Exercise index calculation system

Abstract

A motion index calculation system suitable for obtaining a motion index that matches the physical characteristics of a subject is provided. [Solution] An exercise index calculation device 100 inputs a subject's age and resting heart rate, calculates a target heart rate using the Karvonen method calculation formula based on the subject's age, resting heart rate, and intensity (%), and calculates a baseline NSM using the CLS theory calculation formula based on the calculated target heart rate.The subject then exercises with a target NSM set based on the calculated baseline NSM, reads out measurement data measuring the NSM and heart rate at that time, and calculates an adapted NSM using an individualization formula based on the calculated target heart rate and the read out measurement data.The device also determines parameters of the individualization formula based on the measurement data, and classifies the subject's physical characteristics based on the determined parameters.

Description

[Technical Field]

[0001] The present invention relates to a system for calculating a motion index, and more particularly to a motion index calculation system suitable for obtaining a motion index that matches the physical characteristics of a subject. [Background technology]

[0002] Conventionally, as a technique for calculating a motion index, for example, the technique described in Non-Patent Document 1 is known.

[0003] Non-Patent Document 1 discloses the NSM (Number of Steps per Minute) method. The NSM method is a method of setting a number of steps per minute (number of steps per minute) that is synchronized with a target heart rate during exercise, and providing exercise guidance to patients so that they exercise with the set number of steps per minute as their goal. Because steps per minute is easier to understand and put into practice than speedometers or distance meters, it has been reported that in the group receiving guidance using the NSM method, awareness of the achievement of moderate-intensity exercise and the number of hours of moderate-intensity exercise significantly increased six months after the intervention compared to before the intervention. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Yoshihiro Amakawa and 7 others, "What is the optimal exercise intensity for patients to be aware of even in clinics without specialized exercise instructors?", published April 30, 2022 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the technology described in Non-Patent Document 1, the NSM is calculated only based on the target heart rate of the subject, and therefore the subject's physical characteristics (for example, individual differences in physical response to exercise load) are not fully taken into consideration. As a result, the NSM is not necessarily the optimal exercise index for the subject.

[0006] Therefore, the present invention has been made with a focus on the unresolved problems of the conventional technology, and aims to provide a motion index calculation system that is suitable for obtaining motion indexes that match the physical characteristics of a subject. [Means for solving the problem]

[0007] [Invention 1] In order to achieve the above object, an exercise index calculation system of Invention 1 comprises: a biometric information acquisition means for acquiring biometric information of a subject; a target heart rate calculation means for calculating a target heart rate of the subject that will be a target during exercise, based on the biometric information acquired by the biometric information acquisition means; a standard step number calculation means for calculating a standard number of steps per predetermined time that will be a reference for the subject, based on the target heart rate calculated by the target heart rate calculation means or the biometric information acquired by the biometric information acquisition means; an exercise performance information acquisition means for acquiring exercise performance information of the subject that corresponds to each of one or more target numbers of steps set based on the standard number of steps calculated by the standard step number calculation means; and an adapted step number calculation means for calculating an adapted number of steps per predetermined time that is adapted to the physical characteristics of the subject, based on the target heart rate calculated by the target heart rate calculation means and the exercise performance information acquired by the exercise performance information acquisition means.

[0008] With this configuration, the biological information acquisition means acquires biological information, the target heart rate calculation means calculates a target heart rate based on the acquired biological information, the standard step number calculation means calculates a standard step number based on the calculated target heart rate or the acquired biological information, and the exercise performance information acquisition means acquires exercise performance information, and the appropriate step number calculation means calculates an appropriate step number based on the calculated target heart rate and the acquired exercise performance information.

[0009] Here, biological information includes, for example, the subject's age, resting heart rate, height, weight, information on underlying diseases (e.g., type 2 diabetes, hypertension), information on medication such as beta-blockers that affect heart rate, and information on lifestyle habits that affect autonomic nervous regulation (such as smoking and caffeine intake).

