Motion sickness estimation apparatus, motion sickness estimation method, and control program

Abstract

To estimate whether or not a user is in a motion sickness state without imposing a burden on the user.SOLUTION: An estimation device (10) includes an acquisition unit (11) that acquires, as an electrical signal, a reflected wave of a microwave emitted from a sensor unit (20) to a body of a user, a waveform processing unit (12) that performs waveform processing on the electrical signal, and an estimation unit (13) that estimates whether the user is in a motion sickness state by using a waveform processed by the waveform processing unit (12).SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a motion sickness estimation device that estimates whether a user is suffering from motion sickness without contact. [Background technology]

[0002] People can experience what is known as "motion sickness" when riding in vehicles such as cars, buses, boats, and airplanes. In recent years, people have also become "sick" while using VR and AR devices. Medically, this "sickness" is known as motion sickness or acceleration sickness.

[0003] Various studies have been conducted to estimate and detect the severity of motion sickness. For example, methods for assessing motion sickness include the Simulator Sickness Questionnaire (SSQ), which analyzes subjective assessments, and an electrogastrogram (EGG) that estimates the severity from the electrical activity of the stomach.

[0004] Patent Document 1 describes a detection system that places electrodes on the flanks of a subject and detects motion sickness based on changes in potential derived from the electrodes. Patent Document 2, while not a system for detecting motion sickness, describes an apparatus that places a sensor in the stomach and intestines of a subject and uses the sensor signal to determine the characteristics of food ingested by the subject. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-191572 [Patent Document 2] Special Publication No. 2004-523328 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the SSQ is merely a subjective evaluation and lacks accuracy. Furthermore, when using EGG and the technologies described in Patent Documents 1 and 2, sensors and the like are placed directly on the outside or inside of the subject's body, which is problematic in terms of usability. When subjects suffer from motion sickness, they are often in a vehicle or the like, and placing sensors and the like directly on the subject's body is time-consuming and burdensome for the subject.

[0007] One aspect of the present invention has been made in consideration of the above-mentioned problems, and its purpose is to realize an estimation device or the like that can estimate whether a user is suffering from motion sickness without imposing a burden on the user. [Means for solving the problem]

[0008] In order to solve the above problem, a motion sickness estimation device according to one embodiment of the present invention comprises an acquisition unit that acquires reflected waves of microwaves irradiated onto a user's body as electrical signals, a waveform processing unit that performs waveform processing on the electrical signals, and an estimation unit that uses the waveform processed by the waveform processing unit to estimate whether the user is in a state of motion sickness.

[0009] In order to solve the above problem, a method for estimating motion sickness according to one embodiment of the present invention includes an acquisition step of acquiring reflected waves of microwaves irradiated onto a user's body as electrical signals, a waveform processing step of waveform-processing the electrical signals, and an estimation step of estimating whether the user is in a state of motion sickness using the waveform processed in the waveform processing step. [Effects of the Invention]

[0010] According to one aspect of the present invention, it is possible to estimate whether a user is suffering from motion sickness without imposing a burden on the user. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating an overview of an estimation system according to a first embodiment of the present invention. [Figure 2]FIG. 2 is a functional block diagram showing the configuration of a main part of the estimation system. [Figure 3] FIG. 10 is a diagram illustrating an example of a waveform used for estimation by the estimation device. [Figure 4] 10 is a flowchart showing a flow of processing by the estimation device. [Figure 5] FIG. 10 is a functional block diagram showing a configuration of a main part of an estimation system according to another embodiment of the present invention. [Figure 6] FIG. 10 is a diagram showing an example of an arrangement in which two sensor units are provided. [Figure 7] FIG. 10 is a functional block diagram showing a configuration of a main part of an estimation system according to still another embodiment of the present invention. [Figure 8] FIG. 10 is a diagram illustrating an example of a waveform used for estimation by an estimation device according to another embodiment. [Figure 9] FIG. 10 is a diagram for explaining evaluation of the estimation system. [Figure 10] FIG. 10 is a diagram for explaining evaluation of the estimation system. DETAILED DESCRIPTION OF THE INVENTION

