JP2017123898A - Biological information processing system, electronic instrument, program, and control method for biological information processing system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a biological information processing system, etc. capable of accurately estimating at least one of an oxygen intake of a subject, calorie consumption, and physical strength of the subject without requiring the subject to perform specific exercise when activity amount information of the subject is deficient.SOLUTION: An information processing system 100 includes: an information acquisition part 110 for acquiring at least one of biological information of a subject from a biological sensor 200 and body motion information from a body motion sensor 300; and a processing part 130. The processing part 130 specifies activity intensity of the subject based on at least one of the biological information and the body motion information, performs interpolation processing for deficient activity amount information when the activity intensity of the subject is determined to be first activity intensity, obtains activity amount index information indicating an activity amount index of the subject, and obtains the activity amount index information without performing the interpolation processing for the deficient activity amount information when the activity intensity of the subject is determined to be second activity intensity higher than the first activity intensity.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a biological information processing system, an electronic device, a program, a control method for the biological information processing system, and the like.

  In recent years, there has been an increasing demand for accurately grasping calorie consumption against the background of health-consciousness. It is known that the calorie consumption can be estimated from the oxygen intake, as disclosed in Patent Document 1, for example. In the invention disclosed in Patent Document 1, the oxygen intake is estimated from the heart rate while taking into account individual differences such as physical strength differences, and the calorie consumption corresponding to the individual physical strength is estimated. However, in the invention of Patent Document 1, when estimating the physical strength of the subject, it is necessary to perform a specific exercise (physical strength test) on the subject.

  Further, for example, Patent Document 2 discloses a measuring apparatus that performs physical strength evaluation of a subject using a wearable device.

JP 2002-336219 A JP-A-7-333367

  As described above, in the invention of Patent Document 1, in order to accurately estimate the calorie consumption, it is necessary to estimate the physical strength of the subject in advance. In this case, it is necessary to ask the subject to perform a specific exercise whose relationship between exercise intensity and calorie consumption is known in advance.

  However, performing a specific exercise (physical fitness test) accurately is troublesome and very difficult. For example, even when performing a physical fitness test by walking, which is the simplest exercise, the user (subject) needs some means for accurately measuring the walking distance and speed. Eventually, a device such as a treadmill is used, and the user must obtain the expensive device or go to the place where the device is installed.

  Furthermore, if the user's physical strength or the like changes due to, for example, daily exercise or long-term rest, the relationship between the heart rate and oxygen intake also changes over time. In view of the change over time, if it is attempted to accurately estimate the oxygen intake from the heart rate, it is necessary to periodically perform the troublesome physical fitness test.

  For example, when estimating a user's physical strength from a daily activity state instead of a physical strength test, it is necessary to monitor the daily activity amount of the user. However, not all users carry measurement devices every day and the measurement devices cannot monitor the amount of activity. For example, there are some users who carry a measuring device mainly during exercise and do not carry the measuring device when not exercising. Under such user usage conditions, when the user's physical strength is estimated, the amount of activity on the day when the measuring device is not carried is not considered (reflected), and the user's physical strength may be underestimated. .

  According to some aspects of the present invention, when the activity amount information of the subject may be deficient, the subject's oxygen intake, calorie consumption and It is possible to provide a biological information processing system, an electronic device, a program, a control method for the biological information processing system, and the like that can accurately estimate at least one of the physical strength of the subject.

  One aspect of the present invention is an information acquisition unit that acquires at least one of biological information of a subject detected by a biological sensor and body motion information of the subject detected by a body motion sensor; A processing unit for obtaining activity amount information representing an activity amount of the subject based on one information, and obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information. The processing unit specifies an activity intensity of the subject based on the at least one information of the biological information and the body movement information, and determines that the activity intensity of the subject is the first activity intensity. If it is, the deficit activity amount information determined to be missing is interpolated to obtain the activity amount index information, and the activity intensity of the subject is higher than the first activity intensity. The activity intensity It is when the is related to biological information processing system for determining the activity amount index information without performing the interpolation processing of the missing activity amount information.

  In one aspect of the present invention, the activity intensity of a subject is specified based on at least one of biological information and body motion information. If it is determined that the activity intensity of the subject is the first activity intensity, the missing activity amount information is interpolated to obtain activity amount index information, and the activity intensity of the subject is the second activity intensity. When it is determined that the intensity is strong, the activity amount index information is obtained without performing the interpolation process of the missing activity amount information. Therefore, when the activity amount information of the subject may be missing, even if the subject does not perform specific exercise, at least one of the subject's oxygen intake, calorie consumption, and subject's physical strength Can be accurately estimated.

  In the aspect of the invention, the processing unit may identify the activity intensity of the subject based on the activity amount index information obtained before a deficiency period of the deficient activity amount information. Good.

  Accordingly, it is possible to calculate the activity amount index information by a different method when the activity intensity of the subject is low and when the activity intensity of the subject is high activity intensity.

  In the aspect of the invention, the processing unit may specify the activity intensity of the subject based on preset initial activity amount index information.

  Thereby, even when the activity amount index information is not yet obtained, it is possible to change the calculation method of the activity amount index information according to the activity intensity of the subject.

  Moreover, in one aspect of the present invention, the processing unit determines that the activity intensity of the subject is older than the deficiency period of the deficient activity amount information when it is determined that the activity intensity of the subject is the first activity intensity. The interpolation processing may be performed based on a plurality of activity amount information in a period.

  This makes it possible to obtain activity amount information that matches the activity intensity of the past activity of the subject during the deficit period.

  In the aspect of the invention, the processing unit is acquired in a period before the missing period, and the plurality of activities determined that the activity amount of the subject is included in a given range. Based on the quantity information, the interpolation processing of the missing activity quantity information may be performed.

  As a result, in the activity amount information acquired in the past period from the loss period, the activity amount is not used without the activity amount information whose activity amount is more than the given threshold. It is possible to estimate quantity information.

  In the aspect of the invention, the given range includes activity amount information of a given number or more among the plurality of activity amount information acquired in the past period from the missing period. It may be a range.

  Thereby, it is possible to estimate the missing activity amount information without using the activity amount when the subject protrudes and performs the activity.

  In one aspect of the present invention, the processing unit determines whether or not there is a deficiency period in which the activity amount information is deficient based on the at least one information of the biological information and the body movement information. May be performed.

  As a result, when it is determined that there is a missing period, it is possible to determine to perform interpolation processing of missing activity amount information.

  In one aspect of the present invention, when the wearable device provided with the biological sensor and the body motion sensor is determined not to be attached to the subject, the processing unit has the deficiency period. You may judge.

  Thereby, for example, even when the subject is hardly walking or running, it is possible to determine the presence or absence of a missing period.

  In one aspect of the present invention, the processing unit obtains the activity amount information for each first period, and includes a plurality of activity amount information obtained in a second period longer than the first period. Based on this, the activity amount index information may be obtained for each second period.

  This makes it possible to suppress short-term fluctuations in the activity amount index information.

  In one aspect of the present invention, the processing unit obtains maximum oxygen intake information indicating the maximum oxygen intake of the subject based on the activity amount index information and the user information of the subject. Also good.

  This makes it possible to obtain maximum oxygen intake information according to the physical strength of each subject.

  According to another aspect of the present invention, there is provided an information acquisition unit that acquires at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor; A processing unit for obtaining activity amount information representing an activity amount of the subject based on the at least one information and obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information; The processing unit obtains the activity amount index information by performing interpolation processing of the missing activity amount information determined to be deficient using the activity amount information determined to be the first activity intensity, For the activity amount information determined to be the second activity intensity higher than the first activity intensity, the interpolation processing of the missing activity amount information is performed using the activity amount information of the second activity intensity. Without the activity amount index Related to biological information processing system for determining the distribution.

  In another aspect of the present invention, a biological sensor, a body motion sensor, biological information of the subject detected by the biological sensor, and body motion information of the subject detected by the body motion sensor. An activity amount that acquires at least one information, obtains activity amount information that represents the activity amount of the subject based on the at least one information, and represents an activity amount index of the subject based on the activity amount information A processing unit for obtaining index information, wherein the processing unit specifies an activity intensity of the subject based on the at least one of the biological information and the body motion information, and the activity intensity of the subject Is determined to be the first activity intensity, interpolation of the missing activity amount information determined to be missing is performed to obtain the activity amount index information, and the activity intensity of the subject is First activity intensity Remote if it is determined that the high second activity intensity, there is provided an electronic device for determining the activity amount index information without performing the interpolation processing of said activity amount information.

  Another aspect of the present invention relates to a program that causes a computer to function as each of the above-described units.

  In another aspect of the present invention, at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor is acquired, and the biological information is acquired. Activity amount information representing the amount of activity of the subject is obtained based on the at least one information of the body movement information, and the subject is obtained based on the at least one information of the biological information and the body movement information. If the activity intensity of the subject is determined to be the first activity intensity, interpolation of the missing activity amount information determined to be missing is performed, and the subject's activity intensity is determined. Activity amount index information representing an activity amount index is obtained, and when it is determined that the activity intensity of the subject is a second activity intensity higher than the first activity intensity, the missing activity amount information Without performing the interpolation process Relating to the control method of the biological information processing system for determining the activity amount index information (operating method).

The structural example of the biological information processing system which concerns on this embodiment. Explanatory drawing of various information. Explanatory drawing of the correlation between oxygen intake and heart rate. The flowchart explaining the flow of a process of this embodiment. Explanatory drawing of the zone matched with exercise intensity and exercise intensity. Explanatory drawing of PA-R. FIG. 7A is an explanatory diagram of the correlation between the first intensity exercise time and PA-R, and FIG. 7B is an explanatory diagram of the correlation between the second intensity exercise time and PA-R. Explanatory drawing of biological body history information and body movement history information. Explanatory drawing of the averaging process of activity amount index information. FIG. 10A is a graph of the first intensity exercise time and the travel distance of the subject having the first activity intensity, and FIG. 10B is a graph showing the PA-R of the subject having the first activity intensity. FIG. 11A is a graph of the second intensity exercise time and travel distance of the subject with the second activity intensity, and FIG. 11B is a graph showing PA-R of the subject with the second activity intensity. The flowchart explaining the flow of a process of a 2nd modification. 13A and 13B are configuration examples of wearable devices. Configuration example of wearable device. 15A to 15C are specific examples of realization of the biological information processing system.

