Health management device and health management method

Abstract

To improve technology to encourage drivers to drive safely.SOLUTION: A health management device 30 is provided with a control unit 33 for determining the strength of correlation between a near-miss indicator of a driver 3 driving a vehicle 10 and a score of a health indicator of the driver 3, identifying a health indicator for which the score of the health indicator is equal to or below a first threshold value and the strength of the determined correlation is equal to or above a second threshold value, and selecting a health improvement program to improve the identified health indicators.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a health management device and a health management method. [Background technology]

[0002] Conventionally, there are known technologies for encouraging drivers to drive safely. For example, Patent Document 1 discloses a technology for estimating an accident risk from the driver's biometric data and issuing an alert. Patent Document 2 discloses a technology for predicting the accident risk after a predetermined time from the driver's biometric data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-51216 [Patent Document 2] International Publication No. 2021-251351 Summary of the Invention [Problem to be solved by the invention]

[0004] According to Patent Documents 1 and 2, it is possible to prevent accidents by estimating the risk of an accident and detecting an alert, but they do not mention a fundamental solution such as reducing the risk of an accident by maintaining the driver's health in good condition. Therefore, there is room for improvement in the technology that encourages drivers to drive safely.

[0005] In view of the above circumstances, an object of the present disclosure is to improve technology that encourages drivers to drive safely. [Means for solving the problem]

[0006] A health management device according to one embodiment of the present disclosure is a health management device having a communication unit and a control unit that communicates via the communication unit, wherein the control unit determines the strength of correlation between a near-miss index of a driver of a vehicle and a score of the driver's health index, identifies health indices whose scores are less than a first threshold and whose strength of correlation is greater than or equal to a second threshold, and selects a health improvement program to improve the identified health index.

[0007] A health management method according to one embodiment of the present disclosure includes, using a health management device, determining the strength of correlation between a near-miss index of a driver operating a vehicle and the score of the driver's health index, identifying health indices whose scores are equal to or less than a first threshold and whose strength of correlation is equal to or greater than a second threshold, and selecting a health improvement program for improving the identified health indices. [Effects of the Invention]

[0008] According to one embodiment of the present disclosure, a technique for encouraging drivers to drive safely is improved. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram illustrating a schematic configuration example of a system according to an embodiment of the present disclosure. [Figure 2] 10 is a flowchart showing an example of the operation of the health management device. [Figure 3] FIG. 10 is a schematic diagram illustrating an example of a near-miss index. [Figure 4] FIG. 1 is a schematic diagram showing stress values in a driver's daily activities. [Figure 5] FIG. 10 is a schematic diagram for explaining an example of calculating a health index score. [Figure 6] FIG. 10 is a schematic diagram showing an example of the strength of correlation between the score of a health index and a near-miss index. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described.

[0011] (Outline of the embodiment) An overview of a system 1 according to an embodiment of the present disclosure will be described with reference to Fig. 1. The system 1 includes a vehicle 10, a mobile device 20, a health management device 30, and a wearable device 40. The vehicle 10, the mobile device 20, the health management device 30, and the wearable device 40 are communicably connected to a network 2 including, for example, the Internet and a mobile communication network.

[0012] The vehicle 10 is, for example, an automobile, but is not limited to this and may be any vehicle. The automobile may be, for example, a gasoline-powered vehicle, a BEV (Battery Electric Vehicle), a HEV (Hybrid Electric Vehicle), a PHEV (Plug-in Hybrid Electric Vehicle), or a FCEV (Fuel Cell Electric Vehicle), but is not limited to these. The number of vehicles 10 included in the system 1 may be determined arbitrarily. The vehicle 10 communicates with a mobile device 20, a health management device 30, and a wearable device 40 via a network 2.

[0013] The mobile terminal 20 is a smartphone or a mobile phone carried by the driver 3 of the vehicle 10. The mobile terminal 20 communicates with the vehicle 10, the health management device 30, and the wearable terminal 40 via the network 2. The mobile terminal 20 may be communicably linked with the wearable terminal 40 via Bluetooth (registered trademark) or the like.