[0012] [Embodiment 1] [Outline of Estimation System 1] An embodiment of the present invention will be described in detail below. First, an outline of an estimation system 1 according to this embodiment will be described with reference to FIG. 1. FIG. 1 shows, as an example, a case in which it is estimated whether or not a user H driving a passenger car is suffering from motion sickness. As shown in FIG. 1, the estimation system 1 includes an estimation device 10 and a sensor unit 20. In FIG. 1, the sensor unit 20 is arranged on the surface of the backrest of the seat in which the user H is sitting, opposite the user H. As will be described in detail later, the sensor unit 20 includes a microwave radar, and the estimation system 1 estimates whether or not the user H is suffering from motion sickness by analyzing, with the estimation device 10, reflected waves of microwaves irradiated toward the user's torso and estimating the activity state of the user H's stomach.

[0013] The frequency of peristaltic movement of a human stomach increases during nausea and vomiting, and the fast wave intensity increases during nausea and vomiting. Microwaves also pass through the seat, user H's clothing, etc., and are reflected by user H's internal organs. Therefore, the estimation system 1 can estimate the activity state of user H's stomach by analyzing the waves reflected by user H's stomach, and can estimate whether user H is suffering from motion sickness.

[0014] Furthermore, the estimation system 1 can estimate whether or not the user H is in a state of motion sickness without placing the sensor unit 20 directly inside or outside the body of the user H. Therefore, it can be estimated immediately and continuously, without imposing a burden on the user H, whether or not the user H is in a state of motion sickness or whether or not there are signs of motion sickness.

[0015] [Details of Estimation System 1] Next, the estimation system 1 will be described in detail with reference to Fig. 2. Fig. 2 is a functional block diagram showing the configuration of the main parts of the estimation system 1. As shown in Fig. 2, the estimation system 1 includes an estimation device 10 and a sensor unit 20.

[0016] The estimation device 10 estimates whether or not the user H is suffering from motion sickness, and includes an acquisition unit 11, a waveform processing unit 12, an estimation unit 13, and an output unit .

[0017] The acquisition unit 11 acquires the reflected waves received by the sensor unit 20. The acquisition unit 11 may include a band pass filter (BPF) that extracts only those reflected by the stomach of the user H, i.e., low-frequency signal components that indicate the activity state of the stomach, from the reflected waves. The acquisition unit 11 transmits the extracted low-frequency signal components from the reflected waves to the waveform processing unit 12. Note that communication between the acquisition unit 11 and the sensor unit 20 may be wired or wireless. Furthermore, the acquisition unit 11 may be integrated with the sensor unit 20 (or the receiving unit 22, the receiving antenna 24). In other words, the acquisition unit 11 may also serve as the sensor unit 20 (or the receiving unit 22, the receiving antenna 24).

[0018] The waveform processing unit 12 generates a time-axis waveform from the electrical signal transmitted from the acquisition unit 11. An example of the time-axis waveform is shown in 301 in FIG. 3. In the time-axis waveform shown in 301, the horizontal axis indicates time and the vertical axis indicates amplitude. Furthermore, the waveform processing unit 12 performs an FFT (Fast Fourier Transform) on the time-series data indicated by the electrical signal transmitted from the acquisition unit 11 to generate a frequency spectrum waveform. An example of the frequency spectrum waveform is shown in 302 in FIG. 3. In the frequency spectrum waveform shown in 302, the horizontal axis indicates frequency and the vertical axis indicates intensity.

[0019] The estimation unit 13 estimates whether or not the user H is suffering from motion sickness from the waveform obtained by the waveform processing unit 12. More specifically, the estimation unit 13 performs the estimation by one of the following methods (1) to (3).

[0020] (1) Use a time-axis waveform.

[0021] The estimation unit 13 estimates that the user H is suffering from motion sickness when the amplitude of the time-axis waveform obtained by the waveform processing unit 12 exceeds a first threshold. For example, as shown in time-axis waveform 3011 of 301 in Fig. 3, when the amplitude is equal to or less than the first threshold, it is estimated that the user H is not suffering from motion sickness, i.e., is in a normal state, and when the amplitude exceeds the first threshold, as shown in time-axis waveform 3012, it is estimated that the user H is suffering from motion sickness.

