Health assessment device, health assessment system, and health assessment method

Abstract

To effectively recognize a change in physical condition that a person himself / herself is not aware of.SOLUTION: A server 30 has: a physical condition determination unit 32 that determines a physical condition of a subject by comparing a measured physical condition read from a memory unit that stores the measured physical condition measured from a photographic data obtained by photographing the subject, a self-reported physical condition input by a subject's self-assessment during the photographic period of the photographic data when the measured physical condition is obtained, and a determination threshold 36 for determining the subject's physical condition, with the determination threshold 36; and an advice output unit 34 that changes at least one of a display form and a display content according to the self-reported physical condition when displaying a determination result by the physical condition determination unit 32.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0001] The present invention relates to a physical condition evaluation device, a physical condition evaluation system, and a physical condition evaluation method.

Background Art

[0002] A system for managing a user's physical condition using a wearable device worn by the user has been proposed. Patent Document 1 describes a wearable device including a cough presence / absence determination unit that detects coughs based on environmental sounds collected by a microphone.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Physical condition deterioration includes those that are consciously felt such as chronic diseases, and those that are not consciously felt due to temporary fatigue, etc. Even if a person recognizes that their physical condition is good, small signs indicating a change in physical condition, such as an increased number of blinks or a poor complexion compared to when healthy, may surface on the face or the like. That is, there is a gap between the recognized physical condition and the actually measured physical condition. By making the person aware of this gap state, it is possible to prompt early countermeasures such as rest or a visit to the doctor.

[0005] However, in conventional measuring instruments such as Patent Document 1, although the measured value of the physical condition can be displayed as it is, there is no device for making the person aware of the gap between the recognition of the physical condition and the actual situation. Therefore, a mechanism for effectively making the person aware of the gap state is required rather than simply displaying the measured value of the physical condition.

[0006] Therefore, the main objective of this invention is to effectively enable individuals to recognize changes in their physical condition that they themselves are unaware of. [Means for solving the problem]

[0007] To solve the aforementioned problem, this Disclosure The health assessment device has the following features: Book Disclosure A health condition determination unit determines the health condition of a subject by comparing the measured health condition, which is measured from photographic data of the subject, the subject's self-reported health condition, which is entered during the period of the photographic data taken to determine the measured health condition, and a determination threshold for determining the subject's health condition, which is read from a storage unit, with the determination threshold, which determines the health condition of the subject. When displaying the results of the physical condition determination unit, an output unit is provided that changes at least one of the display format and display content according to the self-reported physical condition. A data learning unit updates the first determination threshold to a second determination threshold calculated based on the second measurement of physical condition if a second measurement of physical condition exists that is newer than the first measurement of physical condition used to calculate the first determination threshold that the physical condition determination unit refers to when making a comparison. It is characterized by having the following features. Other methods will be described later. [Effects of the Invention]

[0008] According to the present invention, it is possible to effectively make a person aware of changes in their physical condition that they themselves are unaware of. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram illustrating the configuration of the health condition evaluation system according to this embodiment. [Figure 2] This is a flowchart outlining the processing of the health condition evaluation system according to this embodiment. [Figure 3] This flowchart shows the details of the blink count determination process according to this embodiment. [Figure 4] This flowchart shows the details of the blink threshold learning process according to this embodiment. [Figure 5] This flowchart shows the details of the closing count determination process according to this embodiment. [Figure 6]This flowchart shows the details of the skin tone determination process according to this embodiment. [Figure 7] This flowchart shows the details of the notification process to the subordinate in this embodiment. [Figure 8] This is a table showing health information related to this embodiment. [Figure 9] This is a table showing the threshold values ​​for determination calculated from the physical condition information related to this embodiment. [Figure 10] This is a hardware configuration diagram of each device in the health condition evaluation system according to this embodiment. [Figure 11] This diagram shows the display screen for the detection / poor health warning when the detection / dry eye flag is ON in this embodiment. [Figure 12] This is a diagram showing the display screen for the "No Sensitivity / Dry Eye Flag" in this embodiment, when it is ON, and displaying the "No Sensitivity / Poor Health" warning. [Modes for carrying out the invention]

[0010] Figure 1 is a diagram showing the configuration of the health assessment system. The health assessment system is configured by connecting a user terminal 10 and a server (health assessment device) 30 via a network 40. The user terminal 10 includes a camera 11, a health condition input unit 12, a display unit 13, and a measurement unit 20. The measurement unit 20 includes a blink count measurement unit 21, an eye-closing time measurement unit 22, and a skin color measurement unit 23. The server 30 includes a storage unit for storing health information 31 and a judgment threshold 36, a health judgment unit 32, a data learning unit 33, an advice output unit (output unit) 34, and an email sending unit 35. Although the illustration shows a server 30 as a health condition evaluation device that has its own memory unit, the memory unit does not need to be an essential component of the health condition evaluation device. It is sufficient if it is present in any other device and is capable of acquiring health condition information 31 and storing necessary information.

