Mind / body estimation indicator, calculation method for mind / body estimation indicator, mind / body estimation method, and mind / body estimation device

Abstract

A mind / body estimation indicator according to the present invention is a parameter value PV that makes it possible to obtain a model function MF that, for the heart rate HR(t) of a subject during every unit time period (t), approximates the relationship between the heart rate HR(t-1) during a first unit time period (t-1) during which the subject is exercising and a change ΔHR(t) in the heart rate that is the difference between the heart rate HR(t-1) and the heart rate HR(t) during a second unt time period (t) subsequent to the first unit time period (t-1) during which the subject is at rest.

Description

Mind-body estimation index, mind-body estimation index calculation method, mind-body estimation method, and mind-body estimation device

[0001] The present disclosure relates to a mind-body estimation index, a method for calculating a mind-body estimation index, a mind-body estimation method, and a mind-body estimation device.

[0002] One of the objectives of the information processing system described in Patent Document 1 is to understand the frailty state of a subject. To achieve this objective, the information processing system generates recovery index information that indicates how the subject is recovering from the response to the occurrence of exercise intensity, based on information on changes in biological data (particularly heart rate and blood pressure) from a moving state to a resting state.

[0003] Patent No. 7065550

[0004] However, in the above-mentioned Patent Document 1, it is not clear how specifically it is desirable to generate the above-mentioned recovery index information using, for example, the heart rate, which is one of the biological data of the subject. In other words, there is a problem that it is not always possible to obtain reliable recovery index information.

[0005] An object of the present disclosure is to provide an index for estimating a subject's physical and mental recovery that is more reliable than conventional indices, particularly an index that takes into account individual differences between subjects and that can be used for movements that the subject does not actually perform.

[0006] In order to solve the above-mentioned problems, the mind-body estimation index according to the present disclosure is a parameter value that realizes a model function that approximates the relationship between the heart rates of a subject for each unit time, the amount of change in heart rate being the difference between the heart rate during a first unit time when the subject is exercising and the heart rate during a second unit time following the first unit time when the subject is at rest, and the amount of change in heart rate.

[0007] According to the mind-body estimation index of the present disclosure, it is possible to provide an index that is more reliable than conventional ones for estimating the subject's mind-body recovery.

[0008] The mind-body estimation system SSS of embodiment 1 will be described. The configuration of the wearable device WT of embodiment 1 is shown. The configuration of the mind-body estimation device SS of embodiment 1 is shown. A flowchart (basic) showing the operation of the mind-body estimation system SSS of embodiment 1. A flowchart (detailed) showing the operation of the mind-body estimation system SSS of embodiment 1. Mathematical formulas for showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 1) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 2) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 3) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 4) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 5) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 6) showing the operation of the mind-body estimation system SSS of embodiment 1. A diagram (part 7) showing the operation of the mind-body estimation system SSS of embodiment 1. FIG. 8 is a diagram (part 8) showing the operation of the mind-body estimation system SSS of embodiment 1. FIG. 9 is a diagram (part 9) showing the operation of the mind-body estimation system SSS of embodiment 1. FIG. 10 is a flowchart (details) showing the operation of the mind-body estimation system SSS of embodiment 2. FIG. 11 is a diagram (part 1) showing the operation of the mind-body estimation system SSS of embodiment 2. FIG. 12 is a diagram (part 2) showing the operation of the mind-body estimation system SSS of embodiment 2. FIG. 13 is a diagram (part 3) showing the operation of the mind-body estimation system SSS of embodiment 2. FIG. 14 is a diagram (part 1) showing the operation of embodiment 3. FIG. 15 is a diagram (part 2) showing the operation of embodiment 3. FIG. 16 shows the hardware configuration of the mind-body estimation system SSS of embodiments 1 to 3. FIG. 17 shows the hardware configuration based on the software realization of the mind-body estimation system SSS of embodiments 1 to 3.

[0009] An embodiment of a mind-body estimation system according to the present disclosure will be described.

[0010] First Embodiment A mind-body estimation system SSS according to a first embodiment will be described.

[0011] <Configuration of First Embodiment> The mind-body estimation system SSS of the first embodiment includes wearable devices WT1 to WTm (m is an integer of 2 or more) and a mind-body estimation device SS, as shown in Fig. 1. The wearable devices WT1 to WTm and the mind-body estimation device SS are connected to each other via a network NW (for example, the Internet) as shown in Fig. 1.

[0012] In the following, to facilitate explanation and understanding, for example, the same name and multiple symbols may be collectively referred to as one name and one symbol, and for example, wearable terminals WT1 to WTm may be collectively referred to as wearable terminal WT.