[0010] Furthermore, biometric information may be configured as information indicating biometric characteristics, information for identifying biometric characteristics (e.g., name, number, ID, code, link information such as URL), or characteristic information related to statistics of biometric characteristics or other characteristics. Biometric information may be configured as, for example, characters, numbers, figures, codes, symbols, images, sounds, or other information. Biometric information may also be configured as keywords related to biometric characteristics (e.g., one or more keywords indicating part of the name of a biometric characteristic).

[0011] In addition, the exercise performance information includes, for example, measurement data during exercise such as NSM, heart rate, heart rate variability (HRV (Heart Rate Variability)), respiratory rate, blood pressure, body temperature, skin temperature, lactate level, rate of perceived exercise (RPE (Rate of Perceived Exertion)), oxygen saturation (SpO2 (Oxygen Saturation)), carbon dioxide output (VCO2 (Carbon Dioxide Output)), muscle oxygenation level (SmO2 (Muscle Oxygen Saturation)), electromyography (EMG), electrocardiogram (ECG), sweat rate, and galvanic skin response (GSR).

[0012] Furthermore, the exercise achievement information may be configured as information indicating the exercise achievement itself, or may be configured as information for identifying the exercise achievement (for example, link information such as a name, number, ID, code, or URL), or as feature information related to statistics or other features of the exercise achievement. Furthermore, the exercise achievement information may be configured as, for example, letters, numbers, figures, codes, symbols, images, sounds, or other information. Furthermore, the exercise achievement information may be configured as keywords related to the exercise achievement (for example, one or more keywords indicating part of the name of the exercise achievement).

[0013] Furthermore, the biometric information acquisition means may, for example, input biometric information from an input device or the like, acquire or receive biometric information from an external terminal or the like, read biometric information from a storage device or storage medium or the like, or generate or calculate biometric information by information processing or the like. Therefore, acquisition includes at least input, acquisition, reception, reading (including search), generation and calculation. The same concept of acquisition applies hereinafter.

[0014] The reference number of steps, the target number of steps, and the suitable number of steps refer to the number of steps per predetermined time, which can be one minute or any other defined time. The predetermined time for the reference number of steps, the target number of steps, and the suitable number of steps can be the same or different.

[0015] The target number of steps may include, for example, the reference number of steps itself, or a number of steps calculated based on the reference number of steps (for example, a number of steps that is a predetermined ratio of the reference number of steps).

[0016] Here, the present system may be realized as a single device, apparatus, terminal, or other device, or as a network system in which multiple devices, apparatus, terminals, or other devices are communicatively connected. In the latter case, each component may belong to any of the multiple devices as long as they are communicatively connected.

[0017] [Invention 2] Furthermore, the exercise index calculation system of Invention 2 is the exercise index calculation system of Invention 1, wherein the biological information is information including the age and resting heart rate of the subject, the target heart rate calculation means calculates the target heart rate based on the age, resting heart rate and exercise intensity information included in the biological information, the standard step number calculation means calculates the standard number of steps based on the target heart rate calculated by the target heart rate calculation means, and the exercise performance information is information including the subject's heart rate during exercise.

[0018] With this configuration, the target heart rate calculation means calculates the target heart rate based on the subject's age, resting heart rate, and exercise intensity information, the standard step number calculation means calculates the standard number of steps based on the calculated target heart rate, and the appropriate step number calculation means calculates the appropriate number of steps based on the target heart rate and the exercise heart rate.

[0019] [Invention 3] Furthermore, the exercise index calculation system of Invention 3 is the exercise index calculation system of Invention 1, wherein the biological information is information including the age, resting heart rate, and height of the subject, the target heart rate calculation means calculates the target heart rate based on the age, resting heart rate, and exercise intensity information included in the biological information, the standard step number calculation means calculates the standard number of steps based on the height and exercise intensity information included in the biological information, and the exercise performance information is information including the subject's heart rate during exercise.

[0020] With this configuration, the target heart rate calculation means calculates the target heart rate based on the subject's age, resting heart rate, and exercise intensity information, the standard step number calculation means calculates the standard number of steps based on the subject's height and exercise intensity information, and the appropriate step number calculation means calculates the appropriate number of steps based on the target heart rate and exercise heart rate.