[0011] The user terminal 10 is a terminal used by a user who is the subject of physical condition evaluation. Below, an example will be described where the subject is an employee working at a company, and the superior of that subordinate receives a report on the subordinate's physical condition by email. The camera 11 takes still images and videos with the subject as the object, and stores, for example, shooting data for the most recent 60 days in the storage unit. The shooting by the camera 11 is, for example, three times a day (in the morning, at noon, and in the evening), and the shooting time for each time is 10 minutes. Note that to observe the physical condition of the subject every day, shooting once a day may be sufficient, but shooting three times a day can also take into account environmental changes such as room illumination.

[0012] The physical condition input unit 12 accepts input of the content of the subject's self-report of physical condition (hereinafter referred to as "self-recognized physical condition") each time the camera 11 shoots. For example, the physical condition input unit 12 causes the current physical condition to be selected from the options of "good, normal, bad" that are pop-up displayed on the display unit 13.

[0013] <000​​​​​​​​​​​

[0014] The server 30 stores the self-reported physical condition and measured physical condition received from the user terminal 10 as physical condition information 31 in the server 30's memory (see Figure 8 for details). The server 30 also stores a judgment threshold 36 in the server 30's memory for evaluating the user's physical condition by comparing it with the physical condition information 31 (see Figure 9 for details). In other words, the memory unit of the server 30 stores the measured physical condition measured from the photographic data of the subject, the subject's self-reported physical condition entered during the period of the photographic data used to determine the measured physical condition, and a judgment threshold 36 for determining the subject's physical condition.

[0015] The threshold 36 for determination is, for example, the following threshold. Threshold a is a threshold value used to compare with the blink count n in the measured physical condition. For example, it is 150-200 times when the person is in good physical condition and 400 times when they are experiencing eye strain. Threshold b is a threshold value used to compare with the brightness m, which represents the user's complexion among the measured physical conditions. These judgment thresholds 36 are updated as needed (threshold learning) to accommodate individual differences in physical condition. On the other hand, the number of times the eyes were closed (p) among the measured physical condition did not vary much from person to person, and since people do not usually close their eyes for more than 3 seconds in normal physical condition, a fixed threshold (whether it was 1 time or more) was used instead of the judgment threshold 36.

[0016] The physical condition determination unit 32 determines the subject's physical condition by comparing the measured physical condition read from the memory unit with the determination threshold 36. In this embodiment, the physical condition determination unit 32 calculates a flag indicating poor physical condition for each symptom, as illustrated below, from the measured physical condition. The dry eye flag is a flag that indicates a suspected symptom of dry eye due to infrequent blinking. The eye strain flag indicates that frequent blinking may suggest symptoms of eye strain. The fatigue flag indicates fatigue due to the occurrence of closed time. The complexion flag indicates that the person's complexion is poor.

[0017] These symptoms are categorized according to the user's perceived physical condition. • If the user perceives their physical condition as poor, the "perceived poor physical condition" flag will be set. For example, if the user perceives their physical condition as poor, the "dry eye" flag will be turned ON if the "dry eye" flag is triggered. • If the self-reported physical condition is not bad (normal or good), the "No detection / Poor physical condition" flag will be set. For example, if the dry eye flag occurs when the self-reported physical condition is good, the "No detection / Dry eye" flag will be turned ON.

[0018] The data learning unit 33 adapts the judgment threshold 36 to individual differences by updating (learning) the judgment threshold 36 based on the measured physical condition over a recent predetermined period (for example, the past week). In other words, if there is a second measured physical condition that is newer than the first measured physical condition used to calculate the first judgment threshold 36 that the physical condition determination unit 32 refers to when making a comparison, the data learning unit 33 updates the first judgment threshold 36 to the second judgment threshold 36 calculated based on the second measured physical condition. Furthermore, the data learning unit 33 may calculate a second judgment threshold 36 using a second measured physical condition when the period during which the self-reported physical condition is not poor continues for a predetermined period of time or longer.