[0013] The wearable device WT is equipped with a function for measuring, for example, the heart rate HR (e.g., as shown in FIG. 7) of the subject HI, and is attachable to the arm, head, etc. The wearable devices WT1 to WTm are used, for example, by subjects HI1 to HIm, who are subjects for whom mental and physical recovery (particularly recovery of cardiac function) is to be estimated; for example, the wearable device WT1 is used by the subject HI1, the wearable device WT2 is used by the subject HI2, and so on, and the wearable device WTm is used by the subject HIm.

[0014] The mind-body estimation device SS is used by an administrator KA who manages the mind-body estimation device SS.

[0015] <Configuration of Wearable Terminal WT> FIG. 2 shows the configuration of the wearable terminal WT according to the first embodiment.

[0016] As shown in FIG. 2, the wearable terminal WT of the first embodiment includes an input / output unit NS(WT), a processing unit SY(WT), a storage unit KI(WT), and a communication unit TU(WT).

[0017] The input / output unit NS (WT) includes a conventionally known sensor or the like, and acquires, for example, the heart rate HR (for example, as shown in FIG. 7) of the subject HI.

[0018] The processing unit SY (WT) performs, for example, processing related to the heart rate HR of the subject HI.

[0019] The storage unit KI (WT) stores, for example, data necessary for processing by the processing unit SY (WT).

[0020] The communication unit TU (WT) communicates via the network NW (shown in FIG. 1), and transmits, for example, the heart rate HR of the subject HI to the mind-body estimation device SS.

[0021] <Configuration of Mind-Body Estimation Apparatus SS> FIG. 3 shows the configuration of the mind-body estimation apparatus SS of the first embodiment.

[0022] As shown in FIG. 3, the mind-body estimation device SS of the first embodiment has an input / output unit NS(SS), a processing unit SY(SS), a memory unit KI(SS), and a communication unit TU(SS).

[0023] The input / output unit NS (SS) is used by the administrator KA to, for example, input data for controlling the operation of the mind-body estimation device SS, and to output data for monitoring the operation.

[0024] The processing unit SY (SS) performs, for example, processing related to estimation of the mind and body of the subject HI.

[0025] The storage unit KI(SS) stores, for example, data necessary for processing by the processing unit SY(SS).

[0026] The communication unit TU (SS) communicates via the network NW, and receives, for example, the heart rate HR of the subject HI from the wearable device WT.

[0027] <Operation of First Embodiment> The operation of the mind-body estimation system SSS of the first embodiment will be described.

[0028] FIG. 4 is a flowchart (basic) showing the operation of the mind-body estimation system SSS of the first embodiment.

[0029] FIG. 5 is a flowchart (details) showing the operation of the mind-body estimation system SSS of the first embodiment.

[0030] FIG. 6 shows mathematical expressions for illustrating the operation of the mind-body estimation system SSS of the first embodiment.

[0031] 7 to 15 are diagrams showing the operation of the mind-body estimation system SSS of the first embodiment.

[0032] The operation of the mind-body estimation system SSS of the first embodiment will be described with reference to FIGS.

[0033] <Basic Operation of Mind-Body Estimation System SSS> The basic operation of the mind-body estimation system SSS of the first embodiment will be described mainly with reference to FIG.

[0034] In the following, for ease of explanation and understanding, it is assumed that the subject whose mind and body is to be estimated is the subject HI1 among the subjects HI1 to HIm (shown in FIG. 1).

[0035] Step S1: The wearable device WT1 (shown in FIGS. 1 and 2) of the subject HI1 acquires the heart rate HR of the subject HI1 for a predetermined period (e.g., 1 minute, 3 minutes, or 5 minutes). The wearable device WT1 transmits the acquired heart rate HR to the mind-body estimation device SS (shown in FIGS. 1 and 3) via the network NW.

[0036] When the mind-body estimation device SS receives the heart rate HR from the wearable device WT1, it calculates the heart rate HR(t) (shown in FIG. 7) which is the heart rate HR per unit time (t) (for example, one minute).

[0037] As a result, the mind-body estimation device SS calculates, for example, heart rate HR(20:59), which is the heart rate HR at 20:59 (1 minute), heart rate HR(21:00), ..., heart rate HR(21:54), which is the heart rate HR at 21:54 (1 minute), and heart rate HR(21:55)(21:55), which is the heart rate HR at 21:55 (1 minute), as shown in Figure 7.

[0038] Step S2: The mind-body estimation device SS calculates a heart rate change ΔHR(t), which is the difference in the heart rate HR(t) of the subject HI1 between adjacent unit times. More specifically, as shown in Figure 8, the mind-body estimation device SS calculates a heart rate change ΔHR(t), which is the difference between the heart rate HR(t-1) of the subject HI1 during a first unit time (t-1) when the subject HI1 is exercising (for example, low-intensity exercise such as walking that the subject HI1 can actually perform), and the heart rate HR(t) during a second unit time (t) following the first unit time (t-1) when the subject HI1 is at rest.

