WO2024034072A1 - 脳活動推定装置、脳活動推定装置を備えた機器および空調装置 - Google Patents

脳活動推定装置、脳活動推定装置を備えた機器および空調装置 Download PDF

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WO2024034072A1
WO2024034072A1 PCT/JP2022/030613 JP2022030613W WO2024034072A1 WO 2024034072 A1 WO2024034072 A1 WO 2024034072A1 JP 2022030613 W JP2022030613 W JP 2022030613W WO 2024034072 A1 WO2024034072 A1 WO 2024034072A1
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Prior art keywords
brain activity
pulse wave
degree
estimation device
analysis
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PCT/JP2022/030613
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English (en)
French (fr)
Japanese (ja)
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怜司 森岡
一雄 吉田
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Carea Corp Co Ltd
Mitsubishi Electric Corp
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Carea Corp Co Ltd
Mitsubishi Electric Corp
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Priority to PCT/JP2022/030613 priority Critical patent/WO2024034072A1/ja
Priority to JP2023565472A priority patent/JPWO2024034072A1/ja
Publication of WO2024034072A1 publication Critical patent/WO2024034072A1/ja
Priority to JP2024109377A priority patent/JP7734799B2/ja
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state

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  • the present disclosure relates to a brain activity estimation device that estimates brain activity of a human body, equipment equipped with the brain activity estimation device, and an air conditioner.
  • the number of blinks of the eyes, the amount of hand or body movement, the degree of opening and closing of the pupils, etc. are determined from moving images or continuously obtained still images acquired by an imaging means, and the degree of concentration of the user is determined based on the determination results.
  • concentration degree estimation device that estimates the degree of concentration (for example, see Patent Document 1). This concentration level estimation device detects that when the concentration level increases, the movements and states of the hands increase, the amount of body movement decreases, the number of blinks decreases, and the pupils tend to dilate.
  • the degree of concentration is estimated based on the assumption that
  • the concentration level estimating device of Patent Document 1 estimates the concentration level based on a pre-assumed premise, such as when the number of blinks decreases, the concentration level increases. However, a decrease in the number of blinks does not necessarily mean that the concentration level has increased, and the concentration level estimation device of Patent Document 1 has room for improvement in improving estimation accuracy.
  • the degree of concentration is considered to be related to the degree of brain activity, but Patent Document 1 does not consider evaluating the degree of brain activity.
  • the degree of brain activity is an indicator related to people's emotions, so if the degree of brain activity can be estimated, it can also be used to estimate people's emotions, and estimating the degree of brain activity will also be useful for future developments. This is important in light of this.
  • the purpose of the present invention is to provide a brain activity estimation device, equipment equipped with the brain activity estimation device, and an air conditioner that can estimate the degree of brain activity with high accuracy. purpose.
  • a brain activity estimation device includes a sensor that detects a pulse wave of a human body, and an analysis unit that analyzes the pulse wave detected by the sensor, and the analysis unit analyzes time-series displacement of the waveform of the pulse wave.
  • An index value that digitizes the pulse wave is generated based on chaos analysis using the pulse wave shape displacement as the analysis source, and the degree of brain activity of the human body is estimated based on the index value.
  • a device includes the above brain activity estimation device and a control device that controls the operation of the device body based on the degree of brain activity estimated by the brain activity estimation device.
  • An air conditioning device includes the above brain activity estimation device, an air conditioning unit that air-conditions an indoor space, and a control device that controls the air conditioning unit based on the degree of brain activity estimated by the brain activity estimation device. It is prepared.
  • a brain activity estimation device performs brain activity estimation based on an index value generated based on a time-series displacement of a pulse wave waveform detected by a sensor. It is possible to estimate the degree of
  • the pulse wave is vital data that is related to the pulsation of the heart and the activity of the nervous system of the brain, and the brain activity estimation device calculates the brain activity based on the index value quantified based on the pulse wave. , the degree of brain activity can be estimated with high accuracy.
  • FIG. 1 is a block diagram showing a configuration of a brain activity estimation device and a usage configuration of the brain activity estimation device according to Embodiment 1.
  • FIG. FIG. 3 is a schematic diagram of the antenna surface of the Doppler sensor according to the first embodiment.
  • FIG. 3 is a schematic diagram of a board component mounting surface of the Doppler sensor according to the first embodiment.
  • FIG. 3 is a diagram showing an example of a pulse wave detected by the Doppler sensor according to the first embodiment.
  • 3 is a diagram showing time-series data of pulse wave shape displacement of the brain activity estimation device according to the first embodiment.
  • FIG. FIG. 3 is a conceptual diagram of an attractor in chaotic analysis of the brain activity estimation device according to the first embodiment.
  • FIG. 2 is a conceptual diagram of Lyapunov indexing in the brain activity estimation device according to the first embodiment.
  • FIG. 3 is a bar graph showing the relationship between the Lyapunov index and various behaviors that involve different brain activities in the central nervous system.
  • FIG. 3 is a diagram showing an example of a change in the Lyapunov index when a human body sequentially performs actions that involve different brain activities.
  • 3 is a flowchart of brain activity estimation processing in the brain activity estimation device according to the first embodiment.
  • FIG. 2 is a block diagram showing a configuration of a brain activity estimation device and a usage configuration of the brain activity estimation device according to a second embodiment. It is a diagram showing Russell's circle of emotions model.
  • FIG. 7 is a diagram illustrating an example of an emotion model of the brain activity estimation device according to Embodiment 2.
  • FIG. 3 is a diagram showing the configuration of an air conditioner according to Embodiment 3.
  • FIG. 3 is a block diagram of an air conditioner according to Embodiment 3.
  • FIG. 1 is a block diagram showing the configuration of the brain activity estimation device 1 and the usage configuration of the brain activity estimation device 1 according to the first embodiment.
  • FIG. 2 is a schematic diagram of the antenna surface of the Doppler sensor 10 according to the first embodiment.
  • FIG. 3 is a schematic diagram of a board component mounting surface of the Doppler sensor 10 according to the first embodiment.
  • the brain activity estimation device 1 is a device that estimates the degree of brain activity in a human body.
  • the brain activity estimation device 1 can objectify the degree of brain activity by quantifying the degree of brain activity in the human body.
  • the brain activity estimation device 1 includes a Doppler sensor 10 and an analysis section 103.
  • the Doppler sensor 10 oscillates a constant sine wave radio wave of approximately 24 GHz called a microwave band or sub-millimeter wave band toward a human body whose brain activity is to be estimated. Blood flow in the human body changes with the pulsation of the heart, and when the distance between the surface of the human body and the Doppler sensor 10 changes, the reflected waves reflected from the surface of the human body change due to the Doppler effect.
  • the Doppler sensor 10 receives reflected waves from the human body according to the movement of blood vessels, and detects the pulse wave of the central nervous system of the human body based on the frequency difference between the reflected waves and the transmitted waves oscillated from the Doppler sensor 10. .
  • a pulse wave is a waveform that indicates changes in the movement of a person's body surface due to heart pulsation, and includes a waveform of changes in blood vessel movement and a waveform of changes in the body surface of the heart region.
  • Blood vessels run throughout the human body, and the Doppler sensor 10 can detect the movement of blood vessels in a part of the human body, such as a part of the head or an arm, even if it is not the heart.
  • a measurement frequency of 60 GHz to 79 GHz is often used due to its high resolution.
  • the purpose here is to detect pulse waves, and it is necessary to analyze minute fluctuations in ultra-low frequency characteristics of about 1 Hz. Therefore, analog detection using the 24 GHz Doppler method is suitable for detecting pulse waves.
  • the Doppler sensor 10 has the advantage of being able to detect the pulse wave of a human body without contact by using radio waves as described above. Therefore, the Doppler sensor 10 can measure a wide range of the human body. Note that the Doppler sensor 10 can measure vital data such as pulse rate, breathing rate, body movement, sleep state, and autonomic nervous balance by analyzing the peak interval of pulse waves. Used for wave detection.
