Health management system and health management method

Abstract

To provide a health management system that can calculate exercise strength when a user rides on a power-assisted bicycle.SOLUTION: A health management system 100 includes: an acquisition unit 24 configured to acquire running data associated with a power value applied on pedals 13 by a user riding on a power-assisted bicycle 10; an estimation unit 25 configured to estimate, based on the acquired running data, a period of time when the power-assisted bicycle runs on an uphill road and to calculate a maximum power value during the estimated period of time; a calculation unit 26 configured to calculate exercise strength of the user based on the calculated maximum power value; and an output unit 27 configured to output exercise strength information indicating the calculated exercise strength.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a health management system using an electric assist bicycle.

Background Art

[0002] Conventionally, an electric assist bicycle that can travel easily by adding an auxiliary driving force by an electric motor to a human driving force such as a pedaling force on a pedal is known. Patent Document 1 discloses a technique related to an electric assist bicycle.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention provides a health management system or the like that can calculate the exercise intensity when a user is riding an electric assist bicycle.

Means for Solving the Problems

[0005] A health management system according to an aspect of the present invention includes an acquisition unit that acquires data related to a power value applied by a user riding an electric assist bicycle to a pedal, an estimation unit that estimates a period during which the electric assist bicycle is traveling uphill based on the acquired data, and calculates a maximum power value during the estimated period, a calculation unit that calculates the exercise intensity of the user based on the calculated maximum power value, and an output unit that outputs exercise intensity information indicating the calculated exercise intensity.

[0006] A health management system according to one aspect of the present invention includes an acquisition unit that acquires attribute information of an electric assist bicycle user, a storage unit that stores table information that converts the attribute information into a maximum oxygen intake, an estimation unit that estimates the user's maximum oxygen intake based on the acquired attribute information and the table information, a calculation unit that calculates the user's exercise intensity based on the estimated maximum oxygen intake, and an output unit that outputs exercise intensity information indicating the calculated exercise intensity.

[0007] A health management method according to one aspect of the present invention is a health management method performed by a computer, comprising: an acquisition step of acquiring data related to the power value applied to the pedals by a user riding an electric assist bicycle; an estimation step of estimating the period during which the electric assist bicycle is traveling uphill based on the acquired data and calculating the maximum power value during the estimated period; a calculation step of calculating the exercise intensity of the user based on the calculated maximum power value; and an output step of outputting exercise intensity information indicating the calculated exercise intensity.

[0008] A program according to one aspect of the present invention is a program for causing a computer to execute the health management method described above.

[0009] A method for generating a machine learning model according to one aspect of the present invention is a method for generating a machine learning model executed by a computer, comprising: an acquisition step of acquiring riding data measured by an electric assist bicycle when a user whose maximum oxygen consumption is known is riding the electric assist bicycle for multiple users; and a generation step of generating the machine learning model for estimating the maximum oxygen consumption from the riding data by training the acquired riding data for multiple users.

[0010] A program according to one aspect of the present invention is a program for causing a computer to execute the method for generating the machine learning model. [Effects of the Invention]

[0011] A health management system, etc., according to one aspect of the present invention, can calculate the exercise intensity when a user is riding an electric assist bicycle. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 is a block diagram showing the functional configuration of a health management system according to an embodiment. [Figure 2] Figure 2 is an external view of the electric assist bicycle and display device included in the health management system according to the embodiment. [Figure 3] Figure 3 is a flowchart showing the calculation process for %VO2max. [Figure 4] Figure 4 shows an example of displaying the %VO2max calculation results (Example 1). [Figure 5] Figure 5 shows an example of displaying the %VO2max calculation results (Example 2). [Figure 6] Figure 6 shows an example of displaying the %VO2max calculation results (Example 3). [Figure 7] Figure 7 is a flowchart of Example 1 of the VO2max estimation process. [Figure 8] Figure 8 shows an example of table information. [Figure 9] Figure 9 is a flowchart of Example 2 of the VO2max estimation process. [Figure 10] Figure 10 is a flowchart of Example 3 of the VO2max estimation process. [Figure 11] Figure 11 is a diagram illustrating the method for estimating the duration of time an electric-assist bicycle is traveling uphill. [Figure 12] Figure 12 shows the correlation between the estimated maximum power value and the measured VO2max value. [Figure 13] Figure 13 is a flowchart of the VO2max update process. [Figure 14] Figure 14 shows an example of how exercise intensity information for multiple users is displayed. [Figure 15]FIG. 15 is a diagram showing a modified example of a method for calculating exercise time. [Figure 16] FIG. 16 is a diagram showing a first modified example of the functional configuration of the health management system according to the embodiment. [Figure 17] FIG. 17 is a diagram showing a second modified example of the functional configuration of the health management system according to the embodiment. [Figure 18] FIG. 18 is a diagram showing a third modified example of the functional configuration of the health management system according to the embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0013] Hereinafter, embodiments will be described with reference to the drawings. Note that all of the embodiments described below show comprehensive or specific examples. The numerical values, shapes, materials, components, arrangement positions and connection forms of the components, steps, order of steps, etc. shown in the following embodiments are merely examples and are not intended to limit the present invention. In addition, among the components in the following embodiments, the components not described in the independent claims are described as optional components.

[0014] Note that each figure is a schematic diagram and is not necessarily drawn precisely. Also, in each figure, the same reference numerals are given to substantially the same configurations, and redundant descriptions may be omitted or simplified.

[0015] (Embodiment) [Configuration] First, the configuration of the health management system according to the embodiment will be described. FIG. 1 is a block diagram showing the functional configuration of the health management system according to the embodiment. FIG. 2 is an external view of the electric assist bicycle and the display device included in the health management system according to the embodiment.

[0016] As shown in Figures 1 and 2, the health management system 100 is a system that calculates the exercise intensity of a user pedaling the electric assist bicycle 10 (a user riding the electric assist bicycle 10) and displays the calculated exercise intensity on the display unit 32 of the display device 30 and the display unit 42 of the administrator terminal 40. According to the health management system 100, users and their administrators can check their own exercise intensity. Specifically, the health management system 100 comprises an electric assist bicycle 10, a server device 20, a display device 30, and an administrator terminal 40.

[0017] First, let me describe the electric assist bicycle 10. The electric assist bicycle 10 is a bicycle that can be ridden on public roads. The electric assist bicycle 10 comprises a frame 11, a front wheel 12f, a rear wheel 12r, pedals 13, an electric motor 14 attached to the frame 11, a battery 15, a control unit 16, a pedaling force sensor 17a, a rotation speed sensor 17b, a memory unit 18, and a communication unit 19.

[0018] The electric assist bicycle 10 assists the forward movement of the vehicle body 11 by driving an electric motor 14 based on the user's pedaling force 13. The electric motor 14 is driven using power supplied from a battery 15. The battery 15 is a secondary battery, such as a lithium-ion battery, and also functions as a power source for the control unit 16, etc.

[0019] The control unit 16 is a control device that drives the electric motor 14. The control unit 16, for example, This is implemented by a microcomputer, but may also be implemented by a processor. The functions of the control unit 16 are realized by the execution of a computer program (software) stored in the storage unit 18 by the hardware, such as a processor or microcomputer, that constitutes the control unit 16.

[0020] Specifically, the control unit 16 determines the magnitude of the assist force (in other words, the auxiliary driving force) generated by the electric motor 14 based on the user's pedaling force 13 and the speed of the electric assist bicycle 10. The pedaling force 13 is obtained from the pedaling force sensor 17a.

[0021] The pedal force sensor 17a is, for example, a magnetostrictive torque sensor. The speed of the electric assist bicycle 10 is calculated based on the number of rotations per unit time of the rear wheel 12r (or front wheel 12f) and the size of the rear wheel 12r (or front wheel 12f). The speed of the electric assist bicycle 10 may also be measured by a sensor such as a Hall IC attached to the rear wheel 12r (or front wheel 12f). The method for detecting the speed of the electric assist bicycle 10 is not particularly limited.

