JP2015131049A - Biological information processing system, electronic apparatus, and server system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a biological information processing system, an electronic apparatus, a server system, etc. capable of accurately performing processing that uses heart rate information by acquiring a base heart rate from the heart rate information, and performing update processing for the base heart rate based on a given update condition.SOLUTION: A biological information processing system 100 includes: a heart rate information acquisition section 110 for acquiring heart rate information of a user; a determination section 120 for determining a base heart rate based on the heart rate information; and an update section 130 for determining an update condition for the base heart rate, and when it is determined that the update condition is satisfied, performing update processing for the base heart rate.

Description

  The present invention relates to a biological information processing system, an electronic device, a server system, and the like.

  2. Description of the Related Art Conventionally, devices and systems that acquire user heart rate information and provide information on the user's health and the like based on the acquired information have been used. The heart rate information may be acquired based on sensor information acquired from, for example, a pulse sensor or a heart rate sensor. Here, the heart rate is the number of beats of the heart, and the pulse rate is the number of times that the pressure caused by the blood pushed out of the heart by the heart beat is transmitted to the peripheral blood vessels, and the artery is beaten, Usually, a healthy person has the same heart rate and pulse rate.

  The heart rate information itself (for example, the heart rate value) can also be used as an index value representing the user's health status, but by performing a given calculation using the heart rate information, You can also ask for information about lifestyle. For example, a method of calculating a user's calorie consumption based on heart rate information and presenting it to the user is known.

  When determining a user's health condition, it is conceivable to perform not only the use of heart rate information acquired by measurement but also a comparison process between the heart rate information and a reference heart rate. In general, the heart rate value varies greatly between users, and it is possible to determine the state of the user appropriately by using a relative value to the reference value instead of the absolute value of the heart rate. Because it becomes.

  For example, Patent Document 1 discloses a determination method using “minimum pulse rate” as the above-mentioned reference. Patent Document 2 discloses a method of calculating a user's calorie consumption based on heart rate information and presenting it to the user.

JP-A-4-180730 JP 2009-285498 A

  As in Patent Document 1, when determining the user's state (stress level, etc.) based on the lowest pulse rate, which is the lowest value of the pulse rate, the pulse rate has become very low due to the influence of the external environment, measurement environment, etc. In such a case, the determination is made based on that, and therefore, an erroneous determination may be made. In other words, an appropriate value must be set for information serving as a reference in determining the health status of the user.

  The conventional technique does not disclose a technique that uses a “basal heart rate”, which will be described later, as a criterion for determination, and therefore does not disclose a technique for appropriately updating the base heart rate depending on the situation.

  According to some embodiments of the present invention, a base heart rate is obtained from heart rate information, and an update process based on a given update condition is performed on the base heart rate, thereby performing processing using the heart rate information. It is possible to provide a biological information processing system, an electronic device, a server system, and the like that are accurately performed.

  According to one aspect of the present invention, a heart rate information acquisition unit that acquires heart rate information of a user, a determination unit that determines a base heart rate based on the heart rate information, and determination of an update condition for the base heart rate And an update unit that performs an update process of the basal heart rate when it is determined that the update condition is satisfied.

  In one aspect of the present invention, the base heart rate is determined from the heart rate information, and the base heart rate is updated based on the determination of the update condition. As a result, the base heart rate can be used as a reference for processing using the heart rate information, and the base heart rate is appropriately updated, so that the processing accuracy can be improved.

  In the aspect of the invention, the update unit may increase the basal heart rate when the update condition is not satisfied for a predetermined period or a predetermined number of times.

  This makes it possible to perform processing for increasing the basal heart rate.

  In the aspect of the invention, the updating unit may count the number of repetitions from the start of measurement of the heart rate information to the end of measurement as the predetermined number of times.

  This makes it possible to perform a predetermined number of counts in units of measurement start and end.

  Moreover, in one mode of the present invention, it further includes a body motion information acquisition unit that acquires body motion information from a body motion sensor, and the update unit includes a period from the measurement start to the measurement end of the number of repetitions. The number of times that the user's body motion is determined to be small based on the body motion information may be counted as the predetermined number of times.

  Thereby, it is possible to perform a predetermined number of counts using the measurement start / end and body motion information.

  In the aspect of the invention, the update unit may determine the update condition of the basal heart rate based on the minimum heart rate obtained from the heart rate information.

  Thereby, the update condition based on the minimum heart rate can be determined.

  In one aspect of the present invention, the update unit may obtain a minimum heart rate by performing a moving average process on the heart rate information acquired in a given minimum heart rate measurement period.

  This makes it possible to obtain the minimum heart rate with high accuracy.

  In the aspect of the invention, the update unit may calculate a heart rate value and a frequency at which each heart rate value is detected based on the heart rate information acquired in the minimum heart rate measurement period. A histogram representing a relationship is obtained, and the value of the heart rate is in a range of x to x + n (x, n is a given positive number), the frequency exceeds a given frequency threshold, and x is the minimum. The minimum heart rate may be obtained.

  This makes it possible to obtain the minimum heart rate with high accuracy.

  In one aspect of the present invention, the determination unit performs a process of replacing a default value of the basal heart rate set by the biological information processing system with a minimum heart rate obtained from the heart rate information, The update unit may perform the update process for updating the basal heart rate determined by the determination unit.

  As a result, it is possible to perform a process for the default value and perform an update process for the subsequent base heart rate.

  Moreover, in one aspect of the present invention, it further includes a body motion information acquisition unit that acquires body motion information from a body motion sensor, and the determination unit is based on the body motion information in a given minimum heart rate measurement period. The minimum heart rate obtained based on the heart rate information of the minimum heart rate measurement period when it is determined that the minimum heart rate measurement period includes a period in which it is determined that the user's body movement is small A number may be the basal heart rate.

  This makes it possible to use body movement information in the processing for the default value.

  In one aspect of the present invention, it further includes a body motion information acquisition unit that acquires body motion information from a body motion sensor, and the update unit is configured to update the basal heart rate based on the body motion information. The determination may be made.

  This makes it possible to use body movement information in the update process.

  In one aspect of the present invention, the predetermined number of times may be the number of times that the update condition of the base heart rate is determined.

  This makes it possible to use the update condition determination count as the predetermined count.

  In one aspect of the present invention, the updating unit may increase the basal heart rate when a predetermined period has elapsed.

  This makes it possible to increase the basal heart rate on condition that a predetermined period has elapsed.

  In one aspect of the present invention, the information processing apparatus may further include a health degree information calculation unit that obtains relative information between the basal heart rate and the heart rate information, and obtains health degree information indicating a health degree based on the relative information. Good.

  This makes it possible to obtain health information from the base heart rate and heart rate information.

  Another aspect of the present invention relates to an electronic device including the biological information processing system.

  Another aspect of the present invention relates to a server system including the biological information processing system.

The structural example of the biological information processing system which concerns on this embodiment. 1 is a detailed configuration example of a biological information processing system according to an embodiment. The structural example of the wearable apparatus containing a biological information processing system. 4A and 4B are examples of the electronic apparatus according to this embodiment. The structural example of the portable terminal device containing a biological information processing system. The example of cooperation of a wearable apparatus and a portable terminal device. The basic flowchart showing the update process of a basal heart rate. The flowchart showing the measurement process performed every several seconds. Explanatory drawing of the method of calculating | requiring the minimum heart rate from a heart rate histogram. Other explanatory drawing of the method of calculating | requiring the minimum heart rate from a heart rate histogram. The figure explaining the update process of the base heart rate using the conventional minimum heart rate. The figure explaining the process which increases a basal heart rate. Relationship diagram between age and basal heart rate. The example of the table showing the relationship between age and a base heart rate. The detailed flowchart explaining the update process of a basal heart rate. The comparative example of the method of this embodiment and the conventional method. The other comparative example of the method of this embodiment and the conventional method. FIG. 18A and FIG. 18B are explanatory diagrams regarding coefficients during body movement and non-body movement. Correlation between basal metabolism calculated by the conventional method and the method of this embodiment. The example of the home screen displayed on a display part. The example of the coefficient setting screen displayed on a display part. The example of the heart rate trend screen displayed on a display part. FIG. 23A and FIG. 23B are screen examples that intuitively present health level information. The example of the analysis screen displayed on a display part.

  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 of this Embodiment Various methods for determining a user's state (health state, etc.) from heart rate information are known. For example, in Patent Document 2, heart rate information (HR) is measured with a heart rate sensor or the like, and the heart rate is obtained. There is known a method for obtaining the calorie consumption from the minute oxygen consumption (VO ₂ ) estimated based on the information. When the calorie intake exceeds the calorie consumption, the user can determine that the metabolic syndrome is suspected of worsening, so the calorie consumption can be used as health information indicating the user's health status. .

In patent document 2, when estimating minute oxygen consumption from heart rate information, the following formula | equation (1) etc. are used.

In the above equation (1), VO _2m is the maximum value of minute oxygen consumption, VO _2r is the minute oxygen consumption in a resting state, HR _m is the maximum value of heart rate information, and HR _r is the heart rate information of resting state. Value. In Patent Document 1, each value of VO _2m , VO _2r , HR _m , and HR _r is obtained, and VO ₂ is obtained from these values and the actually measured HR. Since there is a given relationship between VO ₂ and the amount of calories consumed, the amount of calories consumed can be determined from the estimated VO ₂ .

