JP6755445B2 - Information processing equipment, information processing method, information processing program - Google Patents

Description

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

Conventionally, a head-worn device called a headset is provided with a near-infrared irradiation unit and a near-infrared detection unit to detect changes in blood flow on the surface of the brain, and the detected data is processed by the data processing device. A measurement system that acquires information indicating the activity state of the brain is provided.

Japanese Unexamined Patent Publication No. 2006-320735

For a plurality of users (subjects), the state of brain activity when performing various actions is measured. It is considered that the state of brain activity when a certain behavior is performed is influenced by various properties included in the behavior and the attributes of the user. However, the state of brain activity is rarely analyzed in consideration of various properties included in behavior, user attributes, and the like. It is required to analyze the brain activity state in consideration of the properties included in the behavior, the attributes of the user, etc., and utilize the brain activity state.

An object of the present invention is to provide an information processing device that analyzes a brain activity state measured by a user.

The following means are adopted to solve the above problems.
That is, the first aspect is
A storage unit that stores a detection value that detects blood flow in the head of a user, an identifier of a predetermined action that the user performs when the detection value is detected, and an identifier of the user in association with each other. ,
Based on the detected value stored in the storage unit, a calculation unit that calculates a brain activity value indicating the degree of the brain activity state of the user when the user performs the predetermined action, and a calculation unit.
It is an information processing device provided with.

The aspect of disclosure may be realized by executing the program by an information processing device. That is, the structure of the disclosure can be specified as a program for causing the information processing apparatus to execute the process executed by each means in the above-described embodiment, or as a computer-readable recording medium on which the program is recorded. Further, the structure of the disclosure may be specified by a method in which the information processing apparatus executes the processing executed by each of the above means. The configuration of the disclosure may be specified as a system including an information processing device that performs processing executed by each of the above means.

The steps of writing a program include not only processes performed in chronological order in the order described, but also processes executed in parallel or individually, although not necessarily processed in chronological order. Some of the steps to write the program may be omitted.

According to the disclosed technology, it is possible to provide an information processing device that analyzes a brain activity state measured by a user.

FIG. 1 is a diagram illustrating a configuration involved in information processing of the measurement system according to the embodiment of the present invention. FIG. 2 is a diagram showing a configuration example of a head-mounted device. FIG. 3 is a diagram showing a configuration example of a user terminal. FIG. 4 is a diagram showing a configuration example of the server. FIG. 5 is a diagram showing an example of a personal information table. FIG. 6 is a diagram showing an example of an operation flow of the measurement system of the present embodiment. FIG. 7 is a diagram showing an example of a weight table.

Hereinafter, embodiments will be described with reference to the drawings. The configuration of the embodiment is an example, and the configuration of the invention is not limited to the specific configuration of the disclosed embodiment. In carrying out the invention, a specific configuration according to the embodiment may be appropriately adopted.

[Embodiment]
(Configuration example)
FIG. 1 is a diagram illustrating a configuration involved in information processing of the measurement system according to the embodiment of the present invention. This measurement system detects measurement data (also called a detection value) indicating a change in blood flow from the user's head, and acquires brain activity information (brain activity waveform) indicating the activity state of the user's brain. In addition, this measurement system calculates the basic waveform of the attributes of brain activity from the acquired brain activity information.

As shown in FIG. 1, the measurement system includes head-mounted devices 10-1, 10-2, 10-3, user terminals 20-1, 20-2, 20-3, and a server 30. Here, when the head mounting devices 10-1, 10-2, and 10-3 are generically referred to, they are referred to as the head mounting device 10. Further, when the user terminals 20-1, 20-2, and 20-3 are generically referred to, they are referred to as the user terminal 20. The number of the head-mounted device 10 and the user terminal 20 is not limited to three, respectively.

