JP2017099527A - Mind and body condition diagnosis support device and biological information management system - Google Patents

Abstract

The present invention provides a psychosomatic state diagnosis support apparatus that assists in detecting a stress state of a subject with higher accuracy, and a biological information management system including the psychosomatic state diagnosis support apparatus.
A psychosomatic state diagnosis support device receives biometric information including heart motion information corresponding to the motion of a subject's heart, and calculates an RR interval, which is a contraction interval of the subject's heart, from the heart motion information. Then, the stress state of the subject is detected using the RR interval variation coefficient calculated by dividing the standard deviation of the RR interval in the coefficient calculation period by the average value of the RR intervals in the coefficient calculation period.
[Selection] Figure 1

Description

  The present invention relates to a psychosomatic state diagnosis support apparatus that supports diagnosis of a psychosomatic state of a subject using biometric information, and a biometric information management system including the psychosomatic state diagnosis support apparatus.

  Today, it is called a stress society, but many people suffer from autonomic nerves due to various stresses, and indefinite complaints that make it difficult to identify the cause have occurred. It becomes difficult to maintain daily life. Along with that, it has become a great obstacle in corporate activities and daily family life, and the problem is sharpening.

  Legislation has been developed to deal with such situations, and stress checks are becoming more important than ever. At present, an inquiry by a doctor is adopted as a main method of stress check. For example, Patent Document 1 discloses an apparatus for inspecting a stress level.

  The biometric information acquisition device disclosed in Patent Document 1 acquires fluctuations in peristaltic intervals from body motion including heartbeat vibration of a subject using a vibration sensor attached to a chair, and the degree of stress of the subject or Autonomic nerve activity is calculated.

Japanese Patent Laying-Open No. 2015-66337

  What is stressed is not clarified. For this reason, research and devices for measuring heart rate variability and pulse variability and evaluating autonomic nervous activity have been proposed again over the last 20 years, but have not been effective. For example, the biometric information acquisition device disclosed in Patent Document 1 calculates a ratio (LF / HF) between a low frequency component LF and a high frequency component HF of fluctuation of the peristaltic interval, and compares this ratio with a reference value. Therefore, the degree of stress is determined. However, such a method is not an effective evaluation method.

  The present invention has been made for the above-mentioned circumstances, and an object of the present invention is to provide a psychosomatic state diagnosis support apparatus that supports detection of a stress state of a subject with higher accuracy, and this psychosomatic state diagnosis support. It is to provide a biological information management system provided with a device.

  For example, in occupational fields such as medical care, nursing care, childcare, and early childhood education, there are a large number of retired people due to stress, and clinical psychologists have intervened, but there is a fact that effective results have not been achieved. In light of such circumstances, the present inventors have clarified what stress is and established an evaluation method through a study to solve a specific problem of reducing the number of retirement due to stress in a certain workplace. . There are two types of stress. Stress caused by daily interpersonal, social and physical stimuli, and stress associated with a decrease in the health level of autonomic nerve activity caused by accumulation under certain conditions. The latter leads to depression and cardiovascular disease. On the other hand, stress such as retirement due to stress occurring in the workplace is the former. The stress check using the conventional questionnaire is only for the latter.

  Daily stress appears in the value of sympathetic nerve activity obtained as the ratio (LF / HF) of the low frequency component LF to the high frequency component HF of fluctuation of heart rate variability (pulse variation), but this value changes every moment. Is. Therefore, it is not always possible to measure as in the above-mentioned Patent Document 1, and it is necessary to measure at the moment when the user feels stress. For this reason, unconstrained long-time measurement that does not interfere with human life behavior is required. The inventors have developed a possible device. As a result of measuring biological information including sympathetic nerve activity for a large number of subjects and intensively examining them, the present invention has been found.

  In order to achieve the above object, according to one aspect of the present invention, (1) a biological information input unit to which biological information of a subject detected in time series is input, and the biological information input unit is input A state detection unit that detects the stress state of the subject based on the biological information; and a display unit that displays the stress state of the subject detected by the state detection unit, and the biological information The biometric information including heart motion information corresponding to the motion of the subject's heart is input to the input unit, and the state detection unit detects an RR interval that is a contraction interval of the subject's heart from the heart motion information. And detecting the stress state of the subject using the RR interval variation coefficient calculated by dividing the standard deviation of the RR interval in the coefficient calculation period by the average value of the RR interval in the coefficient calculation period. Psychosomatic diagnosis characterized by It is a support device.

  According to the present invention, since the stress state including the activity state of the entire autonomic nerve can be detected by using the RR interval variation coefficient, the stress state of the subject can be detected with higher accuracy.

  In the present invention, (2) the biological information input unit is input with the biological information in a state in which the subject is artificially stressed over the coefficient calculation period, and the state detection unit includes: The stress state of the subject may be detected by comparing the RR interval variation coefficient during the coefficient calculation period with a first stress state determination reference value. By doing so, for example, the stress state in the life activity of the subject is approximately reproduced by being artificially stressed in a relatively short time such as 2 minutes, and is detected in such a state. By detecting the stress state using the biometric information, the stress state can be detected more accurately in a short time.

  In the present invention, (3) the state detection unit compares the maximum values of the plurality of RR interval variation coefficients in a state determination period including a plurality of the coefficient calculation periods with a first stress state determination reference value. The examiner's stress state may be detected. By doing so, for example, by detecting the stress state using biological information detected over a relatively long time such as one day, the living activity of the subject is more accurately included in such biological information. Therefore, the stress state can be detected with higher accuracy.

  In the present invention, (4) a stress self-evaluation value of the subject is further input to the biological information input unit, and the state detection unit includes a plurality of the state determination periods including a plurality of the coefficient calculation periods. The maximum value of the RR interval variation coefficient is compared with the first stress state determination reference value, and the stress state of the subject is detected by comparing the stress self-evaluation value with the second stress state determination reference value. May be. In this way, the stress state can be detected with higher accuracy by using the stress self-evaluation value in addition to the maximum value of the RR interval variation coefficient.

  In the present invention, (5) the display unit may display a stress diagnosis plot diagram in which points having the coordinates of the stress self-evaluation value and the maximum value of the RR interval variation coefficient are plotted on a two-dimensional plane. Good. In this way, the stress state can be visually grasped.

  In the present invention, (6) the display unit passes through the first stress state determination reference value on the coordinate axis of the maximum value of the RR interval variation coefficient and is parallel to the coordinate axis of the stress self-evaluation value; And at least one second reference line passing through the second stress state determination reference value on the coordinate axis of the stress self-evaluation value and parallel to the coordinate axis of the maximum value of the RR interval variation coefficient is superimposed on the stress diagnosis plot diagram. May be displayed. By doing in this way, the point on a plot figure can be compared visually with at least one of a 1st reference line and a 2nd reference line, and a stress state can be grasped | ascertained more visually. In particular, by displaying both the first reference line and the second reference line, the stress state can be more visually determined depending on where the plurality of quadrants defined by the first reference line and the second reference line are included. I can grasp.

  In the present invention, (7) the first stress state determination reference value is preferably 0.04. Since the first stress state determination reference value is obtained from the RR interval variation coefficient detected in a plurality of subjects whose stress states are known, the stress state can be detected with higher accuracy.

  In the present invention, (8) the state detection unit may detect the diabetic state of the subject by comparing the RR interval variation coefficient in the coefficient calculation period with a diabetic state determination reference value. In this way, in addition to the stress state, the diabetic state can also be detected.

  In the present invention, (9) the state detection unit also detects the diabetic state of the subject by comparing the maximum values of the plurality of RR interval variation coefficients in the state determination period with a diabetic state determination reference value. It may be. For example, by detecting a diabetic state using biological information detected over a relatively long time such as one day, the living activity of the subject is more accurately reflected in such biological information. Diabetes can be detected with higher accuracy.

  In the present invention, (10) it is preferable that the diabetes state determination reference value is 0.022. Since this diabetes state determination reference value is obtained from the RR interval variation coefficient detected in a plurality of subjects whose diabetes state is known, the diabetes state can be detected with higher accuracy.