However, although VO _2m is the maximum value of minute oxygen consumption, it is not realistic to cause the subject to exercise at such a high load that the minute oxygen consumption seems to be maximized. Therefore, VO _2m cannot be obtained from the actual measurement value, and a given statistical value (virtual value) is used. Similarly, HR _m cannot be obtained from an actual measurement value, and a given statistical value is used. Therefore, individual differences among users are not considered for VO _2m and HR _m . Therefore, the above formula (1) is effective when calculating the tendency of the calorie consumption of a group for a certain number of people, but when calculating the calorie consumption per person, the above formula (1 ) Remains a problem.

In addition, HR _r that is the value of heart rate information at rest is used for estimation of VO ₂ based on the above equation (1), but there is also a problem with HR _r . In addition to physical activities (exercises), mental activities can be considered as human activities that involve energy consumption. Also in this mental activity, the value of the heart rate information rises and the calorie consumption also increases. In other words, even if the user is physically resting, there is no mental activity (for example, sleeping), and there is mental activity (for example, performing complex thinking such as calculation, or tension) The value of HR _r is different from that in the case of being in a state.

In Patent Document 1, the fluctuation of HR _{r on} the mental surface is not considered. Therefore, the above equation (1) is effective when a relatively high-load exercise is performed, but the calorie consumption cannot be accurately calculated when the user is not exercising. Conventionally, since the calculation of the calorie consumption was assumed to be during exercise, the problem due to the above equation (1) was not large. For example, in the past, it was only necessary to be able to notify the user of how much energy could be consumed during exercise such as running, and measuring calorie consumption until rest was regarded as important. Absent. However, the amount of calories consumed at rest is also an important indicator in determining the health level of daily activities of users. For example, in the above-described determination of the degree of exacerbation of the metabolic syndrome, the comparison between the calorie intake amount and the calorie consumption amount needs to be performed in units of a given period (for example, 24 consecutive hours) including resting time. In addition, even when various health information is considered, it is highly necessary to accurately calculate the calorie consumption due to mental activity at rest. In this case, the accuracy at rest is a problem (1) The technique used is not appropriate.

Therefore, the present applicant introduces a method using the base heart rate in order to perform processing using the heart rate information with higher accuracy. As an example, the basal heart rate (HR ₀ ) is obtained, and the calorie consumption and the like are calculated using the basal heart rate. In the following description, an example in which the health level information indicating the health state of the user, such as the calorie consumption, is described will be described. However, the use of the basal heart rate for other processes is not hindered.

Here, the base heart rate represents heart rate information when the user is in a deep sleep state. In deep sleep state, unlike HR _r described above, there is no fluctuation in mental activity, and processing using heart rate information accurately not only during exercise (at the time of body movement) but also at rest (at the time of non-body movement). Can be done.

  However, the basal heart rate in the present embodiment is not necessarily heart rate information in the deep sleep state. For example, the time for a deep sleep state during a day's sleep is about several hours, and the time varies greatly among individuals. That is, depending on the user, the period of deep sleep may be very short, or may not exist depending on the day. In consideration of the fact that the time for a deep sleep state tends to decrease as the age increases, the device according to the present embodiment (for example, a wearable device described later with reference to FIG. 4A) may be used. It is conceivable that the heart rate cannot be measured in a deep sleep state. In the first place, it is difficult to determine whether or not the patient is in a deep sleep state unless information such as brain waves is used, and with the device alone according to the present embodiment, it is guaranteed that the obtained heart rate is truly the heart rate in the deep sleep state. It ’s difficult.

  Therefore, the basal heart rate according to the present embodiment is not necessarily limited to the one representing the heart rate in the deep sleep state, and the heart rate information in the deep sleep state is determined by the determination process in the biological information processing system of the present embodiment. It may be determined information. That is, for example, if an algorithm capable of obtaining the base heart rate with a certain degree of accuracy is implemented in the biological information processing system, the information obtained by the algorithm is included in the base heart rate in the present embodiment. To do.

By using the base heart rate, it is possible to perform the processing using the heart rate information with higher accuracy than when using the above-described HR _r or the like. However, even when the base heart rate is used, it is necessary to appropriately update the base heart rate. According to the applicant's investigation, it is known that the daily heart rate fluctuation is small, but since it is biometric information, some fluctuation will occur, so update it using the measured heart rate information etc. It is desirable to do. However, as described above, the base heart rate is a standard for processing using the heart rate information. If the standard fluctuates at a high frequency, the determination result also varies, which is not preferable. Specifically, when the judgment results (calorie consumption, stress level, etc.) are changing, as a user, whether it is due to fluctuations in their life, physical condition, etc. It is impossible to clearly distinguish whether it is due to fluctuation.

  Therefore, the present applicant proposes a method of using the base heart rate and updating the base heart rate based on an appropriate update condition. Specifically, as illustrated in FIG. 1, the biological information processing system 100 according to the present embodiment includes a heart rate information acquisition unit 110 that acquires user's heart rate information, and a base heart rate based on the heart rate information. And an update unit 130 that performs a base heart rate update process when it is determined that the update condition is satisfied and the update condition is satisfied.

  In this way, since the base heart rate can be used and the base heart rate can be appropriately updated, the processing using the heart rate information can be accurately performed. Details of the update condition of the basal heart rate will be described later.

  Hereinafter, a system configuration example of the biological information processing system 100 and the like according to the present embodiment will be described, and then details of the update processing of the base heart rate will be described. Here, how to obtain a default value of the base heart rate (a value that does not depend on the actual value of the heart rate information) is also described. Finally, a process for obtaining health degree information will be described as a specific example of the process using the heart rate information and the base heart rate. When the base heart rate is used, not only the calorie consumption but also other health level information can be obtained. Specifically, it may be determined whether or not the user is in a deep sleep state using the heart rate information and the base heart rate, and deep sleep time information indicating the time in the deep sleep state from the determination is obtained. You may ask for it. Alternatively, it may be determined whether or not the user is stressed using the heart rate information and the base heart rate, and stress information indicating the time during which stress is applied may be obtained from the determination. Good.

2. System Configuration Example FIG. 2 shows a detailed configuration example of the biological information processing system 100 according to the present embodiment. As illustrated in FIG. 2, the biological information processing system 100 includes a heart rate information acquisition unit 110, a determination unit 120, an update unit 130, a body motion information acquisition unit 140, and a health degree information calculation unit 150. However, the biological information processing system 100 is not limited to the configuration of FIG. 2, and various modifications such as omitting some of these components or adding other components are possible. Also, various modifications can be made in the same manner in FIG.

  The heart rate information acquisition unit 110, the determination unit 120, and the update unit 130 are the same as those in FIG. The body motion information acquisition unit 140 acquires body motion information from the body motion sensor. Here, the body motion sensor is a sensor that detects the movement of a user who is an acquisition target of heart rate information, and may be an acceleration sensor, an angular velocity sensor (gyro sensor), It may be a sensor or a combination of a plurality of these sensors. The body motion information acquired by the body motion information acquisition unit 140 is used for basal heart rate update processing and the like. Details will be described later.

  The health degree information calculation unit 150 obtains relative information between the base heart rate and the heart rate information, and obtains health degree information representing the health degree based on the relative information. Here, the health level information is information representing the health condition of the user, and is, for example, calorie consumption, deep sleep time information, stress information, and the like. Details of the processing in the health information calculation unit 150 will be described later.

  In addition, the method of the present embodiment can be applied to an electronic device including the biological information processing system 100 shown in FIGS. The electronic device here may be a biological information measuring device, or in a narrow sense, a wearable device worn by a user. In this case, the measurement by the heart rate sensor is performed in the wearable device that is the electronic apparatus according to the present embodiment.

  Specifically, the electronic device (wearable device 200) according to the present embodiment includes a heart rate sensor 10 (pulse sensor, pulse wave sensor), a body motion sensor 20, and heart rate information acquisition, as shown in FIG. Unit 110, determination unit 120, update unit 130, body movement information acquisition unit 140, health information calculation unit 150, notification unit 210, and communication unit 220. As shown in FIG. 2, the heart rate information acquisition unit 110, the determination unit 120, the update unit 130, the body motion information acquisition unit 140, the health degree information calculation unit 150, and the like in FIG. Corresponding to 100.

  The heart rate sensor 10 is a sensor that measures the heart rate. As the body motion sensor 20, various sensors can be used as described above. When the electronic device is the wearable device 200, the heart rate information acquisition unit 110 acquires sensor information from the heart rate sensor 10 in the device (or a result of signal processing on the sensor information) as heart rate information. Become. Similarly, the body motion information acquisition unit 140 acquires sensor information from the body motion sensor 20 in the device (or the result of signal processing on the sensor information) as body motion information.

  The notification unit 210 notifies (presents) the health level information calculated by the health level information calculation unit 150 to the user. Various notification modes in the notification unit 210 may be considered, and may emit sound or voice, may emit light emitting units such as LEDs, or may vibrate the vibration unit. Good. Further, the notification unit 210 may be realized by a display unit that displays various display screens, and the display unit can be realized by a liquid crystal display, an organic EL display, or the like.

  The communication unit 220 communicates with other devices via various networks. As will be described later with reference to FIG. 6, it is conceivable to notify the calculation result of the wearable device 200 in another device. In this case, the obtained health level information and the health level information are presented. (For example, information on the display screen) is transmitted to the other device.

  An example of the wearable device 200 here is shown in FIGS. 4 (A) and 4 (B). As shown in FIGS. 4A and 4B, wearable device 200 may be a band-type (watch-type) electronic device including band 50, band hole 52, and buckle 54. . In the example of FIGS. 4A and 4B, the wearable device includes the light emitting unit 56 as the notification unit 210, and notifies the user of various information such as health degree information by lighting or blinking of the LED or the like. The wearable device 200 includes a case 58 in which the heart rate sensor 10, the body motion sensor 20, and an electronic board that implements the biological information processing system 100 are stored.

