JP2017123989A - Information processing device, program and information processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing device, etc. for easily evaluating a degree of recovery from tiredness after sleep.SOLUTION: An information processing device 40 includes a first acquisition part for acquiring sleep data including a temporal change in a sleep depth, a first calculation part for calculating a sleep time based on the sleep data acquired by the first acquisition part, a second calculation part for calculating a ratio of a time of a predetermined sleep depth to the sleep time calculated by the first calculation part based on the sleep data acquired by the first acquisition part, a second acquisition part for referring to a storage part that associates and stores the sleep time, the ratio, and a degree of recovery from tiredness, and acquiring a degree of recovery from tiredness associated with the sleep time calculated by the first calculation part and the ratio calculated by the second calculation part, a first specification part for specifying an event related to the sleep based on the sleep data acquired by the first acquisition part, a correction part for correcting the degree of recovery from tiredness acquired by the second acquisition part based on the event specified by the first specification part, and an output part for outputting the degree of recovery from tiredness corrected by the correction part.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus, a program, and an information processing method.

  A fatigue evaluation method for evaluating the fatigue level of a subject using the amount of human herpesvirus contained in body fluid such as saliva as an index is disclosed (Patent Document 1).

  A sleep evaluation device that detects biological information during sleep of a subject, calculates a sleep evaluation score for each item such as sleep depth and sleep rhythm, and determines the sleep type of the subject is disclosed (Patent Document) 2).

JP 2007-330263 A JP2013-165828A

  However, in the evaluation method of Patent Document 1, it is necessary to measure the amount of human herpesvirus contained in the body fluid. In order to measure the amount of human herpesvirus, the amount of human herpesvirus DNA (DeoxyRibonucleic Acid) is measured using a technique such as PCR (Polymerase Chain Reaction). Special measuring devices and reagents are required and time is required. Therefore, it is not suitable for the purpose of simply evaluating the change in the degree of fatigue before and after sleep in daily life, that is, the degree of recovery from fatigue due to sleep.

  Moreover, in patent document 2, it is neither indicated nor suggested about evaluation of the fatigue recovery degree before and after sleep.

  In one aspect, an object of the present invention is to provide an information processing apparatus and the like that easily evaluates the degree of fatigue recovery before and after sleep.

  In one aspect, the information processing apparatus of the present invention calculates a sleep time based on a first acquisition unit that acquires sleep data including temporal changes in sleep depth and the sleep data acquired by the first acquisition unit. 1 calculation unit, a second calculation unit that calculates a ratio of a time that is a predetermined sleep depth based on the sleep data acquired by the first acquisition unit to the sleep time calculated by the first calculation unit, and sleep With reference to the storage unit that stores the time, the ratio, and the degree of fatigue recovery in association with each other, the degree of fatigue recovery associated with the sleep time calculated by the first calculation unit and the ratio calculated by the second calculation unit The first acquisition unit that acquires the first recovery unit that acquires an event related to sleep based on the sleep data acquired by the first acquisition unit, and the degree of fatigue recovery acquired by the second acquisition unit Compensation to be corrected based on the event specified by the specific unit Comprising a part, an output section the correcting section outputs the fatigue degree of correction.

  In one aspect, it is possible to provide an information processing apparatus or the like that simply evaluates the degree of fatigue recovery before and after sleep.

It is explanatory drawing which shows the structure of an information processing system. It is explanatory drawing which shows the record layout of sensor recording DB. It is explanatory drawing which shows the record layout of sleep record DB. It is explanatory drawing which shows the record layout of base point DB. It is explanatory drawing which shows the record layout of midway awakening point DB. It is explanatory drawing which shows the record layout of awakening time fluctuation | variation point DB. It is explanatory drawing which shows the record layout of fatigue recovery degree DB. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is explanatory drawing which shows the screen displayed on an information browsing apparatus. It is a flowchart which shows the flow of a process of a program. 10 is a flowchart showing a flow of processing of a subroutine for calculating a day / night inversion point according to the second embodiment. 10 is a flowchart showing a flow of processing of an apnea calculation subroutine according to a third embodiment. FIG. 10 is an explanatory diagram illustrating a screen displayed on the information browsing device according to the fourth embodiment. It is explanatory drawing which shows the record layout of basic | foundation DB of Embodiment 5. FIG. FIG. 18 is an explanatory diagram illustrating a configuration of an information processing system according to a sixth embodiment. It is explanatory drawing which shows the record layout of sensor recording DB of Embodiment 6. FIG. 18 is a flowchart showing a flow of processing of a subroutine for calculating frequent urination points according to Embodiment 6; 20 is a flowchart illustrating a processing flow of a snoring point calculation subroutine according to the sixth embodiment. FIG. 19 is a functional block diagram illustrating an operation of the information processing apparatus according to the seventh embodiment. FIG. 19 is an explanatory diagram illustrating a configuration of an information processing system according to an eighth embodiment.

[Embodiment 1]
FIG. 1 is an explanatory diagram illustrating a configuration of the information processing system 10. The information processing system 10 includes an information browsing device 35, a client 20, and a server 40 connected via a network 31. Note that a plurality of clients 20 and information browsing devices 35 may be connected to the network 31.

  The information browsing device 35 is an information device that allows the user himself / herself, the user's family, a nurse, a nursing staff member, a counselor, and the like who are objects of measurement of the degree of fatigue recovery to browse the user's degree of fatigue recovery. The information browsing device 35 includes a display unit, an input unit, and a communication unit. The display part of the information browsing apparatus 35 is a liquid crystal display, for example. The input unit of the information browsing device 35 is, for example, a touch panel, a keyboard, a mouse, a pen tablet, or the like.

  The information browsing device 35 is a general-purpose portable information device such as a smartphone, a multifunctional mobile phone, a tablet, or a laptop computer. A portable or wall-mounted information device dedicated to the information processing system 10 may be used as the information browsing device 35.

  The client 20 is a control device that controls a sensor installed in the user's bedroom. The client 20 includes a client CPU (Central Processing Unit) 21, a main storage device 22, an auxiliary storage device 23, a sensor I / F (InterFace) 24, a communication unit 26, and a bus.

  The client 20 may use a home energy management system (HEMS) control device connected to a sensor, a solar cell, or the like attached inside or outside the house. The client 20 may use an information device such as a general-purpose personal computer, a tablet, or a smartphone.

  The client CPU 21 is an arithmetic control device that controls the client 20. As the client CPU 21, one or a plurality of CPUs, a multi-core CPU, or the like is used. The client CPU 21 is connected to each hardware unit constituting the client 20 via a bus.

  The main storage device 22 is a storage device such as SRAM (Static RAM), DRAM (Dynamic RAM), flash memory, or semiconductor memory disk. The main storage device 22 temporarily stores information necessary during the processing performed by the client CPU 21 and a program being executed by the client CPU 21.

  The auxiliary storage device 23 is a storage device such as SRAM, DRAM, flash memory, semiconductor memory disk, or hard disk. The auxiliary storage device 23 stores a program to be executed by the client CPU 21 and various information necessary for executing the program.

  The sensor I / F 24 is an interface that receives signals from various sensors connected to the client 20. The communication unit 26 communicates with the network 31. The communication unit 26 is used for information exchange with the information browsing device 35 and the server 40.

  A sleep sensor 25 is connected to the client 20 via a sensor I / F 24. The sleep sensor 25 is a sheet-like sensor, for example, and is installed between the bed mattress and the sheet. The sleep sensor 25 detects the pulse, respiration, body movement, body temperature, and the like of a person lying on the bed. The sleep sensor 25 and the client 20 may be integrated.

  The sleep sensor 25 may be a sensor that is installed on the bed bed or the wall of the room and detects the pulse, respiration, body movement, body temperature, etc. of the person lying on the bed by microwaves or infrared rays.

  The sleep sensor 25 analyzes the detected information and outputs a sleep state such as a sleep depth and an apnea state to the sensor I / F 24. Note that the sleep sensor 25 does not analyze information, and the client CPU 21 or a server CPU 41 described later may analyze the information.

  The sensor I / F 24 includes a toilet sensor 27 (see FIG. 19) that detects use of the toilet, a door sensor that detects opening and closing of the door, a floor sensor that detects a person walking on the floor around the bed, a room temperature sensor, a humidity sensor, and the like. May be connected.

  The server 40 includes a server CPU 41, a main storage device 42, an auxiliary storage device 43, a communication unit 46, and a bus. The server 40 is an information processing device such as a large computer or a general-purpose personal computer or tablet installed in the data center. The server 40 is an example of an information processing apparatus according to this embodiment.

