Missing data complementing device, biological sensor, missing data complementing method, and program

Abstract

To provide a missing data complementing device capable of appropriately complementing missing of data, a biological sensor, a missing data complementing method, and a program.SOLUTION: A missing data complementing device includes: an acquisition part for acquiring time series measured data related to a plurality of pieces of biological information; and an estimation part which generates object biological information to be biological information including missing data in the measured data and time series data in reference biological information to be biological information other than the object biological information and estimates the missing data by simulation using the time series data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a missing data compensation device, a biosensor, a missing data compensation method, and a program. [Background technology]

[0002] In recent years, wristwatch-type biosensors capable of acquiring various types of bioinformation have become known. For example, some biosensors simply measure, calculate, and acquire the pulse rate, pulse wave, pulse interval, body temperature, number of steps, and calorie consumption data of a user wearing the biosensor in a time series as measurement data of bioinformation (for example, see Patent Document 1).

[0003] The biosensor includes, for example, a photoelectric sensor, an acceleration sensor, or other actual measurement sensor that measures changes in the user's biophysical state, an ultraviolet (UV) sensor, etc. In the biosensor, for example, a control unit converts an actual measurement value by the actual measurement sensor into measurement data of bioinformation, or performs various calculations on the actual measurement value to obtain measurement data of bioinformation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2016-131604 A Summary of the Invention [Problem to be solved by the invention]

[0005] In the conventional biosensor, for example, measurement data for about 5 to 7 days is accumulated. For this reason, since it is necessary to secure a storage capacity, for example, the frequency is set low for the volume pulse wave based on the information actually measured by the optical sensor. For this reason, the accuracy of measuring the volume pulse wave is low, and the measurement data of the heart rate calculated by the volume pulse wave often has missing values. Also, even in normal measurements, missing values ​​may occur in the measurement data due to body movement, etc. In particular, when exercising, missing values ​​often occur in the measurement data. If there are missing values ​​in the measurement data, it may be inconvenient to confirm using the measurement data. For example, pulse rate data is used to determine the user's autonomic nerves, but if the pulse rate data is missing, the accuracy of the determination of the autonomic nerves decreases.

[0006] The present invention has been made in consideration of the above-mentioned circumstances, and has an objective of providing a missing data completion device, a biosensor, a missing data completion method, and a program that can appropriately complete missing data. [Means for solving the problem]

[0007] [1] In order to solve the above problems, one aspect of the present invention is a missing data completion device comprising: an acquisition unit that acquires time-series measurement data relating to a plurality of pieces of biological information; and an estimation unit that estimates the missing data by a simulation using target measurement data, which is measurement data of target biological information, which is biological information including missing data in the measurement data, and reference measurement data, which is measurement data of reference biological information, which is biological information other than the target biological information.

[0008] [2] In one aspect of the present invention, the target biological information is an average heart rate, and the reference biological information includes at least one of number of steps, energy consumption, activity type, body movement type, sleep state, skin temperature, ultraviolet light level, or conversation time.

[0009] [3] In another aspect of the present invention, the estimation unit performs the simulation by utilizing a multiple regression analysis using the target measurement data and the reference measurement data.

[0010] [4] In another aspect of the present invention, the estimation unit performs the multiple regression analysis by a forced entry method.

[0011] [5] Also, one aspect of the present invention includes a measurement sensor that measures a biological change related to a biological change in a living body; an acquisition unit that acquires time-series measurement data related to a plurality of biological information derived based on the biological changes; and an estimation unit that generates time-series data of target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimates the missing data by a simulation using the time-series data. The biosensor includes a complementing unit that complements the missing data estimated by the estimation unit, and a providing unit that generates and provides provided data based on the measurement data from which the missing data has been complemented, wherein the target biometric information is an average heart rate, and the reference biometric information is a biosensor including at least one of the number of steps, energy consumption, activity type, body movement type, sleep state, skin temperature, ultraviolet light level, or conversation time.

[0012] [6] In order to solve the above problem, one aspect of the present invention is a missing data completion method, in which a computer acquires time-series measurement data for a plurality of pieces of biological information, generates time-series data for target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimates the missing data by a simulation using the time-series data.