[0039] As a result, as shown in Figure 8, the mind-body estimation device SS calculates a heart rate change amount ΔHR(12:01) = -15 times, which is the difference between the heart rate HR(12:00), which is the heart rate at 12:00 (corresponding to (t-1)), and the heart rate HR(12:01), which is the heart rate at 12:01 (corresponding to t); and similarly, calculates a heart rate change amount ΔHR(16:31) = -40 times, which is the difference between the heart rate HR(16:30), which is the heart rate at 16:30 (corresponding to (t-1)), and the heart rate HR(16:31), which is the heart rate at 16:31 (corresponding to t).

[0040] Here, the distinction between "when exercising" (i.e., when subject HI1 is in the first state) and "when at rest" (i.e., when subject HI1 is in the second state) is merely subjective, in other words, it is not uniquely determined numerically, for example, on the conventionally known METs.

[0041] Step S3: The mind-body estimation device SS approximates the relationship between the heart rate HR(t-1) and the heart rate change ΔHR(t) using a model function MF. Here, the model function MF corresponds to, for example, the curve equation KH (shown in FIGS. 6 and 17) or the line equation CH (shown in FIGS. 6 and 17).

[0042] When analyzing the relationship between the heart rate HR(t-1) and the amount of change in heart rate ΔHR(t) according to the distribution law "t distribution," the curve equation KH and linear equation CH shown in FIG. 6 are used. On the other hand, when analyzing according to the distribution law "Shifted-Wald distribution" (hereinafter referred to as "SW distribution"), the curve equation KH and linear equation CH (shown in FIG. 17) are used.

[0043] Here, "Y" in the distribution BP(t-1) (shown in FIGS. 6 and 17) corresponds to the amount of change in heart rate ΔHR(t).

[0044] The subscript "i" in the distribution BP(t-1), curve equation KH, straight line equation CH, etc. (shown in Figures 6 and 17) indicates "1 to m" for subjects HI1 to HIm, and the subscript "j" indicates the jth subject of the same subject HI (for example, "1j" indicates the jth subject for subject HI1).

[0045] <t distribution> Regarding the distribution BP(t-1) (shown in FIG. 6), ｉ、ｊ " indicates the expected value, "σ" indicates the variation, and "ν" indicates the degree of freedom (the degree to which outliers are not tolerated). ｉ、ｊ " is expressed by a curve equation KH (shown in FIG. 6), and "z ｉ、ｊ " is expressed by the linear equation CH (shown in FIG. 6).

[0046] <SW distribution> Regarding the distribution BP(t-1) (shown in FIG. 17), ｉ、ｊ ” indicates a skewness of the distribution towards positive values, and “α ｉ、ｊ " indicates the position of the peak in the distribution, and "θ ｉ、ｊ " indicates the lower limit (lower limit value) of the possible values. "θ ｉ、ｊ " is expressed by a curve equation KH (shown in FIG. 17), and "z ｉ、ｊ " is expressed by the linear equation CH (shown in FIG. 17).

[0047] In addition, "γ ｉ、ｊ " is expressed by the associated equation 1 (shown in FIG. 17), and "α ｉ、ｊ is expressed by the associated equation 2 (shown in FIG. 17).

[0048] Returning to FIG. 4 and FIG. 8, the description will continue.

[0049] In step S3, the mind-body estimation device SS approximates the relationship between, for example, the heart rate HR(12:00)=80 beats / minute ("12:00" corresponds to (t-1); shown in FIG. 8) and the amount of change in heart rate ΔHR(12:01)=-15 beats ("12:01" corresponds to t; shown in FIG. 8) using a model function MF (for example, the curve equation KH and the linear equation CH shown in FIG. 6). Similarly, the mind-body estimation device SS approximates the relationship between, for example, the heart rate HR(16:30)=90 beats / minute ("16:30" corresponds to (t-1); shown in FIG. 8) and the amount of change in heart rate ΔHR(16:31)=-40 beats ("16:31" corresponds to t; shown in FIG. 8) using a model function MF (same as above).

[0050] Step 4: The mind-body estimation device SS acquires parameter values ​​PV that realize the model function MF. More specifically, the mind-body estimation device SS acquires parameter values ​​PV (for example, the slope b in the line equation CH) that realize the curve equation KH and the line equation CH (shown in FIGS. 6 and 17) described above. １ｊ (shown in Figures 6 and 17)

[0051] <Operation (Details) of Mind-Body Estimation System SSS> The operation (details) of the mind-body estimation system SSS of the first embodiment will be described mainly with reference to FIG.