[0022] (2) Using frequency spectrum waveforms.

[0023] The estimation unit 13 estimates that the user H is suffering from motion sickness when the intensity of the fast wave frequency exceeds the second threshold in the frequency spectrum waveform obtained by the waveform processing unit 12. In other words, when the peak of the frequency spectrum waveform moves to the fast wave frequency band and the peak value in the fast wave frequency band exceeds the second threshold, the estimation unit 13 estimates that the user H is suffering from motion sickness. For example, as shown in the frequency spectrum waveform 3021 of 302 in FIG. 3, when the intensity of the fast wave frequency band is equal to or less than the second threshold, the estimation unit 13 estimates that the user H is not suffering from motion sickness, i.e., is in a normal state. On the other hand, when the intensity of the fast wave frequency band exceeds the second threshold, as shown in the frequency spectrum waveform 3022, the estimation unit 13 estimates that the user H is suffering from motion sickness. Here, the fast wave frequency band is a band in the frequency spectrum waveform where the frequency is 4 cpm (cycles per minute) or more.

[0024] (3) Use frequency spectrum waveforms.

[0025] The estimation unit 13 estimates that the user H is suffering from motion sickness when the ratio of the fast wave frequency band to the normal wave frequency band, i.e., ΣPfast / ΣPnormal, exceeds a third threshold in the frequency spectrum waveform obtained by the waveform processing unit 12. Here, Pfast indicates the fast wave frequency band portion in the frequency spectrum waveform, and Pnormal indicates the normal wave frequency band portion in the frequency spectrum waveform. The normal wave frequency band is a frequency band of 2 to 4 cpm in the frequency spectrum waveform.

[0026] (4) Both time-domain waveforms and frequency spectrum waveforms are used.

[0027] The estimation unit 13 estimates that the user H is in a motion sickness state when both of the above-mentioned (1) and (2) are satisfied. That is, when the amplitude of the time-axis waveform exceeds a first threshold and the intensity of the peak in the fast wave frequency band in the frequency spectrum waveform exceeds a second threshold, the estimation unit 13 estimates that the user H is in a motion sickness state.

[0028] The output unit 14 outputs the estimation result of the estimation unit 13. The output unit 14 may include a monitor (not shown) or a speaker (not shown) and output the estimation result by these, or may output the estimation result to an external device so that the estimation result is displayed on the external device.

[0029] The sensor unit 20 is a microwave radar and includes a transmitter 21, a receiver 22, a transmitting antenna 23, and a receiving antenna 24.

[0030] The transmitting unit 21 generates microwaves to be emitted from the transmitting antenna 23, and includes a transmitter and the like.

[0031] The receiver 22 performs AD conversion on the reflected wave received by the receiving antenna 24 and transmits the converted signal to the estimation device 10 as an electrical signal.

[0032] The transmitting antenna 23 is an antenna that emits microwaves, and the receiving antenna 24 is an antenna that receives reflected waves of the emitted microwaves.

[0033] [Processing flow in the estimation device 10] Next, a processing flow in the estimation device 10 will be described with reference to FIG. 4. FIG. 4 is a flowchart showing the processing flow in the estimation device 10. As shown in FIG. 4, in the estimation device 10, first, the acquisition unit 11 acquires an electrical signal based on a reflected wave of a microwave irradiated from the sensor unit 20 to the user H (S101, acquisition step). Next, the waveform processing unit 12 performs waveform processing on the electrical signal acquired by the acquisition unit 11 (S102, waveform processing step). Waveform processing refers to generating a time-axis waveform or a frequency spectrum waveform based on the electrical signal. Then, the estimation unit 13 uses the time-axis waveform or the frequency spectrum waveform acquired by the waveform processing unit 12 to estimate whether the amplitude of the time-axis waveform or the intensity of the fast wave in the frequency spectrum waveform exceeds a threshold (S103, estimation step). Then, if the amplitude of the time-axis waveform or the intensity of the fast wave in the frequency spectrum waveform exceeds a threshold (YES in S103), it is estimated that the user H is suffering from motion sickness (S104, estimation step). On the other hand, if the amplitude in the time domain waveform or the intensity of the fast wave in the frequency spectrum waveform does not exceed the threshold in step S103, it is estimated that the user H is not in a motion sickness state, that is, is in a normal state (S105, estimation step).

