JP7402495B2 - Operating method of health information detection device and health information detection device - Google Patents

Description

The present invention relates to a health information detection method and apparatus for detecting a subject's health condition and physical abnormality, and more particularly to a health information detection method and apparatus for providing diabetes risk presentation, stress situation screening, and accurate blood pressure values.

Conventionally, devices and systems have been proposed that manage the health of a subject by attaching a biological information detection device containing various sensors to the subject's wrist and detecting the subject's blood pressure and the like. For example, the information processing system disclosed in Japanese Patent Application Laid-Open No. 2016-134131 (Patent Document 1) mainly targets diabetes, and an information acquisition unit acquires biological information such as blood pressure and pulse rate from a biological information detection device attached to a wrist. , a storage unit that stores the acquired biological information, and a processing unit that performs processing based on the biological information, and the processing unit performs processing while the subject is in the carbohydrate burning zone based on the biological information. Processing is performed to determine exercise information, and information in which the results of the determination process are associated with time information including at least one of date information and time information is displayed as exercise therapy information for diabetes.

Japanese Patent Application Publication No. 2016-134131 JP2017-63997A JP 2018-597 Publication Japanese Patent Application Publication No. 2019-198866

However, the system of Patent Document 1 detects the blood sugar level from the pulse rate, which has a problem with accuracy. Additionally, it does not detect factors that affect health, such as stress and electrocardiogram waveforms, and is not perfect for health management. Furthermore, in Japanese Patent Application Laid-Open No. 2017-63997 (Patent Document 2), biological functions are tested by detecting pulse, breathing, etc. as biological information, but the sensor is large-scale and the test is performed during sleep. It's inconvenient.

The present invention has been made in view of the above-mentioned circumstances, and an object of the present invention is to provide a small-sized device that can be easily attached to the wrist of the arm, and to process and analyze detected electrocardiographic signals and pulses. It is an object of the present invention to provide a health information detection method and device that have a risk presentation function, a stress situation screening function, and a blood pressure detection function that is highly accurate even in response to dynamic changes.

The present invention relates to a method of operating a health information detection device and a health information detection device. , a hook-shaped first electrode for obtaining an electrocardiogram signal is provided at the front or rear of the biological information detection device main body, and the first electrode At least two or more protruding parts that are structured to touch or press against the subject's skin when worn on the body, and to obtain electrocardiographic signals by placing the other hand on the upper surface of the body of the biological information detection device. A method of operating a health information detection device provided with a second electrode, wherein the analysis processing section processes electrocardiographic signal data and pulse data from the body of the biological information detection device, and converts the data into a predetermined table. This is achieved by correspondingly displaying diabetes risk and stress status, as well as calculating and displaying blood pressure values based on blood pressure data measured by a blood pressure monitor.

According to the health information detection method and device according to the present invention, a small biological information detection device can be easily attached to the arm (wrist) of a subject, and electrocardiographic signals and pulse rate data detected by the biological information detection device can be easily attached to the subject. Through processing and analysis, it can provide highly accurate blood pressure values for diabetes, stress, and blood pressure, which are important health issues, as well as presenting the risk of diabetes, screening for stress situations, and dynamic changes in blood pressure.

Detection of biological information consists of two types: electrocardiogram signals and pulse rates, so fewer sensors are required for detecting biological information, and information on diabetes, stress, and blood pressure, which are important for health, can be provided at the same time. Regarding blood pressure, blood pressure data from a conventional sphygmomanometer (blood pressure measurement device) is used for calculation processing (online or offline), and more accurate blood pressure values can be provided.

FIG. 2 is a perspective view showing the appearance and charging state of the biological information detection device used in the present invention. 1 is a schematic diagram showing an example of a usage form of a health information detection device according to the present invention. FIG. 2 is a block diagram showing an example of the internal configuration of a biological information detection device and a mounting table. FIG. 2 is a block diagram showing a configuration example of an analysis processing section according to the present invention. FIG. 2 is a block diagram showing a configuration example of an electrocardiographic signal sensor. FIG. 3 is a waveform diagram showing an example of electrocardiographic signal data and pulse data. FIG. 3 is a waveform diagram showing a normal example and an abnormal example of a pulse waveform. It is a characteristic diagram showing the relationship between autonomic nerve activity (CVRR) and sympathetic nerves. FIG. 3 is a characteristic diagram illustrating the possibility of diabetes with respect to the coefficient CVRR. FIG. 3 is a diagram showing stress display of autonomic nerves. It is a characteristic diagram explaining two regression equations. FIG. 3 is a characteristic diagram for explaining calculation of blood pressure values. FIG. 7 is a bottom view showing another configuration example of the biological information detection device.

