JP3811977B2 - Health information provision device - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
  The present invention relates to a health information providing apparatus that provides information such as a physical condition of a living body based on information of a biological signal, and particularly relates to health information.
[0002]
[Prior art]
  (Conventional example 1)
  It is important to sense the state of the living body for disease prevention, health maintenance or health management of patients undergoing medical treatment, and to inform the person, medical staff, etc. of the health status in an easy-to-understand manner based on the sensed information or past history It is a thing.
[0003]
  As a conventional device for measuring a health condition, for example, there is a health device that measures a pulse wave and determines a health condition from the shape, pulse, etc. of the pulse wave. In this case, the pulse and pulse wave at rest vary depending on the individual. Yes. For the sake of simplicity, the pulse rate will be explained. People who are athletic like normal exercise have a low pulse rate at rest of 50 to 60 pulses / minute (Category 1), and the average person has around 60 to 80. Pulses / minute (Category 2), people with poor lung function, etc. may have 80-90 pulses / minute (Category 3) even at rest. From this, it is possible to simply classify according to the difference in physical strength at rest.
[0004]
  (Conventional example 2)
  On the other hand, many data classification methods have been proposed. For example, in Japanese Patent Laid-Open No. 5-40829 shown in FIG. 13, data input means 101, a data conversion neural network 102 for inputting data by the data input means 101, and data The data reverse conversion neural network 103 that receives the output of the conversion neural network 102, the data input by the data input means 101, and the output of the data reverse conversion neural network are input, and the data conversion neural network 102 and the data reverse conversion neural network There has been proposed a data clustering method characterized by including a data comparison / learning control means 104 for controlling learning in its configuration. Reference numeral 105 denotes clustering means for clustering based on the output from the data conversion neural network. As a result, data conversion by a neural network is performed as a pre-processing of the clustering means, so that a result equivalent to human clustering by looking at the whole can be easily obtained.
[0005]
  (Conventional example 3)
  In addition, the learning control system of the control system includes, for example, a feedback control means that corrects a deviation between a tapping temperature and a set temperature, and a feedforward control means that sets a combustion amount, which is used in a water heater or the like. In order to correct the output error at this time, the learning control is performed so that the output value of the feedforward control means matches the heating load of the water heater. By changing the setting, the control system can be adapted to the user's preference.
It is something to control.
[0006]
  (Conventional example 4)
  Furthermore, as disclosed in JP-A-6-217951, attractor storage means for storing in advance a plurality of patterns of attractors as health management data processing means, biological signal collection means for collecting biological signals, and biological signals An attractor generating means for generating an attractor based on the attraction, a physical condition determining means for determining a physical condition from the attractor stored in the attractor storage means and the attractor generated by the attractor generating means, and a determination by the physical condition determining means A physical condition display means for displaying the physical condition based on the result is provided, and the physical condition is notified from the biological signal. This health care device is an improvement of the device for judging the symptom of a disease from the shape of the attractor so that a person who is not an expert can judge how to see the attractor. That is, the attractor in the normal state is input to the storage means by itself, and the health is determined by comparing the attractors measured according to the value.
[0007]
[Problems to be solved by the invention]
  In the conventional technique, it is difficult in Conventional Example 1 to accurately reflect a person's health state only with a heartbeat. Therefore, it is necessary to perform a process for reflecting heartbeat data to reflect the health condition, and to accurately classify the process to notify the condition.
[0008]
  In addition, except for the case where the absolute value of the measurement data is positively related to the degree of health, there is a case where the judgment criteria must be changed even for the same data depending on the characteristics of the living body, for example, the physical strength of the person and the health condition. For some people, measured biometric data is not a good health condition, but the same value may be a standard state for other people. Conceivable.
[0009]
  Specifically, the distinction between the case where a person with classification 1 in the conventional example catches a cold and the pulse becomes 80 to 90 pulses / minute from the resting time and normal time of other persons of classification 3 They cannot be attached, and both are only classified as classification 3. What is really necessary is that a person who originally has a classification 1 can get an evaluation of "I am weak today." . To be more friendly, the other person in Category 3 originally gets a reputation saying “It is normal today, but let's exercise and improve physical strength.”
[0010]
  Further, Japanese Patent Laid-Open No. 5-40829 of the above-mentioned conventional example 2 can easily obtain a result equivalent to the case where a person looks at the whole and performs subjective clustering with the data inputted by the input means as the preprocessing of the clustering means. In this way, the neural network based on the input data obtained by the input means and its output are fed back to the input. However, although it is based on the information of the input means, it is not such a determination when the same input data must be classified differently according to past history data. Therefore, although it is one method of classification, it is different from the one aimed by the present invention.
[0011]
  In the learning control in the water heater shown in the conventional example 3, the user's preference is set and the initial setting as the standard setting is corrected by controlling in accordance with the setting. In systems with many parameters such as biological data,ScatterIt cannot be controlled. Moreover, it is not possible to classify those that collect biometric data discretely and apply the logic of the control system to those that perform physical condition determination according to the classification. Further, the configuration is different from that of a device that processes biological data, performs classification, and learns the classification.