[0034] As described above, the estimation device 10 according to this embodiment includes an acquisition unit 11 that acquires, as an electric signal, the reflected waves of microwaves irradiated onto the torso of user H from the sensor unit 20, which is a microwave radar, a waveform processing unit 12 that performs waveform processing on the electric signal, and an estimation unit 13 that estimates whether or not user H is suffering from motion sickness using the waveform processed by the waveform processing unit 12. By using microwave radar, it is possible to estimate whether or not user H is suffering from motion sickness without placing the sensor unit 20 directly inside or outside the body of user H. Therefore, it is possible to estimate whether or not user H is suffering from motion sickness without placing a burden on user H.

[0035] [Embodiment 2] Other embodiments of the present invention will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated.

[0036] An estimation system 1A according to this embodiment is shown in Fig. 5. As shown in Fig. 5, the estimation system 1A differs from the estimation system 1 in that it includes two sensor units 20 and an estimation device 10A includes a selection unit 15.

[0037] The acquisition unit 11 of the estimation device 10A acquires an electrical signal based on the reflected wave from each of the two sensor units 20 and transmits the electrical signal to the selection unit 15. The selection unit 15 transmits to the waveform processing unit 12 an electrical signal that satisfies a predetermined condition among the two electrical signals acquired from the acquisition unit 11. Satisfying the predetermined condition may mean, for example, that one of the two electrical signals is a significant electrical signal. A significant electrical signal may be an electrical signal that is easy for the estimation unit 13 to estimate, i.e., a signal whose amplitude changes greatly over time.

[0038] Alternatively, the waveform processing unit 12 may perform waveform processing on each of the two electrical signals acquired by the acquisition unit 11, the selection unit 15 may select one of the signals based on the acquired waveforms, and the estimation unit 13 may perform estimation based on the selected waveform. In this case, the selection unit 15 may select a waveform that has a large change in amplitude or intensity from the waveforms acquired by the waveform processing unit 12.

[0039] As described above, according to the estimation system 1A, estimation is performed using the sensor unit 20 that produces the more significant electrical signal based on the reflected wave, thereby improving the accuracy of estimation as to whether or not the user H is suffering from motion sickness.

[0040] [Embodiment 3] Other embodiments of the present invention will be described below. For ease of explanation, the same reference numerals will be used to designate components having the same functions as those described in the above embodiment, and the description thereof will not be repeated.

[0041] First, Fig. 6 shows an example of the arrangement of the sensor units 20 when there are two sensor units 20. In the example shown in Fig. 6, one of the two sensor units 20 is arranged at a position where microwaves can be irradiated near the fundus of the stomach of user H, and the other is arranged at a position where microwaves can be irradiated near the pre-pyloric fundus of the stomach of user H. In other words, the arrangement positions of the sensor units 20 are made different so that the microwave irradiation positions of the two sensor units 20 are above and below the stomach.

[0042] Fig. 7 shows an estimation system 1B according to this embodiment. As shown in Fig. 7, the estimation system 1B differs from the estimation system 1 in that it includes two sensor units 20 and a phase difference detection unit 121 in a waveform processing unit 12B of an estimation device 10B.

[0043] The phase difference detection unit 121 detects the phase difference between the time-axis waveforms obtained by waveform processing of the electrical signals acquired from the two sensor units 20. Then, the estimation unit 13 estimates whether or not the user H is suffering from motion sickness based on the phase difference between the two time-axis waveforms detected by the phase difference detection unit 121.

[0044] This will be described in detail with reference to Fig. 8. Fig. 8 shows time axis waveforms when user H is in a normal state and when he is suffering from motion sickness.