[0014] The health management device 30 is, for example, a computer such as a server device. The health management device 30 communicates with the vehicle 10, the mobile device 20, and the wearable device 40 via the network 2.

[0015] The wearable device 40 is an information processing terminal worn on the body of the driver 3 of the vehicle 10, and is capable of acquiring biological data such as the number of steps, heart rate, electrocardiogram, blood oxygen level, distance, activity intensity, and sleep time. The wearable device 40 may be, for example, a watch-type device worn on the wrist of the driver 3, but is not limited to a watch-type device. The wearable device 40 communicates with the vehicle 10, the mobile device 20, and the health management device 30 via the network 2. The wearable device 40 may be communicably linked to the mobile device 20 via a wireless communication interface such as Bluetooth.

[0016] The health management device 30 calculates the strength of correlation between the near-miss incident index of the driver 3 who drives the vehicle 10 and the score of the health index of the driver 3, identifies the health index for which the score of the health index is equal to or less than a first threshold and the calculated strength of correlation is equal to or greater than a second threshold, and selects a health improvement program for improving the identified health index.

[0017] As described above, according to this embodiment, health indicators that are highly correlated with so-called near misses are identified, and a health improvement program for improving the identified health indicators is selected. Therefore, even if the driver 3 of the vehicle 10 is unaware of a decline in the health indicators, it is possible to visualize the risk of health-related accidents and encourage the driver 3 to improve the health indicators. Therefore, the technology for encouraging drivers to drive safely is improved in that the probability of preventing health-related accidents is increased.

[0018] Next, each component of the system 1 will be described in detail.

[0019] (Vehicle configuration) As shown in FIG. 1, a vehicle 10 includes a communication unit 11, a measurement unit 12, a storage unit 13, and a control unit 14.

[0020] The communication unit 11 includes one or more communication interfaces that connect to the network 2. The communication interfaces are compatible with mobile communication standards such as, but not limited to, 4G (4th Generation) or 5G (5th Generation). In this embodiment, the vehicle 10 communicates with the mobile device 20, the health management device 30, and the wearable device 40 via the communication unit 11 and the network 2.

[0021] The measurement unit 12 includes one or more sensors. The measurement unit 12 may include a gyro sensor, an acceleration sensor with GPS (G sensor), a geomagnetic sensor, an altitude sensor, an air pressure sensor, etc. However, the sensors are not limited to these.

[0022] The storage unit 13 includes one or more memories. The memories may be, for example, semiconductor memories, magnetic memories, or optical memories, but are not limited to these. Each memory included in the storage unit 13 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 13 stores any information used in the operation of the vehicle 10. For example, the storage unit 13 may store system programs, application programs, embedded software, and the like. The information stored in the storage unit 13 may be updatable with information obtained from the network 2 via the communication unit 11, for example.

[0023] The control unit 14 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The processor may be, for example, a general-purpose processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), or a dedicated processor specialized for a specific process, but is not limited to these. The programmable circuit may be, for example, but is not limited to, an FPGA (Field-Programmable Gate Array). The dedicated circuit may be, for example, but is not limited to, an ASIC (Application Specific Integrated Circuit). The control unit 14 controls the overall operation of the vehicle 10.

[0024] (Mobile device configuration) As shown in FIG. 1, the mobile terminal 20 includes a communication unit 21, a storage unit 22, a display unit 23, and a control unit 24.

[0025] The communication unit 21 includes one or more communication interfaces connected to the network 2. The communication interfaces may be compatible with, but are not limited to, a mobile communication standard such as 4G or 5G, a Bluetooth standard, or a wireless LAN standard. In this embodiment, the mobile terminal 20 communicates with the vehicle 10, the health management device 30, and the wearable terminal 40 via the communication unit 21 and the network 2. The mobile terminal 20 may be communicably linked with the wearable terminal 40 via the Bluetooth standard.

[0026] The storage unit 22 includes one or more memories. Each memory included in the storage unit 22 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores any information used in the operation of the mobile terminal 20. For example, the storage unit 22 may store a system program, an application program, a database, biometric data of the driver 3 received from the wearable terminal 40, and a health management program presented (transmitted) from the health management device 30. The information stored in the storage unit 22 may be updatable with information obtained from the network 2 via the communication unit 21.

