JP2021003560A - Drowsiness calculation device - Google Patents

Abstract

To provide a drowsiness calculation device capable of highly accurately calculating a degree of drowsiness by taking account of peripheral environment.SOLUTION: A smartphone 1 causes: an I/F 18 to acquire a heart rate of a subject and a measurement time measured by a heartbeat sensor 19; a state discrimination part 11 to discriminate an activity state of the subject; and a GPS receiver 12 to acquire current location information of the smartphone 1, where the acquired information is transmitted to a server 2. The server 2 causes: an external communication part 23 to acquire meteorological information of the current location of the subject; and a correction part 24 to read a drowsiness criterion heart rate of the subject from a drowsiness criterion heart rate table 21 according to the activity state and current time, and correct it on the basis of the meteorological information. The corrected drowsiness criterion heart rate is transmitted to the smartphone 1. A drowsiness-degree calculation part 16 of the smartphone 1 calculates a degree of drowsiness of the subject on the basis of the corrected drowsiness criterion heart rate and the heart rate acquired by the I/F 18.SELECTED DRAWING: Figure 1

Description

The present invention relates to a drowsiness calculation device for calculating drowsiness of a subject.

For example, in order to detect the dozing driving of an automobile, it has been proposed to detect the dozing state from the heart rate of the driver (see, for example, Patent Documents 1 and 2).

In Patent Document 1, the average value of the RRI (RR Interval) values of the heartbeat detected by the heartbeat sensor during awakening and a predetermined multiple of the integrated value of the RRI value exceeding the average value are set as threshold values, and the RRI value exceeding the average value is integrated. It is described that if the threshold value is exceeded, it is determined that the patient is dozing.

Patent Document 2 obtains heartbeat interval data in which RRI is time-series based on the heartbeat waveform of a resting subject measured by a sensor. Next, the heartbeat interval data is frequency-analyzed to obtain the frequency analysis result of the heartbeat fluctuation including the power spectral density (PSD) and the total power (TP) of the autonomic nerves with respect to the frequency at a certain time. Next, the frequency analysis result was set as the initial state, and the origin of the drowsiness position and the origin of the awakening position were set based on the estimated drowsiness position and the estimated awakening position included in the initial state. Determine the drowsiness scale. Then, it is described that the drowsiness of the subject is determined based on the drowsiness scale.

Japanese Patent No. 3252586 Japanese Unexamined Patent Publication No. 2014-12042

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

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 based on the resting state, there are daily changes and time zone changes. For example, it is known that the time when drowsiness becomes strong is from 3:00 to 4:00 in the early morning and from 3:00 to 4:00 in the daytime. Therefore, it is difficult to accurately estimate drowsiness only from heartbeat information. In addition, driving is a kind of stress-bearing condition, and the baseline of heart rate differs depending on the road condition (urban area or highway). Furthermore, since the state at the beginning of measurement is not constant, it was not possible to respond to changes such as when sleepy from the beginning and when not sleepy at the beginning.

Further, in order to determine drowsiness, it is necessary to set some reference heart rate or the like as in Patent Documents 1 and 2. However, the heart rate not only varies from person to person, but also fluctuates depending on the time of day and the state of activity. In addition, it varies depending on the weather information such as weather and temperature, and the surrounding environment of the subject such as the type of road and the traffic volume when driving a car.

In the methods described in Patent Documents 1 and 2, since the above-mentioned surrounding environment is not considered at all, accurate detection of drowsiness may not be possible depending on changes in the surrounding environment.

Therefore, in view of the above-mentioned problems, it is an object of the present invention to provide a drowsiness calculation device capable of calculating drowsiness with high accuracy in consideration of, for example, the surrounding environment.

In order to solve the above problems, the invention according to claim 1 is a biometric information acquisition unit that acquires biometric information regarding the heart rate of the subject, and a drowsiness reference heart rate that acquires the drowsiness reference heart rate of the subject. An acquisition unit, an area information acquisition unit that acquires area information related to the area including the current location where the subject is located, and a correction unit that corrects the acquired drowsiness reference heart rate based on the area information. The drowsiness calculation device is characterized by comprising a drowsiness degree calculation unit that calculates information on the drowsiness of the subject based on the corrected drowsiness reference heart rate and the biological information.

It is a schematic block diagram of the drowsiness calculation apparatus which concerns on 1st Example of this invention. It is a schematic block diagram of the smartphone shown in FIG. It is a schematic block diagram of the server shown in FIG. It is a table which showed the discriminant example of the active state in the state discriminating part shown in FIG. It is a table which showed the example of the parameter table shown in FIG. It is explanatory drawing of the display example of the drowsiness display part shown in FIG. It is a table which showed the example of the drowsiness reference heart rate table shown in FIG. This is another configuration example for calculating and setting the drowsiness reference heart rate table. It is explanatory drawing about the interpolation of the drowsiness reference heart rate table. It is a graph which showed the relationship between the reference heart rate and the weather. It is a table that corrects the drowsiness standard heart rate by the weather and temperature. It is a table that corrects the drowsiness standard heart rate by the weather and the discomfort index. It is a graph which showed the relationship between a reference heart rate and a season. It is a table that corrects the drowsiness standard heart rate according to the weather and the season. It is a flowchart of the operation of the drowsiness calculation device shown in FIG. It is a schematic block diagram of the drowsiness calculation device which concerns on the 2nd Example of this invention. It is a schematic block diagram of the smartphone shown in FIG. It is a table that corrects the drowsiness standard heart rate according to the road type and speed. It is explanatory drawing of the display example before subjective evaluation of the drowsiness display part of the drowsiness calculation apparatus which concerns on 3rd Example of this invention. It is explanatory drawing of the display example after subjective evaluation of the drowsiness display part of the drowsiness calculation apparatus which concerns on 3rd Example of this invention.