[0022] The rotation speed sensor 17b measures the rotation speed of the crank. In other words, the rotation speed sensor 17b measures the rotation angle of the crank. The rotation speed sensor 17b is, for example, an optical sensor having a light emitting part and a light receiving part, and measures the rotation speed of the crank based on the number of times the path of light from the light emitting part to the light receiving part is blocked by a light shield located between the light emitting part and the light receiving part, which rotates in conjunction with the crank. The rotation speed sensor 17b is not limited to the optical configuration described above, as long as it can measure the rotation speed of the crank.

[0023] The memory unit 18 is a storage device that stores computer programs and the like executed by the control unit 16. The memory unit 18 is implemented, for example, by semiconductor memory.

[0024] The communication unit 19 is a communication circuit that enables the electric assist bicycle 10 to communicate with the server device 20 via a wide-area communication network. The wide-area communication network includes mobile communication networks and the internet. The communication performed by the communication unit 19 is, for example, wireless communication.

[0025] Specifically, the communication unit 19 transmits the measured value of the user's pedaling force (torque) applied to the pedal 13, obtained from the pedaling force sensor 17a, to the server device 20. The communication unit 19 also transmits the measured value of the crank rotation speed obtained from the rotation speed sensor 17b to the server device 20, or transmits the crank rotation speed per unit time, calculated by the control unit 16 based on the measured value of the rotation speed sensor 17b, to the server device 20. The crank rotation speed per unit time can be calculated, for example, by converting the difference between the most recent measured value of the crank rotation speed and the previous measured value of the crank rotation speed into the crank rotation speed per unit time. When the measured value of the rotation speed sensor 17b is transmitted to the server device 20, the crank rotation speed per unit time is calculated by the server device 20 (information processing unit 22). The unit time is, for example, one minute.

[0026] In the following embodiments, the measured pedaling force is referred to as the torque value, and the number of crank rotations per unit time is referred to as the cadence value.

[0027] Next, the configuration of the server device 20 will be described. The server device 20 calculates the exercise intensity of the user riding the electric assist bicycle 10 (the user pedaling the pedals 13). Specifically, the server device 20 comprises a communication unit 21, an information processing unit 22, and a storage unit 23.

[0028] The communication unit 21 is a communication circuit for the server device 20 to communicate with the electric assist bicycle 10, the display device 30, and the administrator terminal 40, etc. The communication performed by the communication unit 21 is, for example, wired communication, but it may also be wireless communication.

[0029] The information processing unit 22 performs information processing to calculate the user's exercise intensity. The information processing unit 22 is implemented by, for example, a microcomputer, but may also be implemented by a processor. The information processing unit 22 includes, as functional components, an acquisition unit 24, an estimation unit 25, a calculation unit 26, and an output unit 27. The functions of the acquisition unit 24, estimation unit 25, calculation unit 26, and output unit 27 are realized by the execution of a computer program (software) stored in the storage unit 23 by the hardware, such as a microcomputer or processor, that constitutes the information processing unit 22.

[0030] The memory unit 23 is a storage device that stores information necessary for calculating exercise intensity. This information includes computer programs executed by the information processing unit 22. The memory unit 23 is implemented, for example, by a semiconductor memory.

[0031] Next, the display device 30 will be described. The display device 30 receives exercise intensity information, which indicates the exercise intensity calculated by the server device 20, from the server device 20 and displays (visualizes) the received information. The display device 30 displays the information to the user. The display device 30 is a general-purpose portable terminal such as a smartphone or tablet, but it may also be a dedicated terminal for the electric assist bicycle 10, such as a cycle computer. If the display device 30 is a dedicated terminal for the electric assist bicycle 10, the display device 30 can be considered as part of the electric assist bicycle 10. As shown in Figure 1, the display device 30 is attached to the handlebars of the electric assist bicycle 10, for example, but if the display device 30 is a general-purpose portable terminal, it does not need to be attached to the handlebars.

[0032] The display device 30 specifically comprises an input receiving unit 31, a display unit 32, a communication unit 33, an information processing unit 34, and a storage unit 35.

[0033] The input receiving unit 31 receives user input. Specifically, the input receiving unit 31 is implemented by a touch panel or hardware keys (buttons), etc.

[0034] The display unit 32 displays an image (also referred to as the input screen) that the user views in order to input the perceived load. The display unit 32 displays, for example, an image showing the calculated exercise intensity, and other similar information. The display unit 32 is implemented by a display panel such as a liquid crystal panel or an organic EL (Electro Luminescence) panel.

[0035] The communication unit 33 is a communication circuit that enables the display device 30 to communicate with the server device 20 via a wide-area communication network. The communication performed by the communication unit 33 is, for example, wireless communication.

[0036] The information processing unit 34 performs information processing to display information related to the user's exercise intensity. The information processing unit 34 is implemented by, for example, a microcomputer, but may also be implemented by a processor. The information processing unit 34 includes a display processing unit 36 ​​as a functional component. The function of the display processing unit 36 ​​is realized by the execution of a computer program (software) stored in the storage unit 35 by the hardware, such as a microcomputer or processor, that constitutes the information processing unit 34.

[0037] The memory unit 35 is a storage device that stores the information necessary for the above-mentioned information processing. This information includes computer programs executed by the information processing unit 34. The memory unit 35 is implemented, for example, by a semiconductor memory.

[0038] Next, the administrator terminal 40 will be described. The administrator terminal 40 receives exercise intensity information, which indicates the exercise intensity calculated by the server device 20, from the server device 20 and displays (visualizes) the received information. While the display device 30 described above displays information to the user, the administrator terminal 40 displays information to the administrator who manages the user's exercise status. The server device 20 can calculate the exercise intensity of multiple users, and the administrator terminal 40 displays information related to the exercise intensity of multiple users.

[0039] The administrator terminal 40 is, for example, a stationary information terminal such as a personal computer, but it may also be a portable information terminal such as a smartphone or tablet. Specifically, the administrator terminal 40 comprises an input receiving unit 41, a display unit 42, a communication unit 43, an information processing unit 44, and a storage unit 45.

[0040] The input receiving unit 41 receives user input. Specifically, the input receiving unit 41 is implemented using a mouse, keyboard, or touch panel.

[0041] The display unit 42 displays an image (also referred to as the input screen) that the user views in order to input the perceived load. The display unit 42 displays, for example, an image showing the calculated exercise intensity, and other similar information.

[0042] The communication unit 43 is a communication circuit that allows the administrator terminal 40 to communicate with the server device 20 via a wide-area communication network. The communication performed by the communication unit 43 may be, for example, wired communication, but it may also be wireless communication.

[0043] The information processing unit 44 performs information processing to display information related to the user's exercise intensity. The information processing unit 44 is implemented by, for example, a microcomputer, but may also be implemented by a processor. The information processing unit 44 includes a display processing unit 46 and a notification unit 47 as functional components. The functions of the display processing unit 46 and the notification unit 47 are realized by the execution of a computer program (software) stored in the storage unit 45 by the hardware, such as a microcomputer or processor, that constitutes the information processing unit 44.

[0044] The memory unit 45 is a storage device that stores the information necessary for the above-mentioned information processing. This information includes computer programs executed by the information processing unit 44. The memory unit 45 is implemented, for example, by a semiconductor memory.

[0045] [Calculation process for %VO2max (exercise intensity)] The oxygen uptake level (%VO2max) is a well-known indicator of a person's exercise intensity. The oxygen uptake level (%VO2max) is expressed by the following equation 1.

[0046] Oxygen consumption level (%VO2max) = Oxygen consumption (VO2) ÷ Maximum oxygen consumption (VO2max) ··Equation 1

[0047] In some guidelines for improving symptoms such as hypertension, hyperglycemia, and hyperlipidemia, exercise duration is specified based on %VO2max. However, measuring oxygen consumption requires a large-scale exhaled gas analyzer.