Diabetes is a disease name that refers to a state in which the concentration of glucose in the blood (blood sugar, blood sugar level) is high. If diabetes is left untreated, it can lead to various complications such as diabetic neuropathy, diabetic retinopathy, and diabetic nephropathy, which can lead to serious symptoms such as numbness in the hands and feet, blindness, cataracts, and decreased kidney function. Therefore, a system for detecting and treating diabetes is extremely important, and for example, a method for detecting (determining) diabetes has been disclosed in Japanese Patent Application Laid-Open No. 2006-87603. Treatment methods for diabetes vary depending on the cause and degree of progression, but diet therapy, exercise therapy, and pharmaceutical therapy are known, and diet therapy and exercise therapy are particularly important in the early stages. Under these circumstances, presenting the risk of diabetes to subjects is a very important element for health.

In addition, there are reports that stress poses a threat that can take away people's lives, and stress that can lead to death is called "killer stress." Killer stress causes the body to overreact, causing brain cells and blood vessels to be destroyed, resulting in a disease with a high possibility of death. Stress is not limited to negative things; it is known that for positive people, personal success, long vacations, and increased recreation can also be stressful. There are also reports that stress is one of the causes of acute myocardial infarction and stroke, and excessive stress causes hormonal imbalance and bulimia, which eventually leads to obesity, diabetes, and high blood pressure. There is a possibility of this happening, so screening to prevent it from happening is important.

Furthermore, it is a well-known fact that blood pressure values are extremely important factors for human health, and how accurately blood pressure can be measured has become a problem.

The present invention proposes the above-mentioned diabetes risk presentation function and stress screening function based on pulse data and electrocardiographic signal data as biological information, and also proposes the blood pressure data of conventional blood pressure monitors or blood pressure measuring devices (online or It proposes more accurate measurement of blood pressure values through arithmetic processing such as regression processing using offline methods. Regarding blood pressure, the detection accuracy is particularly high for dynamic changes.

FIG. 1 shows the external configuration of a biological information detection device main body 100 used in the present invention and how the internal battery is charged by placing the biological information detection device main body 100 on a charging point 201 of a mounting table 200. As shown in FIG. 2, the information detection device main body 100 is used by securing long bands 101A and 101B with Velcro tape 102 and attaching them to the arm (wrist) of the subject. Although FIG. 2 shows the biological information detection device main body 100 being worn on the wrist of the left hand, it may be worn on the wrist of the right hand.

The usage mode in FIG. 2 shows a case where biometric information detected from the biometric information detection device main body 100 is transmitted wirelessly, and the system in FIG. The analysis processing unit 300 directly receives the biological information, and the analysis processing unit 300, which is a personal computer or the like, processes the data and outputs the analyzed health information. Further, in the system shown in FIG. 2(B), biometric information from the biometric information detection device main body 100 is input to the analysis processing unit 300 via the network 1. The network 1 can include a mobile communication network, a public network such as the Internet, or a fixed telephone network, and can be realized by a WAN (Wide Area Network) or a LAN (Local Area Network), and can be wired or wireless. No question. Further, the system in FIG. 2C shows an example in which the system is connected to the network 1 via the terminal device 2, and the analysis processing unit 300 is connected to the network 1.

In either case, blood pressure data measured with a conventionally known blood pressure monitor (for example, a commercially available electronic blood pressure measuring device) is also input to the analysis processing unit 300 online or offline. In the online case, the measured blood pressure value is directly input as a digital signal by wire or wirelessly, and in the offline case, the measured blood pressure value is manually input by the subject or operator. Further, although FIG. 2 shows an example in which biological information is output wirelessly from the biological information detection device main body 100, wired or optical communication may also be used. The same applies to blood pressure data from a sphygmomanometer.

The biological information detection device main body 100 has built-in electronic elements such as a memory and a battery as described later, and by placing the biological information detection device main body 100 on the charging point 201 of the mounting table 200, the built-in battery (130 ) is charged. Charging is performed using a non-contact charging method that can be performed without using metal contacts or connectors. The biological information detection device main body 100 also includes a means (ACAT (Automatic Change And Transmission)) for reading biological information stored in the memory during charging and outputting it to the outside.