  Hereinafter, this embodiment will be described. In addition, this embodiment demonstrated below does not unduly limit the content of this invention described in the claim. In addition, all the configurations described in the present embodiment are not necessarily essential configuration requirements of the present invention.

1. Outline As disclosed in Patent Document 1 described above, the calorie consumption of a subject can be estimated from, for example, the amount of oxygen intake of the subject. However, since it is difficult to directly determine the oxygen intake in daily life, correlation information (straight line CMR in the graph) indicating the relationship between the oxygen intake and the heart rate is obtained in advance as shown in FIG. The oxygen intake is estimated based on the correlation information and the heart rate acquired from the biological sensor. The correlation information as shown in FIG. 3 includes the maximum oxygen intake of the subject (hereinafter also referred to as VO2max), the maximum heart rate corresponding to VO2max (hereinafter also referred to as HRmax), and the oxygen intake when the subject is at rest. It is determined by the amount (hereinafter also referred to as VO2rest) and the resting heart rate (hereinafter also referred to as HRrest). As shown in FIG. 3, if the correlation information between the oxygen intake and the heart rate is used, for example, when the heart rate is HR ′, it can be estimated that the oxygen intake is VO2 ′, and further VO2 ′. Calorie consumption corresponding to can be obtained.

  Here, the oxygen intake refers to the amount of oxygen necessary for energy generation per minute in aerobic energy metabolism. Therefore, as described above, by measuring the oxygen intake, the amount of energy generated by aerobic energy metabolism, that is, the calorie consumption can be obtained. The maximum value of oxygen intake that can be taken into the body per minute is referred to as maximum oxygen intake (VO2max).

  Moreover, as a person increases exercise intensity, the demand for oxygen increases accordingly, and the amount of oxygen intake increases. Then, for each person, when the exercise is the most intense or the exercise intensity corresponding thereto, the oxygen intake does not increase any more, and the oxygen intake at this time is treated as VO2max. The oxygen intake includes [mL / min] expressed as an absolute value or [mL / kg / min] expressed as a relative value.

  As a method for specifying VO2max, for example, any one of a measurement method using a breath gas analyzer (direct method), a maximum work test, a maximum sub work test, and a test without work is performed. There is a method (indirect method) to estimate by this.

  Specifically, in the direct method using the expiratory gas analyzer, VO2max is obtained by asking the subject to exercise with maximum effort and analyzing the expiratory gas component at that time.

  In the indirect method using the maximum work test, the subject is asked to run for a certain time (or a certain distance) with the maximum effort, and VO2max is estimated from the performance. For example, the maximum work test includes a physical strength test such as a 12-minute running test (Cooper test) and a 20 m shuttle run.

  In the indirect method based on the submaximal work test, the subject is asked to run or walk for a certain period of time (or a certain distance) with a reasonable exercise intensity, and VO2max is estimated from the performance. For example, submaximal work tests include physical strength tests such as an Astrand nomogram and a one-mile walking / jogging test.

  Further, in the indirect method without work, VO2max is estimated from the resting heart rate and the physical activity level based on the questionnaire response. In this case, it is not necessary to ask the subject to perform a work test.

  However, each measurement method has the following problems. For example, the method of measuring VO2max by the direct method has a problem that the subject cannot easily measure the breath gas analyzer because it requires an exhaled gas analyzer. Further, the indirect method of performing the maximum work test requires a maximum effort, and thus there is a problem that it is difficult for non-trainers to implement. And the indirect method of performing sub-maximum work tests can be carried out without difficulty, but it is necessary to carry out the prescribed work (execution in extraordinary life), so it must be measured by ordinary users. There is a problem that is difficult. In addition, as described above, in order to perform a specific exercise (physical fitness test) and perform an accurate measurement, it is necessary to use a device such as a treadmill, and the user obtains the expensive device or the device. There is also a problem that the user himself / herself must go to the place where is installed. Furthermore, the estimation by the questionnaire can be carried out by anyone, but there is a problem that the subjectivity factor is strong and the reliability is low.

  In the invention disclosed in Patent Document 1 described above, it is necessary to perform such a physical strength test in order to estimate calorie consumption while considering individual differences such as physical strength differences. Therefore, while it is possible to acquire calorie consumption in consideration of individual differences, there is a problem that it is troublesome for the user to perform the physical fitness test. Furthermore, as described above, if the user's physical strength and the like change with time, the relationship between the heart rate and oxygen intake also changes with time. Therefore, it is necessary to periodically perform a troublesome physical strength test.

  Therefore, in the present embodiment described below, it is possible to accurately estimate at least one of the oxygen intake amount, calorie consumption, and physical strength of the subject even if the subject does not perform specific exercise. More specifically, even if the subject does not perform a specific physical strength test, VO2max corresponding to the physical strength of the subject is estimated, and an accurate oxygen intake is estimated based on the estimated VO2max. Thereby, it is possible to obtain calorie consumption considering the physical strength of the subject without performing a troublesome physical strength test on the subject.

2. System Configuration Example FIG. 1 shows a configuration example of a biological information processing system 100 according to this embodiment and an electronic apparatus including the biological information processing system 100. The biological information processing system 100 (biological information processing apparatus) of the present embodiment includes an information acquisition unit 110, a processing unit 130, and a storage unit 150 (memory). Examples of electronic devices including the biological information processing system 100 include a wearable device 500 including a biological sensor 200, a body motion sensor 300, and a processing unit 130 (processor 400). The electronic device may include a not-illustrated notification unit (for example, a display unit or an audio output unit). Part or all of the functions of the biological information processing system 100 of the present embodiment are realized by the wearable device 500, for example. However, some or all of the functions of the biological information processing system 100 may be realized by an electronic device (portable electronic device) different from the wearable device 500 such as a smartphone or a server system. A specific configuration example of the biological information processing system will be described later with reference to FIGS. 15A to 15C. The biological information processing system 100 and the electronic apparatus including the biological information processing system 100 are not limited to the configuration in FIG. 1, and various modifications such as omitting some of these components or adding other components. Implementation is possible.

  Next, processing performed in each unit will be described.

  The information acquisition unit 110 acquires biological information of the subject, body movement information of the subject, and user information of the subject. The biological information is detected by the biological sensor 200 provided in the wearable device 500 attached to the subject. Similarly, the body motion information is detected by a body motion sensor 300 provided in the wearable device 500.

  For example, as will be described later with reference to FIG. 15A, the biological information processing system 100 according to the present embodiment is realized by a server system, and the server system acquires biological information and body movement information from the wearable device 500 worn by the user. In this case, the information acquisition unit 110 may be a communication unit that performs communication with the wearable device 500 via a network (a reception unit that receives information from the wearable device 500). The communication unit may be a communication device such as a USB connector (communication terminal) or a wireless antenna, or may be a processor that controls the communication device.

  The biological sensor 200 (biological sensor device) is a sensor that is provided in the wearable device 500 attached to the subject and can acquire biological information of the subject. For example, as shown in FIG. 2, the biological information SI is information representing the biological activity of the subject acquired from the biological sensor 200, for example, the pulse rate (heart rate), body temperature, blood pressure, blood flow, activity time ( Sleep time), activity state (exercise state), and the like. For example, when the information acquisition unit 110 acquires pulse wave information as biological information, a pulse wave sensor is used as the biological sensor 200. The pulse wave information is information relating to the pulse wave of the subject, for example, information indicating the aforementioned pulse rate (heart rate) or the like. The pulse wave sensor is a sensor for detecting pulse wave information (pulse wave signal). For example, a photoelectric sensor including a light emitting unit and a light receiving unit can be considered. It is known that the pulse wave sensor can be realized by various sensors such as a photoelectric sensor and other types of sensors (for example, an ultrasonic sensor). The pulse wave sensor of this embodiment can be widely applied to these sensors. is there. In addition, the biosensor 200 may be configured to include a blood pressure sensor, a temperature sensor, and the like.

  The body motion sensor 300 (body motion sensor device) is a sensor that is provided in the wearable device 500 attached to a subject and can acquire body motion information of the subject. For example, as shown in FIG. 2, the body motion information PI is information indicating the body motion of the subject acquired from the body motion sensor 300. For example, the subject's movement distance, the number of steps, the step length, the movement time, and the movement speed. , Acceleration, exercise time, exercise amount, exercise intensity (activity intensity), exercise content (activity content), and the like. As the body motion sensor 300, for example, an acceleration sensor or the like can be used. In this case, the information acquisition unit 110 acquires acceleration information (or position information) as biological information from the acceleration sensor. In addition, the body motion sensor 300 may be a GPS (Global Positioning System) receiver, for example. In that case, the GPS receiver (body motion sensor 300) acquires position information indicating the current position of the wearable device 500 (subject) based on the radio wave transmitted from the GPS satellite, and the information acquisition unit 110 Then, position information of wearable device 500 (subject) is acquired as body movement information.

  For example, as shown in FIG. 2, the user information UI is information on the subject's profile and the subject's body. For example, the subject's age, sex, height, weight, BMI, lifestyle (exercise habit) This is information indicating occupation. The user information is input by a user from an operation unit (not shown) that can communicate with the processor 400, for example, and is acquired by the information acquisition unit 110 and stored in the storage unit 150. Then, the processing unit 130 described later reads the user information from the storage unit 150 and uses it for various processes.

  The processing unit 130 estimates maximum oxygen intake information indicating the maximum oxygen intake (VO2max) of the subject based on the biological information, the body motion information, and the user information. The maximum oxygen intake information is information indicating VO2max described above. The function of the processing unit 130 can be realized by hardware such as various processors (CPU and the like), ASIC (gate array and the like), a program, and the like. For example, in the example of FIG. 1, the processor 400 realizes the functions of the processing unit 130 and the information acquisition unit 110. However, the present embodiment is not limited to this. For example, the biological information processing system 100 includes a plurality of processors, the first processor realizes the function of the processing unit 130, and the function of the information acquisition unit 110 is the second processor. It is possible to implement various modifications such as

  The biological information and body movement information described above are information acquired when the subject is performing daily life (activity) or exercise, and the user information is information that is registered in the subject in advance, for example. is there. For this reason, no information is obtained by performing a specific physical fitness test on the subject as in the invention of Patent Document 1 described above. In this embodiment, VO2max is estimated based on these pieces of information (biological information, body motion information, user information). Although a specific method for estimating VO2max will be described later, if VO2max can be specified, the oxygen intake (VO2) of the subject can be accurately estimated.

