the visible light emitted from the light emitting / receiving elements 151A and 151B are irradiated to the finger to be inspected, respectively, and the amount of hemoglobin in the blood (hemoglobin oxide (H _b O ₂ ), reduction). Depending on the hemoglobin (H _b ), the reflected light from the finger is incident on the light receiving elements PD1A and PD1B and converted into an electric signal, and the current corresponding to the amount of reflected light is output. It absorbs well, and the reduced hemoglobin absorbs the light near the red light well. The current is detected by the current sensors 152A and 152B, and the detected currents, the voluminous pulse waves RCA and RCB, are input to the data processing unit 170. Absorption rate. The volumetric pulse waves RCA and RCB, which are light amount signals according to the above, are used for calculation of blood vessel age, oxygen saturation, and the like.

Here, the amount of reflected light according to the absorption rate is obtained by the current, but the amount of reflected light (volumetric pulse wave) may be measured by the voltage value across the resistance by inserting a resistance at the place where the current flows.

The 9-axis sensor 160 has the configuration shown in FIG. 13, and includes an acceleration sensor 161 that detects acceleration in the xyz-axis direction and outputs acceleration data αx, αy, and αz, and rotation angle velocity data that detects rotation angular velocity in the xyz-axis direction. It is composed of a gyro 162 that outputs θrx, θry, and θrz, and an azimuth meter (electronic compass) 163 that detects geomagnetism and detects an absolute direction and outputs azimuth data DRx, DRy, and DRz. The accelerometer 161 detects the magnitude of human movement, the gyro 162 detects rotational movement, and the azimuth meter 163 detects the direction of movement (absolute direction). Although it is possible to estimate the attitude and the like with the accelerometer 161 and the gyro 162, it is not possible to correct the error in the yaw direction in terms of Euler angles. Since the gravitational acceleration, which is an index for estimating the posture and the like, runs through the z-axis = yaw axis, the error in the yaw direction can be corrected by adding the azimuth meter 163.

Next, a configuration example of the arithmetic processing unit 300 built in the multifunctional biological information detection device main body 100 or the arithmetic processing unit 300 included in an external device such as a PC (personal computer) will be described with reference to FIG.

The temperature data Th from the temperature sensor 106 is directly output to the outside and input to the operation check unit 310 and the action grasping unit 302. The operation check unit 301 checks whether or not the sensors are operating in a predetermined manner and outputs an operation signal ACc, and the action grasping unit 302 grasps the behavior pattern of the subject such as a shower or exercise and outputs the action signal DAc. do.

The atmospheric pressure PR, which is the atmospheric pressure data from the atmospheric pressure detection unit 140, is directly output to the outside and input to the altitude calculation unit 303. Temperature data Th is also input to the altitude calculation unit 303, and the altitude h is calculated according to, for example, the following equation 4. Note that P ₀ in Eq. 4 is the sea level pressure, and P ₀ = 1013.25 [hPa] may be used.

心電計１３０からの心電データＥＣはピーク検出部３２０に入力され、ピーク検出部３２０において心電データＥＣのピークＲが検出され、検出されたピークＲを示すピーク信号ＰＳはピーク間隔検出部３２１に入力される。ピーク間隔検出部３２１はピーク信号ＰＳに基づいて各データのピーク間隔ＲＲＩを検出し、ピーク間隔ＲＲＩを示すピーク間隔信号ＩＰＳは、心拍数検出部３２３及びＣＶＲＲ(Coefficient of Variation of RR Interval)検出部３２２に入力される。ピーク間隔信号ＩＰＳは心電データＥＣに基づいており、心臓の脈動が心拍数と関連しているので、容易に心拍数ＨＮを検出することができる。

The electrocardiographic data EC from the electrocardiograph 130 is input to the peak detection unit 320, the peak R of the electrocardiographic data EC is detected by the peak detection unit 320, and the peak signal PS indicating the detected peak R is the peak interval detection unit. It is input to 321. The peak interval detection unit 321 detects the peak interval RRI of each data based on the peak signal PS, and the peak interval signal IPS indicating the peak interval RRI is the heart rate detection unit 323 and the CVRR (Coefficient of Variation of RR Interval) detection unit. It is input to 322. Since the peak interval signal IPS is based on the electrocardiographic data EC and the pulsation of the heart is associated with the heart rate, the heart rate HN can be easily detected.

