Information processing device, health condition management system, information processing method and program

Abstract

To attain measurement using proper measurement means according to a difference in measurement condition.SOLUTION: An information processing device comprises: a measurement unit which has a plurality of measurement means for measuring information indicating a user's health condition; and a means determination unit which determines one of the plurality of measurement means. The plurality of measurement means include: first measurement means which measures the information by a non contact method; and second measurement means which measures the information by a contact method. The measurement unit measures the information indicating the health condition by using the determined measurement means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a health condition management system, an information processing method, and a program. [Background technology]

[0002] In recent years, research has been conducted on the relationship between a subject's (user's) autonomic nervous system and health status, and the relationship between chronic sleep deprivation, high stress, and the risk of accidents. In addition, there is a demand for technology to measure the state of the autonomic nervous system and determine health status in various situations, such as mental health measures and measures to prevent unsafe behavior.

[0003] Patent document 1 discloses that a biometric information measuring device includes an illumination unit that irradiates illumination light onto a living organism, an imaging unit that images the living organism, and a calculation unit that calculates biometric information based on the difference between pixel data in an irradiation area, which is an area of the living organism that is irradiated with the illumination light, and pixel data in a non-irradiation area, which is an area of the living organism that is not irradiated with the illumination light, among pixel data associated with each position in an image generated by the imaging unit.

[0004] Furthermore, Patent Document 2 discloses a vital data output method for measuring biometric information of a user with a plurality of cameras arranged around the user. Summary of the Invention [Problem to be solved by the invention]

[0005] However, with conventional technology, it is difficult to consistently obtain equivalent measurement results in very bright places such as outdoors or very dark places such as indoors, and it is not possible to obtain the desired measurement results depending on conditions such as the skin color of the subject (user), making it impossible to perform measurements using appropriate measurement means depending on the measurement conditions.

[0006] The present invention has been made in view of the above, and has an object to enable measurement using an appropriate measurement means. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the object, the present invention comprises a measurement unit having a plurality of measurement means for measuring information indicating a user's health condition, and a means determination unit for determining one measurement means from the plurality of measurement means, wherein the plurality of measurement means include a first measurement means for measuring the information in a non-contact manner and a second measurement means for measuring the information in a contact manner, and the measurement unit measures the information indicating the health condition using the determined measurement means. [Effects of the Invention]

[0008] According to the present invention, it is possible to perform measurements using appropriate measurement means depending on differences in measurement conditions. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration of a health condition management system according to a first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Figure 3] FIG. 3 is a diagram illustrating another example of the hardware configuration of the information processing device. [Figure 4] FIG. 4 is a flowchart showing the overall processing flow of the health condition management system according to the first embodiment. [Figure 5] FIG. 5 is a functional block diagram of the measurement means determination process according to the first embodiment. [Figure 6] FIG. 6 is a diagram showing an example of a screen generated by the screen generation unit. [Figure 7] FIG. 7 is a diagram showing an example of a measurement scene of objective evaluation data. [Figure 8] FIG. 8 is a diagram showing an example of a guide frame used in the first measuring means. [Figure 9] FIG. 9 is a diagram showing an example of a skin region set by the first measuring means. [Figure 10] FIG. 10 is a diagram showing the state of measurement by the second measuring means. [Figure 11] FIG. 11 is a diagram showing an example of a question screen presented to the user. [Figure 12] FIG. 12 is a flowchart showing the flow of the mental health condition determination process in the health condition management system. [Figure 13] FIG. 13 is a flowchart showing the flow of calculation of objective indices in the health status management system. [Figure 14] FIG. 14 is a functional block diagram of the measurement means determination process according to the second embodiment. [Figure 15] FIG. 15 is a flowchart showing the overall processing flow of the health condition management system according to the second embodiment. [Figure 16] FIG. 16 is a flowchart showing the procedure for calculating the SN ratio of the pulse wave signal obtained by each measuring means. [Figure 17] FIG. 17 is a diagram showing an example of the power spectrum of the calculated pulse wave signal. [Figure 18] FIG. 18 is a functional block diagram of the measurement means determination process according to the third embodiment. [Figure 19] FIG. 19 is a flowchart showing the overall processing flow of the health condition management system according to the third embodiment. [Figure 20] FIG. 20 is a functional block diagram of the measurement means determination process according to the fourth embodiment. [Figure 21] FIG. 21 is a flowchart showing the overall processing flow of the health condition management system according to the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of an information processing device, a health condition management system, an information processing method, and a program will be described in detail with reference to the accompanying drawings.

[0011] (First embodiment) 1 is a diagram showing an example of the system configuration of a health condition management system according to a first embodiment. The health condition management system of this embodiment is composed of a measurement terminal 2 used by a user 1, a management terminal 7 used by a related party 6, an analysis device 5 connected to the measurement terminal 2 and the management terminal 7 via a communication network 100 such as the Internet, and an evaluation application 4 running on the measurement terminal 2 and the management terminal 7. The measurement terminal 2 and the management terminal 7 are also connected via the communication network 100. Details of the user 1, the measurement terminal 2, the analysis device 5, the related party 6, the management terminal 7, and the evaluation application 4 are described below.

[0012] User 1 is a subject whose mental health condition is to be evaluated. User 1 is, for example, a company employee. User 1 evaluates his / her mental health condition using a measurement terminal 2 at location A. Location A is, for example, a company office, a location where remote work is performed (home or outside), etc.

[0013] The measurement terminal 2 is a terminal that acquires data related to the mental health state of the user 1. The measurement terminal 2 is an information processing device such as a smartphone, a tablet terminal, a notebook PC (Personal Computer), or a desktop PC. The measurement terminal 2 includes a measurement unit 3 and has an objective evaluation function that acquires the pulse wave, which is objective evaluation data of the user 1.

[0014] The measurement unit 3 is a camera that is built into the measurement terminal 2 or is externally connected. In the case of an external connection, a wired connection such as a USB (Universal Serial Bus) cable or a wireless connection such as Bluetooth (registered trademark) can be used. Here, the measurement unit 3 preferably has a channel with spectral sensitivity in a wavelength range (green light wavelength range or near-infrared light wavelength range) where luminance changes due to pulse can be easily obtained. In this embodiment, the measurement unit 3 preferably has a channel with spectral sensitivity in the green wavelength range, which is determined comprehensively from the relationship with the hemoglobin absorption rate, the spectral transmittance of skin color, pigments, etc.

[0015] The measurement terminal 2 further includes a display unit and an input unit. As will be described later, the measurement terminal 2 may also have a subjective evaluation function for acquiring questionnaire responses from the user 1, which are subjective evaluation data.

[0016] The analysis device 5 is a computer connected to location B on a communication network 100 such as the Internet. The analysis device 5 is an information processing device such as a server, a notebook PC, or a desktop PC. The analysis device 5 has a determination function that analyzes the objective assessment data acquired by the measurement terminal 2 and received via the communication network 100 to determine the mental health state of the user 1, an information management function that centrally manages the objective assessment data and the assessment results, and a notification function that notifies relevant parties 6 of necessary information based on the assessment results. Furthermore, when subjective assessment data is acquired by the measurement terminal 2, the subjective assessment data may be analyzed and determined, managed, and notified in addition to the objective assessment data.

[0017] It should be noted that the judgment function may be provided to the evaluation application 4 of the measurement terminal 2, and after the measurement terminal 2 makes a judgment, the analysis device 5 may manage the information on the judgment result sent from the measurement terminal 2 and notify the relevant person 6. Alternatively, the evaluation application 4 of the management terminal 7 may be provided with the judgment function, and after the management terminal 7 makes a judgment, the analysis device 5 may manage the information on the judgment result sent from the management terminal 7.

