Information processing system, information processing method, and program

Abstract

To provide an information processing system, an information processing method and a program for generating sleep inertia-like symptom information on the basis of activity information of a measurement subject 40 measured by a measuring device for easy confirmation of a sleep inertia-like symptom.SOLUTION: In an information processing system 1 in which a server device 10 and a first user terminal 20 and / or a measurement device 30 are communicably connected to each other via a communication network NW, the server device 10 includes: an activity level acquisition unit that acquires activity level information of the measurement subject 40 measured by the user terminal 30 that is a measurement device; an awakened state estimation unit that estimates an awakened state of the measurement subject; and a sleep inertia-like symptom information generation unit that generates sleep inertia-like symptom information related to a sleep inertia-like symptom that appears in the measurement subject on the basis of at least the activity level information after the awakened state is estimated to be awake.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program for generating sleep inertia-like symptom information. [Background technology]

[0002] Idiopathic hypersomnia is a sleep disorder characterized by daytime sleepiness and drowsiness, which are the primary symptoms of idiopathic hypersomnia. Idiopathic hypersomnia is characterized by persistent sleepiness and sleep inertia, a condition in which sleepiness persists after waking. Sleep inertia can occur after waking from any type of sleep, including morning sleep, daytime sleep, and nighttime sleep.

[0003] As a method for detecting idiopathic hypersomnia, for example, Patent Document 1 describes an invention relating to a marker for detecting idiopathic hypersomnia, which comprises an orexin precursor mutant or a fragment thereof having an amino acid sequence containing a mutation at amino acid residue position 68 of the amino acid sequence shown in SEQ ID NO: 1, or a polynucleotide encoding the mutant or the fragment thereof. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-018308 Summary of the Invention [Problem to be solved by the invention]

[0005] However, for example, the detection method using markers described in Patent Document 1 requires obtaining a sample (body fluid, cells, etc.) from the subject and only detects idiopathic hypersomnia, making it difficult for the subject to easily learn signs or specific symptoms related to sleep inertia (for example, how long after waking up the subject can resume activity). Therefore, there is a need for a digital tool that makes it possible to easily identify sleep inertia-like symptoms.

[0006] The present invention aims to provide an information processing system for generating sleep inertia-like symptom information based at least on activity level information of a subject 40, in order to enable sleep inertia-like symptoms to be easily confirmed. [Means for solving the problem]

[0007] The main invention of the present invention for solving the above problem comprises an activity acquisition unit that acquires activity information of the subject 40, an awake state estimation unit that estimates the awake state of the subject, and a sleep inertia-like symptom information generation unit that generates sleep inertia-like symptom information related to sleep inertia-like symptoms appearing in the subject 40 based at least on the activity information after the awake state has been estimated to be awake.

[0008] Other problems and solutions disclosed in this application will be made clear in the section on preferred embodiments of the invention and the drawings. [Effects of the Invention]

[0009] According to the present invention, in order to easily confirm sleep inertia-like symptoms, an information processing system can be provided for generating sleep inertia-like symptom information regarding sleep inertia-like symptoms appearing in a subject 40 based at least on activity information of the subject 40 after it is estimated that the subject 40 has woken up. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating an example of the overall configuration of an information processing system according to an embodiment of the present invention. [Figure 2]FIG. 2 is a diagram illustrating an example of a hardware configuration of a server device 10. [Figure 3] FIG. 2 is a diagram illustrating an example of the software configuration of the server device 10. [Figure 4] FIG. 2 is a diagram showing an example of the configuration of a subject information storage section 131. [Figure 5] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 6] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 7] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 8] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 9] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 10] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 11] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 12] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 13] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 14] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment of the present invention. [Figure 15] FIG. 2 is a diagram showing a flow of processing executed in the information processing system of the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] The contents of the embodiment of the present invention will be listed and described below. One embodiment of the present invention has the following configuration.