FIG. 15 shows an example of the waveform of the electrocardiographic data EC, and the peak detection unit 320 detects the peaks R1, R2, ... Rn of the electrocardiographic data EC, and the peaks showing the peaks R1, R2, ... Rn. The signal PS is input to the peak interval detection unit 321. The peak interval detection unit 321 detects the interval between peaks R1, that is, the peak interval RRI1 between peak R1 and peak R2, the peak interval RRI2 between peak R2 and peak R3, the peak interval RRI3 between peak R3 and peak R4, and so on. The peak interval signal IPS indicating these peak interval RRIs is input to the heart rate detection unit 323 and the CVRR detection unit 322. The coefficient of variation RRIV of the peak interval was obtained based on the peak signal PS and the peak interval signal IPS of the electrocardiographic data EC, and the activity of the autonomic nerve called CVRR (Coefficient of Variation of RR Interval) was normalized from the coefficient of variation RRIV. Calculate the coefficient CVRR. The calculation method of the coefficient CVRR is known, but when the variance value of the peak interval RRI is σ and the average value of the peak interval RRI is M, it is represented by the following equation 5. The coefficient CVRR is used to determine the activity of the autonomic nerves. Furthermore, the stress level of the autonomic nerve can be measured (stress analysis) in cooperation with the sympathetic nerve.

係数ＣＶＲＲとストレス状況の関係は表１であり、係数ＣＶＲＲが７％以上ではストレスは感じられず、健康的に極めて良好であり、５％～７％未満においても正常（良好）と判断される。また、４％～５％未満は健康的にグレーゾーンであり、混合領域となる。しかし、係数ＣＶＲＲが２％～４％未満になると、何らかのストレス症状が現われ、更に２％未満では健康的に最悪の状況と判断される。このようなストレスの解析は、ストレス解析部（図示せず）において行う。

The relationship between the coefficient CVRR and the stress situation is shown in Table 1. When the coefficient CVRR is 7% or more, no stress is felt, the health is extremely good, and even when the coefficient CVRR is 5% to less than 7%, it is judged to be normal (good). .. In addition, 4% to less than 5% is a healthy gray zone and is a mixed region. However, when the coefficient CVRR is less than 2% to 4%, some kind of stress symptom appears, and when it is less than 2%, it is judged to be the worst health condition. Such stress analysis is performed by the stress analysis unit (not shown).

発受光部１５０の電流計１５２Ａ及び１５２Ｂからの容積脈波ＲＣＡ及びＲＣＢは、いずれも図１７（Ａ）に示すような変曲点を有する波形であり、容積脈波ＲＣＡ及びＲＣＢは酸素飽和度演算部３６０に入力され、容積脈波ＲＣＡは微分部３３０Ａ及びパルストランジットタイム演算部３２４に入力される。容積脈波ＲＣＡは微分部３３０に入力されて微分され（１回目微分）、図１７（Ｂ）に示すような速度脈波ＲＰＷとなる。速度脈波ＲＰＷは更に微分部３３１に入力されて微分され（２回目微分）、図１７（Ｃ）に示すような加速度脈波(second derivative of photoplethysmogram)ＡＰＷとなる。加速度脈波は容積脈波の変曲点をより明瞭にする波形であり、加速度脈波は年代（年齢）によって変化することが知られている。図１８は年代別の波形例を示しており、加齢に伴ってａ波に対してｂ波が浅くなり、ｄ波が深くなるといった変化が現れる。

