Physical and mental state estimation system, physical and mental state estimation method, and recording medium

Abstract

The present invention relates to a physical and mental state estimation system, a physical and mental state estimation method, and a recording medium. The physical and mental state estimation system includes: a heart rate information acquisition unit that acquires heart rate information related to a subject's heart rate; a heart rate variability calculation unit that calculates heart rate variability of a very low frequency component (VLF) based on the acquired heart rate information; and a physical and mental state estimation unit that estimates the subject's concentration / effort state based on the calculated heart rate variability.

Description

Technical Field

[0001] The present invention relates to a physical and mental state estimation system, a physical and mental state estimation method, and a recording medium. Background Art

[0002] Japanese Patent Application Laid-Open No. 2018-088966 discloses a technology for a physical and mental state (mood) estimation system using a subject's heart rate and sympathetic nerve activity index (LF / HF). Summary of the Invention

[0003] The inventors have discovered that there is room for improvement in the estimation accuracy of conventional physical and mental state estimation systems.

[0004] The present invention was developed in response to the aforementioned issues. It estimates a person's physical and mental state based on the value of the very low frequency (VLF) component of heart rate fluctuations (heart rate variability). The present invention provides a physical and mental state estimation system, a physical and mental state estimation method, and a recording medium capable of estimating a subject's physical and mental state with higher accuracy.

[0005] The physical and mental state estimation system involved in the present invention comprises: a heart rate information acquisition unit, which acquires information related to the heart rate of the subject, namely, heart rate information; a heart rate variation calculation unit, which calculates the heart rate variation of the VLF, i.e., very low frequency component, based on the acquired heart rate information; and a physical and mental state estimation unit, which estimates the subject's concentration / effort state based on the calculated value of the heart rate variation.

[0006] The physical and mental state estimating system according to the present invention can estimate the concentration and effort state of a subject with higher accuracy.

[0007] In addition, the heart rate variation calculation unit calculates the heart rate variation for each of VLF1 included in the VLF and VLF2 having a frequency band lower than VLF1, and the physical and mental state estimation unit estimates the concentration / effort state of the subject based on the value of VLF2 and estimates the fatigue state based on the value of VLF1.

[0008] The physical and mental state estimating system according to the present invention can estimate not only the concentration / effort state but also the fatigue state.

[0009] Furthermore, the heart rate variability calculation unit further calculates heart rate variability of HF (high frequency component) and LF (low frequency component), and the physical and mental state estimation unit estimates the fatigue state of the subject in addition to the concentration / effort state based on the VLF, HF, and LF.

[0010] The physical and mental state estimating system according to the present invention can estimate not only the concentration / effort state but also the fatigue state.

[0011] Furthermore, an environment control unit is further provided for controlling the environment of the subject based on the estimated physical and mental state.

[0012] The physical and mental state estimation system according to the present invention can achieve an optimal physical and mental state for a subject by controlling the environment surrounding the subject.

[0013] The environment control unit performs control to improve concentration when the concentration / effort state estimated by the physical and mental state estimation unit is equal to or lower than a predetermined level.

[0014] The physical and mental state estimation system according to the present invention can improve the concentration of a subject when the concentration is reduced.

[0015] The apparatus further includes an environment control unit that controls the environment of the subject based on the estimated physical and mental state, and performs control to alleviate fatigue when the fatigue state estimated by the physical and mental state estimating unit is equal to or higher than a predetermined level.

[0016] The physical and mental state estimation system according to the present invention can relieve fatigue when a subject is tired.

[0017] The system further includes a target setting unit that sets a target for the physical and mental state, and the environment control unit controls the environment surrounding the subject based on the estimated physical and mental state and the set target for the physical and mental state.

[0018] The physical and mental state estimation system according to the present invention includes a target setting unit, and can thereby adjust the physical and mental state of a subject to a desired target physical and mental state.

[0019] The goal setting unit sets a goal based on a predetermined schedule.

[0020] The physical and mental state estimation system according to the present invention can automatically set a goal based on the subject's schedule even when the subject has not set the goal himself or herself.

[0021] The method for estimating a physical and mental state of the present invention includes: a step of obtaining information related to the heart rate of a subject, namely, heart rate information; a step of calculating the heart rate variation of the VLF, namely, the very low frequency component, based on the obtained heart rate information; and a step of estimating the concentration / effort state of the subject based on the calculated value of the heart rate variation.

[0022] The physical and mental state estimating method according to the present invention can estimate the concentration and effort state of a subject with higher accuracy.

[0023] The present invention relates to a recording medium storing a physical and mental state estimation program. The physical and mental state estimation program causes a computer to execute the following steps: obtaining heart rate information related to a subject's heart rate; calculating heart rate variability (VLF, or very low frequency) based on the obtained heart rate information; and estimating the subject's concentration or effort state based on the calculated heart rate variability.

[0024] The physical and mental state estimation program stored in the recording medium according to the present invention can estimate the concentration and effort state of a subject with higher accuracy.

[0025] According to the present invention, a physical and mental state estimation system, a physical and mental state estimation method, and a recording medium can be provided that estimate the physical and mental state of a subject with higher accuracy by estimating the physical and mental state based on the value of the very low frequency component (VLF) of heart rate fluctuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Hereinafter, features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will be described with reference to the accompanying drawings, in which like reference numerals represent like elements, and wherein:

[0027] Figure 1 This is a block diagram showing the physical and mental state estimation system according to the first embodiment.

[0028] Figure 2 This is a diagram showing the relationship between VLF1 and the fatigue state and the relationship between VLF2 and the concentration state according to the first embodiment.

[0029] Figure 3 This is a diagram showing the classification of physical and mental states based on heart rate variability values ​​according to the first embodiment.

[0030] Figure 4 This is a flowchart showing the physical and mental state estimation method involved in the first embodiment.

[0031] Figure 5 This is a block diagram showing a physical and mental state estimation system according to the second embodiment.

[0032] Figure 6 This is a table showing an example of environmental control performed by the environmental control unit 14 of the physical and mental state estimation system according to the second embodiment.

[0033] Figure 7 This is a table showing an example of environmental control performed by the environmental control unit of the physical and mental state estimation system according to the second embodiment.

[0034] Figure 8 This is a flowchart showing the method for estimating a physical and mental state according to the second embodiment.

[0035] Figure 9This is a block diagram showing a physical and mental state estimation system according to the third embodiment.

[0036] Figure 10 This is a table showing an example of environmental control performed by the environmental control unit of the physical and mental state estimation system according to the third embodiment.

[0037] Figure 11 This is a flowchart showing the method for estimating a physical and mental state according to the third embodiment.

[0038] Figure 12 The graph shows the results of subjective evaluation of fatigue before and after the fatigue task (topic) experiment according to Example 1, the results of PVT reaction time, the results of N-back answer time, the results of In(LF / HF), and the results of ccvVLF (%).

[0039] Figure 13 This is a graph showing the results of correlation analysis of the results of the fatigue task test according to Example 1 and the amount of change in heart rate fluctuations before and after the fatigue task test.

[0040] Figure 14 This is a graph showing the comparison between the subjective evaluation "difficulty in concentrating" and the average value of VLF according to Example 2, and the results of correlation analysis.

[0041] Figure 15 This is a graph showing the results of correlation analysis of the results of the fatigue task test according to Example 3 and the amount of change in heart rate fluctuations before and after the fatigue task test.

[0042] Figure 16 This is a graph showing the comparison between the subjective evaluation "difficulty in concentrating" and the average value of VLF2 according to Example 4, and the results of correlation analysis. DETAILED DESCRIPTION

[0043] <Implementation Method 1>

[0044] The physical and mental state estimation system according to the present embodiment is a system for estimating the physical and mental state of the subject by calculating the heart rate fluctuation of the very low frequency component (VLF) based on the heart rate information related to the subject's heart rate and estimating the subject's physical and mental state. In this specification, the calculated VLF is referred to as "VLF" or "VLF signal". Figures 1 to 3 , the physical and mental state estimation system involved in this embodiment is described in detail.