FIG. 2 is a diagram showing a configuration example of a head-mounted device. The head-mounted device 10 has a control unit 11, a wireless communication unit 13, and a pair of sensors 115 and 125 as aspects of information processing. The control unit 11 controls the measurement and communication of the head-mounted device 10. The control unit 11 has, for example, a processor such as a CPU (Central Processing Unit) or a DSP (Digital Signal Processor) and a memory, and executes processing by a computer program, firmware, or the like executably deployed on the memory. .. However, the control unit 11 may be an FPGA (Field Programmable Gate Array) or the like, which is a dedicated hardware circuit that activates the wireless communication unit 13 and the sensors 115 and 125 and executes the cooperation processing with each component. In addition, the control unit 11
A CPU, DSP, dedicated hardware circuit, and the like may be mixed.

The head mounting device 10 has a structure in which it is wound around the user's head in a head-wound shape and mounted, and is fixed to the user's head by tightening a fixing member.

The wireless communication unit 13 is connected to the control unit 11 and the sensors 115 and 125 by a predetermined interface. However, the wireless communication unit 13 may be configured to acquire data from the sensors 115 and 125 via the control unit 11. The wireless communication unit 13 communicates with the user terminal 20 via the network N1. The network N1 is, for example, a network work according to standards such as Bluetooth (registered trademark), wireless LAN (Local Area Network), and ZigBee. The wireless communication unit 13 is an example of the transfer means. However, in this measurement system, the standard of the wireless interface of the wireless communication unit 13 is not limited.

When communicating on the network N1, the user terminal 20 embeds an identifier that identifies the head-mounted device 10 in the header part of the communication header or the user data part (payload part) in the communication data, and the user terminal 20 ( To be able to identify the subject).

Further, in this measurement system, a communication unit that performs wired communication with the wireless communication unit 13 may be provided instead of the wireless communication unit 13. That is, the head-mounted device 10 and the user terminal 20 may be connected by a wired communication interface. The interface for wired communication in this case is not limited, and various interfaces such as USB (Universal Serial Bus) and PCI Express can be used depending on the application of the measurement system.

Both the sensors 115 and 125 irradiate the head with near-infrared rays, receive the near-infrared rays partially absorbed and scattered near the cerebral cortex of the brain, and convert them into electrical signals. The cerebral cortex of the brain has different blood flow, for example, depending on the activity state of the brain. As a result, in each part of the cerebral cortex, the amount of hemoglobin bound to oxygen in the blood and the amount of hemoglobin not bound to oxygen change. Due to changes in the amount of hemoglobin, changes in the amount of oxygen, etc., the absorption characteristics or scattering characteristics of near-infrared rays near the cerebral cortex change. The sensors 115 and 125 convert the near-infrared rays whose light intensity changes due to the change in the near-infrared absorption rate or the change in the transmittance according to the state of blood flow near the cerebral cortex into an electric signal and output the signals. Sensors 115 and 125 are examples of detection means.

The sensors 115 and 125 include, for example, a near-infrared light source that irradiates near-infrared rays and a light receiving unit that receives near-infrared rays. Near-infrared light sources include, for example, LEDs (Light Emitting Diodes), infrared lamps, and the like. Further, the light receiving unit includes a photoelectric element such as a photodiode and a phototransistor, an amplifier, and an AD (Analog Digital) converter. The near-infrared light source and the light receiving unit may not be provided as a pair. For example, a plurality of light receiving units may be provided for one near infrared light source.

FIG. 3 is a diagram showing a configuration example of a user terminal. The user terminal 20 acquires change data of the absorption rate or transmittance of near infrared rays in the vicinity of the user's cerebral cortex from the head wearing device 10, and various information processing related to the activity state of the user's brain. Provide services including. The user terminal 20 is an example of an information processing device (computer). The user terminal 20 is a PC (Personal).
It can be realized by using a dedicated or general-purpose computer such as a computer), a smartphone, a mobile phone, a tablet terminal, a car navigation device, a PDA (Personal Digital Assistant), or an electronic device equipped with a computer. The user terminal 20 can be installed in, for example, a fitness club, a cram school, or the like.