The volumetric pulse waves RCA and RCB from the current meters 152A and 152B of the light emitting / receiving unit 150 are both waveforms having a variation point as shown in FIG. 17 (A), and the volumetric pulse waves RCA and RCB have an oxygen saturation degree. It is input to the calculation unit 360, and the volumetric pulse wave RCA is input to the differentiation unit 330A and the pulse transit time calculation unit 324. The volume pulse wave RCA is input to the differentiation unit 330 and differentiated (first derivative) to become a velocity pulse wave RPW as shown in FIG. 17 (B). The velocity pulse wave RPW is further input to the differentiation unit 331 and differentiated (second derivative) to become an acceleration pulse wave (second derivative of photoplethysmogram) APW as shown in FIG. 17 (C). The acceleration pulse wave is a waveform that makes the inflection point of the volume pulse wave clearer, and it is known that the acceleration pulse wave changes depending on the age (age). FIG. 18 shows an example of waveforms by age, and changes such that the b wave becomes shallower and the d wave becomes deeper with respect to the a wave appear with aging.

The acceleration pulse wave APW is input to the blood vessel age estimation unit 350. First, the exponent SDPTGAI is calculated according to the following equation 6 based on the peak values a, b, c, d, and e of the acceleration pulse wave shown in FIG. 17 (C).
(Number 6)
SDPTGAI ＝ (bcde) / a

Using the index SDPTGAI obtained in the above number 6, the blood vessel age for each gender is estimated by the following number 7.
(Number 7)
Male "estimated age of blood vessels" = 43.47 x SDPTGAI + 65.86
Female "estimated age of blood vessels" = 41.67 x SDPTGAI + 61.75

From the above number 7, it is important to be careful if the estimated blood vessel age is 10 years or older than the actual age, and if the estimated blood vessel age is 20 years or older, the arteriosclerotic diseases such as hypertension, ischemic heart disease, diabetes, and brain It is necessary to positively suspect vascular disease.

In the above description, the blood vessel age is estimated using the volume pulse wave RCA, but the blood vessel age may be estimated based on the acceleration pulse wave obtained by differentiating the volume pulse wave RCB twice.

Along with the estimated blood vessel age, the blood vessel aging deviation value as described below may be obtained. In this case as well, either the volumetric pulse wave RCA or RCB can be used, and the waveform index wi is first calculated from the acceleration pulse wave based on the following equation 8.
(Number 8)
wi ＝ d / a － b / a

Then, statistically, the age group average value wia of the waveform index wi and the age group standard deviation wir are obtained, and the blood vessel aging deviation value is calculated according to the following equation 9.
(Number 9)
Blood vessel aging deviation value = ｜ wia ｜ － ｜ wi / wir ｜ × 10 + 50

When the vascular aging deviation value is 60 or more, there are many cases of hypertension or fundus arteriosclerosis, and when the vascular aging deviation value is 40 or more and less than 60, the waveform is age-appropriate and standard. Become. If the acceleration pulse wave shows an aging waveform and the vascular aging deviation value is high, it is considered that vascular aging is progressing, and it is examined whether the cause is, for example, blood pressure, smoking, high neutral fat, obesity, etc. do. Moreover, even if the cause is not clearly recognized, it is possible to give an opportunity to examine whether or not the cause exists in the lifestyle.

On the other hand, the oxygen saturation (SpO ₂ ) is expressed by the following equation tens, where the concentration of hemoglobin bound to oxygen is (HbO ₂ ) and the concentration of hemoglobin not bound to oxygen is (Hb).
(Number 10)
(SpO ₂ ) = 100 × HbO ₂ ) / (Hb + HbO ₂ )

That is, it is a value multiplied by "100" in order to divide the amount of oxygen actually bound to hemoglobin by the maximum amount of oxygen that can bind to hemoglobin to obtain a percentage. As is clear from this, the oxygen saturation cannot exceed "100". In humans, SpO ₂ at an oxygen partial pressure of 100 (mmHg) is less than 98%, SpO ₂ at an oxygen partial pressure of 80 (mmHg) is about 95%, and SpO ₂ at an oxygen partial pressure of 60 (mmHg) is about 90%. Weak, SpO 2 at an oxygen partial pressure of 40 (mmHg) is about 75%, SpO ₂ at an oxygen partial pressure of 30 (mmHg) is about 60%, and SpO ₂ at an oxygen partial pressure of ₂₀ (mmHg) is about 30%. It is known that there is. That is, it can be seen that the relationship between the oxygen partial pressure and the oxygen saturation is non-linear. Further, the relationship between the oxygen partial pressure and the oxygen saturation is not always as described above because it is affected by the pH and temperature (body temperature) of blood.

