Analysis system and analysis method

Abstract

The analysis system of the present invention is an analysis system for analyzing body information, comprising: a biological data acquisition unit for acquiring the biological data of a user; a circadian rhythm calculation unit for calculating the average circadian rhythm of the user; an autonomic nerve analysis unit for performing autonomic nerve analysis based on changes in the biological data; a weighting coefficient calculation unit for calculating a weighting coefficient for weighting the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit based on the measurement time of the user's biological data and the cycle of the average circadian rhythm; and a body information analysis unit for weighting the autonomic nerve analysis result using the weighting coefficient calculated by the weighting coefficient calculation unit, and estimating changes in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result.

Description

Technical Field

[0001] The present invention relates to an analysis system and an analysis method for analyzing body information. Background Art

[0002] Patent Document 1 discloses a sleepiness prediction device that takes into account daytime activities, time of day, and sleep status. The sleepiness prediction device described in Patent Document 1 measures a sleep state correlation value associated with a subject's sleep state and a daytime activity correlation value associated with the subject's daytime activities. Based on the sleep state correlation value and the daytime activity correlation value, the sleepiness prediction device described in Patent Document 1 calculates a cumulative sleepiness level predicted to have accumulated due to the subject's sleep history and daytime activities, and also calculates a biorhythm sleepiness level based on a biorhythm that varies with the time of day. Based on the cumulative sleepiness level and the biorhythm sleepiness level, the sleepiness prediction device described in Patent Document 1 calculates a total sleepiness level corresponding to the time of day.

[0003] Patent Document 1: Japanese Patent No. 4421507.

[0004] In recent years, there has been a demand for an analysis system and an analysis method capable of analyzing body information. Summary of the Invention

[0005] An analysis system according to one embodiment of the present invention is a system for analyzing biological information, comprising:

[0006] A biometric data acquisition unit that acquires a user's biometric data;

[0007] a circadian rhythm calculation unit for calculating the average circadian rhythm of the user;

[0008] an autonomic nerve analysis unit that performs autonomic nerve analysis based on the changes in the biological data;

[0009] a weighting coefficient calculation unit that calculates a weighting coefficient for weighting the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit based on a measurement time when the biological data of the user is measured and a cycle of the average circadian rhythm; and

[0010] The biological information analysis unit weights the autonomic nerve analysis result using the weighting coefficient calculated by the weighting coefficient calculation unit, and estimates a change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result.

[0011] An analysis method according to one embodiment of the present invention is a method for analyzing biological information using a computer, comprising:

[0012] The step of obtaining the user's biometric data;

[0013] a step of performing autonomic nervous system analysis based on changes in the user's biological data;

[0014] The step of calculating the average circadian rhythm of the user;

[0015] a step of calculating a weighting coefficient for weighting an autonomic nervous system analysis result based on a measurement time of the user's biological data and a cycle of the average circadian rhythm;

[0016] The step of weighting the autonomic nerve analysis result using the calculated weighting coefficient; and

[0017] The step of estimating changes in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nervous system analysis results.

[0018] According to the present invention, physical information can be analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a block diagram showing a schematic configuration of an example of an analysis system according to Embodiment 1 of the present invention.

[0020] Figure 2 This is a block diagram showing a schematic configuration of a measuring device in the analysis system according to the first embodiment of the present invention.

[0021] Figure 3 This is a diagram showing an example of an average circadian rhythm.

[0022] Figure 4 This is a diagram showing a flowchart of an example of calculation of the average circadian rhythm in the analysis system according to the first embodiment of the present invention.

[0023] Figure 5 This is a diagram showing a flowchart of an example of a method for analyzing physical information according to the first embodiment of the present invention.

[0024] Figure 6 This is a diagram showing an example of the relationship between the average circadian rhythm period and the weighting coefficient.

[0025] Figure 7 This is a diagram showing an example of the correlation between the autonomic nervous system analysis results, circadian rhythm changes, and the proportion of light sleep.

[0026] Figure 8 This is a diagram showing an example of determination of the light sleep ratio based on the autonomic nervous system analysis results and circadian rhythm changes.

[0027] Figure 9 This is a diagram showing an example of the correlation between the autonomic nerve analysis results and the time deviation of the circadian rhythm.

[0028] Figure 10 This is a diagram showing an example of determination of a time deviation between an autonomic nervous system analysis result and a circadian rhythm.

[0029] Figure 11 This is a diagram showing an example of output displayed by the analysis system according to the first embodiment of the present invention.

[0030] Figure 12 This is a block diagram showing a schematic configuration of an example of an analysis system according to a second embodiment of the present invention.

[0031] Figure 13 This is a block diagram showing a schematic configuration of an example of an analysis system according to a third embodiment of the present invention.

[0032] Figure 14 This is a schematic diagram of an example of a handheld measuring device.

[0033] Figure 15 This is a schematic diagram of an example of a neck-worn type measuring device.

[0034] Figure 16 This is a schematic diagram of an example of a wristwatch-type measuring device.

[0035] Figure 17 This is a schematic diagram of an example of a chest-adhesive measuring device. DETAILED DESCRIPTION

[0036] (Process of Completion of the Invention)

[0037] In recent years, analyzing circadian rhythms has been used as a method for analyzing users' biological information. Circadian rhythms are 24-hour rhythms that occur in living organisms, and include, for example, daily fluctuations in blood pressure, body temperature, heart rate, and hormone secretions. Circadian rhythms are believed to be related to the autonomic nervous system and sleep.

[0038] Patent Document 1 discloses a sleepiness prediction device that takes into account daytime activities, time of day, and sleep status. The sleepiness prediction device described in Patent Document 1 measures sleep state correlation values and daytime activity correlation values to calculate a cumulative sleepiness level. However, the sleepiness prediction device described in Patent Document 1 requires a large amount of data from the user, placing a burden on the user.

[0039] The sleepiness prediction device described in Patent Document 1 is a device for calculating sleepiness levels, and does not disclose analysis of circadian rhythms or improvement of sleep quality by adjusting circadian rhythms.

[0040] Therefore, the present inventors conducted intensive studies to solve these problems and, as a result, completed the following invention.

[0041] An analysis system according to one embodiment of the present invention is a system for analyzing biological information, comprising:

[0042] A biometric data acquisition unit that acquires a user's biometric data;

[0043] a circadian rhythm calculation unit for calculating the average circadian rhythm of the user;

[0044] an autonomic nerve analysis unit that performs autonomic nerve analysis based on the changes in the biological data;

[0045] a weighting coefficient calculation unit that calculates a weighting coefficient for weighting the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit based on a measurement time when the biological data of the user is measured and a cycle of the average circadian rhythm; and

[0046] The biological information analysis unit weights the autonomic nerve analysis result using the weighting coefficient calculated by the weighting coefficient calculation unit, and estimates a change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result.

[0047] According to such a configuration, changes in circadian rhythm can be estimated and analyzed as one of the biological information.

[0048] In the above analysis system, the biological data may include at least a heart rate or a pulse rate.

[0049] With such a configuration, biological information can be easily analyzed based on the heart rate or pulse rate.

[0050] In the analysis system, the circadian rhythm calculation unit may calculate the average circadian rhythm based on the biological data acquired by the biological data acquisition unit.

[0051] According to such a configuration, the average circadian rhythm can be calculated more accurately based on biological data.

[0052] The analysis system further includes an input unit for inputting the sleep information of the user.

[0053] The circadian rhythm calculation unit may calculate the average circadian rhythm based on the sleep information input through the input unit.

[0054] With such a configuration, the average circadian rhythm can be easily calculated based on the sleep information, and the body information can be easily analyzed.

[0055] The weighting coefficient calculation unit may also increase the weighting coefficient when the measurement time is within a range of -1 / 8 to 3 / 8 of the cycle of the maximum peak value of the average circadian rhythm, as compared to when the measurement time is outside a range of -1 / 8 to 3 / 8 of the cycle of the maximum peak value of the average circadian rhythm.

[0056] According to such a configuration, the estimation accuracy of the circadian rhythm can be improved, and the biological information can be analyzed more accurately.

[0057] The biological information analysis unit may correct the weighted autonomic nervous system analysis result when the heart rate or pulse rate of the user is greater than a predetermined threshold.

[0058] According to such a configuration, the estimation accuracy of the circadian rhythm can be improved, and the biological information can be analyzed more accurately.

[0059] The above-mentioned biological information analysis unit can also estimate the change of the circadian rhythm based on at least one of the deviation of the time of the maximum peak of the circadian rhythm relative to the above-mentioned average circadian rhythm, the deviation of the time of the minimum peak relative to the above-mentioned average circadian rhythm, the reduction of the amplitude and the multi-peaking.

[0060] With such a configuration, the user's physical information can be analyzed in more detail.

[0061] The body information analysis unit may further estimate the sleep quality and activity suitability of the user based on the weighted autonomic nerve analysis result.

[0062] With such a configuration, the user's physical information can be analyzed in more detail.

[0063] The analysis system further includes a prompting unit configured to provide prompt information, wherein the prompt information includes suggestions for improving the circadian rhythm.

[0064] The biological information analysis unit may create the prompt information based on changes in the circadian rhythm.

[0065] According to such a configuration, it is possible to present suggestions for improving the circadian rhythm to the user.

[0066] The body information analysis unit may also calculate and predict the circadian rhythm based on the weighted autonomic nerve analysis result.

[0067] The prompt information includes the average circadian rhythm and the predicted circadian rhythm.

[0068] According to such a configuration, information on changes in circadian rhythm can be presented to the user.

[0069] The analysis system may further include a notification unit configured to notify a timing at which the vital data is measured.

[0070] According to such a configuration, biological data can be acquired at appropriate timing, and the estimation accuracy of the circadian rhythm can be improved.

[0071] The biological data acquisition unit may be built into a patch-type measurement device or a wearable measurement device.

[0072] According to such a configuration, the measurement device can be easily attached to the user, and biological data can be easily acquired.

[0073] The measurement device may include a temperature adjustment unit, which is a device attached to or worn on the neck of the user and adjusts the temperature of the user's neck.

[0074] According to such a configuration, the disturbance of the circadian rhythm can be suppressed.

[0075] The analysis system may further include an activity measurement unit configured to measure activity data of the user.

[0076] The body information analysis unit corrects the weighted autonomic nerve analysis result based on the activity data measured by the activity measurement unit.

[0077] According to such a configuration, the circadian rhythm can be estimated based on the highly reliable autonomic nervous system analysis result, and thus the estimation accuracy of the circadian rhythm can be improved.

[0078] An analysis system according to one embodiment of the present invention is a system for analyzing biological information, comprising:

[0079] one or more assay devices;

[0080] One or more control terminals in communication with the one or more measurement devices; and

[0081] The server communicates with one or more control terminals.

[0082] The one or more assay devices have:

[0083] a biometric data acquisition unit that acquires a user's biometric data; and

[0084] The first communication unit transmits the biometric data acquired by the biometric data acquisition unit to the one or more control terminals.

[0085] The one or more control terminals have:

[0086] a prompting unit for providing prompting information for improving circadian rhythm; and

[0087] The second communication unit transmits the biometric data to the server and receives the prompt information from the server.

[0088] The above server has:

[0089] a circadian rhythm calculation unit for calculating the average circadian rhythm of the user;

[0090] an autonomic nerve analysis unit that performs autonomic nerve analysis based on a change in the user's biological data in the biological data;

[0091] a weighting coefficient calculation unit that calculates a weighting coefficient for weighting the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit based on a measurement time at which the biological data of the user is measured and a cycle of the average circadian rhythm;

[0092] a body information analysis unit that weights the autonomic nervous system analysis result using the weighting coefficient calculated by the weighting coefficient calculation unit, estimates a change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nervous system analysis result, and creates the prompt information based on the change in the circadian rhythm; and

[0093] The third communication unit receives the biological data from the control terminal and transmits the prompt information to the control terminal.

[0094] According to such a configuration, changes in circadian rhythm can be estimated and analyzed as one of the biological information.

[0095] An analysis method according to one embodiment of the present invention is a method for analyzing biological information using a computer, comprising:

[0096] The step of obtaining the user's biometric data;

[0097] a step of performing autonomic nervous system analysis based on changes in the user's biological data;

[0098] The step of calculating the average circadian rhythm of the user;

[0099] a step of calculating a weighting coefficient for weighting an autonomic nervous system analysis result based on a measurement time of the user's biological data and a cycle of the average circadian rhythm;

[0100] The step of weighting the autonomic nerve analysis result using the calculated weighting coefficient; and

[0101] The step of estimating changes in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nervous system analysis results.