The biological information detection device main body 100 has a flat rectangular appearance, and is equipped with bands 101A and 101B made of leather, cloth, rubber, etc. on both sides to be wrapped around the subject's wrist and worn. , Velcro tapes 102A and 102B are layered on each surface of the bands 101A and 101B to fit the wrist in close contact with the wrist. In addition, a hook-shaped first electrode 110 for obtaining electrocardiographic signals is provided at the front (or rear) of the biological information detection device main body 100, and the electrocardiographic signal can be detected by placing the other hand on the upper surface. Protruding second electrodes 111 and 112 are provided for obtaining. A mode changeover switch 113 is provided on the top surface of the biological information detection device main body 100 for switching between a simultaneous transmission mode in which information is output at the same time as detection, and a storage transmission mode in which detected information is temporarily stored in a memory and then output. ing.

Note that the first electrode 110 is structured to come into contact with or press against the subject's skin when the biological information detection device main body 100 is worn on the wrist. Further, although an example is shown in which two electrodes 111 and 112 are provided on the top surface of the biological information detection device main body 100, any number of electrodes may be used as long as the electrodes are at least two or more in combination with the electrode 110. Can be done.

With reference to FIG. 3, a configuration example of the biological information detection device main body 100 and the mounting table 200 will be described.

The biological information detection device main body 100 has a biological information detection section 120, and the biological information detection section 120 is composed of a pulse sensor 121 and an electrocardiographic signal sensor 122. The pulse sensor 121 measures the pulse of the subject and outputs pulse data HR at predetermined time intervals, and the electrocardiogram signal sensor 122 outputs the subject's electrocardiogram signal EC at predetermined time intervals using three electrodes 110 to 112. do. In this example, the electrocardiographic signal sensor 122 has three electrodes, and in order to detect the electrocardiographic signal of the subject, each electrode is attached to the subject's body (in this example, one on the left arm wrist). (2 locations on the right hand side) to measure the potential (potential signal), and output the three measured potential differences at predetermined time intervals as electrocardiographic signal data EC. The potential signal to be measured is weak and is amplified by an amplifier or the like inside the electrocardiographic signal sensor 122, so it is easily affected by noise. Therefore, in order to reduce the influence of noise and improve the S/N ratio, electrodes, amplifiers, etc. are arranged close to each other.

The pulse data HR and electrocardiographic signal data EC output from the biological information detection section 120 are input to the control section 140, and the control section 140 sets the input pulse data HR and electrocardiographic signal data EC for each data in advance. The data are stored in the respective areas in the memory 141. Note that the method of storing the pulse rate data HR and the electrocardiographic signal data EC in the memory 141 is not limited to the method of storing each data in a preset area, but it is also possible to distinguish each data without setting an area. A method may also be used in which an identifier is added to each of the pulse rate data HR and electrocardiographic signal data EC and stored in the memory 141 together with the identifier.

Further, the biological information detection device main body 100 includes a built-in battery 130 that supplies power to each element, and the battery 130 is charged via a charging input section 131. When the charging input unit 131 starts charging, a charging start signal CS is output, and the charging start signal CS is input to the control unit 140.

In this example, the biological information detection device body 100 is placed on the mounting table 200 and the battery 130 is charged. The battery 130 may be charged in a contact manner by inserting a terminal.

When the simultaneous transmission mode is set by the mode changeover switch 113, the control unit 140 transmits the detected biological information RSA to the transmitting unit 142 without storing it in the memory 141, and the transmitting unit 142 transmits the biological information RS. Output to outside. When the storage transmission mode is set by the mode changeover switch 113, the biological information RSA is temporarily stored in the memory 141, and thereafter the information is read out at any time and transmitted to the transmitter 142, and the transmitter 142 transmits the biological information RS to an external device. Output to. In this case, the detected biological information RSA can be stored in the memory 141 and simultaneously transmitted wirelessly to the outside, or it can be simply stored in the memory 141. The transmitting unit 142 converts the input biometric information RSA into a format that can be received by the external analysis processing unit 300, and wirelessly transmits it as biometric information RS. As a wireless transmission method, a Wi-Fi method, a Bluetooth (registered trademark) method, or the like is used.