The user terminal 20 includes a CPU 21, a memory 22, a wireless communication unit 23, a public line communication unit 24, a display unit 25, an operation unit 26, an output unit 27, an imaging unit 28, and a positioning unit 29. , Has a physical sensor unit 2A. The CPU 21 executes the process as the user terminal 20 by the computer program executably expanded in the memory 22. The processing as the user terminal 20 is, for example, a service including various information processing related to the activity state of the user's brain. The CPU 21 that executes such a computer program is an example of the calculation means.

The memory 22 stores a computer program executed by the CPU 21 or data processed by the CPU 21. The memory 22 may include a volatile memory and a non-volatile memory.

The wireless communication unit 23 is the same as the wireless communication unit 13 of the head-mounted device 10. The wireless communication unit 23 is an example of the receiving means. Further, the user terminal 20 may have a communication unit that communicates by wire with the wireless communication unit 13 instead of the wireless communication unit 13.

The public line communication unit 24 communicates with a server on the network N2, for example, a server 30, etc., via the network N2. The network N2 is a public network, for example, a mobile phone network. When the network N2 is a mobile phone network, the public line communication unit 24 connects to the network N2 via a base station of the mobile phone network. However, the network N2 may be a network including an access network to a communication device of an Internet connection provider and the Internet. The access network to the communication device of the Internet connection company is, for example, an optical network provided by the communication company, ADSL (Asymmetric Digital Subscriber Line), or the like. Network N2 is an example of a public wireless network. Further, the public line communication unit 24 is an example of a public wireless communication means. However, in this measurement system, the network N2 is not limited to the public network, for example, a private network such as LAN (Local Area Network), a dedicated line of a company, a business operator, a government office, a school, a research institution, etc. It may be a wide area network such as VPN (Virtual Private Network). Hereinafter, companies, businesses, government offices, schools, research institutes, etc. are also referred to as companies.

The display unit 25 is, for example, a liquid crystal display, an EL (Electro-Luminescence) panel, or the like, and displays output information from the CPU 21. The operation unit 26 is, for example, a push button, a touch panel, or the like, and accepts user operations. The output unit 27 is, for example, a vibrator that outputs vibration, a speaker that outputs sound or voice, and the like. The image pickup unit 28 is, for example, a camera including a solid-state image sensor. As the solid-state image sensor, a CCD (Charge-coupled device) image sensor, a CMOS (Complementary Metal Oxide Semiconductor) image sensor, or the like can be used.

The positioning unit 29 is, for example, a GPS (Global Positioning System) receiver, and is a GPS.
Receives radio waves from satellites and calculates the current position (latitude, longitude, etc.), time, etc. However, the positioning unit 29 is not limited to the one having a GPS receiver. For example, when the public line communication unit 24 is a mobile phone network, the positioning unit 29 may execute positioning based on the distance from the mobile phone base station.

The physical sensor unit 2A is, for example, an acceleration sensor, an angular acceleration sensor, or the like. However, the physical sensor unit 2A may be a temperature sensor, a humidity sensor, a barometric pressure sensor, or a water pressure sensor.

FIG. 4 is a diagram showing a configuration example of the server. The server 30 is connected to the network N2. The server 30 is a general information processing device. The information processing device outputs information by a CPU that performs calculations and controls, a memory and a storage unit that stores data used in calculations, an input unit that accepts information input from users, etc., an image, a voice, and the like. It has an output unit, a communication unit that transmits and receives information to and from other devices, and the like. The server 30 can be realized by using a dedicated or general-purpose computer such as a PC or a workstation (WS, Work Station), or an electronic device equipped with the computer.

The server 30 includes a CPU 31, a memory 32, a public line communication unit 34, a display unit 35, an operation unit 36, and an output unit 37. The CPU 31 executes the process as the server 30 by the computer program executably expanded in the memory 32. The processing as the server 30 is, for example, a service including various information processing related to the activity state of the brain. The CPU 31 that executes such a computer program is an example of the calculation means.

The memory 32 stores a computer program executed by the CPU 31 or data processed by the CPU 31. The memory 32 may include a volatile memory and a non-volatile memory. Further, the memory 32 stores a personal information table T20 including personal information of the user.