[0102] According to such a configuration, changes in circadian rhythm can be estimated and analyzed as one of the biological information.

[0103] An embodiment of the present invention is described below with reference to the accompanying drawings. The following description is illustrative in nature and is not intended to limit the present disclosure, its applications, or its uses. Furthermore, the accompanying drawings are schematic, and the proportions of various dimensions may not necessarily correspond to actual proportions.

[0104] (Implementation Method 1)

[0105] [Overall structure]

[0106] Figure 1 1 is a block diagram showing a schematic configuration of an example of an analysis system 1A according to the first embodiment of the present invention. Figure 1 As shown, the analysis system 1A includes a measurement device 10, a control terminal 20, and a server 30. The analysis system 1A is a system for analyzing the user's physical information. In the first embodiment, the analysis system 1A estimates and analyzes changes in the user's circadian rhythm as physical information.

[0107] <Measurement device>

[0108] The measurement device 10 is a device that measures the biological data of the user. Figure 2 1 is a block diagram showing a schematic configuration of a measuring device 10 in an analysis system 1A according to Embodiment 1 of the present invention. Figure 1 as well as Figure 2 As shown, the measurement device 10 includes a biological data acquisition unit 11 , a first control unit 12 , and a first communication unit 13 .

[0109] The biometric data acquisition unit 11 acquires the user's biometric data. Biometric data includes, for example, daily fluctuations in at least one of the following vital signs: body temperature, heart rate, pulse rate, respiration, brain waves, and blood pressure. In Embodiment 1, the biometric data acquisition unit 11 acquires biometric data including at least heart rate. Alternatively, the biometric data acquisition unit 11 may acquire biometric data including pulse rate instead of heart rate. Heart rate and pulse rate are easy to measure in biometric data, and provide high accuracy in analyzing physical information.

[0110] In Embodiment 1, the biometric data acquisition unit 11 acquires biometric data of the user multiple times while the user is awake. For example, the biometric data acquisition unit 11 acquires biometric data five or more times per day.

[0111] As a condition for measuring biometric data, it is preferred that the user be seated and at rest. Resting refers to a state of being physically inactive and still. Measuring biometric data while the user is at rest improves the accuracy of circadian rhythm estimation based on biometric data, as described later. Furthermore, it is preferred to obtain biometric data while avoiding exercise (including walking), eating, and bathing.

[0112] like Figure 2 As shown, the biological data acquisition unit 11 includes a heart rate measurement unit 14 and a body temperature measurement unit 15. The heart rate measurement unit 14 is a heart rate sensor that measures the user's heart rate. An electrocardiogram sensor or a ballistocardiogram sensor can be used as the heart rate sensor. The body temperature measurement unit 15 is a body temperature sensor that measures the user's body temperature. A chip thermistor or a temperature-measuring resistor can be used as the body temperature sensor.

[0113] Alternatively, it is possible to estimate heart rate during sleep using a mounted body motion sensor. For example, a measurement device 10 equipped with a sheet-type body motion sensor can be placed under a mattress. The control terminal 20 can receive wirelessly output body motion data and transmit it to a server 30. The server 30 can then perform autonomic nervous system analysis on the heart rate.

[0114] Furthermore, when measuring the pulse rate, the biological data acquisition unit 11 may include a pulse sensor. As the pulse rate sensor, a photoelectric pulse sensor, a piezoelectric pulse sensor, or an oxygen saturation sensor can be used.

[0115] Furthermore, the biological data acquisition unit 11 acquires time data when the biological data is acquired. The time data includes the measurement time when the biological data is measured.

[0116] The biological data and time data acquired by the biological data acquisition unit 11 are sent to the first control unit 12 .

[0117] The first control unit 12 comprehensively controls the components of the measurement device 10. The first control unit 12 includes, for example, a memory storing programs and a processing circuit (not shown) corresponding to a processor such as a CPU (Central Processing Unit). In the first control unit 12, the processor executes the program stored in the memory. In the first embodiment, the first control unit 12 controls the biological data acquisition unit 11 and the first communication unit 13.

[0118] The first control unit 12 stores the biological data and time data from the biological data acquisition unit 11 in a memory, and transmits the data to the first communication unit 13 .

[0119] The first communication unit 13 transmits the biological data and time data to the control terminal 20. For example, the first communication unit 13 includes a circuit for communicating with the control terminal 20 in accordance with a predetermined communication standard (e.g., LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), CAN (controller area network), SPI (serial peripheral interface)).

[0120] <Control terminal>

[0121] The control terminal 20 communicates with the measurement device 10 and the server 30. In Embodiment 1, the control terminal 20 functions as a relay between the measurement device 10 and the server 30, and controls the measurement device 10. The control terminal 20 is, for example, a smartphone.

[0122] The control terminal 20 transmits the data acquired by the control terminal 20 to the server 30. The control terminal 20 receives the user's physical information analyzed by the server 30 from the server 30 and displays the information. The control terminal 20 includes an input unit 21, a presentation unit 22, a second control unit 23, and a second communication unit 24.

[0123] The input unit 21 is a device that receives input from the user. User information is input into the input unit 21. In Embodiment 1, the user's sleep information is input into the input unit 21. Sleep information includes the user's bedtime and waking time. The bedtime and waking time can be the current day, the previous day, or the average of several days. Alternatively, the bedtime and waking time can be standard times recognized by the user. The user's bedtime and waking time are used for the simplified calculation of the average circadian rhythm, which will be described later.

[0124] In addition, comments from the user can also be input into the input unit 21. For example, by the user inputting comments related to his or her own actions into the input unit 21, information about the user's actions can be recorded. The input unit 21 can also have selectable buttons. By making comments correspond to selectable buttons, input can be simplified. In this way, the user's operation can be simplified and troubles can be reduced. For example, factors that easily affect autonomic nervous activity and body temperature include walking, exercise, eating, bathing, and sleeping. There are also factors such as going out, working, feeling cold or warm, mood, fatigue, and sleepiness. By making comments correspond to selectable buttons, comment input can be simplified. In addition, the user can also freely input comments into the input unit 21. In this way, comments that do not correspond to selectable buttons can also be input. By inputting these contents before and after the measurement of biological data, the measurement conditions can be limited. By using the contents of the comments as measurement conditions when performing autonomic nervous analysis, the estimation accuracy can be improved.

[0125] Autonomic nervous system function is affected by gender and age. The older the user, the more significantly their autonomic nervous system function declines. In other words, the older the user, the lower their total power. Therefore, in order to adjust the autonomic nervous system analysis results based on gender and age, the user's gender and age information can also be input into input unit 21.

[0126] Information inputted by the input unit 21 is sent to the second control unit 23 .

[0127] The prompting unit 22 is a device that displays prompt information. This prompt information includes the user's physical information analyzed by the server 30 (e.g., circadian rhythm changes and / or autonomic nervous system analysis results), and / or improvement suggestions based on the physical information analysis results. The prompting unit 22 displays prompt information through screen display, sound, and / or vibration. For example, the prompting unit 22 may include a display, a speaker, and / or a vibrator.

[0128] In the first embodiment, the prompting unit 22 also functions as a notification unit for notifying the user of the timing for acquiring the vital data by the measurement device 10. The notification unit notifies the user of the timing for acquiring the vital data when the user is awake. For example, the notification unit notifies the user of the timing for acquiring the vital data when the user is in a resting state. The notification unit may also notify the user of the timing for acquiring the vital data after providing a prompt message instructing the user to assume a resting state. Alternatively, the notification unit may notify the user of the timing for acquiring the vital data by providing a message confirming whether the user is in a resting state before acquiring the vital data. In this case, the notification unit notifies the user of the timing for acquiring the vital data after confirming the user's input to the input unit 21. This allows the acquisition of vital data at a timing suitable for the user's measurement.

[0129] The second control unit 23 centrally controls the components of the control terminal 20. The second control unit 23 includes, for example, a memory storing programs and a processing circuit (not shown) corresponding to a processor such as a CPU (Central Processing Unit). In the second control unit 23, the processor executes the program stored in the memory. In the first embodiment, the second control unit 23 controls the input unit 21, the presentation unit 22, and the second communication unit 24.

[0130] The second communication unit 24 communicates with the measurement device 10 and the server 30. For example, the second communication unit 24 includes a circuit for communicating with the server 30 in accordance with a predetermined communication standard (e.g., LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), CAN (controller area network), SPI (Serial Peripheral Interface)).

[0131] The second communication unit 24 receives the biological data and time data transmitted from the measurement device 10. The second communication unit 24 transmits the biological data and time data to the server 30. The second communication unit 24 also transmits information such as sleep information inputted through the input unit 21 to the server 30.

[0132] <Server>

[0133] The server 30 analyzes the user's physical information based on the biological data and time data received from the control terminal 20, and transmits the analysis results to the control terminal 20. The server 30 includes a storage unit 31, a circadian rhythm calculation unit 32, an autonomic nerve analysis unit 33, a weighting coefficient calculation unit 34, a physical information analysis unit 35, a third control unit 36, and a third communication unit 37.

[0134] The storage unit 31 stores biological data, time data, sleep information, and the like received by the third communication unit 37. Furthermore, the storage unit 31 stores the biological information (circadian rhythm, autonomic nervous system analysis results, and the like) analyzed by the biological information analysis unit 35. The storage unit 31 can be implemented, for example, by a hard disk drive (HDD), an SSD, RAM, DRAM, a ferroelectric memory, a flash memory, a magnetic disk, or a combination thereof.

[0135] The circadian rhythm calculation unit 32 calculates the user's average circadian rhythm based on the biological data and time data acquired by the biological data acquisition unit 11. The average circadian rhythm refers to the average circadian rhythm of the user, and differs for each user.

[0136] Furthermore, calculations of circadian rhythms can result in significant errors and are also affected by changes in the user's bedtimes, waking times, and daytime activities. Furthermore, circadian rhythms can vary between weekdays and weekends. To improve the accuracy of calculating the average circadian rhythm, it is preferable to calculate the average circadian rhythm based on several days' worth of biological data, preferably at least one week's worth. Calculation of the average circadian rhythm will be described later.

[0137] In the first embodiment, the circadian rhythm calculation unit 32 calculates the average circadian rhythm based on the sleep information until more than one week of biological data has been accumulated. Once more than one week of biological data has been accumulated, the circadian rhythm calculation unit 32 replaces the average circadian rhythm calculated based on the sleep information with the average circadian rhythm calculated based on the biological data. Furthermore, in the first embodiment, an example is described in which the circadian rhythm calculation unit 32 replaces the average circadian rhythm calculated based on the sleep information with the average circadian rhythm calculated based on the biological data, but the present invention is not limited to this. For example, the circadian rhythm calculation unit 32 may directly use the average circadian rhythm calculated based on the sleep information instead of replacing the average circadian rhythm calculated based on the sleep information with the average circadian rhythm calculated based on the biological data. Alternatively, the circadian rhythm calculation unit 32 may use the average circadian rhythm calculated based on the biological data to correct the average circadian rhythm calculated based on the sleep information.

[0138] Figure 3 This is a diagram showing an example of an average circadian rhythm. Figure 3 The horizontal axis represents time, and the vertical axis represents body temperature. Figure 3 An example of an average circadian rhythm based on the user's body temperature as biological data is shown. Figure 3 As shown, the exemplary average circadian rhythm based on body temperature has a maximum peak and a minimum peak, and varies periodically.

[0139] Average circadian rhythms can be categorized into different types. However, if the categorization is too detailed, the influence of errors increases, and sometimes the correlation is lost. Therefore, a suitable number of categories is approximately 4 to 8. For example, average circadian rhythms can be categorized based on the period and / or peak time of body temperature fluctuations. The term "period" refers to the interval between the minimum and maximum peak values of body temperature fluctuations.