The analysis processing unit 300 is constructed using a personal computer or the like, and analyzes the health condition and physical abnormalities of the subject based on the received biological information RS and blood pressure data BS from the blood pressure monitor 400. This will be discussed in detail later.

The mounting table 200 is provided with a charging output section 202 for charging the battery 130 of the biological information detection device main body 100, and when the biological information detection device main body 100 is placed on the mounting table 200, the biological information detection device The charging input section 131 of the main body 100 and the charging output section 202 of the mounting table 200 are arranged to exactly face each other, so that the battery 130 can be charged without contact. That is, when the biological information detecting device main body 100 is placed on the mounting table 200 and the charging input section 131 and the charging output section 202 are brought close to each other, the electric power required by the biological information detecting device main body 100 is reduced by a method using electromagnetic induction ( (electromagnetic induction method). Charging input section 131 and charging output section 202 each have a coil, and when current flows through the coil of charging output section 202, magnetic flux is generated, and as a result of being induced by the magnetic flux, current flows through the coil of charging input section 131. current and charging takes place. As the non-contact charging method, a resonance method or the like may be used instead of the electromagnetic induction method. Further, as a power source for charging, a secondary battery such as a nickel cadmium battery or a lithium ion battery, a super capacitor (electric double layer capacitor), or the like is used.

Further, the mounting table 200 is provided with a communication unit 203 that receives the biological information RS wirelessly transmitted from the transmitting unit 142 in the biological information detection device main body 100, and charges the battery 130 of the biological information detection device main body 100. When the body is running, the transmitting unit 142 wirelessly transmits the biometric information RSA stored in the memory 141, the communication unit 203 receives the biometric information RSA, and the received biometric information RSB is output from the communication unit 203 to the outside. Ru. This makes it possible to obtain biometric information in accordance with the environment and conditions for detecting biometric information, and to charge the biometric information detection device main body 100 and obtain the biometric information stored in the memory 141 at the same time.

FIG. 4 shows a configuration example of the analysis processing section 300, in which biological information RS including pulse data HR and electrocardiographic signal data EC is input to the input section 310, and then input to the peak detection section 311 via the input section 310. . The peak detection unit 311 detects each peak of the pulse data HR and the electrocardiographic signal data EC, and the detected peak signal PS is input to the peak interval detection unit 312 and the blood pressure calculation unit 340. The peak interval detection unit 312 detects the peak interval RPI of each data based on the peak signal PS, and the peak interval signal IPS indicating the peak interval RPI is input to the diabetes risk determination unit 320, stress screening unit 330, and blood pressure calculation unit 340. The data is then processed. Further, blood pressure data BS from the blood pressure monitor 400 is input to the blood pressure calculation unit 340 online or offline. Then, the determination signal DJ1 from the diabetes risk determination section 320, the determination signal DJ2 from the stress screening section 330, and the determination signal DJ3 from the blood pressure calculation section 340 are outputted as health information DJ of the subject via the output section 350.

FIG. 5 shows an example of the configuration of the electrocardiographic signal sensor 122, which is composed of three electrodes 110 to 112 and a potential difference calculating section 122A. The three electrodes 110 to 112 are in contact with or pressed against the subject's body, measure the potential of the contact portion, and output potential data e1, e2, and e3, respectively. If the electrode 110 is contacted or pressed by the wrist of the left hand, the electrodes 111 and 112 will be contacted or pressed by two adjacent points on the right hand. Each potential data e1, e2, and e3 is input to the potential difference calculation unit 122A, and the potential difference calculation unit 122A calculates the potential difference according to Equation 1 or Equation 2 below, and outputs electrocardiographic signal data EC at predetermined intervals.
(Number 1)
EC={(e1-e2)+(e1-e3)}/2
(Number 2)
EC=e1-(e2+e3)/2

In such a configuration, as shown in FIG. 2, the biological information detection device main body 100 is worn around the wrist of the subject's left arm, for example, by wrapping it around the bands 101A and 101B, and the right hand is placed on the biological information detection device main body 100. Add. When the biological information detection device main body 100 is worn on the right hand, the left hand is placed on the biological information detection device main body 100 and attached. Thereby, the biological information detection device main body 100 acquires pulse data HR from the pulse sensor 121 and electrocardiographic signal data EC from the electrocardiographic signal sensor 122. Pulse data HR and electrocardiographic signal data EC are input to the control section 140, transmitted to the input section 310 of the analysis processing section 300 via the transmission section 142, and stored in the memory 141. Information is transmitted according to the mode of the mode changeover switch 113.