[0012] [Item 1] an activity level acquisition unit that acquires activity level information of the subject measured by a measurement device; an arousal state estimation unit that estimates the arousal state of the subject; and a sleep inertia-like symptom information generating unit that generates sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject based at least on the activity level information after the wakefulness state is estimated to be wakefulness. An information processing system comprising: [Item 2] the activity level acquiring unit calculates the activity level information based on at least inertial sensor information of the subject measured by the measuring device attached to the subject. 2. The information processing system according to item 1, [Item 3] the activity level acquiring unit calculates the activity level information based on at least electroencephalogram waveform information of the subject measured by the measuring device worn by the subject. 2. The information processing system according to item 1, [Item 4] the activity level acquiring unit calculates the activity level information based on at least external observation information of the subject measured by the measuring device. 2. The information processing system according to item 1, [Item 5] the awake state estimation unit estimates the awake state based on at least electroencephalogram waveform information of the subject. 5. The information processing system according to any one of items 1 to 4. [Item 6] the awake state estimation unit estimates the awake state based on at least inertial sensor information of the subject measured by the measurement device. 5. The information processing system according to any one of items 1 to 4. [Item 7] the wakefulness state estimation unit estimates the wakefulness state based on at least an awakening inducing function that induces awakening of the subject. 5. The information processing system according to any one of items 1 to 4. [Item 8] The awakening induction function includes an alarm function that issues a predetermined notification at a set time. 8. The information processing system according to item 7, [Item 9] The awakening inducing function includes a light turning-on function that turns on a predetermined light at a set time. 8. The information processing system according to item 7, [Item 10] the sleep inertia-like symptom information generation unit generates the sleep inertia-like symptom information based on total activity information within a predetermined time period after the wakefulness state is estimated to be wakefulness. 5. The information processing system according to any one of items 1 to 4. [Item 11] The total activity level information within a predetermined time period after the wakefulness state is estimated to be wakefulness is total activity level information of a predetermined activity intensity or more within the predetermined time period, or total step count information within the predetermined time period. 11. The information processing system according to item 10. [Item 12] the sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on total activity information within a predetermined time period after the person being measured has sat up, after the wakefulness state has been estimated to be wakefulness. 5. The information processing system according to any one of items 1 to 4. [Item 13] The sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on at least one of total time information during which an activity level equal to or greater than a predetermined intensity has occurred after the wakefulness state has been estimated as wakefulness, total time information during which an activity level equal to or less than a predetermined intensity has occurred, information on the elapsed time until the predetermined total activity level has been reached, information on the elapsed time until the predetermined activity intensity has been reached, and information on the elapsed time until the predetermined total number of steps has been reached. 5. The information processing system according to any one of items 1 to 4. [Item 14] The sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on at least one of total time information during which an activity level equal to or greater than a predetermined intensity occurred after the person being measured got up, total time information during which an activity level equal to or less than a predetermined intensity occurred, information on the elapsed time until the predetermined total activity level was reached, information on the elapsed time until the predetermined activity intensity was reached, and information on the elapsed time until the predetermined total number of steps was reached, after the wakefulness state was estimated to be wakefulness and the person being measured got up. 5. The information processing system according to any one of items 1 to 4. [Item 15] The sleep inertia-like symptom information is information indicating the degree of the sleep inertia-like symptom of the subject. 5. The information processing system according to any one of items 1 to 4. [Item 16] The sleep inertia-like symptom information includes information on changes in the degree of sleep inertia-like symptoms calculated based on information indicating the degree of sleep inertia-like symptoms at multiple points in time for the same subject. 5. The information processing system according to any one of items 1 to 4. [Item 17] The device further includes a support information generating unit that generates support information for providing support regarding sleep inertia-like symptoms to the subject or a second user different from the subject, based on at least one of the activity level information of the subject or the sleep inertia-like symptom information. 5. The information processing system according to any one of items 1 to 4. [Item 18] the support information generation unit further refers to electroencephalogram waveform information of the subject or arousal state information indicating the arousal state, and generates the support information. 18. The information processing system according to item 17, [Item 19] The support information generation unit further refers to clinical trial data on sleep inertia-like symptoms to generate the support information. 18. The information processing system according to item 17, [Item 20] the support information generating unit further refers to electronic medical record data related to the subject to generate the support information. 18. The information processing system according to item 17, [Item 21] the support information generating unit further refers to interview result information for the subject to generate the support information. 18. The information processing system according to item 17, [Item 22] The medical interview result information includes a medical interview result regarding the physical condition of the subject. 22. The information processing system according to item 21. [Item 23] The medical interview result information includes a medical interview result regarding the occupation or occupation type of the subject. 22. The information processing system according to item 21. [Item 24] The medical interview result information includes a medical interview result regarding the subject's daytime sleepiness level. 22. The information processing system according to item 21. [Item 25] the support information generation unit presents, to at least a second user, questionnaire result information regarding the degree of daytime sleepiness of the subject and sleep inertia-like symptom information together as the support information. 25. The information processing system according to item 24. [Item 26] a medical interview unit that transmits predetermined question information about the subject, acquires answer information to the question information, and generates the medical interview result information; 26. The information processing system according to item 25. [Item 27] The support information includes support information indicating at least one of the dosage for sleep inertia-like symptoms, the number of times of taking the medicine, the timing of taking the medicine, and information on a predetermined medicine. Item 18. The information processing system according to item 17. [Item 28] The support information includes support information regarding a medical appointment for a sleep inertia-like symptom. 18. The information processing system according to item 17, [Item 29] The sleep inertia-like symptom duration information generating unit generates sleep inertia-like symptom duration information indicating an estimated time that a sleep inertia-like symptom has continued from awakening, based on information on the elapsed time until a predetermined activity intensity occurs, information on the elapsed time until a predetermined total number of steps is reached, or information on the elapsed time until a predetermined total activity level is reached after the awakening state is estimated to be awakening, the support information generation unit further refers to the sleep inertia-like symptom duration information to generate the support information. 18. The information processing system according to item 17, [Item 30] the support information generation unit generates, as the support information, a recommended preparation time from awakening to starting an action for the subject based on the sleep inertia-like symptom duration information. 30. The information processing system according to item 29, [Item 31] the support information generation unit generates, as the support information, recommended awakening induction time information indicating a time for inducing awakening, based on the desired activity start time information of the subject and the sleep inertia-like symptom duration information. 30. The information processing system according to item 29, [Item 32] the support information generating unit estimates a desired activity start time of the subject based on scheduler information of the subject; 32. The information processing system according to item 31, [Item 33] the support information generation unit generates, as the support information, recommended medication time information indicating a time to take medication related to the sleep inertia-like symptom, based on the sleep inertia-like symptom duration information. 30. The information processing system according to item 29, [Item 34] the support information generation unit generates, as the support information, task restriction support information related to a restriction on execution of a predetermined task within a time range including at least a time range indicated by the sleep inertia-like symptom duration information, based on awake time information indicating a time when the awake state was estimated to be awake, restriction target task information, and the sleep inertia-like symptom duration information. 30. The information processing system according to item 29, [Item 35] the support information generation unit generates, as the support information, sleep restriction information regarding sleep restriction within a time range indicated by the sleep inertia-like symptom duration information based on start time information of a predetermined task included in the scheduled task information, based on the scheduled task information and restriction target task information of the subject, and the sleep inertia-like symptom duration information. 30. The information processing system according to item 29, [Item 36] the support information generating unit acquires the scheduled task information based on scheduler information of the subject; 36. The information processing system according to item 35. [Item 37] The method further includes a clinical trial evaluation information generation unit that generates evaluation information regarding an intra-clinical trial evaluation of sleep inertia based at least on the sleep inertia-like symptom information and the clinical trial evaluation criterion information. 37. An information processing system according to any one of items 1 to 36. [Item 38] The clinical trial evaluation information generation unit further refers to clinical trial medication information indicating information on medications taken in the clinical trial, and generates evaluation information regarding an evaluation of the effectiveness of a predetermined medication on sleep inertia. 38. The information processing system according to item 37, [Item 39] An information processing method executed on a computer, comprising: acquiring, by an activity level acquiring unit, activity level information of the subject measured by a measuring device; estimating the state of arousal of the subject by an arousal state estimation unit; and generating, by a sleep inertia-like symptom information generating unit, sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject, based at least on the activity level information after the wakefulness state is estimated to be wakefulness. 1. An information processing method comprising: [Item 40] an activity level acquisition function for acquiring activity level information of the subject measured by a measurement device; an arousal state estimation function for estimating the arousal state of the subject; a sleep inertia-like symptom information generating function that generates sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject based at least on the activity level information after the wakefulness state is estimated to be wakefulness, the sleep inertia-like symptom information generating function being realized in a computer. A program characterized by: [Item 41] the activity level acquiring unit calculates the activity level information based on at least inertial sensor information of the subject measured by the measuring device; the awake state determination unit estimates the awake state based on at least inertial sensor information of the subject. 2. The information processing system according to item 1, [Item 42] the activity level acquiring unit calculates the activity level information based on at least inertial sensor information of the subject measured by the measuring device; the wakefulness determination unit estimates the wakefulness state based on at least electroencephalogram waveform information of the subject. 2. The information processing system according to claim 1, [Item 43] the activity level acquisition unit calculates the activity level information based on at least electroencephalogram waveform information of the subject measured by the measurement device; the wakefulness determination unit estimates the wakefulness state based on at least electroencephalogram waveform information of the subject. 2. The information processing system according to claim 1, [Item 44] the activity level acquisition unit calculates the activity level information based on at least electroencephalogram waveform information of the subject measured by the measurement device; the awake state determination unit estimates the awake state based on at least inertial sensor information of the subject. 2. The information processing system according to claim 1,

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In this specification and the drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted. Furthermore, the features shown in each embodiment can be applied to other embodiments as long as they are not mutually inconsistent.

[0014] <System configuration> 1 is a diagram illustrating an example of the configuration of an information processing system (hereinafter also referred to as "this system") according to an embodiment of the present disclosure. This system generates sleep inertia-like symptom information related to sleep inertia-like symptoms occurring in a subject 40 based on activity level information of the subject 40 measured by a measurement device 30 and the wakefulness state of the subject 40.

[0015] 1 is a diagram showing an example of the overall configuration of an information processing system according to this embodiment. As shown in the figure, in the information processing system 1 according to this embodiment, a server device 10 and a first user terminal 20 and / or a measurement device 30 are connected to each other so as to be able to communicate with each other via a communication network NW, and a second user terminal 50 may also be connected to the communication network NW as necessary. The communication network NW is, for example, the Internet or a LAN (Local Area Network), and is constructed using a public telephone network, a dedicated telephone network, a mobile phone network, Ethernet (registered trademark), a wireless communication path, etc.

[0016] The server device 10 is a computer that manages the entire information processing system. The server device 10 is, for example, a workstation, a personal computer, or a virtual computer logically realized by cloud computing.

[0017] The first user terminal 20 is a device owned by the subject 40 (also referred to as the "first user") who is to be checked for sleep inertia-like symptoms, and includes a smartphone, a personal computer, a tablet terminal, etc. An application program can be installed on the first user terminal 20, and the arithmetic unit of the first user terminal 20 can execute all or part of the functions executed by the arithmetic unit 101 of the server device 10. All or part of the information stored in the storage device 103 of the server device 10 can also be stored in a storage unit built into the first user terminal 20. Conversely, the arithmetic unit 101 of the server device 10 can execute all or part of the functions executed by the first user terminal 20, and all or part of the information stored in the storage device of the first user terminal 20 can also be stored in the storage device 103 built into the server device 10.