[0045] Figure 1 : is a block diagram showing the physical and mental state estimation system involved in embodiment 1. Figure 1 As shown, the physical and mental state estimation system 10 includes a heart rate information acquisition unit 11 , a heart rate variability calculation unit 12 , and a physical and mental state estimation unit 13 .

[0046] [1. Heart rate information acquisition unit]

[0047] Figure 1 The heart rate information acquisition unit 11 shown acquires information related to the subject's heart rate, namely, heart rate information. Examples of the heart rate information acquisition unit 11 include an electrocardiogram (ECG sensor), a heart rate monitor (optical transmission-type pulse wave sensor, optical reflection-type pulse wave sensor), a blood pressure monitor, a pressure sensor (piezoelectric element), a non-contact sensor using facial images, or commercially available wearable devices such as smart watches. The heart rate information acquisition unit 11 can also be embedded in chairs and vehicle seats. The heart rate information is, for example, an electrocardiogram acquired by an ECG, but is not limited to this. For example, information equivalent to an ECG can be acquired based on pulse waves, arterial pressure, or the like as heart rate information.

[0048] [2. Heart rate variability calculation unit]

[0049] Figure 1 The heart rate variability calculation unit 12 shown performs frequency analysis on the subject's heart rate information acquired by the heart rate information acquisition unit 11 and calculates the heart rate variability (HRV) value for each component in a specific frequency band. The term "heart rate variability" in this embodiment includes, for example, heart rate variability, pulse variability, and blood pressure beat-to-beat variability.

[0050] As an example, the calculation of heart rate variability by the heart rate variability calculation unit 12 will be described when an electrocardiogram is acquired as heart rate information using an electrocardiogram as the heart rate information acquisition unit 11. The highest peak in the electrocardiogram acquired by the heart rate information acquisition unit 11 is called the R wave. In an electrocardiogram, the heart rate interval is represented by the interval between R waves, namely the RR interval (RRI). Heart rate variability refers to the periodic fluctuations that occur in the RR interval.

[0051] The heart rate variability calculation unit 12 calculates the heart rate variability time series data obtained by calculating the RR interval based on the electrocardiogram. When the heart rate variability time series data is subjected to frequency analysis, the horizontal axis is represented by frequency (Hz) and the vertical axis is represented by power (msec 2 A graph of power spectral density (PSD) expressed in Hz is provided. Frequency analysis can be performed using a known analysis method. Specifically, for example, a fast Fourier transform (FFT) method, a maximum entropy method (MEM method), or the like can be used.

[0052] The obtained power spectrum density curve has a specific peak in a specific frequency band. The heart rate variation calculation unit 12 calculates (1) the value of the very low frequency component (VLF) as the heart rate variation value for each frequency band. In addition, instead of (1), (2) the values ​​of VLF1 and VLF2 included in the VLF can be calculated. In addition, (3) the values ​​of the VLF, the low frequency component (LF), and the high frequency component (HF) can also be calculated. The details of the calculation of each heart rate variation value will be described later.

[0053] In this embodiment, the VLF value can be used as an indicator of sympathetic nervous system function. In particular, the VLF value can be used to estimate the state of concentration / effort, the VLF1 value can be used to estimate the state of fatigue, and the VLF2 value can be used to estimate the state of concentration / effort. Here, "effort state" refers to a state in which the subject is exerting mental and / or physical effort to achieve a goal. Furthermore, the LF value is considered an indicator of the subject's sympathetic nervous system function, the HF value is considered an indicator of the subject's parasympathetic nervous system function, and the LF / HF value is considered an indicator of the subject's sympathetic nervous system function.

[0054] The heart rate variation calculation unit 12 pre-calculates the normal value (threshold value) of the heart rate variation value of the subject when at rest in order to use the physical and mental state estimation unit 13 to estimate the physical and mental state of the subject as described later. The normal value (threshold value) of the heart rate variation value is a value with a predetermined amplitude. In order to calculate the normal value (threshold value) of the heart rate variation value, the heart rate variation calculation unit 12 obtains heart rate information at least twice. The heart rate variation calculation unit 12 calculates the value of the heart rate variation based on the obtained heart rate information. Alternatively, the average value of the heart rate variation value may be calculated to obtain the standard deviation σ, and the range of ±2σ may be used as the normal value (threshold value) of the heart rate variation value of the subject.

[0055] In addition, without precalculating the normal value (threshold value) of the subject's heart rate variation value, the normal value (threshold value) can also be calculated using the heart rate information or heart rate variation values ​​of two or more other people obtained in advance. "Other people" refers to people other than the subject. The heart rate information or heart rate variation values ​​of other people can also be values ​​obtained by obtaining more than twice from a person different from the subject. The heart rate information or heart rate variation values ​​of other people can also be values ​​obtained more than once from multiple people different from the subject. Alternatively, the average value of the heart rate variation values ​​can be calculated to obtain the standard deviation σ, and the range of ±2σ can be used as the normal value (threshold value) of the heart rate variation value.

[0056] The calculation of the heart rate variation values ​​(1) to (3) described above will be described in more detail below.

[0057] (1) VLF value

[0058] The VLF frequency band can be any value below the frequency that can be transmitted by the sympathetic nerves. Specifically, the VLF frequency band is, for example, 0.0001 to 0.05 Hz, preferably 0.0033 to 0.04 Hz. However, the VLF frequency band is not limited to these and can also be defined by other frequency bands.

[0059] The VLF value can also be calculated as the integrated value (area) of the power within the VLF frequency band. Alternatively, the calculated integrated value (area) of the power can be further divided by the width of the frequency band representing the VLF. The width of the VLF frequency band is obtained by subtracting the lower limit from the upper limit of the VLF frequency band.

[0060] (2) Values ​​of VLF1 and VLF2 included in VLF

[0061] Furthermore, the VLF value of (1) above can also be calculated as a VLF1 component and a VLF2 component, which are further classified. Both VLF1 and VLF2 are frequency bands included in the VLF of (1). The frequency band of VLF1 is a higher frequency band than the frequency band of VLF2. In other words, the frequency band of VLF2 is a lower frequency band than the frequency band of VLF1.

[0062] The correlation between VLF1 and VLF2 will be described in detail. For example, VLF1 and VLF2 can be set so that they are divided into two frequency bands using an arbitrary frequency value as the boundary value. Alternatively, they can be set so that a portion of the low frequency band included in VLF1 overlaps a portion of the high frequency band included in VLF2. Furthermore, the lower frequency limit of VLF1 and the upper frequency limit of VLF2 can be set so that they do not overlap and are separated from each other.

[0063] When VLF1 and VLF2 are set so as to be divided into two frequency bands with an arbitrary frequency value as a boundary value, 0.015 Hz may be used as an example. This value is one tenth of 0.15 Hz, which is often used as a boundary value between LF and HF.

[0064] The values ​​of VLF1 and VLF2 can be calculated using the same calculation method as for VLF in (1) above. That is, the values ​​of VLF1 and VLF2 can also be calculated as the integrated value (area) of the power within each frequency band. Alternatively, the integrated value (area) of the calculated power can be further divided by the width of the frequency band representing VLF1 or VLF2. The width of the frequency bands of VLF1 and VLF2 is calculated by subtracting the lower limit from the upper limit of each frequency band.

[0065] (3) Values ​​of VLF, LF, and HF

[0066] In addition to (1) or (2) above, the value of LF and the value of HF can also be calculated. VLF can also be any one of the VLF of (1) above and VLF1 and VLF2 of (2). The frequency band of LF can be any value that can be transmitted by the sympathetic nerves and parasympathetic nerves. Specifically, it is 0.03 to 0.20 Hz, preferably 0.04 to 0.15 Hz, but not limited to these. The frequency band of HF can be any value that can be transmitted by the parasympathetic nerves. Specifically, it is 0.10 to 0.5 Hz, preferably 0.15 to 0.4 Hz. However, it is not limited to these and can also be defined by other frequency bands.