  Therefore, according to the present embodiment, it is possible to accurately estimate the oxygen intake amount of the subject even if the subject does not perform a specific exercise.

  In the present embodiment, the following configuration may be used. That is, the biological information processing system 100 includes a memory (storage unit 150) that stores information (for example, programs and various data), and a processor 400 that operates based on the information stored in the memory (processing unit 130, hardware). Configured processor). The processor 400 acquires biological information of the subject from the biological sensor 200 (biological sensor device) provided in the wearable device 500, and receives the biological information from the body motion sensor 300 (body motion sensor device) provided in the wearable device 500. The body movement information of the specimen is acquired, and the user information of the subject is acquired from the memory. Then, the processor 400 estimates maximum oxygen intake information indicating the maximum oxygen intake (VO2max) of the subject based on the biological information, the body motion information, and the user information.

  In the processor (processing unit 130), for example, the function of each unit may be realized by individual hardware, or the function of each unit may be realized by integrated hardware. The processor may be, for example, a CPU (Central Processing Unit). However, the processor is not limited to the CPU, and various processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) can be used. The processor may be a hardware circuit based on an ASIC (Application Specific Integrated Circuit). The memory (storage unit 150) may be a semiconductor memory such as SRAM or DRAM, a register, a magnetic storage device such as a hard disk device, an optical disk device or the like. It may be an optical storage device. For example, the memory stores instructions readable by a computer, and the functions of each unit of the processing unit 130 are realized by executing the instructions by the processor. The instruction here may be an instruction of an instruction set constituting the program, or an instruction for instructing an operation to the hardware circuit of the processor.

3. Calculation Method of Calorie Consumption Information Next, the method of this embodiment will be described.

  First, a process for obtaining calorie consumption based on VO2max will be described. As shown in FIG. 2, the processing unit 130 obtains the oxygen intake information OI of the subject based on the estimated maximum oxygen intake information MOI and the biological information SI, and based on the oxygen intake information OI, Calorie consumption information UCI of the sample is obtained. The oxygen intake information OI is information indicating the oxygen intake (VO2) of the subject at a certain timing. The calorie consumption information UCI is information indicating the calorie consumption of the subject in a certain period.

  Here, a more specific description will be given with reference to FIG. As described above, the calorie consumption of the subject can be estimated from, for example, the oxygen intake of the subject, but it is difficult to directly determine the oxygen intake at a certain timing in daily life.

  By the way, it is known that the oxygen intake amount and the heart rate have a (substantially) proportional relationship as shown in the graph of FIG. That is, as shown in the graph of FIG. 3, the higher the heart rate, the higher the oxygen intake. In the graph of FIG. 3, the vertical axis represents oxygen intake (VO2) and the horizontal axis represents heart rate (HR), and shows the correlation between oxygen intake and heart rate. The heart rate can be obtained by a biological sensor such as a pulse wave sensor without imposing a burden on the subject even while the subject is performing daily life.

  Therefore, in the present embodiment, correlation information indicating the relationship between the oxygen intake VO2 and the heart rate HR, that is, the straight line CMR in FIG. 3 is obtained in advance, and this correlation information and the biological sensor (pulse wave sensor) are acquired. Estimate oxygen uptake based on heart rate (pulse rate). For example, when the heart rate is HR ′, it can be estimated that the oxygen intake is VO2 ′.

  As described above, the correlation information (CMR) as shown in FIG. 3 includes the maximum oxygen intake VO2max, the maximum heart rate HRmax corresponding to VO2max, the resting oxygen intake VO2rest, and the rest corresponding to VO2rest. It can be obtained based on the hourly heart rate HRrest.

  However, when the subject of the present embodiment starts wearing the wearable device, the activity amount of the subject has not yet been specified. Therefore, correlation information as indicated by BMR in FIG. 3 is obtained based on user information such as the height, age, and weight of the subject without considering the physical strength of the subject. The correlation between the oxygen intake and the heart rate represented by the straight line BMR is based on the overall tendency obtained statistically, and the correlation between the oxygen intake and the heart rate according to the physical strength of each subject. Is not an accurate indication. Therefore, in this embodiment, the physical strength of each subject is estimated based on the amount of activity of the subject obtained when the subject wears a wearable device and exercises, and the oxygen intake amount in each subject is calculated. A straight line CMR that accurately indicates the correlation of the heart rate is obtained. In the following, the timing at which the straight line BMR is obtained immediately after wearing the wearable device is referred to as a first timing, and the timing at which the straight line CMR is obtained after wearing the wearable device and exercising for a predetermined period as the second timing To do.

  Specifically, in the present embodiment, the processing shown in the flowchart of FIG. 4 is performed, the correlation between the oxygen intake and the heart rate is obtained, and the calorie consumption is obtained. In the present embodiment, a series of processing as shown in FIG. 4 is performed periodically (for example, every day), for example. Moreover, when an instruction is input from the user of the biometric information processing system 100 so as to obtain calorie consumption, the process shown in FIG. 4 may be performed.

  First, the information acquisition unit 110 acquires user information from the storage unit 150 or the like (S101). Next, the information acquisition unit 110 acquires biological information from the biological sensor 200 and acquires body movement information from the body movement sensor 300 (S102). Next, the processing unit 130 specifies activity amount information of the subject based on at least one of the acquired biological information and body motion information (S103).

  Next, the processing unit 130 specifies HRrest based on the biological information (S104). For example, HRrest can be obtained by specifying the lowest heart rate acquired at rest from pulse wave information acquired from a pulse wave sensor. Note that HRrest is updated to a new value as needed. For example, in the example of FIG. 3, it is assumed that the value of HRrest1 is obtained as HRrest at the first timing, and the value of HRrest2 is obtained as HRrest at the second timing.

  And the process part 130 calculates | requires VO2rest based on user information (S105). For example, VO2rest can be obtained by the Harris-Benedict formula using user information such as height, age, weight, and sex. Alternatively, VO2rest may be obtained from a basal metabolism table or the like statistically created in advance, similarly to HRmax. Since VO2rest does not change in the short term, the same value is used at the first timing and the second timing.

  Further, the processing unit 130 obtains PA-R (Physical Activity Rate) as activity amount index information described later based on the activity amount information (S106). The method for obtaining PA-R will be described later in detail with reference to FIG. It should be noted that this processing need not be performed at the first timing when the wearable device is not attached and exercise is not performed.

  Then, the processing unit 130 obtains VO2max according to Jackson's equation (1) using the user information and the obtained PA-R (S107).

VO2max = α + β × P−γ × A−Δ × B + ε × G (1)
In the above equation (1), α is a given intercept, P is PA-R, A is the age of the subject, B is the BMI of the subject, and G is the sex of the subject. . In addition, β is a given PA-R coefficient, γ is a given age coefficient, Δ is a given BMI coefficient, and ε is a given gender coefficient. Since PA-R is not obtained at the first timing, VO2max stored in advance in association with user information such as age may be used.

  Next, the processing unit 130 obtains HRmax based on, for example, user information (S108). For example, HRmax can be obtained based on a correspondence table of age and HRmax statistically created in advance. This correspondence table is stored in the storage unit 150, for example, and the processing unit 130 reads HRmax corresponding to the age of the subject from the correspondence table. The age of the subject is acquired by the information acquisition unit 110 as one of the user information described above. HRmax basically uses a fixed value read from the correspondence table. For example, when a pulse rate sensor detects a heart rate higher than the set HRmax, HRmax is updated. Is also possible. In the example of FIG. 3, the same value is used at the first timing and the second timing.

  Then, the processing unit 130 obtains correlation information between the oxygen intake amount and the heart rate based on VO2rest, VO2max, HRrest, and HRmax (S109). 3 is obtained based on VO2rest, VO2max obtained from the table, HRrest1, and HRmax at the first timing when the wearable device is not worn yet. On the other hand, at the second timing after the subject wears the wearable device and continues to exercise for a while, the straight line CMR of FIG. 3 is obtained based on VO2rest, VO2max (estimated value), HRrest2, and HRmax. be able to.

  Thereafter, the information acquisition unit 110 acquires the current heart rate HR of the subject, and the processing unit 130 obtains the oxygen intake and heart rate correlation information (BMR or CMR) as shown in FIG. 3 and the acquired heart rate. Based on the number HR, the oxygen intake VO2 is estimated (S110). Then, the processing unit 130 estimates calorie consumption based on the oxygen intake (S111). When energy is consumed, oxygen is always used. For example, to consume about 5 kcal, for example, 1 liter of oxygen is used. In step S111, the calorie consumption is estimated from the oxygen intake based on such a correspondence.

  Thereby, in this embodiment, the oxygen intake taken in accordance with the physical strength of the subject can be accurately estimated at a certain timing, and the calorie consumption consumed during that period can be estimated. Therefore, it is possible to obtain calorie consumption considering the physical strength of the subject without performing a troublesome physical strength test on the subject.

4). Next, a method for calculating the maximum oxygen intake information used when estimating the calorie consumption information will be described in detail.

  As illustrated in FIG. 2, the processing unit 130 obtains activity amount index information PAI representing an activity amount index of the subject based on the biological information SI and the body motion information PI, and uses the activity amount index information PAI and the user information UI as the activity amount index information PAI. Based on this, the maximum oxygen intake information MOI is obtained.

  This makes it possible to obtain maximum oxygen intake information according to the physical strength of each subject.

  As shown in FIG. 2, the activity amount index information PAI is information obtained based on activity amount information AI representing the activity amount of the subject. The activity amount information AI is information obtained based on the biological information SI and the body motion information PI (at least one of them).