  In the present invention, (11) the state detection unit further calculates sympathetic nerve information indicating an activity state of the sympathetic nerve of the subject in the coefficient calculation period, and the display unit includes a plurality of the coefficient calculation periods. An autonomic nerve activity plot diagram in which a plurality of points with coordinates of the RR interval variation coefficient and the sympathetic nerve information calculated corresponding to each coefficient calculation period in a plot period including the same is plotted is displayed. You may do it. In this way, by using sympathetic nerve information in addition to the RR interval variation coefficient, the stress state can be detected with higher accuracy and can be visually displayed.

  In the present invention, (12) the display unit may display a plurality of the autonomic nerve activity plot diagrams in each of the plurality of plot periods corresponding to the plurality of life time zones of the subject. By doing in this way, the stress state for every life time slot | zone can be detected, and the stress state which reflected the subject's life activity more can be detected.

  In order to achieve the above object, according to another aspect of the present invention, (13) a biological information input unit to which biological information of a subject detected in time series is input, and input to the biological information input unit A state detection unit that detects the stress state of the subject based on the biometric information that has been performed, and a display unit that displays the stress state of the subject detected by the state detection unit, The biological information including cardiac motion information corresponding to the motion of the subject's heart is input to the biological information input unit, and the state detection unit is a contraction interval of the subject's heart from the cardiac motion information. RR interval is calculated, sympathetic nerve information indicating the sympathetic nerve activity state of the subject and parasympathetic nerve information indicating the activity state of the parasympathetic nerve are calculated using the RR interval in the coefficient calculation period, and the display unit A plurality of coefficient calculation periods Displaying the parasympathetic nerve information calculated corresponding to each of the coefficient calculation periods and a plurality of points having coordinates of the sympathetic nerve information as coordinates in a two-dimensional plane. It is a characteristic psychosomatic state diagnosis support apparatus.

  According to the present invention, by using the sympathetic nerve information and the parasympathetic nerve information, the stress state including the activity state of the entire autonomic nerve can be detected, so that the stress state of the subject can be detected more accurately. Can do.

  In the present invention, (14) the state detection unit may detect the stress state of the subject using an area of a plot shape including a plurality of points in the autonomic nerve balance plot diagram. By doing in this way, a stress state can be detected from the activity state of the whole autonomic nerve.

  In the present invention, (15) the state detection unit may detect the stress state of the subject using the size in the coordinate axis direction of the plot shape including a plurality of points in the autonomic nerve balance plot diagram. Good. By doing in this way, a stress state can be detected from the individual activity state of the sympathetic nerve or the parasympathetic nerve and the balance of these activity states.

  In the present invention, (16) the state detection unit detects a stress state of the subject using a distance between a plot shape including a plurality of points of the autonomic nerve balance plot diagram and a coordinate axis. Also good. By doing so, the stress state can be detected from the offset state of the activity state of the sympathetic nerve and the parasympathetic nerve.

  In the present invention, (17) the state detection unit may detect the stress state of the subject using the smoothness of the periphery of the plot shape including a plurality of points in the autonomic nerve balance plot diagram. . By doing in this way, a stress state can be detected from the state of disorder of the autonomic nerve.

  In the present invention, (18) the display unit may display a plurality of autonomic nerve balance plot diagrams in each of the plurality of plot periods corresponding to the plurality of life time zones of the subject. By doing in this way, the stress state for every life time slot | zone can be detected, and the stress state which reflected the subject's life activity more can be detected.

  In the present invention, (19) the plurality of life time zones may be bedtime, time before going to office, time in office, and time after leaving the office. By doing in this way, each stress state of the life activity in a house and the life activity in a company can be detected.

  In order to achieve the above object, according to another aspect of the present invention, (20) a biological information input unit for inputting biological information of a subject detected in time series, and an input to the biological information input unit are provided. A state detection unit that detects the stress state of the subject based on the biometric information that has been performed, and a display unit that displays the stress state of the subject detected by the state detection unit, The biological information including cardiac motion information corresponding to the motion of the subject's heart is input to the biological information input unit, and the state detection unit is a contraction interval of the subject's heart from the cardiac motion information. An RR interval is calculated, sympathetic nerve information indicating the sympathetic nerve activity state of the subject is calculated using the RR interval in the activity state detection period, and a state determination period including a plurality of the activity state detection periods Each said activity state detection period in A psychosomatic state diagnosis support apparatus characterized by detecting a stress condition using the maximum value and the third quartile of the plurality of the sympathetic information calculated in response to.

  According to the present invention, the stress is calculated using the maximum value of the plurality of sympathetic nerve information and the third quartile calculated for each activity state detection period in the state determination period including the plurality of activity state detection periods. Detect state. By doing so, the stress state of the subject can be detected with higher accuracy.

  In the present invention, (21) When the state detection unit is stressed when the maximum value of the plurality of sympathetic nerve information is 25 or more and the third quartile is 5 or more, It is preferable to determine. By doing in this way, a stress state can be detected more effectively.

  According to another aspect of the present invention, (22) the psychosomatic state diagnosis support device and a biological information detection device are provided, and the biological information detection device detects biological information including the cardiac motion information of the subject. A portable biological information detection device main body that can be attached to the body of the subject, and the biological information detected by the biological information detection device main body is input to the biological information input unit. Is a biological information management system characterized by

  According to the present invention, since the biological information detected by the portable biological information detecting device main body that can be attached to the torso of the subject is used, the biological information can be detected in daily life activities. It is possible to detect a stress state in daily life activities using simple biological information.

  According to the present invention, the stress state is detected using the RR interval variation coefficient calculated from the RR interval of the subject, the sympathetic nerve information indicating the sympathetic nerve activity state, or the parasympathetic nerve information indicating the parasympathetic nerve activity state. Therefore, the stress state of the subject can be detected with higher accuracy.

It is a block diagram which shows the structural example of the biometric information management system concerning 1st Embodiment of this invention. It is a block diagram which shows the structural example of the biometric information detection apparatus of FIG. It is a block diagram which shows the structural example of the control part which the biological information detection apparatus main body of FIG. 2 has. It is a flowchart which shows the operation example of the control part of FIG. It is an image figure which shows the example of an electrocardiogram waveform. It is an image figure which shows the example of the basic waveform and abnormal waveform of the electrocardiogram waveform for 1 beat of a heartbeat. It is a graph which shows the example of the time change of the variation | change_quantity of RR interval. It is a figure which shows the example 1 (stress state detection result) by the display part of the mind-and-body state diagnosis assistance apparatus of FIG. It is a figure which shows the example 2 of a display (stress diagnosis plot figure) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 3A (autonomous nerve activity plot figure 1) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 3B (autonomous nerve activity plot figure 2) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 3C (autonomous nerve activity plot figure 3) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 4A (autonomous nerve balance plot figure 1) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 4B of the display by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. 1 (autonomous nerve balance plot figure 2-normal person). It is a figure which shows the example 4C (autonomous nerve balance plot figure 3-middleman) by the display part of the mind-and-body state diagnosis assistance apparatus of FIG. It is a figure which shows the example 4D (autonomic nerve balance plot figure 4-abnormal person) by the display part of the mind-and-body state diagnosis assistance apparatus of FIG. It is a figure which shows the example 4E (autonomous nerve balance plot figure 5-area of a plot shape) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is a figure which shows the example 4F (autonomous nerve balance plot figure 6-the magnitude | size of the coordinate shape of the plot shape of the plot shape) by the display part of the mind-and-body state diagnosis assistance apparatus of FIG. FIG. 8 is a diagram illustrating a display example 4G (autonomic nerve balance plot diagram 7—distance between plot shape and coordinate axis) by the display unit of the psychosomatic state diagnosis support apparatus of FIG. 1; It is a figure which shows the example 4H (autonomous nerve balance plot figure 8-smoothness of the periphery of a plot shape) by the display part of the psychosomatic state diagnosis assistance apparatus of FIG. It is the figure which plotted the maximum value and the 3rd quartile of the sympathetic nerve information during work in a plurality of subjects. It is the figure which plotted the minimum value and the 1st quartile of the sympathetic nerve information during sleeping in a plurality of subjects. It is a block diagram which shows the structural example of the biometric information management system concerning 2nd Embodiment of this invention. In this invention, it is an image figure which shows the example which mounts | wears with the biological information detection apparatus main body in the multiple places of a subject's body.

  Embodiments of the present invention will be described below in detail with reference to the drawings.

(First embodiment)
The biological information management system according to the first embodiment of the present invention will be described below.