[0012]
  Furthermore, the health management apparatus as shown in the conventional example 4 requires the work of storing the attractor in advance. The work to memorize is troublesome for a person who does not have expertise, and it is impossible to determine when to store the attractor.LikeAs a result, it is conceivable that a wrong judgment is made when a health judgment is made based on the stored attractor. In addition, for health management based on such signals from the heart, a temporary measurement at the time of measurement detects the state of the so-called autonomic nerve that indicates the mutual action of the sympathetic nervous system and the parasympathetic nervous system at that time. Can detect heart disease, but in order to know the person's health status, it is important to know not only the instantaneous value but also the status from the past. There is a need.
[0013]
  By the way, in the health equipment that is used by multiple people, as a necessary condition of the equipment that performs health judgment and provides the information, (1) Even if there is no high-level judge such as a doctor, Can be used in. (2) Identify the use by multiple people. (3) Identify the same person using it many times. (4) Judgment criteria must be set depending on the measurement time (time).
[0014]
[Means for Solving the Problems]
  The present inventionProcessing means for processing data obtained from biological information detecting means or equipment having biological data, classification means for classifying data processed by the processing means, and mapping the data classified by the classification means to other classifications Mapping means; history storage means for storing data history of the classification means; level determination means for determining a load level of a living body from mapping progress data of the history storage means; contents of the history storage means; Display means for displaying the current biological load level determination result by the level determination means, and detecting the load received by the living body over time by the level determination means and displaying the load on the display means by the display means. A health information providing device that can grasp the situation.
[0015]
  Also,Processing means for processing data obtained from biological information detecting means or equipment having biological data, classification means for classifying data processed by the processing means, and mapping the data classified by the classification means to other classifications Mapping means, mapping changing means for changing mapping displacement when data is mapped to another classification, history storage means for storing data history of the classification means, and mapping progress data of the history storage means Level determining means for determining the load level of the living body, and display means for displaying the contents of the history storage means and the current biological load level determination result by the determining means, and the living body receives over time by the level determining means. By grasping the current load and displaying it on the display means, the current health condition is grasped. Theft is a health information providing device that can be.
[0016]
  Further, the processing means includes a biosignal detection means or a reconstruction means for reconstructing a biosignal of a device having biometric data in n dimensions, and a digitization means for digitizing the data into a form that can be classified by the classification means. .
[0017]
DETAILED DESCRIPTION OF THE INVENTION
  Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
[0018]
  1 and 2 are block diagrams showing a configuration of an embodiment of the present invention.
[0019]
  In the present embodiment, 1 is a biological signal detecting means for detecting a biological signal of the living body 2, 3 is a processing means for processing data of the biological signal detected by the biological signal detecting means 1, and 4 is data processed by the processing means 3. Is a classification means for classifying the data classified by the classification means 4 to the second classification section 9 according to the mapping rule of the mapping rule section 8. Is a mapping change unit that changes the mapping method of the mapping unit 7 according to past and current data classified by the classification unit 4, and 11 is a history storage unit that stores a history of data classified by the classification unit 4. Reference numeral 12 denotes display means for displaying data stored in the history storage means 11.
[0020]
  In this configuration, the mapping unit 7 is included in the classification unit 4 in common with the classification unit of the classification unit 4, but may be configured separately. Further, even if the mapping change means 10 is configured as a part of the mapping means 7, the same effect is obtained.
[0021]
  Here, the biological signal detection means 1 is composed of an electrode 14 provided in the detection unit container 13 so as to measure an electrocardiogram and an amplification unit 15 that amplifies a signal detected from the electrode 14 as shown in FIG. .
[0022]
  Further, as shown in FIG. 3, the processing means 3 includes a reconstruction means 16 for reconstructing the biological signal detected by the biological signal detection means 1 in n dimensions, and a quantification means for digitizing the signal into a form that can be classified by the classification means 4. 17.
[0023]
  The operation in the above configuration will be described. The electrocardiographic signal of the living body 2 is detected by the electrode 14 of the living body signal detecting means 1, amplified by the amplifying unit 15, and processed by the processing means 3 after being amplified by the living body signal detecting means 1.
[0024]
  This processing is to perform chaotic signal processing, which is nonlinear signal processing, and frequency analysis processing, which is linear signal processing, as will be described later. The reason for processing the signal is to perform a detailed analysis by processing to accurately grasp the state of the living body or convert it into a plurality of data.
[0025]
  Therefore, the data processed by the processing unit 3 is classified into the first classification unit 6 according to the classification rule of the classification rule unit 5 of the classification unit 4. That is, the classification rule unit 5 has a classification rule based on a standard distribution, and is a rule for classifying where the digitized biological data is located with respect to the standard. The biological data is classified by the first classification unit 6.
[0026]
  Next, the data classified by the classification means 4 is mapped to the second classification section 9 by the mapping means 7 according to the mapping rule of the mapping rule section 8. The past and present data classified by the classification means 4 and the data mapped by the mapping means 7 are stored in the history storage means 11, and the mapping method of the mapping means 7 is mapped in accordance with the history count stored in the history storage means 11. The data is changed by the changing means 10, the data of the classification means 4 is stored in the history storage means 11, and the stored data is displayed on the display means 12.