[0018] The measuring device 30 may be worn by the subject 40 and may be a wearable device, such as a wristwatch, bracelet, ring, eyeglasses, headwear, waistwear, clothing, patch, or earwear (e.g., canal type, ear hook type, ear cuff type). The measuring device 30 may be a device for externally observing the subject 40 and may be an external device, such as a camera, infrared sensor, Doppler sensor, mat sensor, or vital sensor. One or more types of measuring devices 30 may be worn by or installed externally on the subject 40, or a combination of a wearable device and an external device may be used. The measuring device 30 acquires measurement information of the subject 40 in response to an instruction from the subject 40 or an instruction at a predetermined timing, and the information is stored in a memory unit within the measuring device 30 or transmitted to an external device, such as the server device 10 or the first user terminal 20, as appropriate. The measurement information of the subject 40 may be, for example, inertial sensor information such as acceleration information or angular velocity information obtained by an inertial sensor (for example, an acceleration sensor or a gyro sensor, particularly a three-axis acceleration sensor or a three-axis gyro sensor) included in the measuring device 30, or may be electroencephalogram waveform information obtained by an electroencephalogram sensor included in the measuring device 30. It may also be biological information (for example, electrocardiogram waveform, pulse waveform, skin temperature, pulse, blood pressure, body temperature, etc.) obtained via electrodes or a sensor. The biological information may be any biological information that can be obtained from the subject 40 via electrodes or a sensor. Some or all of the inertial sensor information, electroencephalogram waveform information, biological information, and measurement information may be stored in the storage device 103 of the server device 10.

[0019] The measurement device 30 may also be connected to an external device such as the server device 10 or the first user terminal 20 via a communication network NW or a short-range communication interface such as Bluetooth (registered trademark) or BLE (Bluetooth Low Energy), and may transmit and receive various data to and from the external device. The measurement device 30 may also be configured to be able to perform a predetermined operation in response to an operation (including, for example, a touch operation, a swipe operation, or a voice operation) using a sensor or button mounted on the measurement device 30, and may also be configured to display a screen generated by an application in response to the operation.

[0020] The second user terminal 50 is a device used by a second user 60 (e.g., a doctor, a clinical trial practitioner, a family member of a subject, a caregiver of a subject, and a clinical trial management organization such as a medical institution, a medical device company, a pharmaceutical company, or a contract research organization for pharmaceutical products), and includes a personal computer, a smartphone, a tablet terminal, etc. Note that an application program can be installed on the second user terminal 50, and the arithmetic unit of the second user terminal 50 can execute all or part of the functions executed by the arithmetic unit 101 of the server device 10. Also, all or part of the information stored in the storage device 103 of the server device 10 can be stored in a storage device built into the second user terminal 50. Conversely, the arithmetic unit 101 of the server device 10 can execute all or part of the functions executed by the second user terminal 50, and all or part of the information stored in the storage device of the second user terminal 50 can be stored in the storage device 103 built into the server device 10.

[0021] Next, the configuration of the information processing system 1 will be described in detail.

[0022] <Hardware configuration> 2 is a diagram showing an example of the hardware configuration of a computer that realizes the server device 10 in this embodiment. The server device 10 includes an arithmetic unit 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106. These are electrically connected to each other via a bus (not shown). As described above, all or part of the functions executed by the storage device 103 and other devices included in the server device 10 and the arithmetic unit 101 can be included in or executed by other devices such as the first user terminal 20, the measurement device 30, or the second user terminal 50.

[0023] The arithmetic device 101 controls the overall operation of the server device 10, controls the transmission and reception of data between each element, and performs information processing necessary for application execution and authentication processing. The arithmetic device 101 is, for example, a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), and executes programs stored in the storage device 103 and deployed in the memory 102 to perform various information processing.

[0024] The memory 102 includes a main memory configured with a volatile storage device such as a DRAM (Dynamic Random Access Memory) and an auxiliary memory configured with a nonvolatile storage device such as a flash memory or an HDD (Hard Disc Drive). The memory 102 is used as a work area for the arithmetic device 101. It also stores a BIOS (Basic Input / Output System) that is executed when the server device 10 is started up, various setting information, and the like.

[0025] The storage device 103 stores various data and various programs such as application programs, etc. For example, it is a hard disk drive, a solid state drive, a flash memory, or the like.

[0026] The communication interface 104 is an interface for connecting the server device 10 to the communication network NW, and is, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to the public telephone network, a wireless communication device for wireless communication, a USB (Universal Serial Bus) connector or an RS132C connector for serial communication, etc.

[0027] The input device 105 is used to input data, and includes, for example, a keyboard, a mouse, a touch panel, a button, a microphone, a camera (image capturing unit), and the like.

[0028] The output device 106 outputs data and is, for example, a display, a printer, a speaker, or the like.

[0029] <Software configuration> 3 is a diagram illustrating an example of the software configuration of the server device 10 in this embodiment. As illustrated in the diagram, the server device 10 includes functional units, such as an information management unit 110, an activity level acquisition unit 111, an arousal state estimation unit 112, a sleep inertia-like symptom information generation unit 113, a support information generation unit 114, a medical interview unit 115, a sleep inertia-like symptom duration information generation unit 116, a clinical trial evaluation information generation unit 117, and a display information generation unit 118, as well as a subject information storage unit 131. If necessary, the server device 10 may include a second user information storage unit (not shown) that stores information (e.g., user ID, affiliation, contact information, etc.) about a second user (e.g., a doctor, a clinical trial administrator, a subject's family, a caregiver, a clinical trial management organization such as a medical institution, a medical device company, a pharmaceutical company, or a contract drug development organization).

[0030] Each of the above functional units is realized by the arithmetic unit 101 provided in the server device 10 reading out a program stored in the storage device 103 into the memory 102 and executing it, and each of the above storage units is realized as part of the storage area provided by the memory 102 and storage device 103 provided in the server device 10.

[0031] The subject information storage unit 131 stores information about the subject 40. Fig. 4 is a diagram showing an example of the configuration of the subject information storage unit 131. As shown in the figure, the subject information stored in the subject information storage unit 131 includes, in association with a subject ID that identifies the subject 40, basic information (personal information such as name, address, sex, age, occupation, etc.), physical information such as height and weight, measurement information (including biological information), activity level information, sleep inertia-like symptom information, and interview result information.

[0032] The information management unit 110 reads out predetermined information from various storage units and transmits it to the outside (for example, the first user terminal 20 or the second user terminal 50), or receives predetermined information from the outside and stores it in the corresponding storage unit.

[0033] The activity level acquiring unit 111 acquires activity level information stored in a storage unit such as the subject information storage unit 131 in response to a request from the first user terminal 20 or the second user terminal 50. The activity level information may be generated by the activity level acquiring unit 111 and stored in the subject information storage unit 131 or the like, or may be activity level information generated in the measuring device 30, the first user terminal 20, an external server linked to the system, or activity level information stored in a storage unit of the measuring device 30 or the first user terminal 20, or an external database linked to the system (particularly a clinical trial information database), received by the information managing unit 110 of the server device 10 and stored in the subject information storage unit 131 or the like.

[0034] Here, the activity level information will be described. The activity level information may be any information indicating the level of activity in the living body of the subject 40, and may be expressed as an absolute or relative numerical value, or may be expressed as information indicating the level of activity or information indicating the amount of activity. The information used to calculate the activity level may include, for example, information obtained from the measuring device 30 worn by the subject 40. For example, the information may be inertial sensor information such as acceleration information or angular velocity information obtained from an inertial sensor (e.g., an acceleration sensor or a gyro sensor, particularly a three-axis acceleration sensor or a three-axis gyro sensor) included in the measuring device 30, or electroencephalogram (EEG) waveform information obtained from an electroencephalogram (EEG) sensor included in the measuring device 30. The inertial sensor information and the electroencephalogram waveform information may be used in combination. Alternatively, the information may be external observation information (such as image (still image, moving image) information, infrared sensor information, Doppler sensor information, mat sensor information, vital sensor information, or other external sensor information) obtained by an external device observing the subject 40 from the outside. The external observation information may be used in combination with at least one of inertial sensor information and electroencephalogram waveform information. Furthermore, it may be biological information (e.g., electrocardiogram waveform, pulse waveform, skin temperature, pulse rate, blood pressure, body temperature, etc.) acquired via electrodes or sensors, and may be used in combination with at least one of inertial sensor information, electroencephalogram waveform information, and external observation information. The biological information may be any biological information that can be acquired from the subject 40 via electrodes or sensors. When the measuring device 30 is a wearable device, it becomes possible to acquire activity level information more easily, and an example of such a device is a wristwatch-type device equipped with an inertial sensor.