[0140] As a classification of circadian rhythm, for example, as a classification based on the difference between the maximum peak and the minimum peak of body temperature, it can be classified into morning type (maximum peak time: around 16:00, minimum peak time: around 4:00), night type (maximum peak time: around 22:00, minimum peak time: around 10:00), reversed morning type (maximum peak time: around 4:00, minimum peak time: around 16:00), reversed night type (maximum peak time: around 10:00, minimum peak time: around 22:00). In addition, as a classification based on the difference in cycle, it can be classified into a fixed type (around 24 hours), a short cycle type (around 20 hours), a long cycle type (around 28 hours), an unknown type (unable to confirm a clear cycle), etc. In addition, the values of peak time and cycle time are examples, and the values can also be different. In addition, the circadian rhythm usually includes a maximum peak and a minimum peak respectively in 1 day. However, in circadian rhythms, there are cases where multiple peaks occur, including two or more maximum peaks and / or minimum peaks in a day. Multiple peaks are also considered a type of circadian rhythm disorder.

[0141] In the first embodiment, the circadian rhythm is described as the fluctuation of body temperature, but the present invention is not limited to this. The circadian rhythm is calculated based on the fluctuation of biological data. For example, the circadian rhythm can also be calculated based on heart rate, pulse rate, etc.

[0142] The autonomic nervous system analysis unit 33 performs autonomic nervous system analysis based on changes in the biological data. In the first embodiment, the autonomic nervous system analysis unit 33 performs autonomic nervous system analysis based on changes in the user's heart rate in the biological data. Specifically, the autonomic nervous system analysis unit 33 calculates autonomic nervous system activity indicators (LF, HF, LF / HF, TP, ccvTP) based on changes in the user's heart rate while awake. LF is the low-frequency component. HF is the high-frequency component. LF / HF is the ratio of the low-frequency component to the high-frequency component. TP is the total power ((autonomic nervous system activity) = LF + HF)). ccvTP is the value obtained by correcting TP based on the heart rate during the measurement time. The autonomic nervous system analysis unit 33 calculates at least one of LF, HF, LF / HF, TP, and ccvTP as the autonomic nervous system activity indicator.

[0143] The weighting coefficient calculation unit 34 calculates a weighting coefficient K for weighting the autonomic nervous system analysis results analyzed by the autonomic nervous system analysis unit 33 based on the measurement time and the average circadian rhythm period of the user's biological data. In the first embodiment, the weighting coefficient calculation unit 34 calculates the weighting coefficient K based on the measurement time and the average circadian rhythm period of the user's heart rate. The weighting coefficient K is calculated based on the measurement time of the heart rate and the period of the maximum peak value of the average circadian rhythm. The weighting coefficient K is calculated so that it increases when the measurement time of the heart rate is within a predetermined time range that includes the maximum peak value of the average circadian rhythm, compared to when it is outside this time range.

[0144] The autonomic nervous system analysis results can be weighted using a weighting coefficient K. Weighting refers to adjusting the reliability of the autonomic nervous system analysis results. The higher the reliability, the larger the weighting coefficient K, while the lower the reliability, the smaller the weighting coefficient K.

[0145] For example, the peak time of the circadian rhythm can be used as an indicator for determining the user's sleep quality. Therefore, the weighting coefficient calculation unit 34 sets a larger weighting coefficient near the peak time of the circadian rhythm than at other times. For example, the weighting coefficient K near the peak time of the circadian rhythm can be set to "1," while the weighting coefficient K at other times can be set to "0."

[0146] The biometric analysis unit 35 weights the autonomic nervous system analysis results using the weighting coefficient K calculated by the weighting coefficient calculation unit 34 and estimates the change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nervous system analysis results. Thus, the biometric analysis unit 35 analyzes the circadian rhythm disturbance as one of the biometric information.

[0147] For example, the case of using LF / HF as an indicator of autonomic nervous activity will be described. The LF / HF weighted by the weighting coefficient K is called the corrected LF / HF. If this corrected LF / HF increases, there are many cases where circadian rhythm disturbances occur on the same day or a few days later (1 to 3 days later). For example, if the minimum peak time of the average circadian rhythm is around 4 o'clock, the minimum peak time of the circadian rhythm on the same day or a few days later (1 to 3 days later) is delayed to around 7 o'clock. In addition, if the corrected LF / HF increases, the amplitude is also likely to decrease.

[0148] In addition, the description of the case of using the above-mentioned LF / HF is an example, and the use of any one of the autonomic nervous activity indicators of LF, HF, LF / HF, TP, and cccTP can also be used to infer the disorder of the circadian rhythm. For example, the TP or cccTP weighted by the weighting coefficient K is called the corrected TP or corrected cccTP. If the corrected TP or corrected cccTP increases, there are many cases of circadian rhythm disorder on the same day or a few days later (1 to 3 days later). For example, in the circadian rhythm on the same day or a few days later (1 to 3 days later), a decrease in amplitude and a delay in the minimum peak time occur.

[0149] Disruptions in circadian rhythms include deviations in the timing of maximum peaks, deviations in the timing of minimum peaks, reductions in amplitude, and multimodality. These disturbances can occur independently, but are often combined. For example, if the circadian rhythm disturbance is temporary (lasting less than a few days), both a deviation in the timing of peaks and a reduction in amplitude are likely to occur simultaneously.

[0150] In this manner, the biological information analysis unit 35 can estimate changes in the circadian rhythm relative to the average circadian rhythm for the current day or for several days thereafter based on the autonomic nervous system analysis results weighted by the weighting coefficient K. Furthermore, the biological information analysis unit 35 estimates variations in the maximum peak times, variations in the minimum peak times, decreases in amplitude, and multimodality as changes in the circadian rhythm.

[0151] Based on the estimated circadian rhythm changes, the biometric analysis unit 35 creates information suggesting improvements to the circadian rhythm. For example, the biometric analysis unit 35 calculates a predicted circadian rhythm based on the weighted autonomic nervous system analysis results. The predicted circadian rhythm refers to the circadian rhythm in a few hours or days, indicating the degree to which the circadian rhythm will vary from the average circadian rhythm.

[0152] In Embodiment 1, the presentation information includes the average circadian rhythm and the predicted circadian rhythm. The presentation information is stored in the storage unit 31 and transmitted to the control terminal 20 via the third communication unit 37. The control terminal 20 displays the presentation information on the presentation unit 22. Thus, by viewing the presentation information on the presentation unit 22, the user can understand changes (disruptions) in their circadian rhythm relative to the average circadian rhythm.

[0153] The body information analysis unit 35 may also create prompt information including suggestions for adjusting the circadian rhythm, such as breathing techniques, stretching, yoga, aromatherapy, acupuncture (such as circular needles), exercise, and walking.

[0154] The body information (circadian rhythm, autonomic nerve analysis results, etc.) and presentation information analyzed by the body information analysis unit 35 are stored in the storage unit 31 .

[0155] Furthermore, the biological information analysis unit 35 can also estimate REM sleep cycles, sleep depth, bedtime, waking time, sleep duration, etc. based on changes in the circadian rhythm, and analyze the sleep quality.

[0156] The third control unit 36 centrally controls the components of the server 30. For example, the third control unit 36 includes a memory storing programs and a processing circuit (not shown) corresponding to a processor such as a CPU (Central Processing Unit). In the third control unit 36, the processor executes the program stored in the memory. In the first embodiment, the third control unit 36 controls the storage unit 31, the circadian rhythm calculation unit 32, the autonomic nerve analysis unit 33, the weighting coefficient calculation unit 34, the physical information analysis unit 35, and the third communication unit 37.

[0157] The third communication unit 37 communicates with the control terminal 20. For example, the third communication unit 37 includes a circuit for communicating with the control terminal 20 in accordance with a predetermined communication standard (e.g., LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), USB, HDMI (registered trademark), CAN (controller area network), SPI (Serial Peripheral Interface)).

[0158] The third communication unit 37 receives the biological data, time data, and sleep information transmitted from the control terminal 20 , and transmits the biological information and presentation information to the control terminal 20 .

[0159] [action]

[0160] An example of the operation (analysis method) of analysis system 1A will be described. Analysis system 1A calculates the average circadian rhythm on server 30 based on the biological data measured by measurement device 10 or sleep information input through control terminal 20. Analysis system 1A performs autonomic nervous system analysis based on the heart rate in the biological data and weights the autonomic nervous system analysis results using a weighting coefficient K. Based on the weighted autonomic nervous system analysis results, analysis system 1A estimates and analyzes changes in the circadian rhythm relative to the average circadian rhythm.

[0161] Calculation of average circadian rhythm

[0162] An example of calculation of the average circadian rhythm in the analysis system 1A will be described.

[0163] The circadian rhythm calculation unit 32 simply calculates a first circadian rhythm based on the user's sleep information (bedtime and waking time) until at least one week of biological data has been acquired, and uses the first circadian rhythm as the average circadian rhythm. Once at least one week of biological data has been acquired, the circadian rhythm calculation unit 32 calculates a second circadian rhythm based on at least one week of biological data, and uses the second circadian rhythm as the average circadian rhythm. In this manner, the first circadian rhythm simply calculated based on the user's sleep information is set as the average circadian rhythm until sufficient biological data has been accumulated. Once sufficient biological data has been accumulated, the second circadian rhythm calculated based on the biological data is set as the average circadian rhythm.

[0164] In Embodiment 1, the sleep information used in the simplified calculation of the first circadian rhythm is the bedtime and wake-up time information for the current day and the previous day. Furthermore, the sleep information used in the simplified calculation of the first circadian rhythm is not limited to the bedtime and wake-up time information for the current day and the previous day. For example, the sleep information used in the simplified calculation of the first circadian rhythm may also be the average bedtime and wake-up time information for several days, or may be the standard bedtime and wake-up time information recognized by the user.

[0165] In Embodiment 1, the circadian rhythm calculation unit 32 calculates the average circadian rhythm using the user's body temperature as biological data. The average circadian rhythm may be calculated using heart rate, pulse rate, or the like in addition to body temperature.

[0166] Figure 4 This is a flowchart showing an example of calculation of the average circadian rhythm in the analysis system 1A according to the first embodiment of the present invention.

[0167] like Figure 4 As shown, in step ST11, the circadian rhythm calculation unit 32 determines whether the user's sleep information is stored. Specifically, the circadian rhythm calculation unit 32 determines whether the sleep information is stored in the storage unit 31. If there is no sleep information, the process proceeds to step ST12. If there is sleep information, the process proceeds to step ST14.

[0168] The case where there is no sleep information will be described. In step ST12, the circadian rhythm calculation unit 32 obtains sleep information. Specifically, the circadian rhythm calculation unit 32 sends instruction information to the control terminal 20 via the third communication unit 37, and causes the presentation unit 22 of the control terminal 20 to present an instruction to obtain sleep information.

[0169] The user inputs sleep information into input unit 21 in accordance with instructions provided by presentation unit 22. In Embodiment 1, the user inputs bedtime and wake-up time into input unit 21. The sleep information input into input unit 21 is transmitted to server 30 via second communication unit 24 and stored in storage unit 31 of server 30.

[0170] As described above, in step ST12 , the presentation unit 22 of the control terminal 20 presents presentation information urging the user to input sleep information, and the user is prompted to input sleep information via the input unit 21 , thereby acquiring sleep information.

[0171] In step ST13, the circadian rhythm calculation unit 32 calculates a first circadian rhythm based on the sleep information. The first circadian rhythm is a circadian rhythm simply calculated based on the user's bedtime and waking time.

[0172] The circadian rhythm is correlated with the user's bedtime and waking time. For example, a night-time user's bedtime and waking time tend to be relatively late, so the peak of the circadian rhythm tends to be delayed. In one example, the circadian rhythm calculation unit 32 pre-creates a correlation equation or correlation table representing the correlation between bedtime, waking time, and circadian rhythm, and stores it in the storage unit 31. The circadian rhythm calculation unit 32 reads the correlation equation or correlation table from the storage unit 31 and calculates the first circadian rhythm based on the sleep information input by the user and the correlation equation or correlation table.

[0173] Once the user enters sleep information, the user may not be asked to enter this information again. However, over a long period of time (e.g., more than three months), the user's lifestyle may change, and their bedtime and wake-up times may change. Therefore, input may be requested periodically (e.g., every three months). Alternatively, the user may be asked to enter sleep information (bedtime and wake-up time) multiple times. This allows the calculation of average bedtime and average wake-up times.

[0174] Furthermore, a correlation equation or correlation table representing the relationship between bedtime, waking time, and circadian rhythm can be created based on sleep information of multiple users. In this case, the sleep information of multiple users can be accumulated in the storage unit 31 of the server 30, and the correlation equation or correlation table can be created based on the accumulated sleep information.