  The electronic device according to the present embodiment is not limited to the wearable device 200 including the heart rate sensor 10 and the like, and may be another electronic device. For example, the electronic device of the present embodiment may be a mobile terminal device such as a smartphone. In this case, as illustrated in FIG. 5, the mobile terminal device 300 includes a heart rate information acquisition unit 110, a determination unit 120, and an update unit 130 corresponding to the biological information processing system 100, and the heart rate information acquisition unit 110 is wearable. Heart rate information is acquired from the heart rate sensor 10 mounted on the device 200 or the like. In this case, the wearable device 200 and the mobile terminal device 300 are connected by various networks such as short-range wireless communication. Although omitted in FIG. 5, the mobile terminal device 300 may include a notification unit and a communication unit in the same manner as the wearable device 200 of FIG. 3.

  A specific example is shown in FIG. In FIG. 6, the band-type wearable device 200 shown in FIG. 4A and the like and the mobile terminal device 300 such as a smartphone are connected by short-range wireless communication or the like, and the heart rate sensor 10 mounted on the wearable device 200 Information such as the heart rate calculated based on the information is displayed on the display unit of the mobile terminal device 300. In FIG. 6, the wearable device 200 is also provided with the notification unit 210 (light emitting unit 56), so that the wearable device 200 can notify the processing result using the heart rate information. In this case, since the processing result is acquired in the mobile terminal device 300 in the configuration of FIG. 5, the wearable device 200 first transmits sensor information to the mobile terminal device 300, and the mobile terminal device 300 uses the sensor information. The health level information is calculated, and the calculation result is transmitted to the wearable device 200.

  Note that FIG. 6 is a diagram corresponding to the configuration of FIG. 3 as described above. In this case, since the wearable device 200 acquires the processing result using the heart rate information, In particular, no communication or the like is required. However, in that case, since the mobile terminal device 300 does not calculate health information or the like, when performing display on the display unit or the like of the mobile terminal device 300, it is necessary to receive the calculation result from the wearable device 200. .

  Further, the electronic device according to the present embodiment is not limited to the wearable device 200 and the portable terminal device 300, and various devices such as a PC (Personal Computer) can be used.

  Further, the method of the present embodiment can also be applied to a server system including the biological information processing system 100 described above. The configuration example of the server system is the same as that of the mobile terminal device 300 in FIG. In this case, however, the server system may be physically provided at a position far from the user. In this case, even if the processing result in the server system is notified by the notification unit of the server system, the user cannot be recognized. Therefore, in the server system, it is preferable to transmit the calculation result such as health information to a device used by the user, such as the wearable device 200 and the mobile terminal device 300.

  In general, the server system has higher processing performance than the wearable device 200 and the portable terminal device 300, and the storage area of the storage unit is less limited. Therefore, compared to the case where the biological information processing system 100 is included in the wearable device 200 or the like, it is possible to perform processing using the heart rate information at high speed. Further, if the storage area is large, it is possible to store log data of a large number of users and increase the amount of log data per person when log data such as heart rate information is taken. Therefore, by performing highly versatile processing using a large number of user data, and by storing the user's heart rate information in units of several years or decades, the calculation accuracy of the health level information for the user can be improved. We can expect improvement.

  Various communication routes between the wearable device 200 and the server system are conceivable. For example, if the wearable device 200 can be directly connected to the network, the wearable device 200 may directly communicate with the server system via the network. Alternatively, the wearable device 200 first transmits sensor information to the mobile terminal device 300 using short-range wireless communication or the like, and the mobile terminal device 300 transfers the sensor information to the server system via the network. Communication between the wearable device 200 and the server system may be performed via another device.

3. Base Heart Rate Update Processing Next, details of the base heart rate update processing will be described. Specifically, the calculation process of the minimum heart rate and the update process of the base heart rate based on the minimum heart rate will be described first. This corresponds to the update process for reducing the basal heart rate. Next, update processing for increasing the basal heart rate will be described. Further, setting and updating of the default value of the basal heart rate and processing using the body motion information will be described. Finally, the entire update processing of the basal heart rate will be described using a flowchart and the like.

3.1 Calculation of Minimum Heart Rate In this embodiment, the update unit 130 obtains the minimum heart rate based on the heart rate information, and determines the update condition for the base heart rate based on the minimum heart rate. The base heart rate represents the heart rate in the deep sleep state or the heart rate determined to be in the deep sleep state as described above. Therefore, the heart rate assumed to be the smallest among the various states that the target user can take, such as deep sleep state, shallow sleep state, rest, and exercise, is the base heart rate. In other words, if the base heart rate does not fluctuate, the value does not fall below the base heart rate when measuring the heart rate information, and conversely, if the heart rate information value falls below the base heart rate, The number may be updated with a value that is lower than the number. Specifically, as shown in FIG. 7, first, the minimum heart rate is calculated (S101), and it is determined whether the minimum heart rate is below the base heart rate (S102). In the case of Yes in S102, the base heart rate is updated to the value of the minimum heart rate, assuming that the update condition of the base heart rate is satisfied (S103).

  However, in reality, the heart rate may be erroneously detected due to body movement artifacts, etc. depending on the equipment wearing state at the time of heart rate measurement, etc., so if it is simply updated below the lower limit, it will be mistakenly There is a risk of being updated. Therefore, in this embodiment, first, the calculation accuracy of the minimum heart rate in S101 is increased. Here, the minimum heart rate is a value obtained on the basis of heart rate information within the period from the start of measurement of heart rate information to the end of measurement as one unit. From the start of measurement to the end of measurement is, for example, from the press of the start button by the user to the press of the stop button. However, if the device is a band-type device such as FIG. Modifications such as using a period from when it is removed to when it is removed are possible.

  Specifically, the updating unit 130 obtains the minimum heart rate by performing a moving average process on the heart rate information acquired in a given minimum heart rate measurement period. In the minimum heart rate measurement period, the processing shown in the flowchart of FIG. 8 is performed periodically (for example, at a frequency of once every several seconds).

  When this process is started, the heart rate HR at that time is first acquired from the heart rate sensor 10, and the body motion information (for example, acceleration value acc) at that time is acquired from the body motion sensor 20. Then, a moving average is taken for HR (and acc) (S201). When the acquired value is used as it is, the HR has a large variation in value, and particularly the variation near the lower limit in the histogram of FIG. Since the minimum heart rate is a value close to the minimum value as described above, the minimum heart rate is obtained using information near the lower limit having a large variation, and the calculation accuracy is lowered. On the other hand, by taking a moving average, it is possible to suppress variations in values, so that the minimum heart rate can be obtained with high accuracy.

  A specific example is shown in FIG. FIG. 9 is a histogram showing the heart rate value on the horizontal axis and the frequency (frequency) at which the heart rate value appears within the minimum heart rate measurement period on the vertical axis, and A1 (inverted triangle plot) moves. A histogram before averaging processing, A2 (circled plot) is a histogram after moving average processing. In FIG. 9, a given frequency threshold is set so that a very small value estimated as false detection is not set as the minimum heart rate. Here, the frequency with the frequency exceeding 90 and the minimum heart rate is set. The minimum heart rate is used. Still, since the value variation is large in A1 before the moving average process, the frequency may be increased to some extent even in a range where the numerical value of the heart rate is small. In the example of FIG. 9, it is not preferable to employ a heart rate of 41 to 43 as the minimum heart rate. However, if threshold determination is performed, 42 becomes the minimum heart rate.

  On the other hand, by performing the moving average process, as shown in A2, variation in value is suppressed even near the lower limit, and the rise of the graph can be made steep. As a result, the minimum heart rate becomes 44, and the possibility of setting an excessively small value as the minimum heart rate can be suppressed.

  After the moving average process of S201, the body movement state is determined (S202). The state here represents, for example, whether the body movement in the short-term span is large or small. More specifically, the average value of the body movement signal for about 1 minute (or the average value of the difference values). And the threshold value, signal periodicity, and the like. Thereafter, the histogram is updated using the heart rate after the moving average process calculated in S201. Specifically, the frequency of the heart rate value calculated in S201 may be increased by 1.

  In the flowchart of FIG. 8, it is assumed that it is determined whether or not the patient is sleeping based on the body motion information obtained together with the heart rate information in S201. Therefore, in S204, it is determined whether or not the body motion signal is smaller than the predetermined value, and the lengths of the periods that are smaller than the predetermined value are accumulated and stored. Details of the processing using the body motion signal in S201 and S204 will be described later.

  However, even if the moving average process is performed as described above, a unique point may occur in the histogram. A specific example is shown in FIG. In FIG. 10, B1 is a histogram before moving average processing, and B2 is a histogram after moving average processing. When B1 and B2 are compared, variation is suppressed by the moving average process, and the frequency of the heart rate such as 42 and 43 can be suppressed to a lower level in B2 than in B1. As a result, if it is B1, the minimum heart rate was 43, it is possible to exclude 43 from the minimum heart rate.

  In B2, the frequency is high at the heart rate 44, and the minimum heart rate is 44 from the threshold determination. However, in view of the fact that the frequency at 45 to 48 of B2 is very small, the frequency at 44 is a peculiar value generated as a result of measurement error or moving average calculation, and the minimum heart rate is set to 44. Is inappropriate.