[0035] The activity level based on the inertial sensor information may be calculated by the activity level acquiring unit 111 based on at least one of body movement information (behavior information) and posture information of the subject 40 estimated using the inertial sensor information (e.g., acceleration information based on a three-axis acceleration sensor), or calculated as at least one of METs or calories burned from behavior information of the subject 40 (e.g., standing, sitting, walking, running, etc.) estimated using the inertial sensor information, or calculated as the number of steps estimated using the inertial sensor information, or may be expressed as an absolute or relative numerical value, or as information indicating the degree of activity level. Furthermore, the activity level may be calculated from the inertial sensor information using a learning model (e.g., one previously trained using training data) stored in the storage device 103 or an external server.

[0036] The activity level may be calculated based on the electroencephalogram waveform information by a known method, such as extracting alpha wave, theta wave, gamma wave, and / or beta wave components from the electroencephalogram waveform information and calculating the extracted alpha wave, theta wave, gamma wave, and / or beta wave components, their component ratios, or the intensities of each component, or may be expressed as absolute or relative numerical values, or as information indicating the degree of activity level. Furthermore, the activity level may be calculated from the electroencephalogram waveform information using a learning model (e.g., one previously trained using training data) stored in the storage device 103 or an external server.

[0037] The activity level may be calculated based on external observation information, for example, by estimating behavioral information by performing image analysis on image information or analyzing external sensor information using the activity level acquisition unit 111, and may be calculated as at least one of METs, calories burned, or number of steps from the behavioral information, and may be expressed as an absolute or relative numerical value, or may be expressed as information indicating the degree of activity level. Alternatively, the activity level may be calculated from external observation information using a learning model (for example, one previously trained using training data) stored in the storage device 103 or an external server.

[0038] The awake state estimation unit 112 estimates the awake state of the person being measured 40. The awake state of the person being measured 40 may be estimated, for example, based on information obtained from the measuring device 30 worn by the person being measured 40. The information obtained from the measuring device 30 may be, for example, measurement information (including biological information) such as electroencephalogram waveform information or inertial sensor information obtained directly from the measuring device 30 worn by the person being measured 40, or external observation information (image (still image, moving image) information, infrared sensor information, Doppler sensor information, mat sensor information, vital sensor information, and various other external sensor information) obtained by an external device observing the person being measured 40 from the outside. The information may include, but is not limited to, body movement information (information indicating the body movement of the person being measured 40), blood pressure information, heart rate information, and other biological calculated information calculated based on the measurement information. The inertial sensor information, electroencephalogram waveform information, external observation information, and biological information may be a combination of two or more pieces of information selected arbitrarily. For example, the arousal state estimation unit 112 may store arousal estimation reference information in the storage device 103 for comparison with information obtained from the measurement device 30, and estimate whether or not the arousal state indicates arousal based on the result of comparing the read arousal estimation reference information with the information obtained from the measurement device 30. Alternatively, the arousal state estimation unit 112 may include, for example, a learning model related to the information obtained from the measurement device 30 (e.g., a model stored in the storage device 103 or an external server and trained in advance using teacher data), and estimate whether or not the arousal state indicates arousal based on the information obtained from the measurement device 30 as input. If the measurement device 30 is a wearable device, it is possible to more easily estimate the arousal state. Examples of such devices include a wristwatch-type device equipped with an inertial sensor.

[0039] The information obtained from the measuring device 30 worn by the person being measured 40, which is referred to by the activity level acquisition unit 111 and the awake state estimation unit 112, may be the same or different. For example, (A) the activity level acquisition unit 111 calculates the activity level based on at least inertial sensor information of the person being measured 40 measured by the measuring device 30, and the awake state estimation unit 112 estimates the awake state based on at least the inertial sensor information of the person being measured 40; (B) the activity level acquisition unit 111 calculates the activity level based on at least the inertial sensor information of the person being measured 40 measured by the measuring device, and the awake state estimation unit 112 estimates the awake state based on at least the inertial sensor information of the person being measured 40; Examples of such cases include, but are not limited to, (A) the activity level acquisition unit 111 calculates the activity level based on at least the electroencephalogram waveform information of the subject 40 measured by the measurement device, and the awake state estimation unit 112 estimates the awake state based on at least the electroencephalogram waveform information of the subject 40, and (B) the activity level acquisition unit 111 calculates the activity level based on at least the electroencephalogram waveform information of the subject 40 measured by the measurement device, and the awake state estimation unit 112 estimates the awake state based on at least the inertial sensor information of the subject 40.

[0040] Furthermore, the arousal state estimation unit 112 does not necessarily have to estimate the arousal of the subject 40 directly from information obtained from the measurement device 30. For example, the arousal state estimation unit 112 may estimate the arousal state based on an arousal induction function that induces arousal in the subject 40, i.e., estimate that the subject 40 has become aroused due to the induction of arousal by the arousal induction function. The arousal induction function may be any function that induces arousal in the subject 40 by an external stimulus, such as a function that generates a predetermined external stimulus at a set time set by the first user terminal 20. More specifically, the arousal induction function may be, for example, an alarm function that issues a predetermined notification at a set time set by the first user terminal 20, or a light activation function that turns on a predetermined light. The arousal induction function may be a function provided in the first user terminal 20, or a function provided in another device that can be activated or set by the first user terminal 20 via a short-range communication interface or the like.

[0041] The sleep inertia-like symptom information generating unit 113 generates sleep inertia-like symptom information regarding sleep inertia-like symptoms appearing in the subject 40 based at least on the activity level information after the wakefulness state is estimated to be wakefulness. The sleep inertia-like symptoms may be symptoms similar to sleep inertia or symptoms estimated to be sleep inertia, but are not information that can be used to determine whether the symptoms are sleep inertia. However, if a doctor can determine whether the symptoms are sleep inertia using this system, the sleep inertia-like symptoms may be the same as or approximately the same as sleep inertia symptoms.

[0042] A specific example of using the activity level information for generating the sleep inertia-like symptom information will be described. The activity level information used for generating the sleep inertia-like symptom information may be, for example, (1) the total activity level (e.g., total activity amount) within a predetermined time after the wakefulness state of the person being measured 40 is estimated to be wakeful, and in particular, (1-1) the total activity level (e.g., total activity amount) within a predetermined time after the wakefulness state of the person being measured 40 is estimated to be wakeful, or the sum of the activity level (e.g., activity amount) equal to or greater than a predetermined activity intensity (exercise intensity), (1-2) the total number of steps, total calories, or total METs within a predetermined time after the wakefulness state of the person being measured 40 is estimated to be wakeful, but is not limited to these. (2) The total number of steps, total calories, or total METs within a predetermined time after the wakefulness state of the person being measured 40 is estimated to be wakeful. The criteria may include, but are not limited to, (1) the total time during which an activity level (e.g., activity amount) of a predetermined intensity or higher occurred within a predetermined period of time, (2) the total time during which an activity level (e.g., activity amount) of a predetermined intensity or lower occurred within a predetermined period of time after the wakefulness state of the person being measured 40 is estimated to be wakeful, (3) the total time during which an activity level (e.g., activity amount) of a predetermined intensity or lower occurred within a predetermined period of time after the wakefulness state of the person being measured 40 is estimated to be wakeful, (4) the elapsed time until a predetermined total activity level (e.g., total activity amount) is reached after the wakefulness state of the person being measured 40 is estimated to be wakeful, (5) the elapsed time until a predetermined activity intensity is reached after the wakefulness state of the person being measured 40 is estimated to be wakeful, or (6) the elapsed time until a predetermined total number of steps is reached after the wakefulness state of the person being measured 40 is estimated to be wakeful. Furthermore, these criteria target the period after the wakefulness state of the person being measured 40 is estimated to be wakeful, but may be particularly limited to the period after the person being measured 40 gets up. The getting-up activity may be a sitting-up or a standing-up movement, and may be determined based on at least one of body movement information (behavior information) and posture information of the person being measured 40 estimated using inertial sensor information. The default values ​​(default activity level, time, etc.) shown here may be set to any value in advance by the system provider or the user of the system, and may be set to any value as long as the sleep inertia-like symptoms described below can be detected. The default time may be the same as or approximately the same as the wakefulness period, or may be a period shorter than the wakefulness period, such as several tens of minutes or several hours.Furthermore, the sleep inertia-like symptom information may be generated by combining two or more of the above conditions, thereby generating more accurate sleep inertia-like symptom information (such as information indicating the degree of sleep inertia-like symptoms, which will be described later).