The biological information RSC from the input unit 310 is input to the peak detection unit 311, and as shown in FIG. 6, peaks R11, R12, ... R1n of the pulse data HR and peaks R21, R22, ... R2n is detected, and the peak signal PS indicating each peak is input to the peak interval detection section 312 and the blood pressure calculation section 340, and as shown in FIG. 7, the peak intervals RRI1, RRI2, RRI3, ... is detected and input as a peak interval signal IPS to the diabetes risk presentation unit 320, stress screening unit 330, and blood pressure calculation unit 340.

There is a correlation between the electrocardiogram signal data EC and the pulse data HR input (the pulse data HR has a delay from the electrocardiogram signal data EC), and FIGS. 7(A) and (B) show waveform examples of the pulse data HR. However, the same can be said of the electrocardiographic signal data EC. The peak detection section 311 detects peaks R11, R12, R13, and R14 of the pulse data HR, and this peak signal PS is input to the peak interval detection section 312. The peak interval detection unit 312 sequentially detects the intervals between peaks, that is, the interval RRI1 between peak R11 and peak R12, the interval RRI2 between peak R12 and peak R13, and the interval RRI3 between peak R13 and peak R14 (the same applies hereinafter), and detects these peak intervals. A peak interval signal IPS indicating RRI1, .

Autonomic nerve disorder can be evaluated by measuring the degree of variation in pulse rate (heart rate) due to breathing (peak intervals RRI1 to RRI3 in FIG. 7) during electrocardiography. That is, since the cardiac parasympathetic nerve function acts during expiration and does not change during inspiration, autonomic nerve disorder can be evaluated by measuring the rate of change in pulse rate (RRI: RR Interval). FIG. 8 is a characteristic diagram showing the relationship between autonomic nerve activity (CVRR) and sympathetic nerves, and allows the degree of stress of autonomic nerves to be determined.

Therefore, the diabetes risk determination unit 320 calculates the rate of change in the peak interval RRIV based on the peak signal PS and the peak interval signal IPS of the electrocardiographic signal data EC or the pulse data HR, and calculates the coefficient of variation of RR (CVRR) from the rate of change RRIV. A coefficient that normalizes the degree of activity of the autonomic nerve called ``Interval'' is calculated, and the risk of diabetes is presented from the coefficient CVRR (see Equation 3 for calculation of CVRR). In the waveform diagram of FIG. 7, the peak intervals RRI1 to RRI3 vary in normal subjects as shown in FIG. 7(A), and the peak intervals RRI1 to RRI3 are almost uniform in abnormal subjects as shown in FIG. 7(B). There is no variation. Furthermore, according to a presentation by the Department of Diabetes, Department of Laboratory Medicine, University of Tokyo Hospital, there is a relationship between the average value of the coefficient CVRR and diabetes as shown in Table 1 below. In other words, for those aged 30 to 59, the value for healthy people is high at ``3.5%,'' while for diabetic patients it is low at ``2.2%.'' Even for people aged 60 and over, the value for healthy people is ``2.5%.'' It is as high as .8%, and as low as 1.7% or less for diabetic patients.

また、係数ＣＶＲＲに対する糖尿病の可能性を図で示すと、図９のようになる。

Further, the possibility of diabetes with respect to the coefficient CVRR is illustrated in FIG. 9.

The calculation method for the coefficient CVRR is well known, and it is expressed by the following equation 3, where σ is the variance value of the peak intervals and M is the average value of the peak intervals.

上記数３で演算される係数ＣＶＲＲを表１若しくは図９のデータと比較し、糖尿病リスク判定部３２０は、糖尿病のリスクを指示する判定信号ＤＪ１を出力する。糖尿病のリスクとしているのは、本発明装置が医療機器ではなく、ヘルス機器としての健康情報検出装置であることと、「逆は真ならず」であり、本装置で演算された数値であっても、中には自律神経統合失調症やうつ病の可能性があるからである。しかし、糖尿病と診断された人々の係数ＣＶＲＲは、表１の数値に殆どが入ることが分かっている。

The coefficient CVRR calculated using Equation 3 above is compared with the data in Table 1 or FIG. 9, and the diabetes risk determination unit 320 outputs a determination signal DJ1 indicating the risk of diabetes. The risk of diabetes is based on the fact that the device of the present invention is not a medical device but a health information detection device as a health device, and ``the reverse is not true,'' and it is not the numerical value calculated by this device. This is because some patients may have autonomic schizophrenia or depression. However, it is known that most of the coefficients CVRR of people diagnosed with diabetes fall within the values shown in Table 1.