[0052] The mind-body estimation system SSS of the first embodiment "approximates the relationship between the heart rate HR(t-1) and the amount of change in heart rate ΔHR(t) with a curve equation KH" and "sets parameter values ​​(for example, the slope b) that reduce the error between the relationship and the curve equation KH overall." １ｊ ) by a Bayesian estimation method or the like. For the sake of convenience in explanation and understanding, for example, with reference to FIG. 10, a somewhat detailed discussion of whether or not a necessary and sufficient number of heart rates HR(t-1) exist for the same heart rate HR(t-1) will be omitted.

[0053] FIG. 5 shows the operation of the mind-body estimation system SSS of the first embodiment in more detail than FIG.

[0054] Regarding the correspondence between FIG. 5 and FIG. 4, steps S30A to S30E (shown in FIG. 5) are details of step S3 (shown in FIG. 4), and step S40A (shown in FIG. 5) is details of step S4 (shown in FIG. 4).

[0055] In the following, for ease of explanation and understanding, it is assumed that the subject whose mind and body is to be estimated is the subject HI1 out of the subjects HI1 to HIm (shown in FIG. 1), as described above.

[0056] Step S10: As in step S1, the mind-body estimation device SS calculates the heart rate HR(t) (shown in FIG. 7) which is the heart rate HR per unit time (t) (for example, one minute).

[0057] Step S20: As in step S2, the mind-body estimation device SS calculates the heart rate change ΔHR(t), which is the difference between the heart rate HR(t-1) within the first unit time (t-1) and the heart rate HR(t) within the second unit time (t), for subject HI1, as shown in Figure 8.

[0058] Step S30A: The mind-body estimation device SS assigns the relationship between the heart rate HR(t-1) and the heart rate change ΔHR(t) to a two-dimensional coordinate space 2ZK, as shown in Fig. 9. Here, "assigning" means to plot, or to draw, mark, or place a point.

[0059] More specifically, as shown or suggested in Figures 8 and 9, the mind-body estimation device SS assigns a relationship in a two-dimensional coordinate space 2ZK between, for example, heart rate HR(12:00) = 80 beats / minute ("12:00" corresponds to (t-1)) and heart rate change amount ΔHR(12:01) = -15 beats ("12:01" corresponds to t), and similarly assigns a relationship between, for example, heart rate HR(16:30) = 90 beats / minute ("16:30" corresponds to (t-1)) and heart rate change amount ΔHR(16:31) = -40 beats ("16:31" corresponds to t).

[0060] Step S30B: The mind-body estimation device SS creates a distribution BP(t-1) of the heart rate change ΔHR(t) for each heart rate HR(t-1) in the two-dimensional coordinate space 2ZK. The mind-body estimation device SS creates this distribution in accordance with the "t-distribution" (shown in FIG. 6).

[0061] More specifically, as shown in Figure 10, the mind-body estimation device SS creates a distribution BP(t-1) = 90 beats / min in a two-dimensional coordinate space 2ZK, for example, for a heart rate HR(t-1) = 90 beats / min, for five heart rate changes ΔHR(t) (shown by ●) corresponding to the heart rate HR(t-1) = 90 beats / min, i.e., heart rate changes ΔHR(t) = -41, -38, -37, -36, -32, by following the distribution rule "t distribution." Similarly, the mind-body estimation device SS creates a distribution BP(t-1) = 45 beats / min, 46 beats / min, . . . , 99 beats / min, 100 beats / min in the two-dimensional coordinate space 2ZK for other heart rates HR(t-1) = 45 beats / min, 46 beats / min, . . . , 99 beats / min, 100 beats / min by following the distribution law "t-distribution" as shown in Figure 10.

[0062] Step S30C: As shown in FIG. 11, the mind-body estimation device SS acquires the expected value μ(t-1) (shown by ●) in the two-dimensional coordinate space 2ZK based on the distribution BP(t-1) (shown in FIG. 10).

[0063] More specifically, as shown in Figure 11, the mind-body estimation device SS acquires the expected value μ(t-1) (shown by ●) for the distribution BP(t-1) = 90 times / minute in a two-dimensional coordinate space 2ZK, and similarly acquires the expected value μ(t-1) (shown by multiple ●) for the distribution BP(t-1) = 45 times / minute, 46 times / minute, ..., 99 times / minute, and 100 times / minute.

[0064] Step S30D: As shown in Figure 12, in a two-dimensional coordinate space 2ZK, the mind-body estimation device SS creates a virtual curve KK (shown by a dotted line) that optimally passes through the multiple expected values ​​μ(t-1) (in other words, at a position as close as possible to the position of each expected value μ(t-1)) based on the expected value μ(t-1) for each heart rate HR(t-1), in other words, multiple expected values ​​μ(t-1) (shown by multiple ●).

[0065] Step S30E: As shown in FIG. 13, the mind-body estimation device SS approximates the curve equation KH (shown in FIG. 6, shown in solid line) to the virtual curve KK (shown in dotted line) in the two-dimensional coordinate space 2ZK.