[0018] The relevant person 6 is a manager who manages the mental health state of the user 1, and may be, for example, the user 1's boss, a human resources officer at the company to which the user 1 belongs, or industrial health staff such as an industrial physician or public health nurse. The relevant person 6 checks the mental health state of the user 1 using a management terminal 7 at a location C. The location C may be, for example, a company office or a location where remote work is performed (home or outside). The locations A and C may be the same place or different places.

[0019] The management terminal 7 is a terminal used by the person concerned 6 to manage the mental health state of the user 1. The management terminal 7 is an information processing device such as a smartphone, tablet terminal, notebook PC, or desktop PC. The management terminal 7 has a display unit and an input unit, and has a function that allows the person concerned 6 to check the mental health state of the user 1. The configuration of the display unit and input unit may be the same as or different from that of the measurement terminal 2.

[0020] The evaluation application 4 is an application that runs on the measurement terminal 2 and the management terminal 7, and executes processing to realize each of the above functions. The evaluation application 4 is, for example, a native application that runs on an OS (Operating System) or a web application that runs on a browser.

[0021] The information processing devices (measurement terminal 2, management terminal 7) of this embodiment can be realized by a smartphone, a tablet terminal, or the like. FIG. 2 is a diagram showing an example of the hardware configuration of an information processing device realized by a smartphone, a tablet terminal, or the like. As shown in FIG. 2, the information processing devices 2, 7 include a CPU (Central Processing Unit) 401, a ROM (Read Only Memory) 402, a RAM (Random Access Memory) 403, an EEPROM (Electrically Erasable Programmable ROM) 404, a CMOS (Complementary Metal Oxide Semiconductor) sensor 405, an image sensor I / F 406, an acceleration / azimuth sensor 407, a media I / F (Interface) 409, and a GPS (Global Positioning System) receiving unit 411.

[0022] Of these, the CPU 401 controls the overall operation of the information processing device 2, 7. The ROM 402 stores programs executed by the CPU 401 and programs used to drive the CPU 401, such as an IPL (Initial Program Loader). The RAM 403 is used as a work area for the CPU 401. The EEPROM 404 reads and writes various data, such as smartphone programs, under the control of the CPU 401. The CMOS sensor 405 is a type of built-in imaging means that captures an image of a subject (mainly a self-portrait) and obtains image data under the control of the CPU 401. Note that instead of a CMOS sensor, an imaging means such as a CCD (Charge Coupled Device) sensor may also be used. The imaging element I / F 406 is a circuit that controls the operation of the CMOS sensor 405. The acceleration / direction sensor 407 is a sensor such as an electronic magnetic compass or gyrocompass that detects geomagnetism, or an acceleration sensor. The media I / F 409 controls the reading and writing (storage) of data from and to a recording medium 408, such as a flash memory. The GPS receiver 411 receives GPS signals from GPS satellites.

[0023] The information processing device 2, 7 also includes a long-distance communication circuit 412, a CMOS sensor 413, an image sensor I / F 414, a microphone 415, a speaker 416, an audio input / output I / F 417, a display 418, an external device connection I / F 419, a short-distance communication circuit 420, an antenna 420a of the short-distance communication circuit 420, and a touch panel 421.

[0024] The long-distance communication circuit 412 is a circuit that communicates with other devices via the communication network 100. The CMOS sensor 413 is a type of built-in imaging means that captures an image of a subject and obtains image data under the control of the CPU 401. The imaging element I / F 414 is a circuit that controls the driving of the CMOS sensor 413. The measurement unit 3 in the measurement terminal 2 is composed of the CMOS sensor 413 and the imaging element I / F 414.

[0025] The microphone 415 is a built-in circuit that converts sound into an electrical signal. The speaker 416 is a built-in circuit that converts an electrical signal into physical vibrations to produce sounds such as music and voice. The sound input / output I / F 417 is a circuit that processes input and output of sound signals between the microphone 415 and the speaker 416 under the control of the CPU 401. The display 418 is a type of display means (display unit) such as a liquid crystal or organic EL (Electro Luminescence) that displays an image of a subject, various icons, etc. The external device connection I / F 419 is an interface for connecting various external devices. The short-range communication circuit 420 is a communication circuit such as NFC (Near Field Communication) or Bluetooth. The touch panel 421 is a type of pointing device and also a type of input means (input unit) that allows a user to operate the information processing device 2, 7 by pressing the display 418.

[0026] The information processing device 2, 7 also includes a bus line 410. The bus line 410 is an address bus, a data bus, or the like for electrically connecting the components such as the CPU 401 shown in FIG. 2. The information processing device 2, 7 may also include an illuminance sensor for detecting the ambient brightness. For example, the information processing device 2, 7 can adjust the brightness of the display 418 according to the brightness detected by the illuminance sensor to make the display screen easier to view.

[0027] The information processing device of this embodiment (measurement terminal 2, analysis device 5, management terminal 7) can be realized by a notebook PC, desktop PC, server, etc. Fig. 3 is a diagram showing an example of the hardware configuration of an information processing device realized by a notebook PC, desktop PC, server, etc.

[0028] As shown in FIG. 3, the information processing devices 2, 5, and 7 are constructed by a computer and include a CPU 501, a ROM 502, a RAM 503, an HD (Hard Disk) 504, an HDD (Hard Disk Drive) controller 505, a display 506, an external device connection I / F 508, a network I / F 509, a bus line 510, a keyboard 511, a pointing device 512, a DVD-RW (Digital Versatile Disk Rewritable) drive 514, and a media I / F 516.

[0029] The CPU 501 controls the overall operation of the information processing devices 2, 5, and 7. The ROM 502 stores programs used to drive the CPU 501, such as an IPL. The RAM 503 is used as a work area for the CPU 501. The HD 504 stores various data, such as programs. The HDD controller 505 controls the reading and writing of various data from and to the HD 504 under the control of the CPU 501. The display 506 is a type of display means (display unit) that uses a liquid crystal display or organic EL (Electro Luminescence) display to display various information such as a cursor, menu, window, characters, or images.

[0030] The external device connection I / F 508 is an interface for connecting various external devices. In this case, the external device is, for example, a camera that operates as the measurement unit 3 in the measurement terminal 2, or a USB memory, printer, etc. When the measurement unit 3 is a camera having three channels of R (Red), G (Green), and B (Blue), the imaging element is, for example, an optical sensor such as a CCD or CMOS sensor. Note that the measurement unit 3 of the measurement terminal 2 may be a camera externally connected to the measurement terminal 2.

[0031] The network I / F 509 is an interface for performing data communication using the communication network 100. The bus line 510 is an address bus, a data bus, or the like for electrically connecting the components such as the CPU 501 shown in FIG.

[0032] The keyboard 511 is a type of input means (input unit) equipped with multiple keys for inputting characters, numbers, various instructions, etc. The pointing device 512 is a type of input means for selecting and executing various instructions, selecting a processing target, moving a cursor, etc., and may be a mouse, touchpad, trackball, joystick, pen tablet, or the like. The display 506 and pointing device 512 may be realized by a touch panel display that integrates these functions.

[0033] The DVD-RW drive 514 controls reading and writing of various data from and to a DVD-RW 513, which is an example of a removable recording medium. Note that the medium is not limited to a DVD-RW, and may be a DVD-R, etc. The media I / F 516 controls reading and writing (storing) of data from and to a recording medium 515, such as a flash memory.

[0034] 4 is a flowchart showing the overall processing flow of the health condition management system according to this embodiment. Each procedure (step) constituting the processing will be explained below.