  The server CPU 41 is an arithmetic control device that executes a program according to the present invention. As the server CPU 41, one or a plurality of CPUs, a multi-core CPU, or the like is used. The server CPU 41 is connected to each hardware part constituting the server 40 via a bus.

  The main storage device 42 is a storage device such as SRAM (Static RAM), DRAM (Dynamic RAM), flash memory, or semiconductor memory disk. The main storage device 42 temporarily stores information necessary during the processing performed by the server CPU 41 and a program being executed by the server CPU 41.

  The auxiliary storage device 43 is a storage device such as SRAM, DRAM, flash memory, semiconductor memory disk, or hard disk. The auxiliary storage device 43 stores a program to be executed by the server CPU 41, a sensor record DB (DataBase) 51, a sleep record DB 52, a base point DB 53, a midway wake point DB 54, a wake time variation point DB 55, and a fatigue recovery degree DB 56. Yes.

  The sensor record DB 51, the sleep record DB 52, the base point DB 53, the midway awake point DB 54, the awake time fluctuation point DB 55, and the fatigue recovery degree DB 56 may be stored in other storage devices connected via a network.

  The communication unit 46 communicates with the network 31. The communication unit 46 is used for exchanging information with the client 20 and the information browsing device 35 and updating various DBs stored in the auxiliary storage device 43.

  FIG. 2 is an explanatory diagram showing a record layout of the sensor record DB 51. The sensor record DB 51 is a DB that records the name and date and time and the sleep sensor data acquired from the sleep sensor 25 in association with each other. The sleep sensor data acquired from the sleep sensor 25 is, for example, the sleep depth and the number of apneas. The data recorded in the sensor record DB 51 is an example of sleep data according to the present embodiment. The sensor recording DB 51 will be described with reference to FIG.

  The sensor record DB 51 has a name field, a date field, and a sleep sensor data field. In the name field, the name of the user is recorded. In the date field, the date and time when the data is acquired is recorded.

  The sleep sensor data field has a sleep depth field and an apnea frequency field. The sleep depth at the time recorded in the date / time field is recorded in the sleep depth field. The number of apnea events that occurred during the time recorded in the date / time field is recorded in the apnea number field. The sensor record DB 51 has one record for data acquired once for one user.

  The sleep depth will be described. In the present embodiment, the sleep depth is shown in three stages of getting out of bed, shallow sleep, and deep sleep. The bed is not lying on the bed. Getting out of bed includes both the state of being away from the bed and the state of sitting on the bed. Light sleep includes both lying in bed but awake and REM (Rapid Eye Movement) sleep. Deep sleep is a state of sleeping deeper than a REM sleep state.

  The apnea phenomenon will be described. The apnea phenomenon is a phenomenon in which the airflow in the mouth and nose stops for 10 seconds or more during the sleep time. When the apnea phenomenon occurs, it is known that the fatigue recovery effect by sleep is low. In the present embodiment, a phenomenon in which the airflow in the mouth and nose stops for an arbitrary period such as 8 seconds may be defined as an apnea phenomenon and detected.

  In the present embodiment, the data recording interval is 10 minutes. The data recording interval is an example and is not limited to 10 minutes. In the date / time field, a representative time of an interval for recording data is recorded. For example, the top line in FIG. 2 means that the data is recorded from 22:00 to 22:10 on October 10, 2015. In the sleep depth field, the average sleep depth in this time zone is recorded. In the apnea number field, the number of apnea events occurring in this time zone is recorded.

  The sleep sensor data field desirably has a field for recording various data that can be output by the sleep sensor 25, such as body temperature, heart rate, and respiration rate.

  FIG. 3 is an explanatory diagram showing a record layout of the sleep record DB 52. The sleep record DB 52 is a DB that records a name, a date, and a sleep state in association with each other. The sleep record DB 52 will be described with reference to FIG.

  The sleep record DB 52 includes a name field, a date field, a sleep time field, a deep sleep ratio field, a bed leaving time field, a midway awakening frequency field, and an apnea frequency field. In the name field, the name of the user is recorded. The date is recorded in the date field. In the present embodiment, one day is from noon to noon the next day. Therefore, the date field of the record described at the top of FIG. 3 means from noon on October 1, 2015 to noon on the next two days.

  In the sleep time field, a sleep time that is a sum of a light sleep time and a deep sleep time is recorded. In the deep sleep ratio field, the ratio of the deep sleep time during the sleep time is recorded as a percentage. In the leaving time field, the morning wake-up time is recorded. In the midway awakening number field, the number of times of getting out of bed for sleeping, for example, during the sleep is recorded. In the apnea frequency field, the total number of apnea events occurring in one day is recorded.

  The server CPU 41 creates one record of the sleep record DB 52 using the data for one day recorded in the sensor record DB 51. Specifically, the server CPU 41 extracts one day of sensor record records of one user using the name field and date / time field of the sensor record DB 51 as keys. The server CPU 41 extracts, from the extracted sensor record, a record recorded as “light sleep” and a record recorded as “deep sleep” in the sleep depth field. The server CPU 41 calculates the sleep time that is the product of the total number of extracted records and the data recording interval of 10 minutes, and records it in the sleep time field.

  As described above, the server CPU 41 realizes the function of the first calculation unit of the present embodiment. Note that the sleeping time may be calculated by the client CPU 21 and transmitted to the server 40 via the network 31. In this case, the client CPU 21 implements the function of the first calculation unit of the present embodiment.

  The server CPU 41 calculates a deep sleep time ratio expressed as a percentage by dividing the number of deep sleep records by the sum of the number of shallow sleep records and the number of deep sleep records, and enters the deep sleep ratio field. Record.

  As described above, the server CPU 41 realizes the function of the second calculation unit of the present embodiment. The deep sleep time ratio may be calculated by the client CPU 21 and transmitted to the server 40 via the network 31. In this case, the client CPU 21 realizes the function of the second calculation unit of the present embodiment.

  The server CPU 41 records the date and time recorded as getting out in the sleep depth field in the morning in the getting out time field, except for getting out of the room due to awakening in the middle described later.

  The server CPU 41 determines that the awakening is midway when, for example, the number of times the bed is recorded continuously in the sleep depth field is 3 times or less. The server CPU 41 records the number of halfway awakenings in the number of halfway awakenings. In addition, server CPU41 may determine the presence or absence of a bed based on the user's motion which sensors other than the sleep sensor 25, such as a door sensor and a floor sensor, for example, may record in a sleep depth field.

  The server CPU 41 calculates the sum of the values recorded in the apnea number field of the data for one day recorded in the sensor record DB 51 and records it in the apnea number field of the sleep record DB 52.

  In addition, you may create the information which client CPU21 records on the record of sleep record DB52, and you may record in sleep record DB52 via the network 31. FIG. Information that the sleep sensor 25 records in the record of the sleep record DB 52 may be created and recorded in the sleep record DB 52 via the network 31. In such a case, sleep time and the like can be calculated at a time interval finer than the time interval recorded in the sensor recording DB 51.

  FIG. 4 is an explanatory diagram showing a record layout of the base point DB 53. The base point DB 53 is a DB that associates the deep sleep time ratio, the sleep time, and the base point. The base point is a score indicating a basic fatigue recovery degree determined from the deep sleep time ratio and the sleep time.

  The base point DB 53 has a deep sleep time ratio field and a sleep time field. The sleep time field has subfields in increments of 1 hour from the 0 hour field to the 12 hours or more field.

  In the deep sleep time ratio field, numerical values in increments of 10 percent from 0 percent to 90 percent are recorded. In each sleep time field, a base point associated with the sleep time is recorded. The base point is a score obtained by scoring the degree of fatigue recovery when an event such as apnea or awakening is not generated during sleep.

  A specific example will be described. “10” is recorded in the field of the sleep time of 2 hours in the record in which the deep sleep time ratio is 10%. This means that the basic point of the degree of fatigue recovery when the sleep time is 2 hours or more and less than 3 hours and the deep sleep time ratio is 10% or more and less than 20% is 10 points.

  As the base point DB 53, for example, a different DB may be created and used depending on age, sex, and the like. As the base point DB 53, a different DB may be created and used for each individual.

  FIG. 5 is an explanatory diagram showing a record layout of the midway awakening point DB 54. The midway awakening point DB 54 is a DB that associates the number of midway awakenings and the score. When there is an awakening, the degree of recovery from fatigue due to sleep decreases. The midway awakening point is a score for deducting the degree of recovery from fatigue when midway awakening occurs.

