Drowsiness calculation device

Abstract

To provide a drowsiness calculation device that is able to highly accurately calculate drowsiness taking account of the present activity as well as the past activity of the subject.SOLUTION: In a smart phone 1, an I / F18 acquires a heart rate of a subject, measured by a heart beat sensor 19, and a measurement time, and a condition determination unit 11 determines an active state of the subject and transmits it to a server 2. Then, in the server 2, according to the active state transmitted from the smart phone 1, a correction unit 24 acquires a drowsiness reference heart rate from a drowsiness reference heart rate table 21 in which drowsiness reference heart rates of the subject are stored for each active state. Then, the drowsiness reference heart rate acquired by the correction unit 24 is corrected based on a fatigue degree, sleep time, and diet. Next, the corrected drowsiness reference heart rate is transmitted to the smart phone 1, and a drowsiness degree calculation unit 16 of the smart phone 1 calculates a drowsiness degree of the subject on the basis of the corrected drowsiness reference heart rate and the heart rate acquired by the I / F 18.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a drowsiness calculation device that calculates drowsiness of a subject. [Background technology]

[0002] For example, in order to detect whether a driver is dozing while driving an automobile, it has been proposed to detect the dozing state from the driver's heart rate (see, for example, Patent Documents 1 and 2).

[0003] Patent Document 1 describes that a threshold is set to a predetermined multiple of the average value of the RRI (RR Interval) value of the heart rate detected by a heart rate sensor when awake and the integral value of the RRI value that exceeds the average value, and if the integrated RRI value that exceeds the average value exceeds the threshold value, it is determined that the person is dozing.

[0004] Patent Document 2 describes how heart rate interval data is obtained by converting RRI into a time series based on the heart rate waveform of a subject in a resting state measured by a sensor. Next, the heart rate interval data is frequency analyzed to obtain a frequency analysis result of heart rate fluctuations including a power spectral density (PSD) for a frequency at a certain time and a total power (TP) of the autonomic nerves. Next, the frequency analysis result is set as an initial state, and a drowsiness scale in which the origin of the drowsiness position and the origin of the awake position are set is determined based on the estimated drowsiness position and the estimated awake position included in the initial state. Then, the drowsiness of the subject is determined based on the drowsiness scale. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 3252586 [Patent Document 2] JP 2014-12042 A Summary of the Invention [Problem to be solved by the invention]

[0006] In the case of the method described in Patent Document 1, data at the time of initial awakening is used as a reference. However, the initial state differs from person to person, and depending on the level of awakening at the initial state, the average value of the RRI value, etc., may not be set appropriately. Therefore, there is a problem that it is not possible to deal with the difference in the initial state between people.

[0007] The method described in Patent Document 2 has the following problems. The correspondence between heart rate information and drowsiness scale changes due to various factors. Even if a resting state is used as a base, there are changes depending on the day and the time of day. For example, it is known that drowsiness becomes stronger between 3 and 4 in the early morning, and between 3 and 4 in the afternoon during the day. Therefore, it is difficult to estimate drowsiness with high accuracy using only heart rate information. In addition, driving is a kind of stressful state, and the baseline of the heart rate differs depending on the road condition (city or highway). Furthermore, since the state at the start of measurement is not constant, it is not possible to respond to changes such as when the driver is sleepy from the beginning or when the driver is not sleepy at first.

[0008] Furthermore, in order to determine drowsiness, it is necessary to set some kind of reference heart rate, etc., as in Patent Documents 1 and 2. However, heart rate not only varies from person to person, but also varies depending on the time of day and activity state. Furthermore, drowsiness is also influenced by past actions (activities), such as the state of sleep (e.g., whether one slept well the previous day), the state of eating (e.g., whether one has eaten a meal), or the state of fatigue (whether one is tired or not).

[0009] The methods described in Patent Documents 1 and 2 do not take into consideration the activity of the subject, and therefore may not be able to accurately detect drowsiness depending on the activity of the subject.

[0010] In view of the above-mentioned problems, an object of the present invention is to provide a drowsiness calculation device that can calculate drowsiness with high accuracy, for example, by taking into account a subject's current activity and past activity. [Means for solving the problem]

[0011] In order to solve the above problem, the invention described in claim 1 is a drowsiness calculation device characterized by comprising a bio-information acquisition unit that acquires bio-information regarding the subject's heart rate, a fatigue level information acquisition unit that acquires fatigue level information regarding the subject's fatigue level, and a drowsiness level calculation unit that calculates information regarding the subject's current drowsiness based on the bio-information and the fatigue level information. [Brief description of the drawings]

[0012] [Figure 1] 1 is a schematic configuration diagram of a drowsiness calculation device according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a schematic configuration diagram of the smartphone shown in FIG. [Diagram 3] FIG. 2 is a schematic configuration diagram of a server shown in FIG. [Figure 4] 3 is a table showing an example of determining an activity state in a state determining unit shown in FIG. 2 . [Diagram 5] 3 is a table showing an example of the parameter table shown in FIG. 2. [Figure 6] 3 is an explanatory diagram of a display example of a drowsiness display unit shown in FIG. 2. [Figure 7] 4 is a table showing an example of the drowsiness reference heart rate table shown in FIG. 3. [Figure 8] 13 is another configuration example for calculating and setting the drowsiness reference heart rate table. [Figure 9] FIG. 13 is an explanatory diagram of the interpolation of a drowsiness reference heart rate table. [Figure 10] 1 is a table of correction values ​​corresponding to fatigue levels. [Figure 11] 13 is an example of a screen on which a subject inputs a degree of fatigue. [Figure 12] 13 is an example of a screen on which a subject inputs his / her sleep state. [Figure 13] 13 is a table of correction values ​​corresponding to the time elapsed since a meal. [Figure 14] 13 is a flowchart for determining after a meal from position information. [Figure 15] 1 is a table of correction values ​​corresponding to fatigue levels and sleep levels. [Figure 16] 2 is a flowchart of an operation of the drowsiness calculation device shown in FIG. [Figure 17] 17 is a flowchart of an operation for correcting the drowsiness reference heart rate shown in FIG. 16. [Figure 18] 13 is a table of correction values ​​corresponding to destinations in a drowsiness calculation device according to a second embodiment of the present invention. [Figure 19] 13 is a table of correction values ​​corresponding to distances to destinations. [Figure 20] 11 is a table of correction values ​​corresponding to road shapes. [Figure 21] FIG. 13 is an explanatory diagram of a display example before subjective evaluation on a drowsiness display unit of a drowsiness calculation device according to a third embodiment of the present invention. [Figure 22] FIG. 13 is an explanatory diagram of a display example after subjective evaluation of a drowsiness display unit of a drowsiness calculation device according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, a drowsiness calculation device according to an embodiment of the present invention will be described. In the drowsiness calculation device according to an embodiment of the present invention, a biological information acquisition unit acquires biological information related to the heart rate of a subject, an activity state acquisition unit acquires a current activity state that is a current activity state of the subject, and an activity history acquisition unit acquires information on the subject's past activity state. Next, a drowsiness reference heart rate acquisition unit acquires a drowsiness reference heart rate from a drowsiness reference heart rate storage unit in which the drowsiness reference heart rate of the subject is stored for each activity state according to the activity state acquired by the activity state acquisition unit. Next, a correction unit corrects the drowsiness reference heart rate acquired by the drowsiness reference heart rate acquisition unit based on information on the past activity state acquired by the activity history acquisition unit. Then, a drowsiness degree calculation unit calculates information on the subject's drowsiness based on the drowsiness reference heart rate corrected by the correction unit and the biological information acquired by the biological information acquisition unit. In this way, the drowsiness reference heart rate can be corrected based on the subject's past activity state, so that drowsiness can be calculated with high accuracy by considering not only the current activity but also the past activity.