[0175] The case where sleep information is present will be described. In step ST14 , the biological data acquisition unit 11 acquires biological data and time data. Specifically, the circadian rhythm calculation unit 32 reads the biological data and time data from the storage unit 31 .

[0176] In step ST15, the circadian rhythm calculation unit 32 calculates the second circadian rhythm based on the biometric data and time data. Specifically, the second circadian rhythm is estimated by organizing the biometric data measured while the user is awake by measurement time and calculating the period of change in the biometric data, the times of the maximum and minimum peak values, and the amplitude of the change in the biometric data. To improve the accuracy of circadian rhythm estimation, the biometric data acquisition unit 11 preferably acquires five or more pieces of biometric data per day.

[0177] Next, the setting of the average circadian rhythm will be described. In step ST16, the circadian rhythm calculation unit 32 determines whether there is at least one week of biological data. Specifically, the circadian rhythm calculation unit 32 determines whether at least one week of biological data is stored in the storage unit 31. If there is no at least one week of biological data, the process proceeds to step ST17. If there is at least one week of biological data, the process proceeds to step ST18.

[0178] The following describes a case where there is no biological data for more than one week. In step ST17, the circadian rhythm calculation unit 32 sets the first circadian rhythm as the average circadian rhythm. Specifically, if there is no biological data for more than one week accumulated in the storage unit 31, the circadian rhythm calculation unit 32 sets the first circadian rhythm simply calculated based on the sleep information as the average circadian rhythm.

[0179] The following describes the case where there is more than one week of biological data. In step ST18, the circadian rhythm calculation unit 32 sets the second circadian rhythm as the average circadian rhythm. Specifically, when more than one week of biological data is stored in the storage unit 31, the circadian rhythm calculation unit 32 sets the second circadian rhythm calculated based on the biological data as the average circadian rhythm.

[0180] Thus, when no biological data has been accumulated, the circadian rhythm calculation unit 32 uses the first circadian rhythm based on the sleep information as the average circadian rhythm. Furthermore, when biological data has been accumulated for one week or more, the first circadian rhythm is replaced with the second circadian rhythm, which is then used as the average circadian rhythm.

[0181] <Analysis of Body Information>

[0182] Next, an example of analysis of biological information in the analysis system 1A will be described. In the first embodiment, estimation and analysis of circadian rhythm changes as one type of biological information will be described.

[0183] Figure 5 This is a diagram showing a flowchart of an example of a method for analyzing physical information according to the first embodiment of the present invention.

[0184] like Figure 5 As shown, in step ST21, the presentation unit 22 notifies the user of the timing for acquiring the vital data. Specifically, the server 30 transmits information on the timing for acquiring the vital data to the control terminal 20. Upon receiving this information from the server 30, the control terminal 20 displays prompt information instructing the user to acquire the vital data through the presentation unit 22. This notifies the user of the timing for acquiring the vital data and urges the user to acquire the vital data using the measurement device 10.

[0185] The criteria for determining the timing of acquiring biological data are time periods when the autonomic nervous system analysis results are more important (for example, time periods near the peak of the circadian rhythm) and states that can be inferred to be resting (for example, low activity, stable heart rate or pulse rate, and stable body temperature).

[0186] If the user is not in a quiet state, the control terminal 20 may urge the user to be quiet through the prompting unit 22 and allow the user to determine whether the user is in a quiet state. For example, the control terminal 20 may display a message such as "Please be quiet for 5 minutes" on the prompting unit 22, and then display a message such as "If you are quiet, please start measurement" 5 minutes after the message is displayed.

[0187] In step ST22, the biological data acquisition unit 11 acquires the biological data of the user. In the first embodiment, the biological data acquisition unit 11 acquires the body temperature and heart rate of the user as the biological data.

[0188] In step ST23, the biological data acquisition unit 11 acquires time data. Specifically, the biological data acquisition unit 11 acquires the measurement time when the biological data is acquired.

[0189] The relay control terminal 20 transmits the biometric data and time data acquired by the biometric data acquisition unit 11 to the server 30 .

[0190] In step ST24, the autonomic nervous system analysis unit 33 performs autonomic nervous system analysis based on fluctuations in the user's biological data. Specifically, the autonomic nervous system analysis unit 33 calculates an autonomic nervous system activity index based on fluctuations in the heart rate in the biological data acquired in step ST22. The autonomic nervous system analysis unit 33 calculates at least one of LF, HF, LF / HF, TP, and cccTP as the autonomic nervous system activity index. The autonomic nervous system analysis unit 33 organizes the calculated autonomic nervous system activity index by time. Specifically, the autonomic nervous system analysis unit 33 organizes the autonomic nervous system activity index by the time at which the heart rate was measured.

[0191] In step ST25, the biological information analysis unit 35 obtains the average circadian rhythm. Specifically, the circadian rhythm calculation unit 32 obtains the average circadian rhythm based on Figure 4 The average circadian rhythm is calculated using the method shown in FIG.

[0192] In step ST26, the weighting coefficient calculation unit 34 calculates the weighting coefficient K based on the time data and the average circadian rhythm cycle. The weighting coefficient calculation unit 34 calculates the weighting coefficient K for weighting the autonomic nerve analysis results analyzed by the autonomic nerve analysis unit 33 based on the measurement time of the user's biological data (heart rate) and the average circadian rhythm cycle.

[0193] Here, use Figure 6 An example of calculation of the weighting coefficient K will be described. Figure 6 This is a diagram showing an example of the relationship between the average circadian rhythm period and the weighting coefficient K. Figure 6 In the example shown, the weighting coefficient K is adjusted to increase the reliability of the autonomic nervous system analysis results when the measurement time is within the specified time range Qs that includes the maximum peak value of the average circadian rhythm, and to decrease the reliability of the autonomic nervous system analysis results when the measurement time is outside of this time range Qs. Specifically, when the measurement time is within the specified time range Qs that includes the maximum peak value of the average circadian rhythm, the weighting coefficient K is set to "1." When the measurement time is outside of this time range Qs that includes the maximum peak value of the average circadian rhythm, the weighting coefficient K is set to "0."

[0194] The autonomic nervous system analysis results based on the biological data measured within the specified time range Qs are highly reliable data for determining the user's sleep quality. That is, by using the autonomic nervous system analysis results within the specified time range Qs, the accuracy of estimating changes in the circadian rhythm can be improved. The specified time range Qs is preferably set within the range of a period of more than -1 / 8 and less than 3 / 8 of the maximum peak value of the average circadian rhythm. The so-called "range of more than -1 / 8 and less than 3 / 8 of the period of the maximum peak value" means that when one period of the average circadian rhythm chart is divided into 8 equal parts in the time direction, the range is equivalent to more than -1 / 8 and less than 3 / 8 based on the peak position. More preferably, the specified time range Qs is set within the range of a period of less than 1 / 4 of the maximum peak value of the average circadian rhythm.

[0195] exist Figure 6 In the example shown, biological data is acquired at five times, t1 to t5, between 9:00 and 21:00 in one day. Timings t1, t2, t3, t4, and t5 represent around 9:00, around 12:00, around 15:00, around 18:00, and around 21:00, respectively. Figure 6In the example shown, the maximum peak time of the average circadian rhythm is around 3:00 PM. Therefore, the predetermined time range Qs is set to be between 12:00 PM and 12:00 AM. In this case, the weighting coefficient calculation unit 34 sets the weighting coefficient K to "0" at the first timing t1 and to "1" at the second timing t2 through the fifth timing t5.

[0196] also, Figure 6 The calculation of the weighting coefficient K shown is an example, and the calculation of the weighting coefficient K by the weighting coefficient calculation unit 34 is not limited to this. For example, the weighting coefficient K may be set to different values within multiple time ranges. The weighting coefficient K may also be set to increase or decrease in stages based on the peak time of the average circadian rhythm.

[0197] In step ST27, the body information analysis unit 35 weights the autonomic nerve analysis result based on the weighting coefficient K. Specifically, the body information analysis unit 35 multiplies the autonomic nerve activity index calculated by the autonomic nerve analysis unit 33 by the weighting coefficient K. Figure 6 In the case of the example shown, the autonomic nervous activity index outside the predetermined time range Qs is "0", and only the autonomic nervous activity index within the predetermined time range Qs remains.

[0198] In addition, the autonomic nervous analysis results based on the biological data obtained in a resting state are used to estimate the fatigue state. However, for the autonomic nervous analysis results based on the biological data obtained in a state where the heart rate increases significantly due to sympathetic hyperactivity such as exercise or drinking, which is not a resting state, the estimation accuracy of the fatigue state is reduced. Therefore, the body information analysis unit 35 may also correct the weighted autonomic nervous analysis results when the user's heart rate is greater than a specified threshold. For example, the body information analysis unit 35 may also correct the weighted autonomic nervous analysis results when the user's normal (average / median) heart rate increases by a certain amount (for example, an increase of 20% of the average value).

[0199] For example, after weighting the autonomic nervous system analysis result by the weighting coefficient K, the weighted autonomic nervous system analysis result is multiplied by the correction coefficient K1. The correction coefficient K1 may also be 0.5. By multiplying the weighted autonomic nervous system analysis result by the correction coefficient K1, the reliability can be reduced.

[0200] The correction coefficient K1 may also be changed according to the rate of increase in the heart rate. For example, if the heart rate increases by 20% or more and less than 40% of the normal level, the correction coefficient K1 may be set to 0.5, and if the heart rate increases by 40% or more and less than 60% of the normal level, the correction coefficient K1 may be set to 0.25.

[0201] Alternatively, the correction coefficient K1 may be calculated using the following formula 1.

[0202] (Formula 1)

[0203] (Correction coefficient K1) = (normal heart rate) / ((heart rate) - (normal heart rate)) / (constant a)

[0204] Here, the constant a is set to an arbitrary value. For example, the constant a is 5 or more and 20 or less.

[0205] In step ST28, the biological information analysis unit 35 estimates the change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result. For example, if the weighted autonomic nerve analysis result exceeds a predetermined threshold value Sa, the biological information analysis unit 35 predicts a circadian rhythm disturbance.

[0206] In the first embodiment, the predetermined threshold value Sa is determined based on the average value H1 and the standard deviation σ of the autonomic nerve analysis results before weighting. For example, the predetermined threshold value Sa is calculated by the following formula 2.

[0207] (Formula 2)

[0208] (Threshold value Sa) = (average value H1) + (standard deviation σ) × (constant b)

[0209] Here, the constant b is set to an arbitrary value. For example, the constant b is set to 1.5. In addition, the constant b is not limited to 1.5 and can be set to other values.

[0210] When it is determined that the weighted autonomic nerve analysis result exceeds the predetermined threshold value Sa, the biological information analysis unit 35 predicts a shift of the circadian rhythm relative to the average circadian rhythm on the current day or in the next few days.

[0211] For example, the shift amount of the circadian rhythm when the predetermined threshold value Sa is exceeded is calculated by the following equation 3. The shift amount Va is represented by the shift in the maximum peak time of the circadian rhythm.

[0212] (Formula 3)

[0213] (Offset Va) = (Constant b) c ×(constant d)

[0214] C is a power of the constant b. C and d are each set to an arbitrary value.

[0215] The body information analysis unit 35 can also set multiple threshold values Sa. When calculating the first threshold value Sa1, the second threshold value Sa2, and the third threshold value Sa3 by the formula 2, the constant b is set to different values. The offset Va1, Va2, and Va3 of each circadian rhythm relative to the average circadian rhythm when the first threshold value Sa1, the second threshold value Sa2, and the third threshold value Sa3 are exceeded can also be calculated by the formula 3. A correspondence table between the first threshold value Sa1, the second threshold value Sa2, and the third threshold value Sa3 and the offset Va1, Va2, and Va3 can also be created and stored in the storage unit 31. In this case, the body information analysis unit 35 can refer to the correspondence table when each threshold value is exceeded and easily calculate the offset of the circadian rhythm.

[0216] Furthermore, fatigue accumulates, and fatigue that cannot be recovered through sleep or daytime activities will affect the next day's fatigue level. Therefore, when estimating circadian rhythm changes, using data from several days can improve the accuracy of the estimation compared to using data from a single day. In other words, if the autonomic nervous system analysis results, weighted using data from several days, exceed a threshold, the accuracy of the estimated circadian rhythm disturbance is improved compared to using data from a single day. Furthermore, the degree of the disturbance increases.