The volume pulse waves RCA and RCB from the light emitting / receiving unit 150 are input to the oxygen saturation calculation unit 360, and the oxygen saturation ST (SpO ₂ ) is calculated. As shown in FIG. 19, the absorption rates of infrared light and red light (visible light) are different from those of hemoglobin (oxidized hemoglobin and reduced hemoglobin) in the blood vessels of the finger, so that the same place of the finger is used. By irradiating the light emitting and receiving elements 151A and 151B with light having different wavelengths, the oxygen saturation calculation unit 360 calculates the oxygen saturation ST based on the difference in the absorption rates.

Further, the pulse wave RCA is input to the pulse transit time calculation unit 324 together with the electrocardiographic data EC, and as shown in FIG. 20, due to the time difference between the peak RCAP of the volumetric pulse wave RCA and the peak ECP of the electrocardiographic data EC. Calculate the pulse transit time PTT. The pulse transit time PTT is input to the blood pressure calculation unit 340, and the blood pressure BP (systolic blood pressure, diastolic blood pressure) is calculated by the blood pressure calculation unit 340 according to the following number 11. Here, the pulse transit time PTT is calculated based on the volumetric pulse wave RCA and the electrocardiographic data EC, but the pulse transit time PTT is calculated based on the volumetric pulse wave RCB and the electrocardiographic data EC. Is also good.
(Number 11)
Systolic blood pressure (maximum blood pressure) = α ₁・ PTT + β ₁
Diastolic blood pressure (minimum blood pressure) = α ₂ , PTT + β ₂
Here, α ₁ , α ₂ , β ₁ , and β ₂ are constants.

The acceleration data α _x , α _y , and α _z from the acceleration sensor 61 are input to the respiration detection unit 310, the posture detection unit 370, and the integration unit 304. Acceleration data α _x , α _y , α _z are input to the integration unit 304 and integrated to become velocity data SPx, SPy, SPz, and velocity data SPx, SPy, SPz are input to the attitude detection unit 370 and are integrated. , Is input to the integrating unit 305. The velocity data SPx, SPy, SPz are input to the integration unit 305 and integrated to become position data PSx, PSy, PSz, and the position data PSx, PSy, PSz are input to the attitude detection unit 370. Rotational angular velocities θrx, θry, and θrz are also input to the attitude detection unit 370.

The posture detection unit 370 is a posture signal indicating the posture of the subject based on the acceleration data α _x , α _y , α _z , the velocity data SP x, SPy, SPz, the position data PSx, PSy, PSz and the rotational angular velocity θrx, θry, θrz. Output PTS. Further, the directional data DRx, DRy, and DRz are input to the directional calculation unit 380, and a three-dimensional directional signal DRS is output. The posture signal PTS and the directional signal DRS can be used to know the posture and direction of the subject's movement and behavior.