[0027]
  Here, the operation will be described more specifically. The processing means 3 reconstructs the one-dimensional time-series signal data detected by the biological signal detection means 1 into an n-dimensional space by the reconstruction means 16 and digitizes it into a format that can be classified by the classification means 4 by the digitization means 17. I have to do it. The reconstruction means 16 may be based on a method by mapping to an n-dimensional space or a TAKENS method.
[0028]
  There are various methods such as a method for obtaining a correlation dimension, a method for obtaining a Lyapunov exponent, a method for obtaining KS entropy, and a method aiming for simple digitization by projection onto two-dimensional or three-dimensional coordinates as the numerical means 17. There are various methods for obtaining a simple numerical value by projection, such as obtaining an area, obtaining a volume, obtaining a center of gravity, obtaining a size, and obtaining a major axis and a minor axis.
[0029]
  Here, the biological signal detected by the biological signal detection means 1 is a heartbeat waveform as shown in FIG. 4 detected by a signal from the heart. In FIG. 4, the vertical axis represents the output of the heartbeat waveform, the horizontal axis represents time, and the RR interval of the heartbeat waveform is shown at the top of the waveform. The R-R interval of the heartbeat waveform detected by the biological signal detection means 1 is obtained, and the interval between the previous R wave and the current R wave is taken on the horizontal axis with respect to the detected R wave, and the current R wave and the next R wave are taken. Is taken on the vertical axis and reconstructed by the reconstruction means 16 on this coordinate. FIG. 5 shows a reconstructed attractor when two-dimensionally embedded. Here, attractors by the reconstruction means 16 for three heartbeat waveforms are displayed.
[0030]
  However, if you try to classify some sampled data as it is, there will be data overlapping (here, it is assumed that the data does not overlap for simplicity), so-called complicated figure clustering work will occur, so later classification In order to simplify the above, the digitizing means 17 digitizes the data. The digitization here is performed by determining the area and the position of the center of gravity. However, if it cannot be simply separated, clustering means 18 (not shown) performs clustering to obtain representative points. Here, the case where it performs without clustering is demonstrated.
[0031]
  The area represents whether the fluctuation of the RR interval is large or small, and the center of gravity represents the magnitude of the heart rate. It shows that the heart rate increases when going to the upper right and decreases when going to the lower left. Here, the classification means 4 performs classification based on the two numerical values of the area and the center of gravity. FIG. 6 is a diagram in which these two items are digitized by the digitizing means 17 and displayed with the items as axes.
[0032]
  The vertical axis represents the distance when the position (X, Y) of the center of gravity is (X2 + Y2) 0.5, and the horizontal axis represents the area.
[0033]
  Based on this graph, the classification means 4 classifies. Accordingly, the digitizing means 17 represents a means for simplification for classification. For this reason, when a simple form appears in the reconstruction means 16, the digitizing means 17 may not be substantially used as a one-to-one mapping. The items to be digitized by the digitizing means 17 may be selected according to the subsequent purpose.
[0034]
  Here, the case where the above heartbeat waveform is measured will be specifically described as to what kind of classification the first classification unit 6 represents. The heartbeat waveform is processed in advance by the processing means 3 to be numerical data. The data is shown in FIG. As for the person whose heart rate is normal, the person who has a so-called sport heart is low in the data obtained by the digitizing means 17 at rest, and the general person falls into Category 1 in FIG. The person who falls is classified as Category 3 in FIG. 6 even at rest. In this way, the heartbeat attractors are quantified and classified by the classification means 4. This is hereinafter referred to as chaos data.
[0035]
  Next, the second classification by the mapping means 7 is to express the numbers in easy-to-understand words. For example, in the classification 3 in the first classification section 6, “You have physical fitness and will continue to maintain physical fitness in the future. (C category), category 2 "You are a standard healthy body. Please continue to improve your health and keep the current state." (B category), category 1 " Three classes A, B, and C such as (Class A) are provided in the second classification unit 9, so exercise gradually to increase physical strength. Then, the mapping unit 7 associates the first classification unit 6 along the second classification unit 9. Here, class 1 corresponds to class A, class 2 corresponds to class B, and class 3 corresponds to class C.
[0036]
  By the way, in the first measurement in this way, what is mapped to the second classification unit 9 is not changed by the mapping change means 10, that is, zero change is performed. However, when the measurement becomes the second and third times, the mapping changing means 10 may change the data depending on the data. For example, if the person in the C classification in the second classification unit described above is classified into the classification 3 in the first classification unit 6 for the second and third time, both are changed to zero change as the C classification. Next, it is assumed that the chaos data is classification 1 in the fourth measurement. At this time, another way to change the mapping to the classification of X classification of another XYZ classification series, "Did you measure in a resting state? You probably have a cold right now. Take a rest." 10, and the sentence is displayed on the display unit 12.
[0037]
  Unlike the case of just measuring the pulse, the information that is classified as this X classification and having a high probability of being a cold is accurate, and the information from the chaos data is accurate. It is because it is possible to identify. In the conventional example, the classification is simply A. When classification 3 becomes classification 1 due to exercise, it is classified as an LMN classification sequence. Here, the XYZ and LMN classification sequences only indicate that they are different classification sequences. The aim is to prepare a plurality of classification series, change the classification series based on past history, and give advice most suitable for the person.