[0048] In contrast, the health management system 100 calculates the user's %VO2max using a method different from directly measuring oxygen intake using an exhaled gas analyzer, etc., and displays the calculated %VO2max. The %VO2max calculation operation of the health management system 100 is described below. Figure 3 is a flowchart of the %VO2max calculation operation.

[0049] When a user pedals the electric assist bicycle 10, the pedaling force sensor 17a measures the torque value, the rotation speed sensor 17b measures the cadence value, and the control unit 16 stores the riding data, which is time-series data of the torque value and cadence value pair, in the storage unit 18. The communication unit 19 transmits the time-series data stored in the storage unit 18 to the server device 20 at a predetermined timing. For example, the electric assist bicycle 10 transmits the riding data accumulated from when the power is turned on until it is turned off as one riding data set to the server device 20 when the user instructs it to turn off the power. The electric assist bicycle 10 may also transmit the most recent riding data set to the server device 20 when the user instructs it to turn on the power. In other words, the electric assist bicycle 10 transmits riding data periodically, for example, when the power is turned off or on.

[0050] The communication unit 21 of the server device 20 receives driving data, and the acquisition unit 24 acquires the received driving data (S11). In step S11, noise reduction (invalidation of irregular values) is performed on the acquired driving data as necessary.

[0051] The calculation unit 26 calculates the power value applied by the user to the pedal 13 based on the torque value and cadence value included in the acquired riding data (S12). More specifically in step S12, time-series data of the power value is calculated based on the time-series data of the torque value and cadence value pair. For example, if the time-series data of the torque value and cadence value pair is time-series data measured at predetermined time intervals such as 1 second, the time-series data of the power value calculated in step S12 will also be time-series data at predetermined time intervals. The power value [W] can be calculated based on the formula: 2π × crank length [m] × torque value [N·m] × cadence value [rpm] / 60.

[0052] Alternatively, the power value may be calculated by the control unit 16, and time-series data of the power value may be stored in the storage unit 18. In this case, the acquisition unit 24 acquires the time-series data of the power value as driving data in step S11, and the power value calculation process is omitted. Thus, in step S11, the acquisition unit 24 only needs to acquire driving data related to the power value (time-series data of a set of torque value and cadence value, or time-series data of the power value).

[0053] Next, the calculation unit 26 calculates the user's VO2 (oxygen consumption) based on the power value calculated in step S12 (S13). More specifically, in step S13, time-series data of VO2 is calculated based on time-series data of the power value. It is known that there is a proportional relationship between the power value and VO2, and the storage unit 23 has a conversion formula (linear function) for calculating VO2 from the power value stored in advance. The calculation unit 26 can calculate VO2 by substituting the power value into this conversion formula.

[0054] The conversion formula can be generated by a gradual increase in load test on a bicycle ergometer. Specifically, the conversion formula can be obtained by measuring VO2 using an exhaled gas analyzer while changing the load (actual power value) applied to the pedals of a bicycle ergometer in multiple subjects.

[0055] The calculation unit 26 may also calculate VO2 by substituting the moving average of the power values ​​for the most recent predetermined period into the above conversion formula. According to the inventors' knowledge, the predetermined period for calculating the moving average is preferably about 3 minutes (for example, a period of 2 minutes 30 seconds or more and 3 minutes 30 seconds or less). It is known that the power value (input) applied by the user to the pedal 13 and VO2 (output) form a first-order lag system, and the process of converting the moving average of the power values ​​to VO2 is considered appropriate.

[0056] Next, the calculation unit 26 calculates %VO2max (exercise intensity) by dividing the VO2 calculated in step S13 by VO2max (maximum oxygen uptake) based on the above formula 1 (S14). In step S14, more specifically, time-series data of %VO2max is calculated. VO2max is estimated by the estimation unit 25 before the processing in step S14 is performed and is stored in the storage unit 23 in advance. Details of the VO2max estimation method (estimation operation) will be described later.

[0057] Next, the output unit 27 outputs exercise intensity information showing the calculated %VO2max (S15). The exercise intensity information output by the output unit 27 is transmitted to the display device 30 by the communication unit 21, and the communication unit 33 of the display device 30 receives the exercise intensity information. The exercise intensity information is stored in the storage unit 23 along with the user's ID.

[0058] Next, the display processing unit 36 ​​displays an image on the display unit 32 showing the calculation result of %VO2max based on the received exercise intensity information (S16). In other words, the display unit 32 displays an image showing the calculation result in response to a command from the display processing unit 36. The display unit 32 displays %VO2max as a number, for example, but may also display the calculation result of %VO2max as a graph, as shown below.

[0059] As shown in Figure 4, the display processing unit 36 ​​displays, for example, the change in the calculated %VO2max over time in a graph. Figure 4 shows an example 1 of the display of the %VO2max calculation results. In the graph in Figure 4, the horizontal axis represents time, and the vertical axis represents the instantaneous value of %VO2max.

[0060] As mentioned above, guidelines for improving symptoms such as hypertension, hyperglycemia, and hyperlipidemia sometimes recommend exercise that raises %VO2max above the standard value (e.g., 40%). Therefore, in the example in Figure 4, the standard value is shown in the graph, and the period during which %VO2max is above the standard value is shown on the time axis.

[0061] Such graphs allow users to easily understand the changes in %VO2max over time and the periods during which %VO2max was above the baseline value.

[0062] Furthermore, the above guidelines may recommend performing exercise that results in a %VO2max of 150 minutes or more per week for a certain period of time (for example, 150 minutes). Therefore, the display processing unit 36 ​​may display the cumulative value of the time during which the calculated %VO2max is 150 minutes or more (hereinafter also referred to as exercise time). The display processing unit 36 ​​may, for example, display the cumulative value as a number, but may also display the change in the cumulative value over time as a graph, as shown in Figure 5. Figure 5 is a diagram showing an example 2 of the display of the %VO2max calculation results. In the graph in Figure 5, the horizontal axis represents time, and the vertical axis represents the cumulative value of the time during which %VO2max is 150 minutes or more (cumulative time). The cumulative time here is the cumulative time per predetermined period, such as 1 week, 1.5 months (6 weeks), or 3 months (12 weeks), and the time axis can be switched by the user.

[0063] Furthermore, in the example in Figure 5, the aforementioned fixed time period and the baseline pace are illustrated with dashed lines. The graph in Figure 5 can also be described as a graph in which the value increases only during the period when %VO2max is above the baseline value.

[0064] According to graphs like this, users can easily understand how far they have come from reaching the target time for exercise that will result in a %VO2max above a certain threshold.

[0065] Furthermore, as shown in Figure 6, the display processing unit 36 ​​may display the cumulative value of the time (the time the user achieved that exercise intensity) for each calculated magnitude of %VO2max. In other words, the display processing unit 36 ​​may display a histogram of %VO2max in units of time. Figure 6 is a diagram showing example 3 of the display of the %VO2max calculation results. In the graph in Figure 6, the horizontal axis shows the magnitude of %VO2max, and the vertical axis shows the cumulative value over time. The cumulative value is the cumulative value per predetermined period, such as 1 day, 1 week, 1.5 months (6 weeks), or 3 months (12 weeks).

[0066] Such graphs allow users to easily understand the histogram of %VO2max over time.

[0067] The health management system 100 may simultaneously display two or more of the display screens shown in Figures 4-6, or it may selectively display the display screens shown in Figures 4-6 according to user input, etc. Furthermore, the health management system 100 may be implemented as a system capable of displaying only a portion of the display screens shown in Figures 4-6.

[0068] As explained above, the health management system 100 can calculate %VO2max based on the torque value and cadence value included in the data acquired from the electric assist bicycle 10, and display information related to the calculated %VO2max on the display unit 32. %VO2max is an example of an index indicating exercise intensity.