FIG. 5 is a diagram showing an example of a personal information table. In the personal information table T20 of FIG. 5, personal information such as a user's identifier (ID), name, address, date of birth, height, weight, gender, disease (cerebral infarction, diabetes, etc.), pre-existing disease, etc. is stored in the user. It is associated and stored for each.

The public line communication unit 34 communicates with a device on the network N2, for example, a user terminal 20, etc., via the network N2. The network N2 is a public network, for example, a mobile phone network. Further, the public line communication unit 34 is an example of a public wireless communication means.

The display unit 35 is, for example, a liquid crystal display, an EL (Electro-Luminescence) panel, or the like, and displays output information from the CPU 31. The operation unit 36 is, for example, a push button, a touch panel, or the like, and accepts user operations. The output unit 37 is, for example, a vibrator that outputs vibration, a speaker that outputs sound or voice, and the like.

<Operation example>
An operation example of the measurement system of this embodiment will be described.
FIG. 6 is a diagram showing an example of an operation flow of the measurement system of the present embodiment. Each of the head-mounted devices 10 of the measurement system of the present embodiment is worn on the head of the user (subject), and is in a state where the cerebral activity state (cerebral blood flow rate) can be measured. Each of the head-mounted devices 10 is connected to the user terminal 20. It is assumed that the head mounting device 10 has already been calibrated. The user terminal 20 is connected to the server 30.

In S101, the user terminal 20 causes the user wearing the head wearing device 10 to perform a predetermined action. Predetermined actions include, for example, watching videos, playing games, listening to music, exercising, eating and drinking, testing, meditation, sleeping, yoga, playing applications, and the like. The predetermined action may be a specific action in a specific application. Applications include, for example, brain training applications. A unique identifier is assigned to each predetermined action in advance. In addition, each user is assigned a unique identifier in advance. For example, when viewing a moving image, the user terminal 20 causes the display unit 25 to display the moving image, causes the output unit 27 to output audio accompanying the moving image, and causes the user to watch the moving image. Further, when the user terminal 20 is allowed to play a game, the display unit 25 and the output unit 27 are made to output an image or sound for the game, and the operation unit 26 is made to operate the game by the user. Let them play the game. Further, the user terminal 20 outputs a signal for starting and a signal for ending a predetermined exercise as a predetermined action through the display unit 25 and the output unit 27 by images and sounds. The user terminal 20 may accept the input of the operation of the start signal and the end signal of the predetermined exercise as the predetermined action through the input unit 26.

When the user is made to perform a predetermined action, the user terminal 20 causes the head wearing device 10 to measure the brain activity state of the user. Brain activity is measured at a predetermined sampling frequency. Each head-mounted device 10 measures the brain activity state of the user by the sensors 115 and 125, and transmits the detection value indicating the brain activity state to the user terminal 20 via the wireless communication unit 13. When the user terminal 20 receives the detected value (brain activity waveform) indicating the brain activity state from the head wearing device 10 via the wireless communication unit 23, the user terminal 20 stores it in a storage means such as a memory 22. Here, the detected value may be the measured value itself, information processed so that the measured value can be easily transmitted to the user terminal 20, or information summarizing the values measured in a certain period of time. And so on. The detected value may be a value based on the value measured by the head wearing device 10 for the change in blood flow in the head. The detected value indicating the brain activity state is stored as time-series data together with time information, a predetermined action identifier, and a user identifier. The user terminal 20 may constantly acquire a detection value indicating a brain activity state as a measurement result, and extract a detection value indicating a brain activity state while performing a predetermined action. The detected value (cerebral activity waveform) is, for example, a time change of cerebral blood flow and a time derivative of cerebral blood flow.