  • the sensor for detecting the pulse wave of the human body is the Doppler sensor 10 here, the sensor is not limited to the Doppler sensor 10.
  • a 24 GHz to 79 GHz FMCW sensor may be used as the sensor for detecting the pulse wave of the human body.
  • the FMCW sensor can measure pulse waves by detecting changes in distance to the target and converting the speed.
  • the sensor that detects the pulse wave of the human body is not limited to a non-contact type sensor, but may be a contact type sensor that performs detection by contacting the human body.
  • Non-contact sensors have the potential to measure pulse waves with higher accuracy than contact sensors.
  • There are many contact-type devices that measure pulse but photoelectric pulse wave sensors are often used in wearable devices.
  • a pulse wave sensor detects changes in the volume of blood vessels that occur as the heart pumps blood as a waveform, and is equipped with a detector that monitors these changes in volume.
  • the pulse wave sensor can obtain the pulse interval by counting the interval between the peaks of the obtained pulse waves. The number of pulses per minute can be calculated by reciprocating the pulse interval. For example, if the pulse interval is 800 ms (0.8 seconds) on average, the pulse rate is 60 ⁇ 0.8, which is 75 beats per minute.
  • Transmissive pulse wave sensors measure pulse waves by emitting infrared or red light from the body surface and measuring changes in blood flow that occur with the pulsation of the heart as changes in light passing through the body. can.
  • transmission-type pulse wave sensor the areas that can be measured are limited to areas where infrared or red light easily passes through, such as fingertips or earlobes.
  • reflective pulse wave sensors emit infrared light, red light, or light with a green wavelength around 550 nanometers toward a living body, and use a photodiode or phototransistor to detect the reflected light inside the living body. measure.
  • Oxygenated hemoglobin exists in arterial blood and has the property of absorbing incident light. Therefore, a reflective pulse wave sensor can measure a pulse wave signal by sensing in time series the blood flow rate (changes in pressure in blood vessels) that changes with the pulsation of the heart.
  • Reflection-type pulse wave sensors measure reflected light, so they have the advantage of eliminating the need to limit the measurement location, unlike transmission-type pulse wave sensors.
  • a green light source is suitable because the absorption rate of hemoglobin in the blood is high and there is little influence from ambient light, so a green LED should be used for the irradiation light. There are many.
  • contact-type measuring instruments have issues such as being cumbersome to wear and not being able to take measurements from a distance, as they must be worn while taking measurements, so non-contact sensors are suitable when incorporated into equipment. .
  • the sensor that detects the pulse wave of the human body is the Doppler sensor 10.
  • the Doppler sensor 10 includes an antenna section 100, a wireless section 101, an analog circuit section 102, and a substrate section 10a.
  • the antenna section 100 is a section that acquires the pulse wave of the human body, which is central nervous activity.
  • the antenna section 100 includes a TX that is an oscillating section and an RX that is a receiving section.
  • the antenna section 100 has a plurality of (here, 12) antennas 100a, as shown in FIG. TX and RX each have six antennas 100a here.
  • the radio unit 101 generates 24 GHz radio waves called RF (Radio Frequency), oscillates the radio waves from TX, and receives reflected waves from RX.
  • the analog circuit section 102 has a circuit section that converts a frequency component that is a Doppler change of a reflected wave. Further, as shown in FIG. 3, the analog circuit section 102 includes an analog amplification filter section (OPAMP) that extracts and amplifies a necessary frequency band, and an analog-digital conversion section (LDO) that enables numerical analysis.
  • OPAMP analog amplification filter section
  • LDO analog-digital conversion section
  • the board section 10a includes a connector section and a memory for outputting information to the analysis section 103 or a device equipped with the brain activity estimation device 1.
  • the radio section 101, analog circuit section 102, and analysis section 103 are covered with a metal shield case, as shown in FIG. In FIG. 3, the shield case portion is indicated by dots.
  • the analysis unit 103 is a part that analyzes the pulse wave detected by the Doppler sensor 10.
  • the analysis unit 103 generates an index value that digitizes the pulse wave based on chaos analysis using the pulse wave shape displacement, which is a time series displacement of the pulse wave shape (hereinafter referred to as pulse wave shape), as the analysis source, Estimating the degree of brain activity in the human body based on index values. Estimation of brain activity by the analysis unit 103 will be explained again.
  • the analysis unit 103 outputs brain activity information indicating the estimation result of brain activity.
  • Brain activity information is information indicating the degree of brain activity.
  • the brain activity information output from the analysis unit 103 is input to a control content determination unit 104 or a cloud unit 106, which will be described later.
  • the analysis section 103 is composed of a microprocessor unit.
  • the analysis unit 103 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like, and the ROM stores a control program and the like.
  • the analysis unit 103 is not limited to a microprocessor unit.
  • the analysis unit 103 may be configured with something that can be updated, such as firmware.
  • the analysis unit 103 may be a program module that is executed by a command from a CPU (not shown) or the like.
  • the analysis unit 103 may be provided outside the Doppler sensor 10 as a separate body from the Doppler sensor 10, or may be provided on a substrate within the Doppler sensor 10 to perform edge processing within one sensor.
  • the degree of brain activity estimated by the analysis unit 103 can be used to control a device equipped with the brain activity estimation device 1.
  • the device equipped with the brain activity estimation device 1 includes, for example, an air conditioner, and the details thereof will be explained in Embodiment 3 below.
  • a device equipped with the brain activity estimation device 1 includes a control content determining section 104 and a device control section 105.
  • the control content determination unit 104 determines the control content of the device based on the brain activity information obtained by the analysis unit 103, generates control data, and outputs it to the device control unit 105.
  • the device control unit 105 controls various actuators of the device based on control data from the control content determining unit 104.
  • the brain activity information output from the analysis unit 103 may be input to the cloud unit 106.
  • the cloud unit 106 accumulates brain activity information input from the analysis unit 103.
  • the analysis unit 103 may further output the vital data itself, which is pulse wave data obtained by the Doppler sensor 10, and store it in the cloud unit 106.
  • the brain activity information and vital data accumulated in the cloud unit 106 can be visualized by displaying them on the display unit 107.
  • the brain activity information and vital data accumulated in the cloud unit 106 are not limited to visualization, but can be provided to the data collection unit 108 from another cloud via the cloud unit 106 and utilized for various purposes. You can also do that.
  • the display unit 107 is a display such as a liquid crystal panel that performs display.
  • the display unit 107 may be a display unit of a smartphone, or may be one that displays and visualizes brain activity information on an application installed on the smartphone.
  • the degree of concentration is estimated based on pre-assumed assumptions, such as when the number of blinks decreases, the degree of concentration increases. .
  • a decrease in the number of blinks does not necessarily mean that the degree of concentration has increased, and there is room for improvement in estimation accuracy with this method.
  • conventionally there is a device that estimates a person's emotions using an electroencephalograph, but it is not easy to quantify alpha and beta brain waves.
  • estimation devices using electroencephalograms require measurement data over a certain period of time for estimation processing, making estimation impossible in real time, requiring the electroencephalogram to be worn on the head, and complicating the system. There were issues with practicality and analysis time.
  • the brain activity estimation device 1 of the first embodiment uses a pulse wave detected non-contact using the Doppler sensor 10 to estimate the degree of brain activity in the central nervous system in a short period of time by chaotic analysis as described below. It can be estimated by
  • Physiological psychology estimates a person's physiological state and psychological state from physiological changes appearing in biological signals.
  • various biological signals such as electroencephalograms, electrocardiograms, heartbeat intervals, blood pressure, and respiratory tip plethysmography have been analyzed using various techniques, and much knowledge has been obtained.
  • most of the analyzes have been based on linear theory.
  • biological signals include nonlinear properties, and these vary due to a nonlinear property called chaos.
  • Chaos refers to a phenomenon in which although the system's state transition rules are deterministic, the nonlinearity of the system itself creates complexity equivalent to that of a stochastic system.