  Therefore, in the present embodiment, the minimum heart rate may be obtained using not only the frequency at a given heart rate value but also the frequencies at the preceding and following heart rate values. Specifically, the updating unit 130 obtains a histogram representing the relationship between the heart rate value and the frequency at which each heart rate value is detected based on the heart rate information acquired in the minimum heart rate measurement period. In the range of the value of heart rate from x to x + n (x, n is a given positive number), the frequency that exceeds the given frequency threshold and has the minimum value is obtained as the minimum heart rate.

  In the example of FIG. 10, the horizontal axis of the histogram is an integer value, and x is a natural number. In addition, for example, 2 may be used as n. In this case, when the frequency value exceeds all of the threshold values in all of x, x + 1, and x + 2, x is set as the minimum heart rate. In the case of FIG. 10, when x = 44, the frequency at 44 exceeds the threshold, but at x + 1 = 45 and x + 2 = 46, the frequency does not exceed the threshold. Therefore, x = 44 cannot be determined as the minimum heart rate. When x = 48, the frequency exceeds the threshold value in all of 48, 49, and 50, and when x is less than 48, three consecutive heart rate values exceeding the threshold value are not found. Therefore, the minimum heart rate can be determined to be 48, and the minimum heart rate can be obtained with high accuracy.

  However, the method for obtaining the minimum heart rate is not limited to searching for the minimum heart rate value that continuously exceeds the threshold value. For example, a search may be made from the side with the larger value, and a numerical value in which the frequency is monotonously decreased and the decrease width in the monotonic decrease range is maximized may be specified.

  For example, in FIG. 10 (although the upper portion of the graph is missing), it is assumed that the frequency at the heart rate = 52 is the maximum. In this case, the monotonic decrease range (ie, the monotone increase range in the direction from the smaller value to the larger value) when searching from the larger value to the smaller value is 52 to 49, 48 to 47, 44. It becomes three places of ~ 43. In this case, if the reduction width in 52-49, the reduction width in 48-47, and the 44-43 reduction width are compared, the reduction width in 52-49 becomes the maximum. In this case, the lower limit value of the monotonic decrease range, that is, 49 may be set as the minimum heart rate.

  Ideally, the histogram as shown in FIG. 10 has one peak (monotonically increasing in a range smaller than the peak and monotonically decreasing in a range larger than the peak). In FIG. 10, several small mountains other than the original one are generated due to the peculiar point, but the one with the maximum height (increase / decrease width) is the original one. Obviously, this is the mountain you want to detect. That is, by using the above-described method, it is possible to obtain an appropriate minimum heart rate by eliminating a peculiar point as in the method of continuously searching for a value exceeding the threshold value.

  However, the minimum heart rate is 48 in the search for values that continuously exceed the threshold, and the minimum heart rate is 49 in the method for searching for a value with a large decrease, as indicated by the method used. Number values can change. Which method is appropriate depends on the shape of the actually acquired histogram and the like. In addition, when the measurement period includes both a period of sleep state and a period of waking state, the histogram has two peaks: a mountain corresponding to the sleep state and a mountain corresponding to the waking state. As shown, the histogram also changes depending on the usage state of the user. That is, how to obtain the minimum heart rate is not limited to the above method, and various modifications such as using other methods or combining several methods are possible.

3.2 Process for Increasing Base Heart Rate By the above method, the minimum heart rate can be obtained with high accuracy, thereby making it possible to update the base heart rate appropriately. However, the update process described above reduces the basal heart rate, and the update process that increases it is not considered.

  It is known that the basal heart rate varies with environmental changes and temperature. For example, when living in a place where daily altitude is high, the oxygen partial pressure increases by moving to a place where altitude is low, so that it is possible to acquire a normal oxygen intake with a small heart rate. Therefore, there are cases where the basal heart rate changes (particularly decreases) specifically. As mentioned above, simply updating when the minimum heart rate falls below the basal heart rate may lead to a case where the standard becomes inconsistent with the normal living environment after such a specific case. is there.

  A specific example is shown in FIG. In FIG. 11, the base heart rate is indicated by a dotted line, and the minimum heart rate actually measured by the above method (for example, the minimum heart rate per day) is indicated by a solid line. In FIG. 11, the minimum heart rate having a small value is acquired around 50 days and 70 days, due to factors such as environmental changes, and an update process is performed to reduce the base heart rate accordingly. It is assumed that the environment has returned to the original state after about 80 days.

  In this case, the heart rate of the target user returns to a higher level than before the change of the environment as in the case before 50 days have passed, whereas the base heart rate is understood from the area surrounded by C1 in FIG. The number is not updated from a lowered state. As a result, the daily heart rate becomes a large value with respect to the basal heart rate even though the user lives a normal life. Specifically, compared to the magnitude (ratio or increase) of the heart rate measured at C2 in FIG. 11 to the base heart rate, the magnitude (ratio, ratio) of the heart rate measured at C1. (Or increase amount) is large. Therefore, although it is normal life, it is judged that exercise load is too large, stress is felt, and sleep is not enough. For example, there is a portion where the solid line is interrupted at C1 in FIG. 11, which indicates that the user has erroneously determined that he / she is not sleeping but is actually sleeping.

  As disclosed in Patent Document 1, in the process using the heart rate information, a method using a minimum value or a value equivalent thereto as a reference for the target user is widely used. Therefore, a method for updating a reference value to a small value using an actual measurement value has been found in the past, but a method for updating a reference value to a large value is not known.

  Therefore, the applicant performs an update process for increasing the basal heart rate. Specifically, the update unit 130 increases the basal heart rate when the update condition is not satisfied for a predetermined period or a predetermined number of times.

  The update condition here is, for example, the condition of S102 in FIG. Moreover, what is necessary is just to set periods, such as 90 days, as a predetermined period. The updating unit 130 may count the number of repetitions from the start of measurement of heart rate information to the end of measurement as a predetermined number. Specifically, as described above, when the measurement start button is pressed and then the measurement end button is pressed, the count may be made once. Alternatively, the predetermined number of times may be the number of times that the update condition of the base heart rate is determined. In S102 of FIG. 7, it is the number of times the comparison processing between the minimum heart rate and the base heart rate has been performed. In this case, the predetermined number of times can be considered as the number of times the minimum heart rate has been obtained. Therefore, when the measurement itself was performed but the minimum heart rate was not calculated, it was excluded from the number of times, or when the minimum heart rate was calculated multiple times from the start button press to the stop button press It is also possible to count the obtained number of times.

  FIG. 12 is an example of a case where the minimum heart rate is obtained when it is determined that a sleep state is obtained during measurement. In this case, as shown on the horizontal axis, the predetermined number of times can be considered as the number of sleeps. In FIG. 12, since the value of the minimum heart rate became small during the fourth and fifth sleeps, an update process for reducing the base heart rate was performed. However, the minimum heart rate did not fall below the basal heart rate for the next 90 times. In such a case, it is considered that the current base heart rate does not reflect the actual situation of the user, that is, it may have an excessively small value. Therefore, the value of the base heart rate may be increased as shown in FIG. 12, and the value is increased by 1 in FIG. Note that, as described above, the base heart rate that serves as a criterion for the determination process should not be changed greatly, so that it is not preferable to increase the increase rate excessively even when increasing.

  In this way, it is possible to increase not only the update process to decrease the base heart rate, but also increase it, so when the base heart rate decreases due to environmental changes etc. and does not correspond to the user's state However, an appropriate update process can be performed. As described above, since it is easy to decrease the base heart rate using the minimum heart rate, even if the update process for increasing the base heart rate is inappropriate, the base heart rate is set appropriately. It is easy to return to the correct state.

  In addition, it is possible to increase the base heart rate in consideration of changes in the base heart rate depending on the user's age. For example, the update unit 130 may increase the basal heart rate when a predetermined period has elapsed.

  As shown in FIG. 13, the value of the base heart rate tends to increase as the age increases. In other words, even if there is no decrease in the base heart rate due to environmental changes as described above, or no decrease in the base heart rate due to measurement or processing errors, the base heart rate will not match the user's condition over time. Can be considered. Therefore, for example, the base heart rate may be increased in units of three months, half a year, or one year. In the example of increasing when the above update condition is not satisfied, if the update process is performed during a predetermined period or a predetermined number of times, the measurement of the period or the count of the number has been reset, but in this case The update status up to that point need not be considered. Therefore, it is possible to reliably increase the value of the base heart rate regardless of the state of the actually measured heart rate information.

  Of course, due to the increase process when the update condition is not satisfied, it is fully possible that an appropriate update process including fluctuations with age has been performed. The possibility of increasing the value cannot be denied. However, as described above, since it is easy to decrease the value of the excessively increased basal heart rate, it is not a big problem. As described above, it is not preferable that the basal heart rate fluctuates frequently, but it is assumed that the predetermined period here is a period that is long to some extent. Must not.

3.3 Default Value Setting and Update Processing Next, processing related to the default value of the basal heart rate will be described. As described above, the numerical value of the heart rate information has a large individual difference depending on the user, and in order to perform an appropriate process, it is necessary to perform a comparison process with a reference value instead of an absolute value, that is, a base heart rate. For this reason, the use of the biological information processing system 100 (for example, the wearable device 200 of FIG. 4A) has just started, and the user's heart rate information has not been measured to the extent that the base heart rate can be obtained. However, some default value may be set as the base heart rate.

  Specifically, a predetermined value may be set as the default value regardless of the personal data of the user. Alternatively, as shown in FIG. 14, a table that associates predetermined personal data (for example, age) with a default value of the base heart rate is held, and the personal data input from the user at the start of use of the device and the table From the above, a default value may be set. Alternatively, a mathematical formula for obtaining a default value from personal data may be held, and the default value may be set from the calculation result using the mathematical formula.