[0067] The values ​​of VLF or VLF1 and VLF2, LF and HF can be obtained using the same calculation method as the VLF in (1) above. That is, each value can also be calculated as the integral value (area) of the power in each frequency band. In addition, the calculated integral value (area) of the power can be further divided by the width of the frequency band representing each frequency band to obtain the value. The width of the frequency band is obtained by subtracting the lower limit value from the upper limit value of each frequency band. In addition, the heart rate variation calculation unit 12 calculates the "LF / HF value" as an indicator of tension / fatigue from the LF value and the HF value. The LF / HF value is a value calculated by dividing the LF value by the HF value. In addition, the logarithm of the LF / HF value, namely the "ln(LF / HF) value", can also be used.

[0068] Specific examples of heart rate information and heart rate variability have been described above. However, the heart rate information acquired by the heart rate information acquisition unit 11 is not limited to the specific examples as long as it is information related to the heart rate that can at least calculate VLF when the heart rate variability calculation unit 12 performs frequency analysis.

[0069] For example, as another example of heart rate information and heart rate variability, a pulse wave meter can be used as the heart rate information acquisition unit 11 to acquire the pulse as heart rate information. The pulse has a periodicity that is linked to the heart rate. The heart rate variability calculated by the heart rate variability calculation unit 12 includes periodic fluctuations occurring in the pulse interval (PI), i.e., pulse variability. The pulse wave waveform has a gentler peak than an electrocardiogram. However, by obtaining an acceleration pulse wave as a second derivative wave from the acquired pulse waveform, more accurate pulse interval information can be acquired.

[0070] Alternatively, for example, a blood pressure monitor may be used as the heart rate information acquisition unit 11 to acquire the beat interval as heart rate information. The heart rate variability calculated by the heart rate variability calculation unit 12 includes beat interval variability. Specifically, the heart rate variability calculation unit 12 can obtain a time-series waveform of the beat interval acquired as heart rate information and perform frequency analysis on the time-series waveform to obtain the beat interval variability.

[0071] [3. Physical and Mental State Estimation]

[0072] Figure 1 The physical and mental state estimation unit 13 shown estimates the physical and mental state of the subject based on the heart rate variability values ​​calculated by the heart rate variability calculation unit 12. Specifically, the physical and mental state of the subject in this embodiment includes a state of concentration / effort and a state of fatigue. The subject is a person who resides in a somewhat enclosed space where the subject's surrounding environment can be controlled. Examples include a vehicle driver, an aircraft pilot, a person working in an office or store, or a person studying in a classroom or room.

[0073] The following describes the following cases: the case where the heart rate variability calculation unit 12 (1) calculates the value of VLF as the value of heart rate variability, (2) calculates the values ​​of VLF1 and VLF2 included in VLF, and (3) calculates the values ​​of VLF, LF, and HF (LF / HF).

[0074] (1) When the VLF value is calculated

[0075] If the VLF value calculated by the heart rate variability calculation unit 12 is less than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is not concentrating compared to a resting state. Conversely, if the VLF value is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is concentrating or exerting effort compared to a resting state.

[0076] (2) When the values ​​of VLF1 and VLF2 included in VLF are calculated

[0077] Reference Figure 2 To explain. Figure 2 : is a diagram showing the relationship between VLF1 and fatigue state and the relationship between VLF2 and concentration / effort state according to the first embodiment. Figure 2 As shown, in this embodiment, VLF1 indicates fatigue and VLF2 indicates concentration / effort. That is, when the values ​​of VLF1 and VLF2 are calculated, the fatigue can be estimated from the value of VLF1 in addition to the concentration / effort state.

[0078] If the VLF1 value calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is more fatigued than when at rest. Conversely, if the VLF1 value is less than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is no more fatigued than when at rest.

[0079] If the VLF2 value calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is more focused and diligent than when at rest. Conversely, if the VLF2 value is less than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is less focused and distracted than when at rest.

[0080] Next, the estimation of the physical and mental state when the values ​​of VLF1 and VLF2 are combined will be described.

[0081] When the values ​​of VLF1 and VLF2 calculated by the heart rate variability calculation unit 12 are larger than the normal value (threshold value), the physical and mental state estimation unit 13 estimates that the subject is more focused and exerting effort than when at rest and is fatigued.

[0082] When the VLF1 value calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold) and the VLF2 value is less than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is not concentrating as compared to resting state and is distracted and tired.

[0083] When the value of VLF1 calculated by the heart rate variability calculation unit 12 is smaller than the normal value (threshold) and the value of VLF2 is larger than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is concentrating and exerting effort and is not fatigued.

[0084] When the values ​​of VLF1 and VLF2 calculated by the heart rate variability calculation unit 12 are smaller than the normal value (threshold value), the physical and mental state estimation unit 13 estimates that the subject is not concentrating but is distracted and not fatigued.

[0085] (3) When the values ​​of VLF, LF, and HF (LF / HF) are calculated

[0086] Reference Figure 3 To explain. Figure 3 This diagram shows the classification of physical and mental states based on heart rate variability values ​​according to Embodiment 1. As described in (1), VLF indicates a state of concentration / effort. When LF and HF values ​​are calculated in addition to VLF, the physical and mental state estimation unit 13 estimates a state of fatigue in addition to the state of concentration / effort.

[0087] The following describes the estimation of the physical and mental state when the values ​​of VLF, HF, and LF / HF are combined, divided into states (a) to (f). Figure 3 (a) to (f) correspond to.

[0088] (a) Concentrated / high state: large VLF, large LF / HF, less than 20 minutes

[0089] If the VLF value calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold) and the LF / HF value is greater than the normal value (threshold), and the state indicating these values ​​lasts continuously or intermittently for less than 20 minutes in total, the physical and mental state estimation unit 13 estimates that the subject is in a state of concentration / effort and high spirits.

[0090] (b) Fatigue state: large VLF, large LF / HF, more than 20 minutes

[0091] On the other hand, when the VLF value calculated by the heart rate variation calculation unit 12 is greater than the normal value (threshold) and the LF / HF value is greater than the normal value (threshold) in the same manner as in (a), and the state indicating these values ​​continues or intermittently for a total of more than 20 minutes, the physical and mental state estimation unit 13 estimates that there is a high possibility that the subject is fatigued.

[0092] (c) Creative / inspirational state: large VLF and large HF

[0093] When the VLF value calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold) and the HF value is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is in a state suitable for creative work or is inspired by something.

[0094] (d) Rest / relaxation state: VLF is within the normal value (threshold) range, HF is large

[0095] When the VLF value calculated by the heart rate variability calculation unit 12 is within the normal value (threshold) range and the HF value is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is in a resting or relaxed state.

[0096] (e) Distracted / anxious state: small VLF, large LF / HF

[0097] When the VLF value calculated by the heart rate variability calculation unit 12 is smaller than the normal value (threshold) and the LF / HF value is larger than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is in a distracted state with reduced concentration and is feeling anxious.

[0098] (f) Boredom / burnout: small VLF and large HF

[0099] When the VLF value calculated by the heart rate variability calculation unit 12 is smaller than the normal value (threshold) and the HF value is larger than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is in a state of boredom or fatigue.

[0100] Furthermore, as described in (2) above, VLF may be further classified into VLF1 and VLF2. By confirming both the fatigue state indicated by the VLF1 value and the stress / fatigue state indicated by the LF / HF value, the fatigue state can be estimated with higher accuracy.

[0101] Next, the physical and mental state estimation method according to this embodiment will be described.

[0102] Figure 4 Flowchart showing the method for estimating the physical and mental state according to the first embodiment. Figure 1 and Figure 4 As shown, the physical and mental state estimation method involved in this embodiment includes: a step of the heart rate information acquisition unit 11 acquiring heart rate information (step S1); a step of the heart rate variation calculation unit 12 calculating the heart rate variation (step S2); and a step of the physical and mental state estimation unit 13 estimating the physical and mental state (step S3).

[0103] The physical and mental state estimation system and method according to this embodiment use the VLF value as an indicator of concentration and effort. As an example of a test that can estimate fatigue and / or decreased alertness using methods other than heart rate variability, the Psychomotor Vigilance Test (PVT) is known. Furthermore, the N-back task is known as a mentally taxing task.

