JP4641811B2 - Mental immunity measuring device - Google Patents

Description

  The present invention relates to a technique for performing chaos analysis on acquired biological information and measuring mental immunity such as communication and dementia.

  In order to determine whether or not the patient has dementia, a method is used in which a doctor asks a patient a prepared question and the degree of dementia is determined based on the content of the patient's answer. As such, for example, the Hasegawa simple intelligence evaluation scale is well known.

  Patent Document 1 discloses a computer system for supporting certification of long-term care. It is a system that can input answers to questions, etc., on a radar chart.

JP2001-142979

  However, in the determination based on the answers to the above questions, there is a problem that an objective determination cannot be made depending on the knowledge and experience of the person making the determination.

  In addition, there is also a problem that it takes time to make a decision because it is a format of answering a question.

  SUMMARY OF THE INVENTION An object of the present invention is to solve the above-mentioned problems and to provide a device capable of objectively determining mental immunity without relying on the ability of a judge quickly and with less difficulty. .

(1) The communication capability determination device according to the present invention receives biological information measured by a biological information measuring instrument that measures biological information, and provides biological information for a first predetermined time as time delay τ and dimension n. Attractor group constituting means for constructing a time series attractor group in which the attractors are shifted for a second predetermined time shorter than the first predetermined time , and for each attractor constituting the time series attractor group, a Lyapunov exponent of each dimension is calculated. A Lyapunov index calculating means for calculating a representative Lyapunov index representing the Lyapunov index of each dimension; a representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov index based on the time-series representative Lyapunov index for the time series attractor group And determining means for determining communication ability based on the calculated characteristic value.

The time series attractor group is configured from the biological information, and the communication power is determined based on the standard deviation of the Lyapunov exponent of each attractor of the time series attractor group . Therefore, it is possible to make a quick and objective determination.

(2) A communication capability determination auxiliary device according to the present invention receives biological information measured by a biological information measuring instrument that measures biological information, and receives biological information for a first predetermined time as time delay τ and dimension n. Attractor group constituting means for constituting a time-series attractor group in which the attractor is shifted for a second predetermined time shorter than the first predetermined time, and for each attractor constituting the time-series attractor group, a Lyapunov exponent of each dimension is calculated. Lyapunov exponent calculation means for calculating a representative Lyapunov exponent representing the Lyapunov exponent of each dimension, and representative characteristic value calculation for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group Means, for each representative Lyapunov exponent of the calculated time series, the value of each representative Lyapunov exponent is replaced with an angle , And an output means for outputting a vector continuous in time sequence.

  The time series attractor group is configured from the biological information, and the Lyapunov exponent of each attractor of the time series attractor group is output as a vector. As a result, the fluctuation of the characteristic value of the Lyapunov exponent of each attractor in the time series attractor group can be known.

(4) A communication capability display system according to the present invention is a communication capability display system including a terminal device and a server device capable of communicating with the terminal device, wherein the terminal device is a living body from a biological information measuring instrument. A transmission unit configured to transmit information to the server device; a reception unit configured to receive display data from the server device; and a display unit configured to display the received display data. Receiving means for receiving the measured biological information, and receiving the received biological information, the time delay τ and the dimension n are the second time shorter than the first predetermined time for the biological information for the first predetermined time. Attractor group constituting means for constituting a time series attractor group shifted for a predetermined time, and for each attractor constituting the time series attractor group, a Lyapunov finger of each dimension Lyapunov exponent calculation means for calculating a representative Lyapunov exponent representing the Lyapunov exponent of each dimension, a representative characteristic for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group For the calculated time-series representative Lyapunov exponent, the value of each representative Lyapunov exponent is replaced with an angle, and a display data generating unit that generates a vector continuous in time-series order as display data, the generated display data Is provided.

  The time series attractor group is configured from the biological information, and the communication power is determined based on the standard deviation of the Lyapunov exponent of each attractor of the time series attractor group. Therefore, it is possible to make a quick and objective determination.

  “Mental immunity” is a concept including adaptability to the external environment and intellectual functions.

  In the embodiment, the “attractor constituting unit” corresponds to step S9 in FIG.

  In the embodiment, “Lyapunov exponent calculation means” corresponds to steps S10 and S11 in FIG.

  In the embodiment, “representative characteristic value calculation means” corresponds to step S16 in FIG.

  In the embodiment, “determination means” corresponds to step S17 in FIG.