[0035] The evaluation application 4 first selects and determines one measurement means from the multiple measurement means possessed by the measurement unit 3 (step S1). The measurement unit 3 has a means (first measurement means for non-contact measurement) for measuring the pulse wave based on changes in skin brightness from image information of the face, forehead, etc. of the user 1 captured in a non-contact manner by a camera, and a means (second measurement means for contact measurement) for measuring the pulse wave based on changes in skin brightness from image information of the finger, etc. of the user 1 captured in contact with or close to the camera. Details of each measurement means and the calculation of the pulse wave will be described later.

[0036] Next, the evaluation application 4 acquires objective evaluation data regarding the mental health state of the user 1 using the determined measurement means (step S2). The objective evaluation data is a pulse wave, and is acquired using the measurement unit 3 of the measurement terminal 2. In step S2, subjective evaluation data may be acquired together with the objective evaluation data. The subjective evaluation data is data of the results of a questionnaire given to the user 1, and questions are displayed on the display unit of the measurement terminal 2, and the answers of the user 1 to the questions are acquired via the input unit of the measurement terminal 2. The objective evaluation data (and the subjective evaluation data, if acquired) are transmitted to the analysis device 5 via the communication network 100.

[0037] The analysis device 5 judges the mental health state of the user 1 based on the objective evaluation data acquired by the evaluation application 4 (and the subjective evaluation data if acquired) (step S3).

[0038] Next, the analysis device 5 stores the evaluation data (objective evaluation data, subjective evaluation data) acquired in step S2, data such as the mental health status assessment results determined in step S3, and data such as the date and time of the assessment in a storage area such as HD 504 of the analysis device 5 (step S4). The storage area is not limited to HD 504 and can be any storage area as long as it can store and centrally manage the above data.

[0039] Next, the analysis device 5 notifies the relevant person 6 of the necessary information (step S5). The necessary information preferably includes "subjects requiring attention" and "recommendations for preventing the subject's mental health from worsening." The subject requiring attention is a subject determined to have poor mental health in step S3. The recommendation for preventing the subject's mental health from worsening is information useful for improving the mental health of the subject requiring attention. Examples of information useful for improving mental health include information about work style, exercise, food and supplements, sleep, massage, relaxation, mindfulness, healing and fatigue-reducing products, environment, counseling, etc. In this embodiment, the relevant person 6 is notified of the necessary information by having the evaluation application 4 of the management terminal 7 used by the relevant person 6 display the necessary information on the display unit.

[0040] The necessary information may be other than the above. If the user 1 is a subject who requires attention, the user 1 may be notified of a recommendation to prevent the subject from becoming mentally unwell. The notification method may also be other methods as long as the necessary information can be notified to the relevant person 6. For example, the necessary information may be notified to the relevant person 6 by email or the like.

[0041] Step S5 allows the person in charge 6 to know which subjects require special attention, making it possible to take early action before the mental health of the subjects requires special attention deteriorates. In addition, the person in charge 6 can know recommendations for preventing the mental health of the subjects requires special attention from becoming unwell, making it possible to take more effective action against the subjects requires special attention.

[0042] 5 is a functional block diagram of the measurement means determination process according to this embodiment. As shown in FIG. 5, the evaluation application 4 includes a screen generation unit 210, a display control unit 220, a means determination unit 230, and a measurement control unit 240.

[0043] The screen generation unit 210 generates a UI (User Interface) screen that allows the user 1 to select one measurement means from multiple measurement means. The display control unit 220 displays the generated screen on a display unit such as a monitor. FIG. 6 is a diagram showing an example of a screen generated by the screen generation unit 210. In this example, the measurement unit 3 has a non-contact measurement means and a contact measurement means. The user 1 can operate a slide button 610 included in the screen 600 using an input unit such as a pointing device to select a measurement means that suits the user 1's usage situation. After selecting a measurement means, the user 1 can click a measurement start button 620 with a pointing device or the like to start measurement using the selected measurement means.

[0044] For example, if user 1 is doing desk work in an office or remote work location and wants to perform measurements without feeling restricted by a non-contact measurement means, the first measurement means can be selected. On the other hand, if user 1 is away from home, outdoors, or in a dark place and it is difficult to perform non-contact measurement, or if it is difficult to capture the skin color of a facial image or obtain changes in skin color brightness due to wearing a mask, the second measurement means can be selected. In this way, user 1 can select an appropriate measurement means depending on conditions (measurement conditions), such as the environment of the measurement location and the measurement position (indoors or outdoors).

[0045] The means determination unit 230 has a receiving unit 232 that receives input from the input unit, and a determination unit 231 that determines a measurement means. The receiving unit 232 receives information on the selected measurement means and information on the start of measurement, and the determination unit 231 determines one measurement means from among multiple measurement means based on this information. The measurement control unit 240 controls the measurement unit 3 to start measurement according to the determined measurement means. In addition, the screen generation unit 210 generates a screen that notifies the user 1 that measurement will start.

[0046] Fig. 7 is a diagram showing an example of a measurement scene of objective evaluation data in this embodiment. As shown in Fig. 7, user 1 measures objective evaluation data (signals such as pulse waves) used to calculate objective indices, which will be described later, using a measurement unit 3 such as a camera mounted on a measurement terminal 2. Note that although the measurement terminal 2 in Fig. 7 is a laptop computer, as described above, a smartphone or tablet terminal may also be used as the measurement terminal 2. The measurement unit 3 captures an image of user 1 and acquires signals such as the pulse waves of user 1 from the captured image.

[0047] Next, an example of calculating a pulse wave when the user 1 selects a non-contact measurement means (first measurement means) in a usage situation such as that shown in Fig. 7 will be described. The measurement terminal 2 displays an image captured by the measurement unit 3 on a display or the like. In the following description, it is assumed that the image captured by the measurement unit 3 is input to the measurement terminal 2 frame by frame.

[0048] In the first measurement means, it is preferable to adjust the posture of user 1 or the posture of the imaging device using a guide frame or the like so that the face of user 1 is captured within the captured image range. FIG. 8 is a diagram showing an example of a guide frame used in the first measurement means. For example, as shown in FIG. 8, it is preferable to display a guide frame indicated by a dotted line at the start of measurement and have user 1 capture the image from that position. Here, FIG. 8(a) shows an example of a guide frame when the measurement terminal 2 is a notebook PC, and FIG. 8(b) shows an example of a guide frame when the measurement terminal 2 is a smartphone or tablet terminal. The guide frame is displayed superimposed on the face image of user 1 captured by the measurement unit 3, and user 1 can adjust the capture position while looking at the guide frame to perform measurement.

[0049] The measurement terminal 2 calculates the brightness of the skin based on the input image. First, the measurement terminal 2 calculates the coordinates of feature points on the face. Specifically, the coordinates of feature points such as the eyes, mouth, and nose are detected from the facial area of the user 1 in the image. Note that the detection of each part can be realized, for example, by known face detection technology or face authentication technology.

[0050] The measurement terminal 2 sets a skin region of the user 1's body part for acquiring a pulse wave signal and acquires pixel values of the set skin region. The skin region can be set based on the RGB values of the pixels. For example, the measurement terminal 2 acquires pixel values included in a face region detected using face detection technology, and if the RGB value of the pixel falls within the range of RGB values of skin color, it determines that the pixel is included in the skin region and sets the skin region. Alternatively, the skin region can be set based on the coordinate positions of facial feature points detected using face detection technology. In this case, the coordinate positions of feature points such as the eyes, mouth, and nose are detected, and the pixel positions of the skin region can be calculated based on the detected coordinate positions to set the skin region.