In S102, the user terminal 20 transmits a detection value (brain activity waveform) indicating a brain activity state stored in the storage means, together with a predetermined action identifier, a user identifier, and time information, via the public line communication unit 24. And sends it to the server 30. The server 30 receives from each user terminal 20 a detection value indicating a brain activity state, a predetermined action identifier, a user identifier, and time information via the public line communication unit 34. The server 30 stores the received brain activity waveform in the memory 32 in association with a predetermined action identifier (ID), a user identifier (ID), and time information. In the memory 32 of the server 30, brain activity waveforms when various predetermined actions (behaviors) of various users are performed are accumulated. The server 30 associates the time-series data of the brain activity waveform, the identifier of the predetermined action, the start time of the predetermined action, the end time of the predetermined action, and the identifier of the user who performed the predetermined action. , May be stored in the memory 32. The memory 32 of the server 30 may store a brain activity waveform or the like so that a brain activity waveform when a specific user performs a specific predetermined action can be extracted.

Here, it is assumed that brain activity includes attributes (marker attributes) of visual function, auditory function, motor function, short-term memory, and long-term memory. The attributes of brain activity are the unique properties, characteristics, characteristics, etc. of brain activity. The attributes of brain activity (brain activity attributes) are mainly attributes corresponding to the functions of the body. The visual function is a function of acquiring visual information from the eyes. The auditory function is a function of acquiring acoustic information from the ear. The motor function is a function of moving a hand, a foot, or the like. Short-term memory is memory that is retained for a period of several seconds to several tens of seconds after the presentation of the object of memory. Long-term memory is memory that is retained for a longer period of time than short-term memory is retained after the presentation of the object of memory. The types of attributes of brain activity are not limited to these, and brain activity may include many more types of attributes.

Further, it is assumed that each attribute of brain activity has a unique brain activity waveform (basic waveform). That is, for example, when visual information is acquired from the eyes of the user (when the visual function is used), the brain activity waveform includes a brain activity waveform peculiar to the visual function. However, the basic waveform of each attribute of brain activity does not require mathematically strict independence. It is assumed that the brain activity waveform measured by the head-mounted device 10 is the sum of the basic waveforms of each attribute of the brain activity. The size of the basic waveform of brain activity included in the brain activity waveform depends on the size of each attribute of brain activity according to the degree of use.

The server 30 sets in advance a brain activity waveform that is assumed when the user performs a predetermined action for each predetermined action. The defined brain activity waveform is represented as the sum of the unique brain activity waveforms of each attribute of brain activity. The brain activity waveform Sapp1 (brain activity waveform when one predetermined action is performed) defined for a certain predetermined action (appp1) is expressed as follows, for example.

Here, Xa is a brain activity waveform (basic waveform) peculiar to visual function, which is one of the attributes of brain activity, Xb is a basic waveform of auditory function, Xc is a basic waveform of motor function, and Xd is a basic waveform of short-term memory. , Xe is the basic waveform of long-term memory. In addition, Ma1 is the weight of the visual function (attribute value) for the predetermined behavior, Mb1 is the weight of the auditory function for the predetermined behavior, Mc1 is the weight of the motor function for the predetermined behavior, and Md1 is the short-term memory for the predetermined behavior. The weight of, Me1, is the weight of long-term memory for the predetermined action. The weight of the attribute of brain activity with respect to a predetermined behavior is an amount indicating the magnitude of brain activity expected for the attribute. The weight of the attribute of the brain activity with respect to the predetermined behavior indicates the tendency of the brain activity state of the predetermined behavior. The larger the weight (attribute value) of the attribute of a certain brain activity in a certain predetermined action, the more actively the function of the attribute of the brain activity functions when the predetermined action is performed. The weight of each attribute is a tool that supports a predetermined action such as a video creator (when the predetermined action is viewing a video), a game creator (when the predetermined action is a game play), or an application program. Etc. are set in advance by the designer. These tools, application programs, and the like are provided by the user terminal 20 and other control devices. The person who sets the weight of each attribute sets the weight of each attribute according to the characteristics of the predetermined action. The weights of the attributes of the brain activity with respect to the predetermined behavior are stored in the memory 32 of the server 30 as the weight table T10.