[0045] The human stomach normally moves from the top to the bottom, but moves from the bottom to the top during vomiting, etc. Therefore, under normal circumstances, as shown in 801 in Fig. 8, when a time-axis waveform 8011 based on a reflected wave received by the sensor unit 20 corresponding to the upper side of the stomach is compared with a time-axis waveform 8012 based on a reflected wave received by the sensor unit 20 corresponding to the lower side of the stomach, the phase of the time-axis waveform 8012 is delayed by δn.

[0046] On the other hand, in a state of motion sickness such as vomiting, when comparing a time-axis waveform 8021 based on the reflected wave received by the sensor unit 20 corresponding to the upper side of the stomach with a time-axis waveform 8022 based on the reflected wave received by the sensor unit 20 corresponding to the lower side of the stomach, the phase of the time-axis waveform 8022 is advanced by δa, as shown in 802 in Fig. 8. The phase difference δa in the state of motion sickness is greater than the phase difference δn in the normal state.

[0047] Therefore, if the phase difference detection unit 121 detects the phase difference between the time axis waveform based on the reflected wave received by the sensor unit 20 corresponding to the upper side of the stomach and the time axis waveform based on the reflected wave received by the sensor unit 20 corresponding to the lower side of the stomach, the estimation unit 13 can estimate whether or not the user H is in a state of motion sickness from the phase difference detected by the phase difference detection unit 121.

[0048] In other words, when the phase of the time-axis waveform 8021 based on the reflected wave received by the sensor unit 20 corresponding to the upper side of the stomach is θb and the phase of the time-axis waveform 8022 based on the reflected wave received by the sensor unit 20 corresponding to the lower side of the stomach is θp, it can be estimated whether or not user H is suffering from motion sickness depending on whether the phase difference δ = θb - θp exceeds the third threshold.

[0049] [Modification] When two sensor units 20 are used, the estimation unit 13 calculates the coherence Coh in the fast wave frequency band of the frequency spectrum waveform based on the reflected waves received from the two sensor units 20. fbfp and infers whether the user H is suffering from motion sickness based on whether the degree of coherence exceeds a fourth threshold.

[0050] In this case, the degree of coherence Coh fbfp may be calculated using the following formula (1):

[0051]

number

[0052] Here, P(ω) indicates the fast wave frequency band of the frequency spectrum waveform, fb indicates the frequency spectrum waveform (gastric fundus signal or gastric body signal) based on the reflected wave at the gastric fundus or gastric body, i.e., the upper part of the stomach, and fp indicates the frequency spectrum waveform (gastric fundus signal) based on the reflected wave at the pre-pyloric fundus, i.e., the lower part of the stomach. fbfb (ω) represents the power spectrum of the fundus signal or the body signal, and P fpfp (ω) denotes the power spectrum of the antrum signal, and P fbfp (ω) indicates the cross spectrum between the gastric fundus signal (or gastric body signal) and the pre-pyloric fundus signal.

[0053] In the above example, the frequency spectrum waveform is divided into normal waves and fast waves, and the calculation is performed in the fast wave frequency band, but this is not limiting. The degree of coherence may be calculated using the frequency spectrum waveform itself without dividing it into normal waves and fast waves.

[0054] [Evaluation of the estimation system] Next, with reference to Figure 9, an evaluation using the estimation system 1 will be described. Here, an experiment was conducted in which visual and vestibular information was provided to the user (subject) as a task, and sickness was artificially induced. Visual information was provided using a drone flight simulator, with images moving up and down presented via a head-mounted display. Vestibular stimulation was provided by rotating the swivel chair on which the subject was sitting approximately 20° left and right at approximately 0.5 Hz. The task was completed for 15 minutes, with a 15-minute rest period before and after.

[0055] 901 in Fig. 9 compares the results when using estimation system 1 with those when using EGG, a conventional estimation method. 901 shows the transition of intensity in the fast wave frequency band of the frequency spectrum waveform, with the vertical axis representing intensity and the horizontal axis representing time. Waveform 9011 in 901 shows the transition of intensity when using estimation system 1, and waveform 9012 shows the transition of intensity when using EGG.