  • the brain activity estimation device 1 targets chaotic brain activity in the central nervous system. Until now, there has been no research that targets chaotic brain activity in the central nervous system.
  • the brain activity estimation device 1 performs chaotic analysis using pulse wave shape fluctuations, focusing on the movement of the waveform itself, as an analysis source.
  • the pulse wave shape fluctuation is expressed by time series data of displacement of the pulse wave shape, in other words, time series displacement data of the pulse wave shape.
  • the brain activity estimation device 1 generates an index value that digitizes a pulse wave based on chaos analysis based on a pulse wave shape displacement, which is a time-series displacement of the pulse wave waveform shape, and calculates the human body based on the index value. Estimate the degree of brain activity.
  • the first step is a step of calculating a vector specified from time-series data of pulse wave shape displacement and a preset delay time.
  • the second step is to generate an attractor in which vectors are arranged in chronological order in a three-dimensional state space.
  • the third step is to calculate the Lyapunov index, which is an index value, based on the trajectory of the attractor.
  • the pulsation interval of the heart varies from beat to beat, but this variation originates in the brain and is transmitted to the heart through the autonomic nerves. Therefore, in estimating the degree of brain activity, the present inventors considered that not only the autonomic nervous system but also the central nervous system is related to heart pulsation, and investigated the correlation between heart pulsation and brain activity. By discovering this, he came up with the idea of estimating the degree of brain activity in the central nervous system.
  • a change in pulse rate is a time variation in the interval between pulse wave peaks, and is detected using only pulse wave peak information in a pinpoint manner.
  • the time variation in the interval between the peaks of pulse waves may be referred to as one-dimensional pattern pulse displacement or pulse fluctuation.
  • the height of the pulse wave has no relation to the time variation of the interval between the peaks of the pulse wave, and vital data is obtained by converting the frequency only from the displacement of the pulse interval, so it is expressed as one-dimensional.
  • the present inventors believed that complex neural activity information of a living body cannot be determined solely by changes in pulse rate. Then, the present inventors searched for a method different from pinpoint detection using only pulse wave peak information, and came to focus on the displacement of the pulse wave waveform. However, when comparing the complexity of the peak interval fluctuation and the fluctuation of the two-dimensional waveform pattern from waveform to waveform of the pulse wave itself, we find that the fluctuation of the two-dimensional waveform pattern (that is, the fluctuation of the pulse wave shape) The complexity is incomparably greater for one-dimensional pulse interval fluctuations. This is thought to be because pulse wave shape fluctuations are related to the activity of neurons in the six layers of the cerebral cortex. For this reason, the present inventors decided to use chaos as a method for analyzing brain activity instead of conventional analysis methods.
  • the analysis unit 103 obtains a pulse wave from the Doppler sensor 10.
  • FIG. 4 shows an example of a pulse wave detected by the Doppler sensor 10.
  • FIG. 4 is a diagram showing an example of a pulse wave detected by the Doppler sensor 10 according to the first embodiment.
  • the horizontal axis represents time
  • the vertical axis represents analog pulse wave height.
  • pulse wave height means power. Pulse wave height does not refer to the power of only the peak portion where the pulse wave power is highest, but refers to the power for each time in a time series including the peak portion of the pulse wave.
  • the Doppler sensor 10 obtains the analog waveform shown in FIG. 4 and outputs it to the analysis section 103.
  • the pulse wave height decreases as the distance between the Doppler sensor 10 and the human body to be measured increases.
  • the absolute value of the pulse wave height is not necessary information when quantifying the pulse wave.
  • the larger the pulse wave height the clearer the pulse wave shape, and the higher the accuracy of attractor formation, which will be described later. Therefore, it is preferable that the distance from the Doppler sensor 10 to the human body is short.
  • the analysis unit 103 wants to obtain the pulse wave height necessary to ensure accuracy when analyzing the time series data of the pulse wave shape, it may do the following.
  • the analysis unit 103 calculates the deviation of the pulse wave height, and when the deviation is smaller than a preset threshold value, automatically adjusts the input signal amplification factor to pseudo-enlarge the analog pulse wave waveform. By doing so, the shape of the pulse wave becomes clear, and the analysis unit 103 can perform highly accurate analysis even if the distance from the Doppler sensor 10 to the human body is long.
  • the input signal magnification may be 1x.
  • the analysis unit 103 may decide the magnification according to the smallness of the deviation. Specifically, for example, the analysis unit 103 sets the input signal magnification to 2 when the deviation is smaller than the first threshold, and sets the input signal magnification to 2 when the deviation is smaller than the second threshold which is smaller than the first threshold. For example, the signal magnification may be set to 3 times.
  • the analysis unit 103 automatically increases the input signal amplification factor and increases the analog waveform as the distance from the Doppler sensor 10 to the human body increases. This allows the analysis unit 103 to analyze pulse wave shape fluctuations (time-series displacement data of pulse wave shape) even if the distance from the Doppler sensor 10 to the human body is long.
  • the analysis unit 103 automatically increases the input signal amplification factor and increases the analog waveform as the area of the human body becomes smaller and as the deviation in pulse wave height decreases. Thereby, the analysis unit 103 can solve problems that are difficult to analyze when the pulse wave level is small.
  • the analysis unit 103 generates an attractor, which will be described later, from the pulse wave shape.
  • the pulse wave shape is a two-dimensional waveform pattern of a pulse wave. Although it is difficult to find rules in time-series data of pulse wave shape from pulse wave waveform to waveform, when time-series data of pulse wave shape is converted into attractors, it is found that certain patterns exist. There is.
  • An attractor is a set toward which a certain dynamical system evolves over time. When a dynamical system moves from a point close enough to an attractor, the system remains close enough to that attractor. There is no restriction on the trajectory contained in an attractor other than to remain within that attractor.
  • the first embodiment is characterized in that brain activity in the central nervous system is targeted for chaos, and pulse wave shape displacement, which is a time-series displacement of the waveform shape of a pulse wave, is used as the analysis source for chaos analysis.
  • the analysis method itself uses a conventionally known method. Therefore, in the following explanation of chaos analysis, an overview will be provided.
  • the first step is a step of calculating a vector specified from the time series data of pulse wave shape displacement and a preset delay time, as described above.
  • FIG. 5 is a diagram showing time-series data of pulse wave shape displacement of the brain activity estimation device 1 according to the first embodiment.
  • the horizontal axis represents time
  • the vertical axis represents analog pulse wave height.
  • x(0) is the initial value of sensor data obtained during the calculation window length.
  • the calculation window length is an arbitrarily set time length, for example, 20 seconds. The shorter the calculation window length, the faster the calculation, but the longer the calculation window length, the more shape data of the pulse wave shape is available and the higher the accuracy.
  • the analysis unit 103 calculates an appropriate delay time in order to embed the two-dimensional time series change into the d-dimensional state space, in other words, to draw a trajectory in the d-dimensional state space.
  • d is 3 and two-dimensional time-series changes are embedded in a three-dimensional state space, the analysis unit 103 creates a vector with three state variables.
  • ⁇ is a parameter called embedding delay time.
  • the second step is to generate attractors in which vectors are arranged in chronological order in a three-dimensional state space.
  • FIG. 6 is a conceptual diagram of an attractor in the chaotic analysis of the brain activity estimation device 1 according to the first embodiment.
  • a trajectory is obtained.
  • FIG. 6 shows a three-dimensional state space with three coordinate axes: x(i), x(i+ ⁇ ), and x(i+2 ⁇ ).
  • the third step is to calculate the Lyapunov index, which is an index value, based on the trajectory of the attractor.
  • the Lyapunov index is determined by evaluating the instability or divergence of the attractor's trajectory.
  • FIG. 7 is a conceptual diagram of Lyapunov indexing in the brain activity estimation device 1 according to the first embodiment.