[0014] The activity history acquisition unit may also acquire at least one of the subject's past sleep information, past meal information, and fatigue information associated with past activities. In this way, the sleepiness reference heart rate can be corrected taking into account the subject's past sleep information, past meal information, and fatigue information associated with past activities. Therefore, the sleepiness level can be calculated taking these factors into account.

[0015] The activity history acquisition unit may also acquire the examiner's past stay history at specific locations. In this way, the examinee's past activities can be estimated based on specific locations such as restaurants and hot springs where the examinee has stayed in the past, and the sleepiness reference heart rate can be corrected.

[0016] The apparatus may further include an activity prediction unit that predicts the subject's activity, and the correction unit may further correct the drowsiness reference heart rate acquired by the drowsiness reference heart rate acquisition unit based on the activity predicted by the activity prediction unit. In this way, the drowsiness reference heart rate can be corrected taking into account not only past activities but also future plans, such as going out to play.

[0017] The subject's activity prediction unit may further include a destination information acquisition unit that acquires information about the subject's destination, and the activity prediction unit may predict the subject's activity based on the information about the destination. In this way, it is possible to predict future plans, such as whether the destination itself is home or a trip.

[0018] The present invention may further include a current location information acquisition unit that acquires information about the subject's current location, and the correction unit may correct the reference drowsiness heart rate acquired by the reference drowsiness heart rate acquisition unit based on the information about the current location acquired by the current location information acquisition unit when the current activity state acquired by the activity state acquisition unit is driving a vehicle. In this way, when the subject is driving a vehicle, the reference drowsiness heart rate can be corrected according to the conditions of the road on which the subject is currently traveling, etc.

[0019] The biological information acquiring unit may acquire the measurement time of the acquired biological information, and the drowsiness reference heart rate acquiring unit may acquire the drowsiness reference heart rate according to the measurement time and the activity state from a drowsiness reference heart rate storage unit in which the subject's drowsiness reference heart rate is stored for each time and activity state. In this way, the drowsiness reference heart rate can be selected taking into account the time in addition to the activity state. Therefore, drowsiness can be calculated with even greater accuracy.

[0020] The sleepiness calculation unit may calculate information about the subject's sleepiness based on a sleepiness prediction parameter preset for each activity state. In this way, multiple elements included in the biological information about the heart rate can be weighted according to the activity state.

[0021] The device also includes a display unit that displays information about drowsiness to the subject, and an input unit into which the subject's evaluation of the information about drowsiness displayed on the display unit is input. The drowsiness level calculation unit may update the drowsiness reference heart rate stored in the drowsiness reference heart rate storage unit based on the evaluation input by the input unit, and calculate the information about drowsiness. In this way, the subject can subjectively evaluate the information about drowsiness calculated by the drowsiness level calculation unit. The subjective evaluation result is then fed back so that the drowsiness level calculation unit can calculate the information about drowsiness again. Therefore, the information about drowsiness can be calculated more accurately in accordance with the subject's individual sense.

[0022] In addition, in an information processing device according to one embodiment of the present invention, an activity state acquisition unit acquires an activity state of a subject, and an activity history acquisition unit acquires information on the subject's past activity states. Then, a correction unit corrects a drowsiness reference heart rate selected from a drowsiness reference heart rate storage unit in which the subject's drowsiness reference heart rate is stored for each activity state according to the activity state acquired by the activity state acquisition unit, based on the information on the past activity states acquired by the activity history acquisition unit. In this way, it is possible to correct the drowsiness reference heart rate based on the subject's past activity states.

[0023] In addition, in a drowsiness calculation device according to another embodiment of the present invention, a biological information acquisition unit acquires biological information related to the heart rate of a subject, and an activity state acquisition unit acquires a current activity state, which is a current activity state of the subject. Next, an activity prediction unit predicts the activity state of the subject. Next, a drowsiness reference heart rate acquisition unit acquires a drowsiness reference heart rate from a drowsiness reference heart rate storage unit in which the drowsiness reference heart rate of the subject is stored for each activity state according to the current activity state. Next, a correction unit corrects the drowsiness reference heart rate acquired by the drowsiness reference heart rate acquisition unit based on the activity state predicted by the activity prediction unit. Then, a drowsiness degree calculation unit calculates information related to the drowsiness of the subject based on the drowsiness reference heart rate corrected by the correction unit and the biological information acquired by the biological information acquisition unit. In this way, the drowsiness reference heart rate can be corrected taking into account the subject's future plans, so that drowsiness can be calculated with high accuracy taking into account not only current activity but also future activity.

[0024] In addition, in the drowsiness calculation method according to one embodiment of the present invention, the biological information acquisition step acquires biological information related to the subject's heart rate, the activity state acquisition step acquires a current activity state that is the subject's current activity state, and the activity history acquisition step acquires information on the subject's past activity states. Next, the drowsiness reference heart rate acquisition step acquires the drowsiness reference heart rate from a drowsiness reference heart rate storage unit in which the subject's drowsiness reference heart rate is stored for each activity state according to the current activity state. Next, the correction step corrects the drowsiness reference heart rate acquired in the drowsiness reference heart rate acquisition step based on information on the past activity states acquired in the activity history acquisition step. Then, the drowsiness level calculation step calculates information on the subject's drowsiness based on the drowsiness reference heart rate corrected in the correction step and the biological information acquired in the biological information acquisition step. In this way, the drowsiness reference heart rate can be corrected based on the subject's past activity states, so that drowsiness can be calculated with high accuracy by taking into account not only the current activity but also the past activity.

[0025] The above-mentioned drowsiness calculation method may be implemented as a drowsiness calculation program executed by a computer. In this way, the drowsiness reference heart rate can be corrected based on the subject's past activity state using a computer, so that drowsiness can be calculated with high accuracy by taking into account not only the current activity but also the past activity.