[0104] Compared to the correlation between PVT performance and fatigue status estimated using LF / HF values, which are well-known indicators of mental fatigue, PVT performance has a higher correlation with fatigue status estimated using VLF values ​​in this embodiment. Furthermore, as shown in the examples described below, while N-back task performance is correlated with concentration / effort status estimated using VLF values, N-back task performance is not considered to be correlated with LF / HF.

[0105] Furthermore, the following examples show that VLF1 is positively correlated with PVT performance and increases in fatigued states. Furthermore, the following examples show that while VLF1 is not correlated with N-back task performance, VLF2 is negatively correlated with N-back task performance and increases in focused / effortful states. Furthermore, VLF2 has a higher negative correlation with N-back task performance than VLF.

[0106] Therefore, the physical and mental state estimation system and method for estimating physical and mental state according to this embodiment estimate the state of concentration and effort based on the VLF, a value of heart rate variation that has a higher correlation with the state of concentration and effort than the LF / HF. This allows for a higher accuracy estimation of the subject's state of concentration and effort. Furthermore, by using both the VLF1, which correlates with fatigue, and the VLF2, which correlates with the state of concentration and effort, fatigue and the state of concentration and effort can be estimated simultaneously. Furthermore, by using the VLF2, which has a higher negative correlation with N-back task performance than the VLF, the state of concentration and effort can be estimated with even higher accuracy.

[0107] In addition, regarding the correlation between the PVT scores and the N-back task scores and the VLF, VLF1, VLF2, and LF / HF, the specific correlation analysis results will be described later in the examples.

[0108] <Implementation Method 2>

[0109] The physical and mental state estimation system of this embodiment further includes an environment control unit, which controls the environment of the subject based on the physical and mental state estimated by the physical and mental state estimation system of embodiment 1. The environment of the subject refers to the environment of the space where the subject stays. Figure 5 and Figure 6 , the physical and mental state estimation system involved in this embodiment is explained.

[0110] Figure 5 2 is a block diagram showing a physical and mental state estimation system 20 according to Embodiment 2. Hereinafter, the same reference numerals are given to the components common to the physical and mental state estimation system according to Embodiment 1, and only the different components are described.

[0111] The environment control unit 14 controls the environment surrounding the subject based on the estimated physical and mental state. By controlling the environment surrounding the subject, the subject can be placed in an optimal physical and mental state. The environment control unit 14 includes a control content determination unit, but it can also be provided separately. This control content determination unit determines the control content based on the physical and mental state estimated by the physical and mental state estimation unit 13.

[0112] The environment control unit 14 performs control to improve concentration when the concentration / effort state estimated by the physical and mental state estimation unit 13 is below a predetermined level. As described above, when the value of VLF or the value of VLF2 calculated by the heart rate variability calculation unit 12 is less than a normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject's concentration state is low. The environment control unit 14 can perform control to improve concentration when the value of VLF or the value of VLF2 is less than a normal value (threshold). Figure 6 To illustrate the specific control content.

[0113] Figure 6 TABLE 1 is a table showing an example of environmental control performed by the environmental control unit 14 of the physical and mental state estimation system according to Embodiment 2. Figure 6 As shown, by controlling hearing, smell, touch, and vision, the state of concentration can be improved. Each control item of hearing, smell, touch, and vision can be used for environmental control individually or in combination.

[0114] Specifically, when the control item is hearing, a speaker is set in the environment around the subject. The environmental control unit 14 controls to play the rustling sound of leaves blown by the wind at a volume that the subject can hear. When the control item is smell, the environmental control unit 14 controls so that the fragrance of rosemary essential oil, for example, is transmitted to the subject. In addition, other essential oils known to have an effect on improving concentration can also be used. The essential oil can be formed into a mist using an aroma diffuser or a few drops can be dripped on a small device such as an aroma stone. The method is not limited, as long as the subject can smell the fragrance of the essential oil.

[0115] When the control item is tactile sensation, the subject sits on a vibrating chair or a chair with a vibrating seat. The environment control unit 14 obtains a period based on the frequency of the subject's VLF or VLF2 and vibrates the chair according to the period.

[0116] When the control item is vision, lighting capable of varying the illuminance is placed within the subject's field of view. The environmental control unit 14 varies the illuminance. For example, the color of the light can be blue. Methods for varying the illuminance include: oscillating the frequency and illuminance of the subject's VLF signal synchronously with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof; and oscillating the variance of the frequency and illuminance of the subject's VLF signal synchronously with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof.

[0117] The illuminance of the light is randomly varied by "making the frequency of the subject's VLF signal and the illuminance flash synchronously with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof." First, the variance of the predetermined illuminance of the lighting to be used can be statistically calculated, and the frequency of the VLF signal can be flashed synchronously with the variance with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof. Furthermore, the amplitude of the VLF signal frequency can be flashed synchronously with the illuminance of the light with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof.

[0118] The environmental control unit 14 may also flash the light at n times (n is a natural number greater than or equal to 1) or 1 / n times (n is a natural number greater than or equal to 2) the frequency of the VLF signal of the subject, rather than at the frequency of the VLF signal itself. Furthermore, since brain activity is known to function differently on the left and right sides of the subject's field of view, the light may be flashed so that the left and right sides experience different flashing conditions. For example, the left side may be flashed in synchronization with the frequency of the subject's VLF signal and the illuminance, while the right side may be flashed in synchronization with the variance of the illuminance. Alternatively, the left and right sides may be flashed in reverse.

[0119] The environmental control unit 14 can also further perform fatigue relief control when the fatigue state estimated by the physical and mental state estimation unit 13 is above a predetermined level. As described above, when the value of VLF or the value of VLF1 calculated by the heart rate variability calculation unit 12 is greater than the normal value (threshold), the physical and mental state estimation unit 13 estimates that the subject is fatigued. The environmental control unit 14 can perform fatigue relief control when the value of VLF or the value of VLF1 is greater than the normal value (threshold). Figure 7 An example of specific control content will be described below.

[0120] Figure 7 This is a table showing an example of environmental control performed by the environmental control unit of the physical and mental state estimation system according to the second embodiment. Figure 7 As shown in FIG, by controlling hearing, smell, touch, and vision, fatigue can be alleviated. As with the control of the concentration state, each control item of hearing, smell, touch, and vision can be used for environmental control individually or in combination.

[0121] Specifically, if the control item is hearing, a speaker is placed in the environment surrounding the subject. The environmental control unit 14 controls the playback of the sound of a gurgling stream at a volume audible to the subject. If the control item is smell, the environmental control unit 14 controls the transmission of the scent of, for example, juniper essential oil to the subject. Other essential oils known to be effective in relieving fatigue can also be used. Essential oils can be sprayed using an aromatherapy sprayer or a few drops can be applied to a small device such as a diffuser. The method is not limited to allowing the subject to smell the essential oil's fragrance.

[0122] When the control item is tactile sensation, the subject sits on a vibrating chair or a chair with a vibrating seat. The environment control unit 14 obtains a period based on the frequency of the subject's HF and vibrates the chair according to the period.

[0123] If the control item is vision, lighting with variable illuminance is placed within the subject's field of view. The environmental control unit 14 varies the illuminance. Blue light can be used. To vary the illuminance, the frequency of the subject's VLF signal is synchronized with the illuminance.

[0124] Next, the physical and mental state estimation method according to this embodiment will be described.

[0125] Figure 8 Flowchart showing the method for estimating the physical and mental state according to the second embodiment. Figure 5 and Figure 8 As shown, the physical and mental state estimation method according to this embodiment includes, in addition to steps S1 to S3 of the first embodiment, a step (step S4) in which the environment control unit 14 controls the environment around the subject based on the estimated physical and mental state.

[0126] After step S4, a step of determining whether to terminate the control may be further included. For example, the environmental control of step S4 may be continued for a predetermined time, or steps S1 to S3 may be executed after the environmental control of step S4, and the environmental control of step S4 may be repeated until the subject's physical and mental state reaches the desired state.