  • the Lyapunov index is used for this numerical index value. Attractors in three-dimensional state space have orbital instability. Instability can also be referred to as divergence.
  • the Lyapunov index quantifies this orbital instability.
  • the Lyapunov exponent is a measure of how far two trajectories depart from two points close to each other. In other words, the Lyapunov exponent is a value that represents the degree to which orbits that are very close to each other in a dynamical system are moving apart.
  • the larger the Lyapunov index the larger the fluctuation range of the attractor, and the larger the fluctuation range.
  • Lyapunov spectrum A set of these Lyapunov indices is called a Lyapunov spectrum.
  • the enlargement ratio is calculated by repeating this work of stretching the sphere every slide time S.
  • the entire Lyapunov spectrum is calculated by taking the sum and averaging them.
  • the largest Lyapunov index among the calculated Lyapunov indexes is called the maximum Lyapunov index.
  • the degree to which the orbit deviates from the initial value of the system can be determined by looking at the maximum Lyapunov exponent.
  • the Lyapunov index indicates how far the nth state is from the initial state, and in terms of brain activity, the farther from the initial state, the more active the person's brain is. , it can be seen that the brain is not functioning as much as it was in its initial state.
  • the accuracy decreases, but instead of calculating multiple times for each slide time S, the Lyapunov exponent may be calculated by comparing two states using the slide time as one section.
  • the Lyapunov index is calculated as follows. For example, assume that the radius of the sphere is 0.08, the calculation window length is 20 seconds, the slide time is 1 second, and the measurement frequency is 200 Hz. Since the measurement frequency is 200 Hz, the number of data obtained per second is 200, and the number of data obtained in the calculation window length from the start of calculation is 4000.
  • the Lyapunov exponent when the number of dimensions of the state space is three, a Lyapunov spectrum consisting of three dimensions of ⁇ 1, ⁇ 2, and ⁇ 3 is obtained using 4000 pieces of data in 20 seconds from the start of measurement. Then, in the next one second, a Lyapunov spectrum consisting of ⁇ 1, ⁇ 2, and ⁇ 3 is similarly obtained.
  • the output window length is set to 60 seconds, the above operation is repeated for 60 seconds. That is, the first Lyapunov spectrum is obtained in the first 20 seconds, and a Lyapunov spectrum is obtained every second of the slide time in the subsequent 40 seconds, resulting in a total of 40 Lyapunov spectra. Then, the sum of the 40 Lyapunov spectra for each of ⁇ 1, ⁇ 2, and ⁇ 3 is taken and averaged, and the averaged ⁇ 1, ⁇ 2, and ⁇ 3 are determined. The largest Lyapunov exponent among these is the maximum Lyapunov exponent, and this maximum Lyapunov exponent is one piece of data obtained from the total output. In other words, the maximum Lyapunov exponent is taken as the degree to which the system deviates from its initial value.
  • the analysis unit 103 creates an attractor trajectory using 12,000 pieces of data and calculates the Lyapunov index.
  • the output window length is the time required to output one piece of data as described above, and is the time required to calculate one Lyapunov index.
  • the analysis unit 103 creates an attractor trajectory using 30,000 pieces of data and calculates the Lyapunov index.
  • the measurement frequency is preferably about 100Hz to 1000Hz.
  • the output window length is preferably 30 seconds or more and 5 minutes or less. Since the reaction time of the central nervous system is fast, the output window length is preferably shorter than when measuring the autonomic nervous system, and is preferably 30 seconds or more and 60 seconds or less.
  • the analysis unit 103 may perform the following procedure to improve accuracy.
  • the analysis unit 103 may collect and average the output data for each set time. For example, assuming that the output window length is 30 seconds and the set time is 5 seconds, the analysis unit 103 collects one piece of data output based on vital data for 30 seconds before the timing every 5 seconds. When the analysis unit 103 has collected 30 data, it may average the 30 data to obtain one piece of data. In this case, the analysis unit 103 can obtain one piece of highly accurate data (Lyapunov index) after taking 3 minutes to quantify.
  • FIGS. 8 and 9 The following Figures 8 and 9 were created based on the results of an experiment in which the human body was made to perform various actions with different brain activities, and the Lyapunov index was calculated based on the pulse waves obtained from the human body performing the actions. It is a diagram.
  • FIG. 8 is a bar graph showing the relationship between the Lyapunov index and various behaviors that involve different brain activities in the central nervous system.
  • the numerical value on the vertical axis is a value normalized with the case of rest being 1.
  • the value on the vertical axis for any other action than resting is the value obtained by dividing the Lyapunov index for that action by the Lyapunov index for resting.
  • the horizontal axis shows human behavior with different degrees of brain activity.
  • the following four actions were adopted for the actions on the horizontal axis.
  • the four actions are resting, reading articles, typing emails, and copying materials by typing. Reading an article refers to the act of reading materials on a computer or smartphone, for example.
  • copying materials by typing refers to the act of inputting characters written in materials into a computer by typing.
  • Figure 8 shows that compared to light tasks such as resting and reading articles, heavy tasks such as typing e-mails and copying materials by typing, which are said to have a high level of brain activity even by subjective report, are more effective on the vertical axis. It was confirmed that the numbers were large. In other words, it was confirmed that the higher the level of brain activity, the higher the Lyapunov index. Therefore, it was confirmed that there is a relationship between the degree of brain activity in the central nervous system and the Lyapunov index.
  • the degree of concentration is low when resting, and the degree of concentration is high when typing calculations. It has been confirmed that this is also correlated with the subjective evaluation results. Therefore, there is a correlation between behavior, the Lyapunov index, and the degree of concentration, and the Lyapunov index can also be treated as a mental index of the degree of concentration. Specifically, when the Lyapunov index is large, it can be evaluated that the degree of concentration is high, and when the Lyapunov index is small, it can be evaluated that the degree of concentration is low. Therefore, the vertical axis in FIG. 8 can also be seen as an index of concentration.
  • the numerical value on the vertical axis may be regarded as a concentration index indicating the degree of concentration, for example.
  • FIG. 9 is a diagram showing an example of changes in the Lyapunov index when the human body sequentially performs actions with different brain activities.
  • the vertical axis shows the elapsed time (minutes) of the action
  • the horizontal axis shows the Lyapunov index at the time of the action.
  • FIG. 9 shows the change in the Lyapunov index when the behavior changes from a resting state before work, through a calculation typing work state, and then to a calculation typing work stoppage. It was confirmed that the Lyapunov index, which was small in the resting state, increased during calculation typing tasks and decreased when the tasks were stopped. This shows that there is a relationship between the Lyapunov index and the degree of brain activity. In other words, when the Lyapunov index is large, the degree of brain activity is high, and when the Lyapunov index is small, the degree of brain activity is low.
  • the brain activity estimation device 1 estimates the brain activity of the central nervous system based on the Lyapunov index. Specifically, the analysis unit 103 estimates that the degree of brain activity is high if the Lyapunov index is large, and estimates that the degree of brain activity is low if the Lyapunov index is small.
  • the Lyapunov index on the vertical axis in FIG. 9 is a value obtained by calculation using the brain activity estimation device 1 temporarily as an experimental device. Therefore, from FIG. 9, it can be seen that the Lyapunov index is obtained by the brain activity estimation device 1 at one-minute intervals.
  • FIG. 9 proves that the process of calculating the Lyapunov index from the pulse wave can be performed in one minute, which is generally a short time.
  • FIG. 9 shows the experimental results obtained using the brain activity estimation device 1
  • FIG. 9 merely shows the experimental results of measuring the correlation between the Lyapunov index and behavior. It may be created using a device exclusively for experiments instead of the brain activity estimation device 1.
  • FIG. 10 is a flowchart of brain activity estimation processing in the brain activity estimation device 1 according to the first embodiment.
  • the brain activity estimation device 1 includes a step of acquiring pulse wave shape data (step S1), and a step of performing chaotic analysis based on the brain wave shape data (step S2).