FIG. 7 is a diagram showing an example of a weight table. In the weight table T10 of FIG. 7, the name of the predetermined action, the ID of the predetermined action, and the weight (attribute value) of each attribute of the brain activity are stored in association with each other. The weight table T10 includes weights of ID and brain activity attributes for a plurality of predetermined actions. For example, in the name of a predetermined action "action A", the ID is "001", the visual function weight (attribute value) is "3", the auditory function weight is "0", the motor function weight is "1", and the short term. The memory weight is stored as "2" and the long-term memory weight is stored as "0".

In S103, the server 30 calculates the basic waveform for each attribute of the brain activity from the brain activity waveforms of various users stored in the memory 32 when they perform various predetermined actions. The weight of each attribute of brain activity is given by the weight table T10 for each predetermined action. Therefore, the server 30 uses the brain activity waveform stored in the memory 32 and the weights of the weight table T10 to obtain the basic waveforms (Xa, ..., Xe) for each attribute of the brain activity according to the above equation (1). Is calculated. When there are five types of brain activity attributes, in principle, the basic waveform can be calculated if there are five or more predetermined behavioral brain activity waveforms. That is, the basic waveforms (Xa, ..., Xe) for each attribute of brain activity can be calculated by diagonalizing the matrix based on the weights of the attributes of the brain activity of a plurality of predetermined actions. More specifically, for example, when there is a brain activity waveform (Sapp1 to Sapp5) for a predetermined action (app1 to app5), the weight of the attribute of the brain activity to the predetermined action (app1 to app5) (Ma1 to Me1, ... , Ma5 to Me5), the basic waveform (Xa, ..., Xe) for each attribute of brain activity is expressed by the following equation, for example.

By solving this equation (2), the basic waveforms (Xa, ..., Xe) for each attribute of brain activity can be obtained. By using more brain activity waveforms, more accurate basic waveforms can be calculated. The server 30 may calculate the basic waveform after averaging the brain activity waveform for each predetermined action. The server 30 stores the calculated basic waveforms (Xa, ..., Xe) for each brain activity in the memory 32. Further, when the measurement length (temporal length) differs depending on the brain activity waveform, the influence of the difference in the measurement length can be removed by estimating the instantaneous response function from the deconvolution in the square wave.

In S104, the server 30 calculates the brain activity value for the brain activity waveforms of various users stored in the memory 32 when they perform various predetermined actions. The brain activity value is calculated by the amplitude width of the brain activity waveform (difference between the maximum value and the minimum value), the integrated value of the change amount of the brain activity waveform, the change rate of the brain activity waveform, the repetitive reproducibility of the brain activity waveform, and the like. To. The brain activity value is a value indicating the degree of brain activity, and the larger the brain activity value, the more actively the brain is active. The server 30 can calculate the brain activity value for each user, each predetermined action, and each date and time. The server 30 stores the calculated brain activity value in the memory 32. Further, the server 30 displays (outputs) the brain activity value stored in the memory 32 by the display means of the display unit 35 and / or the output unit 37 or the like. The user can confirm the brain activity value displayed on the display means or the like of the display unit 35 and / or the output unit 37, and recognize the result of the predetermined action or the like. Each user can carry out each action in order to activate the brain activity, for example, with reference to the past brain activity value.

Further, the server 30 may calculate the brain activity value for each attribute of the user. For example, when the server 30 calculates the brain activity value when a man aged 20 years or older performs a predetermined action "action A", the server 30 extracts the brain activity waveform of the predetermined action "action A" and uses the brain activity waveform as the brain activity waveform. From the associated users, a male aged 20 years or older is extracted using the personal information table T20 or the like, and the brain activity value is calculated using the extracted brain activity waveform of the user. This makes it possible to calculate the distribution of brain activity values and brain activity values under specific conditions. The server 30 stores the calculated brain activity value and the distribution of the brain activity value in the memory 32. The server 30 displays the brain activity value and the distribution of the brain activity value stored in the memory 32 by the display means of the display unit 35 and / or the output unit 37. The user who confirmed the displayed brain activity value, for example, is the brain activity value of the user who has a specific disease and the brain activity value of the user who does not have the disease when performing a specific predetermined action. If a statistically significant difference occurs in comparison with the above, it can be used to detect a person having the disease by using the brain activity waveform (brain activity value) when the predetermined action is performed.