  The midway awakening point DB 54 has a midway awakening frequency field and a midway awakening point field. The number of halfway awakening that occurs during an overnight sleep is recorded in the number of halfway awakenings. In the midway awakening point field, the number of midway awakening points that are deducted from the degree of fatigue recovery when the number of midway awakenings recorded in the midway awakening frequency field is recorded.

  A specific example will be described. “10” is recorded in the midway awakening point field where the number of midway awakenings is 2 times. This means that when halfway awakening occurs twice, the number of midway awakening points deducted from the degree of fatigue recovery is 10 points.

As the midway awakening point DB 54, for example, a different DB may be created and used depending on age, sex, and the like. The midway awakening point DB 54 may create and use a different DB for each individual.

  FIG. 6 is an explanatory diagram showing a record layout of the awakening time variation point DB 55. The awakening time fluctuation point DB 55 is a DB that associates the fluctuation of the morning awakening time with the score. When the awakening time is irregular, the degree of recovery from fatigue due to sleep decreases. The awakening time fluctuation point is a score for deducting the degree of recovery from fatigue when the fluctuation of the awakening time is large, that is, when the standard deviation of the awakening time is large.

  The awakening time fluctuation point DB 55 has a standard deviation field and an awakening time fluctuation point field. In the standard deviation field, for example, the standard deviation of awakening time for one week is recorded. The unit of the standard deviation field is minutes. By calculating the data to be recorded in the standard deviation field described above, the server CPU 41 realizes the function of the fluctuation amount calculation unit of the present embodiment.

  In the awakening time fluctuation point field, an awakening time fluctuation point that is deducted from the degree of fatigue recovery when the awakening time fluctuates with the standard deviation recorded in the standard deviation field is recorded.

  A specific example will be described. “40” is recorded in the awakening time variation point field of the record whose standard deviation field is 120 minutes. This means that when the standard deviation of the variation of the past awakening time is 120 minutes, the waking time fluctuation point deducted from the fatigue recovery degree is 40 points. As described above, the server CPU 41 realizes the function of the second specifying unit of the present embodiment.

  As the awakening time fluctuation point DB 55, for example, a different DB may be created and used depending on age, sex, and the like. As the awakening time fluctuation point DB 55, a different DB may be created and used for each individual.

  The standard deviation is an example of a representative value indicating the fluctuation amount of the awakening time. Instead of the standard deviation, for example, a time difference between the earliest awakening time and the latest awakening time in a predetermined number of days may be used.

  FIG. 7 is an explanatory diagram showing a record layout of the fatigue recovery degree DB 56. The fatigue recovery degree DB 56 is a DB that associates the name and date with the fatigue recovery degree.

  The fatigue recovery degree DB 56 has a name field, a date field, and a fatigue recovery degree field. In the name field, the name of the user is recorded. In the date field, the date when the sleep sensor data is acquired is recorded. In the fatigue recovery degree field, the fatigue recovery degree calculated based on the data recorded in the sensor recording DB 51 is recorded. The fatigue recovery degree DB 56 has one record for one user's day.

  A method for calculating the degree of fatigue recovery will be described. As described above, the server CPU 41 creates and records records in the sensor record DB 51 and the sleep record DB 52 based on the sleep data recorded in the sensor record DB 51. In the following description, the case where the degree of fatigue recovery on October 7, 2015 is calculated based on the data recorded in the sensor record DB 51 and the sleep record DB 52 will be described as an example.

  The server CPU 41 searches the sleep record DB 52 using the date of October 7, 2015 as a key, and extracts the sleep record record shown in the bottom row of FIG. The server CPU 41 searches the base point DB 53 using the data recorded in the sleep time field and deep sleep ratio field of the extracted sleep record record as keys. Since the sleep time is 6 hours 45 minutes and the deep sleep ratio is 72%, the server CPU 41 is a basic point when the deep sleep ratio is 70% or more and less than 80% and the sleep time is 6 hours or more and less than 7 hours 80. Get a point. As described above, the server CPU 41 realizes the function of the second acquisition unit of the present embodiment.

  The server CPU 41 searches the midway awakening point DB 54 using the data recorded in the extracted midway awakening frequency field as a key. Since the number of halfway awakenings is 1, the server CPU 41 acquires five halfway awakening points. Halfway awakening is an example of an event related to sleep in the present embodiment.

  The server CPU 41 calculates an apnea point using the data recorded in the apnea number field of the extracted sleep record. Since the number of apneas is 2, the apnea point is calculated by, for example, integrating the constant A. In the present embodiment, A is 0.5. The relational expression between the apnea frequency and the apnea point is not limited to a direct proportion. The relational expression between the apnea frequency and the apnea point may be a quadratic or higher function. Apnea is an example of an event related to sleep in the present embodiment.

  The server CPU 41 searches the sleep record DB 52 and extracts, for example, sleep record records for the past week from October 7, 2015. The server CPU 41 calculates the standard deviation of the bed leaving time using the data recorded in the bed leaving time field of the extracted sleep recording record. Since the method for calculating the standard deviation is known, the description thereof is omitted.

  The server CPU 41 calculates a standard deviation of 45 minutes. The server CPU 41 searches the awakening time fluctuation point DB 55 using the calculated standard deviation as a key. The server CPU 41 extracts five points that are waking time fluctuation points when the standard deviation is 40 minutes or more and less than 60 minutes.

  The server CPU 41 calculates the history influence point from the past fatigue recovery degree data. If the state of low fatigue recovery continues, the fatigue recovery level decreases because fatigue has accumulated. The history influence point is a score that reflects the influence of the history of fatigue recovery on the fatigue recovery.

  A method for calculating the history influence point will be described. The server CPU 41 searches the fatigue recovery degree DB 56 and extracts, for example, fatigue recovery degree records for the past 10 days from October 7, 2015. The server CPU 41 uses the data recorded in the fatigue recovery degree field of the extracted record to calculate a history influence point that is an average value of the fatigue recovery degree. Since the calculation method of the average value is known, the description is omitted. In the following, the description will be continued with an example where the history influence point is 67 points.

The recovery point can be calculated by equation (1).
Recovery point = basic point-event point-waking time fluctuation point ... (1)
Event point = midway awakening point + apnea point ...... (2)

The server CPU 41 calculates the recovery point as follows by substituting the above-mentioned points into the equation (1).
Event point = 5 + 0.5 × 2 = 6 points Recovery point = 80-6-5 = 69 points

The degree of fatigue recovery can be calculated by equation (3).
Fatigue recovery level = Recovery point × B + History influence point × C (3)
B + C = 1

  The server CPU 41 calculates the degree of fatigue recovery by substituting the above-mentioned points into the equation (3). For example, when B = 0.7 and C = 0.3, the server CPU 41 records the calculated fatigue recovery degree 68.4 points and date in the fatigue recovery degree DB 56. As described above, the server CPU 41 realizes the function of the fatigue recovery degree recording unit of the present embodiment.

  Summarize the method of calculating the degree of fatigue recovery. Server CPU41 calculates | requires a basic point based on sleep time and the deep sleep time ratio. Server CPU41 calculates | requires an event point based on the event which generate | occur | produced during sleep, such as a halfway awakening point and an apnea point. When these sleep events occur, the degree of recovery from fatigue due to sleep decreases.

  The server CPU 41 obtains an awakening time fluctuation point indicating the influence of fluctuations of the awakening time in the past several days. The server CPU 41 obtains a history influence point indicating a tendency of the degree of fatigue recovery in the past several days. As described above, the server CPU 41 realizes the function of the third specifying unit of the present embodiment.

  The server CPU 41 calculates the recovery point of the day by subtracting the event point and the awakening time fluctuation point from the base point. The server CPU 41 calculates the degree of fatigue recovery for the day based on the recovery point and the history influence point indicating the accumulation of past fatigue.

  That is, the server CPU 41 calculates the fatigue recovery degree by correcting the base point with the event point, the awakening time variation point, and the history influence point. As described above, the server CPU 41 realizes the function of the correction unit of the present embodiment.

  The basic point DB 53, the midway awake point DB 54, the awake time variation point DB 55, and the values of the constants A, B, and C used above are all illustrative examples.

  For example, when the user has physical strength and is less affected by the degree of fatigue recovery in the past, B = 1 and C = 0 may be used. By doing so, the history influence point does not affect the fatigue recovery degree.