[0127] The physical and mental state estimation system and the physical and mental state estimation method involved in this embodiment are equipped with an environmental control unit for controlling the environment around the subject. By controlling the surrounding environment, it is possible to achieve the best physical and mental state for the subject. Therefore, a comfortable environment with high productivity and efficiency and less stress can be achieved. In addition, the environmental control unit can perform control to improve concentration when the value of VLF or the value of VLF2 is less than the normal value (threshold value). Therefore, when the concentration of the subject is reduced, the concentration can be improved. In addition, the environmental control unit can perform control to relieve fatigue when the value of VLF or the value of VLF1 is greater than the normal value (threshold value). Therefore, when the subject is tired, the fatigue can be relieved.

[0128] <Implementation Method 3>

[0129] The physical and mental state estimation system according to this embodiment is further provided with a target setting unit for setting a target of the physical and mental state based on the physical and mental state estimation system according to the second embodiment. Figure 9 , the physical and mental state estimation system involved in this embodiment is explained.

[0130] Figure 9 3 is a block diagram showing a physical and mental state estimation system 30 according to Embodiment 3. Hereinafter, the same reference numerals are given to the components common to the physical and mental state estimation system according to Embodiment 2, and only the different components are described.

[0131] The target setting unit 15 sets the target physical and mental state of the subject. The environmental control unit 14 controls the environment surrounding the subject based on the subject's physical and mental state estimated by the physical and mental state estimation unit 13 and the set physical and mental state target. When setting the target physical and mental state of the subject, the target setting unit 15 may input the subject's desired physical and mental state, or input data obtained by pre-associating the subject's physical and mental state estimated by the physical and mental state estimation unit 13 with the content of the environmental control.

[0132] Here, in embodiment 1, Figure 3 The states (a) to (f) estimated by the physical and mental state estimation unit 13 described above represent the subject's current state. Furthermore, as an example, the physical and mental states of the subject set as targets by the target setting unit 15 in this embodiment are states (a), (c), and (d) among the following states (a) to (f).

[0133] (a) Concentrated / high state: large VLF, large LF / HF, less than 20 minutes

[0134] (b) Fatigue state: large VLF, large LF / HF, more than 20 minutes

[0135] (c) Creative / inspirational state: large VLF and large HF

[0136] (d) Rest / relaxation state: VLF is within the normal value (threshold) range, HF is large

[0137] (e) Distracted / anxious state: small VLF, large LF / HF

[0138] (f) Boredom / burnout: small VLF and large HF

[0139] Reference Figure 10 , an example of specific control content when the target setting unit 15 sets states (a), (c), and (d) as the physical and mental states of the subject as targets will be described.

[0140] Figure 10 TABLE 1 is a table showing an example of environmental control performed by the environmental control unit 14 of the physical and mental state estimation system according to Embodiment 3. Figure 10 As shown, by controlling temperature, hearing, smell, touch, vision (light), vision (other than light), and wind, the subject's physical and mental state can be controlled to states (a), (c), and (d). Each control item of temperature, hearing, smell, touch, vision (light), and vision (other than light) can be used for environmental control individually or in combination.

[0141] For example, when the physical and mental state estimated by the physical and mental state estimation unit 13 is state (e) or (f), the target setting unit 15 sets the target of the physical and mental state to state (a) or (c), and the environment control unit 14 controls the environment around the subject so that the physical and mental state of the subject is adjusted from state (e) or (f) to the focused state, i.e., (a) or (c). Figure 3 and Figure 10 As shown, by performing environmental control of the space where the subject is present, the subject's VLF value can be controlled from a low state (distracted) to a high state (focused).

[0142] Similarly, when the physical and mental state estimated by the physical and mental state estimation unit 13 is one of states (c), (d), and (f), the target setting unit 15 sets the physical and mental state target to (a), which is a focused / high-spirited state, and the environment control unit 14 controls the environment around the subject so that the physical and mental state of the subject is adjusted from states (c), (d), and (f) to state (a). Figure 3 and Figure 10 As shown, by controlling the environment of the subject's space, the subject's HF value state (sluggish / inert (inactive) / drowsy) can be controlled to a state with large LF / HF values ​​(active).

[0143] In addition, when the physical and mental state estimated by the physical and mental state estimation unit 13 is state (b) or (e), the target setting unit 15 sets the target of the physical and mental state to state (c) or (d), and the environment control unit 14 controls the environment around the subject so that the physical and mental state of the subject is adjusted from state (b) or (e) to state (c) or (d). Figure 3 and Figure 10 As shown, by controlling the environment of the subject's space, the subject's LF / HF value state can be controlled from a state with high values ​​(stress / fatigue / activity) to a state with high values ​​(inspiration / relaxation).

[0144] As a specific example, if the subject is considering relaxing, the rest / relaxation state (d) is input into the goal setting unit 15. If the subject is considering focusing on work or study, the concentration state (a) is input into the goal setting unit 15. If the concentration state (a) lasts for more than 20 minutes, it will turn into the fatigue state (b), so the surrounding environment is controlled to adjust to (c) or (d). After approximately 20 minutes in state (c) or (d), the environment around the subject is again controlled to adjust to the concentration state (a). This series of controls can be input into the goal setting unit 15 as desired by the subject, or can be pre-input into the goal setting unit 15 by repeating the above-mentioned environmental controls of (a) and (c) or (d). Furthermore, if the subject is engaged in creative work or considering gaining inspiration, the creative / inspirational state (c) is input into the goal setting unit. In order to improve the subject's concentration and achieve a mental and physical state that is less prone to fatigue, the control content is determined so that the VLF increases without increasing the LF / HF.

[0145] The specific control contents of the environment control unit 14 are the same as those in the first embodiment for the senses of hearing, smell, touch, and vision (light). Here, the temperature and wind are described. Figure 10 As shown, when the control item is temperature, the environment control unit 14 controls the temperature of the space where the subject is located. When the control item is vision (other than light), the environment control unit 14 controls to place plants or play images of plants within the subject's field of vision.

[0146] When the control item is wind, the environmental control unit 14 causes artificial wind to blow in the space where the subject is located. The environmental control unit 14 varies the wind speed in synchronization with the subject's VLF signal. Methods for varying the wind speed include varying the frequency of the subject's VLF signal and the average wind speed in synchronization with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof; and varying the frequency of the subject's VLF signal and the variance of the wind speed in synchronization with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof.

[0147] In the case of "changing the frequency of the subject's VLF signal and the average wind speed in synchronization with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof," the average wind speed generated by the artificial wind device over a predetermined period is first determined. Based on this average, the wind speed is then varied in accordance with the subject's VLF signal.

[0148] The wind speed is randomly varied when the frequency of the subject's VLF signal and the variance of the wind speed are varied with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof. First, the variance of the wind speed within the predetermined wind speeds generated by the device capable of blowing artificial wind can be statistically calculated, and the wind speed can be varied by synchronizing the frequency of the VLF signal and the variance with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof. Alternatively, the wind speed can be varied by synchronizing the amplitude of the VLF signal frequency and the wind speed with the same period or a constant multiple thereof, or with the same phase or a constant multiple thereof.

[0149] The environmental control unit 14 may also change the wind speed by using n times (n is a natural number greater than or equal to 1) or 1 / n times (n is a natural number greater than or equal to 2) the frequency of the VLF signal rather than the frequency of the subject's own VLF signal. Furthermore, since brain activity is known to play different roles on the left and right sides, different wind speeds may be set for the left and right sides of the subject. For example, the frequency of the subject's own VLF signal may be changed synchronously with the wind speed on the left side, while the variance of the frequency of the subject's own VLF signal may be changed synchronously with the wind speed on the right side. Alternatively, the left and right sides may be reversed.

[0150] When controlling the subject to enter a focused state (state (a) or (c)), the environmental control unit 14 changes the wind speed at the frequency of the subject's VLF. Similarly, when controlling the subject to enter a focused / excited state (state (a)), the environmental control unit 14 changes the wind speed at the frequency of the subject's LF.