[0038]
  In this way, the values detected by the biological signal detection means 1 are classified into the first classification means 4, and then mapped by the mapping means 7 (symbol display, sign display, etc. may be used instead of sentences) The change means 10 (in this case, zero change before the third time) can be changed to provide appropriate information. The task of mapping is to make the measured data easy to understand for people by high-level language, or to classify the data easily for humans by using predetermined symbols.
[0039]
  In addition, immediately after the first use, the one corresponding to the standard classification is mapped by changing the mapping displacement sequentially according to the number of times by repeating the measurement, and responding to various states of the measurer from the past history It is possible to provide health information on health that matches the measurer. At first, the user's position with respect to the standard is known, and the user becomes familiar with the standard.
[0040]
  By the way, in the above description, the biological signal detection means 1 has been described based on electrocardiography, but the pulse and other biological information (for example, biological information for preventing diabetes by measuring the urine sugar value in daily urine components). It is also possible to classify the information and provide health information. It is also possible to provide health information by classifying electrocardiogram information, body temperature information, body surface temperature, and the like in combination. In that case, the electrocardiogram information is processed by the processing means 3, but the body temperature information may be zero-processed, that is, may be used as biological information without being processed.
[0041]
  Next, as shown in FIG. 7, the processing means 3 does not have the reconstruction means 10, and there is a method using the frequency analysis means 10a as the digitizing means 17 for signal processing.
[0042]
  For example, 1 / fn fluctuation of the electrocardiogram can be obtained by FFT (spectral analysis by fast Fourier transform), and the slope n can be classified. By setting n as the first classification, health information can be provided by reclassification as the second classification using the relationship between n and aging and the relationship between n and health. Alternatively, it can be divided into a low-frequency component and a high-frequency component by frequency analysis to determine whether the parasympathetic nervous system is positioned or sympathetic nervous system is positioned, and reclassification is performed by weighting the component to give advice on an appropriate health condition.
[0043]
  By the way, although it described as a health information provision apparatus which detects a biological signal and provides information here, it improves the classification which provides health information while learning how to use it by the use history with other devices. Even if used, the same effect can be obtained. It can also be used when data is already classified after being measured as biological information.
[0044]
  The description of the embodiment of the present invention has been given in the embodiment of the invention using the mapping change means 10, but the case where the mapping change means 10 is not used will be described in the embodiment of the invention described later.
[0045]
  Next, another embodiment of the present invention will be described.
[0046]
  FIG. 8 is a block diagram showing the configuration of another embodiment of the present invention. 8 shows a level determination means 19 for determining the load level of the living body from the mapping progress data of the history storage means in the configuration of FIG. 1, and a display means 12 for displaying the determination result in the level determination means 19 and the contents of the history storage means 11. It consists of. Further, as in the case of the configuration of FIG. 1, an arrhythmia detection unit 20 is provided to detect an electrocardiogram turbulence, that is, an arrhythmia, in order to collect the electrocardiogram data more accurately as an electrocardiogram. Thus, the arrhythmia portion and the normal portion are divided and input to the classification means 4.
[0047]
  In the embodiment, the arrhythmia detection means 20 is provided between the processing means 3 and the classification means 4, but it is also possible to detect the arrhythmia directly from the electrocardiogram by increasing the accuracy of the arrhythmia detection means 20, In that case, it may be provided between the biological signal detection means 1 and the processing means 3.
[0048]
  Next, the operation will be explained. The electrocardiographic signal of the living body 2 is detected by the electrode 14 of the living body signal detecting means 1 and amplified by the amplifying unit 15 and then the data of the living body signal (electrocardiographic signal) detected by the living body signal detecting means 1 is obtained. Processing such as chaos signal processing is performed by the processing means 3, and data on the arrhythmia part and the normal pulse part is input to the classification means 4 by the arrhythmia detection means 20 from the data processed by the processing means 3. The normal pulse portion input to the classification unit 4 is classified into the first classification unit 6 according to the classification rule of the classification rule unit 5, and the data classified by the classification unit 4 is mapped to the second classification unit 9 according to the mapping rule of the mapping rule unit 8 Mapping is performed by means 7. The mapping method of the mapping means 7 can be changed by the mapping change means 10 using the data of the history storage means 11.
[0049]
  If the past and present data classified by the classification means 4 are stored in the history storage means 11 and a signal for the living body 2 is collected at a predetermined time (period), the biological data can be obtained from the mapping progress data of the history storage means 11 with respect to time. Is determined by the level determination means 19, and the display result of the determination result of the level determination means 19 and the contents of the history storage means 11 is displayed on the display means 12 to provide information.
[0050]
  As described above, the current health condition can be grasped by detecting and displaying the load received by the living body with time by the level determination means 19. If the time interval is determined according to a predetermined rule, the time data itself is not required.
[0051]
  In addition, by separating the arrhythmia part by the arrhythmia detection means 20 and inputting it to the classification means 4, it is possible to classify the health information related to the heart disease, and display a wider range of health information and disease information.
[0052]
  By the way, the arrhythmia detection means 20 may be performed at the same place as the processing means 3 so long as the arrhythmia portion is automatically recognized when the processing means 3 processes.