Further, the server 30 may calculate the weight of each attribute from the basic waveform for each attribute of the brain activity and the brain activity waveform for the specific predetermined behavior of the specific user based on the equation (1). Good. At this time, for example, the weight of each attribute is calculated by adjusting the weight of each attribute so that the sum of the basic waveform of each attribute multiplied by the weight becomes the brain activity waveform. The server 30 stores the calculated weights of each attribute in the memory 32. The server 30 displays the weight of each attribute stored in the memory 32 by the display means of the display unit 35 and / or the output unit 37. Thereby, by comparing the weight for each attribute of the brain activity set in the predetermined behavior with the weight calculated here, it is possible to confirm which brain activity attribute function is more active. .. The weight of each attribute calculated here may be used as the brain activity value of each attribute.

In addition, the brain activity value of the specific brain activity attribute of the user (group A) having a specific disease and the brain activity value of the specific brain activity attribute of the user (group B) who does not have the specific disease. If there is a statistically significant difference between the two, is it close to the brain activity value of group A using the brain activity value of the attribute of the specific brain activity of an unknown user for the specific disease? By determining whether or not the value is close to the brain activity value, it is possible to determine whether or not the patient may have the disease. In addition, by searching for the attributes of brain activity that cause a significant difference, it is possible to derive the attributes of brain activity for discovering a user having a specific disease. Furthermore, by designing the user to perform a predetermined action having a large weight of the derived brain activity attribute, it becomes easier to find a user having a specific disease by the predetermined action.

Further, the server 30 sets the brain activity value of another person and his / her own brain activity value when a plurality of users perform the same predetermined action on the display means of the display unit 35 and / or the output unit 37. A comparison may be displayed. As a result, the user can grasp, for example, his / her own ranking in the whole. Furthermore, by comparing the past brain activity value of oneself with the current brain activity value of oneself when the same user performs the same predetermined action, the user compared with oneself in the past. You can check the current state of brain activity.

The server 30 has a large difference between the weight of the attribute of the brain activity set for a specific predetermined action (see FIG. 7) and the weight of the attribute of the brain activity calculated from the brain activity waveform when the predetermined action is performed. In that case, the set weight may be changed to the calculated weight. Further, the content of the predetermined action (content of the moving image or content of the game) may be modified so that the calculated weight of the attribute of the brain activity becomes the weight of the set attribute of the brain activity. As a result, it is possible to design a predetermined behavior (creating a moving image, a game, etc.) so as to activate the brain activity state of a specific brain activity attribute.

The server 30 may recalculate the basic waveform of each attribute of brain activity at predetermined intervals or the like. In this case, the server 30 calculates the basic waveform of each attribute of the brain activity by using a statistical method such as outlier removal. It is considered that as the number of samples of the brain activity waveform increases, the basic waveform of each attribute of the brain activity approaches the true value.

Here, the attributes of brain activity are set to 5 types, but the types of attributes of brain activity are not limited to 5 types, and the attributes of brain activity may be further added. When a new brain activity attribute is added, the designer of the existing predetermined behavior sets the weight of the new predetermined behavior attribute so as to reflect the content of the predetermined behavior. Further, when the weight of the attribute of the new brain activity is expressed as a function of the weight of each attribute of the existing brain activity, the weight of the attribute of the new brain activity is determined by using the function.
The configuration of the above embodiment can be implemented by combining them as much as possible.

(Actions and effects of embodiments)
In the measurement system of the present embodiment, the user terminal 20 measures the brain activity state when the user is made to perform a predetermined action by the head wearing device 10. The server 30 acquires the brain activity state transmitted from the plurality of user terminals 20. The server 30 calculates the basic waveform of the brain activity for each attribute of the brain activity based on the measured plurality of brain activity states. In addition, the server 30 calculates and outputs brain activity values for each predetermined action, each user, each user attribute, each brain activity attribute, and the like.