The stress screening unit 330 calculates the rate of change in the peak interval based on the peak signal PS and peak interval signal IPS of the electrocardiogram signal data EC or the pulse data HR, calculates a coefficient CVRR from the rate of change, and calculates an autonomous coefficient from the coefficient CVRR. Perform neurological stress screening. That is, as in the case of diabetes described above, the change rate RRIV of the peak interval is determined based on the peak signal PS and the peak interval signal IPS of the electrocardiogram signal data EC or pulse data HR, and is called CVRR from the change rate RRIV. A coefficient CVRR is calculated by normalizing the degree of autonomic nerve activity. When this coefficient CVRR is 5% or more, stress is not felt and the person is healthy, and even when it is 4 to 5%, it is considered normal. However, when the coefficient CVRR becomes less than 4%, some stress symptoms appear. Therefore, the stress screening section 330 outputs the screening determination signal DJ2 according to Table 2 below. Since the stress on the coefficient CVRR is shown in detail in FIG. 10, the determination signal DJ2 may be output according to FIG.

また、血圧演算部３４０はピーク検出部３１１からのピーク信号ＰＳ（心電信号データＥＣ及び脈拍データＨＲ）を入力すると共に、血圧計４００から血圧データＢＳを入力し、図６に示すように心電信号データＥＣのピークＲ１１、Ｒ１２・・・から脈拍データＨＲのピークＲ２１、Ｒ２２・・・までのパルス遷移時間Ｔｐ（ｎ－１）、Ｔｐｎ、・・・を求める。つまり、下記数４でパルス遷移時間Ｔｐｎを求める。
（数４）
Ｔｐｎ＝Ｒ１ｎ－Ｒ２ｎ

数４で求められたパルス遷移時間Ｔｐｎを用いて、収縮期血圧（最高血圧）ＢＰＳ及び拡張期血圧（最低血圧）ＢＰＤを下記数５と回帰式で表記する。近似は最小自乗法により判定するが直線ではなく、事前に指定した関数の回帰式を求める場合もある（回帰方程式）。

Further, the blood pressure calculation unit 340 inputs the peak signal PS (electrocardiogram signal data EC and pulse rate data HR) from the peak detection unit 311, and also inputs the blood pressure data BS from the sphygmomanometer 400, and as shown in FIG. The pulse transition times Tp(n-1), Tpn, . . . from the peaks R11, R12 . . . of the electrical signal data EC to the peaks R21, R22 . That is, the pulse transition time Tpn is determined using the following equation 4.
(Number 4)
Tpn=R1n-R2n

Using the pulse transition time Tpn obtained by Equation 4, the systolic blood pressure (systolic blood pressure) BPS and diastolic blood pressure (diastolic blood pressure) BPD are expressed using Equation 5 below and a regression equation. Approximation is determined by the method of least squares, but instead of a straight line, a regression equation of a prespecified function may be found (regression equation).

数５内の（α_ｓ、β_ｓ）と（α_ｄ、β_ｄ）を実際に既に用いられている血圧計４００を用いて、ランダムに抽出した被験者から測定した血圧データＢＳを用いて、最小二乗法でフィッティング後、収縮期血圧（最高血圧）ＢＰＳ及び拡張期血圧（最低血圧）ＢＰＤ、パルス遷移時間Ｔｐｎの平均分散とフィッティング係数を計算する。次いで、被験者の血圧を一定時間において数回採取し、それに上記回帰式で求めた血圧値とを比較し、その妥当性を最小二乗法でチェックする。

(α _s , β _s ) and (α _d , β _d ) in Equation 5 are calculated as the minimum After fitting using the square method, the average variance and fitting coefficient of systolic blood pressure (systolic blood pressure) BPS, diastolic blood pressure (diastolic blood pressure) BPD, pulse transition time Tpn are calculated. Next, the subject's blood pressure is sampled several times over a certain period of time, and compared with the blood pressure value obtained using the regression equation described above, and its validity is checked using the method of least squares.