  In the embodiment, the “display data generation unit” corresponds to the constellation graph generation unit 50 and step S89 in FIG.

  “Display data for clearly displaying at least the average value of the representative Lyapunov index” is data that can indirectly and directly indicate the average value, and displays not only the average value itself but also a constellation graph. This is a concept that includes data and the like.

  The “program” is a concept that includes not only a program that can be directly executed by the CPU, but also a source format program, a compressed program, an encrypted program, and the like.

BEST MODE FOR CARRYING OUT THE INVENTION

1. First Embodiment FIG. 1 is a functional block diagram of a mental immunity determination device according to an embodiment of the present invention. The biological information measuring instrument 2 acquires the biological information of the subject person. The attractor constituting unit 4 constitutes an n-dimensional chaotic attractor based on the acquired biological information. The Lyapunov exponent calculation means 8 calculates a Lyapunov exponent based on the configured n chaos attractor, and calculates a representative Lyapunov exponent representing the Lyapunov exponent of each dimension. The representative characteristic value calculating means 10 calculates the characteristic value of the representative Lyapunov exponent based on the time series of the representative Lyapunov exponent. The determination unit 12 determines the mental immunity of the subject based on the calculated characteristic value. In this way, the mental immunity of the subject can be determined based on the biological information of the subject. In this embodiment, the Lyapunov exponent calculation means 8 and the representative characteristic value calculation means 10 constitute the characteristic value calculation means 6.

  FIG. 2 shows a hardware configuration when the mental immunity determination apparatus of FIG. 1 is realized using a CPU. In this embodiment, a case where communication power and dementia level are determined as the mental immunity level will be described. The finger plethysmograph 16 as a biological information measuring instrument includes a cuff 14 for wearing on a finger. The structure of the cuff 14 is shown in FIG. Infrared rays are emitted from the light emitting element 36. The reflected light is incident on the light receiving element 38. The intensity of the reflected light represents the blood flow rate. Therefore, the signal output from the light receiving element 38 is a fingertip volume pulse wave. The finger plethysmograph 16 outputs the signal from the light receiving element 38 as digital data. Data from the fingertip pulse wave measuring instrument 16 can be taken into the CPU 20 via the I / O port 18.

  FIG. 4 shows an example of the finger plethysmogram output from the finger plethysmograph 16. Although it is actually digital data, it is shown as a waveform in the figure.

  A memory 22, a printer 24, a display 26, a hard disk 28, a keyboard / mouse 34, and a CD-ROM drive 40 are connected to the CPU 20. The hard disk 28 stores an operating system (such as WINDOWS (trademark) of Microsoft Corporation) 30, an analysis program 32, a communication ability table 35, and a dementia degree table 37. The analysis program 32 performs its function in cooperation with the operating system 30. The analysis program 32 recorded in the CD-ROM 42 is installed in the hard disk 28 via the CD-ROM drive 40.

  FIG. 5 shows a flowchart of the analysis program 32. The CPU 20 sets i to “0” in step S1. Next, “1” is added to i to set i to “1” (step S2). The CPU 20 takes in the output from the finger plethysmograph 16 and records it on the hard disk 28 (step S3). In this embodiment, data for 3 minutes (36000 points of data) is recorded. In another embodiment, data longer than 3 minutes may be recorded, or data shorter than 3 minutes may be recorded.

  When the finger plethysmogram data for 3 minutes is recorded, the CPU 20 determines whether i = 3 (step S4). Since i = 1 here, step S2 and subsequent steps are executed again. That is, with i = 2, finger plethysmogram data for 3 minutes is recorded.

  In this way, the CPU 20 records the fingertip pulse wave data for three times on the hard disk 28. FIG. 8A, FIG. 8B, and FIG. 8C show the recorded fingertip pulse wave data for three times. 8A is the first finger plethysmogram data, FIG. 8B is the second finger plethysmogram data, and FIG. 8C is the third finger plethysmogram data.

When the recording for three times is completed (when i = 3), the CPU 20 sets i = 0 and j = 0 (step S5). Subsequently, i = 1 and j = 1 are set (steps S6, S).
7) For the first fingertip pulse wave data, the first block is set as the target block (step S8). In this embodiment, as shown in FIG. 8A, 3500 points of data from the top are used as the first block B1.