Acceleration data α _x , α _y , and α _z are input to the respiratory detection unit 310, and the respiratory rate BR is detected. A configuration example of the respiration detection unit 310 will be described with reference to FIG. Accelerometer data α _x , α _y , α _z are input to the selection unit 311 from the acceleration sensor 161, and the two larger acceleration data α _y, α _z , α _x are selected and input to the high-speed sampling unit 312. In the high-speed sampling unit 312, in order to acquire minute body motion acceleration data α _s of 0.01 [G] or less, high-speed sampling higher than the general respiration frequency is performed, and high-speed sampled acceleration data α _y1, α. _z1 and α _x1 are input to the BPF (bandpass filter) 313. The BPF 313 passes through a frequency bandwidth of a frequency of 0.17 to 0.67 [Hz] corresponding to a normal human respiratory rate of 10 to 40 times per minute, and is bandpass filtered by the BPF 313. Acceleration data α _y2, α _z2 and α _x2 are input to the LPF314. The LPF 314 passes the gravity of 0.01 [G] or less, and inputs the low-pass filtered acceleration data α _y3 , α _z3 , and α _x 3 to the fast Fourier transform unit (FTT) 315. The FTT 315 removes noise components and outputs frequency signals α _ys , α _zs , and α _xs , and the frequency signals α _ys , α _zs , and α _zs are input to the comparison unit 316. The larger frequency signal is automatically selected by the comparison unit 316 and input to the respiration calculation unit 317. The respiratory rate BR calculated from the frequency signal by the respiratory calculation unit 317 is output.

In the above, the respiratory rate BR is calculated using a Fourier transform or the like, but when a three-dimensional acceleration sensor 161 is mounted as in this example, acceleration is generated in relation to respiration. , It is also possible to calculate the respiratory rate BRs according to the following equation 12 or 13 based on the acceleration data α _x, α _y , α _z .

(数１３)
ＢＲｓ＝｜α_ｘ｜＋｜α_ｙ｜＋｜α_ｚ｜

上述した演算処理部３００で得られた各データや情報は、表示部１１０に表示されても良く、媒体を通じて或いは通信手段を介して被験者に報知する。更に、以下に述べる睡眠解析を付加しても良い。この場合、睡眠解析部５００は演算処理部３００が内蔵しても良く、外部に設置するようにしても良い。

(Number 13)
BRs = | α _x | + | α _y | + | α _z |

Each data or information obtained by the above-mentioned arithmetic processing unit 300 may be displayed on the display unit 110, and is notified to the subject through a medium or a communication means. Furthermore, the sleep analysis described below may be added. In this case, the sleep analysis unit 500 may be built in the arithmetic processing unit 300, or may be installed outside.

FIG. 22 shows an example of the configuration of the sleep analysis unit 500, which is a temperature change detection unit 501 that detects a change (increase / decrease) in the temperature data Th and outputs a detection result DT1, and a change (increase / decrease) in the heart rate HN. The heart rate change detection unit 502 that detects and outputs the detection result DT2, the respiratory rate change detection unit 503 that detects the change (increase, decrease) of the respiratory rate BR and outputs the detection result DT3, and the sympathetic nerve index SNS. And the sleep degree determination unit 504 that inputs the parasympathetic index PSNS, determines the sleep degree based on the correlation between the two, and outputs the determination result DT4, and the change (elevation) of the sympathetic nerve index SNS and the parasympathetic nerve index PSNS. , Decrease) and output the determination result DT5, and the posture that determines the subject's posture (standing, lying down, lateral decubitus, etc.) based on the posture signal PTS and outputs the determination result DT6. Input the determination unit 510, the energy consumption change detection unit 511 that detects the change (increase, decrease) of the energy consumption EN and outputs the detection result DT7, the detection results DT1 to DT3 and DT7, and the determination results DT4 to DT6. It is composed of a sleep determination unit 520 that outputs sleep analysis information SYA.

The energy consumption EN is detected based on the acceleration data. Exercise intensity (exercise intensity) is an index of the amount of oxygen taken into the body per 1 kg of body weight, but since the amount of oxygen is difficult to understand, the unit METs (Metabolic Equivalent) is used. The unit METs is a unit indicating exercise intensity depending on how many times more energy can be consumed by the exercise when the resting oxygen intake of 3.5 ml / Kg / min is set to "1". The relationship between oxygen consumption and energy consumption EN is the following number 14.