Hereinafter, the drowsiness calculation device according to the embodiment of the present invention will be described. In the drowsiness calculation device according to the embodiment of the present invention, the biological information acquisition unit acquires the biological information regarding the heartbeat of the subject, and the activity state acquisition unit acquires the activity state of the subject. The drowsiness reference heart rate of the subject is stored in the drowsiness reference heart rate acquisition unit for each activity state. The drowsiness reference heart rate is obtained from the drowsiness reference heart rate storage unit according to the activity state acquired by the activity state acquisition unit. Acquire the number, and acquire the area information related to the area including the current location where the subject is located in the area information acquisition department. Next, the drowsiness reference heart rate acquired by the drowsiness reference heart rate acquisition unit in the correction unit is corrected based on the area information acquired by the area information acquisition unit. Then, the information on the drowsiness of the subject is calculated based on the drowsiness reference heart rate corrected by the correction unit by the drowsiness degree calculation unit and the biological information acquired by the biological information acquisition unit. By doing so, the drowsiness reference heart rate can be corrected based on the area information such as the current location of the subject, so that the drowsiness can be calculated with high accuracy in consideration of the surrounding environment.

In addition, the correction unit may correct the drowsiness reference heart rate acquired by the drowsiness reference heart rate acquisition unit based on the current date and time and regional information. By doing so, the drowsiness reference heart rate can be corrected in consideration of the season and the like. Therefore, drowsiness can be calculated with higher accuracy.

In addition, the area information may include weather information of the area. By doing so, the drowsiness reference heart rate can be corrected in consideration of weather information such as the weather, temperature, humidity, atmospheric pressure, and discomfort index of the place where the subject is.

In addition, the area information may include traffic information of the area. By doing so, the drowsiness reference heart rate can be corrected in consideration of the road type of the place where the subject is located, the traffic condition, and the like.

Further, a traveling speed acquisition unit for acquiring the traveling speed of the vehicle on which the subject is riding is further provided, and the activity state of the subject acquired by the activity state acquisition unit is a state of driving the vehicle. In this case, the correction unit may correct the sleepiness reference heart rate acquired by the sleepiness reference heart rate acquisition unit based on the travel speed acquired by the travel speed acquisition unit and the traffic information included in the area information. By doing so, when the subject is driving a vehicle such as a car, the drowsiness reference heart rate can be corrected based on the traffic information.

In addition, a guide unit that searches for a route to a preset destination and provides guidance is further provided, and the guide unit searches for a route to the destination based on the information on drowsiness calculated by the drowsiness calculation unit. You may. By doing so, it is possible to select and guide an appropriate route according to the drowsiness of the subject.

In addition, the biometric information acquisition unit acquires the measurement time of the acquired biometric information, and the drowsiness reference heart rate acquisition unit stores the drowsiness reference heart rate of the subject for each time and activity state. The drowsiness reference heart rate may be acquired from the unit according to the measurement time and the activity state. By doing so, the drowsiness reference heart rate can be selected in consideration of the time as well as the active state. Therefore, drowsiness can be calculated more accurately.

In addition, the drowsiness degree calculation unit may calculate information on the drowsiness of the subject based on the drowsiness prediction parameters set in advance for each activity state. By doing so, it is possible to weight a plurality of elements included in the biological information regarding the heartbeat according to the activity state.

Further, it includes a display unit for displaying information on drowsiness to the subject, and an input unit for inputting the evaluation of the subject for the information on drowsiness displayed on the display unit. Then, the drowsiness degree 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 on drowsiness. By doing so, the subject can subjectively evaluate the information on drowsiness calculated by the drowsiness degree calculation unit. Then, the drowsiness degree calculation unit can calculate the information on drowsiness again by feeding back the subjective evaluation result. Therefore, the information on drowsiness can be calculated more accurately according to the individual sense of the subject.

Further, in the information processing apparatus according to the embodiment of the present invention, the activity state acquisition unit acquires the activity state of the subject, and the area information acquisition unit acquires the area information related to the area including the subject's current location. get. Then, the drowsiness reference heart rate selected from the 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 in the correction unit is set to the region. Make corrections based on the area information acquired by the information acquisition department. By doing so, the drowsiness reference heart rate can be corrected based on the area information such as the present location of the subject.