  The CPU 20 reconfigures the chaos attractor for the finger plethysmogram data of the target block with the embedding dimension as n and the embedding delay as τ according to the Takens embedding theorem (step S9). FIG. 9 shows a procedure for configuring a chaos attractor from fingertip pulse wave data. Time series fingertip pulse wave data is set to w (t) (FIG. 9A). Based on the fingertip pulse wave data, the CPU 20 generates vectors P (i) = w (i), w (i + τ), and w (i + 2τ) (see FIG. 9A). For explanation, a three-dimensional vector is used. Here, τ is an embedding delay.

  This vector P (i) is sequentially plotted in the three-dimensional reconstruction phase space as shown in FIG. 9B. The coordinate axes of this three-dimensional reconstruction phase space are Xi = w (i), Yi = (i + τ), Zi = (i + 2τ). In this way, an attractor as shown in FIG. 9C can be obtained.

  In this embodiment, the embedding dimension n is 4 and the embedding delay τ is 10 points (10 sampling points). The embedding dimension n and the embedding delay τ may be other values. The CPU 20 records the attractor (vector P (i)) calculated in this way on the hard disk 28.

Next, the CPU 20 calculates a Lyapunov exponent for each calculated dimension of the attractor (step S10). The Lyapunov exponent is a scale that measures how far two orbits {x _n } starting from two adjacent points move away from n → infinity for a dynamical system of x _{n + 1} = f (x _n ). is there. The CPU 20 calculates the Lyapunov exponent for each dimension according to the following equation.

  The CPU 20 sets the largest Lyapunov exponent of each of the four dimensions calculated based on the above formula as the representative value as the maximum Lyapunov exponent λ (i, j) (step S11). In this way, the maximum Lyapunov exponent λ (1, 1) is obtained for the data of the first block (j = 1) of the fingertip pulse wave of the first measurement (i = 1). The CPU 20 records this maximum Lyapunov exponent λ (1, 1) on the hard disk 8.

  Next, the CPU 20 determines whether or not the maximum Lyapunov exponent has been calculated for all the blocks of the finger plethysmogram of the first measurement (step S12). If there is an unprocessed block, the process returns to step S7 and "1" is added to j. Here, j = 2. Therefore, the second block is set as a target block (step S8), and the processes in and after step S9 are repeated.

  In this embodiment, as shown in FIG. 8A, the second block B2 has the same number of points (3500 sample points) as the first block B1, and is shifted by 200 sample points. The CPU 20 calculates the maximum Lyapunov exponent λ (1,2) for the second block B 2 and records it on the hard disk 28.

  When the above processing is repeated and the maximum Lyapunov exponent is calculated for all the blocks in the first fingertip pulse wave (step S12), is i = 3 (that is, whether the processing has been completed for all three measured pulse waves) It is determined whether or not (step S13). Here, since i = 1, the process returns to step S6, i = 2 is set, and step S7 and subsequent steps are repeatedly executed for the second fingertip pulse wave (FIG. 8B). Thus, the maximum Lyapunov exponent λ (2,1)... Λ (2, k) of each block can be calculated and recorded for the second fingertip pulse wave.

  Similarly, when the maximum Lyapunov exponent λ (3, 1)... Λ (3, k) is recorded for the third fingertip pulse wave, the CPU 20 proceeds from step S13 to step S14.

  In step S14, first, the maximum Lyapunov exponents λ (1,1), λ (2,1), λ (3,1) of the first block of the first to third fingertip pulse waves are stored in the hard disk. 28, and the average weighted λ (1) by offset weighting is calculated.

  In this embodiment, Weightedλ (1) is calculated as follows. First, the CPU 20 calculates a difference DEF between the maximum value and the minimum value among the maximum Lyapunov exponents λ (1,1), λ (2,1), and λ (3,1) of the first block. Further, an average value M of the maximum Lyapunov exponents λ (1,1), λ (2,1), λ (3,1) of the first block is calculated. When the difference DEF is smaller than the average value M, the average value M is used as Weightedλ (1). On the other hand, when the difference DEF is not smaller than the average value M, the median value of the maximum Lyapunov exponents λ (1,1), λ (2,1), λ (3,1) is used as Weightedλ (1).

  Next, the maximum Lyapunov exponents λ (1,2), λ (2,2), λ (3,2) of the second block are read from the hard disk 28, and its Weighted λ (2) is calculated. The CPU 20 repeats this and calculates Weightedλ of all blocks.