[0026] The drowsiness calculation program may be stored in a computer-readable recording medium. In this way, the program can be distributed as a standalone program in addition to being incorporated into a device, and version upgrades can be easily performed. EXAMPLES

[0027] A drowsiness calculation device according to a first embodiment of the present invention will be described with reference to Figs. 1 to 13. As shown in Fig. 1, a drowsiness calculation device 50 according to this embodiment includes a smartphone 1 and a server 2 as an information processing device. The server 2 is also configured to be able to communicate with a stationary measuring device 3. The stationary measuring device 3 is installed, for example, indoors and is capable of measuring the subject's heart rate and blood glucose level. When these measurements are performed, the measuring results are displayed to the subject and transmitted to the server 2. The server 2 also accumulates the measuring results of the stationary measuring device 3.

[0028] 2, the smartphone 1 includes a state determination unit 11 as an activity state acquisition unit, a GPS receiver 12, a G sensor 13, a parameter table 14, a communication unit 15, a drowsiness level calculation unit 16, a drowsiness level display unit 17 as a display unit, and an I / F 18 as a biological information acquisition unit. A heart rate sensor 19 is connected to the smartphone 1.

[0029] 2 may be configured as, for example, an application program (app). The smartphone 1 may also be configured to include a heart rate sensor 19.

[0030] In this embodiment, the smartphone 1 will be described, but it goes without saying that the present invention is not limited to the smartphone 1, and may be a mobile phone, a tablet-type terminal, a wristwatch-type terminal, etc. Alternatively, the present invention may be an in-vehicle device such as a car navigation system.

[0031] The heart rate sensor 19 may be any known sensor that can obtain at least the heart rate as biometric information related to the heart rate. For example, a wristwatch type or other type of sensor may be used. The heart rate sensor 19 is not limited to one type, and multiple types may be used depending on the activity state described below.

[0032] In the smartphone 1 having the above-described configuration, the state determination unit 11 and the drowsiness level calculation unit 16 function as an arithmetic device such as a CPU (Central Processing Unit). The parameter table 14 functions as a storage medium such as a flash memory. The drowsiness level display unit 17 functions as a display device such as a liquid crystal display. The I / F 18 functions as a communication control unit that communicates with the heart rate sensor 19 and the like. The GPS receiver 12 and the G sensor 13 can be built into the smartphone 1.

[0033] The state determination unit 11 determines the subject's current activity state (current activity state), such as resting, driving, working, sleeping, etc., based on the current location information and acceleration information (the subject's current location and acceleration) of the smartphone 1 input from the GPS receiver 12 and the G sensor 13. That is, the state determination unit 11 acquires the subject's current activity state.

[0034] As is well known, the GPS receiver 12 receives radio waves transmitted from a plurality of GPS (Global Positioning System) satellites, obtains current location information (latitude, longitude), and outputs the information to the state determination unit 11.

[0035] The G sensor 13 is a so-called acceleration sensor, and if it is a three-axis acceleration sensor, it can measure acceleration in three directions of the X, Y and Z axes. The G sensor 13 outputs the measured acceleration to the state determination unit 11.

[0036] An example of determining an activity state in the state determination unit 11 is shown in Fig. 4. Fig. 4 is an example of a table for determining an activity state. In the table in Fig. 4, the activity states shown are as follows: state sta1 is resting, state sta2 is driving, state sta3 is working, and state sta4 is sleeping. Of course, the activity state may include items other than those shown in the figure, such as exercise or heavy work.

[0037] In the table of Fig. 4, if the location information is the living room, the acceleration information is small, the speed is small, and the device is a wristwatch, the state is determined as sta1 (resting). Here, the speed may be calculated from the change in the current location information, for example. The device indicates the type of the heart rate sensor 19 and the location where it is installed, and may be obtained from the heart rate sensor 19 or may be set separately by the subject, etc.

[0038] Furthermore, if the current location information is a vehicle, the acceleration information is medium, the speed is high, and the device is in the driver's seat, the state is determined to be sta2 (driving). If the current location information is an office, the acceleration information is high, the speed is medium, and the device is a chair, the state is determined to be sta3 (working). If the current location information is a bed, the acceleration information is low, the speed is low, and the device is a bed, the state is determined to be sta4 (sleeping). Furthermore, the acceleration information and speed in particular are merely examples, and the specific values, etc. can be changed arbitrarily in the settings as appropriate.

[0039] 4, the current location information, acceleration information, speed information, and device information are used for the determination, but the determination may be made based on only one, two, or three of these items. The determination may also be made based on the heart rate measured by the heart rate sensor 19. For example, a rough determination is possible, such as determining that the user is at rest or asleep when there is little change in the heart rate, or that the user is driving or working when the heart rate is low but fluctuating.

[0040] The parameter table 14 includes a table in which drowsiness prediction parameters, which will be described later, are set for each activity state. Based on the discrimination result of the state discrimination unit 11, the drowsiness prediction parameters corresponding to the activity state are output to the drowsiness level calculation unit 16. An example of the table is shown in FIG.

[0041] As shown in Fig. 5, two types of drowsiness prediction parameters, a and b, are set. In the example of Fig. 5, in the case of state sta1 (resting), the drowsiness prediction parameter a is 0.2 and the drowsiness prediction parameter b is 0.2, and in the case of state sta2 (driving), the drowsiness prediction parameter a is 0.5 and the drowsiness prediction parameter b is 0. In addition, in the case of state sta3 (working), the drowsiness prediction parameter a is 0.1 and the drowsiness prediction parameter b is 0.2, and in the case of state sta4 (sleeping), the drowsiness prediction parameter a is 0.2 and the drowsiness prediction parameter b is 0.2. As is clear from the table of Fig. 5, the drowsiness prediction parameters a and b are set to values ​​in the range of 0 to 1.

[0042] The communication unit 15 communicates with the server 2. The communication unit 15 transmits to the server 2 the heart rate (which may include heart rate fluctuations) measured by the heart rate sensor 19, the measurement time, and the activity state determined by the state determination unit 11. The communication unit 15 receives the corrected reference heart rate for drowsiness transmitted from the server 2. The corrected reference heart rate for drowsiness is a reference heart rate for drowsiness that has been corrected as described below, and the reference heart rate for drowsiness will be described later.

[0043] The drowsiness level calculation unit 16 calculates the drowsiness level (information regarding drowsiness) based on the heart rate measured by the heart rate sensor 19, the drowsiness prediction parameters output from the parameter table 14, and the corrected drowsiness reference heart rate received by the communication unit 15 from the server 2.

[0044] The degree of drowsiness is the difference between the current heart rate and the drowsiness reference heart rate when the current heart rate is lower than the drowsiness reference heart rate, and is calculated so that drowsiness increases according to the rate at which the current heart rate falls below the drowsiness reference heart rate.