  The auxiliary storage device 43 may include an apnea point DB in which the apnea frequency and the apnea point are associated with each other. The server CPU 41 may calculate using a function stored in the auxiliary storage device 43 instead of the midway awakening point DB 54.

  The number of days of past recordings used for calculating the waking time fluctuation point is an example. The number of days of past recordings used for calculating the history influence point is also an example. These days may use different values depending on age, sex, and the like.

  The awakening time variation point is an example of a score indicating whether or not the user's life rhythm is disturbed. Instead of the waking time fluctuation point, for example, a score indicating the fluctuation amount of the bedtime may be used.

As for the history influence point, the data of the most recent day may be influenced more strongly than the old data. For example, the history influence point can be obtained using equation (4).

  8 to 13 are explanatory diagrams showing screens displayed on the information browsing device 35. A screen displayed by the information processing system 10 according to the present embodiment will be described with reference to FIGS. 8 to 13. In the following description, a case where the server CPU 41 controls the screen of the information browsing device 35 via the network 31 will be described as an example. The CPU of the information browsing device 35 may extract data from the DB stored in the auxiliary storage device 43 via the network 31 and display the screens of FIGS. 8 to 13.

  FIG. 8 is a fatigue recovery degree screen displaying the fatigue recovery degree on a specific day. The fatigue recovery degree screen includes a fatigue recovery degree column 61, a name column 62, a sensor state column 63, a pleasant sleep index button 65, a comfortable environment index button 66, a data button 67 of this month, a sleep button 68 of today, and a sleep tendency button 69. It is displayed.

  In the fatigue recovery degree column 61, the fatigue recovery degree recorded in the fatigue recovery degree DB 56 is displayed as a pie chart. Below the pie chart, advice based on information recorded in the sleep recording DB 52 is displayed. The advice may be displayed in different sentences depending on the user, the user's family, the medical staff, the care staff, and the like.

  The name column 62 displays the name of the user. The sensor status column 63 displays the status of various sensors connected to the sensor I / F 24. For example, at the left end of the sensor status column 63 in FIG. 8, it is displayed that the client CPU 21 is watching over the user status by displaying “watching”. Room temperature and humidity are displayed on the right side of the “watching” display. Furthermore, two icons on the right side indicate whether the user is getting out of bed or in bed. The adjacent icon indicates that the toilet sensor 27 and the door sensor are operating.

  On the right side of the screen, a sleep index button 65, a comfort environment index button 66, a data button 67 for this month, a sleep button 68 for today, and a sleep tendency button 69 are displayed. The operation when the selection of each button is accepted will be described below.

  When the selection of the sleep index button 65 is accepted, the server CPU 41 displays a sleep index screen shown in FIG. 9 on the information browsing device 35. On the pleasant sleep index screen, a pleasant sleep index field 76, a name field 62, a sensor state field 63, a fatigue recovery degree button 64, a comfort environment index button 66, a data button 67 of this month, a sleep button 68 of today and a sleep tendency button 69 are displayed. Has been. In addition, description is abbreviate | omitted about the part which is common in the fatigue recovery degree screen demonstrated using FIG.

  In the sleep index column 76, the sleep index is displayed as a pie chart. Below the pie chart, advice based on information related to the sleep index is recorded. The pleasant sleep index is an index indicating the degree of satisfaction with sleep. The good sleep index is calculated by the server CPU 41. The server CPU 41 may acquire a sleep index from the outside via the network 31. The server CPU 41 stores the sleep index in the auxiliary storage device 43.

  When the server CPU 41 accepts selection of the fatigue recovery degree button 64 on the pleasant sleep index screen, the server CPU 41 displays the fatigue recovery degree screen described with reference to FIG.

  When the server CPU 41 accepts the selection of the comfort environment index button 66, the server CPU 41 displays the comfort environment index screen shown in FIG. The comfort environment index screen includes a comfort environment index field 77, a name field 62, a sensor state field 63, a fatigue recovery degree button 64, a comfort environment index button 66, a data button 67 of this month, a sleep button 68 of today, and a sleep tendency button 69. Is displayed. In addition, description is abbreviate | omitted about the part which is common in the fatigue recovery degree screen demonstrated using FIG.

  In the comfort environment index column 77, the comfort environment index is displayed in a pie chart. Below the pie chart, advice based on information related to the comfort environment index is recorded. The comfort environment index is an index indicating whether or not the environment is suitable for sleep. The comfortable CPU is calculated by the server CPU 41. The server CPU 41 may acquire a comfortable environment index from the outside via the network 31. The server CPU 41 stores the comfort environment index in the auxiliary storage device 43.

  When using the information browsing device 35 having a large display unit, the server CPU 41 may display the fatigue recovery degree column 61, the pleasant sleep index column 76, and the comfortable environment index column 77 side by side on one screen. In this case, the server CPU 41 does not display the fatigue recovery degree button 64, the pleasant sleep index button 65, and the comfortable environment index button 66.

  When using the information browsing device 35 having a large display unit, for example, the fatigue recovery degree columns 61 of a plurality of users may be displayed side by side. By doing in this way, a leader can grasp | ascertain the fatigue recovery degree of the some user in training camp for training, for example, and can be reflected in the guidance content.

  When the server CPU 41 accepts selection of the data button 67 for this month, the server CPU 41 displays the index graph screen shown in FIG. On the exponent graph screen, an exponent graph column 78, a name column 62, and a sensor state column 63 are displayed. In addition, description is abbreviate | omitted about the part which is common in the fatigue recovery degree screen demonstrated using FIG.

  In the index graph column 78, a fatigue recovery degree graph 83, a pleasant sleep index graph 84, and a comfortable environment index graph 85 are displayed in a line graph. The horizontal axis of the graph is an index and the unit is percent. The vertical axis of the graph is the date. The solid line in the index graph column 78 is a graph showing the degree of fatigue recovery, the broken line is the sleep index, and the alternate long and short dash line is the comfort environment index. When an operation of a scroll bar (not shown) is accepted, the server CPU 41 changes the date range displayed in the exponent graph column 78.

  The server CPU 41 displays a fatigue recovery degree line graph based on the date and the fatigue recovery degree recorded in the fatigue recovery degree DB 56. The server CPU 41 displays a line graph of the pleasant sleep index and the comfortable environment index based on the pleasant sleep index and the comfortable environment index stored in the auxiliary storage device 43.

  The server CPU 41 may display the fatigue recovery degree button 64, the pleasant sleep index button 65, the comfortable environment index button 66, the sleep button 68 of the day, and the sleep tendency button 69 on the index graph screen as in FIGS. 8 to 10. .

  The server CPU 41 may accept a change in the date range for displaying data by displaying a vertical scroll bar on the right side of the exponent graph column 78.

  When the server CPU 41 accepts the selection of today's sleep button 68, the server CPU 41 displays a one-day data screen shown in FIG. 12 on the information browsing device 35. On the one-day data screen, a sleep depth column 86, an apnea count column 87, and an awakening column 88 are displayed.

  The horizontal axis of the sleep depth column 86, the apnea column 87, and the midway awakening column 88 is time. As described above, in the information processing system 10, one day is from noon to noon the next day. FIG. 12 shows a graph for one day. Both ends of the horizontal axis of the graph are noon.

  The vertical axis of the sleep depth column 86 indicates the sleep depth. As described above, in the present embodiment, the sleep depth is shown in three stages of getting out of bed, shallow sleep, and deep sleep. The vertical axis of the apnea count column 87 indicates the number of apnea events that have occurred during one hour. The halfway awakening column 88 indicates the time when the halfway awakening has occurred by hatching.

  The server CPU 41 displays the name data field 62, the sensor status field 63, the fatigue recovery degree button 64, the pleasant sleep index button 65, the comfortable environment index button 66, the fatigue recovery degree button 64, A good sleep index button 65, a comfortable environment index button 66, a sleep button 68 of today, and a sleep tendency button 69 may be displayed.

  The server CPU 41 may receive a change in the time range for displaying data by displaying a horizontal scroll bar below the midway awakening column 88. The server CPU 41 may accept a change in the range of the period for displaying data and display data having an arbitrary length such as 40 hours.

  When the selection of the sleep tendency button 69 is received, the server CPU 41 displays the sleep tendency screen shown in FIG. On the January data screen, a sleep state field 89, a name field 62, and a sensor state field 63 are displayed. In addition, description is abbreviate | omitted about the part which is common in the fatigue recovery degree screen demonstrated using FIG.