[0151] The goal setting unit 15 of this embodiment may also set a goal based on a predetermined schedule. For example, a target physical and mental state for each time period may be set based on the subject's schedule, so that the subject's physical and mental state during the set time period is the target physical and mental state. Alternatively, the goal setting unit 15 may estimate the optimal physical and mental state for each time period during the subject's schedule based on the subject's schedule and set it as the target physical and mental state.

[0152] When the goal setting unit 15 sets a goal based on a predetermined schedule, even when the subject does not set a goal himself / herself, the goal can be automatically set based on the subject's schedule.

[0153] The physical and mental state estimation system of this embodiment may also include an environmental information acquisition unit for acquiring environmental information surrounding the subject. The environmental information acquisition unit may also acquire environmental information surrounding the subject in advance. The environmental information acquisition unit may also acquire environmental information in parallel with acquiring heart rate information, calculating heart rate variability, and estimating physical and mental state. Based on the acquired environmental information, the environmental control unit may control the environment surrounding the subject to achieve the subject's target physical and mental state. The inclusion of the environmental information acquisition unit enables more precise control of physical and mental state.

[0154] Furthermore, to improve the accuracy of estimating the subject's physical and mental state and enhance the effectiveness of environmental control, a database containing various information can be created and used for setting physical and mental state targets, estimating physical and mental state, and controlling the environment. For example, the database can store personal identification information, environmental information, biological indicators, physical and mental state estimation results, target input information, environmental control details, and data on the physical and mental state after environmental control. Furthermore, the database can also store variability in physical and mental state, such as intraday and seasonal variations, as well as changes and reactivity of physical and mental state caused by environmental control.

[0155] Next, the physical and mental state estimation method according to this embodiment will be described.

[0156] Figure 11 Flowchart showing the method for estimating the physical and mental state according to the third embodiment. Figure 9 and Figure 11 As shown, the physical and mental state estimation method according to this embodiment includes, in addition to steps S1 to S4 of embodiment 2 (steps S12 to S15 of this embodiment), a step in which the target setting unit 15 sets a target for the physical and mental state of the subject (step S11).

[0157] After step S15, a step may also be included to determine whether to terminate control. For example, steps S12 to S14 may be executed after the environmental control in step S15, and the environmental control in step S15 may be repeated until the subject's physical and mental state reaches the set target state. Furthermore, the information registered in the database may be used to set physical and mental state targets, estimate physical and mental states, and perform environmental control.

[0158] The physical and mental state estimation system and method according to this embodiment further include a target setting unit for setting a target physical and mental state of the subject. The target setting unit enables the physical and mental state of the subject to be adjusted to a desired target physical and mental state.

[0159] <Other Implementation Methods>

[0160] The physical and mental state estimation system of the present invention can also realize arbitrary processing for estimating the physical and mental state by having a processor such as a CPU (Central Processing Unit) read and execute a computer program stored in a memory.

[0161] In the above example, the program can be stored using various types of non-transitory computer readable media and supplied to the computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer readable media include magnetic recording media (e.g., floppy disks, magnetic tapes, hard disk drives), optical magnetic recording media (e.g., optical magnetic disks), CD-ROMs (Compact Disc-Read Only Memory), CD-Rs (CD-Recordable), CD-R / Ws (CD-Re Writable), semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). In addition, the program can also be supplied to the computer by various types of transient computer readable media. Examples of transient computer readable media include electrical signals, optical signals, and electromagnetic waves. Transient computer readable media can supply the program to the computer via wired communication paths such as wires and optical fibers, or wireless communication paths.

[0162] Below, refer to Figures 12-16, the present invention is specifically described based on examples, but the present invention is not limited to these examples.

[0163] <Example 1>

[0164] In this example, 37 subjects underwent a fatigue task experiment and their physical and mental states were examined before and after the task. The VLF, the conventionally used index ln(LF / HF), and the subjective fatigue assessment (VAS) were used as indicators of physical and mental states. The fatigue tasks included the Psychomotor Vigilance Test (PVT) and the N-back task.

[0165] First, the heart rate information acquisition unit of the physical and mental state estimation system involved in embodiment 1 obtains 5 minutes of resting heart rate information, and the heart rate variability calculation unit calculates the normal value (threshold) of the value of the resting heart rate variability. Next, after implementing 5 minutes of PVT (first time), a 20-minute N-back task (3-back) is implemented. After the N-back task, a 5-minute PVT is implemented (second time). Next, the heart rate information acquisition unit of the physical and mental state estimation system obtains 5 minutes of electrocardiogram as heart rate information, and the heart rate variability calculation unit calculates the value of ln(LF / HF) and the value of VLF, and calculates the average value among the subjects. In parallel, a subjective evaluation of fatigue (VAS) was also performed before and after the fatigue task.

[0166] Figure 12 This is a graph showing the results of subjective fatigue evaluation before and after the fatigue task experiment in Example 1, the results of PVT reaction time, the results of N-back answer time, the results of ln(LF / HF), and the results of cCVVLF (%). Each graph shows the results of the corresponding t-test. ** indicates p < 0.01, * indicates p < 0.05, p<0.1, indicating a significant difference or a significant trend. Figure 12 The vertical axis shows the results of subjective fatigue evaluation (VAS), PVT reaction time, and N-back answer time in the upper row from the left, and the results of ln(LF / HF) and ccvVLF (%) in the lower row from the left.

[0167] like Figure 12 As shown in the upper left row, the VAS results showed a significant increase after the task compared to before. The increase in VAS values ​​indicates increased fatigue. This indicates that the subjects were fatigued by the N-back task.

[0168] like Figure 12As shown in the middle of the top row, the PVT reaction time results show that the second PVT performed after the N-back task tends to increase compared to the first PVT performed before the N-back task. This increase in PVT reaction time indicates a decrease in performance. This decrease in PVT performance indicates that the subjects were fatigued by the N-back task.

[0169] like Figure 12 As shown in the upper right row of the graph, the N-back response time was measured. The first half of the 20-minute N-back task was defined as the period from 2 to 8 minutes after the start of the task, and the second half was defined as the period from 14 to 20 minutes after the start of the task. The N-back response time was significantly reduced in the second half compared to the first half. This reduction in N-back response time indicates improved performance. This confirms that the subjects completed the N-back task diligently and attentively.

[0170] like Figure 12 As shown on the left side of the lower part of , the average value of ln(LF / HF), which is conventionally used as an indicator of mental fatigue, also increased significantly after the task compared to before the task.

[0171] Figure 12 The cccvVLF (coefficient component of variance VLF) shown on the right side of the lower section is obtained by correcting the power value of the component VLF with the average heart rate interval (RR interval). That is, it is expressed by "ccvVLF (%) = 100 × √ (power of VLF) / average heart rate interval". Figure 12 As shown in Figure 3, ccvVLF also increased significantly after the task compared with before the task.

[0172] Figure 13 This graph shows the results of a correlation analysis of the fatigue task test results and the changes in heart rate variability before and after the fatigue task test in Example 1. ** indicates p < 0.01, and * indicates p < 0.05, indicating significant correlation. Each scatter plot shows the task performance of 37 subjects and the changes in cCVVLF or ln(LF / HF), which indicate physical and mental states, before and after the fatigue task.

[0173] The PVT reaction time means the reaction time to a displayed task. Figure 13The ΔPVT reaction time (in seconds) on the horizontal axis of Figures A and B is the difference between the PVT reaction time before and after the N-back task. For example, if the PVT reaction time after the N-back task is shorter than the PVT reaction time before the N-back task, the ΔPVT reaction time becomes a negative value, indicating an improved performance. On the other hand, if the PVT reaction time after the N-back task is longer than the PVT reaction time before the N-back task, the ΔPVT reaction time becomes a positive value, indicating a decreased performance. In other words, the more positive the ΔPVT reaction time becomes, the more fatigued the subject becomes.