[0013] [7] In order to solve the above problem, one aspect of the present invention is a program that causes a computer to acquire time-series measurement data for multiple pieces of biological information, generate time-series data for target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimate the missing data by simulation using the time-series data. Effect of the Invention

[0014] According to the present invention, it is possible to provide a missing data compensation device, a biosensor, a missing data compensation method, and a program that can appropriately compensate for missing data. [Brief description of the drawings]

[0015] [Figure 1] 1 is a diagram showing an example of a configuration of a biosensor 1 according to an embodiment. [Diagram 2] 4 is a flowchart showing an example of a process of the biosensor 1 of the embodiment. [Diagram 3] FIG. 13 shows HR data in verification 1 (during sleep). [Figure 4] FIG. 13 shows HR data in verification 2 (during sleep). [Diagram 5] FIG. 13 shows HR data in verification 3 (during sleep). [Figure 6] FIG. 13 shows HR data in verification 4 (during exercise (yoga)). [Figure 7] FIG. 13 shows HR data in verification 5 (walking). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] A missing data compensation device, a biosensor, a missing data compensation method, and a program according to an embodiment will be described with reference to FIGS. 1 to 7. FIG.

[0017] The biosensor of the embodiment is, for example, a so-called wristwatch-type biosensor (wearable sensor) that is worn on the wrist by the user. The biosensor of the embodiment measures, for example, various data according to the user's activity, and calculates measurement data of multiple pieces of bioinformation related to various health conditions of the user based on the measured data, such as average heart rate, number of steps, and energy consumption. The biosensor provides the calculated measurement data to the user by including it in the provided data. The biosensor may include, in addition to the currently calculated measurement data, measurement data calculated in the past and stored in memory in the provided data.

[0018] FIG. 1 is a diagram showing an example of the configuration of a biosensor 1 according to an embodiment. The biosensor 1 according to the embodiment includes, for example, an input / output unit 10, a measurement sensor 20, a control unit 30, and a memory 40. The memory 40 stores measurement data 50 and measurement data 60. The input / output unit 10 enables a user to perform an input operation and outputs information to the user. The measurement sensor 20 actually measures biochanges related to changes in a living organism. The biochanges include changes in the living organism itself, such as changes in pulse rate, movement, and skin temperature, as well as changes in the surrounding environment of the living organism, such as changes in the ultraviolet index.

[0019] The control unit 30 directly uses the actual measurement data measured by the actual measurement sensor 20 as the measurement data 50 or the measurement data 60, or estimates and complements missing data in the measurement data 60. The biosensor 1 of the embodiment includes a missing data generating device, and the control unit 30 functions as the missing data generating device.

[0020] The input / output unit 10 includes, for example, an input unit 11 and an output unit 12. The input unit 11 includes, for example, input devices such as a mouse and a keyboard. The input unit 11 may be configured as an interface that connects these input devices to the biosensor 1. The input unit 11 accepts input of various information to the biosensor 1. For example, when a user performs an input operation to request the provision of desired bioinformation, the input unit 11 generates request information according to the input operation and outputs it to the control unit 30. The request information is, for example, the progress of the user's pulse rate and the progress of the number of steps for the past week.

[0021] The output unit 12 includes, for example, an output device such as a display or a speaker. The output unit 12 may be configured as an interface that connects these output devices to the biosensor 1. The output unit 12 outputs various information provided by the control unit 30 to the user. The input / output unit 10 may include, for example, a touch panel equipped with the input unit 11 and the output unit 12.

[0022] The measurement sensor 20 measures biological changes related to changes in the user. The measurement sensor 20 includes, for example, an optical sensor 21, an acceleration sensor 22, a UV sensor 23, a temperature sensor 24, and a microphone 25. The measurement sensor 20 generates various types of measurement data based on the results of measurements by the optical sensor 21, the acceleration sensor 22, the UV sensor 23, the temperature sensor 24, and the microphone 25, and outputs the data to the control unit 30. The measurement sensor 20 may include other sensors, for example, a deep body thermometer that measures deep body temperature, a glucose sensor that detects glucose in interstitial fluid, a posture sensor that detects posture, and the like.