  However, these default values do not take into account individual differences between users. Therefore, in this embodiment, the determination unit 120 determines (updates) the base heart rate based on the heart rate information, thereby changing (updating) the base heart rate using the actual measurement value from the target user. That is, the process in the determination unit 120 can be said to be a base heart rate update process in a broad sense, but in the present embodiment, the process in the determination unit 120 and the process in the update unit 130 may be different.

  Specifically, the determination unit 120 performs processing to replace the default value of the base heart rate set by the biological information processing system 100 with the minimum heart rate obtained from the heart rate information, and the update unit 130 An update process for updating the base heart rate determined by 120 is performed.

  As described above, the update process in the update unit 130 is replaced with the minimum heart rate if the value is decreased, and thus it is not impossible to change the value of the base heart rate within a certain range. . However, the process of increasing the value needs to be determined over a predetermined period or a predetermined number of times as shown in FIG. 12, and it is assumed that the increase width is small as described above.

  Since the default value of the basal heart rate does not consider individual differences, there is a possibility that it is greatly different from the value to be actually used. In this case, if the default value is larger than the ideal value, it is possible to approach the ideal value in a relatively short time even by the same processing as that of the updating unit 130. However, when the default value is smaller than the ideal value, it is preferable that the method of increasing the value little by little for a long time like the updating unit 130 may take a long time to reach the ideal value. I can't say that.

  Therefore, in the present embodiment, when changing from the default value to the value using the actual measurement value, the replacement with the minimum heart rate may be performed regardless of the magnitude relationship between the minimum heart rate and the base heart rate (default value). . In this way, even in the case where the default value is smaller than the ideal value, it is possible to approach the ideal value in a relatively short time.

3.4 Processing Using Body Motion Signal In the present embodiment, body motion information acquired by the body motion information acquisition unit 140 may be used in updating the base heart rate. Specifically, the determination unit 120 determines that the minimum heart rate measurement period includes a period in which it is determined that the user's body movement is small based on the body movement information in a given minimum heart rate measurement period. In this case, the minimum heart rate obtained based on the heart rate information in the minimum heart rate measurement period may be used as the base heart rate.

  As described above, the basal heart rate is a heart rate in a deep sleep state or a state determined to be a deep sleep state, and is the lowest or equivalent to the target user. That is, when changing the base heart rate value from the default value using the actually measured heart rate information, it is necessary to use the minimum heart rate in a deep sleep state or the like. In particular, as described above, in the processing in the determination unit 120, even if the minimum heart rate is larger than the base heart rate (default value), the base heart rate is replaced with the minimum heart rate. Therefore, if the processing is performed by the updating unit 130 or the like, a very high heart rate (for example, a heart rate during exercise) that does not contribute to the calculation of the base heart rate may be used as the base heart rate. Of course, if the minimum heart rate is obtained from the low heart rate information during sleep, the base heart rate is updated with the value, but there is no problem, but depending on the user, the frequency of use during sleep may be very low. There is a risk that an excessively high basal heart rate will be used for a long time.

  Therefore, when obtaining the minimum heart rate, body motion information is also obtained together with the heart rate information, and based on the body motion information, the user is in a sleep state within the target period (minimum heart rate measurement period). It is determined whether or not. Then, the base heart rate is changed from the default value using the minimum heart rate when it is determined that the period of sleep is included. On the other hand, if the user is not in a sleep state within the period, the base heart rate is not changed from the information in the period, so that the value of the base heart rate can be prevented from becoming excessively large.

  Specifically, as shown in S201 of FIG. 8, body motion signals are acquired at a frequency of once every few seconds. At this time, the moving average process may be performed similarly to the heart rate information to suppress variations. Further, since the value of body motion information may not be zero even in an ideal resting state so that the value of gravitational acceleration is detected from the acceleration signal, the difference value (differential value) of the body motion information is obtained in S201. It may be acquired.

  Then, in S204, an average value or a sum of the values obtained in S201 in a given period may be taken and a comparison process with a threshold value may be performed. Various given periods can be considered here, but a time of about 10 minutes is used, for example. If the average value is larger than the threshold value by this comparison process, it can be determined that the user's body movement is large and not in the sleep state during the period, and if the average value is equal to or less than the threshold value, the user body movement is small during the period. It can be determined that the patient is sleeping.

  Furthermore, in one minimum heart rate measurement period, the time determined to be in the sleep state by the above determination is accumulated. Here, if the time determined to be in the sleep state is longer than 3 hours, the user is in the sleep state in the minimum heart rate measurement period, and a value that can be used for processing related to the base heart rate is obtained. It is determined that Conversely, if the sleep state is 3 hours or less, it is determined that the user did not become a sleep state or became a sleep state, but the sleep was not sufficient to become a deep sleep state, It is not used for processing related to basal heart rate.

  In addition, body motion information may be used in the update unit 130, and the update unit 130 may determine an update condition for the basal heart rate based on the body motion information. The point that the base heart rate should ideally be the heart rate in the deep sleep state is the same in the process in the determination unit 120 and the update process in the update unit 130. By using the information in the estimated period, the base heart rate can be appropriately updated.

  More specifically, the updating unit 130 counts, as a predetermined number of times, the number of repetitions determined that the user's body movement is small based on body movement information during a period from the start of measurement to the end of measurement. Also good.

  This makes it possible to appropriately count the predetermined number of times. For example, when counting from pressing the start button to pressing the stop button as one time, depending on the user's use case, repeating the start / stop frequently within one day, the count becomes very large in a short time Is also possible. In an extreme example, if the ON / OFF operation is repeated 90 times within a day, there is a possibility of increasing the basal heart rate during the day.

  However, this predetermined number of times should be the number of times that an inherently significant update determination has been made. There is a possibility that the base heart rate may be updated by obtaining the minimum heart rate corresponding to a state where the heart rate is sufficiently low for the target user and comparing the minimum heart rate and the base heart rate as shown in S102 of FIG. It becomes a judgment. Even if the minimum heart rate is obtained 90 times during exercise, the possibility that the minimum heart rate is lower than the base heart rate is very low, and it cannot be said that the processing is useful from the viewpoint of updating the base heart rate. Even if the minimum heart rate in exercise state etc. exceeds the basal heart rate for a predetermined number of times, it is a natural result, and it is difficult to say that it is appropriate to increase the basal heart rate based on the result. .

  In other words, by using the body motion information in the predetermined number of counts, the process for determining a significant update condition of the basal heart rate is performed a specified number of times. Even after the significant determination is repeated a certain number of times, an increase process is performed when the minimum heart rate does not fall below the basal heart rate. Therefore, a certain degree of validity can be ensured for an increase in the basal heart rate. .

3.5 Summary As a summary of this section, a flowchart showing the update process of the basal heart rate including the above processing will be described, and then the difference between the method of the present embodiment and the conventional method will be described using a graph based on actual measurement values. explain.

  FIG. 15 is a flowchart showing detailed update processing of the basal heart rate. When this process is started, the time when the average value or integrated value of the body motion information (for example, the differential value of the acceleration value) exceeds the body motion threshold over the minimum heart rate measurement period first. The accumulated value is acquired, and it is determined whether the value exceeds a time threshold (for example, 3 hours) (S301). Since the accumulated time value is acquired in accordance with the heart rate information as shown in S204 of FIG. 8, the value may be used.

  In the case of No in S301, in the minimum heart rate measurement period that is the processing target, it cannot be said that the user is taking sufficient sleep, and even if the minimum heart rate is obtained, the minimum heart rate is the base heart rate. It can be determined that it does not correspond to. Therefore, in the flowchart of FIG. 15, the minimum heart rate calculation process itself is not performed, and the process ends.

  On the other hand, in the case of Yes in S301, since the user may have entered a sleep state (particularly a deep sleep state), the minimum heart rate is calculated. In the flowchart of FIG. 15, as described above with reference to FIG. 10, since a method of continuously searching for a value whose frequency exceeds the threshold value from the lower heart rate is assumed, first the search starting point is hr = It is set as hrmin (S302). Since a numerical range smaller than hrmin is not a search target for the base heart rate, a value smaller than the lower limit of the numerical range that the base heart rate can take may be used as hrmin here. For example, if it is considered that there are very few users whose basal heart rate value is less than 30, hrmin = 30 may be set.

  Then, in the range from hrmin to the upper limit hrmax of the search range (S303), a comparison is made between the frequency (frequency) of the histogram and the frequency threshold (for example, 90) to search for the minimum heart rate. hrmax may be a value larger than the upper limit of the numerical range that the base heart rate can take. Here, since the horizontal axis of the histogram is an integer value, it is determined whether the frequency of the heart rate is hr, the frequency of hr + 1, and the frequency of hr + 2 are all smaller than the threshold (S304).

  In the case of Yes in S304, it is determined that hr at that time is not the minimum heart rate, as in the case of heart rate = 44 in FIG. Therefore, the search is continued by performing an update process for increasing hr. Here, hr is incremented (S305), and the loop of S303 is continued.

  On the other hand, in the case of Yes in S304, it can be determined that hr at that time is the minimum heart rate in the minimum heart rate measurement period to be processed as shown in FIG. Therefore, the process goes through the loop of S303 to determine the update condition of the base heart rate using the obtained minimum heart rate.

  Specifically, it is first determined whether or not the current base heart rate is a default value (S306). In the case of Yes in S306, since the process is performed by the determination unit 120, the base heart rate is replaced with the value of the minimum heart rate regardless of the magnitude relationship between the base heart rate and the minimum heart rate (S307). In order to start counting a predetermined number of times, the counter value is reset to 0 (S308), and the process ends.