[0053]
  Next, another embodiment of the present invention will be described with reference to FIG. FIG. 9 shows a configuration diagram of the biological signal detecting means 1 for detecting the signal of the living body 2 as a pulse. Reference numeral 21 denotes a hand of the living body 2, which is installed so as to cover the optical sensor unit 24 when the finger 22 has a container 23 for containing alcohol. The optical sensor unit 24 is connected to the processing means 3 via the amplification unit 25. The other configuration is the same as that described above, but the mapping changing means 10 is not provided because it is specialized in alcohol detection.
[0054]
  Next, the operation in the above configuration will be described. This explains the application as a drinking checker that can grasp how much alcohol remains when drinking. That is, a person (biological body 2) grasps his / her health condition before drinking, detects the load caused by alcohol at the time during or after drinking, and whether the influence of alcohol remains or not enough You can see what you can drink.
[0055]
  Data of the biological signal (pulse signal) detected by the optical signal detection means 1 of the biological signal detection means 1 provided in the container 23 before the person 2 drinks alcohol and detected by the biological signal detection means 1 after amplification by the amplification section 25 The data processed by the processing unit 3 is classified into the first classification unit 6 according to the classification rule of the classification rule unit 5 of the classification unit 4.
[0056]
  The pulse attractor processed by the processing means 3 is input to the classification means 4, and the case where the classification method of the classification means 4 is classified and mapped by a method different from the embodiment of the present invention will be described (various classification methods are available). Can be considered.) That is, it is assumed that a distribution of digitized data obtained from many people's pulse attractors is stored, and the distribution is as shown in FIG. The horizontal axis indicates the rank for classification, and the vertical axis indicates the frequency. The alternate long and short dash line indicates the center of the distribution, and the vertical solid line L indicates the position of the biological data. A person who is weak in alcohol as a classification rank becomes weaker in the order of rank (-1) and rank (-2). When rank (0) is rank (1) and rank (2) as a standard, it indicates that it is strong against alcohol. This distribution shows that there is a large number of standard people with rank (0) (actually, it can be made more accurate by classifying it more finely, but here for the sake of simplicity, it is classified into five levels. I have to).
[0057]
  Now, let us consider a case where the classification rank of the person in the first measurement is as shown in FIGS. That is, FIG. 10A is a graph in which the classification means 4 classifies the values calculated by the processing means 3 when alcohol is not in the body, into the first classification unit 6. For example, this corresponds to the classification of the center of gravity position in FIG. It is displaced by α1 with respect to the standard. Similarly, FIG. 10 (b) shows the elliptical attractor written as an approximation to the width of the attractor in the case of FIG. 5 out of the values calculated by the processing means 3 when alcohol is not in the body. This is a graph classified into the first classification unit 6 by the classification means 4. This person's usual data is classified into a slightly lower rank (-1) than the standard. Β1 displacement relative to standard. Next, from the classification in the first classification unit 6 shown in FIGS. 10A and 10B, the mapping unit 7 uses the second classification unit to make the data standard data of the person as shown in FIG. 10C. 9 is mapped.
[0058]
  The rule of the mapping rule part 8 at this time is a rule that the first data should be stored as a standard value. At this time, the ratio normalized to the standard value (rank 0) with respect to the classification position (rank -1) in the first classification unit 6 is set as the rule of the mapping rule unit 8 from the next time. Here, two rules of α1 ratio and β1 ratio can be made for two items. Then, the distance γ of the ratio of α1 and β1 is set to γ = (K1 · α1 2 + K2 · β1 2) 0.5. Here, K1 and K2 are weighting coefficients. This completes normal data processing.
[0059]
  Next, the second data after a certain period of drinking alcohol will be described. The classification rank in the second measurement of this person is shown in FIG. 10 (d). Since the alcohol has entered the body, the gravity center value calculated by the processing means 3 is classified into the first classification unit 6 by the classification means 4 (vertical solid line L). ) It is classified into a lower rank (-1). The displacement is α2. When this displacement α2 is corrected with the original displacement, it becomes α3 and is classified into the position indicated by the dotted line. Similarly, FIG. 10 (e) classifies items corresponding to the width of the attractor in the classification means 4 into the first classification unit 6 among the values calculated by the processing means 3 after the alcohol enters the body. It is a graph. The displacement is β2. If this displacement β2 is corrected with the original displacement, it becomes β3 and is classified into the position indicated by the dotted line.
[0060]
  Next, in order to compare the classified data with the standard data of the person, the data is mapped to the second classification unit according to the rule of the mapping rule unit 8 previously obtained by the mapping means 7. That is, it is ranked at the position of γ2 by displacing by a displacement of γ1 obtained by calculation. If the classification is finely divided, it is classified into a position such as a solid line L at the position as shown in FIG.
[0061]
  Here, the level determination means 19 with respect to the standard value is checked for deviation, the alcohol influence degree is stored in the history storage means 11 from the value, and displayed on the display means 12, whereby the alcohol can be checked at the time of measurement.
[0062]
  In the configuration having the notification unit 24, if a rank abnormality threshold is provided for the data of the level determination unit 19, a warning can be issued from the notification unit 24 in the case of excessive drinking.
[0063]
  By the way, when comparing with the standard, it is possible to measure directly without mapping to the second classification unit 9 at the time of classification by the first classification unit 6. However, when the first classification unit 6 classifies a plurality of items, weights each item according to the evaluation method, and classifies them collectively, the second classification unit 9 is essential. Therefore, it is possible to accurately grasp the level of the living body by reclassifying from a plurality of items.