[0045] Here, the premise for calculating the drowsiness level will be described. In order to estimate the balance of the autonomic nerves from the heart rate variability, a high frequency component (HF component) corresponding to the respiratory variability and a low frequency component (LF component) corresponding to the Mayer wave, which is a blood pressure variability, are extracted from the time series data on the heart rate variability, and the magnitudes of both are compared. The HF component reflecting the respiratory variability appears in the heart rate variability only when the parasympathetic nerves are tense (activated). On the other hand, the LF component appears in the heart rate variability when the sympathetic nerves are tense and when the parasympathetic nerves are tense.

[0046] The HF component is generally calculated by taking the square root of the sum of the intensities in the HF component region of the power spectrum (0.15 Hz to 0.40 Hz). The HF component (HFS) varies greatly from person to person, so it is normalized for each individual and converted to a value between 1 and 5. HFS = β × √(HF) (1)

[0047] Here, the value of β is determined so that the average value of HFS is 2. HFS values ​​smaller than 1 are set to 1, and values ​​greater than 5 are set to 5. As a result, HFS can range from 1 to 5.

[0048] In the drowsiness level calculation unit 16, drowsiness prediction parameters are a and b, an RR interval (RR Interval) obtained from the current heart rate HR is RR, a reference RR interval obtained from the reference heart rate HR_ref is RR_ref, and a heart rate fluctuation is HF, and then the drowsiness level D is calculated by the following formula (2). D=a×(RR-RR_ref)+b×HFS...(1)

[0049] Here, the heart rate HR is the number of heart beats per minute, so the RR interval is calculated from the heart rate HR using the following formula (3). RR = 60000 / HR (milliseconds) (3)

[0050] Similarly, RR_ref is calculated using the following formula (4). RR_ref = 60000 / HR_ref (milliseconds) (4)

[0051] In this embodiment, the HFS is calculated from the heart rate by the drowsiness level calculation unit 16, but it may be acquired from the heart rate sensor 19. In that case, the biological information acquired by the I / F 18 is the heart rate and high frequency components of heart rate fluctuations.

[0052] Here, the setting of the drowsiness prediction parameters a and b will be described. For example, drowsiness while driving is likely to occur during monotonous driving (on a highway, etc.), and functional decline accompanied by drowsiness occurs. Such functional decline appears as physiological functions such as a drop in heart rate and blood pressure, and abnormalities in eye movement and brain waves fluctuate. Subjective symptoms and fatigue increase significantly, centered on drowsiness, lethargy, and fatigue in the limbs, and a decrease in concentration is also felt. Behavioral ability: Reaction time is significantly extended and variability increases, accuracy decreases, and dangerous situations may occur due to closing the eyes and drowsiness.

[0053] The state of drowsiness is classified as follows according to the function of the autonomic nervous system. <1. If you are not feeling sleepy> This is a state in which the sympathetic nervous system is activated and the parasympathetic nervous system is inhibited. The heart rate HR is high, and the high frequency component HF of the heart rate fluctuation is small. <2. If you have signs of drowsiness> Although the person may not be aware of drowsiness psychologically due to monotonous driving or fatigue, physiological symptoms of drowsiness appear. Sympathetic nervous activity changes from an increased state to an inhibited state, causing the heart rate to decrease. <3. If drowsiness occurs> While the sympathetic nervous system remains suppressed, parasympathetic nervous activity becomes increased, causing the heart rate HR to decrease and the high frequency component HF of the heart rate fluctuations to increase. <4. A state of conflict against sleepiness> When we sense danger, we feel tense in order to resist drowsiness. When we feel a chill, sympathetic nerve activity increases intermittently, and the high frequency component HF of heart rate fluctuation decreases. <5. Unable to resist sleepiness> Tension disappears and dozing sets in. Sympathetic nervous activity is suppressed, so heart rate (HR) drops.

[0054] In the above classification, normal drowsy driving changes from state 1 (when there is no drowsiness) to state 2 (when there are signs of drowsiness), and often reaches state 4 (a state of struggle against drowsiness). On the other hand, when in a resting state, the driver goes from state 1 to state 2, then to state 3 (when drowsiness occurs), and if the driver falls asleep, the driver reaches state 5 (a state in which the driver cannot resist drowsiness).

[0055] Therefore, in equation (2), as shown in Fig. 4, drowsiness prediction parameter b of heart rate fluctuation is set to 0 during calculation while driving. In other words, while driving, drowsiness prediction parameter a of heart rate change is increased and drowsiness prediction parameter b of heart rate fluctuation is decreased. On the other hand, while in a resting state, drowsiness prediction parameter a of the element of heart rate change is decreased and drowsiness prediction parameter b of heart rate fluctuation is increased.

[0056] That is, the drowsiness prediction parameters a and b are coefficients for weighting the RR interval and the heart rate fluctuation, respectively. As described above, the contribution of the RR interval and the heart rate fluctuation to drowsiness differs depending on the activity state, so by weighting each of them, the accuracy of the drowsiness level is improved.

[0057] The drowsiness level calculated by formula (2) falls within a range of values ​​from 1 to 5, with 1 being low drowsiness and 5 being high drowsiness. Also, the drowsiness level D is the difference between the current heart rate and the drowsiness reference heart rate when the current heart rate is lower than the drowsiness reference heart rate, so RR<RR_re f(HR> HR_ref), the drowsiness level is set to 1.

[0058] That is, since the drowsiness prediction parameter changes depending on the activity state, the drowsiness level calculation section 16 calculates the drowsiness level based on the heart rate (biological information), the activity state, and the drowsiness reference heart rate.

[0059] The drowsiness level display unit 17 displays the drowsiness level calculated by the drowsiness level calculation unit 16. The drowsiness level may be displayed simply as a numerical value at that time, or may be displayed as a bar graph or a line graph so that the change over time can be seen.

[0060] An example of the display of the drowsiness level display unit 17 is shown in Fig. 6. Fig. 6(a) is a table showing the heart rate for each hour, the drowsiness reference heart rate, and the calculated drowsiness level. Fig. 6(b) is a bar graph of Fig. 6(a). That is, when the heart rate is measured as in Fig. 6(a), it is displayed to the subject as in Fig. 6(b).

[0061] The I / F 18 is an interface (I / F) to which the heart rate sensor 19 is connected. When the heart rate sensor 19 is connected by wire, the I / F 18 is an interface corresponding to the wired connection, and when the heart rate sensor 19 is connected by wireless, the I / F 18 is an interface corresponding to the wireless connection. That is, the I / F 18 acquires biological information (heart rate, the measurement time, etc.) related to the heart rate of the subject measured in the subject.

[0062] As shown in FIG. 3, the server 2 includes a drowsiness reference heart rate table 21 serving as a drowsiness reference heart rate storage unit, a terminal communication unit 22, an external communication unit 23, a correction unit 24, and an individual history data storage unit 25.

[0063] As is well known, the server 2 is installed in a place of business or the like, and is capable of communicating with a plurality of terminals, such as the smartphone 1, via a network, such as the Internet. As described above, the server 2 is also capable of communicating with the stationary measuring device 3. In the server 2 configured as described above, the drowsiness reference heart rate table 21 and the individual history data storage unit 25 function as a storage medium, such as a hard disk. Furthermore, the terminal communication unit 22 and the external communication unit 23 function as a board for network control, a semiconductor circuit, or the like. Furthermore, the correction unit 24 functions as a calculation device, such as a CPU.