Further, in the drowsiness calculation method according to the embodiment of the present invention, the biological information acquisition step acquires the biological information regarding the heartbeat of the subject, and the active state acquisition step acquires the active state of the subject. .. The drowsiness reference heart rate acquisition process is the drowsiness reference heart rate of the subject according to the activity state acquired by the activity state acquisition unit from the drowsiness reference heart rate storage unit in which the drowsiness reference heart rate of the subject is stored for each activity state. The number is acquired, and the area information acquisition process acquires area information related to the area including the current location where the subject is located. Next, the drowsiness reference heart rate acquired in the drowsiness reference heart rate acquisition step in the correction step is corrected based on the regional information acquired in the regional information acquisition step. Then, the information on the drowsiness of the subject is calculated based on the drowsiness reference heart rate corrected by the correction unit by the drowsiness degree calculation unit and the biological information acquired by the biological information acquisition unit. By doing so, the drowsiness reference heart rate can be corrected based on the area information such as the current location of the subject, so that the drowsiness can be calculated with high accuracy in consideration of the surrounding environment.

Further, the drowsiness calculation program may be used as the drowsiness calculation method described above is executed by a computer. By doing so, the drowsiness reference heart rate can be corrected based on the area information such as the subject's current location using a computer, so that the drowsiness is calculated with high accuracy in consideration of the surrounding environment. be able to.

Further, the above-mentioned drowsiness calculation program may be stored in a computer-readable recording medium. By doing so, the program can be distributed as a single unit in addition to being incorporated in the device, and version upgrades and the like can be easily performed.

The drowsiness calculation device according to the first embodiment of the present invention will be described with reference to FIGS. 1 to 15. As shown in FIG. 1, the drowsiness calculation device 50 according to this embodiment includes a smartphone 1 and a server 2 as an information processing device.

As shown in FIG. 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, and a drowsiness calculation unit 16. The drowsiness display unit 17 as a display unit and the I / F 18 as a biological information acquisition unit are provided. The heart rate sensor 19 is connected to the smartphone 1.

The configuration of the smartphone 1 shown in FIG. 2 may be configured as, for example, an application program (application). Further, the smartphone 1 may be configured to include the heart rate sensor 19.

Although the present embodiment will be described with respect to the smartphone 1, it goes without saying that the present embodiment is not limited to the smartphone 1, and may be a mobile phone, a tablet-type terminal, or the like, or a wristwatch-type or the like. Alternatively, it may be an in-vehicle device such as a car navigation system.

As the heart rate sensor 19, a well-known sensor can be used as long as it is a sensor capable of acquiring at least a heart rate as biological information regarding the heart rate. For example, various forms such as a wristwatch type and a configuration provided in a seat such as a driver's seat can be used. Further, the heart rate sensor 19 is not limited to one type, and a plurality of types may be used properly according to the activity state described later.

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

The state determination unit 11 is resting, driving, working, sleeping, etc. based on the current location information and acceleration information (current location and acceleration of the subject) of the smartphone 1 input from the GPS receiver 12 and the G sensor 13. The activity status of the subject is determined. That is, the state determination unit 11 acquires the activity state of the subject.

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 radio waves to the state determination unit 11.

The G sensor 13 is a so-called acceleration sensor. For example, if it is a 3-axis acceleration sensor, it can measure acceleration in three directions of the XYZ axes. The G sensor 13 outputs the measured acceleration to the state determination unit 11.

FIG. 4 shows an example of determining the active state in the state determination unit 11. FIG. 4 is an example of a table for determining the activity state. In the table of FIG. 4, the content of the state sta1 indicates the resting state, the content of the state sta2 indicates the driving state, the content of the state sta3 indicates the working state, and the content of the state sta4 indicates the active state of sleeping. Of course, the activity status may include items other than those shown in the figure, such as exercise and hard labor.

In the table of FIG. 4, when the position information is the living room, the acceleration information is small, the speed is small, and the device is a wristwatch, the state sta1 (rest) is determined. Here, the speed may be calculated from, for example, a change in the current location information. The device indicates the type and location of the heart rate sensor 19, and may be acquired from the heart rate sensor 19 or may be set separately by the subject or the like.

Further, when the current location information is the vehicle, the acceleration information is medium, the speed is high, and the device is the driver's seat, the state sta2 (during driving) is determined. When the current location information is an office, the acceleration information is large, the speed is medium, and the device is a chair, it is determined to be in the state sta3 (working). When the current location information is a bed, the acceleration information is small, the speed is low, and the device is a bed, the state sta4 (sleep) is determined. In particular, acceleration information and speed are examples, and specific numerical values and the like can be arbitrarily changed by setting as appropriate.

In the table of FIG. 4, the determination is made based on the current location information, the acceleration information, the speed information, and the device information, but the determination may be made by only one item or only two or three items. Further, it may be discriminated by the heart rate measured by the heart rate sensor 19. For example, if the change in heart rate is small, it can be roughly determined as resting or sleeping, and if the heart rate is low but fluctuating, it can be roughly determined as driving or working.

The parameter table 14 includes a table in which drowsiness prediction parameters, which will be described later, are set for each active state. Then, based on the determination result of the state determination unit 11, the drowsiness prediction parameter corresponding to the activity state is output to the drowsiness degree calculation unit 16. An example of the table is shown in FIG.