  In summary, as shown in FIG. 2, the processing unit 130 obtains the activity amount information AI based on the biological information SI and the body motion information PI acquired from the information acquisition unit 110, and determines the obtained activity amount information AI. Based on the above, the activity amount index information PAI is obtained. Accordingly, the processing unit 130 can obtain the maximum oxygen intake information MOI based on the activity amount index information PAI obtained based on the activity amount information AI and the user information UI.

  Here, the activity amount information AI is information representing the activity amount of the subject. For example, the amount of activity includes exercise intensity and exercise time of exercise performed by the subject, movement distance (walking distance, running distance), and the like.

  The exercise rate (HRR: Heart Rate Reserved) is an index of the intensity (strength) of the exercise content performed by the subject. For example, as shown in the table of FIG. Represented by a value of 0 to 100%. In the example of FIG. 5, a section called a zone is associated with a given range of exercise intensity. Here, the table of FIG. 5 is referred to as zone setting information. This zone setting information is a table in which the exercise intensity of the exercise performed by the subject is divided into a plurality of zones, and is stored in the storage unit 150, for example.

Specifically, in the zone setting information of FIG. 5, a range where the exercise intensity (HRR) is s ₁ % or more and less than s ₂ % is set as zone Z1, and a range where s ₂ % or more and less than s ₃ % is set as the zone. Z2 is set, a range that is s ₃ % or more and less than s ₄ % is set as zone Z3, a range that is s ₄ % or more and less than s ₅ % is set as zone Z4, and s ₅ % or more and s ₆ % or less A certain range is set as zone Z5. Corresponding exercise content is set for each zone.

More specifically, “warm-up” is set as the exercise content in the zone Z1 in which the HRR is s ₁ % or more and less than s ₂ %. In the zone Z2 where the HRR is s ₂ % or more and less than s ₃ %, “fat burning exercise” is set as the exercise content, and in the zone Z3 where the HRR is s ₃ % or more and less than s ₄ %, the exercise content is “ “Aerobic exercise” is set. Furthermore, in the zone Z4 where the HRR is s ₄ % or more and less than s ₅ %, “anaerobic exercise” is set as the exercise content, and in the zone Z5 where the HRR is s ₅ % or more and s ₆ % or less, the exercise content is “ “Maximum intensity exercise” is set.

  In the present embodiment, the zone is set based on the HRR that is the exercise intensity based on the carbonone method, but the present embodiment is not limited to this, and for example, the zone may be set based on the HRmax. . The zone can be variously modified such as a fat burning zone, a carbohydrate burning zone, an aerobic exercise zone, an anaerobic exercise zone, and a maximum intensity exercise zone. Furthermore, the exercise content associated with each zone is not limited to the example of FIG. 5, and various modifications can be made.

  The exercise time is, for example, an accumulated time during which the subject exercises within a certain period. The exercise time may be, for example, the total time of all types of exercises performed within a certain period, or may be the time required for each exercise intensity or type of exercise.

  Thereby, for example, the processing unit 130 can obtain the activity amount index information (PA-R) based on the exercise intensity and the exercise time.

  The moving distance is, for example, an accumulated distance that the subject has moved within a certain period. The travel distance may be the sum of the travel distance and the walk distance within a certain period, or the travel distance and the walk distance may be obtained separately.

  The activity amount index information PAI is information representing the activity amount index of the subject. The activity amount index is, for example, the activity level (exercise level) of the activity (exercise) of the subject. More specifically, the activity amount index indicates, for example, an exercise habit calculated from the frequency of exercise (activity) of the subject within a predetermined period (for example, one week), the intensity of exercise to be performed, etc. It is a level expressed by numerical values set automatically. For example, the activity amount index is PA-R (Physical Activity Rate) shown in a table of FIG.

  And, for example, when the activity amount includes the exercise intensity and exercise time described above, the activity amount index information PAI includes the activity level index information PAI as the activity level of the subject among a plurality of activity levels classified by exercise intensity and exercise time. It is information indicating whether it belongs.

  Thus, as in the example of FIG. 6 described later, a plurality of activity levels (for example, PA-R: 0 to 10) are defined based on the exercise intensity and exercise time of the subject, and the subject has a plurality of activity levels. It becomes possible to specify to which activity level the user belongs.

  For example, when the activity amount includes a movement distance, the activity amount index information PAI indicates which activity level the subject belongs to among the plurality of activity levels classified by the movement distance (for example, the travel distance). It may be the information shown.

  Accordingly, as in the example of FIG. 6 described later, a plurality of activity levels (for example, PA-R: 0 to 10) are defined based on the movement distance of the subject, and the subject is selected from the plurality of activity levels. It becomes possible to specify which activity level it belongs to.

  Here, a specific example will be described with reference to FIG. The table in FIG. 6 is an example of a correspondence table between PA-Rs and activity amounts, and is stored in the storage unit 150, for example. The PA-R shown in FIG. 6 is set to classify the activity amount of the subject into 11 levels (or groups) from 0 to 10. For example, in the correspondence table of FIG. 6, for each PA-R of “0 to 10”, the action content corresponding to the PA-R, the corresponding conditions for the travel distance and the walking distance, and the exercise for each zone in one week Appropriate conditions for the total time (total exercise time) are set. However, PA-R is not necessarily set at 11 levels, and can be set at 2 or more given levels. In the example of FIG. 6, a process for estimating the activity level of the subject from the exercise intensity and the exercise time (that is, a process for estimating the activity level from the total exercise time) and a moving distance (at least one of the travel distance and the walking distance). Either of the estimation results may be selected by performing both of the processes for estimating the activity level from the above, or only one of the two estimation processes may be executed. For example, when both estimation processes are performed, the larger PA-R among the obtained PA-Rs may be employed.

Specifically, in the correspondence table of FIG. 6, the behavior of the subject corresponding to PA-R “10” is the same as “running a ₁ km or more per week, or b running ₁ hour or more per week”. Physical activity "is set. Specific examples of physical activities include “jogging, swimming, cycling, rowing, skipping rope, tennis, basketball, handball, etc.”.

Further, “a ₁ km or more” is set as the travel distance of PA-R. Furthermore, “the total value of the exercise time Z5_tm of the zone Z5, the exercise time Z4_tm of the zone Z4, and the exercise time Z3_tm of the zone Z3 is equal to or greater than b ₁ hour” is set as a corresponding condition for the total exercise time. Yes.

In addition, the behavior of the subject corresponding to PA-R “9” is “same as running of a ₂ km or more and less than a ₁ km per week, or running of b ₂ hours or more and b less than ₁ hour per week” Physical activity "is set. Further, “a ₂ km or more and less than a ₁ km” is set as the travel distance corresponding to PA-R “9”. Furthermore, the appropriate conditions for the total movement time "and exercise time Z5_tm zone Z5, it and the exercise time Z4_tm zone Z4, the total value of exercise time Z3_tm zone Z3 is smaller than _{b 2} more hours _{b 1} hour" Is set.

The subject's behavior corresponding to PA-R “8” is as follows: “Running of a ₃ km or more and less than a ₂ km per week, or running for a period of b ₃ hours or more and less than ₂ hours a week. "Activity" is set. Further, “a ₃ km or more and less than a ₂ km” is set as the travel distance corresponding to PA-R “8”. Furthermore, the appropriate conditions for the total movement time "and exercise time Z5_tm zone Z5, and exercise time Z4_tm zone Z4, that the total value of exercise time Z3_tm zone Z3 is smaller than _{b 3} hours or more _{b 2} hours" Is set.

The subject's behavior corresponding to PA-R “7” is “the body of the same degree as a running of a ₄ km or more and less than a ₃ km per week, or a running of b ₄ hours or more and less than ₃ hours a week. "Activity" is set. Further, “a ₄ km or more and less than a ₃ km” is set as the travel distance corresponding to PA-R “7”. Furthermore, the appropriate conditions for the total exercise time, the exercise time Z5_tm the "zone Z5, and exercise time Z4_tm zone Z4, that the total value of exercise time Z3_tm zone Z3 is smaller than _{b 4} hours or more _{b 3} hours "Is set.

The subject's behavior corresponding to PA-R “6” is “the body of the same degree as a running of a ₅ km or more and less than a ₄ km per week, or a running of b ₅ hours or more and b less than ₄ hours per week”. "Activity" is set. Further, “a ₅ km or more and less than a ₄ km” is set as the travel distance corresponding to PA-R “6”. Furthermore, the appropriate conditions for the total exercise time, the exercise time Z5_tm the "zone Z5, and exercise time Z4_tm zone Z4, that the total value of exercise time Z3_tm zone Z3 is _b less than ₅ hours or more _{b 4} hours "Is set.

The subject's behavior corresponding to PA-R “5” is “the body of the same degree as a running of a ₆ km or more and less than a ₅ km per week, or a running of b ₆ hours or more and less than ₅ hours a week. "Activity" is set. Further, “a ₆ km or more and less than a ₅ km” is set as the travel distance corresponding to PA-R “5”. Furthermore, “the total value of the exercise time in all the zones from the zone Z1 to the zone Z5 is b ₇ hours or more” is set as a corresponding condition for the total exercise time.

As the behavior of the subject corresponding to PA-R “4”, “physical activity equivalent to running less than a ₆ km per week or running less than b ₆ hours per week” is set. Further, “less than a ₆ km” is set as the travel distance of the subject corresponding to PA-R “4”, and “c ₁ km or more” is set as the walking distance. Furthermore, “the total value of exercise time in all zones from zone Z1 to zone Z5 is not less than b ₈ hours and less than b ₇ hours” is set as a corresponding condition for the total exercise time.

As the behavior of the subject corresponding to PA-R “3”, “appropriate exercise of b ₉ hours or more per week” is set. In addition, golf, horse riding, gymnastics, table tennis, bowling, weightlifting, garden work, etc. can be illustrated as moderate exercise.

In addition, “less than a ₆ km” is set as the travel distance corresponding to PA-R “3”, and “c ₂ km or more and less than c ₁ km” is set as the walking distance. Furthermore, “the total value of exercise time in all zones from zone Z1 to zone Z5 is not less than b ₁₀ hours and less than b ₈ hours” is set as a corresponding condition for the total exercise time.