[0043] The sleep inertia-like symptom information may include, for example, information indicating the degree of the subject's sleep inertia-like symptoms, and may particularly be classification information (including score information as a more detailed classification). The sleep inertia-like symptom information generator 113 may, for example, set correspondence information in advance, in which reference values ​​are set for the total activity level (e.g., total activity amount), total time, and elapsed time for each predetermined range to separate the categories. By comparing the total activity level information, total time information, and elapsed time information with the correspondence information, the sleep inertia-like symptom information generator 113 can generate a classification indicating the degree of the subject's sleep inertia-like symptoms as the sleep inertia-like symptom information. In this case, the classification may be expressed, for example, by numbers or letters set according to the level of the sleep inertia-like symptoms (for example, three or more levels such as 1 to 5 or A to E, or two levels indicating the presence or absence of sleep inertia-like symptoms), but is not limited thereto.

[0044] As described above, sleep inertia is a condition in which sleepiness persists after waking up from sleep without being completely relieved. Therefore, as illustrated in Figure 5, if the time elapsed until a predetermined total activity level is reached after waking up (after getting up as one example) is relatively long (or if the total activity level within the predetermined period is relatively low, or if the total time during which activity levels equal to or above the predetermined intensity are generated is relatively short, or if the total time during which activity levels equal to or below the predetermined intensity are generated is relatively long, or if the time elapsed until the activity level equals or exceeds the predetermined intensity is relatively long), it can be estimated that the degree of sleep inertia-like symptoms is relatively high. In this case, the corresponding information may be classified so that the degree of sleep inertia-like symptoms is determined to be relatively high for a range of relatively low activity levels.

[0045] In this way, it is possible to generate sleep inertia-like symptom information regarding sleep inertia-like symptoms appearing in the subject 40 based at least on the activity level information of the subject 40 measured by the measuring device 30 after it is estimated that the subject 40 has woken up, and in particular, when the sleep inertia-like symptom information indicates the degree of the sleep inertia-like symptoms, it becomes possible to easily confirm the degree of the sleep inertia-like symptoms.

[0046] <Modification 1 of sleep inertia-like symptom information> Furthermore, the sleep inertia-like symptom information may include, for example, change information on the degree of sleep inertia-like symptoms calculated based on information indicating the degree of sleep inertia-like symptoms for the same subject at multiple points in time, and may also include trend information (especially evaluation information) on the subject's sleep inertia-like symptoms generated based on the change information. That is, when the change information on the degree of sleep inertia-like symptoms indicates, for example, a change from a relatively high level to a relatively low level, it can be determined that the degree of sleep inertia-like symptoms is changing favorably. Therefore, for such change information showing a relatively decreasing trend in the degree, the sleep inertia-like symptom information generating unit 113 generates, as the sleep inertia-like symptom information, trend information indicating a relatively decreasing trend in the degree of sleep inertia-like symptoms (i.e., evaluation information showing favorable conditions). On the other hand, when the change information of the degree of sleep inertia-like symptoms indicates, for example, a change from a relatively low degree to a relatively high degree, it can be determined that the degree of sleep inertia-like symptoms has changed to poor, and therefore, for such change information showing a relatively increasing tendency of the degree, the sleep inertia-like symptom information generating unit 113 generates trend information showing a relatively increasing tendency of the degree of sleep inertia-like symptoms (i.e., evaluation information showing poor) as sleep inertia-like symptom information. Note that the multiple time points may include two or more different time points of days, weeks, months, or years, or may include different time points before and after starting to take a predetermined medicine.

[0047] As illustrated in Figure 6, when the total activity level within a specified period after waking up (after getting up as one embodiment) is used as a criterion for determining the degree of sleep inertia-like symptoms, and the degree of sleep inertia-like symptoms is classified as A to D from low (weak sleep inertia-like symptoms) to high (strong sleep inertia-like symptoms), for example, if the degree of sleep inertia-like symptoms last month was C and the degree of sleep inertia-like symptoms this month is B, it can be determined that the change between these two different points in time is relatively favorable, and trend information (particularly evaluation information) indicating, for example, relatively favorable is generated.

[0048] In this way, it is possible to easily confirm changes in the degree of sleep inertia-like symptoms (i.e., trends and assessments regarding the subject's sleep inertia-like symptoms) from activity level information based on measurement information from the measurement device 30 worn by the subject.

[0049] <Another embodiment 1> Furthermore, the device may include a support information generator 114 that generates support information for providing support regarding sleep inertia-like symptoms to the subject 40 or a second user 60 (e.g., a doctor, a clinical trial investigator, the subject's family, a caregiver, a clinical trial management organization such as a medical institution, a medical device company, a pharmaceutical company, or a contract research organization) different from the subject 40, based on at least one of the activity level information or the sleep inertia-like symptom information of the subject 40. The support information generator 114 may generate, as support information, presentation information for presenting sleep inertia-like symptom information (e.g., information indicating the degree of the sleep inertia-like symptom and trend information indicating a trend in changes in the degree) to the subject 40 or the second user 60. The presentation information is used when the display information generator 118, described below, generates display information, and the presentation information can be displayed on at least one of the first user terminal 20 and the second user terminal 50 using a predetermined user interface. Furthermore, the support information generating unit 114 may create support information by further referring to at least any reference information such as electroencephalogram waveform information of the subject 40 (e.g., electroencephalogram waveform information for any predetermined period such as an entire day), wakefulness state information of the subject 40, clinical trial data related to sleep inertia-like symptoms, electronic medical record data related to the subject 40, and interview result information for the subject 40. That is, when the support information is presentation information, for example, as illustrated in Fig. 7, presentation information may be generated that presents, in addition to sleep inertia-like symptom information, at least any of the above-mentioned electroencephalogram waveform information of the subject 40, wakefulness state information of the subject 40, clinical trial data related to sleep inertia-like symptoms (e.g., transition information on change trends in sleep inertia-like symptoms of other users), electronic medical record data related to the subject 40 (e.g., including medication information, prescription information, and the like), and interview result information for the subject 40. In this system, clinical trial data and electronic medical record data may be input by operation or data reading from the first user terminal 20 or the second user terminal 50 and stored in the memory device 103, or may be stored in an external server (such as a clinical trial server or electronic medical record server) and obtained by external collaboration with the external server and stored in the memory device 103.

[0050] The medical interview result information may include, for example, at least any one of the results of a medical interview regarding the physical condition of the person being measured 40, the results of a medical interview regarding the occupation or type of job of the person being measured 40, and the results of a medical interview regarding the degree of daytime sleepiness of the person being measured 40. The medical interview result information may be information that a second user 60, such as a doctor, conducts a medical interview with the first user terminal 20 and stores the results of the medical interview as person being measured information by inputting the results of the medical interview into the second user terminal 50, or alternatively or additionally, the medical interview result information may be information generated by the medical interview unit 115 as a functional unit of the server device 10.

[0051] The medical interview unit 115 transmits predetermined question information to the person being measured 40 and acquires answer information to the question information from the person being measured 40. The question information may be any question for obtaining the above-mentioned medical interview result, for example, a question inquiring about the subjective score regarding the physical condition of the person being measured 40, a question inquiring about the occupation (e.g., student, office worker, etc.) or type of job (e.g., type of job such as mechanical engineer, doctor, nutritionist, sales clerk, musician, etc.) of the person being measured 40, a question inquiring about the subjective score regarding the degree of daytime sleepiness, or a question regarding the frequency of naps, and the answer information may be an answer regarding the subjective score or an answer regarding the occupation or type of job. The medical interview unit 115 may generate medical interview result information including the acquired answer information. In addition, the response information regarding the degree of daytime sleepiness is not limited to subjective evaluation based on the interview section 115, but may also be used to determine microsleep caused by daytime excessive sleepiness and evaluate the degree of daytime excessive sleepiness based on brain wave waveform information from the measurement device 30, measurement information from a glasses-type wearable device, or blink information based on the analysis results of external observation information from an external device.