[0066] Step S40A: The mind-body estimation device SS calculates a gradient b １ｊ More specifically, the mind-body estimation device SS obtains the gradient b １ｊ is obtained using, for example, the least squares method, the maximum likelihood estimation method, the EM algorithm, the Bayesian estimation method, the variational Bayes method, the gradient descent method, the back propagation method, or the like.

[0067] Here, the slope b shown in FIGS. １ｊ is an example for ease of explanation and understanding, in other words, it has no relation whatsoever to the curve drawn by the virtual curve KK shown in Figures 14 and 15 (including tangents at each point on the curve, etc.).

[0068] Effect of First Embodiment As described above, in the mind-body estimation system SSS of the first embodiment, the slope b of the straight line equation CH (shown in FIG. 6) that realizes the curve equation KH (shown in FIG. 6, shown in solid line) that is approximated to the virtual curve KK (shown in dotted line) that appropriately passes through a plurality of "expected values ​​μ(t-1)" in a plurality of distributions BP(t-1) that follow the distribution law "t distribution" and that indicate the relationship between the heart rate HR(t-1) and the amount of change in heart rate ΔHR(t) is calculated. １ｊ More specifically, as an index for estimating the physical and mental recovery of the subject HI1, a slope b １ｊ can be obtained.

[0069] In the mind-body estimation system SSS of the first embodiment, in addition to the above effects, the above gradient b １ｊ can be obtained while taking into consideration the individual differences unique to the subject HI1, and the above slope b １ｊcan be obtained as an index that can be used even when it is assumed that subject HI1 performs another exercise (e.g., high-intensity exercise such as swimming) that is different from the exercise (such as the walking described above) and that subject HI1 would not actually perform or would not actually be able to perform.

[0070] Second Embodiment A mind-body estimation system SSS according to a second embodiment will be described.

[0071] <Configuration of Second Embodiment> The mind-body estimation system SSS of the second embodiment has the same configuration as the mind-body estimation system SSS of the first embodiment (shown in FIGS. 1 to 3).

[0072] <Operation of Embodiment 2> The basic operation of the mind-body estimation system SSS of embodiment 2 is similar to the basic operation of the mind-body estimation system SSS of embodiment 1 (shown in FIG. 4). However, the detailed operation of the mind-body estimation system SSS of embodiment 2 differs from the detailed operation of the mind-body estimation system SSS of embodiment 1 (shown in FIG. 5). Unlike embodiment 1, which uses an "expected value μ" based on the distribution law "t distribution," embodiment 2 uses a "lower limit θ" based on the distribution law "SW distribution." This difference is clear from a comparison between FIG. 15 of embodiment 1 and FIG. 21 of embodiment 2. In embodiment 1, as shown in FIG. 15, the curve equation KH passes "approximately through the center" of the area showing the relationship between the amount of change in heart rate ΔHR(t) and the heart rate HR(t-1) (the area marked with a black circle in the figure), whereas in embodiment 2, as shown in FIG. 21, the curve equation KH passes "approximately through the lower limit" of the area.

[0073] FIG. 16 is a flowchart (details) showing the operation of the mind-body estimation system SSS of the second embodiment.

[0074] 17 to 21 are diagrams showing the operation of the mind-body estimation system SSS of the second embodiment.

[0075] The operation (details) of the mind-body estimation system SSS of the second embodiment will be described mainly with reference to FIG.

[0076] The mind-body estimation system SSS of embodiment 2, like the mind-body estimation system SSS of embodiment 1, essentially "approximates the relationship between the heart rate HR(t-1) and the amount of change in heart rate ΔHR(t) with a curve equation KH," and thereby "determines, by Bayesian estimation or the like, parameter values ​​(e.g., slope b1j) that minimize the error between the relationship and the curve equation KH overall." As with embodiment 1, for the sake of ease of explanation and understanding, a discussion that may seem to be somewhat detailed, such as whether or not a necessary and sufficient number of heart rates HR(t-1) exist for the same heart rate HR(t-1), will be omitted, for example, with reference to FIG. 18.

[0077] In the following, for ease of explanation and understanding, it is assumed that the subject whose mind and body is to be estimated is the subject HI1 among the subjects HI1 to HIm (shown in FIG. 1), as in the first embodiment.

[0078] Prior to step S30F: The mind-body estimation device SS performs operations similar to steps S10, S20, and S30A of embodiment 1. Specifically, the mind-body estimation device SS first calculates a heart rate HR(t), which is the heart rate HR per unit time (t) (e.g., one minute), as shown in FIG. 7 , as in step S10. The mind-body estimation device SS then calculates a heart rate change ΔHR(t), which is the difference between the heart rate HR(t-1) within a first unit time (t-1) and the heart rate HR(t) within a second unit time (t), as shown in FIG. 8 , as in step S20. Finally, the mind-body estimation device SS maps the relationship between the heart rate HR(t-1) and the heart rate change ΔHR(t) to a two-dimensional coordinate space 2ZK, as shown in FIG. 9 , as in step S30A.