[0051] 9 is a diagram showing an example of a skin region set by the first measurement means. In the example of FIG. 9, the central part of the face including the nose and cheeks is set as skin region 11 (a rectangle indicated by a dashed line), but a part including the eyes and forehead may also be set as the skin region. Furthermore, the user 1 may set skin region 11 by inputting, via an input unit such as a pointing device, parameters such as the coordinates of the four vertices that define the rectangle of skin region 11, the starting position, width, and length.

[0052] Once the pixel values of the skin region 11 are acquired, the measurement terminal 2 calculates the signal values of the R, G, and B signals from the acquired pixel values. For example, the R, G, and B signal values of each pixel included in the skin region 11 are added for each color, and the average value of each color is used as the signal value of the R, G, and B signals. Because the change in pixel value due to the pulse is small, the influence of noise on a pixel-by-pixel basis is large. However, by averaging the signal values of multiple pixels, the influence of noise can be reduced. Note that instead of the average value, other representative indexes such as the median may be used as the signal values of the R, G, and B signals. Next, a pulse wave signal containing a component indicating the pulse wave is obtained from the signal values of the R, G, and B signals. The pulse wave signal p0(n) for the nth frame can be calculated, for example, using the following equation (1):

[0053] p0(n)=a r ×r(n)+a g ×g(n)+a b ×b(n) (1)

[0054] Here, r(n), g(n), and b(n) represent the signal values of the R signal, G signal, and B signal in the nth frame, respectively. r , a g , a b represents the weights of the R, G, and B signals.

[0055] The weight of each signal is, in the simplest example, a r =0, a g =1, a b The combination of a = 0 can be used. It is known that the change in signal value due to the pulse is most noticeable in the G signal, so the G signal can be used as the pulse wave signal. Another example of weighting is a r =-k (where the coefficient k is a positive value), a g =1, a b = 0 may be used. That is, the signal value obtained by subtracting the R signal corrected by the coefficient k from the G signal may be used as the pulse wave signal. This makes it possible to reduce noise components contained in the G signal due to body movement, etc. The coefficient k is preferably determined so as to minimize the noise components contained in the pulse wave signal. Furthermore, the coefficient k may be a value optimized for each frame. Note that the weighting of each signal is not limited to the above-mentioned example, and signal values calculated using other appropriate weights that can acquire and extract components indicating the pulse wave may also be used as the pulse wave signal.

[0056] Next, measurement terminal 2 removes noise components contained in the calculated pulse wave signal. Any method can be used to remove noise components. For example, noise components with periods different from the pulse wave period can be reduced by applying a noise reduction filter with predetermined periodic characteristics to the previously calculated pulse wave signal. As a noise reduction filter, for example, a bandpass filter that passes only frequency components close to the pulse wave period can be used. Alternatively, a filter function with a period close to the pulse wave period can be generated, and noise components with periods different from the pulse wave period can be reduced by calculating the cross-correlation with the target signal. By repeating these processes until the end of the measurement time, the pulse wave signal of user 1 can be acquired and extracted.

[0057] As mentioned above, when working at a desk in an office or remotely, the first measurement method can be selected and facial images can be taken in a non-contact manner, allowing measurements to be taken without any sense of constraint.

[0058] Next, an example of calculating the pulse wave when the user 1 selects the contact-type measurement means (second measurement means) will be described. The second measurement means is a measurement means that measures the pulse wave based on changes in skin brightness from image information acquired by bringing the user 1's finger or the like into contact with or close to the camera (built-in camera (rear camera or front camera) or externally connected camera) of the measurement unit 3 controlled by the measurement terminal 2.

[0059] FIG. 10 is a diagram showing the measurement performed by the second measurement means. In this example, a finger is placed in contact with the built-in camera of the measurement terminal 2 to perform measurement. Here, FIG. 10(a) shows an example in which the measurement terminal 2 is a laptop PC, and FIG. 10(b) shows an example in which the measurement terminal 2 is a smartphone or tablet. In these examples, the camera and the finger are in close proximity to each other, so the entire image acquired by the measurement unit 3 is considered to be the skin area. The measurement terminal 2 acquires pixel values within the image and performs averaging, noise removal, etc. to calculate the pulse wave. The method for acquiring a pulse wave signal from the acquired pixel values is the same as that of the first measurement means. Note that, in the second measurement means, the finger covers the camera portion of the measurement unit 3, so it is preferable to increase the light intensity of the displayed image on the display or turn on a flashlight located close to the measurement unit 3 during measurement.

[0060] In this way, when it is difficult to perform non-contact measurements, such as when you are out and about, outdoors, or in a dark place, or when it is difficult to capture the skin color of a facial image or obtain changes in skin color brightness due to a mask or the like, selecting the second measurement method makes it possible to perform measurements appropriately.

[0061] As described above, the health status management system of this embodiment can select one measurement method from multiple measurement methods and acquire objective evaluation data using the selected measurement method, allowing appropriate measurement to be performed depending on the measurement scene and measurement conditions.Furthermore, the mental health status can be determined based on the objective evaluation data acquired in this manner, making it possible to prevent symptoms such as mental health problems in the subject (user).

[0062] When acquiring subjective assessment data in step S2, the screen generator 210 generates a screen including questions and options, and the display controller 220 displays the generated screen on a display unit such as a monitor, thereby allowing the measurement terminal 2 to accept user 1's answers to the questions. FIG. 11 illustrates an example of a question screen presented to user 1. The question presentation screen 1100 includes a question and answer screen 1110 including the question content and answer options, and a progress button 1120 for proceeding to the next question. The questions are related to mental health status, such as questions about stress, lack of sleep, job satisfaction, and interpersonal relationships. While the answer acquisition method preferably includes multiple options, other methods may be used as long as the answers to the questions can be acquired. For example, a VAS (Visual Analogue Scale) method may be used, in which a scale is displayed and the user is asked to indicate their current state, or free comments may be entered.

[0063] Next, the method of assessment used in step S3 above and the method of calculating the objective index used in the assessment will be described. Figure 12 is a flowchart showing the flow of the mental health status assessment process in the health status management system of this embodiment. As shown in Figure 12, the health status management system of this embodiment assesses the mental health status of user 1 based on an objective index calculated from objective assessment data and a subjective index calculated from subjective assessment data. Each procedure (step) that makes up the process will be described below.

[0064] The evaluation application 4 or the analysis device 5 calculates an objective index based on the objective evaluation data acquired in step S2 (step S40). Here, an example is described in which an autonomic nervous index (e.g., total power (TP)) is calculated from the pulse wave acquired in step S2 using the processing described below and used as an objective index. TP is an index related to the accumulation of fatigue, and it is known that the accumulation of fatigue leads to poor mental health, such as decreased motivation and depression (WO2020 / 091053, etc.). Therefore, using TP as an objective index allows for an objective evaluation of mental health.

[0065] The calculated objective index (autonomic nervous index) is not limited to TP, and any other index that indicates mental health status may be used. For example, HR (Heart Rate: average heart rate), SDNN (Standard Deviation of the Normal-to-Normal intervals: standard deviation of heartbeat intervals), LF (Low Frequency: index mainly indicating sympathetic nervous activity), HF (High Frequency: index indicating parasympathetic nervous activity), LF / HF (index of autonomic nervous function balance), Ln(TP), which is the natural logarithm of TP, may be used. The calculated objective index does not have to be one, and multiple indexes may be used. For example, both the autonomic nervous indexes TP and LF / HF may be used as objective indexes. The calculated objective index may be calculated based on previously acquired objective evaluation data in addition to the objective evaluation data acquired in step S2. For example, the average TP acquired in the most recent month or the change from the previously acquired TP may be used as the objective index. Furthermore, as an objective index, the above-mentioned various indexes may be combined to classify or score the health state.