  Next, the CPU 20 displays the finger plethysmogram, the maximum Lyapunov exponent, the attractor and the like recorded on the hard disk 28 on the display 26 (step S15). Examples of the display are shown in FIGS. FIG. 10 shows the first finger plethysmogram, the maximum Lyapunov exponents λ (1,1) to λ (k, 1) arranged in time series, the attractor, and the like. In this embodiment, a four-dimensional attractor is displayed by vertical, horizontal, height, and color.

  FIG. 11 shows the second fingertip pulse wave, the maximum Lyapunov exponents λ (1,2) to λ (k, 2) arranged in time series, the attractor, and the like. Although not shown, the third fingertip pulse wave, the maximum Lyapunov exponents λ (1,3) to λ (k, 3) arranged in time series, the attractor, and the like are also displayed.

  Next, the CPU 20 calculates the standard deviation of the Lyapunov exponent Weightedλ (1) to Weightedλ (k) (step S16). Further, the CPU 20 refers to the communication power table 35 and the dementia degree table 37 of the hard disk 28, and determines the communication power and the dementia degree based on the calculated standard deviation (step S17).

  FIG. 12 shows an example of the communication power table 35. The rank indicates the degree of communication power. Rank a is “completely communicated”, rank b is “communication to some extent”, and rank c is “almost not communicated”. If the standard deviation exceeds 1.198, it is determined to be rank a, if it is 1.198 to 1.05, it is rank b, and if it is less than 1.05, it is determined to be rank c.

  FIG. 13 shows an example of the dementia degree table 37. The rank indicates the degree of dementia, and the larger the value, the more dementia is progressing. Rank 0 is “no dementia”, rank 1 is “mild”, rank 2 is “moderate”, rank 3 is severe, and rank 4 is “most severe”. If the standard deviation exceeds 1.254, it is rank 0, if it is 1.254 to 1.157, it is rank 1, if it is 1.157 to 1.12, it is rank 2, 1.12. If it is ˜0.964, it is determined to be rank 3, and if it is less than 0.964, it is determined to be rank 4.

  The CPU 20 displays the determination result on the display 26 (step S18). In this way, the communication ability and the degree of dementia can be determined quickly and objectively.

  The communication power table 35 and the dementia degree table 37 are found to be related to the standard deviation of the weighted average Weighted λ of the Lyapunov exponent based on experiments and investigations conducted by the inventors. It was obtained based on what was done.

  FIG. 14 and FIG. 16 show the investigation results of communication ability and standard deviation performed by the inventor. From the results of this experiment / survey, it is clear that the communication ability can be determined based on the standard deviation of Weighted λ of Lyapunov exponent.

  In the communication power table 35 in FIG. 12, the first value (FIG. 12) is an intermediate value between the average of the standard deviation of the person of rank a and the average of the standard deviation of the person of rank b. 1.198), and an intermediate value between the average of the standard deviation of the person of rank b and the average of the standard deviation of the person of rank c is the second value (1.05 in FIG. 12), and the standard deviation is Rank a is greater than 1 value, rank b if the standard deviation is between the first value and the second value, and rank c if the standard deviation is less than the second value. In this embodiment, the intermediate value is the first value and the second value, but values other than the intermediate value may be adopted.

  17 and 19 show the survey results of the dementia degree and standard deviation performed by the inventors. From this experiment / survey result, it can be seen that the degree of dementia can be judged based on the standard deviation of Weighted λ of Lyapunov exponent.

  In the dementia degree table 37 of FIG. 13, an intermediate value between the average of the standard deviation of rank 0 people and the average of the standard deviation of rank 1 people obtained by the experimental survey is a first value (in FIG. 13). 1.254), and an intermediate value between the average of the standard deviation of the person of rank 1 and the average of the standard deviation of the person of rank 2 is set to the second value (1.157 in FIG. 13). If the standard deviation is greater than the first value, rank 0, and if the standard deviation is between the first value and the second value, rank 1 and the standard deviation are the second value and third. Rank 2 if the value is between the values, rank 3 if the standard deviation is between the third value and the fourth value, and rank 4 if the standard deviation is less than the fourth value. In this embodiment, the intermediate value is the first value and the second value, but values other than the intermediate value may be adopted.