  The biological information management system is a system that manages the stress state of the subject, and includes a biological information detection device 1 and a psychosomatic state diagnosis support device 30 as shown in FIG.

  First, the configuration and operation of the biological information detection apparatus 1 will be described.

  FIG. 2 is a block diagram illustrating a configuration example of the biological information detection apparatus 1.

  The biological information detection apparatus 1 includes a portable biological information detection apparatus main body 10 and a mounting table 20. The biological information detecting device main body 10 is attached to the body of the subject, and preferably attached to the chest. The biological information detection apparatus main body 10 includes a biological information detection unit 100, and the biological information detection unit 100 includes a body temperature sensor 110, an acceleration sensor 120, and an electrocardiogram signal sensor 130.

  The body temperature sensor 110 measures the epidermis temperature of the subject and outputs body temperature data T at predetermined intervals. The acceleration sensor 120 detects a three-dimensional movement of the subject and outputs acceleration data α = (αx, αy, αz) in the X direction, the Y direction, and the Z direction at predetermined intervals. Here, αx is the acceleration in the X direction, αy is the acceleration in the Y direction, and αz is the acceleration in the Z direction.

  The electrocardiogram signal sensor 130 has two electrodes, and in order to detect a subject's electrocardiogram signal, each electrode is brought into contact with the subject's body, and a potential (potential signal) is measured. The difference between the two measured potentials is output as ECG signal data E at a predetermined interval. Note that three or more electrodes may be used, and in that case, a plurality of potential differences are calculated. The measured potential signal is weak and is amplified by an amplifier or the like inside the electrocardiogram signal sensor 130, so that it is easily affected by noise. Therefore, in order to reduce the influence of noise and improve the S / N ratio, electrodes, amplifiers, and the like are arranged close to each other. The wiring length between the electrode and the first-stage amplifier is preferably 2 cm or less. The electrocardiogram signal data E is a potential signal corresponding to the motion of the subject's heart, that is, corresponds to heart motion information. Instead of the electrocardiogram signal data E, for example, pulse data detected by a pulse sensor using near infrared rays may be used. In this case, the pulse data corresponds to heart motion information.

  Note that the interval at which the body temperature data T is output, the interval at which the acceleration data α is output, and the interval at which the electrocardiogram signal data E is output may be the same or different. For example, since the skin temperature normally has a small fluctuation, the interval at which the body temperature data T is output may be set longer than the others. Thereby, the amount of data to be acquired can be reduced. Further, the interval at which the body temperature data T is output, the interval at which the acceleration data α is output, and the interval at which the electrocardiogram signal data E is output may be changed instead of a fixed value. When it is assumed that there is a large change in value immediately after exercise or the like, it is possible to obtain appropriate biological information that matches the physical condition by making adjustments such as shortening the output interval.

  The body temperature data T, acceleration data α, and electrocardiogram signal data E (collectively referred to as “biological information data BD”) output from the biological information detection unit 100 are input to the control unit 140. The control unit 140 stores the input body temperature data T, acceleration data α, and electrocardiogram signal data E in respective areas in the memory 150 set in advance for each data. The method of storing the biometric information data BD in the memory 150 is not limited to the method of storing the biometric information data BD in an area set in advance for each data, and an identifier for distinguishing each data is set without specifying the area. Alternatively, it may be added to the acceleration data α and the electrocardiogram signal data E and stored in the memory 150 together with the identifier.

  When the biometric information is set to be transmitted to the outside when detecting the biometric information (hereinafter referred to as simultaneous transmission setting), the control unit 140 outputs the biometric information data BD to the transmission unit 160. The transmission unit 160 converts the input biometric information data BD into a format that can be received by the psychosomatic state diagnosis support apparatus 30 and wirelessly transmits the biometric information signal BS1. As a wireless transmission method, a Wi-Fi method, a Bluetooth (registered trademark) method, or the like is used. In addition, when the biometric information detection apparatus main body 10 detects biometric information, you may employ | adopt the structure which always transmits biometric information outside. In this configuration, the memory 150 may be omitted.

  Note that the time for which the biological information detection unit 100 measures the biological information may be changed for each of the body temperature sensor 110, the acceleration sensor 120, and the electrocardiogram signal sensor 130. As a result, it is possible to take a flexible measure such as shortening the measurement time of biological information that requires a large amount of power for detection, or increasing the measurement time when the physical condition is not good.

  Here, the operation | movement at the time of charging the biometric information detection apparatus main body 10 is demonstrated.

  As shown in FIG. 2, the biological information detection apparatus main body 10 has a charge input unit 170, and the mounting table 20 has a charge output unit 200. When the biological information detecting device main body 10 is placed on the mounting table 20 and the charging input unit 170 and the charging output unit 200 are brought close to each other, the electric power required by the biological information detecting device main body 10 uses electromagnetic induction (electromagnetic induction). System). That is, the charging input unit 170 and the charging output unit 200 each have a coil. When a current flows through the coil of the charging output unit 200, a magnetic flux is generated, and is induced by the magnetic flux. Current flows and charging takes place. Note that as a non-contact charging method, a resonance method or the like may be used instead of the electromagnetic induction method. Moreover, as a power source to be charged which the biological information detecting device body 10 has, 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.

  The mounting table 20 also includes a communication unit 210 that receives a biological information signal wirelessly transmitted from the transmission unit 160 of the biological information detection device body 10 and wirelessly transmits the signal to the outside, and the biological information detection device body 10 is charged. At the same time, a biological information signal is received from the transmitter 160 and wirelessly transmitted to the outside.

  That is, when charging is started by electromagnetic induction, the charging input unit 170 outputs a charging start signal CS to the control unit 140, and when the charging unit CS inputs the charging start signal CS, the biological information stored in the memory 150 is stored. Data BD is output to transmission section 160. The transmission unit 160 converts the input biometric information data BD into a format that can be received by the communication unit 210, and wirelessly transmits the biometric information signal BS2. The communication unit 210 receives the biological information signal BS2, converts it into a format that can be received by the psychosomatic state diagnosis support apparatus 30, and wirelessly transmits the biological information signal BS3. At this time, as a wireless transmission method used by the transmission unit 160 and the communication unit 210, a Wi-Fi method, a Bluetooth (registered trademark) method, or the like is used to convert it into the biological information signal BS2. The method used for the method may be the same as or different from the method used for conversion into the biological information signal BS1 or BS3. However, when the biological information signal BS2 is wirelessly transmitted, the transmission unit 160 and the communication unit 210 are close to each other, so that power consumption can be suppressed by using the short-range communication method.

  As described above, charging is performed by a non-contact charging method, and transmission / reception of biometric information during charging is performed wirelessly, so that an input / output terminal for external connection is unnecessary, and waterproofness can be increased.

  Note that the communication unit 210 of the mounting table 20 may transmit the biological information signal BS3 to the outside by wired communication. In addition, the biological information detection apparatus body 10 may employ a configuration including a charging terminal for supplying power to the charging input unit 170. For example, a USB connector is used for the charging terminal and a USB cable is used. It may be rechargeable. In this case, the mounting table 20 is omitted. Moreover, you may abbreviate | omit a charging mechanism by operating the biometric information detection apparatus main body 10 with a primary battery (coin-type battery etc.).

  In the present embodiment, the transmission unit 160 of the biological information detection device main body 10, the communication unit 210 of the mounting table 20, and the biological information input unit 300 of the sleep state detection device 30 described later transmit and receive biological information signals by wireless communication. However, it is not limited to this. For example, the biological information stored in the memory 150 may be transmitted and received by wired communication between the transmission unit 160 of the biological information detection device main body 10 and the biological information input unit 300 of the sleep state detection device 30 by adopting a USB interface. As long as the object of the present invention is not violated, the communication method (wired / wireless, communication protocol, etc.) between the respective functional units is arbitrary.

  FIG. 3 is a block diagram illustrating a configuration example of the control unit 140, and FIG. 4 is a flowchart illustrating an operation example of the control unit 140.