[0066] If subjective assessment data has been acquired in addition to the objective assessment data in step S2, the assessment application 4 or the analysis device 5 calculates a subjective index based on the subjective assessment data (step S41). Here, an example will be described in which a numerical value of fatigue sensation (1: very tired, 2: tired, 3: neutral, 4: not tired, 5: not tired at all) is used as the subjective index. The subjective value of fatigue, that is, the sense of fatigue, is also known to be an important index in determining mental health status. Therefore, by using the numerical value of fatigue sensation as the subjective index, it is possible to evaluate the subjective mental health status.

[0067] The subjective index to be calculated may be any other index that represents the mental health state. For example, an index that quantifies responses to questions about stress, lack of sleep, etc. may be used. The subjective index to be calculated does not have to be one; multiple indexes may be used. For example, both a numerical value for fatigue and a numerical value for stress may be used as the subjective index. The subjective index to be calculated may be calculated based on previously acquired subjective assessment data in addition to the subjective assessment data acquired in step S2. For example, the average value of fatigue values acquired in the most recent month or the amount of change from the previously acquired numerical value for fatigue may be used as the subjective index. In addition, although the above describes an example in which the objective index is calculated in step S40 and then the subjective index is calculated in step S41, the order of steps S40 and S41 may be reversed, or step S41 may be omitted and only the objective index of step S40 may be used.

[0068] The evaluation application 4 or the analysis device 5 judges the mental health state of the user 1 based on the objective index calculated in step S40 (step S42). In this embodiment, if "TP, which is an objective index, is below a reference value," the mental health state is judged to be poor and an instruction to issue an alert is issued. Here, the reference value may be set to an appropriate value.

[0069] The mental health condition may be determined using other objective indices as long as they can determine the mental health condition of user 1. For example, the above-mentioned HR, SDNN, LF, HF, LF / HF, and Ln(TP), which is the natural logarithm of TP, may be used. The objective indices used do not have to be one, and multiple indices may be used. For example, an evaluation formula may be created using both or all of the autonomic nervous system indices, TP and LF / HF, as parameters, and the calculated evaluation value may be compared with a predetermined reference value to determine the mental health condition.

[0070] The mental health status may be judged not only in two levels, such as good / bad, but also in three or more levels, such as good / caution / needs caution. It is also preferable that the above-mentioned standard values can be changed by the person concerned 6. By doing so, it becomes possible to control the number of users for whom an alert is issued depending on the situation, for example, by tightening the standard values so that all users with poor mental health status can be extracted, or conversely, by loosening the standard values so that only users with particularly poor mental health status can be extracted.

[0071] Furthermore, if the subjective index is derived in step S41, the evaluation application 4 or the analysis device 5 may determine the mental health state of the user 1 based on the subjective index calculated in step S41 as well as the objective index calculated in step S40. For example, if the subjective index, fatigue, is equal to or less than a first reference value, or if the subjective index, fatigue, is greater than the first reference value but the objective index, TP, is equal to or less than a second reference value, the mental health state may be determined to be poor and an alert may be issued. Here, the first and second reference values may be set to appropriate values. Previous studies have shown that when the subjective level of fatigue is high, the objective level of fatigue is often also high. Therefore, the mental health state may be determined to be poor based on the former condition, that is, the subjective index, fatigue, is equal to or less than a first reference value. Furthermore, the above studies have shown that even when the subjective level of fatigue is low, the objective level of fatigue may be high. Therefore, it is also possible to determine that a person's mental health is poor based on the latter condition: "The subjective indicator of fatigue is greater than the first standard value, but the objective indicator of TP is equal to or less than the second standard value."

[0072] When step S3 is performed by the analysis device 5, the analysis device 5 transmits the result of the mental health condition determined in step S42 to the measurement terminal 2 used by the user 1. The evaluation application 4 running on the measurement terminal 2 displays the result of the mental health condition determined in step S42 or the result of the mental health condition received from the analysis device 5 on the display unit of the measurement terminal 2, and notifies the user 1 of the result of the mental health condition.

[0073] Next, the method for calculating the objective index in step S40 will be described. Figure 13 is a flowchart showing the flow of calculation of the objective index in the health condition management system of this embodiment.

[0074] First, the evaluation application 4 or the analysis device 5 calculates the pulse interval from the pulse wave signal (step S50). The pulse wave signal is acquired by the measurement means determined by the means determination unit 230 as described above. The pulse interval can be calculated using existing technology. For example, the time when the pulse wave signal reaches its peak can be detected, and the pulse interval can be calculated from the time interval between adjacent peaks.

[0075] Next, the evaluation application 4 or the analysis device 5 performs resampling so that the time intervals of the time series data of the pulse intervals calculated in step S50 become equal (step S51). The resampling time interval is, for example, 0.25 seconds. The signal value at the elapsed time for resampling may be obtained by, for example, interpolation. The interpolation method may be linear interpolation, spline interpolation, or the like.

[0076] Next, the evaluation application 4 or the analysis device 5 calculates a power spectrum from the time series data of pulse intervals resampled in step S51 (step S52). A known frequency analysis method can be used to calculate the power spectrum. For example, the maximum entropy method can be used to find the power at a specified frequency. Note that parameters such as lag can be set to appropriate values.

[0077] The evaluation application 4 or the analysis device 5 calculates an autonomic nervous index based on the power spectrum calculated in step S52 and sets this as an objective index (step S53). The autonomic nervous index may be, for example, an LF / HF value that indicates the balance of autonomic nervous function or a TP value that indicates the overall function of autonomic nervous function. The LF / HF value and TP value may be calculated using known methods. For example, the power integral value in the frequency band of 0.04 to 0.15 Hz is set as the LF value, and the power integral value in the frequency band of 0.15 to 0.40 Hz is set as the HF value. The LF / HF value can be calculated by taking the ratio of the two, and the TP value can be calculated by taking the sum of the two.

[0078] (Second embodiment) In the first embodiment, the measurement means was determined based on the information of the measurement means selected by the user 1, but the measurement means may also be selected (or determined) based on the reliability of each measurement means.

[0079] 14 is a functional block diagram of the measurement means determination process according to the second embodiment. As shown in FIG. 14, the evaluation application 4 includes a screen generation unit 210, a display control unit 220, a means determination unit 230, and a measurement control unit 240. The means determination unit 230 includes a determination unit 231, a reception unit 232, a pre-measurement unit 233, and an SN (Signal-to-Noise) ratio calculation unit 234.

[0080] The pre-measurement unit 233 performs preliminary measurements (pre-measurements) using each measurement means before starting measurements (main measurements). The pre-measurement time is approximately several seconds, which is enough time to detect multiple pulse wave cycles. The SN ratio calculation unit 234 performs frequency analysis of the pulse wave signal from the results of the pre-measurements by each measurement means, and calculates the SN ratio of the pulse wave signal.

[0081] The determination unit 231 determines one measurement means from among a plurality of measurement means based on the reliability of each measurement means. First, the determination unit 231 selects the measurement means with the highest reliability from each measurement means. Specifically, the determination unit 231 compares the SN ratios calculated for each measurement means and selects the measurement means with the highest SN ratio.

[0082] The screen generation unit 210 generates an image for presenting the selected measurement means to the user 1, and the display control unit 220 displays the generated image on a display or the like to present to the user 1. For example, if the measurement unit 3 has a non-contact measurement means and a contact measurement means as measurement means, and the determination unit 231 selects the non-contact measurement means, an image such as that shown in FIG. 6 is presented to the user 1.