  • the step of performing chaos analysis includes three steps (steps S21 to S23).
  • the first step is to calculate a vector specified from time-series data of pulse wave shape displacement and a preset delay time (step S21).
  • the second step is to generate attractors in which vectors are arranged in chronological order in a three-dimensional state space (step S22).
  • the third step is a step of calculating a Lyapunov index, which is an index value, based on the trajectory of the attractor (step S23).
  • the analysis unit 103 estimates the degree of brain activity based on the Lyapunov index (step S3), and outputs brain activity information indicating the estimation result.
  • the analysis unit 103 converts, for example, the Lyapunov index into a graded numerical value (for example, 1 to 10) indicating the degree of brain activity and outputs the converted value.
  • the stage numerical values are, for example, numerical values indicating that the degree of brain activity is in a state from low to high in order from the smallest to the largest.
  • the analysis unit 103 stores, for example, the Lyapunov index at rest, and calculates the value of the 100th percentile (%) of the Lyapunov index obtained by analysis by the analysis unit 103 with respect to the Lyapunov index at rest.
  • the analysis unit 103 is not limited to outputting the degree of brain activity as a numerical value as described above, but may output it as a graded image, for example.
  • a graded image refers to, for example, an image in which facial expressions differ in stages depending on the degree of brain activity.
  • the brain activity information output from the analysis unit 103 is input to the display unit 107 via the cloud unit 106 and displayed.
  • the brain activity estimation device 1 outputs brain activity information from the analysis unit 103 and visualizes it, so the user can grasp the degree of brain activity.
  • the brain activity information output from the analysis unit 103 may be accumulated in the data collection unit 108 via the cloud unit 106. Further, the brain activity information output from the analysis unit 103 may be directly input to the display unit 107 and displayed without going through the cloud unit 106. Further, the brain activity information output from the analysis unit 103 may be input to the control content determining unit 104 and used to determine the control content of the device including the brain activity estimation device 1.
  • the analysis unit 103 may output concentration degree information indicating the degree of concentration estimated based on the Lyapunov index as brain activity information.
  • the concentration level information may be, for example, the above-mentioned concentration index, or may be a numerical value of 100 percent (%) or a graded image similar to the degree of brain activity. Note that the concentration degree information is information indicating that the larger the Lyapunov index is, the higher the concentration degree is, and the smaller the Lyapunov index is, the lower the concentration degree is. Further, the concentration level information may be information indicating the current concentration level, the chronological concentration level, or both.
  • the brain activity estimation device 1 outputs from the analysis unit 103 that the larger the Lyapunov index is, the higher the degree of concentration is.
  • the analysis unit 103 may output sleepiness degree information instead of the concentration degree information. This allows the user to visually check the sleepiness level.
  • the brain activity estimation device 1 of the first embodiment includes the Doppler sensor 10, which is a sensor that detects the pulse wave of a human body, the analysis unit 103 that analyzes the pulse wave detected by the Doppler sensor 10, Equipped with.
  • the analysis unit 103 generates an index value that digitizes the pulse wave based on chaos analysis using pulse wave shape displacement, which is a time-series displacement of the pulse wave waveform shape, and calculates the human brain based on the index value. Estimate the level of activity.
  • the brain activity estimation device 1 can estimate the degree of brain activity based on the index value generated based on the time-series displacement of the waveform shape of the pulse wave. Furthermore, the pulse wave is vital data that is related to the pulsation of the heart and, in turn, to the activity of the nervous system in the brain. Since the brain activity estimation device 1 estimates brain activity based on index values calculated based on such pulse waves, it can estimate the degree of brain activity with higher accuracy than conventional estimation methods using eye blinks, etc. can.
  • the brain activity estimation device 1 uses a Doppler sensor 10 as a sensor that detects the pulse wave of the human body without contact, and can detect the pulse wave of the human body without contact, and can estimate the degree of brain activity without contact. I can do it. Furthermore, the brain activity estimation device 1 does not use an electroencephalograph. Therefore, the brain activity estimation device 1 does not require the effort of wearing an electroencephalograph, and since it does not use an electroencephalograph, analysis does not take much time and can be performed in a short time.
  • the brain activity estimation device 1 uses the Doppler sensor 10, so it is easy to use because it can perform measurements even at a long distance, and it does not measure personal information such as pupils, so it is more privacy-friendly. can.
  • Chaos analysis consists of a step of calculating a vector specified from time-series data of pulse wave shape displacement and a preset delay time, and a step of generating an attractor in which the vectors are arranged in chronological order in a three-dimensional state space. , and calculating the Lyapunov index, which is an index value, based on the trajectory of the attractor.
  • the brain activity estimation device 1 can calculate the Lyapunov index, which is an index value, by chaos analysis.
  • the analysis unit 103 outputs concentration level information indicating the concentration level estimated based on the index value as brain activity information indicating the estimation result of the degree of brain activity. Furthermore, the analysis unit 103 outputs concentration degree information indicating that the larger the index value is, the higher the concentration degree is, and the smaller the index value is, the lower the concentration degree is.
  • the analysis unit 103 calculates the deviation of the pulse wave height based on the pulse wave shape, and if the deviation is smaller than a preset threshold value, automatically adjusts the input signal amplification factor and adjusts the pulse wave height acquired by the Doppler sensor 10. Increase the waveform.
  • the brain activity estimation device 1 can clarify the shape of the pulse wave, and can improve the accuracy of analysis even if the distance from the Doppler sensor 10 to the human body is long.
  • the brain activity estimation device 1 of the first embodiment analyzes fluctuations in the shape of a pulse wave to estimate the degree of brain activity in the central nervous system.
  • the brain activity estimation device 1 of the second embodiment estimates the degree of activity of the autonomic nervous system based on the pulse interval fluctuation in addition to the degree of brain activity. That is, the brain activity estimation device 1 of the second embodiment estimates both central nervous activity and autonomic nervous activity.
  • the differences between the second embodiment and the first embodiment will be mainly explained, and the configurations not explained in the second embodiment are the same as those in the first embodiment.
  • FIG. 11 is a block diagram showing the configuration of the brain activity estimation device 1 and the usage configuration of the brain activity estimation device 1 according to the second embodiment.
  • the Doppler sensor 10 detects not only the pulse wave of the human body but also the pulse of the human body.
  • the pulse interval is commonly referred to as the RR Interval (RRI).
  • RRI RR Interval
  • the RRI is frequency-converted and converted into various information such as autonomic nervous balance, which will be described later.
  • Vital analysis that analyzes pulse, blood pressure, respiration, etc. differs from body motion analysis and the like in that it analyzes minute fluctuations in ultra-low frequency characteristics of about 1 Hz. For this reason, in vital analysis, analog detection using the Doppler method at 24 GHz is preferable to 60 GHz to 79 GHz, which has high resolution and is often used for distance measurement applications.
  • the Doppler sensor 10 detects the autonomic nervous balance from the displacement of the pulse interval of the human body (one-dimensional pattern pulse displacement).
  • Autonomic nervous balance is the balance between sympathetic and parasympathetic nerves.
  • Autonomic nerve balance is the ratio between LF (Low Frequency) and HF (High Frequency), and is calculated as LF/HF.
  • LF indicates sympathetic nerve activity
  • HF indicates parasympathetic nerve activity.
  • Sympathetic nerves are said to be dominant during the day or in an active state, and parasympathetic nerves are said to be dominant at night or in a sedated state.
  • LF is determined by the integrated value of power in a low frequency band, such as 0.05 Hz to 0.15 Hz, in the characteristic curve.
  • HF is determined by the integrated value of power in a high frequency band such as 0.15 Hz to 0.40 Hz in the characteristic curve.
  • the characteristic curve is a curve obtained by frequency-expanding time series intervals of pulse intervals, and is a curve drawn on a coordinate axis in which the horizontal axis is frequency and the vertical axis is power.