  As shown in FIG. 3, the control unit 140 includes a data processing unit 141, a mode setting unit 142, a switching unit 143, and a data reading unit 144. The data processing unit 141 reads the biometric information data BD (body temperature data T, acceleration data α, electrocardiogram signal data E) output from the biometric information detection unit 100 and outputs it to the memory 150 and the switching unit 143. The mode setting unit 142 inputs a charging start signal CS output from the charging input unit 170 when charging of the biological information detecting apparatus body 10 is started. And the mode setting part 142 determines the output mode of biometric information data BD based on the presence or absence of the input of the charge start signal CS, and the information of ON / OFF of simultaneous transmission setting, and outputs it as the mode signal MS. That is, when the charging start signal CS is input, the “memory data output mode” is set. When the charging start signal CS is not input and the simultaneous transmission setting is ON, the “simultaneous transmission mode” is set, and the charging start signal CS is not input. When the simultaneous transmission setting is OFF, set to “no output mode”. The mode signal MS is input to the switching unit 143. The data reading unit 144 also receives the charging start signal CS, and when the charging start signal CS is input, the biometric information data BD stored in the memory 150 is read and output to the switching unit 143.

  An example of the operation of the control unit 140 will be described with reference to the flowchart of FIG.

  The data processing unit 141 reads the biological information data BD output from the biological information detecting unit 100 (step S1). The read biometric information data BD is stored in the memory 150 (step S2) and simultaneously input to the contact 143a of the switching unit 143.

  If the mode signal MS output from the mode setting unit 142 is “simultaneous transmission mode”, the switching unit 143 is connected to the contact 143a, and the biological information data BD is output to the transmission unit 160 (step S3). If the mode signal MS is “non-output mode”, the switching unit 143 is not connected to either contact, and the biological information data BD is not output. If the mode signal MS is “memory data output mode”, the switching unit 143 is connected to the contact 143b. At this time, when the charging start signal CS is input to the data reading unit 144, the data reading unit 144 reads the biometric information data BD stored in the memory 150 (step S4), and outputs it to the contact 143b of the switching unit 143. The biometric information data BD stored in the memory 150 is output to the transmission unit 160 (step S5).

  For example, the biological information detecting device body 10 is preferably capable of detecting biological information (biological information data BD) for at least 24 hours in order to detect biological information that more accurately reflects the living activities of the subject.

  Next, the configuration and operation of the psychosomatic state diagnosis support apparatus 30 will be described.

  As shown in FIG. 1, the psychosomatic state diagnosis support apparatus 30 includes a biological information input unit 300, a memory 310, a state detection unit 320, and a display unit 330.

  The biological information input unit 300 receives the biological information signal BS1 transmitted from the transmission unit 160 of the biological information detection apparatus body 10 and the biological information signal BS3 transmitted from the communication unit 210 of the mounting table 20, and receives the biological information data BD. The format is restored and stored in the memory 310. Similar to the storage method in the memory 150, the storage method in the memory 310 also distinguishes each data by a method in which the temperature data T, the acceleration data α, and the electrocardiogram signal data E are stored in a preset region. An identifier may be added to each data and stored together with the identifier. When the biological information detection apparatus body 10 adopts a configuration in which biological information is always transmitted to the outside when the biological information is detected, the biological information input unit 300 is transmitted from the transmission unit 160 of the biological information detection apparatus body 10. Only the biological information signal BS1 is received.

  In addition, the biometric information input unit 300 receives a stress self-evaluation value transmitted from an information terminal such as a smart phone, a tablet terminal, or a personal computer. The stress self-evaluation value is, for example, a numerical value indicating the self-stress state calculated by the subject answering the questionnaire. As an example, the information terminal displays questions such as “I don't feel well recently” or “I don't have a clear cause but may be anxious” as an inquiry sheet. The subject selects an answer to each question item from “well applicable”, “substantially applicable”, “somewhat applies”, and “not applicable” and inputs them to the information terminal. The information terminal calculates the stress self-evaluation value by counting the points set in these answers, and transmits the stress self-evaluation value to the biometric information input unit 300 by, for example, wireless communication. The questionnaire may be written on a paper with questionnaires, and it is possible to adopt a configuration in which answers are totaled manually, and the totaled score is input to the information terminal and transmitted to the biometric information input unit 300. .

  When the detection of the biological information of the subject by the biological information detection device 1 is completed and all the acquired biological information data BD is stored in the memory 310, the state detection unit 320 stores the biological information data BD stored in the memory 310. Are used to calculate analysis data AS relating to the stress state of the subject, that is, the stress state of the subject is detected. Information calculated as analysis data AS includes abnormal waveform information, heart rate, instantaneous heart rate, standard deviation of RR interval, average value of RR interval, RR interval variation coefficient (also referred to as “autonomous nerve activity”), sympathy. Neural information, parasympathetic nerve information, posture information, bedtime, bedtime, and body temperature.

  Hereinafter, each information will be described.

  The electrocardiographic signal data E in the biological information data BD read from the memory 310 forms an electrocardiographic waveform as shown in FIG. Regarding this electrocardiogram waveform, the basic waveform for one beat of the heartbeat is a waveform as shown in FIG. 6A, and the peaks and valleys of the waveform are shown in FIG. It is called wave, Q wave, R wave, S wave and T wave. In the electrocardiogram waveform formed from the electrocardiogram signal data E, if a location whose shape is greatly different from this basic waveform is found, the judgment result that there is an abnormal waveform is displayed along with the information (time etc.) of the found location. Use abnormal waveform information. If there is no portion that is largely different from the basic waveform, the determination result that there is no abnormal waveform is used as abnormal waveform information. As a waveform that is largely different from the basic waveform, for example, as shown in FIG. 6B, the T wave overlaps with the R wave and disappears. Such a waveform can be found by extracting a P wave, a Q wave, an R wave, an S wave, and a T wave from the electrocardiographic waveform and by the presence or absence of the T wave.

  The R wave is extracted from the electrocardiogram waveform, the number of R waves extracted every minute is used as the heart rate, the interval between two adjacent R waves (RR interval) is calculated, and the reciprocal of the RR interval is calculated. Use instantaneous heart rate. When the movement of the autonomic nerve instantaneously changes, the instantaneous heart rate immediately changes correspondingly. Therefore, the movement of the autonomic nerve can be analyzed by calculating the instantaneous heart rate. The heart rate and the instantaneous heart rate are time-series information (information in which data calculated at regular time intervals is arranged).

  RR interval variation coefficient (Coefficient of Variation) obtained by calculating the standard deviation of the RR interval and the average value of the RR interval every predetermined coefficient calculation period (for example, 2 minutes), and dividing the standard deviation of the RR interval by the average value of the RR interval. of RR-interval (CVRR). The health level of the autonomic nerve activity can be evaluated using the RR interval coefficient of variation (CVRR). This is because the coefficient is calculated using biological information measured in a short time without limiting the measurement state of the biological information. The present inventors have repeatedly performed long-term measurements on a large number of subjects, so that the RR interval coefficient of variation also changes from moment to moment. Even if the median value in a certain period is used as a parameter, the median value is also fatigued. Finding that it decreases due to drowsiness etc., and further dividing the whole awakening time into multiple areas, finding the median value of each, and determining the maximum value as a parameter, it can be determined more accurately than the conventional questionnaire I found it. Furthermore, the present inventors make a number of measurements, and by performing a two-dimensional plot with the number of judgments from the questionnaire, the threshold value for judging whether the autonomic nerve activity is healthy is 0.04. I found out.

  The RR interval coefficient of variation (CVRR) is used as a means of screening for diseases related to the determination of diabetes and other autonomic nerves, in addition to the health level of autonomic nerve activity. This coefficient is calculated using biological information measured in a short time without limiting the measurement situation, and lacks accuracy for the reasons described above. With our method, more accurate screening is possible.

  Using the RR interval, sympathetic information and parasympathetic information are calculated. Usually, the RR interval fluctuates periodically, and this fluctuation pattern is related to the function of autonomic nerves (Sympathetic Nervous System (SNS), Parasympathetic Nerve (Para-SNS: PSNS)). Since there is knowledge, information on the autonomic nerve can be obtained by analyzing the fluctuation of the RR interval. When the difference RR in the RR interval is plotted with the time as the horizontal axis, for example, a waveform as shown in FIG. 7 (time change waveform of fluctuation amount of the RR interval) is obtained. Sympathetic nerve information and parasympathetic nerve information, which are information related to the autonomic nerve, are calculated. The sympathetic nerve information is an index indicating the activity state of the subject's sympathetic nerve, and the parasympathetic nerve information is an index indicating the activity state of the subject's parasympathetic nerve. For example, 0.04 Hz or more and less than 0.15 Hz is set as a low frequency component LF, and 0.15 Hz or more and less than 0.4 Hz is set as a high frequency component HF, and the frequency components in each band are set to a predetermined activity state detection period (for example, 2 minutes). ), The low frequency component LF is the parasympathetic nerve information, and the ratio (LF / HF) of the low frequency component LF to the high frequency component HF is the sympathetic nerve information. This is a calculation based on the fact that it is generally said that the sympathetic nerve promotes the pulsation of the heart and the parasympathetic nerve suppresses the pulsation of the heart. Sympathetic nerve information and parasympathetic nerve information are time-series information.