[0019] The advice output unit 34 displays the assessment result (current poor physical condition) from the physical condition assessment unit 32 as advice (notifies the subject). Even if the content of the physical condition assessment is the same, the display format and at least one of the displayed content may be changed depending on the self-reported physical condition at the same time period in which the assessment was made (see Figures 11 and 12 for details). This allows you to recognize changes in your physical condition that you are unaware of, more effectively than changes in your physical condition that you are aware of.

[0020] The email sending unit 35 notifies the supervisor via email that the advice output unit 34 has given advice to the subordinate regarding poor health. Generally, for privacy reasons, it is preferable to provide general information such as "Poor health has been detected in the subordinate" rather than conveying the exact same advice given to the subordinate to the supervisor.

[0021] Figure 2 is a flowchart illustrating the overview of the health assessment system's processing. The health input unit 12 has the subordinate input their health condition for the day (self-reported health condition) (S11). The measurement unit 20 receives input of the subordinate's captured image from the camera 11 and causes the skin tone measurement unit 23 to calculate the skin tone (S12). The measurement unit 20 receives input of the subordinate's shooting motion from the camera 11 and causes the blink count measurement unit 21 to calculate the number of blinks and the eye-closing time measurement unit 22 to calculate the number of times the eyes are closed (S13). Server 30 receives the user's self-reported physical condition in S11 and the measured physical condition, which is the result of calculations in S12 and S13, from the user terminal 10, and stores the received information as physical condition information 31.

[0022] The physical condition determination unit 32 determines the number of blinks in the physical condition information 31 using a determination threshold 36 (threshold a) (S14, see Figure 3 for details). The physical condition determination unit 32 determines the number of times the eyes are closed in the physical condition information 31 using a fixed threshold (S15, see Figure 5 for details). The physical condition determination unit 32 determines the complexion in the physical condition information 31 using a determination threshold 36 (threshold b) (S16). The advice output unit 34 notifies the subordinate of the judgment results from S14-S16 as advice regarding their health (S17). The email sending unit 35 also notifies the superior of the advice from S17 via email (S18). The notifications in S17 and S18 support measures to address the subordinate's poor health, such as adjusting their workload. In a workplace setting, colleagues or other individuals may communicate their observations of an employee's physical condition through direct conversation. However, in a teleworking environment, employees have fewer opportunities to learn about their physical condition from the perspective of others. Therefore, implementing automated health notification methods like those described in S17 and S18 allows for early detection of an employee's poor health.

[0023] Figure 3 is a flowchart showing details of the blink count determination process (S14). When the input self-assessed physical condition is poor (S101, Yes), the physical condition determination unit 32 turns on the self-assessment flag (S102). The physical condition determination unit 32 branches as follows by comparing the blink count n of the measured physical condition with the threshold value a (S103). Note that since the normal range of the blink count of a human is generally 100 to 200 times, it is assumed that the upper limit value of 200 times is set as the initial value of the threshold value a. If the blink count n is appropriate (100 ≤ n < a in S103), there is no need to pay attention to the blink count n, so the process of FIG. 3 is terminated.

[0024] If the blink count n is low (n < 100 in S103), there is a high possibility of dry eye, so a dry eye flag for reminding to blink consciously is set to ON. Here, if the self-assessment flag is ON (S104, Yes), the physical condition determination unit 32 turns on the sensed / dry eye flag (S105). On the other hand, if the self-assessment flag is not ON (S104, No), the physical condition determination unit 32 turns on the non-sensed / dry eye flag (S106).

[0025] If the blink count n is high (n ≥ a in S103), there is a possibility of eye strain, so an eye strain flag for reminding is set to ON. Here, if the self-assessment flag is ON (S111, Yes), the physical condition determination unit 32 turns on the sensed / eye strain flag (S112). On the other hand, if the self-assessment flag is not ON (S111, No), the physical condition determination unit 32 turns on the non-sensed / eye strain flag (S113). Further, if the blink count n of the current user is higher than the general standard (Yes in S114 by satisfying "200 ≤ n < 300"), the data learning unit 33 executes the learning process of the blink threshold value a (S115, details are shown in FIG. 4). That is, even if the threshold value a is updated in S115, its maximum value is less than 300. [[ID=!17]]

[0026] Figure 4 is a flowchart showing details of the learning process of the blink threshold value a (S115). The data learning unit 33 does not update the threshold if the self-identification flag is ON (S121, Yes). In other words, poor physical condition is not reflected in the threshold for individual physical condition assessment. The data learning unit 33 also does not update the threshold if physical condition is not stable (S122, No). In other words, unstable physical condition is not reflected in the threshold for individual physical condition assessment.