[0023] The optical sensor 21 measures the user's volume pulse wave by irradiating light such as infrared light onto the living body and measuring the light reflected from within the living body. The optical sensor 21 functions as a pulse sensor. The optical sensor 21 measures the user's volume pulse wave at intervals of, for example, 20 Hz (approximately 0.05 seconds) and generates data related to the user's pulse, for example, PPI (Peak to Peak Interval) data, as measured data. The optical sensor 21 may measure the user's volume pulse wave at intervals greater than or less than 20 Hz.

[0024] The acceleration sensor 22 measures the movement (acceleration) of the user wearing the biosensor 1. The acceleration sensor 22 is, for example, a three-axis sensor. The acceleration sensor 22 measures the user's acceleration in three-dimensional directions and generates data including the acceleration in the three-dimensional directions, for example, ACT (Activity) data, as measured data. The acceleration sensor 22 measures the user's acceleration in the three-dimensional directions at, for example, 20.5 Hz (approximately 0.448 seconds) and generates the ACT data. The acceleration sensor 22 may measure the user's acceleration in the three-dimensional directions at intervals greater than 20.5 Hz or less than 20.5 Hz.

[0025] The UV sensor 23 actually measures the ultraviolet rays in the surroundings. The UV sensor 23 generates data on the amount of ultraviolet rays (UV index) based on the measured amount of ultraviolet rays, for example, UV actual measurement data, as actual measurement data. The temperature sensor 24 actually measures the temperature of the part of the user where the biosensor 1 is worn. The temperature sensor 24 generates data on the user's skin temperature, for example, skin temperature actual measurement data, as actual measurement data, based on the temperature of the part of the user where the biosensor 1 is worn. The microphone 25 collects human voices. The microphone 25 generates data on the presence or absence of a conversation of the user, for example, conversation data indicating that a conversation has taken place, as actual measurement data, based on the state of collection of the human voice.

[0026] The control unit 30 includes, for example, an acquisition unit 31, a data management unit 32, an estimation unit 33, a complementation unit 34, and a provision unit 35. The control unit 30 realizes these functions by, for example, a hardware processor executing a program stored in a storage device such as a memory 40.

[0027] The hardware processor is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. Instead of storing the program in a storage device, the program may be directly built into the circuit of the hardware processor. In this case, the hardware processor realizes the function by reading and executing the program built into the circuit.

[0028] The hardware processor is not limited to being configured as a single circuit, but may be configured as a single hardware processor by combining multiple independent circuits to realize each function. Also, multiple components may be integrated into a single hardware processor to realize each function.

[0029] When an aggregated data generation condition is met, for example, when request information is output by the input unit 11, the control unit 30 aggregates the measurement data 60 stored in the memory 40 to generate aggregated data. The aggregated data is, for example, data obtained by aggregating each value (measurement value) stored in the memory 40 as the measurement data 60, for example, for about 30 days.

[0030] The control unit 30 generates provided data according to the request information based on the generated tabulated data. The control unit 30 outputs the generated provided data to the output unit 12 that output the request information. The output unit 12 displays the output provided data on a display.

[0031] The acquisition unit 31 acquires various types of actual measurement data output by the actual measurement sensor 20. The acquisition unit 31 acquires the acquired actual measurement data as part of the measurement data 50 or the measurement data 60, and stores it in the memory 40. The measurement data 50 stored in the memory 40 includes PPI data 51 and ACT data 52. Due to capacity constraints, the memory 40 stores, for example, the PPI data 51 for the past three days and the ACT data 52 for the past 11 hours, and sequentially deletes older data as time passes.

[0032] The acquisition unit 31 acquires measurement data 60 relating to the user's biometric information in time series based on the acquired actual measurement data. Depending on the type of measurement data 60, the acquisition unit 31 either uses the acquired actual measurement data as the measurement data 60 as it is, or calculates (estimates) the measurement data 60 based on the acquired actual measurement data, thereby acquiring the time series measurement data 60 relating to the biometric information. The acquisition unit 31 acquires the measurement data 60 in units of, for example, one minute. The acquisition unit 31 stores the acquired measurement data 60 in the memory 40. The memory 40 stores the measurement data 60 for the past 30 days, and sequentially deletes the older data. The time series measurement data 60 is an example of time series data.