  The posture information is obtained by integrating acceleration data α = (αx, αy, αz) in the biological information data BD read from the memory 310 and acceleration data α, and velocity data V = (Vx, Vy, Vz). ). Here, Vx is a velocity in the X direction, Vy is a velocity in the Y direction, and Vz is a velocity in the Z direction, which are calculated by integrating αx, αy, and αz, respectively.

  Posture information indicates that the subject's posture is standing (standing, sitting), supine (upward), prone (down), right-sided (right-side down) and left-sided (down on left) As a result of discriminating which state of the posture), the velocity data V and the acceleration data α are discriminated. That is, when the body height direction in the supine position (sleeping state) is the X direction and the body height direction in the standing position (standing position) is the Z direction, the acceleration in the Z direction increases and a certain amount of time Changes from standing to lying or lying to standing are detected by the occurrence of speed, and supine, prone, right-side lying depending on the presence or absence of increased acceleration in the Y and Z directions and the speed generation time Or it detects a change to the left position. The posture information is time-series information.

  The bed entry time is the time when it is detected that the posture information has changed from the standing position to the lying position (sleeping state). The bed leaving time is the time when it is detected from the posture information that the posture has changed from the standing position to the lying position (sleeping state). The period during which the subject leaves the bed is the period from the bed leaving time to the next bed entry time.

  In the present embodiment, since the posture of the subject is automatically detected using the acceleration data α detected by the acceleration sensor 120, each time is detected using one clock built in the biological information detecting device body 10. can do. Therefore, for example, the elapsed time between each time can be detected with high accuracy compared to a configuration in which each time is detected by a plurality of clocks, for example, when the turn-off time of the bedroom is used as the time of entering the bed.

  As for the body temperature, the body temperature data T in the biological information data BD read from the memory 310 is used as it is.

  In addition, as information calculated as analysis data AS, unless it is contrary to the objective of this invention, you may add some information other than the above, and methods other than the above also about calculation of each information You may calculate by.

  The calculated analysis data AS is output to the display unit 330. The display unit 330 displays the input analysis data AS on a display or the like in various modes.

  A display example 1 by the display unit 330 is shown in FIG. FIG. 8 is an example of a diagram displaying a stress state detection result.

  In this display example 1, the subject is artificially stressed over the coefficient calculation period (2 minutes), the RR interval variation coefficient in the coefficient calculation period is calculated, and the RR interval variation coefficient is determined as the stress state determination. 1 shows the result of comparison with the first stress state determination reference value used for the above and the diabetes state determination reference value used for the determination of the diabetic state.

  Specifically, the biological information detecting device main body 10 is attached to the subject, and a 7-digit number is shown to the subject for 5 seconds, and then input to the personal computer or written on paper in the reverse order in the next 15 seconds. The operation is repeated over the coefficient calculation period. Then, the biological information measured during the coefficient calculation period is input to the psychosomatic state diagnosis support apparatus to calculate the RR interval variation coefficient, and is compared with the first stress state determination reference value and the diabetes state determination reference value. In the present embodiment, the first stress state determination reference value is 0.04, and the diabetes state determination reference value is 0.022. These values are obtained from RR interval variation coefficients detected in a plurality of subjects whose stress states are known. These values are preferable as each criterion value, but other values may be used.

  As shown in FIGS. 8A to 8C, the stress state detection results are displayed side by side next to the RR interval variation coefficient. Specifically, when the RR interval variation coefficient (autonomic nerve activity) is greater than 0.04, a detection result of “no stress” is displayed (FIG. 8A), greater than 0.022, and 0. If it is 04 or less, the detection result “strong stress state” is displayed (FIG. 8B), and if it is 0.022 or less, the detection result “diabetes state” is displayed (FIG. 8C). )).

  Or, for example, the 24-hour period during which the subject performs normal living activities is set as a state determination period, and the RR interval variation coefficient (autonomic nerve activity) is calculated every coefficient calculation period (for example, 2 minutes) over the state determination period. The maximum value of the RR interval coefficient of variation in each of the subject's life time zones (to be described later, staying time, before working time, working time and time after leaving the office) is compared with the first stress state determination reference value. By doing so, the stress state may be detected for each life time zone. The state determination period may not be 24 hours, for example, may be a period of not sleeping (period of getting out of bed) or the like, and can be arbitrarily set according to the subject to be examined. Alternatively, as the maximum value of the RR interval variation coefficient, the median value of the RR interval variation coefficient is calculated in each of the plurality of life time zones, and the maximum one of the plurality of median values is adopted, and this is used as the first value. It is good also as a structure compared with a stress condition determination reference value.

  A display example 2 by the display unit 330 is shown in FIG. FIG. 9 is an example of a stress diagnosis plot diagram.

  The stress diagnosis plot diagram uses the subject's stress self-evaluation value (questionnaire score) and the maximum value of the RR interval variation coefficient in a state determination period (for example, 24 hours) including a plurality of coefficient calculation periods (for example, 2 minutes). The points whose coordinates are the stress self-evaluation value (horizontal axis) of the subject and the maximum value (vertical axis) of the RR interval variation coefficient are plotted on a two-dimensional plane. In FIG. 9, biometric information and stress self-evaluation values of a plurality of different subjects are input to the psychosomatic state diagnosis support apparatus 30, and a plurality of points corresponding to each subject are plotted. is there. The higher the stress self-evaluation value, the stronger the stress state.

  In the stress diagnosis plot diagram shown in FIG. 9, a first reference line indicating a first stress state determination reference value used for determination of the maximum value of the RR interval variation coefficient and a second stress state determination reference used for determination of the stress self-evaluation value. A second reference line indicating the value is drawn. The first reference line passes the first stress state determination reference value on the vertical axis and is parallel to the horizontal axis, and the second reference line passes the second stress state determination reference value on the horizontal axis and the vertical axis. Parallel. It is preferable that at least one of the first reference line and the second reference line is displayed, and it is more preferable that both are displayed.

  The stress state can be more visually determined depending on where the plurality of quadrants (I) to (IV) defined by the first reference line and the second reference line are included. When the plotted point is included in the quadrant (I), it is determined that there is no stress because the RR interval variation coefficient is high and the stress self-evaluation value is low. If the plotted point is included in the quadrant (IV), it is determined that some disease due to stress is occurring because the RR interval variation coefficient is low and the stress self-evaluation value is high. When it is included in quadrant (II) or quadrant (III), it is determined that there is a possibility of modulation due to stress.

  In the present embodiment, the first stress state determination reference value is 0.04, and the second stress state determination reference value is 30. These values are found from RR interval variation coefficients and stress self-evaluation values detected in a plurality of subjects whose stress states are known. Regarding the RR interval coefficient of variation, specifically, the present inventors have made a number of measurements to determine whether the autonomic nervous activity is healthy or not by performing a two-dimensional plot with the number of judgments from the questionnaire. It has been found that it is appropriate that the first stress state determination reference value that is the threshold value of the RR interval variation coefficient to be determined is 0.04.

  Display examples 3A to 3C by the display unit 330 are shown in FIGS. 10 to 12 are examples of autonomic nerve activity plot diagrams (CVRR-SNS-Plot).

  The display unit 330 two-dimensionally displays a plurality of points with coordinates of the RR interval variation coefficient and the sympathetic nerve information calculated corresponding to each coefficient calculation period in a plot period including a plurality of coefficient calculation periods (for example, 2 minutes). An autonomic nerve activity plot plotted on a plane is displayed. Specifically, the display unit 330 includes a plurality of plot periods in each of a plurality of plot periods corresponding to a plurality of living time zones (described later, bedtime, pre-working time, working time, and time after leaving the office). Displays a plot of autonomic nerve activity. 10-12, the autonomic nerve activity plot figure for every several life time zone of a subject is displayed.