  • autonomic nervous balance is LF/HF
  • LF when LF becomes relatively large, it can be assumed that sympathetic nerves are dominant and in an excited or activated state, and when LF is relatively small, parasympathetic nerves are dominant. It can be assumed that the patient is in a relaxed state.
  • autonomic nerve balance value is large, it can be assumed that the person is in an excited state, and if the autonomic nerve balance value is small, it can be estimated that the person is in a relaxed state. Therefore, autonomic nervous balance is used as an index indicating the degree of activity of the autonomic nervous system.
  • the degree of activity of the autonomic nervous system When the human body is in an excited or activated state, the degree of activity of the autonomic nervous system is large, and the value of autonomic nervous balance becomes large.
  • the degree of activity of the autonomic nervous system is small, and the value of autonomic nervous balance is small.
  • the Doppler sensor 10 can detect the degree of activity of the autonomic nervous system, and as described above, can also detect the degree of brain activity of the central nervous system based on pulse waves. In other words, the Doppler sensor 10 can detect autonomic nerve activity and central nerve activity by itself.
  • the sensor used in the brain activity estimation device 1 of the second embodiment is not limited to the Doppler sensor 10, as in the first embodiment, and may be any sensor that can measure pulse waves and pulses.
  • the pulse that can be measured by the sensor refers to the pulse rate or pulse movement, and includes the magnitude of the pulse rate or the time-series increase or decrease in the pulse rate, the magnitude of the pulse interval or the time-series increase or decrease in the pulse interval, and the magnitude of the LF. Or it includes a time-series increase/decrease in LF, a magnitude of LF/HF, or a time-series increase/decrease in LF/HF.
  • the analysis unit 103 estimates a person's emotions by applying both the degree of brain activity and the degree of autonomic nervous system activity to an emotion model created in advance.
  • a commonly used emotional model is a psychological model called Russell's circle of emotions model, shown in Figure 12 below.
  • FIG. 12 is a diagram showing Russell's circle of emotions model.
  • This emotional circular model arranges emotions such as happy, peaceful, bored, and nervous on a circular plane on a biaxial plane with Activation (arousal level) on the vertical axis and pleasant (comfort level) on the horizontal axis. This is what I did.
  • the vertical axis is set between drowsiness and wakefulness, and the horizontal axis is set between discomfort and pleasure.
  • the "alertness level on the vertical axis” can conventionally be estimated from images acquired by an imaging means or brain waves acquired by an electroencephalograph. Furthermore, there has conventionally been a technique in which TP (Total Power, unit [ms 2 ]) calculated from autonomic nerves is used as the alertness level. TP is the sum of the LF and HF powers obtained from the one-dimensional pattern pulse displacement. Furthermore, in a certain literature, the "alertness level on the vertical axis" is expressed as entropy, and in this literature, the "alertness level on the vertical axis" is calculated based on LF and HF. With these conventional methods, it has not been possible to estimate the alertness level with high precision without contact.
  • the original data for estimating the alertness level is the pulse interval in both cases of TP and entropy.
  • the degree of arousal on the vertical axis of emotion models conventionally used to estimate emotions can be determined by using the results estimated from data obtained using imaging means or an electroencephalogram, or by using an autonomous method.
  • TP or entropy calculated from nerves is used.
  • a method using an imaging device or an electroencephalograph cannot estimate an index indicating the vertical axis of an emotional model with high precision in a short time.
  • the vertical axis of the emotion model used to estimate emotions is the brain activity of the central nervous system estimated based on chaos analysis using pulse wave shape displacement as the analysis source.
  • the brain activity estimating device 1 uses the degree of brain activity in the central nervous system estimated based on pulse waves as an index set on the vertical axis of the emotion model, and is significantly different from the conventional method in this point.
  • the brain activity estimation device 1 can estimate the degree of autonomic nerve activity as described above. For this reason, in the second embodiment, a unique emotion model is created in advance with the vertical axis as "degree of brain activity of the central nervous system" and the horizontal axis as "degree of autonomic nerve activity", and the brain activity estimation device 1 performs emotion estimation based on this emotion model.
  • FIG. 13 is a diagram showing an example of an emotion model of the brain activity estimation device 1 according to the second embodiment.
  • the vertical axis is the degree of brain activity obtained from pulse wave shape fluctuation, which is central nervous activity.
  • the horizontal axis is pulse fluctuation, which is autonomic nervous system activity.
  • the vertical axis is the degree of brain activity in the central nervous system estimated based on the shape displacement (fluctuation) of the two-dimensional pulse wave pattern.
  • the horizontal axis is the degree of activity of the autonomic nervous system estimated based on the one-dimensional pattern pulse displacement (fluctuation).
  • the emotion model shows the relationship between the degree of brain activity and the degree of autonomic nervous system activity, and emotions.
  • the emotion is an emotion caused by, for example, concentration level, sleepiness level, fatigue level, activity level, relaxation level, and the like.
  • the Lyapunov exponent can take a value in the range of 0 to 15, as an example. Therefore, in the emotion model of FIG. 13, for example, "sleepiness-concentration" on the vertical axis is assigned to 0-15 of the Lyapunov index.
  • a Lyapunov index of 0 indicates ⁇ drowsiness
  • a Lyapunov index of 15 indicates a state of ⁇ concentration
  • a Lyapunov index of 7 indicates the position where the vertical axis intersects the horizontal axis. That's how it is.
  • the assignment of the Lyapunov index to the vertical axis described here is just one example, and the assignment may be made using a numerical value of 100 percent of the Lyapunov index.
  • emotions such as happiness or joy are assigned when the person is "high in concentration” and "high in relaxation.” Furthermore, when the level of concentration is high and the level of excitement or active emotion is high, emotions such as nervousness or excitement are assigned. Furthermore, when the level of concentration is low and the level of relaxation is high, feelings such as relaxing, calming, or calm are assigned. Emotions such as depression or boredom are assigned when the level of concentration is low and the level of excitement or activation is high.
  • the horizontal axis may be the level of comfort, similar to Russell's emotional circle model, and may also be an axis of "pleasure-displeasure.”
  • the brain activity estimation device 1 estimates emotions using the above emotion model.
  • the operation of the brain activity estimation device 1 according to the second embodiment will be explained below.
  • the brain activity estimation device 1 detects the pulse wave and pulse of the human body using the Doppler sensor 10.
  • the analysis unit 103 analyzes the pulse wave detected by the Doppler sensor 10 to estimate the degree of brain activity in the central nervous system, and also estimates the degree of activity in the autonomic nervous system from the pulse.
  • the analysis unit 103 estimates emotions based on the degree of brain activity, the degree of autonomic nervous system activity, and the emotion model. Specifically, the analysis unit 103 identifies the position of the vertical axis in the emotional model from the Lyapunov index, which indicates the degree of brain activity, and determines the position of the horizontal axis in the emotional model from the autonomic nervous balance, which indicates the degree of activity of the autonomic nervous system. Identify.
  • the analysis unit 103 estimates the emotion by applying the specified vertical axis position and horizontal axis position to the emotion model, and outputs emotion information indicating the estimation result as the estimation result.
  • the emotional information may be text such as joy, calmness, depression, or tension, or may be a facial image that can identify the emotion.
  • the "comfort level on the horizontal axis” can be specified by the degree of activity of the autonomic nervous system based on pulse fluctuation. Specifically, the degree of activity of the autonomic nervous system can be determined by the autonomic nervous balance obtained by the Doppler sensor 10 as described above. In other words, the "comfort level on the horizontal axis" can be determined based on vital data detected from the displacement of the pulse interval of the human body.
  • the "horizontal axis comfort level” is not limited to being specified by autonomic nerve balance, but may be specified by other data.
  • “Comfort level on the horizontal axis” can also be determined by any of the following, for example, “changes or deviations in pulse interval,” “changes or deviations in pulse rate,” or “changes in the above-mentioned increase/decrease or balance of LF of the autonomic nervous system.” can also be identified.