According to the measurement system of the present embodiment, it is possible to calculate the distribution of brain activity values and brain activity values under specific conditions (predetermined behavior, user, user attribute, brain activity attribute, or a combination thereof). it can. The user who measured the brain activity state grasps his / her own ranking as a whole, his / her own ranking under a predetermined condition, etc. by comparing the brain activity value of another person with his / her own brain activity value. be able to.

10 Head mounting device
11 Control unit
13 Wireless communication unit
115 sensor
125 sensor
20 User terminal
21 CPU
22 memory
23 Wireless communication unit
24 Public line communication department
25 Display
26 Operation unit
27 Output section
28 Imaging unit
29 Positioning unit
2A physical sensor unit
30 servers
31 CPU
32 memory
34 Public Line Communication Department
35 Display
36 Operation unit
37 Output section

Claims (9)

A storage unit that stores a detection value that detects blood flow in the head of a user, an identifier of a predetermined action that the user performs when the detection value is detected, and an identifier of the user in association with each other. ,
Based on the detected value stored in the storage unit, a calculation unit that calculates a brain activity value indicating the degree of the brain activity state of the user when the user performs the predetermined action, and a calculation unit.
Bei to give a,
The brain activity has multiple brain activity attributes and
The storage unit stores the weight for each brain activity attribute, which indicates the tendency of the state of brain activity, in association with the identifier of the predetermined action for each predetermined action.
The calculation unit calculates the basic waveform for each brain activity attribute based on the detection value stored in the storage unit and the weight for each brain activity attribute of the predetermined action associated with the detection value. Then, the basic waveform for each brain activity attribute is stored in the storage unit.
Information processing device. The calculation unit calculates the brain activity value for each brain activity attribute based on the detection value stored in the storage unit and the basic waveform for each brain activity attribute.
The information processing device according to claim 1 . An output unit that outputs the brain activity value stored in the storage unit,
The information processing apparatus according to claim 1 or 2 . The computer
The detected value obtained by detecting the blood flow rate in the head of the user, the identifier of the predetermined action performed by the user when the detected value is detected, and the identifier of the user are stored in the storage means in association with each other. ,
Based on the detected value stored in the storage means, a brain activity value indicating the degree of the state of the brain activity of the user when the user performs the predetermined action is calculated .
The brain activity has multiple brain activity attributes and
For each predetermined action, the weight for each brain activity attribute, which indicates the tendency of the state of brain activity, is stored in the storage means in association with the identifier of the predetermined action.
The basic waveform for each brain activity attribute is calculated based on the detected value stored in the storage means and the weight for each brain activity attribute of the predetermined action associated with the detected value, and the storage is performed.
The means stores the basic waveform for each brain activity attribute.
Information processing method to do things. The computer
The brain activity value for each brain activity attribute is calculated based on the detected value stored in the storage means and the basic waveform for each brain activity attribute.
The information processing method according to claim 4 , wherein the information processing method is performed. The computer
Outputs the brain activity value stored in the storage means.
The information processing method according to claim 4 or 5 , wherein the information processing method is performed. The computer
The detected value obtained by detecting the blood flow rate in the head of the user, the identifier of the predetermined action performed by the user when the detected value is detected, and the identifier of the user are stored in the storage means in association with each other. ,
Based on the detected value stored in the storage means, a brain activity value indicating the degree of the state of the brain activity of the user when the user performs the predetermined action is calculated .
The brain activity has multiple brain activity attributes and
For each predetermined action, the weight for each brain activity attribute, which indicates the tendency of the state of brain activity, is stored in the storage means in association with the identifier of the predetermined action.
The basic waveform for each brain activity attribute is calculated based on the detected value stored in the storage means and the weight for each brain activity attribute of the predetermined action associated with the detected value, and the storage means. Stores the basic waveform for each brain activity attribute in
An information processing program to do that. The computer
The brain activity value for each brain activity attribute is calculated based on the detected value stored in the storage means and the basic waveform for each brain activity attribute.
The information processing program according to claim 7 for executing the above. The computer
Outputs the brain activity value stored in the storage means.
The information processing program according to claim 7 or 8 for executing the above.