  Specifically, the living time zone of the subject is divided into four hours: 24 hours in bed time, time before leaving office, time in office and time after leaving the office as a period of leaving the bed. . In the present embodiment, the bedtime, the time before going to work, the time to work, and the time after leaving the office are automatically grasped from the posture information described above. For example, the bedtime is from 23:00 to 6:00, The time before going to work may be set in advance as each time, such as 6:00 to 9:00, working time from 9:00 to 18:00, and time after leaving the office from 18:00 to 23:00.

  The inventors have found the following points. That is, (1) When sympathetic nerve activity is high, people tend to be stressed, but when people do responsible work, do difficult work, fight, push their intentions, etc. Sympathetic nerve activity increases even when positive, (2) For example, those who are in a responsible position such as hospital directors, university professors, kindergarten chiefs, nursery school facility managers, etc. are usually in a state of high sympathetic nerve activity, There are few people whose autonomic nervous activity becomes unhealthy, and (3) the degree of autonomic nerve activity changes frequently as well as the magnitude of sympathetic nerve activity. The inventors have found that the relationship between the degree of autonomic nerve activity and the sympathetic nerve activity is important from these points of interest, and have come up with this autonomic nerve activity plot diagram. In this plot, when the autonomic nerve activity level is large, when the sympathetic nerve activity is large, it is considered that there is a positive mental state and active stress is applied. On the other hand, when the autonomic nerve activity level is small, the passive mental state In this case, it is considered that passive stress is applied when sympathetic nerve activity is large. It is thought that it becomes mentally unhealthy when there is much passive stress which is the latter.

  The present inventors approached the reason that a person with high daily stress does not necessarily harm the health of autonomic nerve activity, and a two-dimensional plot of CVRR and sympathetic nerve activity index for each coefficient calculation period (epoch), That is, the autonomic nerve activity plot was invented. People with unhealthy autonomic nervous activity often have high sympathetic nerve activity in areas where the RR interval variation coefficient is small, and those who continue to be healthy even when stress is large are sympathetic nerve activity in areas where the RR interval variation coefficient is large. It has been found that it is appropriate to set the threshold value of the RR interval variation coefficient to 0.05 in this case.

  In the autonomic nerve activity plot diagram, the autonomic nerve information is displayed as a light gray point with 0.05 or less as being in the passive stress region, and if it is greater than 0.05, it is represented as a dark gray point as being in the active stress region. it's shown. Black dots are invalid data and are not used for stress state detection.

  FIG. 10 is a plot of a busy person in a responsible position. It can be seen that the ratio of passive stress is small before leaving the company, but the ratio of passive stress is large after leaving the company. On the other hand, the point where the sympathetic nerve activity is large is seen only in the active stress region, and it is expected that there is no worry of mental unhealth in the subject.

  FIG. 11 is a plot of a depression patient having a problem at home, and it is considered that sympathetic nerve activity is relatively large in the area of passive stress before entering the company and at the time of working, and strong stress is applied.

  FIG. 12 is a plot of a patient with autonomic dysfunction, and a very large point of sympathetic nerve activity (abnormal data) appears in the passive stress area at the time of working at the company, and it is considered that a very strong stress is applied. It is done.

  In this way, the stress state of the subject can be detected from the autonomic nerve activity plot diagram.

  Display examples 4A to 4H by the display unit 330 are shown in FIGS. 13 to 20 are autonomic nerve balance plot diagrams (PSNS-SNS-Plot).

  The display unit 330 has a plurality of points with the parasympathetic nerve information and the sympathetic nerve information calculated corresponding to each activity state detection period in a plot period including a plurality of activity state detection periods (epochs, for example, 2 minutes) as coordinates. Is displayed on a two-dimensional plane. Specifically, the display unit 330 includes a plurality of plot periods in each of a plurality of plot periods corresponding to a plurality of living time zones (described later, bedtime, pre-working time, working time, and time after leaving the office). Displays the autonomic nerve balance plot of. In FIGS. 13-16, the autonomic nerve balance plot figure for every several life time zone of a subject is displayed.

  As in the above-described autonomic nerve activity plot chart, the subject's life time zone is 24 hours in bed time (IV), and time before leaving office (I) and time in office ( II) and time after leaving the office (III). FIG. 13 shows a display example 4A of a plurality of autonomic nerve balance plot diagrams. Also in the present embodiment, the bedtime, the time before leaving the office, the time at the office, and the time after leaving the office are automatically grasped from the posture information described above. For example, the bedtime is from 23:00 to 6:00. The time before going to work may be set in advance as each time, such as 6:00 to 9:00, working time from 9:00 to 18:00, and time after leaving the office from 18:00 to 23:00.

  FIG. 14 shows an example (display example 4B) of an autonomic nerve balance plot of a person whose stress state is normal. In sleep (bed time), the movement of the sympathetic nerve is small, the movement of the parasympathetic nerve is large, and it indicates that the person is sleeping well. In getting up and going to work (time before going to work), the movement of the sympathetic nerves gradually increases, the movement of the parasympathetic nerves gradually decreases, and it shows that it is starting to get excited. In the company (time in office), the movement of the sympathetic nerve is large and the movement of the parasympathetic nerve is small, indicating that it is active. Then, in leaving the office-entering the floor (time after leaving the office), the movement of the sympathetic nerve is gradually reduced, the movement of the parasympathetic nerve is gradually increased, and the excitement is beginning to settle down.

  FIG. 15 shows an example of an autonomic nerve balance plot of a person whose stress state is between normal and abnormal (display example 4C). Throughout the day, the movement of sympathetic and parasympathetic nerves is similar, indicating that the movement of sympathetic nerves increases and the movement of parasympathetic nerves increases, and the movement of sympathetic nerves decreases and the movement of parasympathetic nerves also decreases. .

  FIG. 16 shows an example of an autonomic nerve balance plot diagram of a person whose stress state is abnormal (display example 4D). In sleep (bed time), the movement of the sympathetic nerve is large and the movement of the parasympathetic nerve is small, indicating that the patient is in an excited state even after going to sleep. In getting up and going to work (time before going to work), the movement of the sympathetic nerve gradually decreases, the movement of the parasympathetic nerve gradually increases, and the excitement is being subsided. In the company (time in office), the movement of the sympathetic nerve is small, the movement of the parasympathetic nerve is large, and it shows that there is no feeling of working. Then, in leaving the office-entering the floor (time after leaving the office), the movement of the sympathetic nerve gradually increases and the movement of the parasympathetic nerve gradually decreases, indicating that it is starting to get excited.

  The stress state of the subject is detected using the area of the plot shape in the autonomic nerve balance plot diagram, the size of the plot shape in the coordinate axis direction, the distance between the plot shape and the coordinate axis, and the smoothness of the periphery of the plot shape. The plot shape is a shape surrounding a plurality of points plotted in the autonomic nerve balance plot diagram. Of these plural points, points that are extremely far from other points may not be included in the plot shape as invalid.

  In FIG. 17, the area of the plot shape is (1) large, (2) medium, (3) small in order from the left (display example 4E). If the area of the plot shape is large, the subject is expected to be stress free and the autonomic nerve is relatively active. If the area of the plot shape is small, the subject is not active the autonomic nerve It is expected to be stressed. A stress state may be detected by providing one or a plurality of stages of determination reference values for the area of the plot shape and comparing the determination reference value with the area of the plot shape.

  In FIG. 18, the size of the plot shape in the coordinate axis direction from the left is (1) the size in the vertical axis direction is larger than the size in the horizontal axis direction, and (2) the size in the vertical axis direction and the size in the horizontal axis direction are almost the same. (3) The size in the vertical axis direction is smaller than that in the horizontal axis direction (Display Example 4F). If the size of the plot shape in the vertical axis direction is larger than the horizontal axis size, the subject is in a state where the sympathetic nerve is superior to the parasympathetic nerve, and it is expected that there is no stress in a positive mental state. If the size of the direction is smaller than the size of the horizontal axis direction, the subject is in a state where the parasympathetic nerve is superior to the sympathetic nerve, and it is expected that there is stress in a negative mental state. One or a plurality of determination reference values are provided for the relative size of the plot shape in the vertical axis direction and the horizontal axis direction, or the absolute size in the vertical axis direction and the horizontal axis direction. The stress state may be detected by comparing the size of the plot shape in the axial direction.