  In the sleep state column 89, a sleep time graph 81 and a sleep depth graph 82 are displayed. The sleep time graph 81 is a line graph showing the sleep time of a day. The horizontal axis of the sleep time graph 81 is the sleep time of one day. In FIG. 13, the vertical axis memory of the sleep time graph 81 is not displayed. Instead, a numeric label indicating sleep time is displayed in the vicinity of a point indicating data. The vertical axis of the sleep time graph 81 is the date.

  The horizontal axis of the sleep depth graph 82 is a time starting from noon and ending at noon the next day. The vertical axis of the sleep depth graph 82 is the date. The sleep depth graph 82 is a graph indicating the sleep depth of each date and time by the type of hatching. A fine dot indicates a light sleep. Diagonal hatching indicates deep sleep. The hatching time is not hatched. The time during which data cannot be acquired due to a network communication failure or the like is indicated by “error”.

  The server CPU 41 may accept a change in the date range for displaying data by displaying a vertical scroll bar on the right side of the sleep state column 89.

  The server CPU 41 may display the fatigue recovery degree button 64, the pleasant sleep index button 65, the comfortable environment index button 66, the sleep button 68 of the day, and the sleep tendency button 69 on the sleep tendency screen as in FIGS. 8 to 10. .

  FIG. 14 is a flowchart showing the flow of processing of the program. The flow of processing performed by the server CPU 41 will be described with reference to FIG. Server CPU41 acquires the sleep record record for one day from sleep record DB52 (step S501). Specifically, the server CPU 41 extracts sleep record records for the user's day using the name field and date field of the sleep record DB 52 as keys.

  The server CPU 41 acquires a base point (step S502). Specifically, the server CPU 41 searches the basic point DB 53 using the data recorded in the sleep time field and deep sleep ratio field of the sleep record record extracted in step S501 as a key, and acquires the basic point.

  The server CPU 41 acquires a midway awakening point (step S503). Specifically, the server CPU 41 searches the midway awakening point DB 54 using the data recorded in the midway awakening frequency field extracted in step S501 as a key, and acquires the midway awakening point.

  The server CPU 41 acquires an apnea point (step S504). Specifically, the server CPU 41 calculates an apnea point by substituting the data recorded in the apnea number field of the sleep record record extracted in step S501 into a predetermined function.

  The server CPU 41 acquires a change in the awakening time (step S505). Specifically, the server CPU 41 searches the sleep record DB 52 and extracts, for example, sleep record records for the past week. The server CPU 41 calculates the standard deviation of the bed leaving time using the data recorded in the bed leaving time field of the extracted sleep recording record.

  The server CPU 41 acquires the awakening time variation point (step S506). Specifically, the server CPU 41 searches the awakening time fluctuation point DB 55 using the standard deviation calculated in step S505 as a key, and acquires the awakening time fluctuation point.

  The server CPU 41 acquires the past degree of fatigue recovery (step S507). Specifically, the server CPU 41 searches the fatigue recovery degree DB 56 and extracts and acquires fatigue recovery degree records for a predetermined number of days.

  The server CPU 41 calculates a history influence point (step S508). Specifically, the server CPU 41 uses the data recorded in the fatigue recovery degree field of the fatigue recovery degree record extracted in step S507 to calculate a history influence point that is an average value of the fatigue recovery degree.

  The server CPU 41 calculates a recovery point based on the above equation (1) (step S509). The server CPU 41 calculates the degree of fatigue recovery based on the above equation (3) (step S510).

  The server CPU 41 adds a record to the fatigue recovery degree DB 56 and records the fatigue recovery degree and date calculated in step S510 (step S511). The server CPU 41 ends the process. Server CPU41 displays the screen demonstrated using FIGS. 8-13 on the information browsing apparatus 35 based on the data recorded on fatigue recovery degree DB56 grade | etc., (Step S512).

  According to the present embodiment, it is possible to provide an information processing apparatus and the like that simply evaluates the degree of fatigue recovery before and after sleep, reflecting the influence of events that occur during sleep such as apnea and mid-wake awakening.

  The screens shown in FIGS. 8 to 13 are examples. It is desirable to change a graph or the like displayed on the screen according to the size of the display screen of the information browsing device 35, the place where the information browsing device 35 is used, and the like.

  The base point DB 53 may relate the ratio of shallow sleep time and the sleep time to the degree of recovery from fatigue instead of the deep sleep time ratio.

  The server CPU 41 may display the degree of fatigue recovery compared with other days of the same user on the information browsing device 35. For example, the server CPU 41 displays a message such as “Today's fatigue recovery degree is the 120th day from the top of the last year” on the information browsing device 35.

  The server CPU 41 may display the degree of fatigue recovery compared with other users on the information browsing device 35. For example, the server CPU 41 displays a message such as “Today's fatigue recovery is within the range of the top 10% to 20%” on the information browsing device 35. The comparison target population can be determined by, for example, types such as the same age, same sex, and same occupation.

[Embodiment 2]
The present embodiment relates to an information processing system 10 that deducts the degree of recovery from fatigue when day / night reversal occurs. In the case of daytime / nighttime reversal, that is, when sleeping in the daytime and staying up in the middle of the night, even if the sleep time and the deep sleep ratio are sufficient, the degree of fatigue recovery is low. The day / night reversal point is a point by which the degree of fatigue recovery is deducted when day / night reversal occurs. The present embodiment relates to an information processing apparatus that calculates day-night inversion points and reflects them in the degree of fatigue recovery. Description of portions common to the first embodiment is omitted.

In the present embodiment, the server CPU 41 includes a day / night reversal point as an event point used when correcting the base point. Therefore, in the present embodiment, the event point is calculated using Equation (5) instead of Equation (2) described above.
Event point = midway awakening point + apnea point + day-night reversal point (5)

  FIG. 15 is a flowchart showing the flow of processing of the subroutine for calculating the day / night inversion point according to the second embodiment. The day / night inversion point calculation subroutine determines whether or not day / night inversion has occurred, and if day / night inversion has occurred, gives a day / night inversion point. The server CPU 41 activates a day / night inversion point calculation subroutine between step S504 and step S505 of the program described with reference to FIG. With reference to FIG. 15, the flow of processing of the subroutine for calculating the day / night inversion point will be described.

  The server CPU 41 acquires the sensor record (step S531). Specifically, the server CPU 41 extracts one day of sensor record records for one user using the name field and date / time field of the sensor record DB 51 as keys.

  The server CPU 41 calculates nighttime sleep time (step S532). Nighttime is, for example, a time zone from 1 am to 6 am. A method for calculating the sleep time at night will be described. The server CPU 41 extracts a sensor record record in the night time zone using the date and time field of the sensor record record extracted in step S531 as a key. The server CPU 41 extracts, from the extracted sensor record, a record recorded as “shallow sleep” and a record recorded as “deep sleep” in the sleep depth field. The server CPU 41 stores the nighttime sleep time, which is the product of the total number of extracted records and the data recording interval of 10 minutes, in the auxiliary storage device 43 or the main storage device 42.

  Similarly, the server CPU 41 calculates the daytime sleep time (step S533). The daytime is, for example, a time zone from 1 pm to 6 pm.

  The server CPU 41 determines whether the nighttime sleep time is longer than the daytime sleep time (step S534). If it is determined that the nighttime sleep time is longer (YES in step S534), no reversal of day and night has occurred. The server CPU 41 determines that the day / night inversion point is zero (step S535). The server CPU 41 then ends the process.

  If it is determined that the nighttime sleep time is not longer (NO in step S534), a reversal of day and night has occurred. The server CPU 41 determines that the day / night inversion point is E (step S536). E is a numerical value stored in advance in the auxiliary storage device 43. The server CPU 41 then ends the process.

  According to the present embodiment, it is possible to provide an information processing apparatus that deducts the degree of recovery from fatigue when day / night reversal occurs.

  The night and daytime time zones used in step S532 and step S533 may be changed according to the user's age, occupation, residential area, and the like. The value of the constant E may be a different value depending on the age, sex, etc. of the user.

[Embodiment 3]
The present embodiment relates to an information processing system 10 that deducts the degree of recovery from fatigue when symptoms of sleep apnea syndrome are present. In the case of sleep apnea syndrome, it is known that the degree of recovery from fatigue due to sleep is low.

  In the following description, the symptoms of sleep apnea syndrome are a state in which an apnea phenomenon occurs 30 times or more per day and a state in which an apnea phenomenon occurs 5 times or more per hour. Description of portions common to the first embodiment is omitted.