[0064]
  Next, when the optical sensor unit 25 is provided on the handle 28 of the automobile 27, the driving load level similar to the determination of the drunkenness level is detected. The configuration is the same except that the biological signal detection means 1 is installed at the handle 28 (the configuration in the drawing is omitted).
[0065]
  In the case of driving the automobile 27, for example, when the Lyapunov exponent is obtained by the processing means 3, the value decreases when a certain point of time has elapsed for a long driving. Therefore, the level determination means 19 determines the load level, and displays the information in the case where it does not move continuously in contact with the optical sensor unit 25 in addition to displaying “It is better to take a break”. It is also possible to make a doze determination by storing it in the memory.
[0066]
  Also in this case, if a rank abnormality threshold value is provided for the data of the level determining means 19, a warning can be issued by the notifying means 24 immediately before a drowsy driving.
[0067]
  Further, a body motion sensor 30 is provided as the biological signal detection means 1 in the bed 29, and the body motion level is classified by obtaining the attractor and Lyapunov exponent by the processing means 4 by detecting the electrocardiogram or respiration of the living body with the body motion sensor 30. In addition, it is possible to make a health judgment, an elderly person's presence / absence judgment, and a life / death judgment. The configuration and operation are the same as in the case of the above-described drunkness determination (the configuration in the drawing is omitted).
[0068]
  When the sleeping information is obtained from the signal from the body motion sensor 30, there is no problem in terms of function because there is less restriction on the calculation time even if processing is performed by the method for obtaining the Lyapunov exponent than the method for obtaining the area of the attractor. Think. Also, it is necessary to provide a mapping rule that matches the bed.
[0069]
  Furthermore, it can also be used for health determination in the housing equipment 31 such as a bath. When the biological signal detection means 1 is provided in the bathtub 31 of the bath and the bath 31 is immersed, it is possible to determine the load level or the relax level. If you take a bath with no special heat, your body will recover and the Lyapunov exponent will rise after bathing. This makes it possible to determine the degree of relaxation and display the degree of recovery.
[0070]
  The above drunk degree determination, driving load determination, dozing determination, life / death determination in bed, relaxation degree determination in bath, and determination of breathing degree in the toilet in conjunction with the sphygmomanometer, when listening to music It can be applied to various load judgments such as the degree of relaxation.
[0071]
  Next, an embodiment in which health-related health information corresponding to an individual can be provided will be described with reference to FIG.
[0072]
  FIG. 11 is a block diagram showing the configuration of another embodiment of the present invention. In FIG. 11, biological signal detection means 1 for detecting a biological signal, 32 is an input means for inputting personal information for personal identification of the detected living body detected by the biological signal detection means 1, 3 is a processing means, and 4 is a living body. Classification means for classifying the data detected by the signal detection means 1 into the first classification section 6 according to the classification rule of the classification rule section 5, and 4 is a first classification section according to the classification rule of the classification rule section 5 for the data processed by the processing means 3 6 is a mapping means for mapping the data classified by the classification means 4 to the second classification section 9 according to the mapping rule of the mapping rule section 8, and 10 is the past and current data classified by the classification means 4. Corresponding mapping change means for changing the mapping method of the mapping means 7 in response, 11 is a history storage means for storing the history of data classified by the classification means 4, 12 A display means for displaying the data stored in the history storage means 11.
[0073]
  The data input by the input means 32 is stored once in the history storage means 11, and then the mapping change means 10 is controlled using the data.
[0074]
  There is a key input unit 35 (not shown) having keys such as an alphabet key 33 (not shown) and a numeric keypad 34 (not shown) for inputting a personal identification code as the input means 32. There is a personal identification code input unit 36 that can input a personal identification code by inputting it through the key input unit 35. The input data is displayed on the display means 12.
[0075]
  Here, as shown in FIG. 3, the processing means 3 includes a reconstruction means 16 for reconstructing the biological signal detected by the biological signal detection means 1 in n dimensions, and a quantification means for digitizing the biological signal into a form that can be classified by the classification means 4. 17.
[0076]
  The operation in the above configuration will be described. The electrocardiographic signal of the living body 2 is detected by the electrode 14 of the living body signal detecting means 1, amplified by the amplifying unit 15, and processed by the processing means 3 after being amplified by the living body signal detecting means 1. The data processed by the processing unit 3 is classified into the first classification unit 6 according to the classification rule of the classification rule unit 5 of the classification unit 4. That is, the classification rule unit 5 has a classification rule based on a standard distribution, and is a rule for classifying where the digitized biological data is located with respect to the standard. The biological data is classified by the first classification unit 6. Next, the data classified by the classification means 4 is mapped to the second classification section 9 by the mapping means 7 according to the mapping rule of the mapping rule section 8. The data input by the input means 32, the past and current data classified by the classification means 4 and the data mapped by the mapping means 7 are stored in the history storage means 11, and according to the history number stored in the history storage means 11. The mapping method of the mapping means 7 is changed by the mapping changing means 10, the data of the classification means 4 is stored in the history storage means 11, and the stored data is displayed on the display means 12.