[0174] Figure 13 The ΔN-back response time (in seconds) on the horizontal axis of Figures C and D represents the difference between the time required to answer questions in the first half of the 20-minute N-back task (2 to 8 minutes after the task begins) and the time required to answer questions in the second half (14 to 20 minutes after the task begins). For example, if the time required to answer N-back questions in the second half of the task is shorter than the time required to answer N-back questions in the first half, the ΔN-back response time becomes negative, indicating improved performance. On the other hand, if the time required to answer N-back questions in the second half of the task is longer than the time required to answer N-back questions in the first half, the ΔN-back response time becomes positive, indicating decreased performance. In other words, the more negative the ΔN-back response time becomes, the more focused and diligent the subject is.

[0175] To confirm whether there is a correlation between the ΔPVT reaction time and ΔN-back answer time as the results of the fatigue task experiment, the cCVVLF indicating physical and mental state, and the conventionally used ln(LF / HF), the Pearson product-moment correlation coefficient was calculated.

[0176] Figure 13 The ΔccvVLF on the vertical axes of Figures A and C represents the difference between the cccvVLF values ​​before and after the fatigue task, indicating physical and mental state. The Δln(LF / HF) on the vertical axes of Figures B and D represents the difference between the ln(LF / HF) values ​​before and after the fatigue task, indicating physical and mental state.

[0177] about Figure 13 The correlation coefficients of graphs A to D, that is, the following (A) to (D), are as follows.

[0178] (A) Correlation coefficient between ΔPVT reaction time (s) and ΔccvVLF (%): r = 0.51**

[0179] (B) Correlation coefficient between ΔPVT reaction time (s) and Δln (LF / HF): r = 0.33*

[0180] (C) Correlation coefficient between ΔN-back response time (s) and ΔccvVLF (%): r = -0.37*

[0181] (D) Correlation coefficient between ΔN-back response time (s) and Δln (LF / HF): r = -0.03

[0182] Regarding the correlation with ΔPVT reaction time, the correlation coefficients (A) and (B) confirm a positive correlation. Meanwhile, the correlation coefficient (A) with ΔccvVLF (r = 0.51) is larger than the correlation coefficient (B) with Δln(LF / HF), which has long been known as an indicator of mental fatigue (r = 0.33). This result suggests that ΔccvVLF is more strongly correlated with ΔPVT reaction time than Δln(LF / HF), which has long been known as an indicator of mental fatigue.

[0183] Regarding the correlation with ΔN-back answer time, according to Figure 13 Graph C and the value of the correlation coefficient (C) (r = -0.37) show that ΔccvVLF is negatively correlated with ΔN-back response time. Figure 13 Graph C shows the following results: as performance on the N-back task improves, that is, when concentration and effort are increased, the cccvVLF value also increases. In contrast, based on the correlation coefficient (D) (r = -0.03), Δln(LF / HF), long known as an indicator of mental fatigue, was not found to be correlated with ΔN-back response time.

[0184] Depend on Figure 13 The results show that the ccfVLF has a positive correlation with ΔPVT reaction time and a negative correlation with ΔN-back response time. As mentioned above, a more positive ΔPVT reaction time indicates a more fatigued state for the subject, while a more negative ΔN-back response time indicates a more focused and diligent state for the subject. Therefore, the ccfVLF, which shows a positive correlation with ΔPVT reaction time and a negative correlation with ΔN-back response time, and thus VLF, can be used to estimate fatigue and focused and diligent states.

[0185] <Example 2>

[0186] In this example, eight subjects performed a fatigue task and underwent an environmental control. The mean values ​​of their physical and mental states before and after the fatigue task and the environmental control were compared and analyzed. A two-way repeated measures analysis of variance and multiple comparisons using the Holm method were used to compare the mean values.

[0187] In this embodiment, VLF and the subjective evaluation index of fatigue / alertness RAS (Roken Arousal Scale) are used as indicators of physical and mental state. In this embodiment, "difficulty in concentrating attention", one of the items of RAS, is used as an evaluation indicator. The heart rate information acquisition unit of the physical and mental state estimation system involved in Implementation 2 obtains the heart rate information at rest, and the heart rate variation calculation unit calculates the normal value (threshold) of the value of the heart rate variation (VLF) at rest. Next, a 10-minute VR (Virtual Reality) game is implemented as a fatigue task. Next, the environment control unit of the physical and mental state estimation system controls the environment around the subject.

[0188] The environmental control project in this embodiment uses Figure 10 The visual field (other than light) described in [1] refers to plants. In this example, eight subjects participated in a four-day experiment, each day undergoing one of four different environments, spending 20 minutes in each environment. The heart rate information acquisition unit of the physical and mental state estimation system acquired an electrocardiogram (ECG) between 0 and 5 minutes after the start of the stay in each environment as heart rate information, and the heart rate variability calculation unit calculated the value of the heart rate variability (VLF). In parallel, subjective evaluations of "difficulty concentrating" were conducted at rest before the fatigue task, and after the fatigue task, 20 minutes after the start of the stay in each environment.

[0189] Figure 14 This is a graph showing the comparison of the subjective evaluation of "difficulty in concentrating attention" and the average value of VLF in Example 2 and the results of correlation analysis. The left and middle graphs show the results of multiple comparisons using the Holm method. ** represents p < 0.01, * represents p < 0.05, A p value of < 0.1 indicates a significant difference or trend. The horizontal axes of the left and middle graphs represent environmental conditions. "No green" indicates an environment with no plants within the subject's visual range.

[0190] "Green A" is an environment where plants with smaller and rounder leaves than those of Green C are placed within the subject's field of vision. Figure 3 and Figure 10 As shown, Green A has a creative / inspirational / resting / relaxing effect.

[0191] "Green B" is an environment where plants with slender leaves are placed within the subject's field of vision. Figure 3 and Figure 10 As shown, Green B has the effect of improving concentration.

[0192] "Green C" is an environment where plants with larger leaves than those in Green A are placed within the subject's field of view. Figure 3 and Figure 10 As shown, Green C has the effect of increasing activity.

[0193] Figure 14 The vertical axis of the left graph, "Change Before and After the Fatigue Task," represents the difference between the subjective evaluation of "Difficulty Concentrating" performed before the fatigue task and the subjective evaluation of "Difficulty Concentrating" performed 20 minutes after the start of the stay in the environment. The change shown in the left graph was obtained using covariance analysis to remove the influence of the state before the fatigue task. The change in the subjective evaluation of "Difficulty Concentrating" indicates greater concentration, as more negative values ​​are observed.

[0194] Likewise, Figure 14 The vertical axis of the middle graph, "Change Before and After the Fatigue Task," represents the difference between the lnVLF value calculated as the normal value (threshold) during rest before the fatigue task and the lnVLF value between 0 and 5 minutes after the start of the stay in the environment. The change shown in the middle graph is the result of an analysis of covariance, after removing the influence of the state before the fatigue task.

[0195] like Figure 14 As shown in the left graph, the change in the subjective evaluation of "difficulty concentrating" became a negative value and was the smallest when staying in the green B environment. In other words, when staying in the green B environment, the presence of the slender-leaved plant successfully improved concentration.

[0196] Furthermore, if Figure 14 As shown in the middle figure of the figure, the following results were obtained: when staying in the green B environment, the change in VLF was the largest compared to the environment without green. Next, as shown in the right figure, a correlation analysis was conducted between the change in the subjective evaluation of "difficulty in concentrating" and the change in VLF. The change in the subjective evaluation of "difficulty in concentrating" and ΔlnVLFln (ms 2 ) was r=-0.41(p<0.05), and the change in subjective evaluation of “difficulty in concentrating attention” was related to ΔlnVLFln(ms 2 ) are significantly correlated.

[0197] As described above, it is shown that there is a correlation between the amount of change in the subjective evaluation "difficulty in concentrating" indicating the state of concentration and the VLF, and therefore the VLF can be used to estimate the state of concentration.

[0198] <Example 3>

[0199] In this example, 37 subjects underwent a fatigue task experiment and studied their physical and mental states before and after the task. VLF1 and VLF2 were used as indicators of physical and mental state. The fatigue task also included the Psychomotor Vigilance Test (PVT) and the N-back task.