  FIG. 16 is a flowchart showing a flow of processing of an apnea point calculation subroutine of the third embodiment. The apnea point calculation subroutine is a subroutine for determining whether or not there is a symptom of sleep apnea syndrome and giving an apnea point. The server CPU 41 starts an apnea calculation subroutine instead of executing step S504 of the program described with reference to FIG. With reference to FIG. 16, the processing flow of the apnea calculation subroutine will be described.

  The server CPU 41 acquires sensor recording (step S551). Specifically, the server CPU 41 extracts one day of sensor record records for one user using the name field and date / time field of the sensor record DB 51 as keys.

  The server CPU 41 determines whether or not the total number of apneas is 30 or more (step S552). When the total number of apneas is 30 times or more (YES in step S552), the server CPU 41 determines whether or not apnea occurs 5 times or more in one hour (step S553). If apnea occurs 5 times or more per hour (YES in step S553), both symptoms of sleep apnea syndrome have occurred. The server CPU 41 determines that the apnea point is F (step S554).

  If apnea has not occurred more than 5 times per hour (NO in step S553), one of the symptoms of sleep apnea syndrome has occurred. The server CPU 41 determines that the apnea point is G (step S555). G is a score lower than F.

  If the total number of apneas is less than 30 (NO in step S552), the server CPU 41 determines whether or not apnea occurs 5 times or more in one hour (step S561). When apnea has not occurred 5 times or more in one hour (NO in step S561), neither of the symptoms of sleep apnea syndrome has occurred. The server CPU 41 determines that the apnea point is zero (step S556).

  If apnea occurs 5 times or more per hour (YES in step S561), one of the symptoms of sleep apnea syndrome has occurred. The server CPU 41 determines that the apnea point is G (step S555).

  After step S554, step S555, or step S556 ends, the server CPU 41 ends the process.

  According to this embodiment, it is possible to provide an information processing apparatus that reflects the presence or absence of symptoms of sleep apnea syndrome in the degree of fatigue recovery.

[Embodiment 4]
The present embodiment relates to an information processing system 10 using a sleep sensor 25 that can determine six stages of sleep depth. Description of portions common to the first embodiment is omitted.

  FIG. 17 is an explanatory diagram illustrating a screen displayed on the information browsing device 35 according to the fourth embodiment. FIG. 17 is a day data screen displayed by the server CPU 41 instead of the screen described with reference to FIG. On the one-day data screen, a sleep depth column 86, an apnea count column 87, and an awakening column 88 are displayed.

  The vertical axis of the sleep depth column 86 indicates the sleep depth. In the present embodiment, the sleep depth is displayed in six stages of getting out of bed, REM sleep, stage 1, stage 2, stage 3, and stage 4. “Getting out of bed” indicates the state of “getting out of bed” according to the first embodiment and the state of being awake while lying on the bed. REM sleep, stage 1 and stage 2 correspond to the state of “shallow sleep” in the first embodiment that is not awake. Steps 3 and 4 correspond to “deep sleep” in the second embodiment.

  In the present embodiment, it is desirable to display the sleep depth using a different color for each sleep depth in the sleep depth graph 82 of the sleep tendency screen described with reference to FIG.

  According to the present embodiment, it is possible to realize an information processing apparatus that uses the high-performance sleep sensor 25 to measure the sleep depth in detail and analyze the fatigue recovery degree with high accuracy.

[Embodiment 5]
The present embodiment relates to an information processing system 10 that uses a base point DB 53 that associates deep sleep time, sleep time, and base points. Description of portions common to the first embodiment is omitted.

  FIG. 18 is an explanatory diagram showing a record layout of the base point DB 53 according to the fifth embodiment. The base point DB 53 shown in FIG. 18 is a DB used instead of the base point DB 53 described with reference to FIG.

  The base point DB 53 has a deep sleep time field and a sleep time field. The sleep time field has subfields in increments of 1 hour from the 0 hour field to the 12 hours or more field.

  In the deep sleep time field, numerical values in increments of 1 hour from 0 hours to 12 hours are recorded. In each sleep time field, a base point associated with deep sleep time is recorded.

  A specific example will be described. 40 is recorded in the field where the sleep time is 4 hours in the record where the deep sleep time is 2 hours. This means that when the deep sleep time is 2 hours or more and less than 3 hours and the sleep time ratio is 4 hours or more and less than 5 hours, the basic point of the fatigue recovery degree is 40 points. Note that the deep sleep time cannot be longer than the sleep time. Therefore, no data is recorded in the field indicating such a situation.

  In the present embodiment, the sleep record DB 52 has a deep sleep time field instead of the deep sleep ratio field.

[Embodiment 6]
The present embodiment relates to an information processing system 10 that deducts the degree of recovery from fatigue when nocturia, snoring, and teething occur at night. Nocturia is a phenomenon that occurs due to urination during sleep. The phenomenon of interrupting daytime sleep due to urination is also included in nocturia. When nocturia, snoring, and teething occur, the degree of recovery from fatigue is low even if the sleep time and deep sleep ratio are sufficient.

  The present embodiment relates to an information processing apparatus that calculates a night urination point, a snoring point, and a tooth-growth point and reflects them in the degree of fatigue recovery. Description of portions common to the first embodiment is omitted.

In the present embodiment, the server CPU 41 includes a night urination point, a snoring point, and a toothing point as event points used when correcting the base point. Therefore, in the present embodiment, the event point is calculated using Equation (6) instead of Equation (2) described above.
Event point = midway awakening point + apnea point + night urination point + snoring point + toothing point ...... (6)

  FIG. 19 is an explanatory diagram illustrating a configuration of the information processing system 10 according to the sixth embodiment. The information processing system 10 according to the present embodiment includes a toilet sensor 27 and a microphone sensor 28. The toilet sensor 27 and the microphone sensor 28 are connected to the sensor I / F 24.

  The toilet sensor 27 is a sensor installed in the toilet. The toilet sensor 27 is a sensor that detects the use status of the toilet. In this embodiment, a case where there is a private toilet will be described as an example. When a plurality of people share one toilet, a person who uses the toilet is specified by means such as face authentication or an ID card.

  The microphone sensor 28 is a sensor that detects and analyzes sound generated around the bed. The microphone sensor 28 detects, for example, the occurrence of snoring, teething, sleeping. The microphone sensor 28 may be a simple microphone that detects sound. In this case, it is desirable that the client CPU 21 or the server CPU 41 analyze the sound acquired via the sensor I / F 24 to detect the occurrence of snoring, tooth cramping, sleep, etc.

  FIG. 20 is an explanatory diagram showing a record layout of the sensor record DB 51 of the sixth embodiment. The sensor record DB 51 in FIG. 20 is a DB used instead of the sensor storage DB described with reference to FIG.

  The sensor recording DB 51 of the present embodiment is a DB that records the name and date / time and the data acquired from the sleep sensor 25, the toilet sensor 27, and the microphone sensor 28 in association with each other. The sensor record DB 51 is an example of sleep data according to the present embodiment.

  The sensor record DB 51 includes a name field, a date field, a sleep sensor data field, a toilet sensor data field, and a microphone data field. In the name field, the name of the user is recorded. In the date field, the date and time when the data is acquired is recorded.

  The sleep sensor data field has a sleep depth field and an apnea frequency field. The sleep depth at the time recorded in the date field is recorded in the sleep depth field. The number of apnea events that occurred during the time recorded in the date field is recorded in the apnea number field.

  In the toilet sensor field, the usage state of the toilet is recorded. In the microphone data field, the occurrence state of snoring or the like is recorded. In the microphone data field, occurrence of two or more states such as “snoring, tooth cramping” may be recorded.

  The sensor record DB 51 has one record for data acquired once for one user.

  FIG. 21 is a flowchart showing a flow of processing of a subroutine for calculating frequent urination points at night according to the sixth embodiment. The subroutine for calculating the frequency of nocturia is a subroutine for determining whether the frequency of nocturia occurs or not, and if the frequency of nocturia occurs, assigning the nocturia point. The server CPU 41 starts a night frequent urination point calculation subroutine between step S504 and step S505 of the program described with reference to FIG. With reference to FIG. 21, the flow of processing of a subroutine for calculating frequent urination points will be described.

  The server CPU 41 acquires the sensor record (step S601). Specifically, the server CPU 41 extracts one day of sensor record records for one user using the name field and date / time field of the sensor record DB 51 as keys.

  The server CPU 41 sets the counter “number of toilets at night” to an initial value 0 (step S602). The server CPU 41 sets the counter I to the initial value 1 (step S603).