[0033] The biological information includes, for example, data on average heart rate (HR), number of steps (STP), energy expenditure (EE), activity type (AT), body motion (BM), sleep state (SS), skin temperature (SK), ultraviolet ray index (UV index), and conversation time (CT).

[0034] The acquisition unit 31 acquires HR data 61 relating to HR, STP data 62 relating to STP, EE data 63 relating to EE, AT data 64 relating to AT, BM data 65 relating to BM, SS data 66 relating to SS, SK data 67 relating to SK, UV data 68 relating to UV, and CT data 69 relating to CT.

[0035] The HR data 61 is data in which the HR calculated based on the PPI data 51 output by the actual measurement sensor 20 is arranged in time series. The STP data 62 is data in which the number of steps of the user estimated based on the ACT data 52 output by the actual measurement sensor 20 is arranged in time series.

[0036] The EE data 63 is, for example, data in which EE estimated based on the ACT data 52 output by the actual measurement sensor 20 and pre-stored data such as weight are arranged in chronological order. The AT data 64, BM data 65, and SS data 66 are, for example, data in which AT, BM, and SS calculated based on the ACT data 52 output by the actual measurement sensor 20 are arranged in chronological order.

[0037] The SK data 67 is data in which the skin temperature measurement data output by the measurement sensor 20 is arranged in chronological order. The UV data 68 is data in which the UV measurement data output by the measurement sensor 20 is arranged in chronological order. The CT data 69 is data in which conversation duration estimated based on the conversation data output by the measurement sensor 20 is arranged in chronological order.

[0038] The data management unit 32 determines whether or not the aggregated data generation condition is satisfied. The aggregated data generation condition is satisfied, for example, when the input unit 11 outputs request information. The aggregated data generation condition may be another condition. The aggregated data generation condition may be satisfied, for example, every time a certain amount of time passes, or when a certain amount of actual measurement data, measurement data, and measurement data acquired by the acquisition unit 31 reaches a certain amount, or when a plurality of these conditions are satisfied.

[0039] When the aggregated data generation condition is satisfied, the data management unit 32 judges whether or not the HR data 61 within the time for generating the aggregated data includes missing data. The data management unit 32 provides the result of the judgment as to whether or not the HR data 61 includes missing data to the estimation unit 33 and the complementation unit 34.

[0040] Of the measurement data 60, the HR data 61 is data calculated based on the actual measurement value of the optical sensor 21, and is data that is relatively often missing depending on the conditions of the actual measurement by the optical sensor 21. The other measurement data 60 is data that is almost free of missing data. For this reason, the HR data 61 may be measurement data that includes missing data. On the other hand, the measurement data 60 of biological information other than the HR data 61 can often be measured as measurement data even when the HR data 61 is missing data, and the present invention uses them to complement the missing data of the HR data 61.

[0041] The average heart rate (HR) that is the subject of missing data complementation is an example of target biological information. Energy expenditure (EE), activity type (AT), body movement type (BM), sleep state (SS), skin temperature (SK), ultraviolet light level (UV), and conversation time (CT) are examples of reference biological information other than target biological information.

[0042] When the HR data 61 includes missing data, the estimation unit 33 performs a simulation by multiple regression analysis using the measurement data 60 including the missing data and the measurement data 60 not including the missing data to estimate the missing data. The measurement data 60 including the missing data includes the HR data 61 (hereinafter also referred to as the target measurement data). For the measurement data 60 not including the missing data, a simulation is performed using the STP data 62, EE data 63, AT data 64, BM data 65, SS data 66, SK data 67, UV data 68, and CT data 69 (hereinafter also referred to as the reference measurement data) to estimate the missing data.

[0043] The estimation unit 33 performs a simulation using, for example, multiple regression analysis using the target measurement data and the reference measurement data. The multiple regression analysis is performed, for example, by a forced input method in which all data, the target measurement data and the reference measurement data, are input. The estimation unit 33 may perform the multiple regression analysis by a method other than the forced input method, for example, a stepwise method, but when the amount of data (parameters) to be input is small, it is better to use the forced input method.