[0027] The display unit 23 includes at least one display interface capable of displaying data. The display interface is, for example, an LCD or an organic EL display, but is not limited to these.

[0028] The control unit 24 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The control unit 24 controls the overall operation of the mobile terminal 20.

[0029] (Configuration of health management device) As shown in FIG. 1, the health management device 30 includes a communication unit 31, a storage unit 32, and a control unit 33.

[0030] The communication unit 31 includes one or more communication interfaces connected to the network 2. The communication interfaces may be compatible with, for example, a mobile communication standard, a wired LAN standard, or a wireless LAN standard, but are not limited to these and may be compatible with any communication standard. In this embodiment, the health management device 30 communicates with the vehicle 10, the mobile device 20, and the wearable device 40 via the communication unit 31 and the network 2.

[0031] The storage unit 32 includes one or more memories. Each memory included in the storage unit 32 may function as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 32 stores any information used in the operation of the health management device 30. For example, the storage unit 32 may store a system program, an application program, a database, driving data (acceleration data) received from the vehicle 10, original logic for calculating a health index score, biometric data of the driver 3 received from the wearable device 40, a health index score calculated using the original logic based on the biometric data, and a program for improving the health index. The information stored in the storage unit 32 may be updatable with information obtained from the network 2 via the communication unit 31, for example.

[0032] The control unit 33 includes one or more processors, one or more programmable circuits, one or more dedicated circuits, or a combination thereof. The control unit 33 controls the overall operation of the health management device 30.

[0033] (Wearable device configuration) As shown in FIG. 1, the wearable terminal 40 includes a communication unit 41, a measurement unit 42, and a control unit 43.

[0034] The communication unit 41 includes one or more communication interfaces connected to the network 2. The communication interfaces may be compatible with, but are not limited to, a mobile communication standard such as 4G or 5G, a Bluetooth standard, or a wireless LAN standard. In this embodiment, the wearable terminal 40 communicates with the vehicle 10, the mobile terminal 20, and the health management device 30 via the communication unit 41 and the network 2. The wearable terminal 40 may be communicably linked with the mobile terminal 20 via the Bluetooth standard.

[0035] The measurement unit 42 includes one or more sensors. The measurement unit 42 may include a heart rate sensor, a blood oxygen concentration sensor, a skin temperature sensor, a blood pressure sensor, a three-axis acceleration sensor, a gyro sensor, a geomagnetic sensor, a position information sensor such as a GPS sensor, an ambient light sensor, an altitude sensor, and an air pressure sensor. However, the sensors are not limited to these.

[0036] The control unit 43 is configured by at least one processor, at least one dedicated circuit, or a combination of these. The control unit 43 may include a standard logic for calculating the health index score. The control unit 43 controls each unit of the wearable terminal 40 and executes processes related to the operation of the wearable terminal 40.

[0037] (Operation flow of the health management device) The operation of health management device 30 according to this embodiment will be described with reference to Figure 2. This operation aims to prevent health-related accidents, and involves identifying health indicators that could lead to near misses, which are just one step away from directly leading to an accident, and selecting a health improvement program that will improve the identified health indicators.

[0038] S101: The control unit 33 acquires acceleration data from the vehicle 10.

[0039] The vehicle 10 is equipped with a GPS-equipped acceleration sensor (G sensor) that measures acceleration data during driving. The acceleration data includes, but is not limited to, the distribution of acceleration during acceleration and deceleration of the vehicle 10 and changes in acceleration during driving. The control unit 33 continuously acquires the acceleration data from the vehicle 10 for a predetermined period of time and records it in the memory unit 32.

[0040] S102: The control unit 33 calculates a near miss index experienced by the driver 3 who drives the vehicle 10.

[0041] A near miss refers to an event that makes you feel "close" or "surprised" just before it directly leads to a serious disaster or accident. In this disclosure, a near miss refers to an event that occurs when the driver operates the accelerator or brake, such as suddenly braking, accelerating, or starting suddenly, while driving the vehicle 10, or when the driver operates the steering wheel, such as suddenly steering, but is not limited to these.