[0079] Step S30F: As in step S30B of embodiment 1, mind-body estimation device SS creates a distribution BP(t-1) of heart rate change ΔHR(t) for each heart rate HR(t-1) in two-dimensional coordinate space 2ZK. Unlike step S30B of embodiment 1, which follows the distribution rule "t-distribution," mind-body estimation device SS creates this distribution according to the distribution rule "SW-distribution" as shown in FIG. 18. Even when using the distribution rule "SW-distribution," mind-body estimation device SS creates a distribution BP(t-1)=45 beats / min, 46 beats / min, 99 beats / min, and 100 beats / min, as shown in FIG. 18, in the same way as in embodiment 1 (shown in FIG. 10) which uses the distribution rule "t-distribution."

[0080] Step S30G: The mind-body estimation device SS acquires a lower limit value θ(t-1) in the two-dimensional coordinate space 2ZK based on the distribution BP(t-1) (shown in FIG. 10) in the same manner as in step S30C in embodiment 1, but differs from step S30C in embodiment 1 in that it acquires a lower limit value θ(t-1). As a result, as shown in FIG. 19, the mind-body estimation device SS acquires lower limit values ​​θ(t-1) (shown by multiple ●) for the distribution BP(t-1)=45 times / minute, 46 times / minute, . . . , 99 times / minute, and 100 times / minute.

[0081] Step S30H: As in step S30D of embodiment 1, the mind-body estimation device SS creates a virtual curve KK (shown by underlining) that optimally passes through multiple lower limit values ​​θ(t-1) (shown by multiple ●) in a two-dimensional coordinate space 2ZK, as shown in Figure 20.

[0082] After step S30H: The mind-body estimation device SS performs the same operations as steps S30E and S40A in embodiment 1. More specifically, as in step S30E, the mind-body estimation device SS first approximates a curve equation KH (shown in FIG. 17, shown by a solid line) to a virtual curve KK (shown by an underline) in a two-dimensional coordinate space 2ZK as shown in FIG. 13. Next, as in step S40A, the mind-body estimation device SS calculates a slope b １ｊ (shown in Figures 17, 20 and 21).

[0083] <Effects of the Second Embodiment> As described above, in the mind-body estimation system SSS of the second embodiment, even if the "lower limit value θ" based on the distribution rule "SW distribution" is used, the slope b of the straight line equation CH (shown in FIG. 17) that realizes the curve equation KH (shown in FIG. 17) can be calculated in the same way as in the first embodiment, which uses the "expected value μ" based on the distribution rule "t distribution." １ｊ More specifically, a slope b that is more reliable than conventional recovery index information (described in Patent Document 1) can be obtained as an index for estimating the physical and mental recovery of the subject HI1. １ｊ can be obtained.

[0084] In addition to the above-mentioned effects, the mind-body estimation system SSS of the second embodiment has the same effect as the mind-body estimation system SSS of the first embodiment, in which the gradient b １ｊ can be obtained while taking into consideration the individual differences unique to the subject HI1, and the above slope b １ｊ can be obtained as an index that can be used even when it is assumed that subject HI1 performs another exercise (e.g., high-intensity exercise such as swimming) that is different from the exercise (such as the walking described above) and that subject HI1 would not actually perform or would not actually be able to perform.

[0085] Supplementary Explanation of First and Second Embodiments In the first and second embodiments, the distribution laws are "t distribution" and "SW distribution." Instead of following the "t distribution" and "SW distribution," other distribution laws (for example, normal distribution (Gaussian distribution) and exponentially modified Gaussian distribution (ex-Gaussian distribution)) may be followed.

[0086] In the first and second embodiments, as described above, the slope b １ｊ The gradient b １ｊ Instead of obtaining the above, for example, the fluctuation range A in the curve equation KH (shown in FIGS. 6 and 17) may be obtained.

[0087] Third Embodiment A mind-body estimation system SSS according to a third embodiment will be described.

[0088] <Configuration of Third Embodiment> The mind-body estimation system SSS of the third embodiment has the same configuration as the mind-body estimation system SSS of the first embodiment (shown in FIGS. 1 to 3).

[0089] <Operation of Third Embodiment> FIGS. 22 and 23 are diagrams showing the operation of the third embodiment.