[0083] The reception unit 232 receives input from the user 1. For example, when performing measurement using the measurement means (non-contact measurement means) presented by the measurement terminal 2 as shown in FIG. 6, the user 1 can start measurement by clicking the measurement start button 620. At this time, the determination unit 231 determines the measurement means to be the non-contact measurement means, and the measurement control unit 240 starts measurement according to the determined measurement means. On the other hand, when performing measurement by changing the presented measurement means, the user 1 can change the measurement means using the slide button 610 and click the measurement start button 620 to start measurement using the changed measurement means. At this time, the determination unit 231 determines the measurement means to be the contact measurement means, and the measurement control unit 240 starts measurement according to the determined measurement means.

[0084] As described above, the measurement means may be determined without receiving input from the user 1. In this case, the determination unit 231 compares the reliability of the S / N ratios calculated for each measurement means, and determines the measurement means with the highest reliability as the measurement means to be used for the main measurement.

[0085] Figure 15 is a flowchart showing the overall processing flow of the health status management system according to this embodiment. The difference from the first embodiment is that the reliability of each measurement means is calculated in step S2-1, and the measurement means is determined based on the reliability in step S2-2. The other steps are the same as those in Figure 4, so their explanation will be omitted.

[0086] First, the measurement terminal 2 calculates the reliability of each measurement means (step S2-1). Figure 16 is a flowchart showing the procedure for calculating the SN ratio of the pulse wave signal obtained by each measurement means as the reliability of each measurement means.

[0087] First, the measurement terminal 2 calculates the power spectrum of the pulse wave signal (step S2-1-1). Specifically, the measurement terminal 2 adjusts the number of pieces of data obtained in the preliminary measurement so that they are a power of 2. Next, the measurement terminal 2 performs a fast Fourier transform (FFT) on the adjusted data. Subsequently, the power at each frequency is calculated. FIG. 17 is a diagram showing an example of the calculated power spectrum of the pulse wave signal, with the vertical axis representing power and the horizontal axis representing frequency.

[0088] Next, the measurement terminal 2 detects the pulse wave frequency Fp (step S2-1-2). When the user 1 is at rest, the pulse rate is often about 30 to 120 beats per minute. Therefore, in the power spectrum, a fundamental wave with a strong peak is detected at a frequency corresponding to this pulse rate. That is, the measurement terminal 2 detects the frequency with the greatest power in the frequency band of 0.5 to 2.0 hertz (Hz) in the power spectrum. In the example shown in FIG. 17, a strong peak is detected at a frequency of about 1.0 Hz, as described above. Therefore, in this example, the pulse wave frequency Fp is detected to be about 1.0 Hz.

[0089] Next, the measurement terminal 2 determines the frequency bands of the signal component and the noise component (step S2-1-3). For example, the frequency band Fs of the signal component is determined centered around the pulse wave frequency Fp detected in step S2-1-2. Specifically, the frequency band Fs of the signal component is determined to be a band from (Fp-ΔF) to (Fp+ΔF). Here, ΔF is a preset value. For example, ΔF is set taking into consideration the peak shape of the power spectrum, etc. Furthermore, the frequency band Fs of the signal component may include a frequency band including harmonic peaks that appear at frequencies that are integer multiples of the pulse wave frequency Fp. In the example shown in FIG. 17, ΔF is set to 0.2 Hz, and Fs is determined to be approximately 0.8 to 1.2 Hz.

[0090] On the other hand, the noise component frequency band Fn is determined as a frequency band other than the signal component frequency band Fs. However, the power of frequencies lower than the pulse wave frequency Fp is affected by the body movement of the living body during measurement, etc. Therefore, the measurement terminal 2 may determine the noise component frequency band Fn to be a frequency band higher than the signal component frequency band Fs. In the example shown in FIG. 17, frequencies of about 1.2 Hz or higher are determined as the noise component frequency band Fn. Note that the noise component frequency band Fn may also be determined to include a frequency band lower than the signal component frequency band Fs. The upper frequency limit of the noise component frequency band Fn is (sampling frequency / 2). For example, if the camera frame rate is 30 fps (frames per second), the upper frequency limit of the noise component frequency band Fn is 30 Hz / 2 = 15 Hz.

[0091] Next, the measurement terminal 2 calculates the SN ratio (step S2-1-4). Specifically, first, the measurement terminal 2 calculates the signal component Vs and the noise component Vn based on the frequency band Fs of the signal component and the frequency band Fn of the noise component determined in step S2-1-3. For example, Vs is calculated by averaging the power of the frequency band Fs, and Vn is calculated by averaging the power of the frequency band Fn. Next, the measurement terminal 2 calculates the SN ratio by taking the ratio of the calculated Vs and Vn. Note that the SN ratio may also be calculated using other calculation methods.

[0092] The measurement terminal 2 performs preliminary measurements, including pulse wave measurements, for a few seconds using each of the multiple measurement means, and calculates the SN ratio of each measurement means. Note that, although the SN ratio has been used as the reliability calculated in step S2-1, any index that can evaluate the reliability of the pulse wave, such as the pulse wave signal value of each measurement means, can also be used as the reliability.

[0093] The evaluation application 4 of the measurement terminal 2 determines the measurement means based on the reliability of each measurement means calculated in S2-1 (step S2-2). Specifically, the measurement means with the highest reliability is selected (or determined).

[0094] In this way, according to this embodiment, the reliability of each measurement means is calculated by preliminary measurement, and the measurement means with the highest reliability is selected, thereby making it possible to determine the optimal measurement method according to the measurement conditions. For example, if the reliability of each measurement means varies depending on the measurement conditions and different measurement means are more likely to acquire a pulse wave signal, it is possible to select a measurement means with high accuracy that is suited to the measurement conditions by comparing the reliabilities determined by preliminary measurement.

[0095] (Third embodiment) The measurement means can be determined using information from sensors and the like held by the measurement terminal 2. For example, it is considered preferable to use a non-contact measurement means (first measurement means) for indoor measurements. The appropriate range of indoor illuminance is determined by industrial safety and health regulations, and the illuminance on the work surface when a worker performs normal work is, for example, 150 lux or more. However, since it is difficult to measure the amount of skin brightness fluctuation in environments darker than this or at night, it is preferable to use a contact measurement means (second measurement means) for measurement. In such cases, in this embodiment, the measurement means can be determined based on information from an illuminance sensor.

[0096] 18 is a functional block diagram of the measurement means determination process according to the third embodiment. As shown in FIG. 18, the evaluation application 4 includes a screen generation unit 210, a display control unit 220, a means determination unit 230, and a measurement control unit 240. The means determination unit 230 includes a determination unit 231, a reception unit 232, and a sensor information acquisition unit 235.

[0097] The sensor information acquisition unit 235 acquires sensor information. The sensor information is information indicating the environmental conditions (measurement conditions) of the measurement location, and is information sensed by an illuminance sensor, a GPS receiving unit 411, a temperature sensor, a humidity sensor, etc. The determination unit 231 determines one measurement means from a plurality of measurement means based on the acquired sensor information. First, the determination unit 231 selects one measurement means from a plurality of measurement means based on the acquired sensor information, as will be described later. The operations of the screen generation unit 210, the display control unit 220, the reception unit 232, and the measurement control unit 240 are the same as those described in the second embodiment.

[0098] The determination unit 231 determines the measurement means selected by the determination unit 231 or the measurement means specified by the user 1 as the measurement means to be used for measurement, in accordance with the input of the user 1. Note that the measurement means may be determined without receiving input from the user 1. In this case, the determination unit 231 determines one measurement means from among the plurality of measurement means based on the sensor information.