As the behavior of the subject corresponding to PA-R “2”, “appropriate exercise of b less than ₁₁ hours per week” is set. Further, “less than a ₆ km” is set as the travel distance corresponding to PA-R “2”, and “c ₃ km or more and less than c ₂ km” is set as the walking distance. Furthermore, “the total value of exercise time in all zones from zone Z1 to zone Z5 is b ₁₂ hours or more and less than b ₁₀ hours” is set as a corresponding condition for the total exercise time.

As the behavior of the subject corresponding to PA-R “1”, “behavior that actively uses stairs, occasional breathing or sweating exercise” is set. Further, “less than a ₆ km” is set as the travel distance corresponding to PA-R “1”, and “c ₄ km or more and less than c ₃ km” is set as the walking distance. Furthermore, “the total value of exercise time in all zones from zone Z1 to zone Z5 is not less than b ₁₃ hours and less than b ₁₂ hours” is set as a corresponding condition for the total exercise time.

As the behavior of the subject corresponding to PA-R “0”, “behavior to avoid walking or exercise, behavior to use an elevator, or behavior to get in a car even within walking distance” is set. Further, “less than a ₆ km” is set as the travel distance corresponding to PA-R “0”, and “less than c ₄ km” is set as the walking distance. Furthermore, “the total value of the exercise time in all the zones from the zone Z1 to the zone Z5 is less than b ₁₃ hours” is set as a corresponding condition for the total exercise time.

  In addition, the total value of action, mileage, walking distance, and exercise time of each zone allocated to each PA-R is only an example, and is an estimation formula for estimating user information, the state of the subject, and the maximum oxygen intake. You may change suitably according to.

  Then, the processing unit 130 detects, from the zone setting information, the biological information of the subject, the body motion information, the exercise intensity and the exercise time, the movement distance, etc., which zone Z1 to zone Z5 the exercise of the subject belongs to. Further, PA-R (any one of “0” to “10”) shown in FIG. 6 is determined from the total value of the exercise time for each of the zones Z1 to Z5. For example, the processing unit 130 may determine each applicable condition illustrated in FIG. 6 in descending order of the PA-R value, and determine a PA-R that applies to the motion of the subject.

Further, it is known that there is a correlation as shown in FIGS. 7A and 7B between PA-R and exercise time. Here, the exercise time during which the subject exercised more than the first exercise intensity is defined as the first intensity exercise time, and the subject exercised more than the second exercise intensity that is stronger than the first exercise intensity. Let exercise time be the second intensity exercise time. In the present example, the first intensity exercise time includes, for example, a time during which an exercise of the second exercise intensity or more is performed. FIG. 7A is a graph showing the correlation between the first intensity exercise time (vertical axis) and PA-R (horizontal axis) when the _first exercise intensity is s ₁ %. FIG. 7B is a graph showing the correlation between the second intensity exercise time (vertical axis) and PA-R (horizontal axis) when the second exercise intensity is s ₃ %. In the graphs of FIGS. 7A and 7B, diamond points SD indicate the correlation between exercise time and PA-R determined by Jackson's equation based on statistical data. Then, if a straight line is drawn according to the change tendency of the diamond point SD, a straight line (SL1, SL2) can be drawn, and the correspondence table of FIG. 6 described above is obtained based on these straight lines (SL1, SL2). Can do.

  Specifically explaining the correlation between PA-R and exercise time, first, as shown in FIG. 7A, PA-R and first intensity exercise time are proportional to each other in the range of PA-R from 0 to 5. It can be regarded as a positive proportional relationship. Even when PA-R is in the range of 6 to 10, it can be considered that PA-R and the first intensity exercise time are in a proportional relationship. However, the proportional coefficient when PA-R is 0 to 5 is considered. Is clearly larger than the proportionality coefficient when PA-R is 6-10.

  On the other hand, as shown in FIG. 7B, in the range of PA-R of 6 to 10, the second intensity exercise time and PA-R can be regarded as having a proportional relationship in which the proportionality coefficient is positive. In addition, even if PA-R is in the range of 0 to 5, it can be considered that PA-R and the second intensity exercise time are in a proportional relationship. However, in the example of FIG. The proportionality coefficient when -R is 6 to 10 is clearly larger than the proportionality coefficient when PA-R is 0 to 5.

  Therefore, it can be said that PA-R in the range of 0 to 5 has a stronger correlation with the first intensity exercise time, and PA-R in the range of 6 to 10 has a stronger correlation with the second intensity exercise time. This means that, for example, a user (for example, PA-R is 0 to 5) who is engaged in light exercise such as walking (exercise of the first exercise intensity to the second exercise intensity) is intense such as running. Time when light exercise is performed even by a user (for example, PA-R is 6 to 10) who does not exercise much (exercise more than the second exercise intensity) and is mainly engaged in intense exercise Indicates that it is not much different from other people. In other words, the group of subjects consists of two groups: a group that focuses on light exercise (for example, PA-R is 0 to 5) and a group that focuses on intense exercise (for example, PA-R is 6 to 10). It can be said that it can be divided roughly. And it can be said that there is a difference in exercise time of exercise corresponding to the type of group in each subject of each group.

  Therefore, the processing unit 130 obtains the first intensity exercise time and the second intensity exercise time as the above-described activity amount information, and any one of the first activity level to the i-th activity level based on the first intensity exercise time. Is selected as the activity level of the subject, and activity amount index information is obtained. For example, in the example of FIG. 6, the first activity level corresponds to PA-R “0”, and the i-th activity level corresponds to PA-R “5”. That is, for example, when the subject is divided into the group in which the above-described light exercise is centered, PA-R is selected from 0 to 5.

  Then, the processing unit 130 selects one of the activity levels from the j-th activity level to the k-th activity level (j and k are integers of i <j <k) based on the second intensity exercise time. Select the activity level of the specimen to obtain activity amount index information. For example, in the example of FIG. 6, the j-th activity level corresponds to PA-R “6”, and the k-th activity level corresponds to PA-R “10”. That is, for example, when the subject is divided into a group in which the above-described intense exercise is centered, PA-R is selected from 6 to 10.

  Accordingly, for example, for a subject that mainly performs exercise with an exercise intensity lower than the second exercise intensity, the activity amount index information is obtained based on the first intensity exercise time, and the exercise of the second exercise intensity or higher is performed. For the subject mainly performed, it is possible to obtain activity amount index information based on the second intensity exercise time. As a result, for example, it is possible to change the way of obtaining the activity amount index information according to the exercise habit (exercise style) of the subject.

  As a modified example, the processing unit 130 performs two processes, a process of obtaining PA-R from the first intensity exercise time and a process of obtaining PA-R from the second intensity exercise time. You may select PA-R actually used among PA-R. Further, as in the example of FIG. 6, for example, when the second intensity exercise time is 1 hour or more, the PA-R is preferentially obtained based on the second intensity exercise time, and in other cases, PA-R may be obtained based on the first intensity exercise time.

  In addition, the processing unit 130 determines the first intensity exercise time and the second intensity exercise time in the second period longer than the first period based on the biological information and the body movement information obtained for each first period. Is obtained, and the activity amount index information is obtained based on at least one of the first intensity exercise time and the second intensity exercise time. For example, the first period is one day, and the second period is one week. That is, the information acquisition unit 110 acquires biological information and body motion information, for example, every day. And the process part 130 calculates | requires at least one of 1st intensity | strength exercise time and 2nd intensity exercise time for every week based on biological information and body movement information, and based on this, activity amount index information (PA) -R).

  Thereby, it becomes possible to make it difficult to reflect the short-term change in activity amount of the subject in the activity amount index information. As a result, activity amount index information obtained by evaluating the activity amount of the subject in the long term can be obtained. Then, using the PA-R obtained in this way, the above-described VO2max can be obtained by the above equation (1), and the estimated value of calorie consumption when performing the same exercise greatly fluctuates in a short period of time. Can be prevented.

5. First Modification Next, a modification of the present embodiment will be described.

  In the present modification, the storage unit 150 may store biological history information that is a history of biological information in a given period and body movement history information that is a history of body movement information in a given period. Then, the processing unit 130 may obtain the maximum oxygen intake information based on the biological history information and the body movement history information.

  Here, the biological history information is information in which, for example, a plurality of pieces of biological information acquired in a given period are arranged in time series order (or acquisition order). Similarly, the body motion history information is information in which, for example, a plurality of body motion information acquired in a given period are arranged in time series order (or acquisition order).

  For example, a specific example is shown in FIG. In the example of FIG. 8, the storage unit 150 stores biological history information SHI in which biological information for four days (SI1 to SI4) is arranged in time series. The storage unit 150 also stores body motion history information PHI in which body motion information (PI1 to PI4) for four days is arranged in time series. Then, the processing unit 130 obtains the activity amount information AI of the subject based on the biological history information SHI and the body movement history information PHI, and obtains the activity amount index information PAI based on the obtained activity amount information AI. Then, the processing unit 130 obtains the maximum oxygen intake information MOI based on the activity amount index information PAI and the user information UI.

  This makes it possible to suppress short-term fluctuations in the maximum oxygen intake information. Then, it is possible to suppress short-term fluctuations in the correlation information between the oxygen intake and the heart rate, which is obtained based on the maximum oxygen intake information. As a result, it is possible to prevent the estimated value of calorie consumption when performing the same exercise from fluctuating greatly in a short time.

  In addition, the processing unit 130 may perform averaging processing of a plurality of activity amount index information, and obtain maximum oxygen intake information based on the activity amount index information and user information after the averaging processing.

  For example, a plurality of activity amount index information to be averaged is acquired in time series order (for example, every other day). A specific example is shown in FIG. In the example of FIG. 9, the processing unit 130 obtains the activity amount AI1 of the subject on the first day based on the biological information SI1 and the body motion information PI1 acquired on the first day, and the activity based on the activity amount AI1. The quantity index information PAI1 is obtained. Similarly, the processing unit 130 obtains the activity amount AI2 of the subject on the second day based on the biological information SI2 and the body motion information PI2 acquired on the second day, and the activity amount index based on the activity amount AI2. Information PAI2 is obtained. Further, the processing unit 130 obtains the activity amount index information PAI3 and PAI4 in the same manner on the third day and the fourth day. Then, the processing unit 130 performs an averaging process on the plurality of activity amount index information (PAI1 to PAI4) to obtain the activity amount index information PAI ′ after the averaging process. Therefore, the activity amount index information after the averaging process reflects the past activity amount index information. Then, the processing unit 130 obtains the maximum oxygen intake information MOI based on the user information UI and the activity amount index information PAI ′ after the averaging process.