[0027] On the other hand, if the data learning unit 33 confirms that the user's physical condition remains stable (S122, Yes), it updates the average value of the number of blinks n that exceeds threshold a as the new threshold a (S123). Note that the continuation of stable physical condition in S122 means, for example, that a state where threshold a is exceeded is considered stable physical condition, and this continuation means that stable physical condition has occurred three or more times in five days. This makes it possible to set an appropriate threshold a for determining physical condition for users who have a personal tendency to blink more times n than the average person, even when their physical condition is stable.

[0028] Figure 5 is a flowchart showing the details of the closing count determination process (S15). The physical condition determination unit 32 terminates the process shown in Figure 5 if the number of times the eyes are closed p for the measured physical condition is 0 (S131, p=0), as there is no need to pay attention to the number of times the eyes are closed p. The physical condition determination unit 32 sets the fatigue flag to ON to warn about fatigue if the number of times the eyes are closed p for the measured physical condition is greater than 0 (S131, p > 0). If the self-recognition flag is ON (S132, Yes), the physical condition determination unit 32 turns ON the perceived fatigue flag (S133). On the other hand, if the self-recognition flag is not ON (S132, No), the physical condition determination unit 32 turns ON the unperceived fatigue flag (S134).

[0029] Figure 6 is a flowchart showing the details of the skin tone determination process (S16). If the physical condition determination unit 32 does not have a threshold b for the user whose physical condition is being determined this time (S201, No), it performs an initial setting of threshold b (S202). As an initial setting, for example, the facial color measurement unit 23 excludes photographic data from the first 5 days of camera 11 that the user perceives as having poor physical condition. Then, the physical condition determination unit 32 sets the minimum value of brightness m among the user's facial color (brightness m) measured by the facial color measurement unit 23 from the photographic data that was not excluded as the initial setting value of threshold b.

[0030] The physical condition determination unit 32 branches as follows according to the brightness m of the measured physical condition (S203). If the complexion is bright (m > b), there is no need to pay attention to the brightness m, so the process in Figure 6 is terminated. If the complexion is dark (m≦b), there is a possibility of fatigue, so the complexion flag is set to ON to indicate caution. If the self-recognition flag is ON (S211, Yes), the physical condition determination unit 32 turns on the detection / complexion flag (S212). On the other hand, if the self-recognition flag is not ON (S211, No), the physical condition determination unit 32 turns on the no-detection / complexion flag (S213).

[0031] Furthermore, after executing S213, if the number of times the brightness m exceeds the threshold b is 3 or more in 5 days, indicating that the physical condition remains stable (S214, Yes), the data learning unit 33 updates the highest value of the brightness m that exceeded the threshold b as the new threshold b (S215). Furthermore, the data learning unit 33 updates the threshold b seasonally (S217) to accommodate seasonal changes in the shooting environment (such as the brightness of natural light illuminating the subject) whenever the seasons change, such as every three months (every 60 working days out of 90 days in three months). Note that, as part of the update process in S217, the data learning unit 33 may, similar to S202, adopt the minimum value of brightness m from the camera 11's shooting data for the last five days (excluding shooting data where the user perceives poor physical condition) as the threshold b. Alternatively, the data learning unit 33 may adopt the maximum value of brightness m from the camera 11's shooting data for the last five days (excluding shooting data where the user perceives poor physical condition) as the threshold b, thereby avoiding significant changes to the threshold b.

[0032] Figure 7 is a flowchart detailing the process of notifying the subordinate (S17). The advice output unit 34 determines whether one or more health condition flags (for example, a dry eye flag) have been generated based on the measurement of the physical condition (S301). If the result in S301 is Yes, proceed to S311; otherwise, proceed to S302. If the self-identification flag is ON (self-identified as being unwell) (S302, Yes), the advice output unit 34 warns the subordinate via the display unit 13 that there is a possibility of an undetermined physical ailment other than the measured physical condition calculated from the data captured by the camera 11 (for example, stomach ache) (S303). The S303 warning message might read, for example, "The person appears to be aware of feeling unwell. Please be mindful of adjusting their workload."