[0099] Figure 19 is a flowchart showing the overall processing flow of the health management system according to this embodiment. The difference from the first embodiment is that sensor information is acquired in step S3-1, and a measurement method is determined based on the sensor information in step S3-2. The other steps are the same as those in Figure 4, so their explanation will be omitted.

[0100] First, the measurement terminal 2 acquires sensor information (step S3-1). The sensor information is information indicating measurement conditions, such as illuminance information of the measurement environment sensed by an illuminance sensor, location information of the measurement location sensed by a GPS, temperature information of the measurement location sensed by a temperature sensor, and humidity information of the measurement location sensed by a humidity sensor.

[0101] Next, the evaluation application 4 of the measurement terminal 2 determines a measurement means based on the acquired sensor information (step S3-2). If the sensor information is illuminance information, for example, if the illuminance is outside the appropriate range of the first measurement means, the second measurement means is determined as the measurement means. If the sensor information is location information, for example, the location information is used to determine whether the measurement location is indoors or outdoors, and if it is indoors, the first measurement means is determined as the measurement means, and if it is outdoors, the second measurement means is determined as the measurement means. Note that instead of location information detected by GPS, temperature information detected by a temperature sensor or humidity information detected by a humidity sensor may be used to determine whether the measurement location is indoors or outdoors. Furthermore, sensor information other than the above-mentioned sensor information can be used to determine the measurement means in this embodiment as long as it can determine the measurement environment and measurement location.

[0102] As described above, according to this embodiment, the measurement means is determined based on the sensor information acquired by the means determination unit 230, so that an appropriate measurement means can be determined according to the measurement conditions, and more accurate measurements can be performed.

[0103] (Fourth embodiment) The measurement terminal 2 may determine the measurement means according to the measurement history (information indicating the measurement means determined in the past). For example, it may be difficult to obtain brightness fluctuations from a skin image of the user 1's face because the skin color and blood pressure state vary depending on the race, age, etc. of the user 1. Even in such a case, if the measurement history is saved, it becomes possible to determine an effective measurement means for the user 1 by referring to the measurement history. For example, if the measurement history includes information on the time of measurement, such as the date and time of measurement, and information on the measurement means used for the measurement, it becomes possible to know what measurement means the user 1 has used in the past and under what conditions, and this information can be used to determine the measurement means.

[0104] Fig. 20 is a functional block diagram of a measurement means determination process according to the fourth embodiment. As shown in Fig. 20, the evaluation application 4 has an information storage unit 250 in addition to a screen generation unit 210, a display control unit 220, a means determination unit 230, and a measurement control unit 240. The difference from the first embodiment is that information on the determined measurement means is stored in the information storage unit 250, and the means determination unit 230 determines the measurement means based on the stored information on the measurement means, etc.

[0105] The information storage unit 250 stores measurement history information including information on the measurement means determined by the means determination unit 230 in the recording medium 408, 515, etc. The measurement history information may include, in addition to the information on the measurement means, information on the user 1 (user name, user identifier, etc.), information on the measurement date and time, information on the measurement location (location information), information on the measurement environment (for example, information on temperature, humidity, illuminance, etc.). The measurement history information may be stored in a location other than the recording medium 408, 515. For example, the measurement history information may be stored in the HD 504, DVD-RW, RAM 403, 503, etc. The measurement history information may also be transmitted to the analysis device 5 or the management terminal 7 and stored in the destination recording medium, etc. In this case, the information storage unit 250 is implemented in the analysis device 5 or the management terminal 7.

[0106] The means determination unit 230 reads out the measurement history information stored in the information storage unit 250 and determines the measurement means based on the read out measurement history information. For example, the means determination unit 230 can compare the number of times that user 1 has measured using each measurement means in past measurements and select the measurement means that has been used the most. By operating the input unit, user 1 can measure using the selected measurement means, or can measure using a measurement means different from the selected measurement means. Furthermore, the measurement terminal 2 may determine the measurement means that has been used the most as the measurement means to be used for measurement, without using input from user 1.

[0107] The means determination unit 230 may determine the measurement means using information on the measurement date and time included in the measurement history information. For example, it can select (or determine) the measurement means determined at the past measurement date and time closest to the current measurement date and time as the current measurement means.

[0108] The means determination unit 230 may determine the measurement means using the location information included in the measurement history information. For example, it can select (or determine) the measurement means determined in the past measurement that corresponds to the location information closest to the current measurement location as the current measurement means.

[0109] The means determination unit 230 may determine the measurement means using information on the measurement environment included in the measurement history information. For example, it can select (or determine) as the current measurement means the measurement means determined in a previous measurement that has a measurement environment (temperature, humidity, illuminance, etc.) closest to the current measurement environment (temperature, humidity, illuminance, etc.).

[0110] Figure 21 is a flowchart showing the overall processing flow of the health condition management system according to this embodiment. The differences from the first embodiment are that measurement history information is read out in step S4-1, a measurement means is determined based on the measurement history information in step S4-2, and the determined measurement history information is saved in step S4-3. The other steps are the same as those in Figure 4, so a description thereof will be omitted.

[0111] First, the evaluation application 4 of the measurement terminal 2 reads out the measurement history information stored in a recording medium or the like (step S4-1). Next, the evaluation application 4 determines the measurement means based on the read out measurement history information (step S4-2). Subsequently, the evaluation application 4 stores the measurement history information including the determined measurement means in a recording medium or the like (step S4-3).

[0112] As described above, various information can be stored as measurement history information. For example, when a measurement means is determined by comparing the number of measurements taken with each measurement means in the past, only information on the determined measurement means may be stored. When a measurement means is determined for each different user, information such as a user name or user identifier may be stored along with the determined measurement means. Furthermore, information such as the measurement date and time, measurement location, and measurement environment may be stored as measurement history information.

[0113] In the above, an example has been described in which an information storage unit 250 is added to the functional block diagram of the first embodiment (FIG. 5), but this embodiment may also be realized by adding an information storage unit 250 to the functional block diagram of the second embodiment (FIG. 14) or the functional block diagram of the third embodiment (FIG. 18).

[0114] When realizing this embodiment by adding an information storage unit 250 to the functional block diagram of the second embodiment, the stored measurement history information can include information on the reliability of each measurement means. For example, in this measurement, a new reliability is not calculated, and the measurement means can be selected (or determined) using the most recent reliability information stored. This allows measurement to be started quickly without performing pre-measurement, etc.

[0115] When realizing this embodiment by adding the information storage unit 250 to the functional block diagram of the third embodiment, sensor information can be included in the measurement history information to be stored. For example, the sensor information acquired this time can be compared with the stored sensor information, and the measurement means determined in the past measurement having the sensor information value closest to the current sensor information can be selected (or determined) as the current measurement means.

[0116] As described above, according to this embodiment, the measurement means is determined based on the measurement history information (information indicating the measurement means) stored by the information storage unit 250, so it is possible to perform appropriate measurements using the measurement means used in the past. Furthermore, if the measurement history information includes user information, it is possible to determine an appropriate measurement means for each user, so it is possible to perform measurements with higher accuracy.

[0117] Although various embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. For example, the multiple measurement means may include a non-contact measurement means using a camera, a contact measurement means, or a pulse wave sensor connected to an external device connection interface. User 1 can perform measurements using a measurement means selected from three or more measurement means. Furthermore, the measurement target of the measurement means is not limited to pulse waves; it may be any signal capable of measuring the state of user 1's autonomic nervous system. These novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit and scope of the invention. These novel embodiments and their modifications are within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as set forth in the claims. Furthermore, components from different embodiments and modifications may be combined as appropriate.