そして、運動加速度α_ｍと単位ＭＥＴｓとの関係は図２３に示すようになっており、運動加速度α_ｍを検出することによって、相当するＭＥＴｓを求めることができる。検出された運動加速度α_ｍから単位ＭＥＴｓの値を求め、下記数１５に従って消費エネルギーＥＮが算出される。
（数１５）
ＥＮ＝ＭＥＴｓ×時間（Ｈ）×体重（Ｋｇ）×１．０５

ここで、消費エネルギーＥＮを活動時の運動エネルギーとすると、数１５から単位ＭＥＴｓは、下記数１６によって求められる。
(数１６)
ＭＥＴｓ＝運動エネルギー／（運動時間×体重×１．０５）

上記数１６において、１Ｋｇ毎にすると、１時間当たりの単位ＭＥＴｓは下記数１７となる。よって、運動エネルギーはメタボリック症候群（メタボ）の指標となっており、単位ＭＥＴｓを求めることにより、メタボの解析を行うことができる。このようなメタボの解析は、メタボ解析部（図示せず）において行う。
(数１７)
ＭＥＴｓ＝運動エネルギー／（１Ｈ×１Ｋｇ×１．０５）≒運動エネルギー

図２４（Ａ）は被験者が睡眠のために入床した時点ｔ１、睡眠に入った入眠の時点ｔ２、睡眠から覚めた脱眠の時点ｔ３、起床の時点ｔ４を時間軸で示している。

The relationship between the motion acceleration α _m and the unit METs is as shown in FIG. 23, and the corresponding METs can be obtained by detecting the motion acceleration α _m . The value of the unit METs is obtained from the detected motion acceleration α _m , and the energy consumption EN is calculated according to the following equation 15.
(Number 15)
EN = METs x time (H) x body weight (Kg) x 1.05

Here, assuming that the energy consumption EN is the kinetic energy during activity, the unit METs from the equation 15 can be obtained by the following equation 16.
(Number 16)
METs = kinetic energy / (exercise time x body weight x 1.05)

In the above number 16, the unit METs per hour is the following number 17 for each 1 kg. Therefore, kinetic energy is an index of metabolic syndrome (metabolic syndrome), and the metabolic syndrome can be analyzed by obtaining the unit METs. Such metabolic syndrome analysis is performed by the metabolic syndrome analysis unit (not shown).
(Number 17)
METs = kinetic energy / (1H x 1Kg x 1.05) ≒ kinetic energy

FIG. 24A shows the time point t1 when the subject entered the bed for sleep, the time point t2 when the subject entered sleep, the time point t3 when the subject woke up from sleep, and the time point t4 when waking up.

The temperature data Th is input to the temperature change detection unit 501, the change (increase, decrease) of the temperature data Th is detected based on the threshold value, and the heart rate HN detected by the heart rate detection unit 323 is the heart rate change detection unit 502. The change (increase, decrease) of the heart rate HN is detected based on the threshold value. The temperature data Th changes during sleep onset and sleep onset as shown in FIG. 24 (C), and the heart rate HN is shown in FIG. 24 before going to bed (before time point t1) and before falling asleep (before time point t2). As shown in (C), it maintains a constant heart rate, but it gradually decreases when it falls asleep (after time point t2), and gradually increases when it wakes up from sleep (after time point t3), and has a constant heart rate. Return to a few HNs. Therefore, the temperature change detection unit 501 detects the change in the temperature data Th, the heart rate change detection unit 502 monitors the change in the heart rate HN, and the detection results DT1 and DT2 are input to the sleep determination unit 520 to fall asleep. And sleep onset can be detected as the second factor.

The respiratory rate BR is input to the respiratory rate change detection unit 503, and the change (increased or decreased) of the respiratory rate BR is detected based on the threshold value. The respiratory rate BR maintains a constant respiratory rate as shown in FIG. 24 (E) before going to bed (before time point t1) and before falling asleep (before time point t2), but is in a sleep-onset state (time point). After t2), it gradually decreases, and when waking up from sleep (after time point t3), it gradually increases and returns to a constant respiratory rate. Therefore, the respiratory rate change detection unit 503 monitors the change in the respiratory rate BR, and the detection result DT3 is input to the sleep determination unit 328, whereby sleep onset and sleep onset can be detected as the third factor.