[0042] 3 is a schematic diagram showing an example of a near-miss index. As shown in Fig. 3, the near-miss index is the number or frequency of accelerations exceeding the acceleration at which a near-miss occurs (for example, the near-miss threshold ±0.3 G shown in Fig. 3) based on acceleration data during driving of the vehicle 10.

[0043] S103: The control unit 33 calculates the health index score of the driver 3 who drives the vehicle 10.

[0044] The control unit 33 calculates a health index score based on biological data continuously measured for a predetermined period by the wearable device 40 attached to the body of the driver 3. The wearable device 40 is capable of measuring biological data such as the number of steps, heart rate, electrocardiogram, blood oxygen level, travel distance, activity intensity, sleep time, and sleep quality. In the present disclosure, the health index scores are, but are not limited to, a stress management score S, a fatigue management score T, and a sleep management score U. The control unit 43 of the wearable device 40 calculates the stress level of the driver 3 from the measured heart rate or blood oxygen level, calculates the driver's energy consumption rate while driving from the heart rate, number of steps, travel distance, activity intensity, and so on, and further measures the sleep time and sleep quality (deep sleep time, REM sleep time, light sleep time, etc.).

[0045] The control unit 33 may acquire the driver's biometric data measured by the wearable terminal 40 as described above and calculate the health index score using the original logic stored in the memory unit 32, or may acquire and use the health index score calculated using the standard logic provided in the wearable terminal 40.

[0046] 4 is a schematic diagram showing stress values for the activities of driver 3 throughout the day. Wearable device 40 is worn on the body of driver 3 24 hours a day (although there may be times when it is not worn, such as when bathing), and calculates the stress value of driver 3 in real time from the measured heart rate or blood oxygen concentration, with 100 as the maximum value, and transmits the calculated stress value as biological data to health management device 30. In FIG. 4, a stress value of 50 or higher is defined as a high stress state, and a stress value of 25 or lower is defined as a low stress state.

[0047] FIG. 5 is a schematic diagram showing an example of calculating a health index score. As an example, a calculation example of the stress management score S will be described with reference to FIG. 5. The control unit 33 stores stress value data transmitted from the wearable device 40 in the memory unit 32 each time the stress value data is received as biometric data. The stress management score S is calculated based on stress value data continuously acquired from the wearable device 40 for one month. The stress management score S may be a driving stress management score S1 based on the average stress while driving (for example, the average value of the stress values for the time periods of Driving 1, Driving 2, and Driving 3 in FIG. 4), or a comprehensive score obtained by adding the driving stress management score S1, a high stress risk score S2 based on the proportion of time spent in a high stress state (high stress proportion) during daily activities excluding sleeping time, and a stress relief score S3 based on the proportion of time spent in a low stress state (low stress proportion).

[0048] When the stress management score S is the latter overall score, as shown in Figure 5, the control unit 33 (i) calculates the driving stress management score S1 with a maximum value of 50, (ii) calculates the high stress risk score S2 with a maximum value of 25, and (iii) calculates the stress relief score S3 with a maximum value of 25. The stress management score S is calculated using the following formula (1). Stress management score S = S1 + S2 + S3 (maximum 100) (1)

[0049] The fatigue management score T is a score that quantifies the amount or rate of physical energy consumption while driving. For example, the fatigue management score T is calculated based on data on the driving energy consumption rate continuously acquired from the wearable device 40 for one month. For example, in FIG. 4 , if heavy driving is performed during the driving time period 1, the driving energy consumption rate increases. Similarly, light driving during the driving time period 2 decreases the driving energy consumption rate. If the remaining physical energy at the start of the day is set to a maximum value of 100 upon waking, the remaining physical energy decreases through the driving time periods 1 and 2, and reaches a lower limit when the driver returns home after completing driving time period 3. The control unit 33 stores the driving energy consumption rate data transmitted from the wearable device 40 in the memory unit 32 each time it is received as biometric data. The control unit 33 calculates the fatigue management score T based on the value of the driving energy consumption rate, which is the rate of physical energy consumption while driving, with 100 as the maximum value.