  On the other hand, if the answer is No in S306, that is, if the basal heart rate is not the default value and has already been updated using the actual measurement value of the user, the processing is performed in the updating unit 130, and therefore the minimum heart rate is lower than the basal heart rate. It is determined whether or not is smaller (S309). In the case of Yes in S309, the base heart rate may be replaced with the minimum heart rate value, so the processing of S307 and S308 is performed and the processing is terminated.

  If Yes in S306, it is determined whether the base heart rate is equal to the minimum heart rate (S310). If equal, the counter value for counting the predetermined number of times is reset (S311). In the case of Yes in S310, the update rate of the base heart rate (condition of S309) is not satisfied, but the minimum heart rate based on the actual measurement value is equal to the base heart rate, so the current base heart rate is the user This is a case where it can be determined that the value representing the state is appropriate, in other words, it is not an excessively small value. In that case, since the process of increasing the base heart rate is unnecessary for the time being, the predetermined number of counts may be restarted from zero. Note that S310 and S311 may be omitted from the implementation, and a modification may be made in which the determination of S309 is hr ≦ basal heart rate.

  In the case of No in S310, since the minimum heart rate is larger than the base heart rate, the update process for decreasing the value is unnecessary. Therefore, it is determined whether to perform processing for increasing the basal heart rate. Specifically, it is determined whether the counter for counting the predetermined number is smaller than a given threshold value (90 in this case) (S312). In the case of Yes in S312, since the number of times that the update condition has not been satisfied has not yet reached the predetermined number, the base heart rate is not changed and the counter value is incremented (S313). In the case of No in S312, since the update condition has not been satisfied a predetermined number of times, the base heart rate is increased. Here, the value of the base heart rate is increased by 1 (S314). After the process of S314, the counter value is reset to 0 in order to restart the predetermined number of times (S315), and the process ends.

  Next, differences in the base heart rate and the like between the conventional method and the method of this embodiment are shown in FIGS. In FIG. 16, for the same heart rate information log data, a graph when the base heart rate is updated by obtaining the minimum heart rate by the conventional method, and the base heart rate by obtaining the minimum heart rate by the method of the present embodiment. The graph when updated is shown. As is clear from the figure, since the calculation accuracy of the minimum heart rate is insufficient with the conventional method, the base heart rate decreases excessively as on May 16, August 1, and August 15. However, with the method of this embodiment, the base heart rate can be maintained at an appropriate level.

  FIG. 17 shows a graph of the base heart rate and the minimum heart rate according to the conventional method, and the base heart rate and the minimum heart rate according to the present embodiment. In the range of FIG. 17, there is no fluctuation in the base heart rate. However, as in FIG. 16, the update method for excessively reducing the base heart rate is suppressed in the method of the present embodiment. The value is larger than the conventional method. Then, comparing the minimum heart rate in the conventional method with the minimum heart rate in the present embodiment, the minimum heart rate in the present embodiment is larger than that in the conventional method in most measurements. It turns out that the possibility of calculating too low can be suppressed.

  Moreover, when the minimum heart rate is larger than the base heart rate, there is a possibility that the user is erroneously determined that the user is not in a sleep state. The sleep state determination here is based on heart rate information, which is different from the method using body motion information as in S301 of FIG. FIG. 17 shows an example in which the minimum heart rate itself is not obtained when the sleep state is not determined. In the conventional method, the base heart rate decreases, so as shown in the dashed circle, if there is a day when the minimum heart rate increases, it is determined that the user is not sleeping even if the user is sleeping. End up. On the other hand, in the method of the present embodiment, the base heart rate can be kept at an appropriate level, so that the sleep state may be erroneously determined even on the day when the minimum heart rate becomes high, as shown by the dashed circle. Can be suppressed.

4). Health Level Information Generation Processing Using Base Heart Rate A specific example of processing for obtaining health level information using the base heart rate will be described. Below, after calculating the calculation method of each information, the example of the display screen used when showing the calculated | required health degree information to a user is demonstrated. However, the health information obtained based on the base heart rate is not limited to the following.

4.1 Calculation Method of Calorie Consumption As described above, in the conventional method, the heart rate (HR), maximum VO ₂ (VO _2m ), maximum HR (HR _m ), resting VO ₂ (VO _2r ), resting HR From (HR _r ), VO ₂ was estimated based on the above formula (1), and the minute energy (calorie) consumption EE (EE = VO ₂ × 5/1000 kcal) was obtained. However, VO _2m , HR _m , VO _2r , and HR _r do not consider individual differences so much, and VO _2m and HR _m cannot actually be measured and do not take into account the influence of operation (ACT). The nature is low. In particular, when considering applications such as monitoring the health status of a user over a long period of time (for example, a full day) regardless of whether the patient is moving or not moving, there is a large problem with the method using the above equation (1). .

Therefore, in this embodiment calculates consumed calories based on the basal heart rate HR ₀ described above. Specifically, the above equation (1) is converted into the following equation (2) using the minute energy consumption EE ₀ corresponding to the base heart rate HR ₀ . In the following formula (2), EE represents the calorie consumption per minute, EE ₀ represents EE in the ground state, and EE _m represents the maximum value of EE.

Then, by solving the above equation (2) for EE, the following equation (3) is obtained.

Here, EE ₀ is a value corresponding to the user's minute basal metabolic rate, and since it is known that the basal metabolic rate BM per day can be calculated by various methods, EE ₀ is also determined in advance. You can ask for it. Further, HR ₀ can be determined from the actually measured value as described above, and the value of the heart rate information measured at that time may be used for HR. Therefore, in the above equation (3), if the value of the part indicated by x is determined, the calorie consumption (minute calorie consumption EE) can be obtained.

Here, graphs in which ΔEE of the above equation (3) is plotted on the vertical axis and (ΔHR / HR ₀ ) × EE ₀ of the above equation (3) is plotted on the horizontal axis are plotted in FIG. 18A and FIG. Shown in B). FIG. 18 (A) corresponds to a diagram in which the values during activity (expressed during exercise or body movement) are plotted, and FIG. 18 (B) is during inactivity (when resting or during non-body movement). Specifically, it corresponds to a diagram in which values of resting position, sitting position, standing position, etc.) are plotted.

  As can be seen from the values of the vertical and horizontal axes and the above equation (3), when the plotted points are linearly approximated, the slope of the straight line represents the coefficient x. When comparing FIG. 18 (A) and FIG. 18 (B), the slope of the straight line in FIG. 18 (A) corresponding to the coefficient during body movement is the same as that in FIG. 18 (B) corresponding to the coefficient during non-body movement. Larger than the slope of the straight line. This means that the value of the coefficient x is greater during activity than during inactivity, and when determining energy consumption (calorie consumption), change the coefficient depending on whether it is active or inactive. Indicates that it is necessary to perform an operation.

Further, in the above equation (3), HR-HR ₀ is used as the index value ΔHR representing the degree of variation with respect to the reference value of the heart rate information. In other words, the reference value has become a base heart rate HR _0. However, when waking up, users are often standing or sitting, regardless of whether they are moving or not. As a case, the acts baroreceptor sensitivity, even if were very weak situation any physical activity and mental activities, heart rate is increasing compared to the HR _0. Therefore, in practice, it is desirable to use a value that takes into account the above-mentioned increase, not HR ₀ itself, as the reference value of the heart rate information when determining ΔHR. Therefore, in the present embodiment, coefficients α and β of 1 or more are set, and ΔHR = HR−αHR ₀ during body movement. When not moving, ΔHR = HR−βHR ₀ is used.

In consideration of the point that the coefficient x should be different between body motion and non-body motion, and ΔHR should use the increments α and β of the reference value instead of HR-HR ₀ , The following formulas (4) and (5) are proposed as formulas for calculating calorie consumption. The following equation (4) is an equation for calculating the calorie consumption during body movement, and the following equation (5) is an equation for calculating the calorie consumption during non-body movement.

  As described above with reference to FIGS. 18A and 18B, the coefficient x is used during body movement, and the coefficient y is used during non-body movement. This indicates that x is a correction factor for an increase in SV during body movement, that is, mainly due to muscle exercise (SV represents a single cardiac output, which is the amount of blood released per heartbeat). It can also be said that y is due to a difference indicating a correction coefficient for an increase in SV due to non-body movement, that is, mental activity or posture change. In other words, the calorie consumption per minute (EE) is proportional to the cardiac output (CO) (EE∝CO = SV × HR), but the increase in SV with respect to the increase in HR is different between body movement and non-body movement. Therefore, different coefficients of x and y are used for each.

Further, as described above, α is a correction of a resting heart rate during arousal during body movement. Here, the awake and standing still resting heart rate was confirmed experimentally that increased by about 1.2 times compared to the value HR ₀ basal heart rate. Therefore, α = 1.2 uses α = 1.2 as a normal value. On the other hand, β is a correction of the initial value before mental activity or posture change during non-body movement, but is normally considered to be close to rest. In this embodiment, β = 1.0 is used as a normal value. Shall.

Further, as described above, in order to obtain the calorie consumption from the above equations (4) and (5), it is necessary to determine the value of the minute calorie consumption EE ₀ corresponding to the base heart rate HR _0. is there. If the value per minute is obtained from the basal metabolic rate BM per day, the obtained value corresponds to EE _0, and the widely known Harris-Benedict formula may be used as a method for obtaining BM.