If the correlation is low with only one regression equation above, for example, if there are differences in the data DT due to conditions such as age, as shown in Figure 11, then add a regression equation according to age and use the two regression equations A and B. Let's use the book's regression equation. For example, separate regression equations are calculated for those under 20 years old and those over 20 years old (in FIG. 11, A is under 20 years old and B is over 20 years old).

Furthermore, regarding the rapid increase in blood pressure after exercise, a correction value is introduced according to the pulse rate. First, as shown in FIG. 12, the average value HRm of the pulse rate HR is determined, then the pulse rate HRh(t) which has suddenly increased due to exercise or the like is determined, and the correction value shown in Equation 6 below is calculated. However, γ is determined from a real value.

数６の補正値を静止時の血圧ＢＰＳに乗算し、血圧の変動時の血圧ＢＰｍ（ｔ）を下記数７に従って算出する。

The blood pressure BPS at rest is multiplied by the correction value of Equation 6, and the blood pressure BPm(t) when the blood pressure fluctuates is calculated according to Equation 7 below.

なお、健康情報検出装置をリストに装着する構造は、上述で述べたマジックテープに限定されるものではなく、従来公知の種々の手法を利用できる。また、上述では生体情報検出装置本体１００から解析処理部３００にデータを送信して処理するようになっているが、生体情報検出装置本体１００内に解析処理部３００を内蔵させることも可能である。

Note that the structure for attaching the health information detection device to the wrist is not limited to the Velcro described above, and various conventionally known methods can be used. Furthermore, in the above description, data is transmitted from the biological information detection device main body 100 to the analysis processing section 300 for processing, but it is also possible to incorporate the analysis processing section 300 within the biological information detection device main body 100. .

Furthermore, in the above description, three electrodes 110 to 112 (one on the side and two on the top) are provided on the biological information detection device main body 100, and measurements are made using the left and right hands. As shown, two electrodes 110A and 110B are provided on the bottom surface of the biological information detection device main body 100 and protrude from the bottom surface, and when the biological information detection device main body 100 is attached to the arm, the two electrodes 110A and 110B It may have a structure that presses against or comes into close contact with the skin. If there are two electrodes, for example, electrocardiographic signal data EC can be obtained by detecting "e1-e2".

Reference Signs List 100 Biological information detection device main bodies 101A, 101B Bands 110, 111, 112 Electrodes 113 Mode changeover switch 120 Biological information detection section 121 Pulse sensor 122 Electrocardiographic signal sensor 130 Battery 140 Control section 200 Mounting table 201 Charging point 202 Charging output section 203 Communication Section 300 Analysis processing section 310 Input section 311 Peak detection section 312 Peak interval detection section 320 Diabetes risk determination section 330 Stress screening section 340 Blood pressure calculation section 400 Sphygmomanometer (blood pressure measurement device)

Claims (12)

A biological information detection device main body that includes a biological information detection unit and can be attached to a wrist of a subject, and an analysis processing unit,
A hook-shaped first electrode for obtaining an electrocardiogram signal is provided at the front or rear part of the body of the biological information detection device, and the first electrode is configured to allow the body to detect when the body of the biological information detection device is worn on a wrist. It has a structure that contacts or presses the subject's skin, and has at least two or more protruding second electrodes for obtaining electrocardiographic signals by placing the other hand on the upper surface of the body of the biological information detection device body. A method of operating a health information detection device provided, the method comprising:
The analysis processing unit processes the electrocardiogram signal data and pulse data from the biological information detection device main body, displays diabetes risk and stress status according to a predetermined table, and also displays a blood pressure monitor. Calculates and displays blood pressure values based on blood pressure data measured by
A method of operating a health information detection device characterized by: The second electrode has two electrodes, and the electrocardiographic signal data EC is expressed as follows (where e1 is the measured potential of the first electrode and (e2, e3) are the measured potentials of the second electrode). 2. The method of operating a health information detection device according to claim 1, using equation 1).
(Formula 1)