[0021] [Invention 4] Furthermore, the exercise index calculation system of Invention 4 is the exercise index calculation system of any one of Inventions 1 to 3, wherein the suitable step count calculation means determines parameters of an individualization formula for calculating the suitable step count based on the exercise performance information, calculates the suitable step count using the individualization formula to which the determined parameters are applied, and includes physical characteristic classification means for classifying physical characteristics of the subject based on the parameters determined by the suitable step count calculation means.

[0022] With this configuration, the suitable step count calculation means determines parameters of the individualization formula based on the exercise record information, and calculates the suitable step count using the individualization formula to which the determined parameters are applied.Then, the physical characteristic classification means classifies the physical characteristics of the subject based on the determined parameters. [Effects of the Invention]

[0023] As described above, according to the exercise index calculation system of Invention 1, the appropriate number of steps is calculated based on the subject's biometric information and exercise performance information, so that an exercise index that is more suited to the subject's physical characteristics than conventional methods can be obtained.

[0024] Furthermore, according to the exercise index calculation system of Invention 2, the appropriate number of steps is calculated based on the subject's age, resting heart rate, exercise intensity information, and exercise heart rate, so that an exercise index that is more suitable for the subject's physical characteristics can be obtained.

[0025] Furthermore, according to the exercise index calculation system of Invention 3, the appropriate number of steps is calculated based on the subject's age, resting heart rate, height, exercise intensity information, and exercise heart rate, so that an exercise index that is more suitable for the subject's physical characteristics can be obtained.

[0026] Furthermore, according to the exercise index calculation system of Invention 4, the physical characteristics of the subject are classified based on the parameters of the individualization formula, so that the physical characteristics of the subject can be grasped as a classification. [Brief explanation of the drawings]

[0027] [Figure 1] 1 is a diagram illustrating a hardware configuration of an exercise index calculation device 100. FIG. [Figure 2] 10 is a flowchart showing an exercise index calculation process. DETAILED DESCRIPTION OF THE INVENTION

[0028] An embodiment of the present invention will be described below, with reference to Figures 1 and 2.

[0029] First, the configuration of this embodiment will be described. The calculation formula used in this embodiment will be described below. Hereinafter, the subject refers to a test subject or other person for whom a motion index is to be calculated.

[0030] 1. Karvonen method calculation formula The Karvonen method is a method used to calculate the target heart rate for exercise. As shown in the following formula (1), the Karvonen method calculates a target heart rate that suits the subject's physical characteristics by multiplying the target exercise intensity by the reserve heart rate, which is the difference between the subject's maximum heart rate and resting heart rate, and adding this to the resting heart rate.

[0031] Target heart rate = (maximum heart rate - resting heart rate) x intensity (%) + resting heart rate … (1) In the above formula (1), the maximum heart rate is generally calculated as "220 - age." To obtain a more accurate value, a correction formula of "208 - 0.7 x age" can be used. In the above formula (1), the resting heart rate is the subject's resting heart rate. This varies depending on age, physical fitness level, illness, medication, etc. For example, physically fit individuals tend to have a resting heart rate of 40-60 bpm, while physically fit individuals tend to have a resting heart rate of 80-95 bpm. In the above formula (1), the intensity (%) is the target exercise intensity set according to the purpose of exercise (e.g., 50% of moderate exercise) and is set as a value between 0 and 1.

[0032] By substituting age, resting heart rate, and intensity (%) into the above formula (1), an exercise index suited to the subject's physical characteristics (age and resting heart rate) can be obtained as a heart rate.

[0033] It is important to modify the Karvonen method calculation formula depending on the subject's condition. For drug correction, a correction formula is used to correct for the effects of medications such as beta-blockers, which reduce resting heart rate. For physical fitness correction, adjustments are made to suit the subject's physical fitness level, such as increasing the intensity (%) by 5-10% for highly fit individuals and decreasing it by 5-10% for less fit individuals.

[0034] 2.CLS theoretical calculation formula CLS (Cardiac-Locomotive Synchronization) theory refers to the physiological synchronization of heart rate and movement rhythms during rhythmic exercise such as walking. This synchronization has physiological benefits, such as increasing blood flow to the heart and reducing cardiac workload, improving circulatory efficiency. The CLS theory calculation formula, as shown in equation (2) below, uses the correlation between heart rate and NSM based on CLS theory to calculate baseline NSM from the target heart rate.