  In the above embodiment, the communication power and the degree of dementia are determined using the standard deviation of Weighted λ of the Lyapunov exponent. However, according to the inventor's experiment and investigation, as shown in FIGS. 15 and 16, a relationship is found between the average value of the Lyapunov exponent Weighted λ and the communication power. Similarly, as shown in FIGS. 18 and 19, a relationship is found between the average value of Weighted λ of the Lyapunov exponent and the degree of dementia. Therefore, the communication power table and the dementia degree table can be generated in the same manner as the standard deviation, and the communication power and the dementia degree can be determined based on the average value of Weighted λ of the Lyapunov exponent.

  In addition, according to the experiment of the inventor, even if the average value of the Lyapunov exponent is high, if the state where the standard deviation (fluctuation) is small continues for a long time, it indicates that the subject's high tension state continues. It turns out. In many cases, it has been found that the average value of the Lyapunov exponent is small and the standard deviation is small. Therefore, the CPU 20 can determine the mental immunity of the subject based on both the average value and the standard deviation (fluctuation) of the Lyapunov exponent.

  In the above embodiment, the communication ability and the degree of dementia are determined and output based on the standard deviation and average value, but the standard deviation and average value may be output. In addition, the figure of the attractor may be output, and the operator may make a determination based on the figure.

  In the above-described embodiment, the determination result or the like is output by being displayed on the display. However, the determination result may be printed and output by a printer or the like. Alternatively, the determination result or the like may be output as data to a recording medium or the like.

  Note that the values of Lyapunov exponents Weightedλ (1) to Weightedλ (k) may be converted into angles, and the locus of k vectors may be output as a constellation graph. The CPU 20 calculates angles ξ1 to ξk corresponding to values of Lyapunov exponents Weightedλ (1) to Weightedλ (k). In this embodiment, the angle ξ is increased as the weighted λ increases. Next, as shown in FIG. 20, the CPU 20 draws a vector with an angle ξ1 corresponding to the value of Weightedλ (1) with the origin O as the base point. Furthermore, the vector is drawn with an angle ξ2 corresponding to the value of Weighted λ (2) with the tip of this vector as a base point. By repeating this, a vector is drawn up to an angle ξk corresponding to the value of Weightedλ (k). Note that the length of each vector is the same regardless of the value of Weightedλ.

  FIG. 21 shows the constellation graph drawn in this way. The trajectory marked with a in the figure is a person with communication power a, the trajectory marked with a sign in FIG. And is clearly distinguishable. Therefore, if the communication force is ranked in advance according to the region where the locus reaches, the operator can easily determine the communication force by looking at the constellation graph.

  FIG. 22 shows the relationship with the degree of dementia for the same constellation graph. As with communication, the degree of dementia can be clearly distinguished. Therefore, if the degree of dementia is ranked in advance according to the region where the locus reaches, the operator can easily determine the degree of dementia by looking at this constellation graph. As described above, when the constellation graph is used, the average value of the Lyapunov exponent and the fluctuation (corresponding to the standard deviation) can be displayed simultaneously.

  In the above embodiment, the correspondence between the value of Weighted λ and the angle is determined in advance, and the angle of each vector is determined according to this. However, when comparing a plurality of subjects, the weighted λ (1) to (k) of each subject is set to 180 degrees with the maximum value and 0 degrees with the minimum value, and the maximum The angle may be determined based on the ratio between the weighted λ having the value of λ and the weighted λ having the minimum value. That is, the angle ξij of each vector may be determined by the following equation.

ξij = 180 * (λij − λmin) / (λmax − λmin)
In addition, i is a block number and is 1 to k, and j is 1 to m indicating a target person (in the case of m people). λij is Weightedλ of the block i of the subject j. λmax is the maximum value of all blocks of all subjects, and λmin is the minimum value of all blocks of all subjects.

  When comparing a plurality of persons, the constellation graph area can be used effectively by determining the angle as described above.

  In the above embodiment, the blood flow at the fingertip is measured as the biological information. However, blood flow may be measured from other parts such as the earlobe. Further, as biometric information, not only blood flow such as a finger vein group but also an electrocardiographic waveform, a respiration rate, and the like may be used. In addition, information obtained by measuring vibration emitted from the body using a piezoelectric sensor or the like may be used.

  In the above embodiment, the maximum Lyapunov exponent in each dimension is used as the representative Lyapunov exponent. However, the Lyapunov exponent of any one dimension may be used as the representative Lyapunov exponent. The average of the Lyapunov exponents in each dimension may be used as the representative Lyapunov exponent.