  The server CPU 41 sets the I-th record among the records acquired in step S601 as a determination target (step S604). The server CPU 41 determines whether or not urination is recorded in the toilet sensor field of the determination target record (step S605). The presence or absence of urination is determined, for example, based on whether or not the amount of urination is greater than or equal to a predetermined amount.

  If it is determined that urination is recorded (YES in step S605), the server CPU 41 determines whether or not the time before urination is sleeping (step S606). Specifically, the server CPU 41 determines whether or not the sleep depth indicating that the user is sleeping is recorded in the sleep depth field of the record immediately before the determination target record.

  When the three levels of sleep depth described using FIG. 12 are recorded in the sleep depth field, the sleep depth indicating that the user is sleeping is “deep sleep”. As described above, the “shallow sleep” in FIG. 12 includes the state where the person is lying on the bed but is awake. When the six levels of sleep depth described with reference to FIG. 17 are recorded in the sleep depth field, the sleep depth indicating sleep is “REM sleep” and “stage 1” to “stage 4”. Up to. As described above, the sleep depth indicating that the user is sleeping varies depending on the sleep sensor 25 to be used.

  Note that the server CPU 41 may determine whether or not sleeping is recorded in the sleep depth field of two or more previous records to be determined, for example. By doing in this way, it is possible to detect night urination even when the bedroom is separated from the toilet and when walking is slow and it takes time to reach the toilet.

  If it is determined that the user is sleeping (YES in step S606), the server CPU 41 determines whether the time after urination is sleeping (step S607). Specifically, the server CPU 41 determines whether or not the sleep depth indicating that the user is sleeping is recorded in the sleep depth field of the record immediately after the determination target record. In addition, server CPU41 may determine whether it is recorded that it is sleeping, for example in the sleep depth field of the record 2 back of determination object.

  If it is determined that the user is sleeping (YES in step S607), the server CPU 41 adds 1 to the counter “number of toilets at night” (step S608). When it is determined that the user is not sleeping before urination (NO in step S606) and the user is not sleeping after urination (NO in step S607) after the end of step S608, the server CPU 41 obtains in step S601. It is determined whether or not the processing of all the records has been completed (step S609).

  If it is determined that the process has not been completed (NO in step S609) and if it is determined that no urination has been recorded (NO in step S605), the server CPU 41 adds 1 to the counter I (step S611). The server CPU 41 returns to step S604.

  If it is determined that the process has been completed (YES in step S609), the server CPU 41 determines a night urination point (step S610). Specifically, the server CPU 41 calculates the apnea point by substituting the value of the counter “number of toilets at night” into a predetermined function. The predetermined function is stored in the auxiliary storage device 43 in the form of an equation or a table. Thereafter, the server CPU 41 ends the process.

  The function for obtaining the night urination point may be a function of “the number of toilets at night” and the amount of urination. In this case, the server CPU 41 also records the amount of urination recorded in the toilet sensor data field of the record being determined in step S608.

  The toilet sensor may be a sensor that detects opening and closing of a toilet door. In this case, it is recorded in the sensor record DB 51 that urination has been performed when opening / closing of the toilet door is detected.

  The toilet sensor may be a sensor that detects lighting of a toilet. In this case, it is recorded in the sensor record DB 51 that urination has been performed when lighting of the toilet is detected.

  By a process similar to that of the subroutine described with reference to FIG. 21, an awakening event can be detected and a midway awakening point can be calculated. Specifically, in step S605, the presence or absence of getting out of bed is determined instead of urination. In step S606 and step S607, the sleep state before and after leaving the bed is determined. Based on the number of midway awakenings counted in this way, midway awakening points are calculated. The calculated midway awakening point is added to the event point described using Equation (6).

  The presence / absence of getting out of bed is determined using the sleep depth field of the sensor record DB 51. The presence or absence of getting out of the floor may be determined using data of a floor sensor arranged around the bed or a door sensor arranged on the door of the bedroom.

  FIG. 22 is a flowchart showing a process flow of a snore point calculation subroutine according to the sixth embodiment. The snoring point calculation subroutine is a subroutine for determining whether snoring and toothing have occurred, and to give a snoring point and toothing point when snoring and toothing have occurred. The server CPU 41 starts a snoring point calculation subroutine immediately before or immediately after the nocturia point calculation subroutine described with reference to FIG. The process flow of the snore point calculation subroutine will be described with reference to FIG.

  The server CPU 41 acquires sensor recording (step S621). Specifically, the server CPU 41 extracts one day of sensor record records for one user using the name field and date / time field of the sensor record DB 51 as keys.

  The server CPU 41 calculates the number of snores (step S622). Specifically, the number of records in which occurrence of snoring is recorded in the microphone data field among the records extracted in step S621 is calculated.

  The server CPU 41 determines a snoring point (step S623). Specifically, the server CPU 41 calculates a snore point by substituting the number of snores calculated in step S622 into a predetermined function. The predetermined function is stored in the auxiliary storage device 43 in the form of an equation or a table. Thereafter, the server CPU 41 ends the process.

  The server CPU 41 calculates the number of times of tooth brushing (step S624). Specifically, among the records extracted in step S621, the number of records in which occurrence of toothpaste is recorded in the microphone data field is calculated.

  Server CPU41 determines a tooth-growth point (step S625). More specifically, the server CPU 41 calculates the toothpaste point by substituting the number of toothpastes calculated in step S624 into a predetermined function. The predetermined function is stored in the auxiliary storage device 43 in the form of an equation or a table. Thereafter, the server CPU 41 ends the process.

  The server CPU 41 may calculate the number of occurrences of sleeping in the same manner, calculate sleeping points, and add them to the event points described using Expression (6).

  If there is a possibility that the microphone sensor 28 may detect a sound confusing to snoring, for example, from a radio or the like, it is desirable that the server CPU 41 calculates only the number of snoring during sleep in step S622. Specifically, among the records extracted in step S621, the server CPU 41 calculates the number of records in which the occurrence of snoring is recorded in the microphone data field and the sleep depth field is recorded.

  Similarly, when there is a possibility that the microphone sensor 28 may detect a sound that is confused with toothpaste, it is desirable that the server CPU 41 calculates only the number of toothpastes during sleep in step S624. Specifically, among the records extracted in step S621, the server CPU 41 calculates the number of records in which the occurrence of toothing is recorded in the microphone data field and the sleep depth field is recorded.

  Only one of the toilet sensor 27 and the microphone sensor 28 may be used. The toilet sensor 27 and the microphone sensor 28 may be connected to the server 40 via the network 31.

  Data acquired from the toilet sensor 27 and the microphone sensor 28 may be recorded in a DB different from the sensor recording DB 51. In this case, data detected by each sensor may be recorded at different intervals.

[Embodiment 7]
FIG. 23 is a functional block diagram illustrating operations of the information processing apparatus according to the seventh embodiment. The information processing apparatus operates as follows based on control by the server CPU 41.

  The first acquisition unit 91 acquires sleep data including temporal changes in sleep depth. Specifically, the sleep data is acquired via the sleep sensor 25, the sensor I / F 24, the client 20, and the network 31. The acquired sleep data is recorded in the sensor record DB 51. The first calculation unit 92 calculates sleep time based on the sleep data acquired by the first acquisition unit 91. The second calculation unit 93 calculates the ratio of the time that is the predetermined sleep depth to the sleep time calculated by the first calculation unit 92 based on the sleep data acquired by the first acquisition unit 91. The second acquisition unit 94 refers to the storage unit that stores the sleep time, the ratio, and the degree of fatigue recovery in association with each other, that is, the sleep time calculated by the first calculation unit 92 and the second calculation unit with reference to the base point DB 53. The degree of fatigue recovery associated with the ratio calculated by 93 is acquired. The 1st specific | specification part 95 specifies the event regarding sleep based on the sleep data which the 1st acquisition part 91 acquired. Events include, for example, mid-wake awakening, apnea, nocturia, snoring, and tooth decay. The correcting unit 96 corrects the degree of fatigue recovery acquired by the second acquiring unit 94 based on the event specified by the first specifying unit 95. The output unit 97 outputs the fatigue recovery degree corrected by the correction unit 96.

[Embodiment 8]
The eighth embodiment relates to a mode in which the information browsing device 35, the client 20, the server 40, and the program 71 are operated in combination.

  FIG. 24 is an explanatory diagram illustrating a configuration of the information processing system 10 according to the embodiment. The configuration of the present embodiment will be described using FIG. Note that description of portions common to the first embodiment is omitted.