[0035] Baseline NSM = 0.923 × target heart rate + 11.398 … (2) In the above formula (2), "0.923" and "11.398" are constants based on the correlation that exists between heart rate and NSM based on CLS theory. The above formula (2) is a standard relationship that does not take into account the subject's physical characteristics (height, weight, age, etc.), but by substituting the target heart rate calculated in the above formula (1), it is possible to obtain an exercise index as NSM that matches the subject's physical characteristics (age and resting heart rate).

[0036] 3. Height correction formula Height was classified into seven groups (from 148 cm to 178 cm or higher in 5 cm intervals), and a unified equation was derived using stepwise regression analysis. Individual regression equations were derived for each height group from data collected over a total of 16,970 days, and a regularity was confirmed in which the linear coefficient a increases and the constant term b decreases as height increases.

[0037] An example of a regression equation by height is as follows: Group A (148-152[cm]): y=15.473x+58.899 Group B (153-157[cm]): y=15.966x+57.715 … Group G (178[cm] or more): y=18.591x+51.656 Here, y indicates NSM and x indicates METs.

[0038] The following equation (3) was derived by stepwise regression analysis. NSM=(0.1×L-0.60)METs+(93.87-0.23×L) …(3) In the above equation (3), L represents height [cm] and METs represents exercise intensity.

[0039] Using the above formula (3), it is now possible to calculate the appropriate NSM for a person of any height at any exercise intensity.

[0040] The height correction formula is a formula for calculating METs by modifying the above formula (3), as shown in the following formula (4).

[0041] METs=(NSM-93.87+0.23×L) / (0.1×L-0.60) …(4) By substituting the baseline NSM calculated in equation (2) above into equation (4), an exercise index (METs) that matches the subject's physical characteristics (age, resting heart rate, and height) can be obtained.

[0042] Next, the hardware configuration of the exercise index calculation device 100 will be described. FIG. 1 is a diagram showing the hardware configuration of the exercise index calculation device 100. As shown in FIG.

[0043] As shown in FIG. 1, the exercise index calculation device 100 is composed of a CPU (Central Processing Unit) 30 that controls calculations and the entire system based on a control program, a ROM (Read Only Memory) 32 that stores the control program and the like for the CPU 30 in advance in a predetermined area, a RAM (Random Access Memory) 34 that stores data read from the ROM 32 and the like and calculation results required in the calculation process of the CPU 30, and an I / F (Interface) 38 that mediates the input and output of data to and from external devices, and these are connected to each other and capable of sending and receiving data by a bus 39, which is a signal line for transferring data.

[0044] External devices connected to the I / F 38 include an input device 40 consisting of a keyboard, mouse, etc. that can input data as a human interface, a storage device 42 that stores data, tables, etc. as files, and a display device 44 that displays a screen based on an image signal.

[0045] Next, the operation of this embodiment will be described. FIG. 2 is a flowchart showing the exercise index calculation process.

[0046] The CPU 30 is composed of an MPU (Micro-Processing Unit) or the like, and starts a predetermined program stored in a predetermined area of ​​the ROM 32, and executes the exercise index calculation process shown in the flowchart of Fig. 2 in accordance with the program. When the exercise index calculation process is executed in the CPU 30, as shown in Fig. 2, the process first proceeds to step S100.

[0047] In step S100, biological information including the subject's age, resting heart rate, and height is input from the input device 40, and the process proceeds to step S102, where exercise intensity information including intensity (%) is input from the input device 40.

[0048] Next, the process proceeds to step S104, where the target heart rate is calculated by substituting the subject's age, resting heart rate, and intensity (%) contained in the biological information and exercise intensity information input in steps S100 and S102 into the Karvonen method calculation formula (1) above. In formula (1) above, the maximum heart rate is calculated as "220 - age." The calculated target heart rate is stored in RAM 34.

[0049] Next, the process proceeds to step S106, where the target heart rate calculated in step S104 is substituted into the CLS theoretical calculation formula (2) above to calculate the baseline NSM. The calculated baseline NSM is stored in the RAM 34.