[0217] Furthermore, the biological information analysis unit 35 may also use the biological data changes on the day the biological data was measured (the estimated result of the circadian rhythm up to that time) for analysis, thereby improving the estimation accuracy of the circadian rhythm changes.

[0218] In this manner, the biological information analysis unit 35 estimates the change (disruption) of the circadian rhythm with respect to the average circadian rhythm based on the weighted autonomic nerve analysis result.

[0219] In step ST29, the biometric analysis unit 35 analyzes changes in the circadian rhythm. Specifically, the biometric analysis unit 35 calculates the predicted circadian rhythm for the next several hours or days based on the weighted autonomic nervous system analysis results. The biometric analysis unit 35 creates presentation information that includes the average circadian rhythm and the predicted circadian rhythm.

[0220] The biological information analysis unit 35 transmits the presentation information to the control terminal 20. The control terminal 20 presents the presentation information via the presentation unit 22. Thus, the user can understand the changes in the circadian rhythm by viewing the presentation information presented by the presentation unit 22.

[0221] The body information analysis unit 35 may also create prompt information including suggestions for improving circadian rhythms based on changes in circadian rhythms. For example, if the body information analysis unit 35 estimates circadian rhythm disruption, decreased sleep quality, or decreased activity suitability, it may create prompt information including suggestions for improvement and / or target values for the autonomic nervous system analysis results.

[0222] For example, if the corrected LF / HF ratio is high, the body information analysis unit 35 may offer suggestions for improvement, such as breathing exercises, stretching, yoga, aromatherapy, or acupuncture (such as circular needles) along with the target value of the autonomic nervous system analysis results. Alternatively, if the corrected TP ratio is high, the body information analysis unit 35 may offer suggestions for improvement, such as exercise or walking, along with the target value of the autonomic nervous system analysis results. The body information analysis unit 35 may also perform another autonomic nervous system analysis after the user implements these suggestions for improvement and determine whether the target value of the autonomic nervous system analysis results has been achieved.

[0223] When the weighted autonomic analysis results (e.g., corrected TP, corrected LF / HF, and other autonomic activity indicators) are large, sleep quality will also change with changes in circadian rhythms. Indicators of sleep quality include, for example, sleep time, the time or proportion of light sleep (e.g., wakefulness, REM sleep, deep sleep stage 1, etc.) within sleep time, REM sleep cycles, the number / frequency of awakenings during sleep, the difference between going to bed and falling asleep, and the difference between waking up and waking up. Among them, the proportion of light sleep that occupies sleep time is particularly highly correlated with the weighted autonomic analysis results (e.g., corrected TP, corrected LF / HF). When the weighted autonomic analysis results are large, the proportion of light sleep increases. When the weighted autonomic analysis results (e.g., corrected TP, corrected LF / HF) are large, there are cases where sleep quality changes after the circadian rhythm changes (e.g., 1 to 3 days later), and there are cases where the circadian rhythm changes after the sleep quality changes.

[0224] In addition, if the circadian rhythm is disturbed or the sleep quality is reduced, the daytime performance of the next day and thereafter will be reduced. Activity suitability is used to indicate whether it is suitable for performance. Activity suitability can also be calculated based on the circadian rhythm, sleep quality, and autonomic nervous system analysis results (unweighted LF / HF, TP) up to the previous day. The main factors for the decline in activity suitability are the disturbance of the circadian rhythm and the reduction in sleep quality up to the previous day, and the reduction in unweighted TP on the current day. Although LF / HF also has an impact, the LF / HF values suitable for performance vary greatly from person to person, so by accumulating user data, LF / HF can also be included in the factors for activity suitability calculation.

[0225] Thus, the corrected TP is correlated with sleep quality for the day and beyond. For example, if the corrected TP increases, sleep quality is likely to decline. Furthermore, the autonomic nervous system analysis results are correlated with activity suitability. For example, if the unweighted TP increases, activity suitability is likely to increase. If activity suitability increases, it is suitable for activities. The corrected LF / HF is correlated with circadian rhythm disturbances for the day and beyond. For example, if the corrected LF / HF increases, circadian rhythm disturbances are likely to occur.

[0226] Using the correlation described above, the biological information analysis unit 35 can analyze changes in the circadian rhythm and calculate biological information such as sleep quality and activity suitability.

[0227] The analysis system 1A can estimate and analyze changes in circadian rhythm by executing steps ST21 to ST29 described above.

[0228] [Regarding the correlation between autonomic nervous system analysis results, circadian rhythm changes, and sleep quality]

[0229] use Figures 7-11 This section describes examples of the correlation between autonomic nervous system analysis results, circadian rhythm changes, and sleep quality.

[0230] Figure 7 This figure shows an example of the correlation between autonomic nervous system analysis results, circadian rhythm changes, and the proportion of light sleep. Figure 8 This shows an example of determining the proportion of light sleep based on the autonomic nervous system analysis results and circadian rhythm changes. Figure 7 and Figure 8 In the autonomic nervous system analysis, ccvTP is used as the result. ccvTP indicates the activity of the autonomic nervous system. Generally speaking, ccvTP values are higher in healthy young people and gradually decrease with age. Furthermore, ccvTP values are higher in healthy people and lower in those experiencing fatigue and stress.

[0231] exist Figure 7 and Figure 8 In the ccvTP, after being weighted by the weighting coefficient K, it is normalized to have an average value of 0 and a threshold value of 1. Hereinafter, the ccvTP normalized by the weighting coefficient K is referred to as the normalized ccvTP. The weighting coefficient K is set to "1" within the period that is between -1 / 8 and 3 / 8 of the maximum peak value of the average circadian rhythm, and is set to "0" in the rest of the range.

[0232] Figure 7 and Figure 8The standardized ccvTP in this context refers to the maximum value within a period that is between -1 / 8 and 3 / 8 of the average circadian rhythm's maximum peak value. If the standardized ccvTP value is above a threshold of 1, the standardized ccvTP is considered high. The standardized ccvTP threshold can also be set to a different value by the user.

[0233] exist Figure 7 and Figure 8 The threshold value of the light sleep ratio is set to 30%. If the light sleep ratio is below the threshold value of 30%, it is determined that the sleep is deep. The threshold value of the light sleep ratio can also be set to different values according to the user.

[0234] like Figure 7 As shown in FIG, if the normalized ccvTP increases, the proportion of people in light sleep tends to increase. In other words, if the normalized ccvTP increases, the sleep tends to become lighter. On the other hand, if the normalized ccvTP decreases, the proportion of people in light sleep tends to decrease. In other words, if the normalized ccvTP decreases, the sleep tends to become deeper. Figure 7 There are parts that are different from the above trends because errors are included.

[0235] like Figure 8 As shown, when the normalized ccvTP is greater than a threshold of 1 and the ratio of light sleep is greater than a threshold of 30% (see Figure 8 In the area A1), it can be determined that the standardized ccvTP is high and the sleep is shallow. In addition, when the standardized ccvTP is less than the threshold 1 and the proportion of shallow sleep is less than the threshold 30% (refer to Figure 8 In the area A2), it can be determined that the standardized ccvTP is low and the sleep is deep. Figure 8 Among them, areas A3 and A4 outside areas A1 and A2 are misjudgments.

[0236] As described above, there is a correlation between the normalized ccvTP and the sleep quality. Therefore, based on the correlation between the normalized ccvTP and the sleep quality, the sleep quality can be determined from the normalized ccvTP.

[0237] Figure 9 This figure shows an example of the correlation between autonomic nervous system analysis results and circadian rhythm timing deviations. Figure 10 This shows an example of determining the time deviation between the autonomic nervous system analysis result and the circadian rhythm. Figure 9 and Figure 10 In the analysis, LF / HF was used as the autonomic nerve analysis result.

[0238] exist Figure 9 and Figure 10In , LF / HF is the corrected LF / HF weighted by the weighting coefficient K. The weighting coefficient K is set to "1" in a period of not less than -1 / 8 and not more than 3 / 8 of the maximum peak value of the average circadian rhythm, and is set to "0" in the rest of the range.

[0239] exist Figure 9 and Figure 10 In this context, the corrected LF / HF refers to the maximum value within a cycle that is between -1 / 8 and 3 / 8 of the maximum peak value of the average circadian rhythm. The threshold for the corrected LF / HF is set to 6. If the corrected LF / HF value is greater than or equal to the threshold of 6, the corrected LF / HF is determined to be high. The threshold for the corrected LF / HF can also be set differently by the user.

[0240] Figure 9 and Figure 10 The time deviation of the circadian rhythm in the calculation refers to the deviation from the predicted circadian rhythm based on the corrected LF / HF calculation for the average circadian rhythm. The threshold value of the time deviation of the circadian rhythm is set to 2 hours. If the time deviation of the circadian rhythm is more than 2 hours, it is determined that the time deviation of the circadian rhythm is large. In addition, when it is difficult to determine the peak due to a decrease in the amplitude of the circadian rhythm or multi-peaking, the time deviation is uniformly set to -10 hours. In addition, the threshold value of the time deviation of the circadian rhythm can also be set to a different value according to the user. The time deviation of the circadian rhythm can also be the average of the deviations between the minimum peak time and the maximum peak time after the corrected LF / HF measurement and the minimum peak time and the maximum peak time of the next day.

[0241] like Figure 9 As shown in FIG, if the corrected LF / HF increases, the time deviation of the circadian rhythm tends to increase. In other words, if the corrected LF / HF increases, the change of the circadian rhythm tends to increase. On the other hand, if the corrected LF / HF decreases, the time deviation of the circadian rhythm tends to decrease. In other words, if the corrected LF / HF increases, the change of the circadian rhythm tends to decrease. In addition, Figure 9 There are parts that are different from the above trend because errors are included.

[0242] like Figure 10 As shown, when the corrected LF / HF is greater than the threshold of 6 and the time deviation of the circadian rhythm is greater than the threshold of 2 hours (refer to Figure 10 In the area B1 and B2), it can be determined that the corrected LF / HF is high and the time deviation of the circadian rhythm is large. In addition, when the corrected LF / HF is less than the threshold value 6 and the time deviation of the circadian rhythm is less than the threshold value 2h (refer to Figure 10 In the area B3), it can be determined that the corrected LF / HF is low and the time deviation of the circadian rhythm is small. Figure 10Among them, areas B4 to B6 other than areas B1 to B3 are misjudged.

[0243] As described above, there is a correlation between the corrected LF / HF and the circadian rhythm timing deviation. Therefore, based on the correlation between the corrected LF / HF and the circadian rhythm timing deviation, the circadian rhythm timing deviation can be determined based on the autonomic nerve analysis results of the corrected LF / HF.

[0244] [About the output displayed by the parsing system]

[0245] Figure 11 An example of output displayed by the analysis system according to the first embodiment of the present invention is shown. Figure 11 As shown, the presentation unit 22 presents the information for improving circadian rhythms, generated by the biological information analysis unit 35. Specifically, the presentation unit 22 presents information including the average circadian rhythm and the predicted circadian rhythm. Thus, by viewing the information presented by the presentation unit 22, the user can understand changes (disruptions) in their circadian rhythm relative to the average circadian rhythm.

[0246] [Effect]

[0247] According to the analysis system 1A of the first embodiment, the following effects can be achieved.

[0248] The analysis system 1A includes a biological data acquisition unit 11, a circadian rhythm calculation unit 32, an autonomic nerve analysis unit 33, a weighting coefficient calculation unit 34, and a body information analysis unit 35. The biological data acquisition unit 11 acquires biological data. The circadian rhythm calculation unit 32 calculates the user's average circadian rhythm. The autonomic nerve analysis unit 33 performs autonomic nerve analysis based on changes in the biological data. The weighting coefficient calculation unit 34 calculates a weighting coefficient K for weighting the autonomic nerve analysis results analyzed by the autonomic nerve analysis unit based on the time at which the user's biological data was measured and the period of the average circadian rhythm. The body information analysis unit 35 weights the autonomic nerve analysis results using the weighting coefficient K calculated by the weighting coefficient calculation unit 34 and estimates changes in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis results. This configuration allows changes in the circadian rhythm to be analyzed as part of the body information. Furthermore, there is the advantage of reducing the burden on the user when analyzing body information.

[0249] The circadian rhythm calculation unit 32 calculates the average circadian rhythm based on the biological data acquired by the biological data acquisition unit 11. With such a configuration, the average circadian rhythm can be accurately calculated based on the biological data.