[0064] The drowsiness reference heart rate table 21 includes a table in which the drowsiness reference heart rate of the subject is stored for each time and activity state. Based on the discrimination result of the state discrimination unit 11 received from the smartphone 1, the drowsiness reference heart rate corresponding to that activity state is output to the correction unit 24. An example of the table is shown in FIG.

[0065] Here, the drowsiness reference heart rate is the heart rate at which drowsiness occurs. In other words, it is the heart rate below which a person feels drowsy. The drowsiness reference heart rate is calculated by, for example, determining the minimum heart rate and standard deviation of the heart rate during a certain activity such as while driving, and adding the standard deviation to the minimum heart rate.

[0066] 7, drowsiness reference heart rates are set for each of states sta1 to sta3 at one-hour intervals from time t1 to t4. Times t1 to t4 indicate times such as 12:00 PM and 1:00 PM, for example.

[0067] The drowsiness reference heart rate is calculated based on the standard deviation calculated based on information such as the heart rate obtained from the heart rate sensor 19. For example, to calculate the reference heart rate at 12 p.m., the heart rate for a predetermined period around 12 p.m. is obtained, the minimum heart rate and standard deviation are found as described above, and the standard deviation is added to the minimum heart rate. Then, the activity state at that time is obtained from the state determination unit 11 of the smartphone 1, and set in the table as the drowsiness reference heart rate at 12 p.m. for that activity state.

[0068] 1, or may be calculated by the server 2 after information such as the heart rate is transmitted to the server 2. Alternatively, the drowsiness reference heart rate may be calculated and set separately in a configuration as shown in Fig. 8 and transferred to the drowsiness reference heart rate table 21 of the server 2. The initial calibration unit 31 calculates the drowsiness reference heart rate by the above-mentioned method.

[0069] In addition, since a person performs various activities in a day, even if the heart rate is measured for 24 hours, it is difficult to fill in all the times and activity states in the drowsiness reference heart rate table 21 (FIG. 9(a)). Therefore, the data may be interpolated so that the blank parts are smooth based on the data before and after the blank parts (FIG. 9(b)).

[0070] Furthermore, the portion filled in by interpolation in the initial state may be updated (added) to the calculated value when the drowsiness reference heart rate is calculated by a subsequent measurement. Furthermore, other interpolated values ​​may be updated based on the update (addition). In this way, the accuracy of the drowsiness reference heart rate can be improved. In other words, the drowsiness reference heart rate table 21 also functions as a drowsiness reference heart rate setting unit that adds or updates the drowsiness reference heart rate based on the measurement time of the heart rate and the subject's activity state detected by the state discrimination unit 11 (activity state acquisition unit).

[0071] The device communication unit 22 receives the heart rate (including heart rate fluctuations), the measurement time, and the activity state determined by the state determination unit 11 from the smartphone 1. The received heart rate, measurement time, and activity state are used by the correction unit 24 and stored in the individual history data storage unit 25. The device communication unit 22 also transmits the corrected drowsiness reference heart rate corrected by the correction unit 24 to the smartphone 1. That is, the device communication unit 22 functions as an activity state acquisition unit in the server 2.

[0072] The external communication unit 23 acquires information on the heart rate and blood glucose level (including the measurement time) from the stationary measuring device 3. The acquired information on the heart rate and blood glucose level is stored in the individual history data storage unit 25 and accumulated.

[0073] The correction unit 24 reads and acquires the drowsiness reference heart rate from the drowsiness reference heart rate table 21 based on the current time and the activity state determined by the state determination unit 11. Then, the correction unit 24 acquires information on past activity from the heart rate, activity state, blood glucose level, etc. stored in the individual history data storage unit 25, and corrects the drowsiness reference heart rate based on the information. The current time may be referenced from a clock function built into the server 2, or may be acquired from an external NTP (Network Time Protocol) server or the like. In other words, the correction unit 24 also functions as a drowsiness reference heart rate acquisition unit and a date and time acquisition unit.

[0074] Here, a method for the correction unit 24 to obtain (calculate) information on past activities will be described. In this embodiment, the information on past activities for correcting the sleepiness reference heart rate includes fatigue level, sleep time, and meal status (whether or not after a meal).

[0075] First, the degree of fatigue will be described. The degree of fatigue indicates the degree of current fatigue due to past activities (fatigue information associated with past activities). Since the degree of fatigue makes one sleepy, the accuracy of the calculation of the degree of sleepiness can be improved by correcting the sleepiness reference heart rate according to the degree of fatigue. The degree of fatigue can be calculated from the high frequency component HF of the heart rate fluctuation described above and the low frequency component LF of the heart rate fluctuation. The low frequency component LF can be obtained by frequency analysis of the RR interval, similar to the high frequency component HF. The RR interval is the reciprocal of the heart rate as shown in formula (3), and therefore can be calculated from the heart rate accumulated in the individual history data storage unit 25. The degree of fatigue is calculated by the following formula (5). Fatigue level HRV = LF / HF (5)

[0076] And, if the average fatigue HRV is av, If HRV>av, you are not tired at all. If av≧HRV>0.75×av, you are slightly tired. If 0.75×av≧HRV>0.5×av, you are tired. If 0.5×av≧HRV>0.25×av, you are very tired. If HRV is 0.25 × av or higher, you are very tired. It can be classified into five stages.

[0077] The above classification is then set as level 0 for "not tired at all," level 1 for "slightly tired," level 2 for "fairly tired," level 3 for "very tired," and level 4 for "very tired," and the drowsiness reference heart rate is corrected according to the fatigue level as shown in FIG. 10. In the example of FIG. 10, when the fatigue level is 4, 10 is added to the read-out drowsiness reference heart rate. This added value becomes the corrected drowsiness reference heart rate. The added correction value is just an example, and can be changed by appropriate settings.

[0078] The degree of fatigue can also be calculated from the results of an activity meter (activity sensor) not shown. An activity meter detects acceleration changes caused by body movements at frequencies of 2 to 3 Hz at 0.01 G or more. It is known that the average amount of activity during wakefulness measured by such an activity meter is about 200 times per minute. Therefore, the current amount of activity is classified as follows. If your activity level is >400, you are not tired at all. If 400 or more activity level > 350, you feel a little tired. If 350 or more activity > 250, you are tired. If your activity level is 250 or higher and your activity level is 200, you are very tired. If your activity level is 200 or higher, you are very tired.

[0079] If the heart rate is classified in this way, the drowsiness reference heart rate can be corrected using Fig. 10. Therefore, the fatigue level can also be calculated by periodically transmitting the measurement results of the activity meter to the server 2 and storing the results in the individual history data storage unit 25.