[0069] Such a health management system 100 can help determine the user's %VO2max while riding an electric assist bicycle 10.

[0070] Furthermore, as shown in the display example in Figure 5, when the displayed exercise time is such that %VO2max is equal to or greater than the reference value, the display processing unit 36 ​​needs to calculate the exercise time based on the exercise intensity information. Here, the calculation of the exercise time may be performed by the server device 20, and the server device 20 may provide exercise time information indicating the exercise time to the display device 30.

[0071] In this case, the calculation unit 26 calculates the time during which the %VO2max calculated in step S14 is equal to or greater than the reference value as the exercise time, and the output unit 27 outputs exercise time information indicating the calculated exercise time. The exercise time information output by the output unit 27 is transmitted to the display device 30 by the communication unit 21, and the communication unit 33 of the display device 30 receives the exercise time information.

[0072] [Example 1 of VO2max estimation behavior] As shown in Equation 1 above, the value of VO2max is necessary to calculate %VO2max. Since there are individual differences in the value of VO2max, there is room for consideration as to how to estimate the value of VO2max. Below, we will explain Example 1 of the user's VO2max estimation process. Figure 7 is a flowchart of Example 1 of the VO2max estimation process.

[0073] First, the acquisition unit 24 acquires the user's attribute information (S21). This attribute information is input to the input receiving unit 31 of the display device 30, for example, during the initial setup when a user starts using the health management system 100. The attribute information input to the input receiving unit 31 is transmitted from the communication unit 33 of the display device 30 to the communication unit 21 of the server device 20, and then acquired by the acquisition unit 24. Specifically, the attribute information includes the user's gender, age, weight, and whether or not they have an exercise habit.

[0074] Next, the acquisition unit 24 acquires table information from the storage unit 23 for converting attribute information into VO2max (S22). Figure 8 shows an example of table information. As shown in Figure 8, the table information specifies the value of VO2max per unit body weight (unit: ml / kg / min) according to the user's gender, age, and whether or not they have an exercise habit. Such table information is statistically generated, for example, based on the measured VO2max values ​​of a large number of subjects with various attributes. Note that the table information may also take into account the user's height.

[0075] Next, the estimation unit 25 estimates the user's VO2max based on the attribute information obtained in step S21 and the table information obtained in step S22 (S23). The estimation unit 25 identifies the value of VO2max per unit body weight by comparing the user's gender, age, and whether or not they have exercise habits, which are included in the attribute information, with the table information, and estimates the user's VO2max by multiplying the identified value by the user's weight, which is included in the attribute information.

[0076] [Example 2 of VO2max estimation behavior] The estimation unit 25 may estimate the user's VO2max using a machine learning model. The machine learning model is constructed, for example, using the measured VO2max values ​​of a large number of subjects with various attributes used to create the table information in Figure 8 as training data, and outputs the user's VO2max when the user's attribute information is input.

[0077] Furthermore, the machine learning model may be constructed using time-series data (torque and cadence values) of electric assist bicycles 10 from a large number of subjects whose VO2max is known as training data. Below, we will describe Example 2 of the VO2max estimation process using such a machine learning model. Figure 9 is a flowchart of Example 2 of the VO2max estimation process.

[0078] First, the acquisition unit 24 acquires driving data (S31). This process is the same as step S11 in Figure 3.

[0079] Next, the estimation unit 25 estimates the user's VO2max based on the acquired driving data and the machine learning model (S32).

[0080] The machine learning model here is, for example, a trained model that can take one or more features calculated from the user's driving data as input and output the user's VO2max. The machine learning model is constructed using features calculated from the driving data of a large number of subjects whose VO2max is known as training data. In step S32, the estimation unit 25 can estimate VO2max by calculating features from the driving data acquired in step S31 and inputting the calculated features into the machine learning model. In addition to driving data, the training data may also include information indicating the condition of the road surface during driving (such as the slope of the road surface).

[0081] Furthermore, the machine learning model may be a model to which deep learning is applied, and which can estimate VO2max using the driving data itself as input. In this case, the calculation of features by the estimation unit 25 is omitted.

[0082] Thus, a machine learning model for estimating maximum oxygen uptake from riding data is generated by acquiring riding data for multiple users, which consists of torque and cadence values ​​measured by the electric assist bicycle 10 when a subject (user) with a known VO2max is riding the electric assist bicycle 10, and then training the model with the acquired riding data from multiple users. The generation of such a machine learning model may be performed by the server device 20 or by another computer not shown in the figure. Training the model with riding data here includes both training the model with features obtained from the riding data and training the model with the riding data itself.

[0083] [Example 3 of VO2max estimation behavior] By the way, as explained in step S13 of Figure 3, VO2 and power value are proportional, so VO2max can be considered to be the maximum power that the user can exert. In the operation of the electric assist bicycle 10, the user's maximum power is exerted when the electric assist bicycle 10 is traveling uphill (traveling in the direction of going uphill).

[0084] Below, we will describe Example 3 of the VO2max estimation process based on running data while running uphill. Figure 10 is a flowchart of Example 3 of the VO2max estimation process.

[0085] First, the acquisition unit 24 acquires driving data (S41). This process is the same as step S11 in Figure 3. As mentioned above, the driving data is time-series data of torque values ​​and cadence values. Noise reduction (invalidation of irregular values) is performed on the driving data acquired in step S41 as needed.

[0086] Next, the estimation unit 25 calculates the power value applied by the user to the pedal 13 based on the torque value and cadence value included in the acquired driving data (S42). This process is similar to step S12 in Figure 3, and more specifically, time-series data of the power value is calculated based on the time-series data of the torque value and cadence value pair.

[0087] Next, the estimation unit 25 estimates the period during which the electric assist bicycle 10 is traveling uphill based on the time-series data of power values ​​(S43). Figure 11 is a diagram illustrating the method for estimating the period during which the electric assist bicycle 10 is traveling uphill, and Figure 11 shows the time-series data of power values ​​and the time-series data of cadence values. Figure 11(b) is an enlarged view of the area enclosed by the dashed frame in Figure 11(a).

[0088] After diligent research, the inventors discovered that when the electric assist bicycle 10 is traveling uphill, the power output is higher than when it is traveling on a flat road, and that both the power output and cadence decrease over time (monotonically). The decrease in power output and cadence over time is thought to be due to the user gradually becoming fatigued.

[0089] Therefore, in step S43, the estimation unit 25 identifies a period in which the power value is greater than a predetermined value, and in which the power value and the cadence value corresponding to that power value decrease. In Figure 11, the area enclosed by the solid line frame in Figure 11(b) corresponds to such a period.

[0090] In the example shown in Figure 11, the predetermined value is, for example, the average value of the power values ​​over the entire period of the time-series data of power values ​​calculated in step S42 (80 [W]). The predetermined value is the average value of all power values ​​calculated so far and may be updated each time driving data is acquired. Alternatively, the predetermined value may be a fixed value such as 100 [W]. If the predetermined value is a fixed value, it is determined empirically or experimentally as appropriate by the designer of the health management system 100.

[0091] In addition, in step S43, a requirement may be added that the duration of the specified period must be greater than or equal to a predetermined length, in addition to the two requirements mentioned above. The predetermined length is, for example, about 5 to 10 seconds. This excludes periods in which the power value and cadence value decrease in the short term, that is, periods in which it is estimated that the electric assist bicycle 10 is not traveling uphill.

[0092] Next, the estimation unit 25 estimates the user's VO2max based on the maximum power value during the estimated period (S44). In the example in Figure 11, the maximum power value during the specified period is 175 [W]. The estimation unit 25 can estimate VO2max by substituting the maximum power value into the conversion formula used in step S13 of Figure 3. The estimated VO2max is stored in the storage unit 23.