[0090] As shown in FIG. 22, the mind-body estimation system SSS of the third embodiment estimates the gradient b １ｊ (For example, as shown in FIGS. 6, 15, 17, and 21), and more specifically, for example, for "subject HI1", the slope b １ｊ This makes it possible to estimate changes over time in the physical and mental recovery of subject HI1 (for example, whether the progress of recovery is stable or unstable, or whether the progress of recovery is gradually speeding up or gradually slowing down).

[0091] In contrast to the above, the mind-body estimation system SSS of the third embodiment simultaneously calculates the gradient b １ｊ More specifically, for example, for "Subject HI1", "Subject HI2", and "Subject HI3", the slope b １ｊ This makes it possible to estimate individual differences in the physical and mental recovery of subjects HI1, HI2, and HI3 (for example, whether the progress of recovery among the three subjects is similar, or whether only subject HI1 is recovering slowly, or whether only subject HI3 is recovering quickly).

[0092] <Hardware Configuration of the Embodiments> FIG. 24 shows the hardware configuration of the mind-body estimation system SSS of the first to third embodiments.

[0093] To perform the above-described functions, the mind-body estimation system SSS of the first to third embodiments includes a processing circuit SYO, as shown in FIG. 24, and may further include an input circuit NYU and an output circuit SYU as necessary.

[0094] The processing circuit SYO is dedicated hardware that realizes the functions of the wearable terminal WT and the processing units SY(WT) and SY(SS) of the mind-body estimation device SS (shown in FIGS. 2 and 3).

[0095] The processing circuit SYO is, for example, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof.

[0096] The input circuit NYU and the output circuit SYU exchange inputs and outputs related to the operation of the processing circuit SYO with, for example, the wearable terminal WT and the outside of the mind-body estimation device SS.

[0097] <Hardware Configuration Based on Software Realization of the Embodiments> FIG. 25 shows the hardware configuration based on software realization of the mind-body estimation system SSS of the first to third embodiments.

[0098] As shown in FIG. 25, the mind-body estimation system SSS of the first to third embodiments includes a processor PRO and a memory circuit KIO, and may further include an input circuit NYU and an output circuit SYU as required.

[0099] The processor PRO is a CPU (also called a central processing unit, processing device, arithmetic unit, microprocessor, microcomputer, or DSP (Digital Signal Processing)) that executes programs. The processor PRO realizes the functions of the wearable terminal WT and the processing unit SY(WT) and processing unit SY(SS) of the mind-body estimation device SS (shown in FIGS. 2 and 3).

[0100] The processor PRO realizes the above-mentioned functions by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in the memory circuit KIO.

[0101] The processor PRO realizes the above-mentioned functions by reading and executing the above-mentioned programs from the memory circuit KIO. The above-mentioned programs can also be said to cause a computer to execute the procedures and methods of the wearable terminal WT and the processing unit SY(WT) and processing unit SY(SS) of the mind-body estimation device SS.

[0102] Here, the memory circuit KIO is, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), etc., as well as a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc), etc.

[0103] Of the functions of the wearable terminal WT, the processing unit SY(WT) of the mind-body estimation device SS, and the processing unit SY(SS), some of the functions may be realized by a processing circuit SYO (shown in Figure 24), while other functions may be realized by a processor PRO (shown in Figure 25).

[0104] As described above, the functions of the wearable terminal WT, the processing unit SY(WT) of the mind-body estimation device SS, and the processing unit SY(SS) can be realized by hardware, software, firmware, or a combination of these.

[0105] The input circuit NYU and the output circuit SYU exchange inputs and outputs related to the operation of the processor PRO with, for example, the wearable terminal WT and the outside of the mind-body estimation device SS.

[0106] <Example of Configuration> The mind-body estimation index, the method for calculating the mind-body estimation index, the mind-body estimation method, and the mind-body estimation device according to the present disclosure have, for example, the following configuration.

[0107] [Item 1] A mind-body estimation index that is a parameter value PV that realizes a model function MF that approximates the relationship between the heart rate HR(t-1) of a subject for each unit time (t), the heart rate change ΔHR(t), which is the difference between the heart rate HR(t-1) within a first unit time (t-1) when the subject is exercising and the heart rate HR(t) within a second unit time (t) following the first unit time (t-1) when the subject is at rest, and the heart rate change ΔHR(t).

[0108] [Item 2] For the subject's heart rate HR(t) for each unit time (t), a heart rate change ΔHR(t) is calculated, which is the difference between the heart rate HR(t-1) during a first unit time (t-1) when the subject is exercising and the heart rate HR(t) during a second unit time (t) following the first unit time (t-1) when the subject is at rest. A two-dimensional coordinate space (2ZK ), the gradient (b １ｊ ) is an index for estimating mind and body.