[0044] The estimation unit 33 performs a simulation in which the reference measurement data to be combined with the target measurement data is changed, and derives a model for calculating missing data (hereinafter, a missing data calculation model) using the reference measurement data that results in a combination with the lowest significance probability. The estimation unit 33 substitutes the reference measurement data at each time point into the derived missing data calculation model, thereby calculating an estimate of the target measurement data that has become missing data as estimated data.

[0045] The complementing unit 34 complements the estimated data calculated by the estimation unit 33 to the HR data 61, which is the target measurement data. The complementing unit 34 complements the missing data of the HR data 61 with the estimated data, thereby generating HR data 61 that has no missing data and is filled with data at each time.

[0046] The providing unit 35 generates provided data according to the requested information based on the measurement data 60 including the HR data 61 supplemented with the estimated data. For example, when the requested information is a change in the user's pulse rate for the past week, the providing unit 35 obtains the change in the pulse rate based on the HR data 61, outputs it to the output unit 12 as provided data, and provides it to the user by having the output unit 12 output it.

[0047] Next, the processing of the biosensor 1 of the embodiment will be described. Here, the processing in the control unit 30 of the biosensor 1 will be mainly described. Fig. 2 is a flowchart showing an example of the processing of the biosensor 1 of the embodiment. First, the control unit 30 in the biosensor 1 determines whether or not the acquisition unit 31 has acquired actual measurement data output by the input unit 11 (step S101).

[0048] If it is determined that the actual measurement data has been acquired, the acquisition unit 31 generates measurement data and measurement data using the acquired actual measurement data and stores the measurement data in the memory 40 (step S103). If it is determined that the actual measurement data has not been acquired, the acquisition unit 31 skips the process of step S103 and proceeds to the process of step S105.

[0049] Next, the data management unit 32 determines whether or not the tally data generation condition is satisfied when request information is output by the input unit 11, a certain period of time has elapsed, etc. (Step S105). If it is determined that the tally data generation condition is not satisfied, the biosensor 1 ends the process shown in FIG.

[0050] When it is determined that the aggregate data generation condition is satisfied, the data management unit 32 reads out the measurement data 60 for 30 days stored in the memory 40 from the memory 40 (step S107). Next, the data management unit 32 determines whether or not the HR data 61 of the read measurement data includes missing data (step S109).

[0051] When it is determined that the HR data 61 includes missing data, the estimation unit 33 performs a simulation using multiple regression analysis by a forced input method using the target measurement data (HR data 61) and the reference measurement data (STP data 62 to CT data 69) (step S111). The estimation unit 33 derives a missing data calculation model based on the result of the simulation (step S113).

[0052] When deriving the missing data calculation model, the estimation unit 33 selects 1 to 8 reference measurement data with different combinations from the eight reference measurement data, and performs multiple regression analysis using each combination of reference measurement data and the HR data 61. The estimation unit 33 derives the missing data calculation model using the combination of reference measurement data with the lowest significance probability as a result of the multiple regression analysis and the HR data 61.

[0053] Next, the estimation unit 33 substitutes the reference measurement data at the time when the missing data occurred into the derived missing data calculation model (step S115). The estimation unit 33 generates estimated data that is estimated as the missing data by substituting the reference measurement data into the missing data calculation model (step S117).

[0054] Next, the complementing unit 34 complements the estimated data generated by the estimation unit 33 as data at the time when the missing data occurred in the HR data 61, and generates HR data 61 without the missing data (step S119). Also, if it is determined in step S109 that the HR data 61 does not include missing data, the data management unit 32 advances the process to step S121.

[0055] Next, the providing unit 35 generates provision data according to the request information using the measurement data without missing data (step S121). The providing unit 35 outputs the generated provision data to the output unit 12 and causes the output unit 12 to output the data, thereby providing the data to the user (step S123). In this way, the biosensor 1 ends the process shown in FIG. 2.

[0056] When target measurement data including missing data occurs, the biosensor 1 of the embodiment estimates the missing data by performing a simulation using the target measurement data and the reference measurement data. Therefore, the complementary data for complementing the missing data can be made highly accurate, and the missing data can be appropriately complemented.