However, in this embodiment, EE ₀ may be obtained based on the minute cardiac output CO (CO is the product of the heart rate HR and the single cardiac output SV). Oxygen binds to hemoglobin in the lung, is transported by the heart, is released and used in tissues (brain and muscle), and the rest is excreted in the lung. VO ₂ and minute cardiac output CO are in a proportional relationship (VO ₂ ∝CO = HR × SV∝EE).

In the slow wave sleep phase (deep sleep) of the electroencephalogram, the applicant of the present invention has a heart rate of basal heart rate HR ₀ , and a cardiac coefficient CI (CI = CO / BSA) has discovered the principle that there are few individual differences. Then, by applying the principle, the following formula (6) for determining the cardiac output during sleep (CO ₀ ) as a value with little individual difference was created.
CO ₀ = 6.9 × Age− ^0.25 × BSA (6)

The above formula (6) and the standard blood hemoglobin concentration (male 15 g / dl, female 13.5 g / dl), arterial oxygen saturation (97.5%) and venous oxygen saturation (75%), The basal metabolic rate BM can be estimated from the oxygen consumption corresponding to CO ₀ so that the oxygen consumption can be estimated from the amount of oxygen bound to 1 g of hemoglobin (1.34 ml) and EE can be estimated from VO _2. Become.

Specifically, when each of the above values is used with the blood hemoglobin concentration as Hb, and the value for unit conversion is also calculated, the estimated basal metabolic rate BM is obtained by the following equation (7), and Hb By putting specific values, the male basal metabolism estimate BM _m and the female basal metabolism estimate BM _f are expressed by the following equation (8).
BM = CO ₀ x Hb x 1.34 x (0.975-0.75) x 10 x 60 x 24 x 5/1000 (7)
Male BM _m = 325.6 × CO ₀
Female BM _f = 293.0 × CO ₀ (8)

For subjects of various ages and genders, the basal metabolism determined by the conventional Harris-Benedict formula is taken on the vertical axis, and the estimated basal metabolism obtained by the above formulas (6) and (8) is taken on the horizontal axis. A diagram of the case is shown in FIG. In this case, the correlation coefficient is r = 0.96, and it can be said that the correlation is very high. That is, the basal metabolic rate (and the minute calorie consumption EE ₀ ) can be calculated with high accuracy even by the method using the above formulas (6) and (8) proposed by the present applicant.

  Next, a method for determining x in the above formula (4) and y in (5) will be described. Specifically, a given standard value may be used, or may be obtained from an actual measurement value when a constant load exercise is performed.

First, a method using standard values will be described. As shown in the above formula (3), x takes a value close to (EE _m / EE ₀ −1) / (HR _m / HR ₀ −1). It is known that EE _m is 20-70 years old, and statistically from age (Age), male takes 49-0.29 Age, female takes 41-0.33 Age, and HR _m is 220-Age. It is known to take a value. Further, EE ₀ can be obtained from the above-mentioned BM _m or BM _f , and HR can be obtained using an actual measurement value from a heartbeat sensor or the like.

When x was calculated using these values, the average was close to the value (about 5) of 4.8 ± 1.5 (standard deviation SD). Therefore, when x is unknown, a value of 5 is used as a standard value. Note that the value of y is a statistically calculated value obtained by measuring HR and VO ₂ during mental activity, and 1.5 is used as the standard value of y in this embodiment.

However, in the above x determination method, an approximate value of x is obtained by using statistical values for EE _m and HR _m that are difficult to measure. Therefore, the problem that it is difficult to deal with individual differences for each user remains as in the method of Patent Document 1 shown in the above formula (1) and the like.

Therefore, in this embodiment, the value of x may be obtained from an actual measurement value. Specifically, the following expression (9) is obtained by modifying the above expression (4).

HR ₀ and EE ₀ on the right side can be obtained by the above-described method, and it has been found that it is preferable to use 1.2 for α. Further, since the value of the heart rate information HR is also obtained from a heart rate sensor or the like, if the value of EE can be obtained, x can be determined from the actual measurement value. Here, in view of the purpose of the method of the present embodiment for determining the minute calorie consumption EE, it is necessary in advance to target any activity state (including body motion and non-body motion). The value of EE cannot be determined. However, if the exercise is known to be a predetermined exercise load, the calorie consumption EE due to the exercise can be calculated in advance. For example, when the stepping exercise of 2 steps for 1 second is continued for 3 minutes (exercise of about 3 Mets), it is known that the calorie consumption per minute EE satisfies the following formula (10).
EE = 3 × 1.05 × weight / 60 (10)

In this way, when an instruction to perform a predetermined exercise can be given to the user, all values of HR ₀ , EE ₀ , α, HR, and EE can be determined and acquired. (9) makes it possible to obtain x from the actual measurement value.

4.2 Calculation of Deep Sleep Time Information Lack of sleep (for example, when deep sleep time is 4 hours or less) is known to have a significant impact on the autonomic nerves of the following day and adverse health effects. In the evaluation, sleep time is an important index value. In particular, the time of a deep sleep state (or a brain wave slow wave sleep state) in a deeper sleep among sleep states is important as an index value representing the sleep state. For example, if the sleep time itself is long but the deep sleep time is short, there is an adverse effect on health, and this may lead to subjective symptoms such as being unable to get tired even though it should be sleeping.

  Therefore, in this embodiment, information about whether or not the user is in a deep sleep state (in a narrow sense, information on deep sleep time, which is a time in the deep sleep state in 24 hours) is also calculated as health degree information. And

As described above, the value of the heart rate information HR takes a value close to the value of the base heart rate HR ₀ in the deep sleep state. Therefore, it is only necessary to determine whether or not the user is in a deep sleep state by comparing HR and HR ₀ . However, since there are variations in the value of HR Even the deep sleep state, the value of HR is greater than HR ₀ is sufficiently considered. Therefore, the value used for comparison with HR is not HR ₀ itself, but has a certain margin and uses the value of HR ₀ × (sleep coefficient). That is, when the following formula (11) holds, it is determined that the user is in a deep sleep state, and the integrated value of the time when the following formula (11) is met in 24 hours is set as the deep sleep time. Here, although the sleep coefficient of the following formula (11) is different for each user, for example, a statistically calculated value such as 1.12 may be used.
HR ≦ HR ₀ × (sleep factor) (11)

4.3 Calculation of stress information Stress information representing the load applied to the user can also be used as an index value representing the degree of health. Here, stress information includes physical stress caused by physical activity during physical movement (physical stress) and psychological stress caused by mental activity during non-physical movement (mental stress). You could think so.

The degree of load due to physical stress and mental stress is greatly reflected in the value of the heart rate information HR. Here, it is known that heart rate increases during non-body movements mainly due to brain activity, so it is necessary to integrate the time when heart rate increases above a certain level during non-body movements. Can evaluate mental stress. As a guideline, a stress coefficient is provided, and when HR satisfies the following equation (12), it is determined that there is mental stress to the extent that the user should pay attention, and the integrated value of the time when the following equation (12) holds Is an index value for mental stress (mental stress information).
HR ≧ HR ₀ × (stress coefficient) (12)

  Here, since the value of the stress coefficient varies depending on the individual, it may be input from the outside. However, when the stress coefficient is unknown or when the operation burden on the user is reduced, the stress coefficient = 1.8 or the like may be used as a statistically obtained value.

  On the other hand, physical stress represents the load on the user during body movement, and the value can be obtained by mainly considering the increase in heart rate due to muscle activity. Specifically, similar to the above-described mental stress, the determination by the above equation (12) may be performed. However, in the case of physical stress, the difference is that the body motion is targeted.

  Various methods for determining whether the body is moving or not moving can be considered. For example, the processing may be performed based on sensor information from a body motion sensor. If the body motion sensor is an acceleration sensor, it can be determined that the body is moving when the acceleration detection value, which is sensor information from the sensor, is large, and if the acceleration detection value is smaller than the body motion, non-body motion is detected. It can be determined that it is time. Alternatively, instead of the magnitude of the acceleration detection value itself, the frequency characteristic of the acceleration detection value (e.g., corresponding to the pitch during walking or running exercise) may be obtained, and whether it is during body movement or non-body movement may be determined therefrom. . That is, the body motion sensor of the present embodiment is sufficient if it is a sensor that can determine whether it is body motion or non-body motion in the calculation of stress information, and an acceleration sensor may be used, or other sensors may be used. It may be used. A method for determining whether the body is moving or not based on the sensor information is also arbitrary.

  The stress information obtained in this way is preferable for mental stress as the accumulated time value is smaller, and for physical stress, the accumulated time is a reasonable value (large enough to prevent exercise shortage and not overloaded). It can be used as an index value for determining that a smaller value is preferable.

4.4 Display Control As described above, in the method of the present embodiment, the calorie consumption, deep sleep time, and stress information can be acquired as health information. The acquired health level information may not be easily understood by simply displaying the value of the health level information. Therefore, in the present embodiment, the obtained health degree information is presented in a form that is easy to understand at a glance for a user (or a user's attending physician, health advisor, or the like) using a technique such as graphing.

  Hereinafter, specific examples of the display screen will be described with reference to FIGS. 20 to 24, but the format of the display screen in the present embodiment is not limited to this. Further, in view of providing a certain amount of information at a time, it is assumed that the screen of FIG. 20 or the like is displayed on the mobile terminal device 300 or the like of FIG. 6, but the wearable device 200 has a sufficiently large display unit. If the display screen is simplified, the display screen for information presentation may be displayed on the display unit of the wearable device 200.