  In the above embodiment, the Lyapunov exponent is calculated based on a four-dimensional attractor. However, the Lyapunov exponent may be calculated based on attractors having three dimensions or less and five dimensions or more.

  In the above embodiment, the standard deviation or average is used as the characteristic value of the Lyapunov exponent, but other characteristic values such as a maximum value and a minimum value may be used.

  In the above embodiment, the finger vein group is measured three times, but only one measurement may be performed. In this case, it is not necessary to calculate Weighted λ, and the maximum Lyapunov exponent can be used as it is. Moreover, you may make it measure a finger plethysmogram twice or less 4 times or more.

  In the above, Weightedλ with offset weighting is used, but other average values such as a simple average may be used.

  In the above-described embodiment, the apparatus is realized by a single computer. However, the apparatus may be realized by a plurality of computers such as a computer that acquires and records biometric information and a computer that performs determination processing. In this case, the data exchange between computers can use not only online communication via the Internet, LAN, etc., but also data exchange using a recording medium.

Note that the first embodiment and its modifications can also be applied to the second embodiment described below.

2. Second Embodiment FIG. 23 shows a functional block diagram of a mental immunity determination system according to another embodiment of the present invention. In this example, the system includes the biological information measuring instrument 2, the mobile phone 58, and the server device 60 that can communicate with the mobile phone 58. The biological information measured by the biological information measuring instrument 2 is transmitted to the server device 60 by the transmission unit 3 of the mobile phone 58. The biological information measuring instrument 2 and the mobile phone 58 may be connected online or may transmit biological information data via a recording medium.

  The receiving means 5 of the server device 60 receives biological information from the mobile phone 58. The attractor constituting unit 4 constitutes a time-series attractor based on the biological information. The Lyapunov exponent calculation means 8 calculates a time-series Lyapunov exponent based on the attractor. The constellation graph generation means 50 generates a constellation graph by converting the time-series Lyapunov exponent into an angle. The transmission means 52 transmits the generated constellation graph data to the mobile phone 58.

  The receiving means 54 of the mobile phone 58 receives constellation graph data. The display unit 56 displays a constellation graph based on the received constellation graph data.

  FIG. 24 shows a schematic configuration of this system. As in the first embodiment, the finger plethysmograph 16 as a biological information measuring instrument includes a cuff 14 for wearing on a finger. Note that the function of the finger plethysmograph 16 may be incorporated in the mobile phone 58. The mobile phone 58 and the server device 60 can communicate with each other via the Internet 62.

  FIG. 25 shows a hardware configuration of the mobile phone 58. In the figure, functional parts necessary for a call are omitted. The fingertip pulse wave measuring instrument 16, the display 86, the memory 82, the numeric keypad 35, and the communication circuit 37 are connected to the CPU 80 via the I / O port 18. The communication circuit 37 is a circuit for connecting to the Internet 62. The numeric keypad 35 is used by the user for input. The memory 82 stores a browser program and a processing program for connecting to the server device 60 and displaying information from the server device 60. The display 86 is for performing display.

  FIG. 26 shows a hardware configuration of the server device 60. A memory 22, a communication circuit 25, a display 26, a hard disk 28, a keyboard / mouse 34, and a CD-ROM drive 40 are connected to the CPU 20. An operating system (such as WINDOWS (trademark) from Microsoft) 30 and an analysis program 30 are recorded on the hard disk 28. The analysis program 32 performs its function in cooperation with the operating system 30. The analysis program 32 recorded in the CD-ROM 42 is installed in the hard disk 28 via the CD-ROM drive 40. The communication circuit 25 is a circuit for connecting to the Internet.

  27 and 28 show a flowchart of the browser program / processing program of the mobile phone 58 and a flowchart of the analysis program 32 of the server device 60. When the user measures the finger plethysmogram with the finger plethysmograph 16, the CPU 80 captures the finger plethysmogram data and records it in the memory 82 (step S 51). Subsequently, the CPU 80 transmits the pulse wave data to the server device 60 via the communication circuit 37 (step S52).

  The CPU 20 of the server device 60 records the pulse wave data received via the communication circuit 25 on the hard disk 28 (step S81). The CPU 20 executes steps S82 to S88 for the recorded pulse wave data, and calculates a time-series maximum Lyapunov exponent. The processes of steps S82 to S88 are the same as steps S6 to S12 of the first embodiment. However, the embodiment of FIG. 27 is different in that the maximum Lyapunov exponent is calculated only for one fingertip pulse wave. Therefore, the weighted λ with offset weighting is not calculated as in the first embodiment, and the subsequent processing is performed using the maximum Lyapunov exponent as it is.