[0050] The sleep management score U is calculated based on sleep time data continuously acquired over 10 months. Because sleep time data can only be acquired once a day, the sleep management score U is calculated based on data continuously acquired over 10 months, for example. The control unit 33 stores the sleep time values transmitted from the wearable device 40 in the memory unit 32 on the basis of the days they are received as biometric data. The control unit 33 calculates the sleep management score U based on the sleep time values, with 100 as the maximum value. However, the sleep management score U may be a sleep time score U1 based on the sleep time values, or a composite score of the sleep time score U1 and the sleep quality score U2. In the former case, for example, the recommended sleep time may be set to 7 hours, and the sleep management score U may be set to 100 when the sleep time is 7 hours or longer. In the latter case, the sleep management score U may be a composite score obtained by dividing the sleep time score U1 and the sleep quality score U2, with 50 as the maximum value. The sleep quality score U2 may be calculated based on the duration of deep sleep, REM sleep, or light sleep measured by the wearable device 40, in addition to the stress level during sleep, for example.

[0051] S104: The control unit 33 calculates the strength of correlation between the near-miss incident index of the driver 3 who drives the vehicle 10 and the score of the health index of the driver 3.

[0052] The strength of the correlation between the health index score and the near-miss index is calculated when the number of data items reaches a predetermined number. In other words, the stress management score S and fatigue management score T, which are based on data that can be obtained in real time, are calculated based on data obtained continuously for, for example, one month. On the other hand, the strength of the correlation with the sleep management score U, which can only be obtained daily, is calculated based on data obtained continuously for, for example, ten months.

[0053] The strength of the correlation between the near-miss incident index and the health index score is defined as the absolute value of the correlation coefficient for events where the significance level is below a specified value. The significance level is a standard for determining whether an event is significant, and 0.05 is usually used.

[0054] Figure 6 is a schematic diagram showing an example of the strength of correlation between health index scores and near-miss incident indexes. As shown in Figure 6, a correlation coefficient is calculated for events with a P value < 0.05. The P value is a number used to compare with the significance level, and when the P value < significance level, it is concluded that there is a significant difference in the event. In other words, a P value < 0.05 indicates that the probability of the event occurring is 95% or higher, and the lower the P value, the higher the probability of occurrence, resulting in an assessment of a higher degree of significance.

[0055] S105: Control unit 33 identifies health indices whose scores are equal to or less than first threshold value α and whose strength of correlation is equal to or greater than second threshold value β.

[0056] The control unit 33 selects health indexes whose scores are equal to or less than the first threshold value α. In FIG. 6, for example, the first threshold value α for the driving stress management score S1 is set to 30 with respect to a maximum value of 100, and the first threshold value α for the sleep time score U1 is set to 25 with respect to a maximum value of 100. Then, it is assumed that drivers A, B, C, D, and E are determined to have either the driving stress management score S1 or the sleep time score U1 equal to or less than the first threshold value α. Note that in S105 of FIG. 2, the requirement that "the score of the health index is equal to or less than the first threshold value α and the strength of the determined correlation is equal to or greater than the second threshold value β" is abbreviated as "meeting a predetermined requirement."

[0057] As described above, the strength of the correlation is defined by the absolute value of the correlation coefficient. The correlation coefficient is a coefficient that represents the relationship between two variables, and the strength of the linear (proportional) relationship is expressed as a number between 1 and -1. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation is determined to be. Specifically, the absolute value of the correlation coefficient is interpreted as follows: (i) when the value is between 0.0 and 0.2, there is almost no correlation; (ii) when the value is between 0.2 and 0.4, there is a slight correlation; (iii) when the value is between 0.4 and 0.7, there is a significant correlation; and (iv) when the value is between 0.7 and 1.0, there is a strong correlation. In Figure 6, if the second threshold β is set to 0.2, among Persons A through E whose health index scores were determined to be equal to or less than the first threshold α, Persons B and C whose absolute values of the correlation coefficients for their driving stress management scores S1 exceed the second threshold β are determined to have a correlation between the near-miss index and driving stress. On the other hand, for persons D and E, whose absolute values of the correlation coefficients for the sleep time score U1 exceed the second threshold value β, it is determined that there is a correlation between the near-miss incident index and the sleep time.