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 the state sta1 (rest), the drowsiness prediction parameter a is 0.2, the drowsiness prediction parameter b is 0.2, and in the state sta2 (during driving), the drowsiness prediction parameter a is 0.5. , The drowsiness prediction parameter b is set to 0. Further, 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 (sleep), the drowsiness prediction parameter a is 0.2 and the drowsiness prediction parameter b. 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.

The communication unit 15 communicates with the server 2. The communication unit 15 transmits the heart rate (which may include heart rate fluctuation) measured by the heart rate sensor 19, the measurement time, the activity state determined by the state determination unit 11, and the current location information obtained by the GPS receiver 12 to the server 2. Send to. The communication unit 15 receives the corrected drowsiness reference heart rate transmitted from the server 2. The corrected drowsiness reference heart rate is a drowsiness reference heart rate corrected later, and the drowsiness reference heart rate will be described later.

The drowsiness calculation unit 16 is drowsy based on the heart rate measured by the heart rate sensor 19, the drowsiness prediction parameter output from the parameter table 14, and the corrected drowsiness reference heart rate received by the communication unit 15 from the server 2. Calculate the degree (information about drowsiness).

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 according to the rate at which the current heart rate falls below the drowsiness reference heart rate. It is calculated to increase drowsiness.

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

For the HF component, the square root is generally used for the total value of the intensities of the HF component region (from 0.15 Hz to 0.40 Hz) of the power spectrum. The HF component (HFS) varies greatly among individuals, and is normalized for each individual and converted to a value from 1 to 5.
HFS = β × √ (HF) ・ ・ ・ (1)

Here, the value of β is determined so that the average value of HFS is 2. If the HFS value is less than 1, it is set to 1, and if it is larger than 5, it is set to 5. As a result, HFS is a value from 1 to 5.

In the drowsiness calculation unit 16, the drowsiness prediction parameters are a and b, the RR interval (RR Interval) obtained from the current heart rate HR is RR, the reference RR interval obtained from the reference heart rate HR_ref is RR_ref, and the heart rate fluctuation is HFS. Then, the drowsiness degree D is calculated by the following equation (2).
D = a × (RR-RR_ref) + b × HFS ... (2)

Here, since the heart rate HR is the number of heartbeats per minute, the RR interval is calculated from the heart rate HR by the following equation (3).
RR = 60000 / HR (milliseconds) ... (3)

Similarly, RR_ref is calculated by the following equation (4).
RR_ref = 60000 / HR_ref (milliseconds) ... (4)

In this embodiment, the HFS is obtained from the heart rate by the drowsiness calculation unit 16, but it may be obtained from the heart rate sensor 19. In that case, the biological information acquired by the I / F18 is a high-frequency component of the heart rate and the heart rate fluctuation.

Here, setting the drowsiness prediction parameters a and b, respectively, will be described. For example, drowsiness during driving is likely to occur during monotonous driving (highway, etc.), and functional deterioration accompanied by drowsiness occurs. In terms of physiological function, such functional deterioration appears while the heart rate, blood pressure, etc. are calmed down, and abnormalities of eye movements and brain waves are agitated. Subjective symptom The feeling of fatigue is greatly increased mainly by drowsiness and drowsiness, tiredness of limbs, and a decrease in concentration is also strongly felt. Behavioral Ability There is a large increase in reaction time, increased variability, reduced accuracy, and even dangerous conditions due to eye closure and drowsiness.

The drowsiness state is classified as follows according to the function of the autonomic nerve.
<1. If you are not drowsy>
It is a state in which the sympathetic nerve is enhanced and the parasympathetic nerve is suppressed. The heart rate HR is large, and the high-frequency component HF of heart rate fluctuation is small.
<2. If there are signs of drowsiness>
Due to monotonous driving and fatigue, there is little psychological awareness of drowsiness, but the signs appear physiologically. The heart rate drops as sympathetic activity changes from hyperactive to suppressed.
<3. If you feel drowsy>
The sympathetic nerve remains suppressed, but the parasympathetic nerve activity changes to an hyperactive state, so that the heart rate HR decreases and the high-frequency component HF of the heart rate fluctuation increases.
<4. Conflict against drowsiness>
Tension occurs to feel dangerous and to resist drowsiness. The sympathetic nerve activity is intermittently increased and the high-frequency component HF of the heartbeat fluctuation is decreased when the person is sick.
<5. A state that cannot withstand drowsiness>
The tension disappears and the person begins to fall asleep. Heart rate HR decreases because sympathetic nerve activity is suppressed.

According to the above classification, in normal drowsiness driving, the state changes from 1 state (when there is no drowsiness) to 2 states (when there are signs of drowsiness), and further reaches 4 states (conflict state against drowsiness). There are many. On the other hand, in the resting state, the state changes from 1 to 2, changes to 3 (when drowsiness occurs), and when sleeping, it reaches 5 (drowsiness cannot be resisted).

That is, the drowsiness prediction parameters a and b are coefficients for weighting each of the RR interval and the heartbeat fluctuation. As described above, since the RR interval and the heartbeat fluctuation have different contributions to drowsiness depending on the active state, the accuracy of drowsiness is improved by weighting each of them.