[0093] Figure 12 shows the correlation between the maximum power values ​​calculated by the processing in steps S41 to S44 for five subjects and the measured VO2max values ​​of those five subjects. As shown in Figure 12, there is a strong correlation between the maximum power values ​​calculated by the processing in steps S41 to S44 and the measured VO2max values ​​of the five subjects, indicating that Example 3 of the VO2max estimation process is a highly valid estimation method.

[0094] Alternatively, another method for determining the duration during which the electric assist bicycle 10 is traveling uphill could be to use a GNSS (Global Navigation Satellite System) module, such as a GPS (Global Positioning System) module.

[0095] For example, the display device 30 attached to the electric assist bicycle 10, or the electric assist bicycle 10 itself, may be equipped with a GNSS module, and the riding data may include time-series data of the coordinates (latitude and longitude) of the electric assist bicycle 10's current position. In such a case, the estimation unit 25 can identify the period during which the electric assist bicycle 10 is riding uphill by querying another server device that manages map information (topographic information) for the slope of the road surface while the electric assist bicycle 10 is riding.

[0096] [VO2max update behavior] VO2max can be considered an indicator of a user's aerobic exercise capacity. If a user's aerobic exercise capacity improves, the VO2max value will increase, and if the user's aerobic exercise capacity decreases, the VO2max value will decrease. Therefore, a user's VO2max value may be updated. Figure 13 is a flowchart of the VO2max update process.

[0097] For example, the electric assist bicycle 10 collects riding data from the time it is turned on until it is turned off, and when the user instructs it to turn off the power, it sends this single riding data to the server device 20.

[0098] The communication unit 21 of the server device 20 receives one run data, and the acquisition unit 24 acquires the received run data (S51).

[0099] Next, the estimation unit 25 calculates the power value applied by the user to the pedal 13 based on the torque value and cadence value included in the acquired driving data (S52). This process is similar to step S12 in Figure 3, and more specifically, time-series data of the power value is calculated based on the time-series data of the torque value and cadence value pair.

[0100] Next, the estimation unit 25 determines whether the time-series data of the power value includes a period in which the electric assist bicycle 10 is traveling uphill (S53). Similar to the process in step S43, the estimation unit 25 determines whether there is a period in which the power value is greater than a predetermined value and in which the power value and the cadence value corresponding to that power value decrease. The estimation unit 25 may also determine whether there is a period in which the power value is greater than a predetermined value and in which the power value and the cadence value corresponding to that power value decrease, and in which the duration is greater than or equal to a predetermined length.

[0101] If the estimation unit 25 determines that the time-series data of the power value includes a period in which the electric assist bicycle 10 is traveling uphill (Yes in S53), it updates the VO2max (S54). Specifically, the estimation unit 25 replaces (overwrites) the VO2max stored in the memory unit 23 with the VO2max based on the maximum power value during that period.

[0102] On the other hand, if the estimation unit 25 determines that the time-series data of the power value does not include the period during which the electric assist bicycle 10 is traveling uphill (No in S53), it does not update the VO2max.

[0103] As explained above, when the estimation unit 25 calculates a new maximum power value, it updates the VO2max stored in the memory unit 23 with the newly calculated maximum power value. As a result, the calculation unit 26 can calculate the user's exercise intensity based on the latest VO2max stored in the memory unit 23.

[0104] As the initial value of VO2max (or maximum power value), the VO2max (or the corresponding maximum power value) is determined based on the user's attribute information and the table information (Figure 8) that converts the attribute information to maximum oxygen consumption, as explained in Example 1 of the VO2max estimation operation. The initial value refers to the value used when VO2max has never been estimated, such as immediately after the introduction of the health management system 100.

[0105] [Displaying information on the administrator terminal] As described above, the server device 20 can acquire riding data from multiple users from multiple electric assist bicycles 10, estimate the exercise intensity of multiple users, and store the exercise intensity information of multiple users in the storage unit 23. The exercise intensity information is distinguished by being stored in the storage unit 23 in association with the user's ID.

[0106] Here, the output unit 27 of the server device 20 may output exercise intensity information for multiple users. Once the outputted exercise intensity information for multiple users is transmitted from the server device 20 to the administrator terminal 40, the display processing unit 46 can display information related to the exercise intensity of multiple users on the display unit 42 based on the exercise intensity information of multiple users. Figure 14 shows an example of the display of exercise intensity information for multiple users.

[0107] In the example shown in Figure 14, the display unit 42 shows the exercise time, achievement rate, ranking, status, and estimated VO2max for five users.

[0108] Exercise time is the cumulative value of the time during which the %VO2max (calculated by the calculation unit 26) indicated by the exercise intensity information exceeds a certain threshold. If the exercise time is updated and displayed daily, administrators can monitor the exercise status of multiple users.

[0109] When the exercise time is displayed in this manner, the display processing unit 46 needs to calculate the exercise time based on the exercise intensity information. Here, the calculation of the exercise time may be performed by the server device 20 (calculation unit 26), and the server device 20 (output unit 27) may provide exercise time information indicating the exercise time to the administrator terminal 40. This eliminates the need for the display processing unit 46 to calculate the exercise time.

[0110] The achievement rate shows the percentage of exercise time achieved relative to the target value (target time). The ranking is the ranking of exercise time among multiple users (ranking based on exercise intensity), with the user with the longest exercise time ranking first. The status indicates the degree of achievement, and is displayed with symbols such as "○" for 100% or more, "△" for 50% or less but less than 100%, and "×" for less than 50%.

[0111] The VO2max estimate is the VO2max calculated (estimated) by the estimation unit 25. When the VO2max estimate is displayed in this way, the maximum oxygen consumption information indicating the VO2max calculated by the estimation unit 25 is provided from the server device 20 to the administrator terminal 40.

[0112] Specifically, the output unit 27 of the server device 20 outputs maximum oxygen consumption information, which represents the calculated VO2max (maximum oxygen consumption corresponding to the maximum power value). The maximum oxygen consumption information output by the output unit 27 is transmitted to the administrator terminal 40 by the communication unit 21, and the communication unit 43 of the administrator terminal 40 receives the maximum oxygen consumption information. As a result, the display processing unit 46 can display VO2max on the display unit based on the maximum oxygen consumption information output by the output unit 27.

[0113] The VO2max estimate displayed in Figure 14 is estimated based on Example 3 of the VO2max estimation process and is updated daily. In other words, the maximum oxygen consumption information is provided from the server device 20 to the administrator terminal 40 each time the VO2max estimated by the estimation unit 25 is updated.

[0114] The estimated VO2max can be considered an indicator of a user's aerobic exercise capacity, and if the estimated VO2max is updated and displayed daily, administrators can track the progress of fitness improvements for multiple users.

[0115] The display processing unit 46 may also display the fluctuations (changes over time) of the estimated VO2max value and the update frequency. As mentioned above, the estimated VO2max value is not updated unless the user rides the electric assist bicycle 10 uphill. If it is displayed that the estimated VO2max value remains at its initial value, or that the update frequency of the estimated VO2max value is low, the administrator can investigate the cause and take steps to improve it (or encourage the user to improve it).

[0116] Incidentally, the administrator terminal 40 manages the exercise time of multiple users and can notify users who are deemed to be not getting enough exercise. For example, the notification unit 47 notifies a user if it determines that the user is not getting enough exercise based on their exercise time. The notification unit 47 can determine, for example, that a user whose status is marked with an "X" is not getting enough exercise.

[0117] Notifications from the notification unit 47 are possible because the user's ID and the recipient (email address, etc.) are associated. The content of the notification may be an alert or it may be something like encouraging the user's exercise.

[0118] [Variations in the method for calculating exercise time] In the above embodiment, the calculation unit 26 calculates the time during which %VO2max is equal to or greater than a reference value as exercise time, and does not treat the time during which %VO2max is less than a reference value as exercise time. However, if the time during which %VO2max is less than a reference value continues for a predetermined period of time or less (i.e., %VO2max falls below a reference value in the short term), such time may be treated as exercise time. Figure 15 shows a modified example of this method for calculating exercise time.