FIG. 20 shows an example of a home screen displayed when the wearable device 200 and the mobile terminal device 300 are connected. On the home screen, cover information such as personal information input mode such as age, gender, height, weight, ID, data file management, input / output management by communication, basal heart rate (HR ₀ ) setting and initial coefficient setting is displayed. . This will be specifically described below.

In the area D1 in FIG. 20, settings for the wearable device 200 are performed. Specifically, in a state where the wearable device 200 and the mobile terminal device 300 are connected, the information acquired by the wearable device 200 is captured by the mobile terminal device 300 by pressing the capture button D11. The information to be captured may be only information that is a result of calculation based on HR and HR ₀ , such as calorie consumption, deep sleep time, and stress information, or sensor information of the heart rate sensor 10 and the body motion sensor 20. All of the above may be targeted, and various modifications can be made.

  In D12, user information can be registered, a clock can be set, and the like. Detailed description of the function of D12 is omitted.

  D2 represents the file name under which the information captured by the capture button D11 is saved. Specifically, the latest data file may be displayed at D21, and the data file captured in the past may be displayed at D22.

  D3 is a button for displaying time change (HR trend) of heart rate information, analysis result based on the information, etc. in the acquired information (assuming that all HR values are acquired here). is there. An example of a display screen when these buttons are pressed will be described later.

D4 is a button for instructing to save and delete the data file. D5 is an area for performing advance preparation in the calculation of health information. Specifically, D51 is a button for calling a screen for setting a coefficient for obtaining health degree information. When D51 is pressed, the screen transitions to the screen of FIG. In FIG. 21, the value of HR _0, the coefficient used for calculating calorie consumption such as x, y, α, and β, the sleep coefficient used for calculating deep sleep time, and the stress coefficient used for calculating stress information Etc. can be set. As described above, x can be set from an actual measurement value. In this case, the user may press a x calculation button indicated by E1 to start a given exercise. It is also possible to set the load value of the exercise performed at the time of setting from the actual measurement value of x (in the above example, the stepping exercise of 2 steps for 1 second is continued for 3 minutes). Corresponds to (Mets). The acceleration coefficient is a value that represents a threshold value of an acceleration detection value for determining whether the body motion is in motion or non-body motion when an acceleration sensor is used as the body motion sensor. The value of the acceleration coefficient in FIG. 21 varies depending on the range of the acceleration sensor and the like, and the unit is not g or m / s ^{2 based on} the standard gravitational acceleration.

D52 is a button used when HR ₀ is set. As described above, HR ₀ is updated with the heart rate information, since such a case to re-set by the instruction of the user's explicit is considered, setting the HR ₀ if the button D52 is pressed Processing shall be performed.

FIG. 22 is an example of a screen displayed when the HR trend button D31 in FIG. 20 is pressed. Figure 22 is a diagram showing the time variation of the time change, and on the basis of HR and HR ₀ computed consumed calorie values of the heart rate information HR at 24 hours continuous. In FIG. 22, the graph indicated by F1 represents the time change of HR, and the graph indicated by F2 represents the time change of the calorie consumption. Even from FIG. 22, it is possible to know the user's biological information (lifestyle information) such as being in a sleep state from about 0:00 to about 6:30.

  However, it is desirable that the health degree information is presented to a certain extent in an easy-to-understand manner. In this embodiment, the display screens of FIGS. 23A and 23B are displayed in the area indicated by D6 in FIG. It is good also as a thing (FIG. 20 is an example which displayed the screen of FIG. 23 (B)).

  FIG. 23B is a graph that displays the daily calorie consumption, deep sleep time, and stress information together. The deep sleep time in FIG. 23B is a time when the user is in a deep sleep state, and ACT (−) is a time when the body is not moving and is not subjected to mental stress. MentalS represents the time during non-movement and in a state where mental stress is applied. Also, PhysicalS represents the time during physical movement and physical stress, and ACT (+) represents the time during physical movement and no physical stress. In addition, the calorie consumption per 24 hours is displayed at the center of the pie chart.

  By using FIG. 23B, it becomes possible to intuitively understand the ratio of the deep sleep time of 24 hours, the time when mental stress was applied, the time when physical stress was applied, and the like. Specifically, a healthy lifestyle has enough rest (sleep), moderate physical activity (physical stress), little mental stress (mental stress), and a balance between calorie intake and calorie consumption. Since it is considered that the user is taken, the user's health condition can be easily grasped by looking at FIG. 23B from such a viewpoint.

  However, the wearable device 200 and the like according to the present embodiment may be considered not to be worn continuously for 24 hours in consideration of charging of the device. In that case, since consideration is given to presenting the relative relationship of each time in an easy-to-see manner in FIG. 23B, even data that is less than 24 hours is converted into 24 hours and presented ( For example, when the wearing time is 12 hours, processing such as doubling the value of each time is performed). For this reason, it may be assumed that the actual time becomes difficult to understand.

  Therefore, a method may be used in which the actual time is displayed as it is as shown in FIG. In FIG. 23A, both the vertical axis and the horizontal axis are time (unit: hour), G1 is mental stress, G2 is not moving (and no mental stress is felt), G3 is moving ( G4 represents the actual time of physical stress.

  Further, the deep sleep time may be expressed by a triangular area in FIG. In this case, it is assumed that the deep sleep time is expressed not by the area of the triangular area but by the color or the like. For example, if the deep sleep time is sufficient (7 hours or more), the color may be green, if it is a little (4-7 hours) yellow, and if it is clearly insufficient (4 hours or less), the color may be red. .

  In addition, the graph display of FIG. 23A, FIG. 23B, and the like has an intuitive and easy-to-understand characteristic, but it is difficult to grasp an accurate value. Therefore, when the analysis button shown in A32 of FIG. 20 is pressed, the analysis screen shown in FIG. 24 may be displayed. On the analysis screen, for example, as shown in FIG. 24, the user's personal information and the parameters used for the calculation of the health level information are displayed, and specific values of the actually measured health level information are displayed. . At this time, a graph such as FIG. 23B may be displayed at the same time. By displaying the analysis screen of FIG. 24 and the like, it becomes possible to know a more accurate value.

  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. Further, the configuration and operation of the biological information processing system 100 and the like are not limited to those described in the present embodiment, and various modifications can be made.

10 heart rate sensors, 20 body motion sensors, 50 bands, 52 band holes,
54 buckle, 56 light emitting unit, 58 case, 100 biological information processing system,
110 heart rate information acquisition unit, 120 determination unit, 130 update unit,
140 body motion information acquisition unit, 150 health information calculation unit, 200 wearable device,
210 Notification unit, 220 Communication unit

Claims (15)

A heart rate information acquisition unit for acquiring the heart rate information of the user;
A determination unit that determines a base heart rate based on the heart rate information;
An update unit that performs an update process of the basal heart rate when it is determined that the update condition of the basal heart rate is satisfied and the update condition is satisfied;
A biological information processing system comprising: In claim 1,
The update unit
A biological information processing system, wherein the base heart rate is increased when the update condition is not satisfied for a predetermined period or a predetermined number of times. In claim 2,
The update unit
The biological information processing system, wherein the number of repetitions from the start of measurement of the heart rate information to the end of measurement is counted as the predetermined number of times. In claim 3,
It further includes a body motion information acquisition unit that acquires body motion information from the body motion sensor,
The update unit
The living body characterized in that the number of times that the user's body movement is determined to be small based on the body movement information in the period from the start of measurement to the end of measurement is counted as the predetermined number of repetitions. Information processing system. In any one of Claims 1 thru | or 4,
The update unit
A biological information processing system, wherein the update condition of the base heart rate is determined based on a minimum heart rate obtained from the heart rate information. In claim 5,
The update unit
A biological information processing system characterized in that a moving average process is performed on the heart rate information acquired during a given minimum heart rate measurement period to obtain the minimum heart rate. In claim 6,
The update unit
Based on the heart rate information acquired in the minimum heart rate measurement period, a histogram representing the relationship between the heart rate value and the frequency at which each heart rate value is detected is obtained, and the heart rate value is x Biological information characterized in that x having a frequency exceeding a given frequency threshold and having a minimum value is obtained as the minimum heart rate within a range of ˜x + n (x, n is a given positive number). Processing system. In any one of Claims 1 thru | or 7,
The determination unit
A process of replacing the default value of the basal heart rate set by the biological information processing system with the minimum heart rate obtained from the heart rate information,
The update unit
A biological information processing system that performs the update process for updating the basal heart rate determined by the determination unit. In claim 8,
It further includes a body motion information acquisition unit that acquires body motion information from the body motion sensor,
The determination unit
Based on the body motion information in a given minimum heart rate measurement period, when it is determined that the minimum heart rate measurement period includes a period in which the user's body motion is determined to be small, the minimum heart rate A biological information processing system, wherein the minimum heart rate obtained based on the heart rate information in a measurement period is the base heart rate. In any one of Claims 1 thru | or 8.
It further includes a body motion information acquisition unit that acquires body motion information from the body motion sensor,
The update unit
A biological information processing system that determines the update condition of the basal heart rate based on the body motion information. In claim 2,
The biological information processing system, wherein the predetermined number of times is the number of times that the update condition of the base heart rate is determined. In claim 1,
The update unit
A biological information processing system that increases the basal heart rate when a predetermined period has elapsed. In any one of Claims 1 to 12,
A biological information processing system, further comprising: a health degree information calculation unit that obtains relative information between the base heart rate and the heart rate information and obtains health degree information representing a health degree based on the relative information.   An electronic apparatus comprising the biological information processing system according to claim 1.   A server system comprising the biological information processing system according to claim 1.