  As a result, for example, even if the activity amount index of the newly acquired activity amount index information is significantly different from the previous value, it is possible to suppress fluctuations in the estimated maximum oxygen intake information. That is, the same effect as the process for obtaining the maximum oxygen intake information can be obtained based on the above-described biological history information and body movement history information.

6). Second Modified Example Next, a second modified example will be described. As described above, for example, when the user's activity amount index information is estimated from the daily activity state instead of the physical fitness test, it is necessary to monitor the user's daily activity amount. However, in practice, not all users wear the wearable device 500 every day, and each sensor of the wearable device 500 cannot always monitor the biological information and body movement information of the subject. For example, there are some users who wear the wearable device 500 mainly during exercise and do not wear the wearable device 500 when not exercising. In addition, there are users who do not regularly exercise (training) and commute and work in daily life, but forget to wear the wearable device 500 depending on the day. . Under such usage conditions of the user, when estimating the physical strength of the user, the amount of activity on the day when the wearable device 500 is not worn is not considered (reflected), and the physical strength of the user is underestimated. There are possibilities.

  Therefore, in this modification described below, when the activity amount information of the subject may be missing, the subject's oxygen intake is accurately estimated even if the subject does not perform a specific exercise. To be able to.

  Here, as described above, the population of the subject includes a group that mainly performs light exercise such as walking (for example, PA-R is 0 to 5) and a group that mainly performs intense exercise such as running ( For example, it has been statistically revealed that PA-R is roughly divided into two groups of 6 to 10). In this example, PA-R is obtained using, for example, the correspondence table of FIG. Therefore, when a person who is engaged in light exercise mainly asks for PA-R, walking in daily life such as commuting becomes a judgment material. May be underestimated.

  On the other hand, when a person who mainly performs intense exercise finds PA-R, walking in daily life is not the main judgment material, but training time and travel distance are the main judgment materials. Therefore, even if the wearable device 500 is forgotten on a day when exercise such as training is not performed, the calculation of PA-R is hardly affected.

  Therefore, in the present modification, the case where the activity intensity of the subject is the first activity intensity (hereinafter also referred to as the low activity intensity) and the second case where the activity intensity of the subject is higher than the first activity intensity. PA-R is calculated by a different method in the case of activity intensity (hereinafter also referred to as high activity intensity).

  Specifically, first, the information acquisition unit 110 acquires at least one of the biological information of the subject detected by the biological sensor 200 and the body motion information of the subject detected by the body motion sensor 300. In this modification, the biological sensor 200 and the body motion sensor 300 are not necessarily provided in the wearable device 500, and may be provided in an electronic device other than the wearable device 500.

  Then, the processing unit 130 obtains activity amount information representing the activity amount of the subject based on at least one of the biological information and the body motion information, and represents the activity amount index of the subject based on the activity amount information. Find activity index information. At this time, the processing unit 130 specifies the activity intensity of the subject based on at least one of biological information and body motion information. Then, when it is determined that the activity intensity of the subject is the first activity intensity (low activity intensity), the processing unit 130 performs an interpolation process of the missing activity amount information that is determined to be missing. Find quantity index information. On the other hand, when it is determined that the activity intensity of the subject is the second activity intensity (high activity intensity) higher than the first activity intensity, the processing unit 130 performs the interpolation process of the missing activity amount information. Find activity index information without doing it.

  Therefore, when the activity amount information of the subject may be missing, it is possible to accurately estimate the oxygen intake amount of the subject without the subject performing specific exercise.

  Here, each case will be specifically described. First, using FIG. 10A and FIG. 10B, a method for calculating the PA-R of a person performed mainly with a low activity intensity exercise (light exercise) will be described.

  The graph of FIG. 10A is a graph showing the first intensity exercise time and the travel distance of the subject performed mainly with the exercise of the first activity intensity (low activity intensity), and the wearable device 500 is worn for about 3 months. And obtained by performing measurement. In the graph of FIG. 10A, the horizontal axis represents the date, the left vertical axis and the solid line represent the first intensity exercise time, and the right vertical axis and the dotted line represent the travel distance. In this example, activity amount information (first intensity exercise time, travel distance) is obtained every day.

  The graph of FIG. 10B shows the PA-R calculated according to the number of days when activity information could not be acquired due to forgetting to wear the wearable device 500 when performing the exercise as shown in FIG. The first intensity exercise time is shown. In the graph of FIG. 10B, the horizontal axis indicates the number of days for which activity information could not be acquired, the left vertical axis and rhombus points indicate PA-R, and the right vertical axis and triangle points indicate The cumulative first intensity exercise time for one week is shown. The example in FIG. 10B is an example of PA-R calculation that is not appropriate as will be described later.

  As shown in FIG. 10A, the subject of this example exercises for a long time about once every two months, but does not exercise well in all other cases. Therefore, as shown in FIG. 10B, the PA-R when the wearable device 500 is not forgotten is calculated as “3”. If the wearable device 500 is forgotten for one day (and two days), the exercise time in daily life is not added, so the cumulative first intensity exercise time is reduced. Decreases to “2”. Furthermore, when the wearable device 500 is left for three days, the accumulated first intensity exercise time further decreases, and the PA-R also decreases to “1”.

  However, on the day when the subject forgets to wear the wearable device 500, it is presumed that the subject was commuting or working like other days. Activity information) should be interpolated. That is, the calculation example of PA-R shown in FIG. 10B is not appropriate, and it is considered that the PA-R of the subject is “3” even though the loss period is originally several days.

  Therefore, in this modification, when it is estimated that the activity intensity of the subject is a low activity intensity in this way, the processing unit 130 performs the interpolation process of the missing activity amount information to obtain the activity amount index information. .

  Here, the missing activity amount information is activity amount information that could not be acquired by the biological information processing system. For example, as described above, when the subject forgets to wear the wearable device 500 or the like provided with at least one of the biological sensor 200 and the body motion sensor 300, at least one of the biological information and the body motion information in the meantime. Cannot be obtained, and the amount of activity cannot be calculated. In the present embodiment, such information indicating the amount of activity that was originally acquired but not acquired is referred to as missing activity amount information. A period during which activity amount information could not be acquired although it was supposed to be acquired is called a deficit period.

  Specifically, first, the processing unit 130 determines whether or not there is a missing period in which the activity amount information is missing, based on at least one of the biological information and the body motion information.

  As a result, when it is determined that there is a missing period, the processing unit 130 can determine to perform the interpolation process of the missing activity amount information.

  Specifically, the processing unit 130 determines that there is a loss period when it is determined that the wearable device 500 provided with the biological sensor 200 and the body motion sensor 300 is not attached to the subject.

  Thereby, for example, even when the subject is hardly walking or running, it is possible to determine the presence or absence of a missing period. However, in this modification, the processing unit 130 determines whether or not the subject is walking based on the biological information and the body motion information, and determines whether or not there is a deficiency period based on the presence or absence of walking. You may judge.

  Then, the processing unit 130 specifies the activity intensity of the subject based on the activity amount index information (PA-R) obtained before the deficient period of the deficient activity amount information.

  Here, the activity intensity of the subject is an average activity intensity of activities that the subject normally performs. The activity intensity of the subject can be obtained from activity amount index information obtained based on at least one of biological information and body motion information.

  For example, if the processing unit 130 determines that the PA-R obtained before the deficit period is included in the first range (for example, 0 to 5), the activity intensity of the subject is low ( If it is determined that the PA-R obtained before the deficit period is included in the second range (for example, 6 to 10), the activity intensity of the subject is determined. Is determined to have a high activity intensity (second activity intensity).

  Thereby, as will be described later, it is possible to calculate PA-R by a different method when the activity intensity of the subject is a low activity intensity and when the activity intensity of the subject is a high activity intensity, etc. It becomes possible.

  Further, for example, when the activity amount index information is not yet obtained (for the initial setting period), the processing unit 130 specifies the activity intensity of the subject based on the preset initial activity amount index information. The initial activity amount index information is initial information of activity index information that can be set in the storage unit 150 or the like in advance. The initial activity amount index information may be set by the subject via the operation unit (not shown) at the start of use of the wearable device 500, for example. For example, the initial PA-R is set to “5” as the initial activity amount index information, and it is determined whether the initial PA-R “5” is included in the first range or the second range. By doing so, the activity intensity of the subject may be obtained. The initial PA-R is not limited to “5”.

  This makes it possible to change the PA-R calculation method according to the activity intensity of the subject, even if the activity amount index information has not yet been obtained.

  Furthermore, in this modification, the activity intensity of the subject is not always determined based on PA-R. For example, the determination may be made based on biological history information and body movement history information acquired before the loss period.

  Then, when it is determined that the activity intensity of the subject is the first activity intensity, the processing unit 130 is based on a plurality of activity amount information in a period earlier than the deficient period of the missing activity amount information. Perform interpolation processing. For example, the missing activity amount information is interpolated based on a plurality of activity amount information acquired in the period from the missing period to one week ago.

  This makes it possible to obtain activity amount information that matches the activity intensity of the past activity of the subject during the deficit period. In other words, it is possible to obtain activity amount information as if the subject was wearing the wearable device 500 during the deficit period.

  More specifically, the processing unit 130 is acquired based on a plurality of pieces of activity amount information acquired in a period before the missing period and determined that the activity amount of the subject is included in the given range. Interpolate missing activity information. That is, the processing unit 130 performs interpolation processing of missing activity amount information by performing trim averaging on activity amount information acquired in a period before the missing period.

  Here, the given range is a range in which activity amount information of a given number or more is included among a plurality of activity amount information acquired in a period before the missing period. In other words, the given range is a range in which K pieces of activity amount information are included in N pieces of activity amount information acquired in a period before the missing period (N and K are positive integers). And K <N). The given number is the number of pieces of activity amount information indicating the amount of activity included in the given range among a plurality of pieces of activity amount information acquired in a period before the missing period. The given range and the given number can be set in advance and stored in the storage unit 150, for example.