[0119] As shown in Figure 15, the calculation unit 26 does not treat the time T1 during which the state in which %VO2max is below the reference value continues for a longer period than a predetermined time as exercise time. On the other hand, the calculation unit 26 treats the time T2 during which the state in which %VO2max is below the reference value continues for a longer period than a predetermined time as exercise time. The predetermined time is, for example, 3 minutes.

[0120] This calculation method allows you to obtain almost the same exercise time as when calculating %VO2max from instantaneous power values, even when using a moving average of power values.

[0121] [Example 1 of the functional configuration of a health management system] In the above embodiment, the calculation of %VO2max and the estimation of VO2max were mainly performed by the server device 20. In other words, the health management system 100 was mainly realized by the server device 20, which included an acquisition unit 24, an estimation unit 25, a calculation unit 26, and an output unit 27. However, in the health management system 100, the calculation of %VO2max and the estimation of VO2max may be performed by the electric assist bicycle 10. Figure 16 shows a modified example 1 of the functional configuration of the health management system 100.

[0122] As shown in Figure 16, the health management system 100a comprises an electric assist bicycle 10a and a display device 30a. The main difference between the electric assist bicycle 10a and the electric assist bicycle 10 is the configuration of the control unit 16a.

[0123] The control unit 16a is a control device implemented by a microcomputer or processor. The control unit 16a comprises, as functional components, an acquisition unit 16b, an estimation unit 16c, a calculation unit 16d, and an output unit 16e. The functions of the acquisition unit 16b, estimation unit 16c, calculation unit 16d, and output unit 16e are realized by the execution of a computer program (software) stored in the storage unit 18 by the hardware, such as the processor or microcomputer, that constitutes the control unit 16a.

[0124] The acquisition unit 16b, estimation unit 16c, calculation unit 16d, and output unit 16e can perform the same processing as the acquisition unit 24, estimation unit 25, calculation unit 26, and output unit 27. In the above embodiment, the acquisition unit 24, estimation unit 25, calculation unit 26, and output unit 27 may be replaced with the acquisition unit 16b, estimation unit 16c, calculation unit 16d, and output unit 16e.

[0125] Another difference between the electric assist bicycle 10a and the electric assist bicycle 10 is that the communication unit 19a communicates with the communication unit 33a of the display device 30a, and exercise intensity information, exercise time information, and maximum oxygen consumption information are provided from the electric assist bicycle 10a to the display device 30a. The communication unit 19a is a communication circuit that performs short-range wireless communication with the communication unit 33a according to a communication standard such as BLE (Bluetooth® Low Energy) or Wi-Fi®. If the display device 30a is a cycle computer provided by the electric assist bicycle 10a, the communication unit 19a may be a communication circuit that performs wired communication with the communication unit 33a.

[0126] Thus, the health management system 100a may be implemented by an electric assist bicycle 10a including an acquisition unit 16b, an estimation unit 16c, a calculation unit 16d, and an output unit 16e. In the case where the display device 30a in the health management system 100a is a cycle computer provided by the electric assist bicycle 10a, the health management system 100a can be said to be implemented by an electric assist bicycle 10a including an acquisition unit 16b, an estimation unit 16c, a calculation unit 16d, an output unit 16e, and a display processing unit 36.

[0127] [Variation 2 of the functional configuration of the health management system] Furthermore, in the health management system 100, the calculation of %VO2max and the estimation of VO2max may be performed by the display device 30. Figure 17 shows a modified example 2 of the functional configuration of the health management system 100.

[0128] As shown in Figure 17, the health management system 100b comprises an electric assist bicycle 10b and a display device 30b. The main difference between the display device 30b and the display device 30 lies in the configuration of the information processing unit 34b.

[0129] The information processing unit 34b performs information processing to display information related to the user's exercise intensity, as well as calculating %VO2max and estimating VO2max. The information processing unit 34b is implemented by, for example, a microcomputer, but may also be implemented by a processor. The information processing unit 34b comprises, as functional components, an acquisition unit 34c, an estimation unit 34d, a calculation unit 34e, an output unit 34f, and a display processing unit 36. The functions of the acquisition unit 34c, estimation unit 34d, calculation unit 34e, output unit 34f, and display processing unit 36 ​​are realized by the execution of a computer program (software) stored in the storage unit 35 by hardware such as a microcomputer or processor that constitutes the information processing unit 34b.

[0130] The acquisition unit 34c, estimation unit 34d, calculation unit 34e, and output unit 34f can perform the same processing as the acquisition unit 24, estimation unit 25, calculation unit 26, and output unit 27. In the above embodiment, the acquisition unit 24, estimation unit 25, calculation unit 26, and output unit 27 may be replaced with the acquisition unit 34c, estimation unit 34d, calculation unit 34e, and output unit 34f.

[0131] Another difference between the display device 30b and the display device 30 is that the communication unit 33b communicates with the communication unit 19b of the electric assist bicycle 10b, and torque values, cadence values, etc., are provided from the electric assist bicycle 10b to the display device 30b. The communication unit 33b is a communication circuit that performs short-range wireless communication with the communication unit 19b according to a communication standard such as BLE or Wi-Fi (registered trademark). If the display device 30b is a cycle computer provided by the electric assist bicycle 10, the communication unit 33b may be a communication circuit that performs wired communication with the communication unit 19b.

[0132] Thus, the health management system 100b may be implemented by a display device 30b (a portable terminal or cycle computer) that includes an acquisition unit 34c, an estimation unit 34d, a calculation unit 34e, and an output unit 34f.

[0133] [Variation 3 of the functional configuration of the health management system] In the health management system 100, the electric assist bicycle 10 and the display device 30 do not have a communication unit that connects to a wide-area communication network, and it is conceivable that a mobile terminal may be used as a relay for communication between the electric assist bicycle 10 and the display device 30 and the server device 20. Figure 18 shows a modified example 3 of the functional configuration of the health management system 100.

[0134] As shown in Figure 18, the health management system 100c comprises an electric assist bicycle 10c, a server device 20, a display device 30c, an administrator terminal 40, and a mobile terminal 50.

[0135] Since the only difference between the electric assist bicycle 10c and the electric assist bicycle 10c is the communication unit 19c, the diagrams of components other than the communication unit 19c are omitted. The communication unit 19c is a communication circuit that performs short-range wireless communication with the first communication unit 51 of the mobile terminal 50, for example, according to a communication standard such as BLE or Wi-Fi (registered trademark).

[0136] Since the display device 30c differs from the display device 30 only in its communication unit 33c, the other components are not shown in the diagram. The communication unit 33c is a communication circuit that performs short-range wireless communication with the first communication unit 51 of the mobile terminal 50, for example, according to a communication standard such as BLE or Wi-Fi (registered trademark).

[0137] The mobile terminal 50 is a general-purpose mobile terminal such as a smartphone or tablet. The mobile terminal 50 includes a first communication unit 51 and a second communication unit 52.

[0138] The first communication unit 51 is a communication circuit that performs short-range wireless communication with the communication unit 19c of the electric assist bicycle 10c and the communication unit 33c of the display device 30c.

[0139] The second communication unit 52 is a communication circuit that communicates with the communication unit 21 of the server device 20 via a wide-area communication network. The wide-area communication network here includes mobile communication networks and the internet.

[0140] Such a health management system 100c can perform the same operations as the health management system 100.

[0141] [Effects, etc.] The following describes examples of inventions that can be obtained from the disclosures in this specification, and explains the effects and other benefits that can be obtained from these examples.