[0118] Furthermore, each function of the above-described embodiments can be realized by one or more processing circuits. Here, the term "processing circuit" in this specification includes a processor programmed to execute each function by software, such as a processor implemented by an electronic circuit, as well as devices such as an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and conventional circuit modules designed to execute each of the above-described functions.

[0119] The devices described in each embodiment represent only one of multiple computing environments for implementing the embodiments disclosed herein. In one embodiment, the measurement terminal 2 includes multiple computing devices, such as a server cluster. The multiple computing devices are configured to communicate with each other via any type of communication link, including a network or shared memory, and perform the processes disclosed herein. Similarly, the analysis device 5 and the management terminal 7 may each include multiple computing devices configured to communicate with each other.

[0120] Furthermore, the measurement terminal 2, the analysis device 5, and the management terminal 7 can be configured to share the disclosed processing steps, for example, the steps in FIG. 4, in various combinations. For example, the process executed by the evaluation application 4 of the measurement terminal 2 can be executed by the analysis device 5 or the management terminal 7. Similarly, the function of the evaluation application 4 of the measurement terminal 2 can be executed by the analysis device 5 or the management terminal 7. Furthermore, the elements of the measurement terminal 2, the analysis device 5, and the management terminal 7 may be integrated into one server device or may be separated into multiple devices.

[0121] Note that the information processing devices 2, 5, and 7 according to each embodiment are not limited to notebook PCs, desktop PCs, smartphones, tablet terminals, etc., as long as they are devices equipped with a communication function. The information processing devices 2, 5, and 7 may be, for example, an image forming device, a PJ (Projector), an IWB (Interactive White Board: a white board with an electronic blackboard function that allows mutual communication), an output device such as digital signage, a HUD (Head Up Display) device, industrial machinery, an imaging device, a sound collecting device, a medical device, a network home appliance, an automobile (Connected Car), a mobile phone, a game console, a PDA (Personal Digital Assistant), a digital camera, or a wearable PC.

[0122] Furthermore, the algorithm for selecting (or determining) a measurement means in the determination unit 231 in the second to fourth embodiments may be an algorithm generated by the learning effect of machine learning. For example, the algorithm of the second embodiment that selects (or determines) a measurement means based on the calculated S / N ratio of each measurement means, the algorithm of the third embodiment that selects (or determines) a measurement means based on acquired sensor information, and the algorithm of the fourth embodiment that selects (or determines) a measurement means based on saved measurement history information may each be generated by applying machine learning technology. Here, machine learning is a technology for enabling a computer to acquire human-like learning capabilities, and refers to a technology in which a computer autonomously generates an algorithm required for judgments such as data identification from previously acquired learning data and applies this algorithm to new data to make predictions. The learning method for machine learning may be any of supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning, or may be a combination of these learning methods. Any learning method for machine learning is acceptable.

[0123] For example, aspects of the present invention are as follows. <1> An information processing device comprising: a measurement unit having a plurality of measurement means for measuring information indicating a user's health condition; and a means determination unit for determining one measurement means from the plurality of measurement means, wherein the plurality of measurement means include a first measurement means for measuring the information in a non-contact manner and a second measurement means for measuring the information in a contact manner, and the measurement unit measures the information indicating the health condition using the measurement means determined by the means determination unit. <2> The method further includes a reliability acquisition unit that acquires the reliability of the plurality of measurement means, and the means determination unit determines one measurement means based on the reliability acquired by the reliability acquisition unit. <1> The information processing device is described in the above. <3> The method further includes a sensor information acquisition unit that acquires sensor information indicating measurement conditions, and the means determination unit determines one measurement means based on the sensor information acquired by the sensor information acquisition unit. <1> or <2> The information processing device is described in the above. <4> The method further includes an information storage unit that stores information indicating the determined measurement means, and the means determination unit determines one measurement means based on the information indicating the measurement means stored in the information storage unit. <1> ~ <3> The information processing device according to any one of the above items. <5> A health status management system having an information processing device operated by a user and an analysis device capable of communicating with the information processing device, wherein the information processing device comprises a measurement unit having a plurality of measurement means for measuring information indicating the health status of the user, and a means determination unit for determining one measurement means from the plurality of measurement means, wherein the plurality of measurement means include a first measurement means for measuring the information in a non-contact manner and a second measurement means for measuring the information in a contact manner, and the measurement unit measures the information indicating the health status using the measurement means determined by the means determination unit. <6> This information processing method includes a means determination step of determining one measurement means from a plurality of measurement means that measure information indicating a user's health condition, and a measurement step of measuring the information indicating the health condition using the measurement means determined in the means determination step, wherein the plurality of measurement means include a first measurement means that measures the information in a non-contact manner and a second measurement means that measures the information in a contact manner. <7> This program causes a computer to function as a means determination unit that determines one measurement means from multiple measurement means included in a measurement unit that measures information indicating a user's health condition, where the multiple measurement means include a first measurement means that measures the information in a non-contact manner and a second measurement means that measures the information in a contact manner, and causes the measurement unit to measure the information indicating the health condition using the measurement means determined by the means determination unit. [Explanation of symbols]

[0124] 1 user 2. Measurement terminal, information processing device 3. Measurement section 4 Evaluation Application 5. Analysis equipment, information processing equipment 6. Stakeholders 7 Management terminal, information processing device 210 Screen generation section 220 Display control unit 230 Means Decision Unit 240 Measurement control section [Prior art documents] [Patent documents]

[0125] [Patent Document 1] Patent No. 6763719 [Patent Document 2] Patent No. 7340801

Claims

1. a measurement unit having a plurality of measurement means for measuring information indicating the health state of a user; a means determination unit that determines one measurement means from the plurality of measurement means; Equipped with the plurality of measuring means include a first measuring means that measures the information in a non-contact manner and a second measuring means that measures the information in a contact manner; the measurement unit measures the information indicating the health condition using the measurement means determined by the means determination unit; Information processing device.

2. further comprising a reliability acquisition unit that acquires the reliability of the plurality of measurement means; the means determination unit determines one measurement means based on the reliability acquired by the reliability acquisition unit. The information processing device according to claim 1 .

3. further comprising a sensor information acquisition unit that acquires sensor information indicating measurement conditions; the means determination unit determines one measurement means based on the sensor information acquired by the sensor information acquisition unit. The information processing device according to claim 1 .

4. further comprising an information storage unit for storing information indicating the determined measuring means; the means determination unit determines one measurement means based on the information indicating the measurement means stored in the information storage unit. The information processing device according to claim 1 .

5. A health condition management system having an information processing device operated by a user and an analysis device capable of communicating with the information processing device, The information processing device includes: a measuring unit having a plurality of measuring means for measuring information indicating the health condition of the user; a means determination unit that determines one measurement means from the plurality of measurement means; Equipped with the plurality of measuring means include a first measuring means that measures the information in a non-contact manner and a second measuring means that measures the information in a contact manner; the measurement unit measures the information indicating the health condition using the measurement means determined by the means determination unit; Health status management system.

6. a means determination step of determining one measurement means from a plurality of measurement means for measuring information indicating the user's health state; a measuring step of measuring the information indicating the health condition using the measuring means determined in the means determining step; Equipped with the plurality of measuring means include a first measuring means that measures the information in a non-contact manner and a second measuring means that measures the information in a contact manner; Information processing methods.

7. Computer, The device functions as a means determination unit that determines one measurement means from a plurality of measurement means included in a measurement unit that measures information indicating a user's health condition, the plurality of measuring means include a first measuring means that measures the information in a non-contact manner and a second measuring means that measures the information in a contact manner; a program that causes the measurement unit to measure the information indicating the health condition using the measurement means determined by the means determination unit;

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