[0058] S106: The control unit 33 selects a health improvement program for improving the identified health index.

[0059] The control unit 33 may adjust the content of the action, the duration of the action, or the conditions of the action, etc., based on the programs for improving each health index stored in the memory unit 32, depending on the strength of the correlation with the near-miss index, but the method of program selection is not limited to this.

[0060] S107: The control unit 33 presents the selected program to the driver 3.

[0061] The control unit 33 may transmit (i) a graph showing the correlation between the near-miss index and the health index, and (ii) a selected health management program (for example, a sleep course in the case of Mr. D) to the mobile terminal 20 of the driver 3 (for example, Mr. D in FIG. 6). The control unit 33 may also display actions (ToDo list) and the degree of achievement of the actions on the display screen of the mobile terminal 20 of Mr. D, who will be a participant, in accordance with the progress of the program participation.

[0062] As described above, the health management device 30 of this embodiment determines the strength of correlation between the near-miss index of the driver 3 who drives the vehicle 10 and the score of the driver 3's health index, identifies health indexes whose health index scores are below a first threshold and whose determined strength of correlation is above a second threshold, and selects a health improvement program to improve the identified health index.

[0063] With this configuration, health indicators that are highly correlated with so-called near misses are identified, and a health improvement program for improving the identified health indicators is selected. Therefore, even if the driver 3 of the vehicle 10 is unaware of a decline in the health indicators, it is possible to visualize the risk of health-related accidents and encourage the driver 3 to improve the health indicators. This improves the likelihood of preventing health-related accidents, thereby improving the technology for encouraging drivers to drive safely.

[0064] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art may make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions included in each component or step can be rearranged so as not to be logically inconsistent, and multiple components or steps can be combined or divided into one.

[0065] For example, in the above-described embodiment, the configuration and operation of health management device 30 may be distributed among multiple computers that can communicate with each other. Also, for example, an embodiment in which some or all of the components of health management device 30 are provided in mobile terminal 20 is possible. For example, communication unit 21, storage unit 22, display unit 23, and control unit 24 provided in mobile terminal 20 may implement some or all of the functions of health management device 30.

[0066] Also possible is an embodiment in which, for example, a general-purpose computer functions as the health management device 30 according to the above-described embodiment. Specifically, a program describing the processing content for realizing each function of the health management device 30 according to the above-described embodiment is stored in the memory of the general-purpose computer, and the program is read and executed by a processor. Therefore, the present disclosure can also be realized as a program executable by a processor, or a non-transitory computer-readable medium storing the program. [Explanation of symbols]

[0067] 1 System 2 Network 3. Driver 10 vehicles 11 Communications Department 12 Measurement section 13 Storage section 14 Control Unit 20 Mobile devices 21 Communications Department 22 Memory section 23 Display section 24 Control Unit 30 Health management equipment 31 Communications Department 32 Storage section 33 Control Unit 40 Wearable devices 41 Communications Department 42 Measurement section 43 Control Unit

Claims

1. The Communications Department and a control unit that performs communication using the communication unit, The control unit calculates the strength of correlation between a near-miss index of a driver who drives a vehicle and a score of the driver's health index, identifies a health index for which the score of the health index is equal to or less than a first threshold and the strength of the correlation is equal to or greater than a second threshold, and selects a health improvement program to improve the identified health index.

2. The health management device according to claim 1, The near miss index is the number or frequency at which the vehicle acceleration data exceeds the acceleration at which a near miss is experienced.

3. The health management device according to claim 1, A health management device, wherein the health index scores are a stress management score, a fatigue management score, and a sleep management score calculated based on biometric data measured by a wearable terminal attached to the driver's body.

4. The health management device according to claim 1, The strength of the correlation is defined as the absolute value of the correlation coefficient for an event whose significance level is equal to or less than a predetermined value.

5. Health management devices Obtaining a strength of correlation between a near-miss incident index of a driver who drives a vehicle and a score of a health index of the driver; Identifying health indicators whose scores are equal to or less than a first threshold and whose strength of correlation is equal to or greater than a second threshold; selecting a health improvement program to improve the identified health indicators; A health management method that implements the following.

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