[0057] In addition, the biosensor 1 of the embodiment uses multiple regression analysis when performing a simulation, so the accuracy of the complementary data can be improved. In addition, the biosensor 1 of the embodiment performs multiple regression analysis by a forced input method, so the accuracy of the complementary data can be improved. Since the measurement data 60 used in the multiple regression analysis is only the data stored in the memory 40, the forced input method can prevent the calculation load from becoming too large.

[0058] Next, we will explain the results of a verification performed on a simulation to estimate missing data. The verification was performed by collecting measurement data 60 for three patterns of user behavior, "sleeping," "exercise (yoga)," and "walking," generating a time series of measurement data 60 in which HR data 61 was missing, and simulating the missing values.

[0059] In the verification, a missing data calculation model was derived by multiple regression analysis using data with reference measurement data for 5 minutes before and after the time when the missing data occurred as parameters as measurement data for estimating the missing data. The multiple regression analysis was performed using the forced entry method, and the missing data was estimated using the derived missing data calculation model.

[0060] (Verification 1: Sleeping, missing data = 1) First, as the measurement data during the user's sleep, the measurement data of each reference biological information of HR, STP, EE, AT, BM, SS, SK, UV, and CT was acquired at 1-minute intervals for a total of 11 minutes (t1 to t11). In verification 1, the missing data was set as 1, and one of the HR data, here the HR data at t=6, was missing. FIG. 3 is a diagram showing the HR data in verification 1 (during sleep). FIG. 3(a) is a time series display of the acquired HR data, FIG. 3(b) is a time series display with the missing time=t6, and FIG. 3(c) is a time series display with the missing data complemented. The missing time is the time when the measurement data was missing. The time series display of each measurement data of STP, EE, AT, BM, SS, SK, UV, and CT is omitted.

[0061] In verification 1, the reference biological information was changed and a missing data calculation model was generated by multiple regression analysis using the forced entry method, and the correlation coefficient (=R) of each missing data calculation model was compared. As a result, the missing data calculation model generated with the three reference biological information of SK, AT, and BM had the highest correlation coefficient. The missing data calculation model generated using the measurement data of the three reference biological information of SK, AT, and BM is shown in the following formula (1). In addition, the summary, coefficient, and results of the analysis of variance of the missing data calculation model shown in formula (1) are shown in Table 1. Missing HR(t) = 0.72*AT(t) + 0.804*BM(t) - 0.819*SK(t) + 86.811…(1) [Table 1]

[0062] In the above formula (1), t=6 is substituted to calculate the complementary data, and the calculated complementary data is complemented to the missing time in Fig. 3(b) to generate the time-series measurement data shown in Fig. 3(c). The measurement data at t=6 shown in Fig. 3(c) is close to the measurement data at t=6 shown in Fig. 3(a).

[0063] (Verification 2: During sleep, missing data = 2) In verification 2, measurement data during sleep was acquired in the same way as in verification 1. Measurement data was acquired for a total of 12 minutes (t1 to t12) at 1-minute intervals. In addition, two pieces of HR data, here HR data at t=6 and 7, were missing, with two pieces of missing data. Figure 4 shows HR data in verification 2 (during sleep). Figure 4(a) shows a time series display of the acquired HR data, (b) shows a time series display with missing times t=6 and 7, and (c) shows a time series display with the missing data complemented.

[0064] In verification 2, a missing data calculation model was generated by multiple regression analysis using the forced entry method as in verification 1, and the correlation coefficients were compared. As a result, the missing data calculation model generated using the three reference biological information items SK, AT, and BM had the highest correlation coefficient. The missing data calculation model generated using the three items SK, AT, and BM is shown in (2) below. In addition, the summary, coefficients, and results of analysis of variance for the missing data calculation model shown in (2) are shown in Table 2. Missing HR(t) = -1.691*AT(t) - 0.529*BM(t) - 1.164*SK(t) + 103.971…(2) [Table 2]

[0065] In the above formula (2), t=6,7 is substituted to calculate the complementary data, and the calculated complementary data is complemented to the missing time in Fig. 4(b) to generate the time series measurement data shown in Fig. 4(c). The t=6,7 measurement data shown in Fig. 4(c) is close to the t=6,7 measurement data shown in Fig. 4(a).