[0033] If the self-recognition flag is ON (self-reported poor physical condition) (S311, Yes), the advice output unit 34 outputs a warning based on the perceived poor physical condition flag from the display unit 13 (S312). The S312 warning message, for example, when the "Sensing Dry Eye" flag is ON, will read: "You appear to be aware of feeling unwell. A possible cause is dry eye. Treatment involves eye drops. Be mindful of adjusting your work schedule." Note that "dry eye" in the warning message refers to a symptom corresponding to each symptom flag, and "eye drop treatment" refers to a method corresponding to each symptom. If the self-reported flag is not ON (self-reported good health, normal) (S311, No), the advice output unit 34 outputs a warning based on the undetected / poor health flag from the display unit 13 (S313). The warning message in S313 is, for example, if the undetected / dry eye flag is ON, "There may be an unseen health problem. A possible health problem is dry eye. The solution is eye drops."

[0034] Figure 8 is a table showing health condition information 31. The table in Figure 8 records, for each shooting date and time (date, acquisition time), the self-reported physical condition entered, various measured physical conditions (number of blinks, number of eye closures, skin tone color code), and the judgment result of those measured physical conditions (blink threshold a exceeded, skin tone threshold b exceeded). The second row of the table is the data format of the past items shown in the first row (DATE for date, TIME for time, CHAR for character, INT for integer, etc.). "Blink threshold a exceeded = T" indicates that the measured physical condition exceeded the blink threshold a (True), and "Blink threshold a exceeded = F" indicates that the measured physical condition did not exceed the blink threshold a (False).

[0035] Figure 9 is a table showing the judgment threshold 36 calculated from the physical condition information 31. The table in Figure 9 associates each threshold with its value (blink threshold a, complexion threshold b) and its update date (blink threshold a update date, complexion threshold b update date). Similar to Figure 8, the second row of the table in Figure 9 shows the data format of the past items shown in the first row.

[0036] Figure 10 is a hardware configuration diagram of each device in the health assessment system. Each device in the health assessment system (user terminal 10, server 30) is configured as a computer 900, each having a CPU 901, RAM 902, ROM 903, HDD 904, communication I / F 905, input / output I / F 906, and media I / F 907. The communication interface 905 is connected to an external communication device 915. The input / output interface 906 is connected to the input / output device 916. The media interface 907 reads and writes data to the recording medium 917. Furthermore, the CPU 901 improves and controls each processing unit by executing a program (also called an application or app) loaded into the RAM 902. This program can also be distributed via a communication line or by recording it on a recording medium 917 such as a CD-ROM and distributing it that way.

[0037] Figure 11 shows the display screen for the detection / poor health warning (S312) when the detection / dry eye flag is ON. In Figure 11, the subordinate's work screen (desktop screen) displays the source code editor 102 for work. At this point, the advice output unit 34, upon receiving notification that the detection / dry eye flag has been turned ON, displays the detection / warning message display 101 on the desktop screen.

[0038] Figure 12 shows the display screen for the "No Sensitivity / Dry Eye Flag" when the "No Sensitivity / Dry Eye Flag" is ON, and displays the "No Sensitivity / Poor Health Warning" (S313). Similar to Figure 11, the subordinate's work screen (desktop screen) in Figure 12 displays the source code editor 112 for work. At this point, the advice output unit 34, upon receiving the notification that the "no detection / dry eye" flag has been turned ON, displays the "no detection / warning message" 111 on the desktop screen.

[0039] As illustrated by the examples listed below, the advice output unit 34 makes the "no-perceived / dry eye" flag more prominent than the "perceived / dry eye" flag by changing at least one of the display format and / or display content according to the user's self-reported physical condition, even if the physical condition assessment is the same. This allows the user to become clearly aware of symptoms they are unaware of, while displaying symptoms they are aware of in a way that does not interfere with their work. - The "no detection / warning message display 111" is displayed larger than the "detection / warning message display 101." The "no detection / warning message display 111" is displayed closer to the center of the screen than the "detection / warning message display 101." The undetected / warning message display 111 is displayed in front of the overlapping source code editor 112, while the detected / warning message display 101 is displayed behind the overlapping source code editor 102 or in a way that it does not overlap. The message displayed in the "No Detection / Warning Message Display 111" shows more detailed information about the symptoms than the message displayed in the "Detection / Warning Message Display 101". Alternatively, the no-detection / warning message display 111 may be included in the display targets, while the detection / warning message display 101 may be excluded from the display targets.

[0040] Furthermore, the present invention is not limited to the embodiments described above, and it goes without saying that various other applications and modifications can be taken as long as they do not depart from the gist of the invention as described in the claims. For example, the embodiments described above describe the configuration of the health condition evaluation system in detail and concretely in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those that include all the components described. Also, it is possible to replace a part of the configuration of one embodiment with a component of another embodiment. It is also possible to add a component of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, replace, or delete other components for a part of the configuration of each embodiment.