[0052] In this way, it is possible to generate support information to provide support to the subject 40 or second user 60 regarding the sleep inertia-like symptoms of the subject 40. In particular, by checking the support information displayed on the first user terminal 20 or the second user terminal 50, the second user 60, such as a doctor, can confirm the state of the sleep inertia-like symptoms of the subject 40, and make decisions based on comprehensive reference information when considering changing the dosage, number of doses, timing of taking a specified medication for sleep inertia-like symptoms, or the type of prescribed medication.

[0053] Other support information will also be described. Based on the sleep inertia-like symptom information (and also referring to the above-mentioned reference information as necessary), the support information generating unit 114 may generate at least any of support information regarding the dosage of the medication to be taken for the sleep inertia-like symptoms of the subject 40, support information regarding the number of times to take the medication, support information regarding the timing of taking the medication, support information regarding the type of prescribed medication, support information including an explanation of at least one of the medication to be taken and medication other than the medication to be taken (for example, information on the seller, efficacy, price, comparison information between multiple different medications, etc.), and support information regarding appointment for medical examination.

[0054] More specifically, the support information generation unit 114 may generate support information for, for example, a doctor or other second user 60, regarding the dosage, number of doses, timing of dosing, and type of prescribed medication for the medication to be taken.For example, if the trend information (evaluation information) that is information about the sleep inertia-like symptoms of the subject 40 indicates a poor condition, particularly if the trend information indicating a poor condition continues for a certain period of time, the support information may include advice information encouraging the subject 40 to change (e.g., increase or decrease) or not change the dosage or number of doses, or advice information encouraging the prescription of a specific medication.The support information may also include medication information (e.g., dosage information, number of doses, timing of dosing) and type of prescribed medication information for other subjects whose sleep inertia-like symptoms and trends (evaluations) indicated by the trend information (evaluation information) are similar to those of the subject 40. The support information generation unit 114 may estimate which advice information or medication information or prescription drug type information of other subjects to present by, for example, storing support estimation reference information for comparison with sleep inertia-like symptom information (and further referring to the above-mentioned reference information as needed) in the storage device 103, and estimating the information based on the result of comparing the read support estimation reference information with the acquired sleep inertia-like symptom information (and further referring to the above-mentioned reference information as needed). Alternatively, the support information generation unit 114 may include, for example, a learning model (e.g., stored in the storage device 103 or an external server and trained in advance using training data) related to sleep inertia-like symptom information (and further referring to the above-mentioned reference information as needed) and advice information, and medication information or prescription drug type information of other subjects, and estimate the information using the sleep inertia-like symptom information (and further referring to the above-mentioned reference information as needed). Note that, in order to enable the current medication information and prescription drug information to be grasped, in this system, the current medication information or prescription drug information may be stored as subject information by user operation, or may be accessible from an external server through system integration.

[0055] As a result, as illustrated in Fig. 8, for example, if the trend information, which is sleep inertia-like symptom information, indicates good but the subjective score for daytime sleepiness is high (strong), the support information generating unit 114 can determine that the daytime excessive sleepiness has not improved even though the sleep inertia-like symptoms are tending to improve, and therefore it becomes possible to generate advice information as support information that recommends continued use of the medication (or a change to increase the dosage or frequency of administration, or prescribing a specific medication such as a stronger medication that further improves daytime excessive sleepiness), thereby making it possible to objectively support the judgment of a doctor, etc. Note that in Fig. 8, for the sake of explanation, the sleep inertia-like symptom information and reference information (e.g., medical interview result information) are also displayed together with the advice information, but this is not limiting, and at least one of the sleep inertia-like symptom information and the reference information may be configured not to be displayed.

[0056] On the other hand, the support information generation unit 114 may generate support information regarding medical appointments for the person being measured 40 or a second user 60 such as a family member or caregiver.For example, if the trend information (evaluation information) that is the sleep inertia-like symptom information of the person being measured 40 indicates a poor condition, particularly if the trend information indicating a poor condition continues for a certain period of time, the support information may include at least one of the following: support information recommending that the person being measured 40 make a medical appointment for a sleep inertia-like symptom if a medical appointment has not been made; support information recommending that the schedule for the medical appointment for the person being measured 40 for a sleep inertia-like symptom be brought forward if a medical appointment has been made; and explanatory information regarding hospitals that can treat sleep inertia-like symptoms (such as the hospital name, hospital address, and hospital contact information). In order to be able to grasp the status of medical appointments related to the sleep inertia-like symptoms of the subject 40, in this system, if at least information that allows grasping the medical appointment information of the subject 40 is stored as subject information by user operation, or if the date of the medical appointment can be referenced on an external server through system cooperation (for example, cooperation with a system that makes medical appointments, or cooperation with a scheduler system), it will be possible to grasp whether there are any medical appointments, the date of the current medical appointment, etc., and therefore appropriate recommendations will be possible. In addition, when cooperation with a scheduler system is established, the relevant schedule may be determined from, for example, the plan content information, hospital name information, text information and tag information of symptom name information input as a schedule.

[0057] As a result, when the trend information, for example, the sleep inertia-like symptom information, indicates poor, the support information generating unit 114 can generate support information that recommends that the subject 40 make an appointment for a medical examination regarding the sleep inertia-like symptoms, as exemplified in Fig. 9, and can provide objective support to the subject 40 himself, his family, caregivers, etc. in making a decision about making an appointment for a medical examination. Note that, for the sake of explanation, in Fig. 8, the sleep inertia-like symptom information is also displayed together with the support information regarding the appointment for a medical examination, but this is not limiting, and the sleep inertia-like symptom information may be configured not to be displayed.

[0058] In this way, support information for subject 40 and second user 60 can be easily generated from the sleep inertia-like symptom information (and the above-mentioned reference information as needed).

[0059] <Another embodiment 2> Furthermore, the device may be provided with a sleep inertia-like symptom duration information generation unit 116 that generates sleep inertia-like symptom duration information indicating an estimated time that sleep inertia-like symptoms have continued since awakening, based on information on the elapsed time until a predetermined activity intensity occurs, such as those exemplified in (4) to (6) above, or information on the elapsed time until a predetermined total number of steps is reached, or information on the elapsed time until a predetermined total activity level (e.g., total activity amount) is reached. The sleep inertia-like symptom duration information generation unit 116 may generate sleep inertia-like symptom duration information by directly using the elapsed time information until a predetermined activity level is reached, as illustrated in Figure 10, or may generate as sleep inertia-like symptom duration information an aggregate value such as the average or maximum value of elapsed time information for a predetermined period (e.g., the last few days, the last few weeks, the last few months, the last few years, etc.) or a predetermined number of times (e.g., several times, tens of times, hundreds of times, thousands of times, tens of thousands of times, etc.), or may generate as sleep inertia-like symptom duration information by adding a predetermined time (e.g., any time such as 10 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, etc.) to the elapsed time information.

[0060] Here, the support information generating unit 114 may generate, as support information, recommended preparation time information indicating a recommended preparation time from awakening to starting an activity in the subject 40, based on the sleep inertia-like symptom duration information. That is, the support information generating unit 114 may generate the recommended preparation time information by directly using the sleep inertia-like symptom duration information, or may generate, as the recommended preparation time information, an aggregate value such as an average value or a maximum value of the sleep inertia-like symptom duration information for a predetermined period (e.g., for the most recent few days, the most recent few weeks, the most recent few months, the most recent few years, etc.) or a predetermined number of times (e.g., several times, tens of times, hundreds of times, thousands of times, tens of thousands of times, etc.), or may generate the recommended preparation time information by adding a predetermined time (e.g., any time such as 10 minutes, 30 minutes, 1 hour, 2 hours, or 3 hours) to the sleep inertia-like symptom duration information.

[0061] In this way, by referring to the sleep inertia-like symptom duration information or the recommended preparation time information displayed on the first user terminal 20, the subject 40 can know, for example, what time he or she should wake up to ensure that he or she has enough time to start an activity.