[0066] (Verification 3: During sleep, missing data = 3) In verification 3, measurement data during sleep was acquired in the same way as in verification 1. Measurement data was acquired for a total of 13 minutes (t1 to t13) at 1-minute intervals. In addition, three pieces of HR data were missing, here HR data at t=6, 7, and 8, with three pieces of missing data. Figure 5 shows HR data in verification 3 (during sleep). Figure 5(a) shows a time series display of the acquired HR data, (b) shows a time series display with missing times=t6, 7, and 8, and (c) shows a time series display with missing data complemented.

[0067] In verification 3, a missing data calculation model was generated by multiple regression analysis using the forced entry method as in verification 1, and the correlation coefficients were compared. As a result, the missing data calculation model generated with SK and BM as the two reference biological information had the highest correlation coefficient. The missing data calculation model generated with SK and BM as the two reference biological information is shown in the following formula (3). In addition, the summary, coefficients, and results of analysis of variance of the missing data calculation model shown in formula (3) are shown in Table 3. Missing HR(t)=-2.863*BM(t)+38.128*SK(t)-1256.157…(3) [Table 3]

[0068] In the above formula (3), t=6, 7, 8 is substituted to calculate the complementary data, and the calculated complementary data is complemented to the missing time in Fig. 5(b) to generate the time series measurement data shown in Fig. 5(c). The t=6, 7, 8 measurement data shown in Fig. 5(c) is close to the measurement data for t=5, 6, 7 shown in Fig. 5(a).

[0069] (Verification 4: During exercise (yoga), missing data = 1) In verification 4, measurement data was acquired while the user was exercising (yoga). Measurement data was acquired for a total of 11 minutes (t1 to t11) at 1-minute intervals. In addition, one piece of HR data was missing, in this case the HR data at t=6, with the missing data being set as 1. FIG. 6 is a diagram showing HR data in verification 4 (during exercise (yoga)). FIG. 6(a) is a time series display of the acquired HR data, (b) is a time series display with the missing time t=6, and (c) is a time series display with the missing data complemented.

[0070] In verification 4, a missing data calculation model was generated by multiple regression analysis using the forced entry method as in verification 1, and the correlation coefficients were compared. As a result, the missing data calculation model generated with SK and EE as the two reference biological information had the highest correlation coefficient. The missing data calculation model generated with SK and EE as the two reference biological information is shown in the following formula (4). In addition, the summary, coefficients, and results of the analysis of variance of the missing data calculation model shown in formula (4) are shown in Table 4. Missing HR(t) = -4.274*EE(t) + 5.535*SK(t) - 98.082 ... (4) [Table 4]

[0071] In the above formula (4), t=6 is substituted to calculate the complementary data, and the calculated complementary data is complementary to the missing time in Fig. 6(b), generating the time-series measurement data shown in Fig. 6(c). The t=6 measurement data shown in Fig. 6(c) is close to the t=6 measurement data shown in Fig. 6(a).

[0072] (Verification 5: Walking, missing data = 1) In verification 5, measurement data was acquired when the user was active while walking. Measurement data was acquired for a total of 11 minutes (t1 to t11) at 1-minute intervals. In addition, one piece of HR data, the HR data at t=6 in this case, was missing, with the missing data being set as 1. FIG. 7 (walking) is a diagram showing HR data in verification 5. FIG. 7(a) is a time series display of the acquired HR data, (b) is a time series display with the missing time t=6, and (c) is a time series display with the missing data complemented.

[0073] In verification 5, a missing data calculation model was generated by multiple regression analysis using the forced entry method as in verification 1, and the correlation coefficients were compared. As a result, the missing data calculation model generated with the reference biological information set as four items, SK, AT, STP, and EE, had the highest correlation coefficient. The missing data calculation model generated with the four items, SK, AT, STP, and EE, is shown in the following formula (5). In addition, the summary, coefficients, and results of analysis of variance of the missing data calculation model shown in formula (5) are shown in Table 5. Missing HR(t)=-0.228*STP(t)+11.187*EE(t)+21.273*AT(t)-30.333*SK(t)+945.114…(5) [Table 5]

[0074] In the above formula (5), t=6 is substituted to calculate the complementary data, and the calculated complementary data is complementary to the missing time in Fig. 7(b), generating the time-series measurement data shown in Fig. 7(c). The t=6 measurement data shown in Fig. 7(c) is close to the t=6 measurement data shown in Fig. 7(a).