The blood pressure value is
The peak signal PS from the peak detection section of the analysis processing section is input to the blood pressure calculation section of the analysis processing section , and the blood pressure data BS from the sphygmomanometer is inputted to the blood pressure calculation section of the analysis processing section. Find the pulse transition time Tpn to the peak of the pulse rate data HR, and use the pulse transition time Tpn to express the systolic blood pressure BPS and diastolic blood pressure BPD using the following (Equation 2) and regression equation,
(Formula 2)

(α _s , β _s ) and (α _d , β _d ) in the above (Equation 2) are calculated using the least squares method using the blood pressure data BS measured from randomly selected subjects using the blood pressure monitor. After fitting, the average variance and fitting coefficient of the systolic blood pressure BPS, the diastolic blood pressure BPD, and the pulse transition time Tpn are calculated, and the blood pressure of the subject is sampled several times in a certain period of time, and the blood pressure value obtained using the regression equation is calculated. 3. The method of operating a health information detection device according to claim 1, wherein the validity of the comparison is checked using a least squares method. Regarding the increase in blood pressure after exercise, calculate the correction value shown below (Equation 3) based on the average value HRm of pulse rate HR and the pulse rate HRh(t) during exercise (however, γ is determined from a real number). At the same time,
(Formula 3)

4. The method of operating a health information detection device according to claim 1, wherein the blood pressure at rest is calculated by multiplying the correction value by the blood pressure at rest to calculate the blood pressure at a time of fluctuation. The biological information detection device includes:
5. The electrocardiographic signal data and the pulse rate data are acquired by the subject touching or pressing the other hand of the biological information detection device body attached to one of the wrists of the subject. A method for operating the health information detection device according to any one of the items. 6. The method of operating a health information detection device according to claim 1, wherein the electrocardiographic signal data and the pulse data are transmitted and received wirelessly. A biological information detection device main body that includes a biological information detection unit and can be attached to a subject's wrist;
The electrocardiographic signal data and pulse rate data from the body of the biological information detection device are processed to display diabetes risk and stress status according to a predetermined table, as well as blood pressure data measured by a sphygmomanometer. an analysis processing unit that calculates and displays a blood pressure value based on the
A hook-shaped first electrode for obtaining an electrocardiogram signal is provided at the front or rear part of the body of the biological information detection device, and the first electrode is configured to provide a first electrode when the body of the body of the biological information detection device is worn on a wrist. It has a structure that contacts or presses the subject's skin, and has at least two or more protruding second electrodes for obtaining electrocardiographic signals by placing the other hand on the upper surface of the body of the biological information detection device. A health information detection device comprising: The second electrode has two electrodes, and the electrocardiographic signal data EC is expressed as follows (where the measured potential of the first electrode is e1 and the measured potential of the second electrode is (e2, e3)). The health information detection device according to claim 7, which uses equation 1).
(Formula 1)

The blood pressure value is
The peak signal PS from the peak detection section is inputted to the blood pressure calculation section of the analysis processing section, and the blood pressure data BS from the sphygmomanometer is inputted, and from the peak of the electrocardiographic signal data EC to the peak of the pulse rate data HR. Find the pulse transition time Tpn, and use the pulse transition time Tpn to express the systolic blood pressure BPS and diastolic blood pressure BPD using the following (Equation 2) and regression equation,
(Formula 2)

(α _s , β _s ) and (α _d , β _d ) in the above (Equation 2) are calculated using the least squares method using the blood pressure data BS measured from randomly selected subjects using the blood pressure monitor. After fitting, calculate the average variance and fitting coefficient of systolic blood pressure BPS, diastolic blood pressure BPD, and pulse transition time Tpn, collect the subject's blood pressure several times at a certain time, and compare it with the blood pressure value obtained by the regression equation. , the health information detection device according to claim 7 or 8, wherein the validity thereof is checked by a least squares method. Regarding the increase in blood pressure after exercise, based on the average value HRm of pulse rate HR and the pulse rate HRh(t) during exercise (however, γ is determined from a real number), the following (Equation 3) correction value is calculated. Along with calculating,
(Formula 3)

The health information detection device according to claim 9, wherein the blood pressure at rest is multiplied by the correction value to calculate the blood pressure at the time of fluctuation. The health information detection device according to any one of claims 7 to 10, wherein transmission and reception between the biological information detection device main body and the analysis processing section are performed wirelessly. The health information detection device according to claim 11, wherein the transmission and reception is performed via a terminal device and a network.

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