[0142] Invention 1 is a health management system 100 comprising: an acquisition unit 24 that acquires driving data related to the power value applied to the pedals 13 by a user riding an electric assist bicycle 10; an estimation unit 25 that estimates the period during which the electric assist bicycle 10 is traveling uphill based on the acquired driving data and calculates the maximum power value during the estimated period; a calculation unit 26 that calculates the user's exercise intensity based on the calculated maximum power value; and an output unit 27 that outputs exercise intensity information indicating the calculated exercise intensity. In the above embodiment, exercise intensity is also referred to as %VO2max.

[0143] Such a health management system 100 can calculate the exercise intensity when a user is riding the electric assist bicycle 10 by estimating the duration that the electric assist bicycle 10 is traveling uphill.

[0144] Invention 2 is a health management system 100 of Invention 1, wherein the estimation unit 25 calculates a power value based on the torque value and cadence value included in the acquired driving data, and estimates that the period during which the calculated power value is greater than a predetermined value and during which the power value and cadence value decrease is the period during which the electric assist bicycle 10 is driving uphill.

[0145] This health management system 100 can calculate the exercise intensity when a user is riding an electric assist bicycle 10 using this specific estimation method.

[0146] Invention 3 is a health management system 100 of Invention 1 or 2, further comprising a storage unit 23 in which the calculated maximum power value is stored, an estimation unit 25 updates the maximum power value stored in the storage unit 23 with the newly calculated maximum power value when a new maximum power value is calculated, and a calculation unit 26 calculates the user's exercise intensity based on the latest maximum power value stored in the storage unit 23.

[0147] Such a health management system 100 can update the maximum power value and calculate the exercise intensity when the user is riding the electric assist bicycle 10 based on the latest maximum power value (the user's latest exercise ability).

[0148] Invention 4 is a health management system 100 of Invention 3, wherein the memory unit 23 stores a maximum power value as an initial value, which is determined based on the user's attribute information and table information that converts the attribute information into maximum oxygen uptake. In the above embodiment, maximum oxygen uptake is also referred to as VO2max.

[0149] Such a health management system 100 can calculate the exercise intensity when a user is riding the electric assist bicycle 10, using initial values, even if the electric assist bicycle 10 has never been ridden uphill.

[0150] Invention 5 is a health management system 100 comprising: an acquisition unit 24 that acquires user attribute information of an electric assist bicycle 10; a storage unit 23 that stores table information that converts the attribute information into a maximum oxygen intake; an estimation unit 25 that estimates the user's maximum oxygen intake based on the acquired attribute information and the table information; a calculation unit 26 that calculates the user's exercise intensity based on the estimated maximum oxygen intake; and an output unit 27 that outputs exercise intensity information indicating the calculated exercise intensity.

[0151] Such a health management system 100 can calculate the exercise intensity when a user is riding an electric assist bicycle 10 by estimating the user's maximum oxygen uptake based on table information.

[0152] Invention 6 is a health management system 100 which is one of Inventions 1 to 4, wherein the calculation unit 26 calculates a power value based on the torque value and cadence value included in the acquired running data, and calculates exercise intensity by dividing the oxygen consumption determined by the calculated power value by the maximum oxygen consumption determined by the maximum power value.

[0153] Such a health management system 100 can calculate the exercise intensity when a user is riding an electric-assist bicycle 10 by dividing the oxygen intake by the maximum oxygen intake.

[0154] Invention 7 is a health management system 100 of Invention 6, wherein the calculation unit 26 calculates exercise intensity by dividing the oxygen consumption, which is determined by the moving average of the calculated power values, by the maximum oxygen consumption, which is determined by the maximum power value.

[0155] Such a health management system 100 can calculate the exercise intensity when a user is riding an electric assist bicycle 10 by dividing the oxygen consumption, which is determined by the moving average of power values, by the maximum oxygen consumption, which is determined by the maximum power value.

[0156] Invention 8 is a health management system 100 of Invention 6 or 7, wherein the calculation unit 26 calculates the time during which the calculated exercise intensity is equal to or greater than a standard value as the exercise time, and the output unit 27 outputs exercise time information indicating the calculated exercise time.

[0157] Such a health management system 100 can calculate exercise time.

[0158] Invention 9 is a health management system 100 according to Invention 8, wherein the calculation unit 26 treats the time during which the calculated exercise intensity is less than a standard value as exercise time if that time is within a predetermined time.

[0159] Such a health management system 100 can treat periods of low exercise intensity as exercise time.

[0160] Invention 10 further includes a display processing unit 36 ​​that displays the user's exercise intensity on a display unit 32 based on the outputted exercise intensity information, and the display processing unit 36 ​​displays at least one of the following on the display unit 32: the change in exercise intensity over time, the cumulative value of the time when the exercise intensity is above a standard value, the change in the cumulative value over time, and the cumulative value of the time when the exercise intensity of each magnitude is obtained. This is a health management system 100 according to any of Inventions 1 to 9.

[0161] Such a health management system 100 can display various information related to exercise intensity.

[0162] Invention 11 is a health management system 100 of any of Inventions 1 to 9, further comprising a display processing unit 46 that displays the user's exercise intensity and the exercise intensity of other users on a display unit 42 based on the outputted exercise intensity information.

[0163] Such a health management system 100 can display the exercise intensity of multiple users.

[0164] Invention 12 is a health management system 100 of Invention 11, wherein the display processing unit 46 displays the ranking of exercise intensity for multiple users on the display unit 42.

[0165] Such a health management system 100 can display rankings of exercise intensity for multiple users.

[0166] Invention 13 is a health management system 100 of any of Inventions 1 to 12, further comprising a notification unit 47 that notifies the user if it is determined that the user is not getting enough exercise based on the outputted exercise intensity information.

[0167] Such a health management system 100 can notify users who are not getting enough exercise.

[0168] Invention 14 is a health management system 100 of Invention 3 or 4, wherein the output unit 27 further outputs maximum oxygen intake information indicating the maximum oxygen intake corresponding to the calculated maximum power value, and the health management system 100 further includes a display processing unit 46 that displays the user's maximum oxygen intake on the display unit 42 based on the output maximum oxygen intake information.

[0169] Such a health management system 100 can display the user's maximum oxygen intake.

[0170] Invention 15 is a health management system 100 of any of Inventions 1 to 14, which is implemented by a server device 20 including an acquisition unit 24, an estimation unit 25, a calculation unit 26, and an output unit 27.

[0171] This health management system 100 can calculate the user's exercise intensity through information processing performed by the server device 20.

[0172] Invention 16 is a health management system 100 of any of Inventions 1 to 14, implemented by a portable terminal including an acquisition unit 24, an estimation unit 25, a calculation unit 26, and an output unit 27. In other words, Invention 16 is a health management system 100b, implemented by a display device 30b as a portable terminal including an acquisition unit 34c, an estimation unit 34d, a calculation unit 34e, and an output unit 34f.

[0173] This health management system 100b can calculate the user's exercise intensity through information processing performed by the display device 30b.

[0174] Invention 17 is a health management system 100 of any of Inventions 1 to 14, implemented by an electric assist bicycle 10 including an acquisition unit 24, an estimation unit 25, a calculation unit 26, and an output unit 27. In other words, Invention 17 is a health management system 100a, implemented by an electric assist bicycle 10a including an acquisition unit 16b, an estimation unit 16c, a calculation unit 16d, and an output unit 16e.

[0175] Such a health management system 100a can calculate the user's exercise intensity through information processing performed by the electric assist bicycle 10a.

[0176] Invention 18 is a health management method performed by a computer such as a health management system 100. The health management method includes an acquisition step of acquiring riding data related to the power values ​​applied to the pedals 13 by a user riding an electric assist bicycle 10; an estimation step of estimating the period during which the electric assist bicycle 10 is riding uphill based on the acquired riding data and calculating the maximum power value during the estimated period; a calculation step of calculating the user's exercise intensity based on the calculated maximum power value; and an output step of outputting exercise intensity information indicating the calculated exercise intensity.