[0056] 9, it can be seen that when the estimation system 1 is used, the intensity in the fast wave frequency band increases during the time period when the subject is given a task, just as when the conventional EGG is used. Therefore, it can be seen that when the estimation system 1 is used, it is possible to accurately estimate whether the subject is in a state of motion sickness, just like when the conventional EGG is used.

[0057] 9 is a graph showing the correlation between the results when the estimation system 1 is used and the subject's subjective evaluation. Here, the subject's subjective evaluation was rated on a five-point scale from 1 to 5 as follows: 1: Nothing at all 2: Slightly uncomfortable 3: Quite uncomfortable but tolerable 4: It's very uncomfortable, but I can tolerate it for another minute or so 5: Uncomfortable and unbearable The vertical axis of 902 in Figure 9 is the intensity of the reflected wave obtained by the estimation system 1 [V 2 9, the higher the subjective evaluation of the subject, the stronger the intensity, and it can be seen that there is a correlation between the subjective evaluation of the subject and the measurement results by the estimation system 1.

[0058] Next, an evaluation using the estimation system 1B will be described with reference to FIG. 10. The tasks given to the subject are the same as those described above. 1001 in FIG. 10 is a diagram comparing the case where the estimation system 1B is used with the case where EGG, a conventional estimation method, is used. 1001 shows the transition of the phase difference, with the vertical axis representing the phase difference and the horizontal axis representing time. Waveform 1011 in 1001 is the transition of the phase difference when the estimation system 1B is used, and waveform 1012 is the transition of the phase difference when EGG is used.

[0059] 10, it can be seen that when the estimation system 1B is used, the phase difference is large during the time period when the subject is given a task, just as when the conventional EGG is used. Therefore, it can be seen that when the estimation system 1B is used, it is possible to accurately estimate whether or not the subject is in a state of motion sickness, just like when the conventional EGG is used.

[0060] 10 is a graph showing the correlation between the results when the estimation system 1B is used and the subject's subjective evaluation. The subject's subjective evaluation is rated on a five-point scale from 1 to 5 as described above.

[0061] The vertical axis of 1002 in Fig. 10 represents the degree of dispersion of the reflected wave obtained by the estimation system 1B [V 2 As shown by 1002 in Fig. 10, the degree of variance increases as the subjective evaluation of the subject increases, and it can be seen that there is a correlation between the subjective evaluation of the subject and the measurement results obtained by the estimation system 1B.

[0062] As described above, even when the estimation system 1 and estimation system 1B according to this embodiment are used, it is possible to accurately estimate whether or not a subject is suffering from motion sickness, just as when a conventional EGG is used. In the estimation system 1 (1A, 1B) according to this embodiment, it is not necessary to directly place the sensor unit 20 inside or outside the user's body, and the user's condition can be easily estimated even when the subject is performing some kind of work, activity, etc. In other words, it is possible to provide an easy-to-use estimation system 1 (1A, 1B).

[0063] [Software implementation example] The functions of the estimation device 10 (hereinafter referred to as the "device") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block of the device (particularly, the waveform processing unit 12 and the estimation unit 13).

[0064] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0065] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0066] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0067] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0068] 〔summary〕 The motion sickness estimation device according to aspect 1 of the present invention comprises an acquisition unit that acquires reflected waves of microwaves irradiated onto a user's body from a microwave radar as electrical signals, a waveform processing unit that performs waveform processing on the electrical signals, and an estimation unit that uses the waveform processed by the waveform processing unit to estimate whether the user is in a state of motion sickness.

[0069] In the motion sickness estimation device of aspect 2 of the present invention, in aspect 1, the waveform processing unit generates a time-axis waveform from the electrical signal, and the estimation unit estimates whether the user is in a motion sickness state based on whether the amplitude of the time-axis waveform exceeds a first threshold.

[0070] In the motion sickness estimation device of aspect 3 of the present invention, in aspect 1 or 2, the waveform processing unit generates a frequency spectrum waveform from the electrical signal, and the estimation unit estimates whether the user is in a motion sickness state based on whether the peak of the frequency spectrum waveform moves to the fast wave frequency band and whether the intensity of the fast wave frequency exceeds a second threshold.