[0062] Furthermore, the support information generating unit 114 may generate, as support information, recommended awakening induction time information indicating a recommended time for inducing awakening of the person being measured 40, based on the desired activity start time information and the sleep inertia-like symptom duration information of the person being measured 40. More specifically, as illustrated in FIG. 11 , the recommended awakening induction time may be calculated by tracing back the sleep inertia-like symptom duration from the desired activity start time of the person being measured 40. The desired activity start time information may be set by an input operation by the person being measured 40. Alternatively, or in addition, schedule information may be acquired from a scheduler function used by the person being measured 40 or an externally linked scheduler system, and the time at which a predetermined schedule is scheduled to start may be set. The support information generating unit 114 may then set the start time of the above-mentioned awakening induction function (e.g., an alarm function, a light-on function, etc.) to the recommended awakening induction time indicated by the generated recommended awakening induction time information. Alternatively, or in addition, information on recommended awakening induction times may be displayed on first user terminal 20 of person under measurement 40.

[0063] Furthermore, the support information generator 114 may generate, as support information, recommended medication time information indicating a recommended time to take a medication for sleep inertia-like symptoms, based on the sleep inertia-like symptom duration information. More specifically, as illustrated in FIG. 12 , the recommended medication time may be calculated by going back the sleep inertia-like symptom duration from the planned start time of the activity of the subject 40, or by going back a predetermined time (e.g., 10 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, etc.) from the back-tracking time. The medication for sleep inertia-like symptoms is a medication that has the effect of promoting wakefulness in the subject 40, and the medication may be recommended to be taken at a timing whose medication effect duration includes at least the time period going back the sleep inertia-like symptom duration from the planned start time of the activity, and further includes the planned time period of the activity.

[0064] 13 , the support information generation unit 114 generates, as support information, task restriction information related to a restriction on the execution of a predetermined task within a range including at least the time indicated by the sleep inertia-like symptom duration information, based on the wakefulness time information indicating the time during which the wakefulness state is estimated to be awake, the restriction-target task information, and the sleep inertia-like symptom duration information. The restriction-target task information is information that identifies one or more tasks to be restricted. The restriction-target tasks may involve various activities, such as going out, working, studying, or doing housework. Furthermore, the support information generation unit 114 may generate, as support information, task restriction information such as advice information encouraging a user to refrain from driving a car or going out at least within the time indicated by the sleep inertia-like symptom duration information if a user plans to go out, or advice information encouraging a user to refrain from driving a car or going out at least within the time indicated by the sleep inertia-like symptom duration information if a user plans to perform work requiring concentration, such as mechanical work. The restricted task information may be common to each user, or may be associated with one or more restricted task information that matches a different theme, for example, for each gender, age (generation), occupation, or job type of the subject 40.When this system is linked to a scheduler system, the relevant tasks may be analyzed, for example, from the planned content information entered as a schedule, text information, or tag information of the planned task information.

[0065] 14 , the support information generator 114 generates, as support information, sleep restriction information related to sleep restriction within the elapsed time range indicated by the sleep inertia-like symptom duration information, based on the scheduled task information, restricted task information, and sleep inertia-like symptom duration information of the subject. Similarly to the above, the restricted task information may be common to each user. Alternatively, one or more restricted task information items may be associated with different themes, such as the gender, age (generation), occupation, or type of work of the subject 40. When the system is linked with a scheduler system, the relevant tasks may be analyzed from, for example, the schedule content information input as a schedule, text information, or tag information of the scheduled task information.

[0066] This makes it possible to generate sleep inertia-like symptom duration information, recommended awakening induction time information, recommended medication information, and task restriction information from at least sleep inertia-like symptom information (and further the above-mentioned reference information as needed).

[0067] <Another embodiment 3> The clinical trial evaluation information generation unit 117 may further include a clinical trial evaluation information generation unit that generates clinical trial evaluation information related to an intra-clinical trial evaluation of sleep inertia based at least on the sleep inertia-like symptom information and the clinical trial evaluation criterion information. That is, when activity level information is acquired in a clinical trial related to sleep inertia-like symptoms (particularly, a clinical trial to confirm the effectiveness of a predetermined drug for sleep inertia-like symptoms), the clinical trial evaluation information generation unit 117 generates evaluation information related to an intra-clinical trial evaluation of sleep inertia (particularly, an evaluation of the effectiveness of the predetermined drug). That is, considering that the purpose of the clinical trial is to evaluate the effectiveness of a predetermined drug for sleep inertia, for example, if information indicating the degree of the subject's sleep inertia-like symptoms indicates a relatively low degree of sleep inertia-like symptoms as a result of referring to and comparing the information indicating the degree of the subject's sleep inertia-like symptoms with the clinical trial evaluation criterion information, the drug should be considered to be working and the sleep inertia-like symptoms should be alleviated, and the clinical trial evaluation information generation unit 117 may generate intra-clinical trial evaluation information (particularly, evaluation information on effectiveness) based at least on the information indicating the degree of the subject's sleep inertia-like symptoms and the clinical trial evaluation criterion information. Furthermore, the change information indicating the degree of the above-mentioned change may be used to generate in-clinical trial evaluation information (especially efficacy evaluation information).

[0068] This makes it possible to easily confirm the in-clinical trial evaluation of sleep inertia-like symptoms from at least the sleep inertia-like symptom information (and further the above-mentioned reference information as necessary).

[0069] The display information generation unit 118 generates display information for a predetermined display on the display unit of the first user terminal 20 or the second user terminal 50. Note that if the predetermined display is controlled by an application on the first user terminal 20 or the second user terminal 50, rather than by a web service via a web browser, some or all of the functions of the display information generation unit 118 may be provided in the arithmetic processing unit of the first user terminal 20 or the second user terminal 50.

[0070] In this way, it is possible to provide an information processing system for generating sleep inertia-like symptom information based on activity information of the subject 40 measured by a measuring device, particularly to make it possible to easily confirm sleep inertia-like symptoms.

[0071] FIG. 15 is a diagram showing the flow of processing executed in the information processing system of this embodiment.

[0072] First, as preprocessing for this process, the information management section 110 of the server device 10 accepts input of information about the subject 40 (e.g., a patient) and stores the subject information in the subject information storage section 131. The subject information may be input by the second user 60 via the second user terminal 50, or may be input by the subject 40 via the first user terminal 20.

[0073] Next, in step S101, the activity level acquisition unit 111 and the wakefulness state estimation unit 112 acquire activity level information and wakefulness state information stored in a storage unit such as the subject information storage unit 131 in response to a request from the first user terminal 20 or the second user terminal 50.

[0074] Next, in step S102, the sleep inertia-like symptom information generating unit 113 generates sleep inertia-like symptom information related to the sleep inertia-like symptoms appearing in the subject based on the activity level information and wakefulness state information acquired in step S101.

[0075] Next, in step S103, the display information generating unit 118 generates display information including the sleep inertia-like symptom information to be displayed on the display unit of the first user terminal 20 or the second user terminal 50.

[0076] Next, in step S104, the information management unit 110 transmits the display information to the first user terminal 20 or the second user terminal 50.

[0077] As described above, according to this embodiment, it is possible to provide an information processing system for generating sleep inertia-like symptom information based on activity information of the subject 40 measured by a measuring device, in particular to make it possible to easily confirm sleep inertia-like symptoms. [Explanation of symbols]

[0078] 1. Information Processing Systems 10 Server device 20 Test subject terminal 30 User terminals 40 Subject 50 Second user terminal 60 Second User NW communication network

Claims

1. an activity level acquisition unit that acquires activity level information of the subject measured by a measurement device; an arousal state estimation unit that estimates the arousal state of the subject; and a sleep inertia-like symptom information generating unit that generates sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject based at least on the activity level information after the wakefulness state is estimated to be wakefulness. An information processing system comprising:

2. the activity level acquiring unit calculates the activity level information based on at least inertial sensor information of the subject measured by the measuring device attached to the subject.

2. The information processing system according to claim 1, wherein:

3. the activity level acquiring unit calculates the activity level information based on at least electroencephalogram waveform information of the subject measured by the measuring device worn by the subject.