[0050] Next, proceeding to step S108, measurement data including the subject's heart rate and NSM when the subject exercises at each of multiple target NSMs set based on the baseline NSM calculated in step S106 is read from storage device 42. For example, multiple different target NSMs are set, such as baseline NSM x 90% (target NSM1), baseline NSM x 95% (target NSM2), and baseline NSM x 100% (target NSM3). The subject exercises with target NSM1 as the target, and NSM1 and heart rate 1 are measured, and measurement data including actual NSM1 and actual heart rate 1 are stored in storage device 42. Similarly, the subject exercises with target NSM2 as the target, and NSM2 and heart rate 2 are measured, and measurement data including actual NSM2 and actual heart rate 2 are stored in storage device 42. Similarly, the subject exercises with target NSM3 as the target, and NSM3 and heart rate 3 are measured, and measurement data including actual NSM3 and actual heart rate 3 are stored in storage device 42. In step S108, the measurement data thus stored is read out from the storage device 42.

[0051] Next, the process proceeds to step S110, where parameters of an individualization formula for calculating an adapted NSM that is adapted to the subject's physical characteristics are determined based on the measurement data read out in step S108. The individualization formula is defined as follows.

[0052] First, the relationship between heart rate and NSM is expressed as a linear model using regression analysis. Heart rate = a × NSM + b …(5) In the above equation (5), the regression coefficients a and b are calculated by the least squares method, which calculates the regression line that best fits the measurement data (pairs of measured NSM and measured heart rate) so that the sum of squares of the residuals is minimized.

[0053] The regression coefficient a represents the slope of the regression line, and as shown in the following equation (6), it is the covariance S between the measured NSM and the measured heart rate. xy The variance of the measured NSM, S xx It can be calculated by dividing by

[0054] a=Sxy / S xx …(6) The regression coefficient b represents the intercept of the regression line, and is expressed as the mean value μ of the measured heart rate as shown in the following equation (7): y From the regression coefficient a, the mean value μ of the measured NSM x It can be calculated by multiplying the value by 1 and subtracting the result.

[0055] b=μ y -a×μ x …(7) Second, the individualization formula is a formula for calculating the adapted NSM by modifying the above formula (5), as shown in the following formula (8).

[0056] NSM = (heart rate - b) / a, Adapted NSM = (target heart rate - b) / a …(8) The determined parameters a and b and the calculated adapted NSM are stored in the storage device 42.

[0057] Next, the process proceeds to step S112, where the physical characteristics of the subject are classified based on the parameters a and b of the individualization formula determined in step S110. The classification criteria are as follows.

[0058] Regarding the regression coefficient a, (1) a<0.60: low response type, (2) 0.60≦a≦0.85: standard response type, (3) a>0.85: high response type. Regarding the regression coefficient b, (1) b ≥ 30: high walking ability, (2) 15 ≤ b < 30: standard walking ability, (3) b < 15: low walking ability The classification information of the classified physical characteristics of the subject is stored in the storage device 42.

[0059] Next, the process proceeds to step S114, where the personalization formula to which the parameters a and b determined in step S110 are applied is displayed on the display device 44.

[0060] Next, the process proceeds to step S116, and the classification information of the subject's physical characteristics classified in step S112 is displayed on the display device 44. For example, if a=0.9 and b=-3, this fits the classification criteria and is displayed as "high response type" and "low walking ability."

[0061] When the process of step S116 is completed, the series of processes ends and the process returns to the original process. [Example]

[0062] Next, an example will be described. Subject T is 65 years old, 168 cm tall, and has a resting heart rate of 68 beats / min. The intensity (%) is set to 50%.

[0063] 1. The target heart rate is calculated as 112 beats / min using the Karvonen method (1) above.

[0064] Target heart rate = (220 - 65 - 68) x 0.5 + 68 = 112 beats per minute 2. The baseline NSM is obtained as 114 steps / min using the CLS theoretical calculation formula (2) above.