[0109] [Item 3] A method for calculating an index for mind-body estimation, comprising: (1) a step of calculating a heart rate HR(t) of a subject for each unit time (t); (2) a step of calculating a heart rate change ΔHR(t), which is the difference between the heart rate HR(t-1) in a first unit time (t-1) in which the subject is exercising and the heart rate HR(t) in a second unit time (t) subsequent to the first unit time (t-1) in which the subject is at rest; (3) a step of approximating the relationship between the heart rate HR(t-1) and the heart rate change ΔHR(t) using a model function MF; and (4) a step of acquiring a parameter value PV that realizes the model function MF.

[0110] [Item 4] (1) calculating the heart rate HR(t) of a subject for each unit time (t); (2) calculating a heart rate change ΔHR(t) which is the difference between the heart rate HR(t-1) in a first unit time (t-1) when the subject is exercising and the heart rate HR(t) in a second unit time (t) following the first unit time (t-1) when the subject is at rest; (3) assigning the relationship between the heart rate HR(t-1) and the heart rate change ΔHR(t) to a two-dimensional coordinate space (2ZK); (4) creating a distribution BP(t-1) of a plurality of heart rate changes ΔHR(t) corresponding to each heart rate HR(t-1) in the two-dimensional coordinate space (2ZK) according to a distribution rule; (5) a step of acquiring an expected value (μ) from the distribution BP(t-1); (6) a step of creating a virtual curve (KK) based on the expected value (μ) corresponding to each heart rate HR(t-1); (7) a step of approximating the virtual curve (KK) to a curve equation (KH) of the distribution law; and (8) a step of approximating the slope (b １ｊ and a step of acquiring a mental and physical state.

[0111] [Item 5] A mind-body estimation method, comprising a step of estimating a degree of mental and physical recovery of the subject using the mind-body estimation index according to item 1 or 2.

[0112] [Item 6] A mind-body estimation device including an estimation unit that estimates a degree of recovery of the mind and body of the subject using the indices for mind-body estimation according to item 1 or 2.

[0113] The mind-body estimation index, the method for calculating the mind-body estimation index, the mind-body estimation method, and the mind-body estimation device according to the present disclosure can be used, for example, to provide an index that is more reliable than conventional ones for estimating the mental and physical recovery of a subject.

[0114] SSS: Mind-body estimation system, WT: wearable terminal, SS: mind-body estimation device, NW: network.

Claims

1. A mind-body estimation index, which is a parameter value that realizes a model function that approximates the relationship between the subject's heart rate for each unit of time, including the amount of change in heart rate, which is the difference between the heart rate during a first unit of time when the subject is exercising and the heart rate during a second unit of time following the first unit of time when the subject is at rest, and the heart rate change amount.

2. A mind-body estimation index, which is the difference between the heart rate change amount, which is the difference between the heart rate during a first unit of time when the subject is exercising and the heart rate during a second unit of time following the first unit of time when the subject is at rest, for the subject's heart rate for each unit of time, and the gradient of the linear equation of the distribution law, which is obtained based on the curve equation of the distribution law, which is approximated to a virtual curve created based on expected values ​​corresponding to each heart rate, obtained from the distribution of multiple heart rate changes corresponding to each heart rate, which is created according to a distribution law in a two-dimensional coordinate space to which the relationship between the heart rates is assigned.

3. A method for calculating indices for mind-body estimation, comprising: (1) a step of calculating the heart rate of a subject for each unit time; (2) a step of calculating a change in heart rate, which is the difference between the heart rate during a first unit time in which the subject is exercising and the heart rate during a second unit time subsequent to the first unit time in which the subject is at rest; (3) a step of approximating the relationship between the heart rate and the change in heart rate using a model function; and (4) a step of acquiring parameter values ​​that realize the model function.

4. A method for calculating an index for mind-body estimation, comprising: (1) a step of calculating the heart rate of a subject for each unit time; (2) a step of calculating a heart rate change amount, which is the difference between the heart rate within a first unit time during which the subject is exercising and the heart rate within a second unit time subsequent to the first unit time during which the subject is at rest; (3) a step of assigning the relationship between the heart rate and the heart rate change amount to a two-dimensional coordinate space; (4) a step of creating, for each heart rate in the two-dimensional coordinate space, a distribution of multiple heart rate change amounts corresponding to the heart rate in accordance with a distribution law; (5) a step of obtaining an expected value from the distribution; (6) a step of creating a virtual curve based on the expected value corresponding to each heart rate; (7) a step of approximating the virtual curve to a curve equation of the distribution law; and (8) a step of obtaining the slope of a linear equation of the distribution law based on the curve equation.

5. A mind-body estimation method comprising a step of estimating the degree of recovery of the subject's mind and body using the mind-body estimation index according to claim 1 or 2.

6. A mind-body estimation device comprising an estimation unit that estimates the degree of recovery of the subject's mind and body using the indices for mind-body estimation set forth in claim 1 or claim 2.

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