2. The information processing system according to claim 1, wherein:

4. the activity level acquiring unit calculates the activity level information based on at least external observation information of the subject measured by the measuring device.

2. The information processing system according to claim 1, wherein:

5. the awake state estimation unit estimates the awake state based on at least electroencephalogram waveform information of the subject.

5. An information processing system according to claim 1, wherein:

6. the awake state estimation unit estimates the awake state based on at least inertial sensor information of the subject measured by the measurement device.

5. An information processing system according to claim 1, wherein:

7. the wakefulness state estimation unit estimates the wakefulness state based on at least an awakening inducing function that induces awakening of the subject.

5. An information processing system according to claim 1, wherein:

8. The awakening induction function includes an alarm function that issues a predetermined notification at a set time.

8. The information processing system according to claim 7,

9. The awakening inducing function includes a light turning-on function that turns on a predetermined light at a set time.

8. The information processing system according to claim 7,

10. the sleep inertia-like symptom information generation unit generates the sleep inertia-like symptom information based on total activity information within a predetermined time period after the wakefulness state is estimated to be wakefulness.

5. An information processing system according to claim 1, wherein:

11. The total activity level information within a predetermined time period after the wakefulness state is estimated to be wakefulness is total activity level information of a predetermined activity intensity or more within the predetermined time period, or total step count information within the predetermined time period.

11. The information processing system according to claim 10.

12. the sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on total activity information within a predetermined time period after the person being measured has sat up, after the wakefulness state has been estimated to be wakefulness.

5. An information processing system according to claim 1, wherein:

13. The sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on at least one of total time information during which an activity level equal to or greater than a predetermined intensity has occurred after the wakefulness state has been estimated as wakefulness, total time information during which an activity level equal to or less than a predetermined intensity has occurred, information on the elapsed time until the predetermined total activity level has been reached, information on the elapsed time until the predetermined activity intensity has been reached, and information on the elapsed time until the predetermined total number of steps has been reached.

5. An information processing system according to claim 1, wherein:

14. The sleep inertia-like symptom information generating unit generates the sleep inertia-like symptom information based on at least one of total time information during which an activity level equal to or greater than a predetermined intensity occurred after the person being measured got up, total time information during which an activity level equal to or less than a predetermined intensity occurred, information on the elapsed time until the predetermined total activity level was reached, information on the elapsed time until the predetermined activity intensity was reached, and information on the elapsed time until the predetermined total number of steps was reached, after the wakefulness state was estimated to be wakefulness and the person being measured got up.

5. An information processing system according to claim 1, wherein:

15. The sleep inertia-like symptom information is information indicating the degree of the sleep inertia-like symptom of the subject.

5. An information processing system according to claim 1, wherein:

16. The sleep inertia-like symptom information includes information on changes in the degree of sleep inertia-like symptoms calculated based on information indicating the degree of sleep inertia-like symptoms at multiple points in time for the same subject.

5. An information processing system according to claim 1, wherein:

17. The device further includes a support information generating unit that generates support information for providing support regarding sleep inertia-like symptoms to the subject or a second user different from the subject, based on at least one of the activity level information of the subject or the sleep inertia-like symptom information.

5. An information processing system according to claim 1, wherein:

18. the support information generation unit further refers to electroencephalogram waveform information of the subject or arousal state information indicating the arousal state, and generates the support information.

18. The information processing system according to claim 17.

19. The support information generation unit further refers to clinical trial data on sleep inertia-like symptoms to generate the support information.

18. The information processing system according to claim 17.

20. the support information generating unit further refers to electronic medical record data related to the subject to generate the support information.

18. The information processing system according to claim 17.

21. the support information generating unit further refers to interview result information for the subject to generate the support information.

18. The information processing system according to claim 17.

22. The medical interview result information includes a medical interview result regarding the physical condition of the subject.

22. The information processing system according to claim 21.

23. The medical interview result information includes a medical interview result regarding the occupation or occupation type of the subject.

22. The information processing system according to claim 21.

24. The medical interview result information includes a medical interview result regarding the subject's daytime sleepiness level.

22. The information processing system according to claim 21.

25. The support information generation unit presents, to at least a second user, questionnaire result information regarding the degree of daytime sleepiness of the subject and sleep inertia-like symptom information together as the support information.

25. The information processing system according to claim 24.

26. a medical interview unit that transmits predetermined question information about the subject, acquires answer information to the question information, and generates the medical interview result information; 26. An information processing system according to claim 25.

27. The support information includes support information indicating at least one of the dosage for sleep inertia-like symptoms, the number of times of taking the medicine, the timing of taking the medicine, and information on a predetermined medicine.

18. The information processing system according to claim 17.

28. The support information includes support information regarding a medical appointment for a sleep inertia-like symptom.

18. The information processing system according to claim 17.

29. The sleep inertia-like symptom duration information generating unit generates sleep inertia-like symptom duration information indicating an estimated time that a sleep inertia-like symptom has continued from awakening, based on information on the elapsed time until a predetermined activity intensity occurs, information on the elapsed time until a predetermined total number of steps is reached, or information on the elapsed time until a predetermined total activity level is reached after the awakening state is estimated to be awakening, the support information generation unit further refers to the sleep inertia-like symptom duration information to generate the support information.

18. The information processing system according to claim 17.

30. the support information generation unit generates, as the support information, a recommended preparation time from awakening to starting an action for the subject based on the sleep inertia-like symptom duration information.

30. The information processing system according to claim 29.

31. the support information generation unit generates, as the support information, recommended awakening induction time information indicating a time for inducing awakening, based on the desired activity start time information of the subject and the sleep inertia-like symptom duration information.

30. The information processing system according to claim 29.

32. the support information generating unit estimates a desired activity start time of the subject based on scheduler information of the subject; 32. The information processing system according to claim 31 .

33. the support information generation unit generates, as the support information, recommended medication time information indicating a time to take medication related to the sleep inertia-like symptom, based on the sleep inertia-like symptom duration information.

30. The information processing system according to claim 29.

34. the support information generation unit generates, as the support information, task restriction support information related to a restriction on execution of a predetermined task within a time range including at least a time range indicated by the sleep inertia-like symptom duration information, based on awake time information indicating a time when the awake state was estimated to be awake, restriction target task information, and the sleep inertia-like symptom duration information.

30. The information processing system according to claim 29.

35. the support information generation unit generates, as the support information, sleep restriction information regarding sleep restriction within a time range indicated by the sleep inertia-like symptom duration information based on start time information of a predetermined task included in the scheduled task information, based on the scheduled task information and restriction target task information of the subject, and the sleep inertia-like symptom duration information.

30. The information processing system according to claim 29.

36. the support information generating unit acquires the scheduled task information based on scheduler information of the subject; 36. An information processing system according to claim 35.

37. The method further includes a clinical trial evaluation information generation unit that generates evaluation information regarding an intra-clinical trial evaluation of sleep inertia based at least on the sleep inertia-like symptom information and the clinical trial evaluation criterion information.

5. An information processing system according to claim 1, wherein:

38. The clinical trial evaluation information generation unit further refers to clinical trial medication information indicating information on medications taken in the clinical trial, and generates evaluation information regarding an evaluation of the effectiveness of a predetermined medication on sleep inertia.

38. An information processing system according to claim 37.

39. An information processing method executed on a computer, comprising: acquiring, by an activity level acquiring unit, activity level information of the subject measured by a measuring device; estimating the state of arousal of the subject by an arousal state estimation unit; and generating, by a sleep inertia-like symptom information generating unit, sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject, based at least on the activity level information after the wakefulness state is estimated to be wakefulness.

1. An information processing method comprising:

40. an activity level acquisition function for acquiring activity level information of the subject measured by a measurement device; an arousal state estimation function for estimating the arousal state of the subject; a sleep inertia-like symptom information generating function that generates sleep inertia-like symptom information related to a sleep inertia-like symptom appearing in the subject based at least on the activity level information after the wakefulness state is estimated to be wakefulness, the sleep inertia-like symptom information generating function being realized in a computer. A program characterized by:

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