  The information processing system 10 includes an information browsing device 35, a client 20, and a server 40 connected via a network 31. The information browsing device 35 is an information device including a display unit, an input unit, and a communication unit. The client 20 includes a client CPU 21, a main storage device 22, an auxiliary storage device 23, a sensor I / F 24, a communication unit 26, and a bus. A sleep sensor 25 is connected to the client 20 via a sensor I / F 24.

  The server 40 includes a server CPU 41, a main storage device 42, an auxiliary storage device 43, a communication unit 46, a reading unit 47, and a bus. The reading unit 47 is a device that reads the portable recording medium 72, and specifically, for example, a micro SD (Secure Digital) card slot, a disk drive, or the like.

  The program 71 is recorded on a portable recording medium 72. The server CPU 41 reads the program 71 via the reading unit 47 and stores it in the auxiliary storage device 43. The server CPU 41 may read a program 71 stored in a semiconductor memory 73 such as a flash memory mounted in the server 40. Further, the server CPU 41 may download the program 71 from another server computer (not shown) connected via the communication unit 46 and store it in the auxiliary storage device 43.

  A portion of the program 71 executed by the client 20 is transmitted to the client 20 via the communication unit 46 and the network 31 and stored in the auxiliary storage device 23. A portion of the program 71 executed by the information browsing device 35 is transmitted to the information browsing device 35 via the communication unit 46 and the network 31 and stored in the auxiliary storage device of the information browsing device 35. Note that each of the client 20 and the information browsing device 35 may download the program via the network 31.

  The server CPU 41 reads the program 71 for executing the above-described various software processes from the portable recording medium 72 or the semiconductor memory 73 or downloads it from another server computer (not shown) via the communication unit 46. The program 71 is installed as a control program for the server 40, loaded into the main storage device 42, and executed by the server CPU 41.

  The client CPU 21 reads the transmitted program 71 from the auxiliary storage device 23. The program 71 is installed as a control program for the client 20, loaded into the main storage device 22, and executed by the client CPU 21. Similarly, the program 71 is installed as a control program for the information browsing device 35, loaded into the main storage device of the information browsing device 35, and executed by the CPU of the information browsing device 35. As described above, the information processing system functions as the information processing system 10 described above as a whole.

The technical features (components) described in each embodiment can be combined with each other, and new technical features can be formed by combining them.
The embodiment disclosed this time is to be considered as illustrative in all points and not restrictive. The scope of the present invention is defined not by the above-described meaning but by the scope of the claims, and is intended to include all modifications within the meaning and scope equivalent to the scope of the claims.

10 Information processing system 20 Client 21 Client CPU
22 Main storage device 23 Auxiliary storage device 24 Sensor I / F
25 Sleep sensor 26 Communication unit 27 Toilet sensor 28 Microphone sensor 31 Network 35 Information browsing device 40 Server (information processing device)
41 Server CPU
42 Main storage device 43 Auxiliary storage device 46 Communication unit 47 Reading unit 51 Sensor record DB
52 Sleep Record DB
53 Base DB
54 Midway Awakening Point DB
55 Awakening Time Change Point DB
56 Fatigue Recovery DB
61 Fatigue recovery column 62 Name column 63 Sensor status column 64 Fatigue recovery button 65 Comfort sleep index button 66 Comfort environment index button 67 Data button of the month 68 Sleep button of the day 69 Sleep tendency button 71 Program 72 Portable recording medium 73 Semiconductor memory 76 Pleasant sleep index column 77 Comfortable environment index column 78 Index graph column 81 Sleep time graph 82 Sleep depth graph 83 Fatigue recovery graph 84 Pleasure sleep index graph 85 Comfortable environment index graph 86 Sleep depth column 87 Apnea count column 88 Mid awakening column 89 Sleep Status column 91 First acquisition unit 92 First calculation unit 93 Second calculation unit 94 Second acquisition unit 95 First identification unit 96 Correction unit 97 Output unit

Claims (8)

A first acquisition unit that acquires sleep data including temporal changes in sleep depth;
A first calculation unit that calculates sleep time based on the sleep data acquired by the first acquisition unit;
A second calculation unit that calculates a ratio of time that is a predetermined sleep depth based on the sleep data acquired by the first acquisition unit to the sleep time calculated by the first calculation unit;
Fatigue recovery associated with the sleep time calculated by the first calculation unit and the ratio calculated by the second calculation unit with reference to the storage unit that stores the sleep time, the ratio, and the fatigue recovery degree in association with each other. A second acquisition unit for acquiring the degree;
A first specifying unit for specifying an event related to sleep based on sleep data acquired by the first acquiring unit;
A correction unit that corrects the degree of fatigue recovery acquired by the second acquisition unit based on the event specified by the first specification unit;
An information processing apparatus comprising: an output unit that outputs the degree of fatigue recovery corrected by the correction unit. A fluctuation amount calculation unit that calculates a fluctuation amount from sleep data of a predetermined number of days acquired by the first acquisition unit;
A second specifying unit that specifies a first degree of influence on the degree of fatigue recovery based on the amount of change calculated by the change amount calculating unit;
The correction unit corrects the degree of fatigue recovery acquired by the second acquisition unit based on the event specified by the first specification unit and the first influence specified by the second specification unit. Information processing device. A fatigue recovery degree recording section for recording the fatigue recovery degree corrected by the correction section;
A third specifying unit for specifying a second degree of influence on the fatigue recovery degree based on the past fatigue recovery degree for a predetermined number of days recorded in the fatigue recovery degree recording part;
The correction unit corrects the degree of fatigue recovery acquired by the second acquisition unit based on the event specified by the first specification unit and the second influence specified by the third specification unit. Information processing device. A fluctuation amount calculation unit that calculates a fluctuation amount from sleep data of a predetermined number of days acquired by the first acquisition unit;
A second specifying unit that specifies a first degree of influence on the degree of fatigue recovery based on the amount of change calculated by the change amount calculating unit;
A fatigue recovery degree recording section for recording the fatigue recovery degree corrected by the correction section;
A third specifying unit for specifying a second degree of influence on the fatigue recovery degree based on the past fatigue recovery degree for a predetermined number of days recorded in the fatigue recovery degree recording part;
The correction unit includes the degree of fatigue recovery acquired by the second acquisition unit, the event specified by the first specifying unit, the first influence specified by the second specifying unit, and the first specified by the third specifying unit. The information processing apparatus according to claim 1, wherein the correction is performed based on the degree of influence. The sleep data includes temporal changes in the number of apnea occurrences,
The first specifying unit specifies that a sleep apnea event has occurred when it is determined that an apnea of a predetermined number of times or more has occurred,
The information processing apparatus according to any one of claims 1 to 4, wherein the correction unit corrects the degree of fatigue recovery to be low when the first specifying unit specifies occurrence of sleep apnea. A third acquisition unit for acquiring the time when the toilet was used;
The first specifying unit is a predetermined range before and after the time when the toilet is used based on the time when the toilet acquired by the third acquisition unit is used and the sleep data acquired by the first acquisition unit. If it is determined that the sleep depth of the time is a predetermined depth, an event of nocturia occurs,
The information processing apparatus according to any one of claims 1 to 5, wherein the correction unit corrects the degree of fatigue recovery to be low when the first specifying unit specifies occurrence of frequent urination at night. Get sleep data that includes temporal changes in sleep depth,
Calculate sleep time based on the sleep data,
Based on the sleep data, a time at a predetermined sleep depth is calculated as a ratio of the sleep time,
Referring to the storage unit that stores the sleep time, the ratio of the time that is the predetermined sleep depth to the sleep time, and the fatigue recovery degree, the calculated sleep time and the fatigue recovery degree that is related to the calculated ratio Acquired,
Based on the sleep data, identify events related to sleep,
Correct the acquired degree of fatigue recovery based on the identified event,
A program that causes a computer to execute a process that outputs the corrected fatigue recovery level. Get sleep data that includes temporal changes in sleep depth,
Calculate sleep time based on the sleep data,
Based on the sleep data, a time at a predetermined sleep depth is calculated as a ratio of the sleep time,
Referring to the storage unit that stores the sleep time, the ratio of the time that is the predetermined sleep depth to the sleep time, and the fatigue recovery degree, the calculated sleep time and the fatigue recovery degree that is related to the calculated ratio Acquired,
Based on the sleep data, identify events related to sleep,
Correct the acquired degree of fatigue recovery based on the identified event,
An information processing method for causing a computer to execute a process of outputting a corrected degree of fatigue recovery.

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