[0077]
  Here, the operation will be further described in detail. The part for obtaining the heartbeat waveform and processing it by the processing means 3 is the same as in the above-described embodiment of the invention. Further, the method of classification by the first classification unit 6 and part of the mapping are also as described above. Therefore, a description will be given of mapping from data.
[0078]
  The second classification by the mapping means 7 is to express the numbers in easy-to-understand words. For example, in the third classification in the first classification section 6, “You have physical strength and please try to maintain physical strength in the future.” (Class C), in Category 2, “You are a standard healthy body. Continue to improve your health and keep it current.” (Class B), in Category 1, “You are inferior. Please exercise gradually and try to increase your physical strength. ”(Class A) Three classifications A, B, and C are provided in the second classification unit 9. Then, the mapping unit 7 associates the first classification unit 6 along the second classification unit 9. Here, class 1 corresponds to class A, class 2 corresponds to class B, and class 3 corresponds to class C.
[0079]
  By the way, the data input to the personal identification code input unit 36 in the input means 32 can be used to determine whether or not this measurer is the first person. If it is the first measurement person, what is mapped to the second classification unit 9 as the first measurement is not changed by the mapping change means 10, that is, zero change is performed. However, when the personal identification code is input by the input unit 32 and the measurement is performed for the second and third times, the mapping changing unit 10 may change the data depending on the data. For example, in the case where the above-mentioned C category person is classified as category 1 both for the second time and the third time, both are changed to zero as C category. Next, it is assumed that the chaos data is classification 1 in the fourth measurement. At this time, it was changed by the mapping change means 10 to the classification of X classification “Did you measure in a resting state? Perhaps you are currently suffering from a cold. Take rest.” The sentence is displayed on the means 12.
[0080]
  In this way, classification is performed by recognizing the first measurement or the second measurement by the value detected by the biological signal detection means 1 and the input means 32. That is, by repeating the measurement, it is possible to display information corresponding to various states of the measurer from the past history and provide health information that matches the measurer.
[0081]
  In some cases, the input means 32 includes a personal identification code input unit 36 for inputting a personal identification code and a physical information input unit 37 for inputting personal physical information. The data of the personal identification code input unit 36 and the physical information input unit 16 are displayed on the display means 12.
[0082]
  In the above configuration, the operation is the same as the case where the personal identification code input unit 36 is provided. The data input to the personal identification code input unit 36 and the data input to the physical information input unit 16 in the input unit 32 can determine whether this measurer is the first person or what type of person it is. If it is the first measurer and a person with a standard figure, the mapping change means 10 does not change what is mapped to the second classification unit 9 as the first measurement, that is, zero change is performed.
[0083]
  On the other hand, in the body information input unit 16, the person who is small from the standard figure is shifted to the second classification part 9 for the mapping, so that only one classification is shifted to the one with the poor physical strength. With respect to the mapping, the mapping is changed by the mapping changing means 10 so that the mapping is shifted to the one where the physical strength is superior. From this state, when the personal identification code is further input by the input means 32 and the measurement becomes the second and third times, the mapping changing means 10 may change the data depending on the data. For example, in the case where the above-mentioned C class person is classified as class 3 both in the second time and the third time, both are changed to zero change as the C class. Next, it is assumed that the chaos data is classification 1 in the fourth measurement. At this time, it was changed by the mapping change means 10 to the classification of X classification “Did you measure in a resting state? Perhaps you are currently suffering from a cold. Take rest.” The sentence is displayed on the means 12.
[0084]
  After the values detected by the biological signal detection means 1 are classified into the first classification means 4 in this way, the mapping means 7 corresponds to the text, and the standard classification immediately after the device is used. By repeating the measurement, the display corresponding to various states of the measurer can be performed from the past history, and health information that matches the measurer can be provided.
[0085]
  Next, as another embodiment of the present invention, a personal identification code input unit 36 for inputting a personal identification code and a life information input unit 17 for inputting personal life information are provided as input means 32. . The data of the personal identification code input unit 36 and the life information input unit 38 are displayed on the display means 12.
[0086]
  Life information input unit 38 has become a disturbed life, such as lack of sleep from standard lifeWaitFor those who are in the second classification unit 9, the classification is shifted to one that is inferior in physical strength, and for those who are in the rest state from the standard lifestyle in the life information input unit 38, the second classification unit As for the mapping to 9, the mapping is changed by the mapping changing means 10 so as to shift the classification to the one where the physical strength is superior.
[0087]
  By the way, as the life information, the time measuring means 39 may be provided, and the measurement date and time is automatically input at the time of measurement, and the information may be input for comparison with standard data corresponding to the life rhythm. Moreover, it is good also as an input for making it contrast with an individual's standard life pattern.
[0088]
  In this state, when the personal identification code is further input by the input unit 32 and the measurement is performed for the second time and the third time, the mapping changing unit 10 may change the data depending on the data. For example, in the case where the above-mentioned C class person is classified as class 3 both in the second time and the third time, both are changed to zero change as the C class. Next, it is assumed that the chaos data is classification 1 in the fourth measurement. At this time, the mapping was changed to the classification of X classification "Did you measure in a resting state? You probably have a cold now. Take a rest without disturbing your life." The text is changed by the means 10 and the sentence is displayed on the display means 12.