  • the source data for specifying the position of the "comfort level on the horizontal axis” is the pulse rate. In other words, the "comfort level on the horizontal axis" is determined by the pulse rate.
  • the emotional information output from the analysis unit 103 is input to the display unit 107 via the cloud unit 106 and displayed.
  • the brain activity estimation device 1 outputs the emotional information from the analysis unit 103 and visualizes it, so that the user can understand the emotion.
  • the emotional information output from the analysis section 103 may be accumulated in the data collection section 108 via the cloud section 106.
  • the emotional information output from the analysis unit 103 may be directly input to the display unit 107 and displayed without going through the cloud unit 106.
  • the emotional information output from the analysis unit 103 may be input to the control content determination unit 104 and used to determine the control content of the equipment of the brain activity estimation device 1.
  • the human body when the sympathetic nerve of LF is high or the ratio of autonomic nerve balance expressed as LF/HF is high, the human body is in a good state for work and study. Furthermore, when the human body is "high in concentration” and “excitement or active emotion is high,” the human body is in a state of rapidity.
  • the analysis unit 103 indicates that the work efficiency of the human body is high when the degree of brain activity is higher than a first preset threshold and the degree of autonomic nervous system activity is higher than a second preset threshold. Work efficiency information may also be output. In this way, in addition to outputting emotional information based on the emotional model, the analysis unit 103 may output other information specified from the degree of brain activity and the degree of activity of the autonomic nervous system.
  • the brain activity estimation device 1 of the second embodiment can obtain the same effects as the first embodiment, and can also estimate the degree of activity of the autonomic nervous system by detecting the pulse using the Doppler sensor 10. . Therefore, the brain activity estimation device 1 estimates the degree of activity of the autonomic nervous system in addition to the degree of brain activity of the central nervous system, and combines the degree of brain activity of the central nervous system, the degree of activity of the autonomic nervous system, and the emotional model. Emotions can be estimated from .
  • the brain activity estimation device 1 since central nervous activity and autonomic nervous activity can be measured with a single Doppler sensor 10, the brain activity estimation device 1 does not need to be worn and can be used for short periods of time and over long distances, compared to devices that use an electroencephalograph or imaging means. Emotion estimation can be performed.
  • Embodiment 3 relates to a device including the brain activity estimation device 1 described in Embodiment 1 or Embodiment 2, and in particular, an air conditioner will be described here.
  • FIG. 14 is a diagram showing the configuration of an air conditioner 201 according to the third embodiment.
  • the air conditioner 201 is equipment that air-conditions an indoor space 271 that is an air-conditioned space.
  • Air conditioning refers to adjusting the temperature, humidity, cleanliness, airflow, etc. of air in an air-conditioned space, and specifically includes heating, cooling, dehumidification, humidification, and air cleaning.
  • an air conditioner 201 is installed in a house 203.
  • the air conditioner 201 is a heat pump type air conditioner that uses, for example, HFC (hydrofluorocarbon) as a refrigerant.
  • the air conditioner 201 is equipped with a vapor compression type refrigerant circuit, and operates by obtaining electric power from a commercial power source, power generation equipment, power storage equipment, etc. (not shown).
  • the air conditioner 201 includes an outdoor unit 211 provided outside the house 203, an indoor unit 213 provided inside the house 203, and a remote controller 255 operated by the user.
  • the outdoor unit 211 and the indoor unit 213 are connected via a refrigerant pipe 261 through which refrigerant flows and a communication line 263 through which various signals are transferred.
  • the air conditioner 201 cools the indoor space 271 by blowing out conditioned air, for example, cold air, from the indoor unit 213, and heats the indoor space 271 by blowing out warm air.
  • the outdoor unit 211 includes a compressor 221, a four-way valve 222, an outdoor heat exchanger 223, an expansion valve 224, an outdoor blower 231, and an outdoor unit control section 251.
  • the indoor unit 213 includes an indoor heat exchanger 225, an indoor blower 233, and an indoor unit control section 252.
  • the refrigerant pipe 261 connects the compressor 221, the four-way valve 222, the outdoor heat exchanger 223, the expansion valve 224, and the indoor heat exchanger 225 in an annular manner.
  • the air conditioner 201 has a refrigerant circuit configured by connecting a compressor 221 , a four-way valve 222 , an outdoor heat exchanger 223 , an expansion valve 224 , and an indoor heat exchanger 225 through a refrigerant pipe 261 .
  • the refrigerant circuit circulates refrigerant to operate a refrigeration cycle.
  • the compressor 221 compresses the refrigerant and circulates it through the refrigerant pipe 261. Specifically, the compressor 221 compresses a low-temperature, low-pressure refrigerant, and discharges the high-pressure, high-temperature refrigerant to the four-way valve 222 .
  • the compressor 221 includes an inverter circuit that can change the operating capacity according to the drive frequency. The operating capacity is the amount of refrigerant that the compressor 221 pumps out per unit time. The compressor 221 changes its operating capacity according to instructions from the outdoor unit control section 251.
  • the four-way valve 222 is installed on the discharge side of the compressor 221.
  • the four-way valve 222 switches the flow direction of the refrigerant in the refrigerant pipe 261 depending on whether the air conditioner 201 is operating in a cooling operation, a dehumidifying operation, or a heating operation.
  • the expansion valve 224 is installed between the outdoor heat exchanger 223 and the indoor heat exchanger 225, and decompresses and expands the refrigerant flowing through the refrigerant pipe 261.
  • the expansion valve 224 is an electronic expansion valve whose opening degree can be changed and controlled. The expansion valve 224 changes its opening according to instructions from the outdoor unit control section 251 to adjust the pressure of the refrigerant.
  • the outdoor heat exchanger 223 exchanges heat between the refrigerant flowing through the refrigerant pipe 261 and the air in the outdoor space (external space) 272 that is outside the indoor space 271.
  • the outdoor blower 231 is provided near the outdoor heat exchanger 223, sucks air from the outdoor space 272, and sends the sucked air to the outdoor heat exchanger 223.
  • the air sent to the outdoor heat exchanger 223 is blown out into the outdoor space 272 after exchanging heat with the refrigerant flowing through the refrigerant pipe 261 .
  • the indoor heat exchanger 225 exchanges heat between the refrigerant flowing through the refrigerant pipe 261 and the air in the indoor space 271.
  • the indoor blower 233 is provided near the indoor heat exchanger 225, sucks air from the indoor space 271, and sends the sucked air to the indoor heat exchanger 225.
  • the air sent to the indoor heat exchanger 225 exchanges heat with the refrigerant flowing through the refrigerant pipe 261, and then is blown out into the indoor space 271.
  • the air heat-exchanged by the indoor heat exchanger 225 is supplied to the indoor space 271 as conditioned air. As a result, the indoor space 271 is air-conditioned.
  • the outdoor unit control section 251 controls the operation of the outdoor unit 211.
  • the indoor unit control section 252 controls the operation of the indoor unit 213.
  • a remote controller 255 is arranged in the indoor space 271.
  • the remote controller 255 transmits and receives various signals to and from an indoor unit control section 252 included in the indoor unit 213.
  • the remote controller 255 includes a display section 255a as shown in FIG. 15, which will be described later.
  • the display unit 255a includes a touch screen, a liquid crystal display, an LED (Light Emitting Diode), and the like.
  • the remote controller 255 also includes a push button (not shown).
  • the remote controller 255 functions as a command reception unit that receives various commands from the user, and a display unit that displays various information to the user.
  • the user inputs commands to the air conditioner 201 by operating the remote controller 255.
  • the command is, for example, a command to switch between operation and stop, or a command to switch the operating mode, set temperature, set humidity, air volume, air direction, or timer.
  • the air conditioner 201 operates according to the input command.
  • FIG. 15 is a block diagram of an air conditioner 201 according to the third embodiment.
  • the air conditioner 201 includes a control device 250, an air conditioner 280, and the brain activity estimation device 1 of the first embodiment. Further, an information device 290 operated by a user is connected to the air conditioner 201 via a network N.