  For example, in the example of FIG. 10A, it is assumed that the subject has forgotten to wear the wearable device 500 on March 1, 2015 and cannot acquire the activity amount information. In this case, the missing activity amount information is interpolated based on the activity amount information acquired from February 22, 2015 to February 28, 2015. Here, in the example of FIG. 10A, the subject exercises for 140 minutes or longer on February 28, 2015. However, since this subject does not usually perform such long-term exercise, the data on February 28, 2015 should not be used when performing the interpolation process. For example, when interpolation processing is performed using data on February 28, 2015, the subject usually performs such exercise at a pace of about once a week. This is because the physical strength is overestimated. Therefore, in this example, for example, the given range described above is set to 0 minutes to 40 minutes. In this case, among the first intensity exercise times acquired from February 22, 2015 to February 28, 2015, the first intensity exercise time for five days is included in the given range. In this example, the average value of the first intensity exercise times for the five days is obtained, and the obtained average value is set as the first intensity exercise time estimated to have been performed on March 1, 2015. In this way, a calculation method for obtaining an average value by excluding abnormal values is called a trim average. In FIG. 10A, such a trim average value of the first intensity exercise time is illustrated by a dotted line.

  As a result, in the activity amount information acquired in the past period than the loss period, the activity amount information whose activity amount is significantly different from other activity amounts (greater than a given threshold) is not used. It is possible to estimate activity amount information. In other words, it is possible to estimate missing activity amount information based on the activity amount acquired when the subject performs a normal activity. As a result, it is possible to suppress overestimation or underestimation of the activity amount index information of the subject.

  Also, by estimating the missing activity amount information using only the activity amount information whose activity amount falls within the given range, the missing amount can be obtained without using the activity amount at the time when the subject performed a prominent activity. It is possible to estimate activity amount information.

  Further, as in the example of FIGS. 10A and 10B, the processing unit 130 obtains activity amount information for each first period, and a plurality of activity amounts obtained within a second period longer than the first period. Based on the information, the activity amount index information is obtained for each second period. In the example of FIG. 10A, the first period is one day, and in the example of FIG. 10B, the second period is one week. However, the first period and the second period are not limited to these.

  This makes it possible to suppress short-term fluctuations in the activity amount index information.

  Next, with reference to FIGS. 11A and 11B, a method for calculating the PA-R of a person performed mainly with a high activity intensity exercise (violent exercise) will be described.

  The graph of FIG. 11A is a graph showing the second intensity exercise time and the travel distance of the subject performed mainly with the exercise of the second activity intensity (high activity intensity), and wearing the wearable device 500 for one week. It was obtained by measuring. In the graph of FIG. 11A, the horizontal axis represents the elapsed day, the left vertical axis and the solid line represent the second intensity exercise time, and the right vertical axis and the dotted line represent the travel distance. Also in this example, activity amount information (second intensity exercise time, travel distance) is obtained every day.

  The graph of FIG. 11B shows the PA-R calculated according to the number of days when activity information could not be acquired because the wearable device 500 was forgotten to be worn when exercise as shown in FIG. The mileage is shown. In the graph of FIG. 11B, the horizontal axis indicates the number of days for which activity information could not be acquired, the left vertical axis and rhombus points indicate PA-R, and the right vertical axis and square points indicate The cumulative mileage for one week is shown.

  As shown in FIG. 11A, the subject of this example performs exercise of the second activity intensity for 2-3 hours for three days in one week, but the second activity intensity on the other days. Do not exercise. On the day when the exercise of the second activity intensity is performed, the mileage is long, but on the other days, the mileage is close to zero (however, only the second day is exceptional). Since the subject of this example has a relatively long mileage for one week, PA-R is calculated based on the cumulative mileage for one week as shown in FIG. 11B. For example, as illustrated in FIG. 11B, the PA-R when the wearable device 500 is not forgotten is calculated as “10”. If the wearable device 500 is forgotten to be attached for one day (here, it is assumed that the wearable device 500 is forgotten to be attached to the first day), the cumulative travel distance does not decrease, so the PA-R may be set to “10”. desirable. Furthermore, when the wearable device 500 is forgotten to be attached for 2 days (for example, it is assumed that the wearable device 500 is forgotten to be attached to the 5th day), or when the wearable device 500 is forgotten to be attached for 3 days (for example, the 7th day) In addition, since the cumulative travel distance does not decrease, it is desirable that PA-R also remains “10”.

  In addition, in this way, the subject mainly performing exercise with high activity intensity is not consciously exercising for living daily life like the subject of FIG. 10A, but consciously as training. Most of them are exercising. Therefore, such a subject rarely forgets to wear the wearable device 500 when training, and often does not wear the wearable device 500 on a day when training is not performed. Therefore, as in the example of FIG. 10B described above, when it is estimated that the exercise of high activity intensity was performed in the same manner as other days even when the wearable device 500 was forgotten to be worn, In other words, training was performed, and the physical strength of the subject was overestimated.

  Therefore, when the activity intensity of the subject is estimated to be high activity intensity in this way, the processing unit 130 obtains the activity amount index information without performing the interpolation process of the missing activity amount information. In other words, the processing unit 130 calculates PA-R on the assumption that the activity amount (travel distance, second intensity exercise time, etc.) indicated by the missing activity amount information is zero. As described above with reference to FIG. 6, PA-R is calculated based on the exercise time, travel distance, and the like of the high activity intensity for the subject that mainly performs the exercise of high activity intensity. Therefore, even if the amount of activity on the day when high activity intensity exercise is not performed is zero, there is almost no influence on the calculation of PA-R.

  Next, the processing flow of the second modification will be described with reference to the flowchart of FIG.

  First, the information acquisition unit 110 acquires user information (S201). Furthermore, the information acquisition unit 110 acquires biological information from the biological sensor 200, for example, and acquires body movement information from the body movement sensor 300 (S202). And the process part 130 calculates | requires activity amount information for every day, for example based on at least one of biometric information and body movement information (S203).

  Next, the processing unit 130 determines whether, for example, one week has passed since the processing of step S202 is performed for the first time (or from the date when the subject wears the wearable device 500 for the first time) (S204). If it is determined that one week has not elapsed, the process returns to step S202 and the process is repeated.

  On the other hand, when the processing unit 130 determines that one week has elapsed since the processing of step S202 for the first time, it is determined whether there is a missing period from the present time to one week before (S205). . If it is determined that there is a missing period, the processing unit 130 determines whether the activity intensity of the subject is the first activity intensity (S206). If the processing unit 130 determines that the activity intensity of the subject is estimated to be the first activity intensity, the processing unit 130 performs an interpolation process of the missing activity amount information (S207).

  In contrast, if the processing unit 130 determines that there is no missing period from the present time to one week ago, the processing unit 130 proceeds to step S208 without performing the processing of step S207. In addition, when the processing unit 130 determines that there is a missing period and determines that the activity intensity of the subject is not the first activity intensity, the process proceeds to step S208 without performing the process of step S207.

  Next, the processing unit 130 calculates PA-R based on the activity amount information acquired from the present time to one week ago (S208), and estimates VO2max based on the calculated PA-R. (S209). The above is the processing flow of this modification. In the present modification, a series of processes shown in FIG. 12 are performed every day even after one week or more has elapsed since the process of step S202 for the first time.

  Further, in the above embodiment, after determining the normal activity intensity of the subject, it is determined whether or not to perform the interpolation process of the missing activity amount information, and the PA-R is obtained. The present invention is not limited to this, and it is also possible to perform the above-described processing without determining the normal activity intensity of the subject. For example, in the above-described example, only the activity amount information of the first activity intensity is used in the interpolation process of the missing activity amount information. On the other hand, since the activity amount information of the second activity intensity is not included in the given range described above, it is not used in the interpolation process. That is, the processing unit 130 obtains activity amount index information by performing interpolation processing of missing activity amount information determined to be missing using the activity amount information determined to be the first activity intensity. Then, for the activity amount information determined to be the second activity intensity, the processing unit 130 does not perform the missing activity amount information interpolation process using the activity amount information of the second activity intensity, and the activity amount index information. Ask for.

7). Specific Example of Wearable Device FIGS. 13A, 13B, and 14 show an example of an external view of a wearable device 500 (wearable device) that acquires biological information and body motion information. The wearable device 500 according to the present embodiment includes a band unit 10, a case unit 30, and a sensor unit 40. As shown in FIGS. 13A and 13B, the case portion 30 is attached to the band portion 10. As shown in FIG. 14, the sensor unit 40 is provided in the case unit 30 and includes the biological sensor 200 and the body motion sensor 300 described above.

  The band unit 10 is for wrapping around the user's wrist and wearing the wearable device 500. The band part 10 has a band hole 12 and a buckle part 14. The buckle portion 14 has a band insertion portion 15 and a projection portion 16. The user inserts one end side of the band unit 10 into the band insertion unit 15 of the buckle unit 14 and inserts the protrusion 16 of the buckle unit 14 into the band hole 12 of the band unit 10, so that the wearable device 500 is placed on the wrist. Installing. In addition, the band part 10 is good also as a structure which has a buckle instead of the buckle part 14. FIG.

  The case unit 30 corresponds to the main body unit of the wearable device 500. Various components of the wearable device 500 such as the sensor unit 40 and a circuit board (not shown) are provided inside the case unit 30. That is, the case part 30 is a housing for housing these components.

  The case part 30 is provided with a light emitting window part 32. The light emitting window 32 is formed of a light transmissive member. The case portion 30 is provided with a light emitting portion as an interface mounted on a flexible substrate, and light from the light emitting portion is emitted to the outside of the case portion 30 through the light emitting window portion 32. The case unit 30 may be provided with a display unit such as an LCD (Liquid Crystal Display) instead of the light emitting unit, or may be provided with a display unit and a light emitting unit.

  The wearable device 500 is worn on the user's wrist as shown in FIG. 15A and the like, and the biological information and the body movement information are measured in the worn state.