Then, the drowsiness degree calculated by the equation (2) is in the range of numerical values of 1 to 5. The degree of drowsiness is 1 for less drowsiness and 5 for greater drowsiness. Further, the drowsiness degree 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. Therefore, in the case of RR <RR_ref (HR> HR_ref), the drowsiness degree is Let it be 1.

That is, since the drowsiness prediction parameter changes depending on the activity state, the drowsiness degree calculation unit 16 calculates the drowsiness degree based on the heart rate (biological information), the activity state, and the drowsiness reference heart rate.

The drowsiness degree display unit 17 displays the drowsiness degree calculated by the drowsiness degree calculation unit 16. The drowsiness level may be displayed only by the numerical value at that time, or may be displayed by a bar graph, a line graph, or the like so that the change in time series can be understood.

A display example of the drowsiness level display unit 17 is shown in FIG. FIG. 6A is a table showing the heart rate for each time, the drowsiness reference heart rate, and the calculated drowsiness level. FIG. 6B is a bar graph of FIG. 6A. That is, when the heart rate is measured as shown in FIG. 6A, it is displayed to the subject as shown in FIG. 6B.

The I / F 18 is an interface (I / F) to which the heart rate sensor 19 is connected. The I / F 18 is an interface corresponding to a wired connection when the heart rate sensor 19 is a wired connection, and an interface corresponding to a wireless connection when the heart rate sensor 19 is a wireless connection. That is, the I / F18 acquires biological information (heart rate, measurement time, etc.) related to the subject's heart rate measured by the subject.

As shown in FIG. 3, the server 2 includes a drowsiness reference heart rate table 21 as a drowsiness reference heart rate storage unit, a terminal communication unit 22, an external communication unit 23 as a regional information acquisition unit, and a correction unit 24. , A personal history data storage unit 25, and the like.

As is well known, the server 2 is installed in a business office or the like, and can communicate with a plurality of terminals such as smartphones 1 via a network such as the Internet. The server 2 can also communicate with a weather server or the like, which will be described later, via a network such as the Internet. In the server 2 having the above-described configuration, a storage medium such as a hard disk functions as the drowsiness reference heart rate table 21 and the personal history data storage unit 25. Further, in the terminal communication unit 22 and the external communication unit 23, a substrate for network control, a semiconductor circuit, and the like function. In addition, an arithmetic unit such as a CPU functions as the correction unit.

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. Then, based on the determination result of the state determination unit 11 received from the smartphone 1 and the current time, the drowsiness reference heart rate corresponding to the active state is output to the correction unit 24. An example of the table is shown in FIG.

Here, the drowsiness reference heart rate is the heart rate at the time of drowsiness occurrence. That is, the heart rate below this heart rate is the heart rate at which a person feels drowsy. As a method of calculating the drowsiness reference heart rate, for example, the minimum heart rate and the standard deviation of the heart rate in a certain active state such as driving are obtained, and the standard deviation is added to the minimum heart rate.

In the case of the example of FIG. 7, the drowsiness reference heart rate is set for each state sta1 to st3 at 1-hour intervals of times t1 to t4. Times t1 to t4 indicate times such as 0:00 pm and 1:00 pm.

The drowsiness reference heart rate is calculated based on the standard deviation or the like calculated based on the information such as the heart rate acquired from the heart rate sensor 19. For example, when calculating the reference heart rate at midnight, obtain the heart rate for a predetermined period near midnight, obtain the minimum heart rate and standard deviation as described above, and add the standard deviation to the minimum heart rate. It can be calculated with. Then, the activity state at that time is acquired from the state determination unit 11 of the smartphone 1 and set in the table as the drowsiness reference heart rate at midnight of the activity state.

Further, the drowsiness reference heart rate may be calculated and set by the smartphone 1 shown in FIG. 1, or information such as the heart rate may be transmitted to the server 2 and calculated by the server 2. It may be calculated and set separately with the configuration as shown above, 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 method described above.

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

Further, the portion filled by interpolation in the initial state may be updated (added) to the calculated value when the drowsiness reference heart rate is calculated by the subsequent measurement. Further, other interpolated values may be updated based on the update (addition). By doing so, the accuracy of the drowsiness reference heart rate can be improved. That is, the drowsiness reference heart rate table 21 adds or updates the drowsiness reference heart rate based on the measurement time of the heart rate and the activity state of the subject detected by the state determination unit 11 (activity state acquisition unit). It also functions as a number setting unit.

The terminal communication unit 22 receives from the smartphone 1 the heart rate (including heart rate fluctuation), the measurement time, the activity state determined by the state determination unit 11, and the current location information obtained by the GPS receiver 12. Further, the terminal communication unit 22 transmits the corrected drowsiness reference heart rate corrected by the correction unit 24 to the smartphone 1. That is, the terminal communication unit 22 functions as an activity state acquisition unit in the server 2.

Based on the current location information of the smartphone 1 received by the terminal communication unit 22, the external communication unit 23 receives the weather, temperature, and humidity as regional information of the location from an external weather server or the like via an external network such as the Internet. , Acquire weather information such as pressure. That is, the external communication unit 23 has acquired the area information related to the area including the current location where the subject is located.