[0065] Baseline NSM = 0.923 x 112 + 11.398 = 114 [steps / min] 3. Set the target NSM as follows:

[0066] Target NSM1 = Baseline NSM x 90% = 114 x 0.90 = 103 [steps / min] Target NSM2 = Baseline NSM x 95% = 114 x 0.95 = 109 [steps / min] Target NSM3 = Baseline NSM x 100% = 114 x 1.00 = 114 [steps / min] The subject exercises with the goal of achieving the target NSM1, and the NSM1 and heart rate 1 are measured. The actual NSM1 was 92 [steps / min], and the actual heart rate 1 was 85 [beats / min].

[0067] The subject exercises to achieve the target NSM2, and the NSM2 and heart rate 2 are measured. The actual NSM2 was 104 [steps / min], and the actual heart rate 2 was 96 [beats / min].

[0068] The subject exercises to achieve the target NSM3, and the NSM3 and heart rate3 are measured. The actual NSM3 was 115 [steps / min] and the actual heart rate3 was 107 [beats / min].

[0069] 4. Average value of measured NSM μ x is 103.7, the average value of the actual heart rate μ y is 96. From these, the covariance S of the measured NSM and the measured heart rate is xy is 84.33, and the variance of the measured NSM S xx is 88.22. Using the above equations (6) and (7), we obtain a = 0.956 and b = -3.1, respectively.

[0070] a=84.33 / 88.22=0.956 b=96-0.956×103.7=-3.1 The adapted NSM is obtained as 120 [steps / min] using the individualization formula (8) above.

[0071] Compatible NSM = (112 + 3.1) / 0.956 = 120 [steps / min] In this case, the exercise intensity is calculated as 4 [METs] using the height-corrected calculation formula (4) above.

[0072] (120-93.87+0.23×168) / (0.1×168-0.60)=4 Next, the coefficient of determination is calculated to evaluate the goodness of fit (goodness of fit) of the personalization formula. The coefficient of determination is an index that shows how much of the variability in the measurement data can be explained by the personalization formula, with the closer it is to 1, the higher the fit. The coefficient of determination is obtained as 0.9994 using the formula below. The obtained coefficient of determination is very close to 1, which shows that the personalization formula explains the measurement data very well. Therefore, an extremely strong linear relationship is observed between heart rate and NSM for subject T.

[0073] R 2 =1-(residual sum of squares / total sum of squares)=0.9994 Here, the residual sum of squares is the sum of the squares of the differences (residuals) between the actual heart rate at each measurement point and the predicted value by the individualization formula. The total sum of squares is the sum of the squares of the differences between the actual heart rate and its average value, and represents the variability of the entire data.

[0074] Next, we verify the correlation. The coefficient of determination is obtained as 0.9994 using the following formula. This value indicates an extremely strong correlation.

[0075] R 2 =1-(residual sum of squares / total sum of squares)=0.9994 Here, the residual sum of squares is the sum of all the squares of the residuals, and the total sum of squares is the sum of all the squares of the differences between each value and its average value.

[0076] 5. For subject T, a=0.956 and b=-3.1 were obtained, so subject T's physical characteristics were classified as "high response type" and "low walking ability."

[0077] Subject T can exercise at an exercise intensity that suits his / her physical characteristics by setting his / her own suitable NSM in a metronome application on a mobile terminal such as a smartphone and walking to the rhythm of that setting. In addition, subject T can receive exercise guidance that suits his / her physical characteristics by providing his / her classification information, for example, "high response type" and "low walking ability," to a medical institution or the like.

[0078] Next, the effects of this embodiment will be described. In this embodiment, the subject's biometric information is input, a target heart rate is calculated based on the input biometric information, a baseline NSM is calculated based on the calculated target heart rate, measurement data of the subject corresponding to each of multiple target NSMs set based on the calculated baseline NSM is read, and an adapted NSM is calculated based on the calculated target heart rate and the read measurement data.

[0079] This allows the adapted NSM to be calculated based on the subject's biological information and measurement data, making it possible to obtain a movement index that is more suited to the subject's physical characteristics than in the past.

[0080] Furthermore, in this embodiment, the target heart rate is calculated based on the subject's age, resting heart rate, and intensity (%), and the adapted NSM is calculated based on the target heart rate and exercise heart rate.