[0250] The analysis system 1A includes an input unit 21 for inputting a user's sleep information. The circadian rhythm calculation unit 32 calculates the user's average circadian rhythm based on the sleep information input via the input unit 21. This configuration makes it possible to easily calculate the average circadian rhythm based on the sleep information. For example, if sufficient biological data has not been accumulated, the average circadian rhythm can be calculated based on the user's sleep information. If the biological data contains insufficient information, the calculation of the circadian rhythm may contain errors. Therefore, by calculating the average circadian rhythm based on the user's sleep information until at least one week of biological data has been accumulated, errors in analyzing the body information can be reduced.

[0251] When the measurement time is within the range of -1 / 8 to 3 / 8 of the average circadian rhythm's maximum peak value, the weighting coefficient calculation unit 34 increases the weighting coefficient K compared to when the measurement time is within a range other than the above range. This configuration allows for more accurate analysis of the user's physical information. By increasing the weighting of the autonomic nervous system analysis results within the range of -1 / 8 to 3 / 8 of the average circadian rhythm's maximum peak value, physical information can be analyzed with greater accuracy. The autonomic nervous system analysis results within the range of -1 / 8 to 3 / 8 of the average circadian rhythm's maximum peak value have a high correlation with circadian rhythm disturbances. Therefore, by increasing the weighting coefficient within this range, the accuracy of estimating circadian rhythm changes can be improved.

[0252] The weighting coefficient K may be set for each user or according to the type of autonomic nerve analysis result.

[0253] When the user's heart rate exceeds a predetermined threshold, the body information analysis unit 35 corrects the weighted autonomic nervous system analysis results. This structure reduces the reliability of autonomic nervous system analysis results based on biological data acquired in a state where the heart rate is significantly elevated due to sympathetic hyperactivity, such as exercise or drinking, which is not a resting state. This allows for higher-precision analysis of body information. For example, a state where the heart rate is temporarily higher than usual, such as during exercise or drinking, reduces the accuracy of the estimated circadian rhythm disturbance. By reducing the reliability of the autonomic nervous system analysis results when the user is not in such a resting state, the accuracy of the estimated circadian rhythm changes can be improved.

[0254] The biometric information analysis unit 35 estimates circadian rhythm changes based on at least one of the deviation of the circadian rhythm's maximum peak time relative to the average circadian rhythm, the deviation of the circadian rhythm's minimum peak time relative to the average circadian rhythm, a decrease in amplitude, and multiple peaks. This allows for more accurate analysis of biometric information. Circadian rhythm disturbances can occur, for example, due to jet lag or shift work, with deviations in the maximum peak time and minimum peak time, or without a decrease in amplitude such as a decrease in body temperature at night. By distinguishing these factors, the accuracy of the estimation of their impact on sleep quality and other factors can be improved.

[0255] The body information analysis unit 35 estimates the user's sleep quality and activity suitability based on the weighted autonomic nervous system analysis results. With this configuration, more detailed body information of the user can be analyzed.

[0256] The analysis system 1A includes a presentation unit 22 that presents information including suggestions for improving circadian rhythm. The biological information analysis unit 35 generates the information based on the aforementioned changes in circadian rhythm. This configuration allows the user to improve their circadian rhythm by presenting suggestions for improving their circadian rhythm.

[0257] The biometric analysis unit 35 calculates a predicted circadian rhythm based on the weighted autonomic nervous system analysis results. The presentation information includes the average circadian rhythm and the predicted circadian rhythm. This configuration allows the user to be presented with the average circadian rhythm and the predicted circadian rhythm, and to be informed of changes in the circadian rhythm.

[0258] The analysis system 1A includes a notification unit 22 for notifying the user of the timing for measuring the vital data. With such a configuration, the user can understand the appropriate timing for measuring the vital data and can measure the vital data in an appropriate state.

[0259] In addition, while the first embodiment describes an example in which the analysis system 1A includes a measurement device 10, a control terminal 20, and a server 30, the present invention is not limited thereto. The analysis system 1A may implement these components using a single device or multiple devices. For example, the measurement device 10 and the control terminal 20 may be integrally formed. The measurement device 10, the control terminal 20, and the server 30 may also be integrally formed. The measurement device 10 and the server 30 may also be integrally formed.

[0260] The components constituting the analysis system 1A may also be implemented by devices other than the measuring device 10, the control terminal 20, and the server 30. For example, the components included in the measuring device 10, the control terminal 20, and the server 30 may also be included in other devices. As an example, the measuring device 10 may also have an input unit 21, a prompt unit 22, and / or an autonomic nerve analysis unit 33. The control terminal 20 may also have a biological data acquisition unit 11, a circadian rhythm calculation unit 32, an autonomic nerve analysis unit 33, and / or a weighting coefficient calculation unit 34. The server 30 may also have an input unit 21 and / or a prompt unit 22. In addition, the measuring device 10, the control terminal 20, and the server 30 may also include Figure 1 Alternatively, the measuring device 10, the control terminal 20 and the server 30 may be reduced to Figure 1 The components shown.

[0261] In the first embodiment, an example is described in which the analysis system 1A includes one measurement device 10 and one control terminal 20 , but the present invention is not limited thereto. The analysis system 1A may include one or more measurement devices 10 and one or more control terminals 20 .

[0262] When the analysis system 1A includes multiple measurement devices 10 and / or multiple control terminals 20, information acquired by the multiple measurement devices 10 and / or multiple control terminals 20 can be aggregated into the server 30. Since the server 30 can analyze the physical information using information acquired from multiple users, the accuracy of the estimated physical information can be improved.

[0263] While the example described in Embodiment 1 illustrates that the biometric data includes daily fluctuations in at least one of the following vital signs: body temperature, heart rate, pulse rate, respiration, brain waves, and blood pressure, the present invention is not limited to this. Biometric data may also include data other than these vital signs. For example, if the measurement device 10 includes the autonomic nervous system analysis unit 33, that is, if the measurement device 10 performs autonomic nervous system analysis based on heart rate, the biometric data may also include autonomic nervous system activity indicators (LF, HF, LF / HF, TP, ccvTP).

[0264] While the first embodiment describes an example in which the analysis system 1A analyzes body information using heart rate as biometric data, the present invention is not limited to this example. For example, the analysis system 1A may analyze body information using at least heart rate or pulse rate as biometric data. This facilitates acquisition of biometric data and improves the accuracy of biometric information analysis.

[0265] In the first embodiment, the example in which the biological information analysis unit 35 corrects the weighted autonomic nervous system analysis results when the user's heart rate exceeds a predetermined threshold is described, but the present invention is not limited to this. The biological information analysis unit 35 may also correct the weighted autonomic nervous system analysis results when the user's pulse rate exceeds a predetermined threshold.

[0266] In Embodiment 1, the analysis method is described as an example in which the components included in the measurement device 10, the control terminal 20, and the server 30 execute each step. However, the present invention is not limited to this embodiment. Each step of the analysis method can also be executed by a computer. The computer includes a processor and a memory storing a program executed by the processor.

[0267] While the example of calculating the average circadian rhythm based on sleep information and biological data has been described in the first embodiment, the present invention is not limited to this. For example, the average circadian rhythm may be calculated based on biological data instead of using sleep information. In this case, the circadian rhythm calculation unit 32 may calculate the average circadian rhythm after accumulating biological data for more than one week. That is, the circadian rhythm calculation unit 32 may not calculate the average circadian rhythm until more than one week of biological data has been accumulated. Furthermore, the average circadian rhythm may be calculated based on sleep information instead of biological data. Alternatively, the average circadian rhythm may be calculated based on information other than sleep information. For example, the user may input information indicating whether the user is a morning type or an evening type into the input unit 21. The circadian rhythm calculation unit 32 may also calculate the first circadian rhythm based on the type information input by the user. The circadian rhythm calculation unit 32 may use any information as long as it can calculate the user's average circadian rhythm.

[0268] In the first embodiment, the analysis method includes steps ST21 to ST29 , but the present invention is not limited thereto. The analysis method may include additional steps, reduce some steps, or implement multiple steps in one step.

[0269] In the first embodiment, the example in which the biological data acquisition unit 11 includes the heart rate measurement unit 14 and the body temperature measurement unit 15 is described, but the present invention is not limited to this. The biological data acquisition unit 11 only needs to include equipment capable of acquiring biological data. For example, the biological data acquisition unit 11 may also include a pulse rate measurement unit, an activity level measurement unit, and the like.

[0270] While the first embodiment describes an example in which the biometric data acquisition unit 11 acquires biometric data while the user is awake, the present invention is not limited to this embodiment. For example, the biometric data acquisition unit 11 may also acquire biometric data while the user is asleep. Thus, the circadian rhythm calculation unit 32 can calculate the average circadian rhythm using biometric data from the user's sleep, in addition to the user's awake biometric data. As a result, an average circadian rhythm more suitable for the user can be calculated.

[0271] In the first embodiment, the example in which the presentation unit 22 also functions as the notification unit has been described, but the present invention is not limited thereto. The presentation unit 22 and the notification unit may be separate components.

[0272] In the first embodiment, the circadian rhythm calculation unit 32 calculates the average circadian rhythm using the fluctuation of the user's body temperature. However, the present invention is not limited to this. For example, the circadian rhythm calculation unit 32 may calculate the average circadian rhythm using heart rate, pulse rate, or autonomic nervous system activity index.

[0273] In the first embodiment, the autonomic nerve analysis unit 33 performs autonomic nerve analysis based on the fluctuation of the user's heart rate. However, the present invention is not limited to this. For example, the autonomic nerve analysis unit 33 may perform autonomic nerve analysis based on the fluctuation of the user's pulse rate.

[0274] In the first embodiment, the example in which the time data is acquired by the measurement device 10 is described, but the present invention is not limited to this. For example, the time data may be acquired by the control terminal 20. In this case, the control terminal 20 transmits a measurement start instruction to the measurement device 10 and receives the measurement data from the measurement device 10. In this case, the control terminal 20 may also add the control terminal 20's time data and input information to the measurement data and transmit it to the server 30.

[0275] (Implementation Method 2)

[0276] The analysis system according to the second embodiment of the present invention will be described. In the second embodiment, the differences from the first embodiment will be mainly described. In the second embodiment, the same or equivalent components as those in the first embodiment are designated by the same reference numerals. In the second embodiment, overlapping descriptions with those in the first embodiment will be omitted.

[0277] use Figure 12 An example of an analysis system according to the second embodiment will be described. Figure 12 This is a block diagram showing a schematic configuration of an example of an analysis system 1B according to a second embodiment of the present invention.

[0278] The second embodiment is different from the first embodiment in that an activity amount measuring unit 16 is provided.

[0279] like Figure 12 As shown, the analysis system 1B further includes an activity amount measurement unit 16. In the second embodiment, the measurement device 10A includes the activity amount measurement unit 16.

[0280] <Activity Measurement Unit>

[0281] The activity measurement unit 16 is an activity meter that measures the user's activity. The activity measurement unit 16 is, for example, an accelerometer. The activity measurement unit 16 is controlled by the first control unit 12. The user's activity data measured by the activity measurement unit 16 is transmitted to the first control unit 12. The first control unit 12 transmits the activity data to the control terminal 20 via the first communication unit 13. The control terminal 20 receives the activity data from the measurement device 10A via the second communication unit 24 and transmits the activity data to the server 30.

[0282] In the second embodiment, since the analysis system 1B includes the activity amount measurement unit 16 , for example, the following processing can be realized.

[0283] [An Example of Calculation Processing of Average Circadian Rhythm Based on Activity Data]

[0284] The circadian rhythm calculation unit 32 can also calculate the first circadian rhythm based on the activity data. For example, the circadian rhythm calculation unit 32 estimates the user's bedtime and waking time based on the activity data, and calculates the first circadian rhythm based on the estimated bedtime and waking time of the user. For example, when the activity data is less than a specified threshold, and the state of the activity data less than the specified threshold continues for a specified time, the circadian rhythm calculation unit 32 determines that the user has gone to bed and estimates the bedtime. On the other hand, when the activity data is greater than the specified threshold, the circadian rhythm calculation unit 32 determines that the user has gotten up and estimates the waking time. In the case where the activity meter is an acceleration sensor, the determination of going to bed and getting up can be performed based on the posture (lying position, sitting / standing position) according to the acceleration information, so the determination accuracy can be improved by combining the activity and posture.