[0075] In the above embodiment, the biosensor 1 generates a missing data calculation model based on the measurement data, but the missing data calculation model may be generated including the actual measurement data of the measurement data. Also, in the above embodiment, the providing unit 35 generates the provided data including the measurement data 60, but the providing unit 35 may generate the provided data including the measurement data 50 such as the PPI data 51 and the ACT data 52 in addition to the measurement data 60. The user may be able to perform the input operation of the input unit 11 by including the provision of the measurement data in the request information.

[0076] In the above embodiment, the missing data generation device is provided as the control unit 30 in the biosensor 1, but the missing data generation device may be provided in an external device outside the biosensor 1. In this case, the missing data generation device may be provided in a server or the like as an external device that can be connected to the biosensor 1 by wire or wirelessly via a network.

[0077] In addition, in the above embodiment, measurement data is generated at one-minute intervals, but measurement data may also be generated at intervals less than one minute, for example, at 30-second intervals, or more than one minute, for example, at five-minute intervals.

[0078] The embodiments of the present invention have been described above with reference to the drawings. However, the missing data completion device, the biosensor, the missing data completion method, and the program are not limited to the above-mentioned embodiments, and various modifications, substitutions, combinations, and / or design changes can be made without departing from the spirit of the present invention.

[0079] The effects of the above-described embodiments of the present invention are merely examples. Therefore, the embodiments of the present invention may have other effects that a person skilled in the art can recognize from the description of the above-described embodiments in addition to the above-described effects. [Explanation of symbols]

[0080] 1 Biometric sensor, 10...input / output section, 11...input section, 12...output section, 20...actual measurement sensor, 21...light sensor, 22...acceleration sensor, 23...UV sensor, 24...temperature sensor, 25...microphone, 30...control section, 31...acquisition section, 32...data management section, 33...estimation section, 34...complementation section, 35...provision section, 40...memory, 50...measurement data, 51...PPI data, 52...ACT data, 60...measurement data, 61...HR data, 62...STP data, 63...EE data, 64...AT data, 65...BM data, 66...SS data, 67...SK data, 68...UV data, 69...CT data

Claims

1. an acquisition unit that acquires time-series measurement data relating to a plurality of pieces of biological information; and an estimation unit that estimates the missing data by a simulation using target measurement data, which is measurement data of target biological information, which is biological information including missing data in the measurement data, and reference measurement data, which is measurement data of reference biological information, which is biological information other than the target biological information. A device for filling in missing data.

2. The target biological information is an average heart rate, The reference biological information includes at least one of the number of steps, energy consumption, activity type, body movement type, sleep state, skin temperature, ultraviolet light level, and conversation time.

2. The missing data repair device according to claim 1.

3. the estimation unit executes the simulation by utilizing a multiple regression analysis using the target measurement data and the reference measurement data.

3. The missing data repair device according to claim 2.

4. The estimation unit performs the multiple regression analysis by a forced input method.

4. The missing data repair device according to claim 3.

5. A measurement sensor that measures a biological change related to a biological change; an acquisition unit that acquires time-series measurement data relating to a plurality of pieces of biological information derived based on the biological changes; an estimation unit that generates time series data of target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimates the missing data by a simulation using the time series data; a completion unit that completes the missing data estimated by the estimation unit; a providing unit that generates and provides provision data based on the measurement data from which the missing data has been complemented; The target biological information is an average heart rate, The reference biological information includes at least one of the number of steps, energy consumption, activity type, body movement type, sleep state, skin temperature, ultraviolet light level, and conversation time. Biometric sensors.

6. The computer Acquire time-series measurement data relating to multiple pieces of biological information; generating time series data of target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimating the missing data by a simulation using the time series data; Missing data imputation methods.

7. On the computer, Acquire time-series measurement data relating to multiple pieces of biological information; generating time series data of target biological information, which is biological information including missing data in the measurement data, and reference biological information, which is biological information other than the target biological information, and estimating the missing data by a simulation using the time series data; program.

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