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 read drowsiness reference heart rate is corrected based on the weather information received by the external communication unit 23. The current time may be referred to from the clock function built in the server 2, or may be acquired from an external NTP (Network Time Protocol) server or the like. That is, the correction unit 24 also functions as a drowsiness reference heart rate acquisition unit and a date / time acquisition unit.

The drowsiness reference heart rate varies depending on the weather, for example, as shown in FIG. FIG. 10 is a graph showing the relationship between the reference heart rate and the weather (weather). As shown in FIG. 10, when it is cloudy, the drowsiness reference heart rate tends to be higher than when it is sunny. Therefore, for example, as shown in FIG. 11, the drowsiness reference heart rate is corrected based on the weather and the temperature. In the case of the example of FIG. 11, if it is cloudy and the temperature is very warm, 10 is added to the read drowsiness reference heart rate. This added value becomes the corrected drowsiness reference heart rate.

Also, as shown in FIG. 12, the drowsiness reference heart rate may be corrected based on the weather and the discomfort index. The discomfort index is an index that quantitatively expresses the heat and humidity of summer, and it is said that if it exceeds 75, 10% of the population will be uncomfortable, and if it exceeds 80, everyone will be uncomfortable. In the case of the example of FIG. 12, when it is cloudy and the discomfort index is 65 to 70, 10 is added to the read drowsiness reference heart rate. This added value becomes the corrected drowsiness reference heart rate. Corrections such as those in FIG. 12 are effective when used in the summer.

Alternatively, it may be corrected based on the season and the weather. The drowsiness reference heart rate fluctuates with the seasons, for example, as shown in FIG. FIG. 13 is a graph showing the relationship between the reference heart rate and the season. As shown in FIG. 13, the drowsiness reference heart rate tends to be higher in spring than in other seasons. Therefore, for example, as shown in FIG. 14, the drowsiness reference heart rate is corrected based on the season and the weather. In the case of the example of FIG. 14, in the case of rain and summer, 9 is added to the read drowsiness reference heart rate. The season may be determined from the acquired date and time (date). That is, the drowsiness reference heart rate may be corrected based on the current date and time and regional information (weather).

The personal history data storage unit 25 stores historical data such as heart rate, measurement time, and activity status received by the terminal communication unit 22 for each individual. The server 2 may create or update the drowsiness reference heart rate table 21 based on the history data stored in the personal history data storage unit 25.

FIG. 15 shows a flowchart of the operation of the drowsiness calculation device having the above-described configuration. First, in step S11, the communication unit 15 transmits the user ID and password assigned to each user from the smartphone 1 to the server 2. The server 2 authenticates the user with the user ID and password transmitted from the smartphone 1 in step S21.

Next, the communication unit 15 transmits the activity state determined by the state determination unit 11 from the smartphone 1 and the current location information obtained by the GPS receiver 12 to the server 2. The server 2 receives the activity status and the current location information transmitted from the smartphone 1 in step S22. That is, this step functions as an activity state acquisition process.

Next, the server 2 acquires the weather information about the current location received in step S22 from the external communication unit 23 in step S23. Then, in step S24, the correction unit 24 reads the drowsiness reference heart rate from the activity state and the current time from the drowsiness reference heart rate table 21, and the read drowsiness reference heart rate is based on the weather information received by the external communication unit 23. It is corrected and transmitted to the smartphone 1 as the corrected drowsiness reference heart rate. That is, step S24 functions as a drowsiness reference heart rate acquisition step and a correction step.

Next, the smartphone 1 receives the corrected drowsiness reference heart rate from the server 2 in step S13. Subsequently, in step S14, the drowsiness degree calculation unit 16 calculates the drowsiness degree and displays it on the drowsiness degree display unit 17. That is, step S14 functions as a drowsiness calculation step. Then, in step S15, 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. The history data may be transmitted when the current location information is transmitted in step S12.

Next, the server 2 stores the received history data in the personal history data storage unit 25.

According to this embodiment, in the smartphone 1, the I / F 18 acquires the heart rate and the measurement time of the subject measured by the heart rate sensor 19, and the state determination unit 11 determines the activity state of the subject. , The GPS receiver 12 obtains the current location information of the smartphone 1 and transmits these to the server 2. Then, in the server 2, the external communication unit 23 acquires the weather information of the subject's current location, and the correction unit 24 sets the subject's drowsiness reference heart rate according to the activity state and the current time in the drowsiness reference heart rate table 21. Read from and correct based on weather information. Then, the corrected drowsiness reference heart rate (corrected drowsiness reference heart rate) is transmitted to the smartphone 1, and the test is performed based on the corrected drowsiness reference heart rate and the heart rate acquired by the I / F 18 by the drowsiness degree calculation unit 16 of the smartphone 1. Calculate the drowsiness of a person. By doing so, the drowsiness reference heart rate can be corrected based on the weather information such as the weather, temperature, humidity, atmospheric pressure, and discomfort index of the subject's current location, so the surrounding environment called the weather information is also taken into consideration. It is possible to calculate drowsiness with high accuracy.