[0285] The circadian rhythm calculation unit 32 reads a correlation equation or a correlation table representing the correlation between the bedtime, waking time, and circadian rhythm from the storage unit 31. The circadian rhythm calculation unit 32 calculates the first circadian rhythm using the user's estimated bedtime and waking time and the read correlation equation or correlation table. The threshold value for the estimated bedtime activity data and the threshold value for the estimated waking time activity data may be different or the same.

[0286] Like this, in embodiment 2, embodiment 1 can also be Figure 4 Steps ST11 to ST13 are replaced with a simplified calculation process of the first circadian rhythm based on the above-mentioned activity data. Alternatively, the circadian rhythm calculation unit 32 may simply calculate the first circadian rhythm based on both the sleep information and the activity data.

[0287] According to such a configuration, the accuracy of the simplified calculation of the first circadian rhythm can be improved.

[0288] [An Example of Resting State Determination Processing Based on Activity Level Data]

[0289] The measurement device 10A may also determine whether the user is resting based on the activity data, and acquire the user's biometric data via the biometric data acquisition unit 11 when the user is resting. For example, the measurement device 10A determines that the user is not resting when the activity data exceeds a predetermined threshold, and determines that the user is resting when the activity data is below the predetermined threshold. This determination is made by the first control unit 12. In the measurement device 10A, the biometric data acquisition unit 11 acquires biometric data when the user is resting.

[0290] When the activity data is above a predetermined threshold, the measurement device 10A transmits information indicating that the user is not resting to the control terminal 20 via the first communication unit 13. Based on the information indicating that the user is not resting, the control terminal 20 creates prompt information urging the user to rest and displays the prompt information on the prompt unit 22. Alternatively, when the activity data is below a predetermined threshold, the measurement device 10A transmits information indicating that the user is resting to the control terminal 20 via the first communication unit 13. Based on the information indicating that the user is resting, the control terminal 20 notifies the user of the timing for acquiring biological data via the notification unit.

[0291] This configuration allows acquisition of biological data while the user is resting, thereby improving the accuracy of autonomic nervous system analysis and the accuracy of estimating circadian rhythm changes.

[0292] [An example of autonomic nervous system analysis based on activity data]

[0293] The body information analysis unit 35 may also correct the weighted autonomic nervous system analysis results based on the activity data. For example, the body information analysis unit 35 obtains the activity data via the control terminal 20. If the activity data exceeds a predetermined threshold, the body information analysis unit 35 reduces the correction factor K1. Alternatively, the body information analysis unit 35 may adjust the correction factor K1 based on the heart rate data and intelligent information derived from the activity data.

[0294] This configuration makes it possible to determine whether the user is resting based on activity data. Furthermore, if the physical information analysis unit 35 determines that the user is not in a stable state, the reliability of the autonomic nervous system analysis results can be reduced. This improves the accuracy of the autonomic nervous system analysis results and the accuracy of estimating circadian rhythm changes.

[0295] Furthermore, when utilizing both heart rate data and activity level data, it is possible to determine based on the activity level data whether changes in heart rate are due to exercise or stress. This allows the correction coefficient K1 to be varied depending on the cause of the heart rate increase, thereby improving the accuracy of the autonomic nervous system analysis results.

[0296] Furthermore, the processing based on the activity amount data measured by the activity amount measurement unit 16 described in the second embodiment may be performed in whole or in part.

[0297] In the second embodiment, an example in which the active mass measuring unit 16 is included in the measuring device 10A has been described, but the present invention is not limited thereto. For example, the active mass measuring unit 16 may be included in the control terminal 20 .

[0298] In the second embodiment, the measurement device 10A determines whether the user is resting based on the activity data, but the present invention is not limited thereto. The control terminal 20 or the server 30 may also determine whether the user is resting based on the activity data.

[0299] The following describes an example in which the control terminal 20 includes an activity measurement unit 16 and a GPS (Global Positioning System). In this case, the second control unit 23 calculates the user's acceleration and position based on the activity data measured by the activity measurement unit 16 and GPS information, and calculates the user's exercise intensity and movement history. This allows analysis of the user's behavior, eliminating the need for the user to input information into the input unit 21.

[0300] The control terminal 20 may also control the measuring device 10 to start the measurement of the biological data by the user inputting to the input unit 21 (for example, pressing the start button). The measurement of the biological data is preferably performed when the user is in a resting state. Therefore, the control terminal 20 determines whether a large body movement has occurred during the measurement based on the activity data (acceleration) measured by the activity measurement unit 16. Moreover, if it is determined that a large body movement has occurred, a warning alarm or the like is prompted from the prompt unit 22. In addition, if there is a possibility that the calculation accuracy of the autonomic nervous activity index has been significantly reduced, the measurement may be automatically repeated.

[0301] Alternatively, rather than the user initiating measurement, the control terminal 20 may determine that the user is in a resting state based on the activity data (acceleration) and automatically initiate measurement by the measurement device 10A. Alternatively, if no significant change in the activity data (acceleration) occurs within a predetermined period of time, the control terminal 20 may determine that the user is in a resting state and automatically initiate measurement by the measurement device 10A.

[0302] In addition, the control terminal 20 may also measure the activity data (acceleration) on a regular basis and calculate the exercise intensity. The control terminal 20 may also determine whether the user is walking, exercising, or resting based on the exercise intensity before and after the measurement, and determine the reliability of the analysis result. For example, when the user was previously determined to be exercising, the control terminal 20 may also reduce the reliability of the measurement. Since it takes time to reach a resting state after exercising or walking, the control terminal 20 may not start the specified time measurement. Furthermore, the heart rate / pulse rate may be measured on a regular basis, and the time period required for the analysis to continue in the resting state during or after the measurement may be extracted, and the data of this time period may be used for analysis.

[0303] (Implementation 3)

[0304] The analysis system according to the third embodiment of the present invention will be described. In the third embodiment, the differences from the second embodiment will be mainly described. In the third embodiment, the same or equivalent components as those in the second embodiment are designated by the same reference numerals. In the third embodiment, overlapping descriptions with those in the second embodiment will be omitted.

[0305] use Figure 13 as well as Figure 14 An example of an analysis system according to the third embodiment will be described. Figure 13 This is a block diagram showing a schematic configuration of an example of an analysis system 1C according to a third embodiment of the present invention. Figure 14 This is a schematic diagram of an example of a handheld measuring device.

[0306] The third embodiment is different from the second embodiment in that it includes a first measurement device 10B and a second measurement device 10C. In the third embodiment, the first measurement device 10B is a handheld device.

[0307] like Figure 13 As shown, the analysis system 1C includes a first measurement device 10B and a second measurement device 10C.

[0308] <First measuring device>

[0309] The first measurement device 10B includes a biological data acquisition unit 11a, a first control unit 12a, and a first communication unit 13a. The biological data acquisition unit 11a includes a heart rate measurement unit 14 and a pulse rate measurement unit 17. In the first measurement device 10B, the heart rate data measured by the heart rate measurement unit 14 and the pulse rate data measured by the pulse rate measurement unit 17 are transmitted to the first control unit 12a. The first control unit 12a transmits the heart rate data and pulse rate data to the control terminal 20 via the first communication unit 13a.

[0310] <Second measuring device>

[0311] The second measurement device 10C includes a biological data acquisition unit 11b, an activity measurement unit 16, a first control unit 12b, and a first communication unit 13b. The biological data acquisition unit 11b includes a body temperature measurement unit 15. In the second measurement device 10C, body temperature data measured by the body temperature measurement unit 15 and activity data measured by the activity measurement unit 16 are transmitted to the first control unit 12b. The first control unit 12b transmits the body temperature data and activity data to the control terminal 20 via the first communication unit 13b.

[0312] The first control units 12a and 12b and the first communication units 13a and 13b in the first measurement device 10B and the second measurement device 10C are the same as the first control unit 12 and the first communication unit 13 in the first embodiment, and therefore detailed descriptions thereof are omitted.

[0313] like Figure 14 As shown, the first measurement device 10B is a handheld measurement device. In the first measurement device 10B, the biological data acquisition unit 11a that detects heart rate and pulse rate has electrocardiographic sensors (electrocardiographic electrodes) 14A and 14B and a photoelectric pulse sensor 17A mounted in a portable handheld housing.

[0314] The first measurement device 10B is a handheld measurement device that, when held by the user, can acquire electrocardiographic signals and photoelectric pulse waves and measure heart rate, pulse rate, and body temperature. The first measurement device 10B includes a main body 110 shaped like a roughly rotating ellipsoid that is grasped by the user's thumb and four fingers of one hand (e.g., the right hand) during measurement. A plate-shaped flange 118 is projected from the side of the main body 110 in a direction approximately orthogonal to the direction in which the stopper 111 is projected (i.e., lateral). The flange 118 is provided to extend along the axial direction of the main body 110 (i.e., from the base end toward the tip end).

[0315] The first ECG electrode 14A is disposed so as to contact the fingers (e.g., index finger and / or middle finger) of one hand (e.g., right hand) when the main body 110 is grasped by the hand. Alternatively, the first ECG electrode 14A may be disposed so as to contact the thumb of one hand (e.g., right hand).

[0316] On the other hand, a second electrocardiographic electrode 14B, for example, shaped like an ellipse, is disposed on the front surface (and / or back surface) of the flange 118 for detecting electrocardiographic signals. Specifically, the second electrocardiographic electrode 14B is disposed so that it comes into contact with the fingers (e.g., thumb and / or index finger) of the other hand (e.g., left hand) by pinching (gripping) the flange 118. In other words, when the user grasps the main body 110 and flange 118 of the first measurement device 10B, the first and second electrocardiographic electrodes 14A and 14B come into contact with each other through the user's left and right hands (fingertips), thereby acquiring an electrocardiographic signal corresponding to the potential difference between the user's left and right hands.

[0317] A photoelectric pulse sensor 17A is provided in the main body 110. This sensor comprises a light-emitting element and a light-receiving element, and detects a photoelectric pulse wave from the tip of the thumb, which is restrained by the stopper 111. This sensor optically detects the photoelectric pulse wave by utilizing the light absorption properties of hemoglobin in the blood.

[0318] As described above, the analysis system 1C may include multiple measurement devices 10B and 10C. Furthermore, the first measurement device 10B may be a handheld device. Specifically, the biological data acquisition unit 11a (heart rate measurement unit 14 and pulse rate measurement unit 17) may be attached to a handheld measurement device. This facilitates measurement of heart rate and pulse rate.

[0319] Furthermore, in the third embodiment, the first measurement device 10B is described as a handheld device, but the present invention is not limited thereto. For example, the second measurement device 10C may also be a handheld device.

[0320] While the third embodiment describes an example in which the biological data acquisition unit 11a includes the heart rate measurement unit 14 and the pulse rate measurement unit 17, the present invention is not limited thereto. For example, the biological data acquisition unit 11a may include either the heart rate measurement unit 14 or the pulse rate measurement unit 17. Alternatively, the biological data acquisition unit 11a may include the body temperature measurement unit 15.

[0321] In the third embodiment, the second measurement device 10C is described as including the activity measurement unit 16 , but the present invention is not limited thereto. For example, the first measurement device 10B may include the activity measurement unit 16 , and the control terminal 20 may include the activity measurement unit 16 .

[0322] (Implementation 4)

[0323] The analysis system according to the fourth embodiment of the present invention will be described. In the fourth embodiment, the differences from the second embodiment will be mainly described. In the fourth embodiment, the same or equivalent components as those in the second embodiment are designated by the same reference numerals. In the fourth embodiment, overlapping descriptions with those in the second embodiment will be omitted.

[0324] In embodiment 4, use Figures 15-17 The following describes an example where the measuring device is a wearable device or an adhesive device. Figure 12 The configuration of the analysis system 1B according to the second embodiment shown is the same, so detailed description thereof will be omitted.

[0325] Neck-worn devices

[0326] Figure 15 FIG. 1 is a schematic diagram of an example of a neck-worn measuring device 10D. Figure 15 As shown, the measuring device 10D includes a generally U-shaped neckband 120 that is elastically worn so as to clamp the user's neck from the back, and a pair of sensor units 121 and 122 disposed at both ends of the neckband 120, which contact the user's neck. Sensor unit 122 (121) primarily includes a rectangular, planar ECG electrode (conductive fabric) 14C. In addition to the aforementioned configuration, one sensor unit 122 also includes a photoelectric pulse sensor 17B. Photoelectric pulse sensor 17B optically detects photoelectric pulse waves by utilizing the light absorption properties of hemoglobin in the blood.