[0071] In the motion sickness estimation device of aspect 4 of the present invention, in aspect 1 or 2, the waveform processing unit generates a frequency spectrum waveform from the electrical signal, and the estimation unit estimates whether the user is in a motion sickness state based on whether the ratio of the fast wave frequency band to the normal wave frequency band of the frequency spectrum waveform exceeds a third threshold.

[0072] In the motion sickness estimation device of aspect 5 of the present invention, in any of aspects 1 to 3, the waveform processing unit generates a time-axis waveform and a frequency spectrum waveform from the electrical signal, and the estimation unit estimates whether the user is in a motion sickness state based on whether the amplitude of the time-axis waveform exceeds a first threshold, the peak of the frequency spectrum waveform moves to a fast wave frequency band, and the intensity of the fast wave frequency exceeds a second threshold.

[0073] A motion sickness estimation device according to aspect 6 of the present invention is any of aspects 1 to 5, wherein the acquisition unit acquires reflected waves of the two microwaves irradiated to the user as electrical signals, and includes a selection unit that selects a reflected wave from the two reflected waves that satisfies a predetermined condition, and the estimation unit estimates whether the user is suffering from motion sickness using a waveform obtained by processing the electrical signal corresponding to the reflected wave selected by the selection unit in the waveform processing unit.

[0074] In the motion sickness estimation device of aspect 7 of the present invention, in aspect 1, the acquisition unit acquires the reflected waves of the two microwaves irradiated to the user as electrical signals, the waveform processing unit waveform-processes the electrical signals of the two reflected waves, and the estimation unit estimates whether the user is in a state of motion sickness using the phase difference between the two waveform-processed waveforms.

[0075] In the motion sickness estimation device of aspect 8 of the present invention, in aspect 1, the acquisition unit acquires the reflected waves of the two microwaves irradiated to the user as electrical signals, the waveform processing unit waveform processes the electrical signals of the two reflected waves, and the estimation unit estimates whether the user is in a motion sickness state based on whether the degree of coherence of the two waveform-processed waveforms exceeds a fourth threshold.

[0076] A method for estimating motion sickness according to aspect 9 of the present invention includes an acquisition step of acquiring reflected waves of microwaves irradiated onto a user's body as electrical signals, a waveform processing step of waveform-processing the electrical signals, and an estimation step of estimating whether the user is in a state of motion sickness using the waveform processed in the waveform processing step.

[0077] A motion sickness estimation method according to aspect 10 of the present invention is the same as aspect 9, in which, in the waveform processing step, a time-axis waveform is generated from the electrical signal, and in the estimation step, it is estimated whether the user is suffering from motion sickness based on whether the amplitude of the time-axis waveform exceeds a first threshold.

[0078] A motion sickness estimation method according to aspect 11 of the present invention is, in aspect 9 or 10, such that in the acquisition step, the reflected waves of the two irradiated microwaves are acquired as electrical signals, in the waveform processing step, the electrical signals of the two reflected waves are waveform processed, and in the estimation step, the phase difference between the reflected waves of the two waveform-processed microwaves is used to estimate whether the user is in a state of motion sickness.

[0079] The estimation device according to each aspect of the present invention may be realized by a computer. In this case, the control program for the estimation device, which causes the computer to operate as each part (software element) of the estimation device, thereby realizing the estimation device on the computer, and the computer-readable recording medium on which the control program is recorded, also fall within the scope of the present invention.