  In step S89, the CPU 20 generates a constellation graph based on the calculated time-series maximum Lyapunov exponent λ (j).

  The CPU 20 calculates angles ξ1 to ξk corresponding to the values of the maximum Lyapunov exponents λ (1) to λ (k). In this embodiment, the angle ξ is increased as λ increases. Next, as shown in FIG. 20, the CPU 20 draws a vector at an angle ξ1 corresponding to the value of λ (1) with the origin O as a base point. Further, the vector is drawn with an angle ξ2 corresponding to the value of λ (2) with the tip of the vector as a base point. By repeating this, a vector is drawn up to an angle ξk corresponding to the value of λ (k). Note that the length of each vector is the same regardless of the value of λ.

  The constellation graph chart generated in this way is shown in FIG. The drawing area of the graph is shown in three areas A, B, and C, for example, and a constellation graph 105 is displayed thereon. Mental immunity is higher in the order of areas A, B, and C. The CPU 20 transmits data of this constellation graph chart to the mobile phone 58 via the communication circuit 25.

  The CPU 80 of the mobile phone 58 receives this via the communication circuit 37 (step S53) and displays it on the display 86 (step S54). Thereby, the user can see a constellation graph chart as shown in FIG. The mental immunity can be determined depending on which region the constellation graph 105 is located. Further, the magnitude of the fluctuation can be known from the degree of shaking of the constellation graph 105. Further, when the fluctuation is small, the constellation graph becomes linear, and the constellation graph reaches the outer radius line 300. When the fluctuation is large, the zigzag of the constellation graph increases and does not reach the radius line 300. Therefore, the degree of approach to the radius line 300 can also be used as one of the indexes.

In the above embodiment, the mobile phone 28 is a terminal device, such as a PDA or a PC, can be used as a terminal device if a device that can connect to the Internet.

  Further, a constellation graph is recorded on the server device side in association with the biometric data acquisition date for each user, and as shown in FIG. 30, not only the current constellation graph 105a but also the past constellation graph 105b are recorded as a history. You may make it show as. At this time, as shown in the figure, it is preferable to display the measurement date in the vicinity of each constellation graph. By showing the history in this way, the user can know the change in the mental immunity.

  In this embodiment, the measurement is performed only once. However, as in the first embodiment, the measurement may be performed a plurality of times, and Weightedλ may be calculated to perform the processing.

  In this embodiment, the constellation graph is transmitted to the mobile phone 28 for display. However, instead of (or in addition to) the constellation graph, the mental immunity calculated in the first embodiment is transmitted. May be displayed.

  In the first and second embodiments, the determination of the communication ability and the degree of dementia is performed, but psychological aspects such as other mental immunity such as movement, meal, excretion, bathing, change of clothes, and adjustment. A similar determination can be made for data.

It is a functional block diagram of the mental immunity determination apparatus by one Embodiment of this invention. 2 is a hardware configuration when the mental immunity determination apparatus of FIG. 1 is realized using a CPU. It is a figure which shows the sensor part of the fingertip pulse measuring instrument. It is an example of the finger pulsation group measured by the finger pulsation group measuring device 16. It is a flowchart of an analysis program. It is a flowchart of an analysis program. It is a flowchart of an analysis program. It is an example of the finger vein group from the 1st measurement to the 3rd measurement. It is a figure which shows the structure process of an attractor. This is a display screen for finger veins, time series Lyapunov exponents, attractors, etc. This is a display screen for finger veins, time series Lyapunov exponents, attractors, etc. It is a figure which shows the example of a communication ability table. It is a figure which shows the example of a dementia degree table. This is a result of an experimental investigation on the relationship between the standard deviation of Lyapunov exponent and communication ability. This is a result of an experimental investigation on the relationship between the standard deviation of Lyapunov exponent and communication ability. FIG. 14 summarizes the data of FIGS. 14 and 15. This is a result of an experimental investigation of the relationship between the Lyapunov exponent average and communication ability. This is a result of an experimental investigation of the relationship between the Lyapunov exponent average and communication ability. The data of FIG. 17 and FIG. 18 are collected. It is a figure explaining the process which draws a constellation graph. It is an example of a constellation graph. It is an example of a constellation graph. It is a functional block diagram of the system by a 2nd embodiment. It is a structural example of the system of FIG. This is a hardware configuration of the mobile phone 28. This is a hardware configuration of the server device 60. 4 is a flowchart showing processing of a mobile phone 28 and a server device 60. 4 is a flowchart showing processing of a mobile phone 28 and a server device 60. It is an example of a constellation graph. It is an example of a constellation graph.