The sympathetic nerve index SNS and the parasympathetic nerve index PSNS are input to the sleep degree determination unit 504 and the crossover determination unit 505, and the sleep degree determination unit 504 is in deep sleep based on the correlation between the sympathetic nerve index SNS and the parasympathetic nerve index PSNS. It is determined whether or not there is light sleep, and the determination result DT4 is input to the sleep determination unit 520. Further, as shown in FIG. 24 (G), the sympathetic nerve index SNS is high and the parasympathetic nerve index PSNS is low before falling asleep. Since the neural index PSNS gradually increases and crosses, sleep onset can be detected by detecting this crossing. Similarly, when sleep is deprived, the sympathetic nerve index SNS gradually increases and the parasympathetic nerve index PSNS gradually decreases and crosses. Therefore, sleep deprivation can be detected by detecting this crossover or change. Therefore, the crossover determination unit 505 monitors the crossover or change of the sympathetic nerve index SNS and the parasympathetic nerve index PSNS, and inputs the determination result DT5 to the sleep determination unit 520 to detect sleep onset and sleep onset as the fourth factor. can do.

As shown in FIG. 24 (B), the user is in a standing position before entering the floor (before time point t1), and as shown in FIG. 24 (F), the acceleration data α has an acceleration α _y with respect to gravity of 1 [. G], but since the posture is in the supine position when entering the floor (after the time point t1), the acceleration α _y becomes “0”. Then, when the person wakes up (after the time point t4), the acceleration α _y with respect to gravity becomes 1 [G] again. Before entering the bed (before time point t1), the acceleration α _z parallel to gravity is “0” because it is in a standing position, but when entering the bed and in a lying position (after time point t2), the acceleration α _z becomes gravity. Since it acts in the direction, the acceleration α _z becomes 1 [G], and when it wakes up (after the time point t4), the acceleration α _z with respect to gravity becomes “0” again. The posture of such a subject is determined by the posture detection unit 370 and the posture determination unit 510, and is an important element of sleep analysis. Since the posture of the subject can be determined from the accelerations α _x , α _y , and α _z of the xyz axis, and the lateral decubitus position can also be determined, the number of times of turning over during sleep can be detected.

The energy consumption EN is input to the energy consumption change detection unit 511, and the change in the energy consumption EN is detected based on the threshold value. The energy consumption EN maintains a constant size as shown in FIG. 24 (F) before going to bed (before time point t1) and before falling asleep (before time point t2), but is in a sleep onset state (time point). It decreases when (after t2), rises when waking up from sleep (after time point t3), and returns to a certain size. Therefore, the energy consumption change detection unit 511 monitors the change in the energy consumption EN, and the detection result DT7 is input to the sleep determination unit 520, so that sleep onset and sleep onset can be detected as the fifth factor. The sleep determination unit 520 outputs the sleep analysis information SYA based on the detection result and the determination result.

100 Biometric information detection device Main unit 101 Button switch 102 USB terminal 102A Transmitter 103A, 103B Mode switch 104 Charging lamp 105 Capacity lamp 106 Battery 107 Temperature sensor 108A, 108B Conductive material 109A, 109B Recess 110 Display unit 130 Electrocardiograph 131 Potential difference calculation Unit 140 Atmospheric pressure detection unit 141 Atmospheric pressure meter 142 Change rate calculation unit 150 Light emitting / receiving unit 151A, 151B Light receiving / receiving element 152A, 152B Current sensor 160 9-axis sensor 161 Acceleration sensor 162 Gyro 163 Direction meter (electronic compass)
170 Data processing unit 180 Memory 200A, 200B Electrode piece 201A, 201B Engagement member 202A, 202B Convex part 203A, 203B Electrode material 204A, 204B Lead wire 300 Calculation processing unit 300A Output unit 301 Operation check unit 302 Action grasping unit 303 Advanced calculation Unit 304, 305 Integration unit 310 Respiration detection unit 320 Peak detection unit 321 Peak interval detection unit 322 CVRR detection unit 323 Heart rate detection unit 324 Pulse transit time (PTT) calculation unit 331, 332 Differentiation unit 340 Blood pressure calculation unit 350 Vascular age estimation Unit 360 Oxygen saturation calculation unit 370 Attitude detection unit 380 Direction calculation unit 311 Selection unit 312 High-speed sampling unit 313 BPF
314 LPF
315 Fast Fourier Transform (FFT)
316 Comparison unit 317 Respiratory calculation unit 400 PC 401 USB terminal 402 Lead wire 500 Sleep analysis unit 501 Temperature change detection unit 502 Heart rate change detection unit 503 Respiratory rate change detection unit 504 Sleep degree determination unit 505 Crossover determination unit 510 Posture determination unit 511 Energy consumption change detection unit 520 Sleep determination unit