  In FIG. 19, the distance between the plot shape and the coordinate axis in order from the left is (1) the vertical axis is in contact with the horizontal axis (distance is almost 0), and (2) the vertical axis is separated from the horizontal axis by the distance dp. Are in contact with each other, (3) are in contact with the vertical axis and are separated from the horizontal axis by a distance ds, (4) are separated from the vertical axis by a distance dp, and are separated from the horizontal axis by a distance ds, (Display example 4G). When the plot shape is away from the vertical axis, the subject determines that the parasympathetic nerve is active, and when away from the horizontal axis, the subject determines that the sympathetic nerve is active. However, depending on the subject, the distance between the plot shape and the coordinate axis is usually far away, and determination is necessary based on the difference from the usual state.

  In FIG. 20, the shapes around the plot shape in order from the left are (1) smooth and beautifully arranged with no corners, (2) generally smooth but somewhat cornered, (3) many corners are violently disturbed (Display example 4H). If the shape of the periphery of the plot shape is smooth, the subject's autonomic nerve is expected to be stable and free of stress, and if disturbed, the subject's autonomic nerve is unstable and stressed Is expected. One or a plurality of stages of determination reference values for determining the smoothness of the peripheral shape of the plot shape are provided, and the stress state is detected by comparing the determination reference value with the smoothness of the peripheral shape of the plot shape. Also good.

  FIG. 21 is a graph plotting the maximum value of the sympathetic nerve information during work in the plurality of subjects and the third quartile, and FIG. 22 is the sympathetic nerve information during sleeping in the plurality of subjects. It is the figure which plotted the minimum value of and the 1st quartile. The state detection unit 320 includes a maximum value and a plurality of sympathetic nerve information calculated corresponding to each activity state detection period in a state determination period (during work) including a plurality of activity state detection periods (for example, 1 minute). The stress state is detected using the third quartile of the sympathetic nerve information. Specifically, the maximum value of the plurality of sympathetic nerve information is greater than or equal to the threshold (preferably 25, more preferably 27), and the third quartile is the threshold (preferably 5). At the time described above, it is determined that the subject is in a strong stress state (Large Stress Subject; LS subject). The display unit 330 displays the determination result.

  The present inventors have obtained a quartile distribution of the size of sympathetic nerve information (an index of sympathetic nerve activity) obtained for each active state detection period with respect to a predetermined state determination period from the measurement results of a large number of subjects. When described in the method, the maximum value is 27 or more and the 75% value from the minimum value side (that is, the third quartile, in other words, the next quartile of the maximum value) is 5 or more. In this case, the subject was found to be feeling great stress.

  Specifically, the present inventors calculated sympathetic nerve information during work (time in office) for a plurality of kindergarten teachers who are subjects, and analyzed the plurality of sympathetic nerve information. The number of teachers who are examinees is 19, and the age is 40.7 ± 15.1 years old. For some teachers, sympathetic nerve information is calculated multiple times for different occasions, and the number of sympathetic nerve information to be analyzed is 30. FIG. 21 shows a plot of the maximum value of the sympathetic nerve information calculated for each teacher and the third quartile. In FIG. 21, the horizontal axis is a number assigned to each of a plurality of teachers, and the vertical axis is the size of sympathetic nerve information. For some teachers, a plurality of sympathetic nerve information calculated from biological information measured a plurality of times is plotted. Based on the plot of FIG. 21, the threshold value of the maximum value of the plurality of sympathetic nerve information of each subject is set to 25 (more certainly 27), and the threshold value of the third quartile is set to 5. By doing so, it is possible to discriminate those who feel great stress. In FIG. 21, the teachers of Nos. 4, 8, and 10 are identified as those who feel a great stress.

  In addition, the present inventors consider that the sympathetic nerve information should show the lowest value during bedtime, and sympathy is performed for discrimination of a subject (Remain Stress Subject; RS subject) in which stress remains (accumulates). Neural information was analyzed. As a result, when the minimum value of sympathetic nerve information was larger than 0.8 and the first quartile was larger than 0.1, the subject found that the stress remained.

  Specifically, sympathetic nerve information during sleep (bed time) was calculated for a plurality of kindergarten teachers who were the same subjects as above, and the sympathetic nerve information was analyzed. The number of subjects and the number of sympathetic nerve information to be analyzed are the same as described above. FIG. 22 is a diagram in which the minimum value of the sympathetic nerve information calculated for each teacher and the first quartile are plotted. In FIG. 22, the horizontal axis is a number assigned to each of a plurality of teachers, and the vertical axis is the size of sympathetic nerve information. For some teachers, a plurality of sympathetic nerve information calculated from biological information measured a plurality of times is plotted. Based on the plot of FIG. 22, the threshold value of the minimum value of the plurality of sympathetic nerve information of each subject is set to 0.8, and the threshold value of the first quartile is set to 0.1. It is possible to discriminate those who remain stressed. In FIG. 22, teachers No. 4, No. 5, and No. 14 are determined to be those who remain stressed.

  The present inventors have made a paper on the relationship between sympathetic information and stress [Stress of Kindagarten tears: How we tried to detect and to reduce it by using a small and wearable ECG and ceramics. / Shirouzu, Shigenori; Seno, Yumeka; Tobioka, Ken; Masaki, Takeo; Yasumatsu, Kiyotaka; Mishima, Norio; Sugano, in Hisanobu / Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE] Disclosed.

  In the present embodiment, if accurate electrocardiographic signal data E is obtained, the apparatus for detecting biological information is not limited to the biological information detecting apparatus 1, and the length of the activity state detection period (analysis epoch) is also one minute. Not limited to. Values other than those described above may also be used as the threshold values. The state determination period may not be during work or sleeping, for example, it may be a period of not sleeping (period of getting out of bed) or 24 hours, and can be arbitrarily set according to the subject to be examined It is.

  Furthermore, by tracking changes in sympathetic nerve activity over time in this way, it is possible to identify the cause / partner that caused the stress, and to give a warning of stress / bullying if analyzed in real time. Monitoring from child bullying and monitoring stress for workplace stress monitoring can be constructed.

(Second Embodiment)
FIG. 23 is a block diagram illustrating a configuration example (second embodiment) including a psychosomatic state diagnosis support apparatus 50 in which a storage unit is added to the first embodiment illustrated in FIG. 1. In the configuration of the first embodiment, by adding an accumulation unit so that analysis data calculated by the psychosomatic state diagnosis support apparatus can be accumulated, it is possible to investigate a secular change such as a stress state. Note that in the second embodiment, identical symbols are assigned to configurations identical to those in the first embodiment and descriptions thereof are omitted.

  The accumulation unit 500 receives the analysis data AS output from the state detection unit 320 and stores the analysis data AS together with information about the time (for example, date) when the biological information data BD is acquired. Information relating to time is input from the outside when, for example, the biological information detection apparatus 1 adds the biological information data BD and the state detection unit 320 extracts the information from the analysis data AS or when the analysis data AS is stored. It is set by doing. Display unit 530 displays analysis data AS corresponding to the designated time.

  Note that the storage unit 500 may save the biological information data BD stored in the memory 310. Accordingly, when a new analysis function is added to the state detection unit 320, the past biological state can be analyzed by using the biological information data BD stored in the memory 310. In addition, the storage unit may be prepared separately from the psychosomatic state diagnosis support apparatus, instead of being added to the psychosomatic state diagnosis support apparatus. As a result, a large amount of data can be stored.

  In the above-described embodiments (the first embodiment and the second embodiment), the three sensors of the body temperature sensor, the acceleration sensor, and the electrocardiogram signal sensor are used. However, other sensors may be additionally used. . For example, an oxygen saturation sensor for measuring arterial blood oxygen saturation may be added and used to analyze the biological state of the subject. An infrared color photoelectric pulse wave sensor may be used.

  Furthermore, the number of biological information detection device main bodies to be worn on the subject is not limited to one, and a plurality of biological information detection device main bodies may be provided as shown in FIG. By acquiring biological information from multiple locations on the subject's body, it becomes possible to detect symptoms that are difficult to detect with only biological information from one location (for example, early detection of cerebral infarction or myocardial infarction, etc.) It becomes possible to grasp the health condition including the stress state of the subject and the physical abnormality more precisely. When using a plurality of biological information detection device main bodies, for example, a unique number is assigned to the biological information detection device main body so that it can be determined from which biological information detection device main body the acquired biological information is obtained. The unique number is added to the biological information signal transmitted by the biological information detecting device.