Further, the server 2 adds or updates the drowsiness reference heart rate stored in the drowsiness reference heart rate table 21 based on the time when the heart rate is measured and the activity state of the subject determined by the state determination unit 11. There is. By doing so, it is possible to update or add the change in the drowsiness reference heart rate for each time of the active state due to the daily life of the subject at any time.

In addition, the drowsiness calculation unit 16 calculates the drowsiness of the subject based on the drowsiness prediction parameters set in advance for each active state. By doing so, the heart rate and the heart rate fluctuation can be weighted according to the activity state.

Further, since the drowsiness display unit 17 is provided, the subject can specifically perceive his / her drowsiness, and can take measures such as breaks and exercises based on the displayed drowsiness. it can.

In the above-described embodiment, the drowsiness prediction parameter and the drowsiness reference heart rate were selected based on the activity state and the measurement time, but even if the drowsiness prediction parameter and the drowsiness reference heart rate are selected based only on the activity state. Good. However, it is preferable to consider the measurement time because an appropriate drowsiness prediction parameter and drowsiness reference heart rate can be selected.

Further, in addition to the drowsiness degree display unit 17, the drowsiness degree may be notified by voice. Alternatively, voice notification may be given when the degree of drowsiness is above a certain level.

Further, the server 2 may include both or one of the parameter table 14 and the drowsiness degree calculation unit 16, and the smartphone 1 may include both or one of the drowsiness reference heart rate table 21 and the correction unit 24. Further, the smartphone 1 may be provided with the configuration without using the server 2.

Next, the drowsiness calculation device according to the second embodiment of this embodiment will be described with reference to FIGS. 16 to 18. The same parts as those in the first embodiment described above are designated by the same reference numerals, and the description thereof will be omitted.

In this embodiment, as shown in FIG. 16, the external communication unit 23 of the server 2 is based on the current location information of the smartphone 1A received by the terminal communication unit 22, for example, an external traffic information server or the like via the Internet or the like. From, traffic information such as the road type (expressway or general road, urban area is suburb, etc.) and traffic volume of the road at the relevant position is acquired. That is, in this embodiment, traffic information is included as regional information.

As shown in FIG. 17, the smartphone 1A has a route search unit 1a added to the configuration of the first embodiment. The route search unit 1a searches for a route from the current location acquired by the GPS receiver 12 to the destination input to the touch panel or the like, and guides the display unit 17A by indicating the route, an arrow, or the like.

Further, the route search unit 1a searches for a route to the destination based on the drowsiness degree calculated by the drowsiness degree calculation unit 16. The route search unit 1a may be configured as a navigation application program (navigation application).

Next, the correction of the correction unit 24 of the server 2 in this embodiment will be described. In this embodiment, the drowsiness reference heart rate is corrected based on the traffic information received by the external communication unit 23, for example, as shown in FIG. 18, based on the speed (congestion degree) of the vehicle and the road type. The speed may be calculated from a plurality of current location information, or may be calculated and transmitted by the smartphone 1. In either case, the terminal communication unit 22 functions as a traveling speed acquisition unit. In the case of the example of FIG. 18, when the speed is 80 km / h or more and the road type is an expressway, 10 is added to the read drowsiness reference heart rate. This added value becomes the corrected drowsiness reference heart rate.

The smartphone 1 that has received the corrected drowsiness reference heart rate calculates the drowsiness degree and displays it on the display unit 17A in the same manner as in the first embodiment. Further, the route search unit 1a searches for a route based on the degree of drowsiness, for example, avoiding the highway when the degree of drowsiness is high because a highway or a straight straight road tends to be sleepy. Alternatively, the route may be changed according to the change in drowsiness (when the drowsiness becomes high, the person is guided to approach the service area, or the person gets off the highway, etc.). That is, the route to the destination is searched based on the information on the drowsiness degree calculated by the drowsiness degree calculation unit 16.

The correction based on the traffic information and the selection of the route based on the drowsiness degree as in the present embodiment may be performed when the active state determined by the state determination unit 11 is the driving state.

According to this embodiment, the correction unit 24 corrects the drowsiness reference heart rate read from the drowsiness reference heart rate table 21 based on the traveling speed received by the terminal communication unit 22 and the traffic information received by the external communication unit 23. ing. By doing so, when the subject is driving a vehicle such as a car, it is possible to correct the drowsiness reference heart rate based on the surrounding environment such as traffic information.

Further, a route search unit 1a for searching and guiding a route to a preset destination is provided, and the route search unit 1a determines a route to the destination based on the drowsiness degree calculated by the drowsiness degree calculation unit 16. I try to search. By doing so, it is possible to select and guide an appropriate route according to the drowsiness of the subject.

It may be combined with the first embodiment. That is, both traffic information and weather information may be corrected. At this time, instead of simply adding the correction value, weighting may be performed with a predetermined coefficient or the like. Alternatively, a table in which weather information and traffic information are combined may be created, for example, a table for correction according to weather and road type.

Next, the drowsiness calculation device according to the third embodiment of this embodiment will be described with reference to FIGS. 19 and 20. The same parts as those of the first and second embodiments described above are designated by the same reference numerals, and the description thereof will be omitted.