Explanation of symbols

DESCRIPTION OF SYMBOLS 2 ... Biological information measuring device 4 ... Attractor structure means 8 ... Lyapunov exponent calculation means 10 ... Representative characteristic value calculation means 12 ... Determination means 12

Claims (6)

The biological information measured by the biological information measuring instrument for measuring the biological information is received, and the second time shorter than the first predetermined time is set as the attractor for the biological information for the first predetermined time as time delay τ and dimension n . Attractor group constituting means for constituting a time series attractor group shifted by a predetermined time ,
For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
Determination means for determining communication ability based on the calculated characteristic value;
Communication ability judging device with   The biological information measured by the biological information measuring instrument for measuring the biological information is received, and the second time shorter than the first predetermined time is set as the attractor for the biological information for the first predetermined time as time delay τ and dimension n. Attractor group constituting means for constituting a time series attractor group shifted by a predetermined time,
  For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
  Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
  For the calculated time-series representative Lyapunov exponents, output means for replacing each representative Lyapunov exponent value with an angle, and outputting as a vector continuous in time-series order,
  A communication ability judgment assisting device equipped with.   A communication capability display system comprising a terminal device and a server device capable of communicating with the terminal device,
  The terminal device
  Transmitting means for transmitting biological information from the biological information measuring instrument to the server device;
  Receiving means for receiving display data from the server device;
  A display for displaying the received display data,
With
  The server device
  Receiving means for receiving biological information measured by a biological information measuring instrument for measuring biological information;
  In response to the received biometric information, the time delay τ and the dimension n are used as a time series attractor group in which the attractor is shifted for a second predetermined time shorter than the first predetermined time with respect to the first predetermined time of biometric information. Attractor group configuring means to configure,
  For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
  Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
  With respect to the calculated time series representative Lyapunov exponent, the value of each representative Lyapunov exponent is replaced with an angle, and display data generation means for generating a vector that is continuous in time series as display data,
  Transmitting means for transmitting the generated display data;
Communication capacity display system equipped with.   A server device capable of communicating with a terminal device,
  Receiving means for receiving biological information from the terminal device;
  The biological information measured by the biological information measuring instrument for measuring the biological information is received, and the second time shorter than the first predetermined time is set as the attractor for the biological information for the first predetermined time as time delay τ and dimension n. Attractor group constituting means for constituting a time series attractor group shifted by a predetermined time,
  For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
  Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
  With respect to the calculated time series representative Lyapunov exponent, the value of each representative Lyapunov exponent is replaced with an angle, and display data generation means for generating a vector that is continuous in time series as display data,
  Transmitting means for transmitting the generated display data to the terminal device;
A server device comprising:   A program for causing a computer to function as a communication ability judging device,
  The biological information measured by the biological information measuring instrument for measuring the biological information is received, and the second time shorter than the first predetermined time is set as the attractor for the biological information for the first predetermined time as time delay τ and dimension n. Attractor group constituting means for constituting a time series attractor group shifted by a predetermined time,
  For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
  Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
  Determination means for determining communication ability based on the calculated characteristic value;
  A program for realizing this with a computer.   A program for causing a computer to function as a communication ability determination auxiliary device,
  The biological information measured by the biological information measuring instrument for measuring the biological information is received, and the second time shorter than the first predetermined time is set as the attractor for the biological information for the first predetermined time as time delay τ and dimension n. Attractor group constituting means for constituting a time series attractor group shifted by a predetermined time,
  For each attractor constituting the time series attractor group, the Lyapunov exponent for each dimension is calculated, and the Lyapunov exponent calculation means for calculating the representative Lyapunov exponent representing the Lyapunov exponent for each dimension,
  Representative characteristic value calculating means for calculating a standard deviation of the representative Lyapunov exponent based on the time-series representative Lyapunov exponent for the time series attractor group,
  For the calculated time-series representative Lyapunov exponents, output means for replacing each representative Lyapunov exponent value with an angle, and outputting as a vector continuous in time-series order,
  A program for realizing this with a computer.

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