[0081] This allows the adapted NSM to be calculated based on the subject's age, resting heart rate, intensity (%), and exercise heart rate, making it possible to obtain an exercise index that is more suited to the subject's physical characteristics.

[0082] Furthermore, in this embodiment, parameters of the personalization formula are determined based on the measurement data, an adapted NSM is calculated using the personalization formula to which the determined parameters are applied, and the subject's physical characteristics are classified based on the determined parameters.

[0083] As a result, the subject's physical characteristics are classified based on the parameters of the individualization formula, and the subject's physical characteristics can be grasped as a classification.

[0084] In this embodiment, step S100 corresponds to the biological information acquiring means of invention 1, step S104 corresponds to the target heart rate calculating means of invention 1 or 2, step S106 corresponds to the reference step number calculating means of invention 1 or 2, and step S108 corresponds to the exercise performance information acquiring means of invention 1. Furthermore, step S110 corresponds to the optimal step number calculating means of invention 1 or 4, and step S112 corresponds to the physical characteristic classifying means of invention 4.

[0085] [Modification] In the above embodiment and its modified examples, the baseline NSM calculated by the CLS theory calculation formula (2) above is used, but this is not limiting, and the NSM calculated by the above formula (3) can also be used. In the above formula (3), the exercise intensity [METs] is given a target value, similar to the intensity (%). This allows the appropriate NSM to be calculated based on the subject's age, resting heart rate, height, exercise intensity, and exercise heart rate, making it possible to obtain an exercise index that is more suited to the subject's physical characteristics.

[0086] Furthermore, in the above embodiment and its modifications, the measured NSM is used in the above equation (5), but this is not limiting, and a target NSM can also be used.

[0087] Furthermore, in the above embodiment and its modified example, the calculated adapted NSM is stored in the storage device 42, but this is not limiting and the following configuration can also be adopted.

[0088] In the first configuration, a device such as a smartphone or fitness band (hereinafter simply referred to as "device") stores a suitable NSM and notifies the user of a rhythm corresponding to the suitable NSM. For example, if the device vibrates at the rhythm of the suitable NSM, the user will know that they should walk at that rhythm. Notification methods include vibration and the output of rhythmic sounds.

[0089] In the second configuration, the device stores the appropriate NSM, measures the subject's exercise NSM, and notifies the subject when the exercise NSM is within a predetermined range (hereinafter referred to as the "appropriate NSM range") based on the appropriate NSM. For example, if the subject starts walking and the exercise NSM increases and falls within the appropriate NSM range, the device vibrates, allowing the subject to know that they should walk at the rhythm when the notification was issued.

[0090] In the third configuration, the device stores the appropriate NSM, measures the subject's exercise NSM, and notifies the subject when the exercise NSM falls outside the appropriate NSM range. For example, if the subject starts walking and the exercise NSM is still low and outside the appropriate NSM range, the device vibrates, and when the exercise NSM falls within the appropriate NSM range, the device stops vibrating. This allows the subject to know that they should walk at the rhythm when the notification was stopped.

[0091] Furthermore, in the above embodiment and its modified example, the target NSM is calculated using the Karvonen method calculation formula, but the present invention is not limited to this, and a configuration in which the target NSM is calculated using another calculation formula can be adopted.

[0092] Furthermore, in the above embodiment and its modified examples, the reference NSM is calculated using the CLS theoretical calculation formula or (3) above, but this is not limiting and a configuration in which the reference NSM is calculated using another calculation formula can be adopted.

[0093] Furthermore, in the above-described embodiment and its modified examples, multiple target NSMs are set, but this is not limiting, and only one target NSM can be set. When only one target NSM is set, the parameters of the individualization formula can be determined based on two pieces of data, for example, the exercise record and the state at rest (NSM0, resting heart rate).

[0094] Furthermore, in the above embodiment and its modified examples, the individualization equation is defined as a regression line, but is not limited to this and can be defined as a higher-order curve or a multiple regression equation.

[0095] In addition, in the above-described embodiment and its modified examples, the exercise index calculation device 100 is realized as a single device, but is not limited to this, and can also be realized as a network system. As an example of a network system, part or all of the functions of the exercise index calculation device 100 can be configured as a virtual server on a server that provides a cloud computing service.