The basic configuration of this embodiment is the same as that of the first embodiment (FIGS. 1 to 3). Further, the configuration of the second embodiment (FIGS. 16 and 17) may be used.

The drowsiness level shown in the first embodiment is calculated based on the heart rate or the like measured by the heart rate sensor 19, but the calculated value may deviate from the sense of the subject. Therefore, in this embodiment, the subject subjectively evaluates the drowsiness level displayed on the drowsiness level display unit 17, and corrects (updates) the drowsiness reference heart rate based on the evaluation.

A specific example will be described with reference to FIGS. 19 and 20. 19 and 20 are display examples of the drowsiness degree display unit 17, and show a change in heart rate, a drowsiness reference heart rate graph 171 and a drowsiness degree graph 172, and a drowsiness degree level. For this drowsiness level, the evaluation of the subject can be input by touching the displayed numerical value. That is, in the examples of FIGS. 19 and 20, the drowsiness display unit 17 is a touch panel.

In the case of FIG. 19, the drowsiness reference heart rate is 74. The current drowsiness level is 4 from the calculation result of the drowsiness level calculation unit 16. At this time, the subject subjectively evaluates the current degree of drowsiness.

As a result of the subjective evaluation, when the subject evaluates that the drowsiness level is 2, the subject selects and operates the drowsiness level 2 of the input unit 9 as shown in FIG. Then, the drowsiness reference heart rate correction unit 10 corrects the drowsiness reference heart rate according to the subjective evaluation. In the case of FIG. 19, it is corrected to 72. Then, the drowsiness degree is recalculated based on the corrected drowsiness reference heart rate, and the drowsiness degree graph 6b is also corrected (FIG. 20).

Next, the correction of the drowsiness reference heart rate in the drowsiness reference heart rate correction unit 10 will be described. When the equation (2) described in the first embodiment is modified, the following equation (5) is obtained.
RR_ref = RR- (Db x HFS) / a ... (5)

Therefore, the modified RR_ref can be calculated by substituting the subjective evaluation value into D in Eq. (5). Then, the drowsiness degree is recalculated by the equation (2) based on the modified RR_ref. Since RR_ref and the drowsiness reference heart rate HR_ref have the relationship shown in the equation (4) described in the first embodiment, the drowsiness reference heart rate HR_ref can be easily calculated by modifying the equation (4). can do.

The modified drowsiness reference heart rate HR_ref is used to display the change in heart rate and the drowsiness reference heart rate graph 6a, and is also transmitted to the server 2 to indicate the drowsiness reference at the corresponding activity state and time in the drowsiness reference heart rate table 21. Heart rate corrections are also made.

According to this embodiment, a touch panel is provided for inputting a subjective evaluation of the subject for the drowsiness level presented on the drowsiness level display unit 17. Then, the drowsiness degree calculation unit 16 corrects the drowsiness reference heart rate based on the subjective evaluation input by the touch panel, and calculates the drowsiness degree again based on the corrected drowsiness reference heart rate. By doing so, the subject can subjectively evaluate the drowsiness degree calculated by the drowsiness degree calculation unit 16. Then, the drowsiness degree calculation unit 16 can calculate the drowsiness degree again by feeding back the subjective evaluation result. Therefore, it is possible to more accurately calculate information on drowsiness tailored to the individual subject.

The present invention is not limited to the above examples. That is, those skilled in the art can carry out various modifications according to conventionally known knowledge within a range that does not deviate from the gist of the present invention. Of course, such a modification is included in the category of the present invention as long as it still has the configuration of the drowsiness calculation device of the present invention.

1, 1A smartphone 2 server (information processing device)
11 Status determination unit (activity status acquisition unit)
12 GPS receiver (running speed acquisition unit)
13 G sensor 14 Parameter table 15 Communication unit 16 Drowsiness calculation unit 17, 17A Drowsiness display unit (display unit, input unit)
18 I / F (Biological Information Acquisition Department)
19 Heart rate sensor 1a Route search unit (guidance unit)
21 Drowsiness standard heart rate table (Drowsiness standard heart rate storage unit)
22 Terminal communication unit (biological information acquisition unit, activity status acquisition unit, running speed acquisition unit)
23 External Communication Department (Regional Information Acquisition Department)
24 Correction unit (drowsiness standard heart rate acquisition unit)
25 Individual history data storage unit 50 Drowsiness calculation device S25 History data reception (biological information acquisition process)
S22 Activity status, current location acquisition (activity status acquisition process)
S24 Correct and transmit the reference heart rate (drowsiness reference heart rate acquisition process, correction process)
S14 Drowsiness calculation (drowsiness calculation process)

Claims (1)

A biometric information acquisition unit that acquires biometric information regarding the subject's heartbeat,
The drowsiness reference heart rate acquisition unit for acquiring the drowsiness reference heart rate of the subject, and
The area information acquisition department that acquires area information related to the area including the current location where the subject is located, and
A correction unit that corrects the acquired drowsiness reference heart rate based on the regional information, and
A drowsiness degree calculation unit that calculates information on the drowsiness of the subject based on the corrected drowsiness reference heart rate and the biological information.
A drowsiness calculation device characterized by comprising.

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