  Although this embodiment of the present invention has been described above, the present invention is not limited to these examples. As long as a person skilled in the art appropriately adds, deletes, and modifies the design of each of the above-described embodiments, and appropriately combines the features of each of the embodiments, the gist of the present invention is included. And within the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Biological information detection apparatus 10 Biological information detection apparatus main body 20 Mounting stand 30, 50 Psychosomatic state diagnosis support apparatus 100 Biological information detection part 110 Body temperature sensor 120 Acceleration sensor 130 ECG signal sensor 140 Control part 141 Data processing part 142 Mode setting part 143 Switching unit 143a, 143b Contact 144 Data reading unit 150 Memory 160 Transmitting unit 170 Charging input unit 200 Charging output unit 210 Communication unit 300 Biological information input unit 310 Memory 320 Status detection unit 330, 530 Display unit 500 Storage unit E ECG signal data T body temperature data V velocity data AS analysis data BD biological information data BS1, BS2, BS3 biological information signal MS mode signal dp, ds distance LF low frequency component HF high frequency component CS charge start signal

Claims (22)

A biological information input unit for inputting biological information of the subject detected in time series;
A state detection unit that detects a stress state of the subject based on the biological information input to the biological information input unit;
A display unit for displaying a stress state of the subject detected by the state detection unit,
The biological information including the heart motion information corresponding to the motion of the subject's heart is input to the biological information input unit,
The state detection unit calculates an RR interval that is a contraction interval of the subject's heart from the heart motion information, and calculates a standard deviation of the RR interval in a coefficient calculation period as an average value of the RR intervals in the coefficient calculation period. A psychosomatic state diagnosis support apparatus, wherein a stress state of the subject is detected using an RR interval variation coefficient calculated by dividing. The biological information input unit is input with the biological information in a state in which the subject is artificially stressed over the coefficient calculation period,
The psychosomatic state diagnosis support apparatus according to claim 1, wherein the state detection unit detects the stress state of the subject by comparing the RR interval variation coefficient in the coefficient calculation period with a first stress state determination reference value. .   The state detection unit detects a stress state of the subject by comparing a maximum value of the plurality of RR interval variation coefficients in a state determination period including the plurality of coefficient calculation periods with a first stress state determination reference value. The psychosomatic state diagnosis support apparatus according to claim 1. In the biological information input unit, the subject's stress self-evaluation value is further input,
The state detection unit compares a maximum value of the plurality of RR interval variation coefficients in a state determination period including the plurality of coefficient calculation periods with a first stress state determination reference value, and determines the stress self-evaluation value as a second stress. The psychosomatic state diagnosis support apparatus according to claim 1, wherein a stress state of the subject is detected by comparing with a state determination reference value.   5. The psychosomatic state diagnosis support apparatus according to claim 4, wherein the display unit displays a stress diagnosis plot diagram in which points having the coordinates of the stress self-evaluation value and the maximum value of the RR interval variation coefficient are plotted on a two-dimensional plane. .   A first reference line passing through the first stress state determination reference value and parallel to the coordinate axis of the stress self-evaluation value on the coordinate axis of the maximum value of the RR interval variation coefficient; and the stress self-evaluation value 6. At least one of a second reference line that passes through the second stress state determination reference value and is parallel to the coordinate axis of the maximum value of the RR interval variation coefficient on the coordinate axis is superimposed and displayed on the stress diagnosis plot diagram. The psychosomatic state diagnosis support device described.   The psychosomatic state diagnosis support apparatus according to any one of claims 2 to 6, wherein the first stress state determination reference value is 0.04.   The psychosomatic state diagnosis support apparatus according to claim 2, wherein the state detection unit also detects the diabetic state of the subject by comparing the RR interval variation coefficient in the coefficient calculation period with a diabetic state determination reference value.   The psychosomatic state according to claim 3, wherein the state detection unit also detects the diabetic state of the subject by comparing a plurality of maximum values of the RR interval variation coefficient in the state determination period with a diabetic state determination reference value. Diagnosis support device.   The psychosomatic state diagnosis support apparatus according to claim 8 or 9, wherein the diabetes state determination reference value is 0.022. The state detection unit further calculates sympathetic nerve information indicating an activity state of the subject's sympathetic nerve in the coefficient calculation period,
The display unit displays a plurality of points with coordinates of the RR interval variation coefficient and the sympathetic nerve information calculated corresponding to each of the coefficient calculation periods in a plot period including the plurality of coefficient calculation periods. The psychosomatic state diagnosis support apparatus of Claim 1 which displays the autonomic nerve activity plot figure plotted above.   The psychosomatic state diagnosis support apparatus according to claim 11, wherein the display unit displays a plurality of the autonomic nerve activity plot diagrams in each of the plurality of plot periods corresponding to a plurality of life time zones of the subject. A biological information input unit for inputting biological information of the subject detected in time series;
A state detection unit that detects a stress state of the subject based on the biological information input to the biological information input unit;
A display unit for displaying a stress state of the subject detected by the state detection unit,
The biological information including the heart motion information corresponding to the motion of the subject's heart is input to the biological information input unit,
The state detection unit calculates an RR interval that is a contraction interval of the subject's heart from the heart motion information, and indicates the sympathetic nerve activity state of the subject using the RR interval in an activity state detection period. Calculate parasympathetic information and parasympathetic information indicating the activity state of the parasympathetic nerve,
The display unit is configured to two-dimensionally represent a plurality of points with coordinates of the parasympathetic nerve information and the sympathetic nerve information calculated corresponding to each of the activity state detection periods in a plot period including the plurality of activity state detection periods. A psychosomatic state diagnosis support device that displays an autonomic nerve balance plot diagram plotted on a plane.   The psychosomatic state diagnosis support apparatus according to claim 13, wherein the state detection unit detects a stress state of the subject using an area of a plot shape including a plurality of points of the autonomic nerve balance plot diagram.   The mind and body according to claim 13 or 14, wherein the state detection unit detects a stress state of the subject by using a size in a coordinate axis direction of a plot shape including a plurality of points of the autonomic nerve balance plot diagram. Condition diagnosis support device.   16. The detection according to claim 13, wherein the state detection unit detects a stress state of the subject using a distance between a plot shape including a plurality of points in the autonomic nerve balance plot diagram and a coordinate axis. The psychosomatic state diagnosis support apparatus according to any one of the above.   The said state detection part detects the stress state of the said subject using the smoothness of the periphery of the plot shape containing the several point of the said autonomic nerve balance plot figure. The psychosomatic state diagnosis support apparatus according to 1.   18. The display unit according to claim 13, wherein the display unit displays a plurality of the autonomic nerve balance plot diagrams in each of the plurality of plot periods corresponding to a plurality of life time zones of the subject. The psychosomatic state diagnosis support device described.   The psychosomatic state diagnosis support apparatus according to claim 12 or 18, wherein the plurality of life time zones are a bedtime, a time before leaving the office, a time at the company, and a time after leaving the office. A biological information input unit for inputting biological information of the subject detected in time series;
A state detection unit that detects a stress state of the subject based on the biological information input to the biological information input unit;
A display unit for displaying a stress state of the subject detected by the state detection unit,
The biological information including the heart motion information corresponding to the motion of the subject's heart is input to the biological information input unit,
The state detection unit calculates an RR interval that is a contraction interval of the subject's heart from the heart motion information, and indicates the sympathetic nerve activity state of the subject using the RR interval in an activity state detection period. The sympathetic nerve information is calculated, and the maximum value and the third quadrant of the plurality of sympathetic nerve information calculated corresponding to each of the activity state detection periods in the state determination period including the plurality of activity state detection periods A psychosomatic state diagnosis support device characterized by detecting a stress state using a rank.   The said state detection part determines that the said subject has stress when the maximum value of the said several sympathetic nerve information is 25 or more and a 3rd quartile is 5 or more. Psychosomatic condition diagnosis support device. The psychosomatic state diagnosis support apparatus according to claim 1, and
A biological information detection device,
The living body information detecting device has a portable living body information detecting device main body that can be attached to the body of the subject to detect living body information including the heart motion information of the subject,
The biological information management system, wherein the biological information detected by the biological information detection device main body is input to the biological information input unit.