  • the control device 250 controls the entire air conditioner 201. Furthermore, the control device 250 controls the operation of the device body, in other words, the air conditioning unit 280, based on the degree of brain activity estimated by the brain activity estimation device 1.
  • the control device 250 includes the above-mentioned outdoor unit control section 251 and indoor unit control section 252.
  • the control device 250 includes the control content determining section 104 and the device control section 105 described in the first embodiment.
  • the control content determination section 104 and the device control section 105 may be provided in either the outdoor unit control section 251 or the indoor unit control section 252.
  • the outdoor unit control section 251 includes a control section 251a, a storage section 251b, a clock section 251c, and a communication section 251d. These parts are connected via a bus (not shown).
  • the control unit 251a controls the entire outdoor unit.
  • the storage unit 251b is composed of a memory such as RAM or ROM, and stores data necessary for control.
  • the clock section 251c is a section that clocks time.
  • the communication unit 251d is an interface for communicating with the indoor unit control unit 252 via the communication line 263 (see FIG. 14).
  • the outdoor unit control section 251 is connected to the indoor unit control section 252 by a communication line 263, as shown in FIG.
  • the outdoor unit control section 251 operates in cooperation with the outdoor unit control section 251 by receiving various signals via the indoor unit control section 252 and the communication line 263.
  • the indoor unit control section 252 includes a communication section 252a that communicates with the outdoor unit control section 251 and the remote controller 255.
  • the communication unit 252a is an interface for communicating with the outdoor unit control unit 251 and the remote controller 255.
  • the communication unit 251d is further connected to the information device 290 via the network N.
  • the communication unit 252a performs a process of receiving various commands from a user from the remote controller 255, and a process of transmitting various commands received from the remote controller 255 to the indoor unit control unit 252.
  • the communication unit 252a also performs a process of transmitting notification information to the remote controller 255 to notify the user.
  • the outdoor unit control section 251 and the indoor unit control section 252 are configured by a microprocessor unit.
  • the outdoor unit control department 251 and the indoor unit control unit 252 have a CPU (Central Processing Unit), ROM (READ ONLY MEMORY) and RAM (RANDOM ACCESSSSSSSSSSSSSMORY), etc., and control ROM. Programs, etc. are remembered .
  • the outdoor unit control section 251 and the indoor unit control section 252 are not limited to microprocessor units.
  • the outdoor unit control section 251 and the indoor unit control section 252 may be configured with something that can be updated, such as firmware.
  • the outdoor unit control section 251 and the indoor unit control section 252 may be program modules that are executed by instructions from a CPU (not shown) or the like.
  • the control device 250 has an outdoor unit control section 251 and an indoor unit control section 252, and is configured separately into an outdoor unit 211 and an indoor unit 213, but it is possible to It may be configured with one control section.
  • the air conditioning unit 280 is a part that air-conditions the indoor space 271, and corresponds to the refrigerant circuit, outdoor blower 231, and indoor blower 233 in FIG.
  • the air conditioner 201 configured as described above controls the operation of the air conditioner 280 based on the degree of brain activity estimated by the brain activity estimation device 1. That is, the control device 250 controls the operation of the air conditioner 280 according to the degree of concentration based on the degree of brain activity. Specifically, for example, when the user's concentration level is low, the control device 250 performs the following control to encourage the user to wake up. In the past, it has been confirmed that users wake up when exposed to wind. Therefore, when the degree of concentration is low, the control device 250 increases the rotation speed of the indoor blower 233 to increase the amount of air blown.
  • the control device 250 may perform the following control.
  • the control device 250 causes the wind to intermittently hit the user by a swing motion that moves up and down the vertical wind direction plates (not shown) provided on the indoor unit 213, or by moving the left and right wind direction plates (not shown) left and right.
  • the wind is made to hit the user intermittently by the swing motion.
  • the air conditioner 201 can prompt the user to wake up when the user's concentration level is low.
  • a temperature about 1°C lower than what the user feels is an appropriate temperature has the effect of cooling the brain and improves work efficiency. Particularly during heating, it is said that if the set temperature is too high, the degree of concentration will decrease. Therefore, when the degree of concentration is low, the control device 250 performs control to adjust the indoor set temperature to a temperature slightly lower than the current temperature.
  • the control device 250 lowers the rotation speed of the indoor fan 233 to reduce the amount of air blown, or controls the upper and lower wind direction plates as upwardly as possible so that the user is not exposed to the wind. do.
  • the control device 250 controls vertical wind direction plates (not shown) and left and right wind direction plates (not shown) to prevent wind from hitting the user. control one or both of With the above control, the air conditioner 201 can prevent the user's consciousness from turning toward the wind due to the wind hitting the user, thereby reducing the user's concentration level. In other words, the air conditioner 201 has the effect that a highly concentrated user can work without being conscious of the wind.
  • the remote controller 255 is positioned as a part of the components of the air conditioner 201, but the information device 290 is positioned as a device owned by the user.
  • the information device 290 has a display section 281 such as a liquid crystal panel, and various information is displayed on the display section 281.
  • Information device 290 is comprised of, for example, a smartphone or a tablet.
  • An application for displaying the concentration level of the human body is installed in the information device 290.
  • the information device 290 can also be used in place of the remote controller 255 by installing an application for air conditioning control.
  • the information device 290 When operated by the user, the information device 290 starts an application, acquires information related to the degree of brain activity estimated by the brain activity estimation device 1 via the network N, and displays the information on the display unit 281. indicate.
  • the information related to the degree of brain activity may be information indicating the degree of brain activity, concentration level information regarding concentration level, or emotional information regarding emotions.
  • the concentration level information is information indicating the current concentration level, the time-series concentration level, or both.
  • the control device 250 of the air conditioner 201 transmits information related to the degree of brain activity obtained by the brain activity estimation device 1 via the communication unit 252a in response to a request from the information device 290. Processing is performed to transmit the information to the device 290 and display it on the information device 290. Note that although information related to the degree of brain activity is displayed on the information device 290 here, it may be displayed on the display unit 255a of the remote controller 255.
  • the air conditioner 201 displays and visualizes the concentration level information on the information device 290 or the display unit 255a of the remote controller 255, so that the user can visually check the concentration level.
  • the device equipped with the brain activity estimation device 1 is the air conditioner 201
  • the device is not limited to the air conditioner 201, and can be incorporated into various devices such as electrical equipment, cars, or entertainment equipment.
  • the brain activity estimation device 1 can be incorporated into, for example, a labor management device or a learning management device.
  • the brain activity estimation device 1 can be incorporated into equipment in a wide range of fields such as healthcare, labor, education, sleep, mindfulness, meditation, customer service, marketing, or sports mental training.
  • the device can control the device using the brain activity determination results and present the brain activity determination results to the user. In this way, by applying the brain activity estimation device 1 to various devices, it is possible to visualize and present the degree of brain activity, degree of concentration, and emotion to the user of the device.
  • 1 Brain activity estimation device 10 Doppler sensor, 10a substrate section, 100 antenna section, 100a antenna, 101 radio section, 102 analog circuit section, 103 analysis section, 104 control content determination section, 105 device control section, 106 cloud section, 107 Display unit, 108 Data collection unit, 201 Air conditioner, 203 House, 211 Outdoor unit, 213 Indoor unit, 221 Compressor, 222 Four-way valve, 223 Outdoor heat exchanger, 224 Expansion valve, 225 Indoor heat exchanger, 231 Outdoor blower , 233 indoor blower, 250 control device, 251 outdoor unit control section, 251a control section, 251b storage section, 251c clock section, 251d communication section, 252 indoor unit control section, 252a communication section, 255 remote controller, 255a display section, 261 Refrigerant piping, 263 Communication line, 271 Indoor space, 272 Outdoor space, 280 Air conditioning section, 281 Display section, 290 Information equipment, 300 Air conditioning system.

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