[0096] Furthermore, in the above-described embodiment and its modifications, the exercise index calculation device 100 is configured to use the storage device 42, but is not limited to this, and can also be configured to use an external storage device such as a database server.

[0097] Furthermore, in the above embodiment and its variants, the processing shown in the flowchart of FIG. 2 is executed by executing a program stored in advance in ROM 32. However, this is not limiting, and the program showing these procedures may be read from a storage medium on which the program is stored, into RAM 34, and executed.

[0098] Furthermore, the above-described embodiments and their modifications can be applied to each other. Furthermore, the present invention is not limited to the above-described embodiment and its modifications, but can also be applied to other cases within the scope of the present invention. [Explanation of symbols]

[0099] 100...Movement index calculation device, 30...CPU, 32...ROM, 34...RAM, 38...I / F, 39...bus, 40...input device, 42...storage device, 44...display device

Claims

1. a biometric information acquisition means for acquiring biometric information of a subject; a target heart rate calculation means for calculating a target heart rate of the subject during exercise based on the biological information acquired by the biological information acquisition means; a reference step number calculation means for calculating a reference number of steps per predetermined time period as a reference for the subject based on the target heart rate calculated by the target heart rate calculation means or the biological information acquired by the biological information acquisition means; an exercise performance information acquisition means for acquiring exercise performance information of the subject corresponding to one target number of steps or each of a plurality of target numbers of steps different from the reference number of steps, which is set based on the reference number of steps calculated by the reference number of steps calculation means; and an adapted step number calculation means for calculating an adapted number of steps per predetermined time that is adapted to the subject's physical characteristics, based on the target heart rate calculated by the target heart rate calculation means and the exercise performance information corresponding to each of the one or more target step numbers acquired by the exercise performance information acquisition means.

2. In claim 1, the suitable step number calculation means determines parameters of an individualization formula for calculating the suitable step number based on the exercise record information, and calculates the suitable step number using the individualization formula to which the determined parameters are applied; A motion index calculation system comprising a physical characteristic classifying means for classifying the physical characteristics of the subject based on the parameters determined by the suitable step number calculation means.

3. A biometric information acquisition means for acquiring biometric information of a subject; a target heart rate calculation means for calculating a target heart rate of the subject during exercise based on the biological information acquired by the biological information acquisition means; a reference step number calculation means for calculating a reference number of steps per predetermined time period as a reference for the subject based on the target heart rate calculated by the target heart rate calculation means or the biological information acquired by the biological information acquisition means; an exercise performance information acquisition means for acquiring exercise performance information of the subject corresponding to each of one or more target step numbers set based on the reference step number calculated by the reference step number calculation means; an adapted step count calculation means for determining parameters of an individualization formula for calculating an adapted number of steps per predetermined time period that is adapted to physical characteristics of the subject, based on exercise performance information corresponding to each of one or more target step counts acquired by the exercise performance information acquisition means, and for calculating the adapted number of steps using the individualization formula to which the determined parameters have been applied, based on the target heart rate calculated by the target heart rate calculation means.

4. In claim 3, A motion index calculation system comprising a physical characteristic classifying means for classifying the physical characteristics of the subject based on the parameters determined by the suitable step number calculation means.

5. In any one of claims 1 to 4, the biological information is information including the subject's age and resting heart rate, the target heart rate calculation means calculates the target heart rate based on age, resting heart rate, and exercise intensity information included in the biological information; the reference step number calculation means calculates the reference step number based on the target heart rate calculated by the target heart rate calculation means; The exercise index calculation system, wherein the exercise performance information is information including the subject's heart rate during exercise.

6. In any one of claims 1 to 4, The biological information includes the subject's age, resting heart rate, and height, the target heart rate calculation means calculates the target heart rate based on age, resting heart rate, and exercise intensity information included in the biological information; the reference step number calculation means calculates the reference step number based on height and exercise intensity information included in the biological information; The exercise index calculation system, wherein the exercise performance information is information including the subject's heart rate during exercise.

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