[0080] Alternatively, a screen such as that shown in FIG. 11 may be displayed on the smartphone 1 to allow the subject to make a selection.

[0081] Next, the sleep time will be described. The sleep time indicates whether or not the person slept well the previous night. In other words, it indicates past sleep information. If the person did not sleep well, he or she is likely to become sleepy afterwards, so by correcting the sleepiness reference heart rate based on the sleep time, the accuracy of the calculation of the sleepiness level can be improved. Since the heart rate decreases during sleep, the sleep time can be estimated from the heart rate accumulated in the individual history data storage unit 25. The sleep time is then classified as follows: If your sleep time is >8 hours, you slept well. If your sleep time is 8 hours or more and >6 hours, you slept normally. If your sleep time is 6 hours or more and > 2 hours, you didn't sleep well. If 2 hours or more sleep time > 0 hours, you were unable to sleep. If 0 hours = sleep time, you didn't sleep at all.

[0082] Then, similar to the fatigue level, the above classification is set to level 0 for "slept well," level 1 for "slept normally," level 2 for "did not sleep well," level 3 for "did not sleep at all," and level 4 for "drowsiness reference heart rate," and the drowsiness reference heart rate is corrected according to the sleep level in the same manner as in Fig. 10. Of course, the correction value may be a value different from the example in Fig. 10.

[0083] Regarding sleep, a screen such as that shown in FIG. 12 may be displayed on the smartphone 1 to allow the subject to make a selection.

[0084] Next, the eating situation will be described. The eating situation indicates whether or not a meal was recently eaten (such as the degree of fullness). In other words, it indicates past meal information. This makes it possible to determine whether or not a meal was eaten by detecting an increase in blood glucose level. Since blood glucose levels are accumulated in the individual history data storage unit 25, the determination is made from that data.

[0085] Then, a correction value is added according to the time elapsed since the meal, as shown in Fig. 13. For example, if it is within one hour after the meal, 3 is added to the read drowsiness reference heart rate. The correction value is just an example, and can be changed by appropriate settings.

[0086] The meal status may also be determined from the subject's location information. For example, if the subject stays near (within a certain range of) a specific location such as a restaurant for a certain period of time or more and the heart rate increases, the subject is determined to be eating. The subject's location information can be determined by acquiring information from the GPS receiver 12 of the smartphone 1. That is, in this case, the individual history data storage unit 25 also stores the subject's past location information (stay history).

[0087] The flowchart for meal determination will be described with reference to Fig. 14. The flowchart in Fig. 14 may be performed by the correction unit of the server 2, but may also be performed by the smartphone 1 and only the results may be transmitted. When executed by the server 2, map information, location information, etc. are provided by the server 2 itself or are obtained from an external source. When executed by the smartphone 1, a map application or a navigation application (navigation application) must be installed.

[0088] First, in step S11 of Fig. 14, the current location is acquired. Next, in step S12, it is determined whether or not there is a facility for eating within a certain range, and if there is (YES), the staying time is acquired in step S13. On the other hand, if there is no facility for eating within the certain range (NO), the process returns to step S11 (determining that no meal has been eaten).

[0089] Next, in step S14, it is determined whether or not the user stayed for a certain period of time or more, and if the user stayed for a certain period of time or more (if YES), the heart rate is obtained in step S15. Then, in step S16, it is determined whether or not the heart rate has increased, and if it has increased (if YES), it is determined that the user has eaten. On the other hand, if the user has not stayed for a certain period of time or more in step S14 (if NO) or if the heart rate does not increase in step S16 (if NO), the process returns to step S11 (it is determined that the user has not eaten). In other words, a stay of a certain period of time or more in a facility (specific location) where meals are served becomes a past stay history.

[0090] Correction may also be made based on information about multiple past activities. For example, correction may be made by adding values ​​as shown in Fig. 15 depending on both the fatigue level and sleep time. In the case of Fig. 15, if the fatigue level is 4 and the sleep level is 4, 10 is added.

[0091] Furthermore, the meal situation may also be combined. For example, in FIG. 15, the correction values ​​of the fatigue level and the sleep situation may be calculated, and then the correction value of the meal situation may be calculated, and the two correction values ​​may be added together.

[0092] The individual history data storage unit 25 stores, for each individual, the heart rate, measurement time, activity state, etc. received by the terminal communication unit 22, and the heart rate, blood glucose level, measurement time, etc. transmitted from the stationary measuring device 3. The server 2 may create or update the drowsiness reference heart rate table 21 based on the history data stored in the individual history data storage unit 25.

[0093] 16 shows a flowchart of the operation of the drowsiness calculation device 50 having the above-mentioned configuration. First, in step S21, the smartphone 1 transmits a user ID and a password assigned to each user to the server 2 via the communication unit 15. The server 2 performs user authentication using the user ID and password transmitted from the smartphone 1 in step S31.

[0094] Next, the activity state determined by the state determination unit 11 of the smartphone 1 is transmitted from the communication unit 15 to the server 2. The server 2 receives the activity state transmitted from the smartphone 1 in step S32. That is, this step functions as an activity state acquisition step.

[0095] Next, in step S33, the server 2 acquires (reads) the history data (heart rate, blood glucose level, etc.) accumulated in the individual history data storage unit 25. Then, in step S34, the correction unit 24 reads out the drowsiness reference heart rate from the drowsiness reference heart rate table 21 based on the activity state and the current time, corrects the read drowsiness reference heart rate based on the above-mentioned fatigue level, etc., and transmits it to the smartphone 1 as the corrected drowsiness reference heart rate. That is, step S34 functions as a drowsiness reference heart rate acquisition step, an activity history acquisition step, and a correction step.

[0096] Next, in step S23, the smartphone 1 receives the corrected drowsiness reference heart rate from the server 2. Then, in step S24, the drowsiness level calculation unit 16 calculates the drowsiness level and displays it on the drowsiness level display unit 17. That is, step S24 functions as a drowsiness level calculation step. Then, in step S25, the history data (heart rate, measurement time, etc.) is transmitted to the server 2. That is, this step functions as a biological information acquisition step. Note that the history data may be transmitted when the activity state is transmitted in step S22.

[0097] Next, in step S 35 , the server 2 stores the received history data in the individual history data storage unit 25 .

[0098] Fig. 17 shows a flowchart of the operation of step S34 (correcting and transmitting the drowsiness reference heart rate) in Fig. 16. First, in step S41, the fatigue level and the sleeping time are calculated, and in step S42, a correction value is calculated from the fatigue level and the sleeping time based on the table shown in Fig. 15.

[0099] Next, in step S43, it is determined whether or not the patient has eaten after a meal based on the blood glucose level and position information as described above. If the patient has eaten after a meal, the correction value calculated in step S43 is further added with a correction value based on the table shown in Fig. 13 (+α), and the drowsiness reference heart rate corrected with the correction value calculated in step S44 is calculated as the corrected drowsiness reference heart rate in step S45 and transmitted. On the other hand, if the patient has not eaten after a meal, the drowsiness reference heart rate corrected with the correction value calculated in step S42 is calculated as the corrected drowsiness reference heart rate in step S45 and transmitted.