[0177] This health management method allows for the calculation of the exercise intensity a user experiences while riding the electric assist bicycle 10 by estimating the duration that the electric assist bicycle 10 is traveling uphill.

[0178] Invention 19 is a health management method performed by a computer, such as a health management system 100. The computer includes a storage unit 23 that stores table information for converting attribute information into maximum oxygen consumption. The health management method includes an acquisition step of acquiring attribute information of a user of an electric assist bicycle 10, an estimation step of estimating the user's maximum oxygen consumption based on the acquired attribute information and table information, a calculation step of calculating the user's exercise intensity based on the estimated maximum oxygen consumption, and an output step of outputting exercise intensity information indicating the calculated exercise intensity.

[0179] This health management method can calculate the exercise intensity when a user is riding an electric assist bicycle 10 by estimating the user's maximum oxygen uptake based on table information.

[0180] Invention 21 is a method for generating a machine learning model that is executed by a computer such as a health management system 100. The method for generating a machine learning model includes an acquisition step of acquiring riding data measured by an electric assist bicycle 10 when a user whose maximum oxygen uptake is known is riding the electric assist bicycle 10 for multiple users, and a generation step of generating a machine learning model for estimating maximum oxygen uptake from riding data by training the acquired riding data for multiple users.

[0181] This method of generating machine learning models can create models for estimating maximum oxygen uptake from running data.

[0182] (Other embodiments) Although embodiments have been described above, the present invention is not limited to the embodiments described above.

[0183] For example, in the above embodiment, the health management system was implemented by multiple devices. In this case, the components of the health management system (especially the functional components) may be distributed among the multiple devices in any way.

[0184] Furthermore, the health management system may be implemented by a single device. For example, the health management system may be implemented as a single device corresponding to the electric assist bicycle, server device, or display device in the above embodiment.

[0185] Furthermore, in the above embodiment, the processing performed by a specific processing unit may be performed by another processing unit. Also, the order of multiple processing units may be changed, or multiple processing units may be executed in parallel.

[0186] Furthermore, in the above embodiment, each component may be realized by executing a software program suitable for each component. Each component may also be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0187] Furthermore, each component may be implemented by hardware. For example, each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or they may be separate circuits. Also, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0188] Furthermore, general or specific embodiments of the present invention may be implemented as a system, apparatus, method, integrated circuit, computer program, or recording medium such as a computer-readable CD-ROM. Alternatively, they may be implemented as any combination of a system, apparatus, method, integrated circuit, computer program, and recording medium.

[0189] For example, the present invention may be implemented as an electric assist bicycle, server device, display device, or administrator terminal according to the above embodiment. Furthermore, the present invention may be implemented as a health management method executed by a computer, such as a health management system, or as a program for causing a computer to execute such a health management method. The present invention may also be implemented as a computer-readable non-temporary recording medium on which such a program is stored.

[0190] Furthermore, the present invention may be implemented as a method for generating machine learning models to be executed by a computer, such as a health management system, or as a program for causing a computer to execute such a method for generating machine learning models. The present invention may also be implemented as a computer-readable non-temporary recording medium on which such a program is recorded.

[0191] Furthermore, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art could conceive, or forms realized by arbitrarily combining the components and functions of each embodiment without departing from the spirit of the present invention. [Explanation of Symbols]

[0192] 10, 10a, 10b, 10c Electric-assist bicycles 13 pedals 20 Server Devices 23 Memory section 16b, 24, 34c acquisition part 16c, 25, 34d Estimation section 16d, 26, 34e calculation section 16e, 27, 34f output section 30b Display device (mobile terminal) 32, 42 display section 36, 46 Display Processing Unit 47 Notification Department 100, 100a, 100b, 100c Health Management System

Claims

1. An acquisition unit that acquires data related to the power values ​​applied to the pedals by a user riding an electric assist bicycle, An estimation unit estimates the period during which the electric assist bicycle is traveling uphill based on the acquired data, and calculates the maximum power value during the estimated period. A calculation unit that calculates the user's exercise intensity based on the calculated maximum power value, The system includes an output unit that outputs exercise intensity information indicating the calculated exercise intensity. Health management system.

2. The estimation unit, Based on the torque and cadence values ​​included in the acquired data, the power value is calculated. The period during which the calculated power value is greater than a predetermined value, and during which the power value and cadence value decrease, is estimated to be the period during which the electric assist bicycle is traveling uphill. The health management system according to claim 1.

3. The health management system further includes a storage unit in which the calculated maximum power value is stored. When the estimation unit calculates a new maximum power value, it updates the maximum power value stored in the storage unit with the newly calculated maximum power value. The calculation unit calculates the user's exercise intensity based on the latest maximum power value stored in the storage unit. The health management system according to claim 1.

4. The storage unit stores a maximum power value as an initial value, which is determined based on the user's attribute information and table information that converts the attribute information into a maximum oxygen intake. The health management system according to claim 3.

5. The calculation unit described above, Based on the torque and cadence values ​​included in the acquired data, the power value is calculated. The exercise intensity is calculated by dividing the oxygen consumption determined by the calculated power value by the maximum oxygen consumption determined by the maximum power value. The health management system according to claim 1.

6. The calculation unit calculates the exercise intensity by dividing the oxygen consumption, which is determined by the moving average of the calculated power values, by the maximum oxygen consumption, which is determined by the maximum power value. The health management system according to claim 5.

7. The calculation unit calculates the time during which the calculated exercise intensity is equal to or greater than the standard value as the exercise time. The output unit outputs motion time information indicating the calculated motion time. The health management system according to claim 5.

8. The calculation unit described above, If the duration for which the calculated exercise intensity is below the standard value is within a predetermined time, that time will be treated as the exercise time. The health management system according to claim 7.

9. The health management system further includes a display processing unit that displays the user's exercise intensity on a display unit based on the outputted exercise intensity information. The display processing unit displays on the display unit at least one of the following: the change in exercise intensity over time, the cumulative value of the time during which the exercise intensity exceeds a reference value, the change in the cumulative value over time, and the cumulative value of the time during which each magnitude of exercise intensity is obtained. The health management system according to claim 1.

10. The health management system further includes a display processing unit that displays the user's exercise intensity and the exercise intensity of other users on a display unit, based on the outputted exercise intensity information. The health management system according to claim 1.

11. The display processing unit displays the ranking of multiple users in terms of exercise intensity on the display unit. The health management system according to claim 10.

12. The health management system further includes a notification unit that notifies the user if it is determined that the user is not getting enough exercise based on the outputted exercise intensity information. The health management system according to claim 1.

13. The output unit further outputs maximum oxygen consumption information indicating the maximum oxygen consumption corresponding to the calculated maximum power value. The health management system further includes a display processing unit that displays the user's maximum oxygen intake on the display unit based on the outputted maximum oxygen intake information. The health management system according to claim 3.

14. The health management system is implemented by a server device including the acquisition unit, the estimation unit, the calculation unit, and the output unit. A health management system according to any one of claims 1 to 13.

15. The health management system is implemented by a mobile terminal including the acquisition unit, the estimation unit, the calculation unit, and the output unit. A health management system according to any one of claims 1 to 13.

16. The health management system is implemented by an electric assist bicycle including the acquisition unit, the estimation unit, the calculation unit, and the output unit. A health management system according to any one of claims 1 to 13.

17. A health management method performed by a computer, A data acquisition step to obtain data related to the power values ​​applied to the pedals by a user riding an electric assist bicycle, An estimation step of estimating the period during which the electric assist bicycle is traveling uphill based on the acquired data, and calculating the maximum power value during the estimated period, A calculation step of calculating the user's exercise intensity based on the calculated maximum power value, The step includes an output step of outputting exercise intensity information that indicates the calculated exercise intensity. Health management method.

18. A program for causing the computer to execute the health management method described in claim 17.

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