[0089]
  After the values detected by the biological signal detection means 1 are classified in the classification means 4 in this way, the mapping means 7 corresponds to the text, and the measurement corresponding to the standard classification immediately after using the device is measured. By repeating the above, it is possible to display information corresponding to various states of the measurer from the past history and provide health information that matches the measurer.
[0090]
  By the way, although it demonstrated by the structure input to the log | history memory | storage means 11 as an input method of the input means 32, as shown in FIG. 12, it treats with the data processed with the process means 3, and is classified into the classification | category means 4 as one of several data. You may make it input. When viewed from the classification unit 4, the data of the input unit 32 is handled as a part of the data of the substantial processing unit 3, so that the operation is the same as the configuration without the input unit 32.
[0091]
【The invention's effect】
  As described above, the health information providing apparatus of the present inventionNextIt has an effect like
[0092]
  (1) Although raw data alone cannot be judged by ordinary people, mapping information to words that can be understood by ordinary people enables even amateur people to receive health information.
[0093]
  (2) Since correction by correction or learning is added according to the number of data histories, the information to be provided becomes information that matches the information to be provided. In other words, when it is used for the first time, the standard classification is performed and the health information provided in the past is provided. This is a smart health information provision device.
[0094]
  (3) By reconstructing the biological signal, the data can be easily classified.
[0095]
  For example, the health state of the living body which is based on the balance between the exchangeable nerve and the auxiliary exchange nerve of the living body is directly viewed by the chaotic signal processing, which is optimal for providing health information on the healthy state.
[Brief description of the drawings]
FIG. 1 is a block diagram showing a configuration of a health information providing apparatus according to an embodiment of the present invention.
FIG. 2 is a block diagram showing a configuration of a biological signal detection unit of the health information providing apparatus according to the embodiment.
FIG. 3 is a block diagram showing the configuration of processing means in the embodiment
FIG. 4 is a diagram showing an example of data detected by a biological signal detection unit in the same embodiment
FIG. 5 is a view showing an example of data obtained by processing each heartbeat waveform by the processing means in the embodiment;
FIG. 6 is a diagram showing an example of data processed by the processing means in the embodiment
FIG. 7 is a block diagram showing the configuration of processing means in the embodiment
FIG. 8 is a block diagram showing a configuration of a health information providing apparatus according to another embodiment of the present invention.
FIG. 9 is a block diagram showing a configuration of a biological signal detection unit in the embodiment.
FIG. 10A is a graph in which values calculated by the processing means when alcohol is not in the body are classified into the first classification unit by the classification means.
  (B) A graph in which values calculated by the processing means when alcohol is not in the body are classified into the first classification unit by the classification means
  (C) Graph classified into the second classification unit by each mapping means from the classification of the first classification unit shown in (a) and (b)
  (D) A graph in which the value calculated by the processing means when alcohol is in the body is classified into the first classification by the classification means
  (E) A graph in which the value calculated by the processing means when alcohol is in the body is classified into the first classification by the classification means
  (F) Graph classified into the second classification unit by each mapping means from the classification of the first classification unit shown in (d) and (e)
FIG. 11 is a block diagram showing a configuration of a health information providing apparatus according to another embodiment of the present invention.
FIG. 12 is a block diagram showing a configuration of a health information providing apparatus according to another embodiment of the present invention.
FIG. 13 is a block diagram showing a configuration of a classification method according to an embodiment of a conventional invention.
[Explanation of symbols]
  1 Biological signal detection means
  3 Processing means
  4 classification means
  7 Mapping means
  10 Mapping change means
  11 History storage means
  12 Display means
  16 Reconfiguration means
  17 Digitization means
  18 Frequency analysis means
  19 Level judgment means
  20 Arrhythmia detection means
  23 containers
  24 detector
  25 Notification means
  27 Handle
  28 Dozing detection unit
  29 Bedding
  31 Housing equipment
  32 Input means
  36 Personal identification code input section
  37 Physical information input part
  38 Living information input section

Claims (3)

Processing means for processing data obtained from biological information detecting means or equipment having biological data, classification means for classifying data processed by the processing means, and mapping the data classified by the classification means to other classifications and mapping means, history storage means for storing a history of data of said classification means, and determining the level decision means for loading levels of biogenic from the mapping course data of the history storage means, the contents of the history memory means and the Display means for displaying the current biological load level determination result by the level determination means, and detecting the load received by the living body over time by the level determination means and displaying the load on the display means by the display means. Health information provision device that can grasp the situation . Processing means for processing data obtained from biological information detecting means or equipment having biological data, classification means for classifying data processed by the processing means, and mapping the data classified by the classification means to other classifications Mapping means, mapping changing means for changing mapping displacement when data is mapped to another classification, history storage means for storing data history of the classification means, and mapping progress data of the history storage means Level determining means for determining the load level of the living body, and display means for displaying the contents of the history storage means and the current biological load level determination result by the determining means, and the living body receives over time by the level determining means. By grasping the current load and displaying it on the display means, the current health condition is grasped Doo health information providing device that can. The processing means digitizes the biological signal of the biological information detection means or the device having the biological data so that the reconstruction means for reconstructing the data in n dimensions and the classification means can classify the reconstructed data in the n dimensions The health information providing apparatus according to claim 1, wherein the health information providing apparatus includes a digitizing unit.

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