[0100] In the flowchart of Fig. 17, the drowsiness reference heart rate is corrected each time, but for the fatigue level and the sleep time, correction values ​​may be determined on the morning of the day, and a drowsiness reference heart rate table may be generated based on the correction values. Then, the drowsiness reference heart rate may be read out from the generated drowsiness reference heart rate table based on the activity state and the current time. For meals, the values ​​in the generated drowsiness reference heart rate table may be corrected.

[0101] According to this embodiment, in the smartphone 1, the I / F 18 acquires the subject's heart rate measured by the heart rate sensor 19 and the measurement time, and the state discrimination unit 11 discriminates the subject's activity state and transmits it to the server 2. Next, in the server 2, the correction unit 24 acquires the drowsiness reference heart rate from the drowsiness reference heart rate table 21 in which the drowsiness reference heart rate of the subject is stored for each activity state according to the activity state transmitted from the smartphone 1. Next, the drowsiness reference heart rate acquired by the correction unit 24 is corrected based on the fatigue level, sleep time, and meal situation calculated from the history data read from the individual history data storage unit 25. Then, the corrected drowsiness reference heart rate (corrected drowsiness reference heart rate) is transmitted to the smartphone 1, and the drowsiness level calculation unit 16 of the smartphone 1 calculates the drowsiness level of the subject based on the corrected drowsiness reference heart rate and the heart rate acquired by the I / F 18. By doing this, the drowsiness reference heart rate can be corrected based on the subject's past activity state, so that drowsiness can be calculated with high accuracy by taking into account not only current activity but also past activity.

[0102] In addition, the correction unit 24 acquires information on the subject's past activity state, such as the degree of fatigue, the duration of sleep, and the state of meals. In this way, the sleepiness reference heart rate can be corrected in consideration of the subject's degree of fatigue, the duration of sleep, and the state of meals. Therefore, the degree of sleepiness can be calculated taking these factors into account.

[0103] Furthermore, the server 2 adds or updates the drowsiness reference heart rate stored in the drowsiness reference heart rate table 21 based on the time the heart rate was measured and the subject's activity state determined by the state determination unit 11. In this way, it is possible to update or add at any time the change in the drowsiness reference heart rate for each hour of the subject's activity state in daily life.

[0104] In addition, the drowsiness level calculation unit 16 calculates the drowsiness level of the subject based on a drowsiness prediction parameter preset for each activity state. In this way, the heart rate and heart rate fluctuation can be weighted according to the activity state.

[0105] Furthermore, since the sleepiness level display unit 17 is provided, the subject can specifically perceive his or her own sleepiness and can take appropriate measures, such as taking a break or exercising, based on the displayed sleepiness level.

[0106] It is not necessary to acquire all three pieces of information on the subject's fatigue level, sleep time, and dietary status, and it is also possible to acquire only one of them.

[0107] In addition, although the location information is used to determine whether or not a meal has been eaten, the present invention is not limited to this. For example, after staying at a relaxation facility such as a hot spring, tension is relieved and it is easy to become sleepy. Therefore, the sleepiness reference heart rate may be corrected if the user has stayed at a relaxation facility for a certain period of time or more. Whether or not the user has stayed at a relaxation facility can be determined in the same manner as in the flowchart of FIG. 14. In other words, staying at a relaxation facility (specific location) for a certain period of time or more becomes a past stay history.

[0108] In the above-described embodiment, the drowsiness prediction parameter and the drowsiness reference heart rate are selected based on the activity state and the measurement time, but the drowsiness prediction parameter and the drowsiness reference heart rate may be selected based only on the activity state. However, it is preferable to also take the measurement time into consideration, since this allows the selection of an appropriate drowsiness prediction parameter and drowsiness reference heart rate.

[0109] Furthermore, the drowsiness level may be notified by voice in addition to by the drowsiness level display unit 17. Alternatively, when the drowsiness level is equal to or higher than a certain level, the drowsiness level may be notified by voice.

[0110] In addition, the server 2 may include both or either of the parameter table 14 and the drowsiness level calculation unit 16, and the smartphone 1 may include both or either of the drowsiness reference heart rate table 21 and the correction unit 24. Also, the smartphone 1 may be configured to include all of these components without using the server 2. EXAMPLES

[0111] Next, a sleepiness calculation device according to a second embodiment of the present invention will be described with reference to Figures 18 to 20. Note that the same parts as those in the first embodiment described above are given the same reference numerals and the description thereof will be omitted.

[0112] In the first embodiment, the drowsiness reference heart rate is corrected based on information about past activities, but in this embodiment, future activities are predicted and the corresponding correction is performed. The configuration is the same as that shown in Figs. 1 to 3. That is, the correction unit 24 functions as an activity prediction unit. Note that in this embodiment, the smartphone 1 is described, but as will be clear from the following description, it may be a car navigation system.

[0113] For example, if a subject drives a vehicle such as a car and returns home, it is predicted that the subject will engage in relatively calm and relaxing activities such as resting and sleeping, and will be less tense and more likely to become sleepy. On the other hand, if a subject goes to a tourist spot on a trip, it is predicted that the subject will engage in relatively active activities such as shopping and walking, and will be in a high mood and less likely to become sleepy.

[0114] Therefore, when a destination is set in a navigation app or the like of the smartphone 1, the information is transmitted to the server 2. Then, based on the destination, a correction is performed by further adding a correction value, for example, as shown in FIG. 18, to the corrected drowsiness reference heart rate corrected in the first embodiment. Note that the smartphone 1 may transmit information such as "home" or "recreation spot" instead of the destination information. That is, the terminal communication unit 22 functions as a destination information acquisition unit that acquires information regarding the destination to which the subject is heading, and the correction unit 24 predicts the subject's activity based on the information regarding the destination.

[0115] Also, even when heading to a tourist spot, one does not often feel sleepy when setting off, but may become sleepy at an intermediate point, so in addition to the correction in FIG. 18, a correction may be made according to the distance from the intermediate point to the destination as shown in FIG. 19. In the distance in FIG. 19, 0% is the departure point and 100% is the destination. Also, correction may be made according to the ratio of the time required from the intermediate point to the time required from the departure point to the destination, not limited to the distance. In that case, the correction can be made in the same way even if the distance in FIG. 19 is changed to the required time.

[0116] According to this embodiment, the correction unit 24 predicts the subject's activity and corrects the drowsiness reference heart rate read from the drowsiness reference heart rate table 21 based on the predicted activity. In this way, the drowsiness reference heart rate can be corrected taking into consideration not only past activities but also future plans, such as going out to play.