Claims (7)

The structure is such that the main body of the device equipped with the display unit can be attached to the subject.
The main body of the device includes a temperature sensor that measures temperature and skin temperature, an electrocardiograph, a barometric pressure detector that detects barometric pressure and changes in barometric pressure, a light emitting / receiving unit in two different wavelength ranges, an accelerometer, a gyro, and an accelerometer. Equipped with a 9-axis sensor consisting of an accelerometer,
Temperature data from the temperature sensor, electrocardiographic data from the electrocardiograph, barometric pressure data from the barometric pressure detection section, volumetric pulse wave from the light emitting / receiving section, acceleration data from the acceleration sensor, rotation from the gyro. Wearable multifunctional biological information characterized by being provided with an arithmetic processing unit that calculates and processes angular velocity data and orientation data from the azimuth meter and displays and outputs biological information about the subject on the display unit. Detection device. The arithmetic processing unit is provided outside the main body of the apparatus, and processes the temperature data, the electrocardiographic data, the pressure pressure data, the volume pulse wave, the acceleration data, the rotation angle velocity data, and the orientation data. The wearable multifunctional biometric information detection device according to claim 1, which is to be transmitted to the arithmetic processing unit by wire or wirelessly. The wearable multifunctional biological information detection device according to claim 1, wherein the arithmetic processing unit is provided inside the main body of the device. The wearable multifunctional biological information detection device according to claim 2 or 3, wherein the arithmetic processing unit includes a sleep analysis unit and can analyze metabolic syndrome and stress. The wearable multifunctional biological information detection device according to claim 2 or 3, wherein a sleep analysis unit, a stress analysis unit, and a metabolic syndrome analysis unit are provided outside the arithmetic processing unit. The wearable multifunctional biological information detection device according to any one of claims 1 to 5, wherein the display unit can display a plurality of measurement data, determination results, and the like by switching a mode switch. The arithmetic processing unit
An operation check unit that checks whether the sensors are operating in a predetermined manner based on the temperature data, and an operation check unit.
A behavior grasping unit that grasps the behavior pattern of the subject based on the temperature data,
An altitude calculation unit that calculates altitude based on the temperature data and barometric pressure data,
A CVRR detection unit that detects CVRR based on the peak interval of the electrocardiographic data, and
A heart rate detection unit that detects the heart rate based on the peak interval,
A pulse transit time calculation unit that calculates a pulse transit time based on the volumetric pulse wave and the electrocardiographic data, and a pulse transit time calculation unit.
A blood pressure calculation unit that calculates blood pressure based on the pulse transit time,
A blood vessel age estimation unit that estimates blood vessel age based on the acceleration pulse wave obtained from the volume pulse wave, and a blood vessel age estimation unit.
An oxygen saturation calculation unit that calculates oxygen saturation from two volume pulse waves with different wavelength regions,
A respiratory rate detector that detects the respiratory rate based on the acceleration data,
The acceleration data, the velocity data obtained by integrating the acceleration data, the position data obtained by integrating the velocity data, the rotational acceleration data, the posture detecting unit for detecting the posture and the motion direction of the subject based on the orientation data, and the posture detecting unit.
The wearable multifunctional biometric information detection device according to any one of claims 1 to 6.

Images