8). Specific Implementation Example of Biological Information Processing System Next, an example of a specific apparatus that implements the biological information processing system 100 according to the present embodiment will be described. The functions of the biological information processing system 100 according to the present embodiment may be realized by the server system 600, for example. An example in this case is FIG. 15A, for example, the biological information processing system 100 that is the server system 600 is connected to the wearable device 500 (electronic device) via the network NE, and the biological information of the subject is received from the wearable device 500. And body movement information is acquired. The wearable device 500 worn by the user needs to be small and light, so that there are large restrictions on the processing performance of the processing unit in the battery and the apparatus, or the data storage capacity. On the other hand, since the server system 600 has relatively small resource constraints, for example, it estimates VO2max based on biological information, body movement information, and user information, and performs a process for obtaining calorie consumption at a high speed, or more data. (Biological history information, body movement history information, etc.) can be held.

  The biological information processing system 100 only needs to be able to acquire various types of information collected by the wearable device 500, and is not limited to the one directly connected to the wearable device 500. For example, as illustrated in FIG. 15B, the wearable device 500 may be connected to another processing apparatus 700, and the biological information processing system 100 may be connected to the processing apparatus 700 via a network NE. As the processing device 700 in this case, for example, a mobile terminal device such as a smartphone used by a user wearing the wearable device 500 can be considered. The connection between the wearable device 500 and the processing device 700 may be the same as that of the network NE, but it is also possible to use short-range wireless communication or the like.

  In addition, the biological information processing system 100 according to the present embodiment may be realized not by the server system 600 but by a processing device 700 (electronic device, portable terminal device in a narrow sense) such as a smartphone. A configuration example in this case is shown in FIG. 15C. Mobile terminal devices such as smartphones are often limited in processing performance, storage area, and battery capacity compared to the server system 600, but sufficient processing performance can be ensured in consideration of recent performance improvements. It is also possible. Therefore, if a request for processing performance or the like is satisfied, a smartphone or the like can be used as the biological information processing system 100 according to the present embodiment as illustrated in FIG. 15C.

  Furthermore, in consideration of improvement in terminal performance, usage pattern, or the like, an embodiment in which the wearable device 500 (electronic device) includes the biological information processing system 100 according to the present embodiment is not denied as described above. In this case, the information acquisition unit 110 acquires information from the biological sensor 200 and the body motion sensor 300 in the same device. When the biological information processing system 100 is mounted on the wearable device 500, the biological information processing system 100 has low demand for data analysis, storage, etc. for a large number of users. Target users. In other words, the possibility of satisfying the user's request is sufficiently conceivable in the processing performance of the wearable device 500 and the like.

  That is, the method of the present embodiment is a process for estimating VO2max based on the information acquisition unit that acquires the biological information of the subject, the body motion information of the subject, and the user information of the subject, and the various pieces of acquired information. Can be applied to a terminal device (biological information processing device, biological information analyzing device, biological information measuring device, biological information detecting device).

  In the above description, the biological information processing system 100 is realized by any one of the server system 600, the processing device 700, and the wearable device 500. However, the present invention is not limited to this. For example, the biological information and body movement information, user information acquisition processing, and VO2max estimation processing may be realized by distributed processing of a plurality of devices. Specifically, the biological information processing system 100 may be realized by at least two or more of the server system 600, the processing device 700, and the wearable device 500. Alternatively, another device may perform part of the processing of the biological information processing system 100, and the biological information processing system 100 according to the present embodiment can be realized by various devices (or combinations of devices).

  In addition, the biological information processing system and the electronic device according to the present embodiment may realize part or most of the processing by a program. In this case, the biological information processing system, the electronic device, and the like of the present embodiment are realized by a processor such as a CPU executing the program. Specifically, a program stored in a non-temporary information storage device is read, and a processor such as a CPU executes the read program. Here, an information storage device (device readable by a computer) stores programs, data, and the like, and functions as an optical disk (DVD, CD, etc.), HDD (hard disk drive), or memory (card type). It can be realized by memory, ROM, etc. A processor such as a CPU performs various processes according to the present embodiment based on a program (data) stored in the information storage device. That is, in the information storage device, a program for causing a computer (an apparatus including an operation unit, a processing unit, a storage unit, and an output unit) to function as each unit of the present embodiment (a program for causing a computer to execute the processing of each unit). Is memorized.

  As a result, the processing of this embodiment can be realized by a program. The program may be a program that is read and executed by a processing unit (for example, a DSP) of a device such as a smartphone.

  Although the present embodiment has been described in detail as described above, it will be easily understood by those skilled in the art that many modifications can be made without departing from the novel matters and effects of the present invention. Accordingly, all such modifications are intended to be included in the scope of the present invention. For example, a term described at least once together with a different term having a broader meaning or the same meaning in the specification or the drawings can be replaced with the different term in any part of the specification or the drawings. The configurations and operations of the biological information processing system and the electronic device are not limited to those described in the present embodiment, and various modifications can be made.

10 ... Band part, 12 ... Band hole, 14 ... Buckle part, 15 ... Band insertion part,
16 ... Projection part, 30 ... Case part, 32 ... Light emission window part, 40 ... Sensor part,
DESCRIPTION OF SYMBOLS 100 ... Biological information processing system, 110 ... Information acquisition part, 130 ... Processing part,
150 ... storage unit, 200 ... biological sensor, 300 ... body motion sensor,
400 ... processor, 500 ... wearable device, 600 ... server system,
700 ... Processing device

Claims (14)

An information acquisition unit for acquiring at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor;
A processing unit for obtaining activity amount information representing an activity amount of the subject based on the at least one information, and for obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information;
Including
The processor is
Based on the biological information and the at least one information of the body movement information, the activity intensity of the subject is specified,
When it is determined that the activity intensity of the subject is the first activity intensity, the activity amount index information is obtained by interpolating the missing activity amount information determined to be missing,
When it is determined that the activity intensity of the subject is the second activity intensity higher than the first activity intensity, the activity amount index is not performed without performing the interpolation process of the missing activity amount information. A biological information processing system characterized by obtaining information. In claim 1,
The processor is
A biological information processing system that identifies the activity intensity of the subject based on the activity amount index information obtained before a deficiency period of the deficient activity amount information. In claim 1 or 2,
The processor is
A biological information processing system, wherein the activity intensity of the subject is specified based on preset initial activity amount index information. In any one of Claims 1 thru | or 3,
The processor is
When it is determined that the activity intensity of the subject is the first activity intensity, the interpolation process is performed based on a plurality of activity amount information in a period earlier than the deficient period of the missing activity amount information. A biological information processing system characterized by In claim 4,
The processor is
Based on the plurality of activity amount information acquired in the past period from the defect period and determined that the activity amount of the subject is included in a given range, the defect activity amount information A biological information processing system characterized by performing an interpolation process. In claim 5,
The given range is
The living body information processing system characterized in that it is in a range including a given number or more of activity amount information among the plurality of activity amount information acquired in the past period from the missing period. In any one of Claims 1 thru | or 6.
The processor is
A biological information processing system that determines whether or not there is a deficient period in which the activity amount information is deficient, based on at least one of the biological information and the body movement information. In claim 7,
The processor is
A biological information processing system, wherein when the wearable device provided with the biological sensor and the body motion sensor is determined not to be worn on the subject, the biological information processing system is determined to be the deficient period. In any one of Claims 1 thru | or 8.
The processor is
The activity amount information is obtained every first period,
The biological information processing system, wherein the activity amount index information is obtained for each second period based on a plurality of activity amount information obtained in a second period longer than the first period. In any one of Claims 1 thru | or 9,
The processor is
A biological information processing system that obtains maximum oxygen intake information indicating a maximum oxygen intake of the subject based on the activity amount index information and user information of the subject. An information acquisition unit for acquiring at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor;
A processing unit for obtaining activity amount information representing an activity amount of the subject based on the at least one information, and for obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information;
Including
The processor is
Using the activity amount information determined as the first activity intensity, interpolating the missing activity amount information determined to be missing to obtain the activity amount index information,
For the activity amount information determined to be the second activity intensity higher than the first activity intensity, the interpolation processing of the missing activity amount information is performed using the activity amount information of the second activity intensity. The biological information processing system characterized in that the activity amount index information is obtained. A biological sensor,
A body motion sensor,
At least one information of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor is acquired, and based on the at least one information, the subject A processing unit for obtaining activity amount information representing an activity amount of a specimen, and obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information;
Including
The processor is
Based on the biological information and the at least one information of the body movement information, the activity intensity of the subject is specified,
When it is determined that the activity intensity of the subject is the first activity intensity, the activity amount index information is obtained by interpolating the missing activity amount information determined to be missing,
When it is determined that the activity intensity of the subject is the second activity intensity higher than the first activity intensity, the activity amount index is not performed without performing the interpolation process of the missing activity amount information. An electronic device characterized by seeking information. An information acquisition unit for acquiring at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor;
As a processing unit for obtaining activity amount information representing an activity amount of the subject based on the at least one information and obtaining activity amount index information representing an activity amount index of the subject based on the activity amount information,
Make the computer work,
The processor is
Based on the biological information and the at least one information of the body movement information, the activity intensity of the subject is specified,
When it is determined that the activity intensity of the subject is the first activity intensity, the activity amount index information is obtained by interpolating the missing activity amount information determined to be missing,
When it is determined that the activity intensity of the subject is the second activity intensity higher than the first activity intensity, the activity amount index is not performed without performing the interpolation process of the missing activity amount information. A program characterized by seeking information. Obtaining at least one of the biological information of the subject detected by the biological sensor and the body motion information of the subject detected by the body motion sensor;
Based on the at least one information of the biological information and the body movement information, activity amount information representing the activity amount of the subject is obtained,
Based on the biological information and the at least one information of the body movement information, the activity intensity of the subject is specified,
When it is determined that the activity intensity of the subject is the first activity intensity, the activity amount indicating the activity amount index of the subject is obtained by performing interpolation processing of the missing activity amount information determined to be missing. Seeking index information,
When it is determined that the activity intensity of the subject is the second activity intensity higher than the first activity intensity, the activity amount index is not performed without performing the interpolation process of the missing activity amount information. A control method for a biological information processing system, characterized by obtaining information.