[0117] In addition, the server 2 acquires information about the subject's destination from the smartphone 1 via the terminal communication unit 22, and the correction unit 24 predicts the subject's activity based on the information about the destination. In this way, it is possible to predict future plans, such as whether the destination itself is home or a trip, etc.

[0118] In the above description, the correction is made in addition to the past activity status as described in the first embodiment, but it is also possible to correct only the future prediction as described in this embodiment.

[0119] In addition, when driving a vehicle, if the road on which the vehicle is currently traveling is monotonous, the driver is likely to become sleepy, whereas if the road is complex, the driver will need to concentrate and will be awake. Therefore, in addition to correction based on past and future predictions, correction may be made based on the shape of the road on which the vehicle is currently traveling, as shown in FIG. 20.

[0120] That is, the device communication unit 22 acquires information on the subject's current location. Next, when the subject's current activity state acquired by the device communication unit 22 is driving a vehicle, the correction unit 24 corrects the drowsiness reference heart rate acquired according to the activity state based on the information on the current location. Note that driving a vehicle can be determined by the state determination unit 11, as described above.

[0121] Also, the route guided by a navigation application or the like may be changed based on the drowsiness level calculated by the smartphone 1. That is, the smartphone 1 may further include a guidance unit (navigation application) that searches for and provides guidance on a route to a preset destination, and the guidance unit may search for a route to the destination based on the drowsiness level calculated by the drowsiness level calculation unit 16. EXAMPLES

[0122] Next, a sleepiness calculation device according to a third embodiment of the present invention will be described with reference to Fig. 21 and Fig. 22. Note that the same parts as those in the first and second embodiments described above are given the same reference numerals and the description thereof will be omitted.

[0123] The basic configuration of this embodiment is similar to that of the first embodiment (FIGS. 1 to 3). The drowsiness level shown in the first embodiment is calculated based on the heart rate measured by the heart rate sensor 19, but the calculated value may differ from the subject's sense. Therefore, in this embodiment, the subject subjectively evaluates the drowsiness level displayed on the drowsiness level display unit 17, and the drowsiness reference heart rate is corrected (updated) based on the evaluation.

[0124] A specific example will be described with reference to Fig. 21 and Fig. 22. Fig. 21 and Fig. 22 are display examples of the drowsiness level display unit 17, and show a graph 171 of the change in heart rate and the reference heart rate for drowsiness, a graph 172 of the drowsiness level, and the drowsiness level. The subject's evaluation of the drowsiness level can be input by touching the displayed numerical value. That is, in the examples of Fig. 17 and Fig. 18, the drowsiness level display unit 17 is a touch panel.

[0125] In the case of Fig. 21, the drowsiness reference heart rate is 74. The current drowsiness level is 4 according to the calculation result of the drowsiness level calculation unit 16. At this time, the subject subjectively evaluates the current drowsiness level.

[0126] If the subject evaluates the drowsiness level to be 2 as a result of the subjective evaluation, the subject selects 2 as the drowsiness level on the input unit 9, as shown in Fig. 21. Then, the drowsiness reference heart rate correction unit 10 corrects the drowsiness reference heart rate in accordance with the subjective evaluation. In the case of Fig. 21, it is corrected to 72. Then, the drowsiness level is recalculated based on the corrected drowsiness reference heart rate, and the drowsiness level graph 6b is also corrected (Fig. 22).

[0127] Next, a description will be given of the correction of the drowsiness reference heart rate in the drowsiness reference heart rate correction unit 10. The formula (2) explained in the first embodiment is modified to obtain the following formula (6). RR_ref=RR-(Db×HFS) / a...(6)

[0128] Therefore, by substituting the subjective assessment value for D in equation (6), a corrected RR_ref can be calculated. Then, the drowsiness level is recalculated by equation (2) based on the corrected RR_ref. Note that, since RR_ref and the drowsiness reference heart rate HR_ref have the relationship shown in equation (4) described in the first embodiment, the drowsiness reference heart rate HR_ref can be easily calculated by modifying equation (4).

[0129] The corrected drowsiness reference heart rate HR_ref is used to display the graph 6a of the change in heart rate and the drowsiness reference heart rate, and is also transmitted to the server 2, where the drowsiness reference heart rate at the corresponding activity state and time in the drowsiness reference heart rate table 21 is corrected. In addition to the drowsiness reference heart rate, various correction values ​​may be corrected. Since the correction values ​​described in the first and second embodiments may vary among individuals, it is advisable to feed back the results of the subjective evaluation.

[0130] According to this embodiment, a touch panel is provided for inputting the subject's subjective evaluation of the drowsiness level presented on the drowsiness level display unit 17. The drowsiness level calculation unit 16 then modifies the drowsiness reference heart rate based on the subjective evaluation inputted through the touch panel, and recalculates the drowsiness level based on the modified drowsiness reference heart rate. In this manner, the subject can subjectively evaluate the drowsiness level calculated by the drowsiness level calculation unit 16. The subjective evaluation result is then fed back so that the drowsiness level calculation unit 16 can calculate the drowsiness level again. Therefore, information related to drowsiness tailored to each individual subject can be calculated with higher accuracy.

[0131] The present invention is not limited to the above-mentioned embodiment. In other words, a person skilled in the art can implement the present invention by modifying it in various ways according to the conventionally known knowledge without departing from the gist of the present invention. As long as the configuration of the drowsiness calculation device of the present invention is still included in the scope of the present invention even after such modification, it is of course included in the scope of the present invention. [Explanation of symbols]

[0132] 1. Smartphone 2. Server (information processing device) 3. Stationary Measuring Instruments 11 Status determination unit (activity status acquisition unit) 12 GPS receiver (current location information acquisition unit) 13 G Sensor 14 Parameter Table 15 Communications Department 16 Drowsiness calculation unit 17 Drowsiness level display unit (display unit, destination information acquisition unit, input unit) 18 I / F (biometric information acquisition section) 19 Heart Rate Sensor 21 Drowsiness level reference heart rate table (storage section of drowsiness reference heart rate) 22 Terminal communication unit (biometric information acquisition unit, activity status acquisition unit, current location information acquisition unit, destination information acquisition unit) 23 External Communications Department 24 Correction unit (sleepiness reference heart rate acquisition unit, activity history acquisition unit, activity prediction unit) 25 Individual history data storage section 50 Sleepiness Calculator S25 Receiving history data (biometric information acquisition process) S22 Activity status acquisition (activity status acquisition process) S23 History data acquisition (activity history acquisition process) S24: Correct the reference heart rate and transmit it (drowsiness reference heart rate acquisition process, correction process) S14 Drowsiness calculation (drowsiness calculation process)

Claims

[Claim 1] A biological information acquiring unit that acquires biological information related to a heartbeat of a subject; a fatigue level information acquiring unit for acquiring fatigue level information relating to the fatigue level of the subject; a sleepiness level calculation unit that calculates information regarding a current sleepiness of the subject based on the biological information and the fatigue level information; A drowsiness calculation device comprising:

Images