[0041] Furthermore, some or all of the above configurations, functions, and processing units may be implemented in hardware, for example, by designing them as integrated circuits. Broadly defined processor devices such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application Specific Integrated Circuits) may be used as hardware. Furthermore, each component of the health assessment system according to the above-described embodiment may be implemented on any hardware, as long as the respective hardware can send and receive information from each other via a network. Also, the processing performed by a certain processing unit may be implemented by a single piece of hardware, or by distributed processing using multiple pieces of hardware. [Explanation of Symbols]

[0042] 10 User terminals 11 Cameras 12. Health Input Section 13 Display section 20 Measurement section 21 Blinking Count Measurement Unit 22 Closing time measurement unit 23. Skin tone measurement unit 30 Server (Health Assessment Device) 31. Health Information (Memory Section) 32. Health Assessment Department 33 Data Learning Department 34. Advice Output Section (Output Section) 35 Email Sending Section 36. Threshold for determination (memory unit) 40 Networks

Claims

1. A health condition determination unit determines the health condition of a subject by comparing the measured health condition, which is measured from photographic data of the subject, the subject's self-reported health condition, which is entered during the period of the photographic data taken to determine the measured health condition, and a determination threshold for determining the subject's health condition, which is read from a storage unit, with the determination threshold, and the health condition determination unit determines the health condition of the subject. When displaying the results of the physical condition determination unit, an output unit is provided that changes at least one of the display format and display content according to the self-reported physical condition. The data learning unit is characterized by having a data learning unit that, when a second measured physical condition exists that is newer than the first measured physical condition used to calculate the first judgment threshold that the physical condition determination unit refers to when making a comparison, updates the first judgment threshold to a second judgment threshold calculated based on the second measured physical condition. A device for evaluating physical condition.

2. The data learning unit is characterized in that it calculates the second judgment threshold using the second measured physical condition when the period during which the self-reported physical condition is not poor continues for a predetermined period of time or longer. The physical condition evaluation device according to claim 1.

3. A health condition evaluation system comprising a health condition evaluation device according to claim 1 or claim 2 and a user terminal, The user terminal has a health condition input unit that receives the self-reported health condition input, and a measurement unit that measures the measured health condition from the captured data. The measurement unit extracts the subject's eyes from the captured data and includes the number of blinks of those eyes in the measurement data. The physical condition determination unit is characterized by determining the physical condition of the subject by comparing the number of blinks included in the measured physical condition with the determination threshold. Health assessment system.

4. A health condition evaluation system comprising a health condition evaluation device according to claim 1 or claim 2 and a user terminal, The user terminal has a health condition input unit that receives the self-reported health condition input, and a measurement unit that measures the measured health condition from the captured data. The measurement unit extracts the subject's eyes from the captured data and includes the number of times the eyes are closed for a predetermined period of time or longer in the measurement of the subject's physical condition. The physical condition determination unit is characterized by determining the physical condition of the subject by comparing the number of times the eyes are closed for a predetermined period of time or longer, which is included in the measured physical condition, with the determination threshold. Health assessment system.

5. A health condition evaluation system comprising a health condition evaluation device according to claim 1 or claim 2 and a user terminal, The user terminal has a health condition input unit that receives the self-reported health condition input, and a measurement unit that measures the measured health condition from the captured data. The measurement unit extracts the color code of the subject's face region captured in the image data, and includes the subject's complexion, determined from the color code, in the measurement unit. The physical condition determination unit is characterized by determining the physical condition of the subject by comparing the subject's complexion, which is included in the measured physical condition, with the determination threshold. Health assessment system.

6. The health assessment device comprises a health determination unit, an output unit, and a data learning unit. The physical condition determination unit determines the physical condition of the subject by comparing the measured physical condition, which is measured from the photographic data of the subject, the subject's self-reported physical condition, which is entered during the period of the photographic data taken when determining the measured physical condition, and the determination threshold for determining the subject's physical condition, which is read from the storage unit, with the determination threshold, thereby determining the subject's physical condition. The output unit, when displaying the judgment result from the physical condition judgment unit, changes at least one of the display format and the display content according to the self-reported physical condition. The data learning unit is characterized in that, if there is a second measured physical condition that is newer than the first measured physical condition used to calculate the first judgment threshold that the physical condition determination unit refers to when making a comparison, it updates the first judgment threshold to a second judgment threshold calculated based on the second measured physical condition. Methods for evaluating physical condition.

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