[0327] In this way, a neck-worn device is worn around the user's neck. Neck-worn devices can measure pulse rate using a photoelectric pulse sensor or heart rate using an electrocardiogram sensor with multiple electrocardiogram electrodes. Neck-worn devices can cause relatively greater discomfort during exercise, but this discomfort is less noticeable in daily life. Furthermore, measurement stability is second only to chest-worn devices, allowing for adequate measurement of autonomic nervous system activity. Furthermore, since surface temperature near the carotid artery is close to deep body temperature, deep body temperature can be estimated, similar to chest-worn devices, and circadian rhythms can also be estimated based on deep body temperature.

[0328] The measuring device 10D may also include a temperature regulating unit for regulating the temperature of the user's neck. For example, when there is no decrease from the maximum peak value of the circadian rhythm, that is, when the amplitude is small, the neck is cooled by cooling the temperature regulating unit, thereby lowering the user's body temperature and suppressing the disturbance of the circadian rhythm (reduction in amplitude). In addition, if the deep body temperature does not decrease when falling asleep, the sleep quality is reduced, so the deep body temperature is lowered by cooling the neck by using the temperature regulating unit, thereby promoting falling asleep. The temperature regulating unit has, as a cooling component, a Peltier element, a fan and / or a blower. Thus, the neck can be cooled by utilizing the Peltier effect, air supply to the neck, and the heat of vaporization of water.

[0329] Furthermore, if the circadian rhythm does not rise from its minimum peak value, i.e., if the amplitude is small, the user's body temperature can be raised by heating the neck with the temperature control unit, thereby suppressing circadian rhythm disturbances (reduction in amplitude). The temperature control unit, as a heating component, may include, for example, a resistor, an infrared device, and / or a heater resistor. Thus, the neck can be warmed by radiating infrared rays or directly heating the neck.

[0330] In addition, the temperature adjustment unit only needs to have at least one of the functions of cooling and heating.

[0331] <Watch-type device>

[0332] Figure 16 FIG. 1 is a schematic diagram of an example of a wristwatch-type measuring device 10E. Figure 16 As shown, this wristwatch-type measurement device 10E includes a main body 130, a strap 131 attached to the main body 130, and a pulse sensor 132 disposed on the back of the main body 130. A photoelectric pulse sensor 17C is disposed on the inner surface of the pulse sensor 132. Therefore, when a user wears this wristwatch-type measurement device 10E on one wrist (e.g., the left hand), the photoelectric pulse sensor 17C contacts the user's wrist, allowing the user to measure the pulse rate, etc.

[0333] <Breast-attached device>

[0334] Figure 17 FIG. 1 is a schematic diagram of an example of a chest-attached measuring device 10F. Figure 17 As shown, the measurement device 10F includes a main body 140 that can be attached to the user's chest, and two (or more) electrocardiographic electrodes (gel electrodes) 14D that are detachably attached to the main body 140. When using the measurement device 10F to measure electrocardiographic signals, the measurement device 10F is attached (worn) to the chest, with the electrocardiographic electrodes (gel electrodes) 14D in contact with the chest. This allows electrocardiographic signals to be detected via the electrocardiographic electrodes (gel electrodes) 14D.

[0335] As the ECG electrode 14D, for example, silver / silver chloride, conductive gel, conductive rubber, conductive plastic, metal, conductive cloth, a capacitive coupling electrode with a metal surface coated with an insulating layer, etc. can be used. As the metal, for example, stainless steel, Au, and other corrosion-resistant materials with little metal allergy are preferred. As the conductive cloth, for example, textiles, braids, and non-woven fabrics composed of conductive threads with conductivity can be used. In addition, as the conductive thread, for example, a material with a surface coated with a resin wire such as Ag, a material coated with carbon nanotubes, or a material coated with a conductive polymer such as PEDOT can be used. In addition, a conductive polymer wire with conductivity can also be used.

[0336] For chest-mounted devices, it's preferable to use an ECG sensor with multiple ECG electrodes to measure heart rate. Chest-mounted devices offer higher measurement stability. Furthermore, since they're attached to the torso, they can also estimate deep body temperature (core temperature) based on heat flux from the body's surface temperature, and thus circadian rhythms. Alternatively, they can be secured to the chest with a strap instead of adhesive tape.

[0337] In addition, in embodiment 4, an example of wearing a wearable device on the neck and arm is described, but it is not limited to this. The wearable device can also be worn on a part other than the neck and arm. For example, the wearable device can also be worn on the chest. In addition, an example of sticking an adhesive device on the chest is described, but it is not limited to this. The sticking device can also be stuck on a part other than the chest. For example, the sticking device can also be stuck on the neck or arm. In such a structure, it can also play a role in Figures 15-17 The effects of the wearable device and the adhesive device are shown.

[0338] While Embodiment 4 describes an example in which the wearable device and the adhesive patch incorporate electrocardiogram electrodes 14C and 14D as the heart rate measurement unit 14 and photoelectric pulse sensors 17B and 17C as the pulse rate measurement unit 17, the present invention is not limited to this. For example, the wearable device and the adhesive patch may also incorporate a body temperature measurement unit 15 and / or an activity level measurement unit 16. This allows for easy acquisition of body temperature and / or activity level data as the user's biological data.

[0339] Although the present invention has been fully described in connection with the preferred embodiments with reference to the accompanying drawings, various modifications and variations will be apparent to those skilled in the art. It should be understood that such modifications and variations are encompassed within the scope of the present invention as long as they do not depart from the scope of the invention as defined in the appended claims.

[0340] The analysis system of the present invention can be applied to analysis of user's physical information, for example.

[0341] Description of Reference Numerals

[0342] 1A, 1B, 1C…Analysis system; 10, 10A, 10B, 10C, 10D, 10E, 10F…Measurement device; 11, 11a, 11b…Biological data acquisition unit; 12, 12a, 12b…First control unit; 13, 13a, 13b…First communication unit; 14…Heart rate measurement unit; 14A, 14B, 14C, 14D…ECG electrodes; 15…Body temperature measurement unit; 16…Activity measurement unit 17…pulse rate measuring unit; 17A, 17B, 17C…photoelectric pulse sensor; 20…control terminal; 21…input unit; 22…prompt unit; 23…second control unit; 24…second communication unit; 30…server; 31…storage unit; 32…circadian rhythm calculation unit; 33…autonomic nervous system analysis unit; 34…weighting coefficient calculation unit; 35…physical information analysis unit; 36…third control unit; 37…third communication unit.

Claims

1. An analysis system that analyzes physical information and is capable of acquiring biometric data from a biometric data acquisition unit, wherein the biometric data acquisition unit acquires the biometric data of a user, the analysis system comprising: a circadian rhythm calculation unit for calculating the average circadian rhythm of the user; an autonomic nerve analysis unit that performs autonomic nerve analysis based on the changes in the biological data; and The body information analysis unit weights the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit using a weighting coefficient set based on the measurement time of the user's biological data and the cycle of the average circadian rhythm, and estimates the change of the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result. When the measurement time is within a predetermined range including the maximum peak value of the average circadian rhythm, the weighting coefficient is increased compared to when the measurement time is within other time ranges.

2. The analysis system according to claim 1, wherein: The above analysis system further comprises a weighting coefficient calculation unit, The weighting coefficient calculation unit sets the weighting coefficient based on a measurement time at which the biological data of the user is measured and a cycle of the average circadian rhythm.

3. The analysis system according to claim 1 or 2, wherein: The biological data includes at least heart rate or pulse rate.

4. The analysis system according to claim 1 or 2, wherein: The circadian rhythm calculation unit calculates the average circadian rhythm based on the biological data acquired by the biological data acquisition unit.

5. The analysis system according to claim 1 or 2, wherein: It also includes an input unit for inputting the sleep information of the user. The circadian rhythm calculation unit calculates the average circadian rhythm based on the sleep information input through the input unit.

6. The analysis system according to claim 1 or 2, wherein: The weighting coefficient calculation unit increases the weighting coefficient when the measurement time is within the range of -1 / 8 to 3 / 8 of the cycle of the maximum peak value of the average circadian rhythm, as compared to when the measurement time is outside the range of -1 / 8 to 3 / 8 of the cycle of the maximum peak value of the average circadian rhythm.

7. The analysis system according to claim 1 or 2, wherein: When the user's heart rate or pulse is greater than a predetermined threshold, the body information analysis unit corrects the weighted autonomic nerve analysis result.

8. The analysis system according to claim 1 or 2, wherein: The biological information analysis unit estimates changes in the circadian rhythm based on at least one of a deviation of the time of the maximum peak of the circadian rhythm from the average circadian rhythm, a deviation of the time of the minimum peak from the average circadian rhythm, a decrease in amplitude, and multimodality.

9. The analysis system according to claim 1 or 2, wherein: The body information analysis unit further estimates the sleep quality and activity suitability of the user based on the weighted autonomic nerve analysis result.

10. The analysis system according to claim 1 or 2, wherein: The device further comprises a prompting unit configured to provide prompt information, wherein the prompt information includes suggestions for improving the circadian rhythm. The biological information analysis unit creates the prompt information based on changes in the circadian rhythm.

11. The analysis system according to claim 10, wherein: The body information analysis unit calculates and predicts the circadian rhythm based on the weighted autonomic nerve analysis result. The prompt information includes the average circadian rhythm and the predicted circadian rhythm.

12. The analysis system according to claim 1 or 2, wherein: The device further includes a notification unit configured to notify a timing of measuring the vital data.

13. The analysis system according to claim 1 or 2, wherein: The biological data acquisition unit is built into a patch-type measurement device or a wearable measurement device.

14. The analysis system according to claim 13, wherein: The measurement device includes a temperature adjustment unit that is attached to or worn on the user's neck and adjusts the temperature of the user's neck.

15. The analysis system according to claim 1 or 2, wherein: further comprising an activity measurement unit for measuring activity data of the user, The body information analysis unit corrects the weighted autonomic nerve analysis result based on the activity data measured by the activity measurement unit.

16. The analysis system according to claim 15, wherein: The activity amount measuring unit is mounted on an adhesive-type measuring device or a wearable-type measuring device.

17. An analysis system that analyzes physical information and is capable of acquiring biological data from one or more measurement devices, wherein the one or more measurement devices include a biological data acquisition unit that acquires the biological data of a user, the analysis system comprising: One or more control terminals in communication with the one or more measurement devices; and The server communicates with one or more control terminals. The one or more control terminals have: a prompting unit for providing prompting information for improving circadian rhythm; and The second communication unit transmits the biometric data to the server and receives the prompt information from the server. The above server has: a circadian rhythm calculation unit for calculating the average circadian rhythm of the user; an autonomic nerve analysis unit that performs autonomic nerve analysis based on a change in the user's biological data in the biological data; a body information analysis unit that weights the autonomic nerve analysis result analyzed by the autonomic nerve analysis unit using a weighting coefficient set based on the measurement time of the user's biological data and the cycle of the average circadian rhythm, estimates a change in the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nerve analysis result, and creates the prompt information based on the change in the circadian rhythm; and a third communication unit, receiving the biometric data from the control terminal and sending the prompt information to the control terminal; When the measurement time is within a predetermined range including the maximum peak value of the average circadian rhythm, the weighting coefficient is increased compared to when the measurement time is within other time ranges.

18. The analysis system according to claim 17, wherein: The above analysis system further comprises a weighting coefficient calculation unit, The weighting coefficient calculation unit sets the weighting coefficient based on a measurement time at which the biological data of the user is measured and a cycle of the average circadian rhythm.

19. A method for analyzing body information by computer, comprising: The step of obtaining the user's biometric data; a step of performing autonomic nervous system analysis based on changes in the user's biological data; The step of calculating the average circadian rhythm of the user; a step of weighting the autonomic nervous system analysis result using a weighting coefficient set based on a measurement time at which the biological data of the user is measured and a cycle of the average circadian rhythm; and The step of estimating the change of the circadian rhythm relative to the average circadian rhythm based on the weighted autonomic nervous system analysis results, When the measurement time is within a predetermined range including the maximum peak value of the average circadian rhythm, the weighting coefficient is increased compared to when the measurement time is within other time ranges.

20. The analysis method according to claim 19, wherein: The method further includes setting the weighting coefficient based on the time at which the biological data of the user is measured and the cycle of the average circadian rhythm.

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