[0080] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0081] According to the present invention, objective and immediate evaluation by biometric measurements is possible, instead of subjective evaluation methods such as conventional questionnaires. Furthermore, it is not restrictive like electrogastrograms (EGGs), and highly usable detection is possible. Therefore, it is possible to estimate motion sickness, visually-induced sickness, spatial disorientation, etc., and it is useful for monitoring passengers in automobiles, airplanes, ships, etc., and for monitoring users when using VR / AR devices. [Explanation of symbols]

[0082] 1, 1A, 1B Estimation System 10, 10A, 10B Estimation Device 11 Acquisition Department 12, 12B Waveform processing section 13 Estimation part 14 Output section 15 Selection section 20 Sensor section 21 Transmitter 22 Receiving unit 23 Transmitting Antenna 24 receiving antenna 121 Phase difference detection unit

Claims

1. an acquisition unit that acquires reflected waves of microwaves irradiated onto the user's body as electrical signals; a waveform processing unit that processes the waveform of the electrical signal; an estimation unit that estimates whether the user is suffering from motion sickness using the waveform processed by the waveform processing unit.

2. the waveform processing unit generates a time-axis waveform from the electrical signal; The motion sickness estimation device according to claim 1 , wherein the estimation unit estimates whether the user is in a motion sickness state based on whether the amplitude of the time-axis waveform exceeds a first threshold value.

3. the waveform processing unit generates a frequency spectrum waveform from the electrical signal; The motion sickness estimation device of claim 1, wherein the estimation unit estimates whether the user is in a motion sickness state based on whether the peak of the frequency spectrum waveform moves to a fast wave frequency band and whether the intensity of the fast wave frequency exceeds a second threshold.

4. the waveform processing unit generates a frequency spectrum waveform from the electrical signal; The motion sickness estimation device according to claim 1, wherein the estimation unit estimates whether the user is in a motion sickness state based on whether a ratio of a fast wave frequency band to a normal wave frequency band in the frequency spectrum waveform exceeds a third threshold.

5. the waveform processing unit generates a time-axis waveform and a frequency spectrum waveform from the electrical signal; The motion sickness estimation device of claim 1, wherein the estimation unit estimates whether the user is in a state of motion sickness based on whether the amplitude of the time-axis waveform exceeds a first threshold, the peak of the frequency spectrum waveform moves to a fast wave frequency band, and the intensity of the fast wave frequency exceeds a second threshold.

6. the acquisition unit acquires reflected waves of the two microwaves irradiated to the user as electrical signals; a selection unit that selects a reflected wave that satisfies a predetermined condition from the two reflected waves; A motion sickness estimation device as described in any one of claims 1 to 5, wherein the estimation unit estimates whether the user is in a state of motion sickness using a waveform obtained by processing an electrical signal corresponding to a reflected wave selected by the selection unit in the waveform processing unit.

7. the acquisition unit acquires reflected waves of the two microwaves irradiated to the user as electrical signals; the waveform processing unit processes waveforms of the electrical signals of the two reflected waves; The motion sickness estimation device according to claim 1 , wherein the estimation unit estimates whether the user is suffering from motion sickness using a phase difference between the two waveform-processed reflected waves.

8. the acquisition unit acquires reflected waves of the two microwaves irradiated to the user as electrical signals; the waveform processing unit processes waveforms of the electrical signals of the two reflected waves; The motion sickness estimation device according to claim 1 , wherein the estimation unit estimates whether the user is in a motion sickness state based on whether the degree of coherence of the two waveform-processed reflected waves exceeds a fourth threshold.

9. an acquiring step of acquiring reflected waves of microwaves irradiated to the user's body as electrical signals; a waveform processing step of waveform-processing the electrical signal; an estimation step of estimating whether or not the user is suffering from motion sickness using the waveform processed in the waveform processing step.

10. In the waveform processing step, a time-axis waveform is generated from the electrical signal; 10. The method for estimating motion sickness according to claim 9, wherein in the estimating step, it is estimated whether or not the user is in a state of motion sickness based on whether or not the amplitude of the time-axis waveform exceeds a first threshold value.

11. In the acquiring step, reflected waves of the two irradiated microwaves are acquired as electrical signals; In the waveform processing step, the electrical signals of the two reflected waves are waveform processed; 10. The method for estimating motion sickness according to claim 9, wherein in the estimating step, it is estimated whether or not the user is suffering from motion sickness using a phase difference between the reflected waves of the two waveform-processed microwaves.

12. 2. A control program for causing a computer to function as the motion sickness estimation device according to claim 1, the control program causing a computer to function as the waveform processing unit and the estimation unit.

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