[0200] First, the heart rate information acquisition unit of the physical and mental state estimation system according to Embodiment 1 acquires 5 minutes of resting heart rate information, and the heart rate variability calculation unit calculates the normal value (threshold) of the resting heart rate variability value (VLF1 and VLF2 values). Next, after performing a 5-minute PVT, a 20-minute N-back task (3-back) is performed. After the N-back task, another 5-minute PVT is performed. Next, the heart rate information acquisition unit of the physical and mental state estimation system acquires 5 minutes of electrocardiogram as heart rate information, and the heart rate variability calculation unit calculates the VLF1 and VLF2 values, and then averages them across subjects.

[0201] Figure 15 These graphs show the results of a correlation analysis between the performance of a fatigue task experiment and the change in heart rate variability before and after the fatigue task experiment, according to Example 3. Each scatter plot shows the performance of 37 subjects and the change in lnVLF1 or lnVLF2, which indicates their physical and mental state, before and after the fatigue task. ** indicates p < 0.01, and * indicates p < 0.05, indicating a significant correlation.

[0202] The PVT reaction time means the reaction time to a displayed task. Figure 15 The ΔPVT reaction time (in seconds) on the horizontal axis of Figures A and B is the difference between the PVT reaction time before and after the N-back task. For example, if the PVT reaction time after the N-back task is shorter than the PVT reaction time before the N-back task, the ΔPVT reaction time becomes negative, indicating improved performance. In other words, the more negative the ΔPVT reaction time becomes, the more likely the subject is focused and diligent. On the other hand, if the PVT reaction time after the N-back task is longer than the PVT reaction time before the N-back task, the ΔPVT reaction time becomes positive, indicating decreased performance. In other words, the more positive the ΔPVT reaction time becomes, the more fatigued the subject is.

[0203] Figure 15The ΔN-back response time (in seconds) on the horizontal axis of Figures C and D represents the difference between the time required to answer questions in the first half of the 20-minute N-back task (2 to 8 minutes after the task begins) and the time required to answer questions in the second half (14 to 20 minutes after the task begins). For example, if the time required to answer N-back questions in the second half of the task is shorter than the time required to answer N-back questions in the first half, the ΔN-back response time becomes negative, indicating improved performance. On the other hand, if the time required to answer N-back questions in the second half of the task is longer than the time required to answer N-back questions in the first half, the ΔN-back response time becomes positive, indicating decreased performance. In other words, the more negative the ΔN-back response time becomes, the more focused and diligent the subject is.

[0204] To confirm whether there is a correlation between the ΔPVT reaction time and ΔN-back answer time as the results of the fatigue task experiment and the changes in lnVLF1 and lnVLF2 indicating physical and mental states, the Pearson product-moment correlation coefficient was calculated.

[0205] Figure 15 The ΔlnVLF1 on the vertical axis of Figures A and C is the difference between the lnVLF1 values ​​before and after the fatigue task experiment, which indicates physical and mental state. The ΔlnVLF2 on the vertical axis of Figures B and D is the difference between the lnVLF2 values ​​before and after the fatigue task experiment, which indicates physical and mental state.

[0206] about Figure 15 The correlation coefficients of graphs A to D, that is, the following (A) to (D), are as follows.

[0207] (A) Correlation coefficient between ΔPVT reaction time (s) and ΔlnVLF1: ​​r = 0.36*

[0208] (B) Correlation coefficient between ΔPVT reaction time (s) and ΔlnVLF2: r = 0.36*

[0209] (C) Correlation coefficient between ΔN-back response time (s) and ΔlnVLF1: ​​r = -0.21

[0210] (D) Correlation coefficient between ΔN-back response time (s) and ΔlnVLF2: r = -0.46**

[0211] Regarding the correlation with ΔPVT reaction time, the correlation coefficients in (A) and (B) show that both VLF1 and VLF2 have similar correlations. VLF1 has a strong positive correlation with ΔPVT reaction time, and when PVT reaction performance decreases due to fatigue, the VLF1 value increases.

[0212] Regarding the correlation with the ΔN-back response time, the correlation coefficient value (C) (r = -0.21) indicates that VLF1 is almost not correlated with the ΔN-back response time. In contrast, the correlation coefficient value (D) (r = -0.46) indicates that VLF2 is significantly correlated with the ΔN-back response time. Figure 15 Graph D shows the following results: as performance on the N-back task improves, that is, when concentration and effort are increased, the VLF2 value also increases. Furthermore, when comparing the results of the VLF in Example 1 with the results of the VLF2 in this example, the negative correlation between the VLF2 and the N-back response time is higher than that of the VLF.

[0213] Depend on Figure 15 The results show that VLF1 and VLF2 are correlated with ΔPVT reaction time, while VLF2 is negatively correlated with ΔN-back response time. As mentioned above, a more positive ΔPVT reaction time indicates a more fatigued state for the subject, and a more negative ΔN-back response time indicates a more focused / effortful state for the subject. This indicates that VLF1, which has a positive correlation with ΔPVT reaction time, can be used to estimate fatigue. Furthermore, VLF2, which has a positive correlation with ΔPVT reaction time and a negative correlation with ΔN-back response time, can be used to estimate both fatigue and focused / effort states. Therefore, by combining VLF1 and VLF2 for evaluation, fatigue and focused / effort states can be distinguished and estimated.

[0214] <Example 4>

[0215] In this example, eight subjects performed a fatigue task and underwent an environmental control. The mean values ​​of their physical and mental states before and after the fatigue task and the environmental control were compared and analyzed. A two-way repeated measures analysis of variance and multiple comparisons using the Holm method were used to compare the mean values.

[0216] In this embodiment, VLF2 and the subjective assessment of "difficulty concentrating" as one of the RAS items were used as indicators of physical and mental state. The heart rate information acquisition unit included in the physical and mental state estimation system according to Implementation 2 acquired resting heart rate information, and the heart rate variability calculation unit calculated the normal value (threshold) of the resting heart rate variability value (VLF2). Next, a 10-minute VR (Virtual Reality) game was administered as a fatigue task. Next, the environmental control unit included in the physical and mental state estimation system controlled the environment surrounding the subject.

[0217] The environmental control project in this embodiment uses Figure 10 The visual field (other than light) described in [1] refers to plants. In this example, eight subjects participated in a four-day experiment, each day undergoing one of four different environments, spending 20 minutes in each environment. The heart rate information acquisition unit of the physical and mental state estimation system acquired an electrocardiogram (ECG) between 0 and 5 minutes after the start of each environment as heart rate information, and the heart rate variability calculation unit calculated the heart rate variability value (VLF2). Concurrently, subjective assessments of "difficulty concentrating" were performed before the fatigue task, during rest, after the fatigue task, and 20 minutes after the start of each environment.

[0218] Figure 16 This graph shows the results of a comparison of the subjective evaluation of "Difficulty Concentrating" and the average value of VLF2, as well as the correlation analysis results, in Example 4. The horizontal axes of the left and center graphs represent environmental conditions. The environmental conditions for "No Green," "Green A," "Green B," and "Green C" are the same as those described in Example 2. The left and center graphs show the results of multiple comparisons using the Holm method. ** indicates p < 0.01, and * indicates p < 0.05, respectively, indicating significant differences.

[0219] Figure 16 The vertical axis of the left graph, "Change Before and After the Fatigue Task," represents the difference between the subjective evaluation of "Difficulty Concentrating" performed before the fatigue task and the subjective evaluation of "Difficulty Concentrating" performed 20 minutes after the start of the stay in the environment. The change shown in the left graph was obtained using covariance analysis to remove the influence of the state before the fatigue task. The change in the subjective evaluation of "Difficulty Concentrating" indicates greater concentration, as more negative values ​​are observed.

[0220] Likewise, Figure 16 The vertical axis of the middle graph "Changes before and after the fatigue task experiment" represents lnVLF2ln (ms) calculated as a normal value (threshold) during rest before the fatigue task. 2 ) value and lnVLF2ln(ms) between 0 and 5 minutes after staying in the environment 2The variation shown in the middle graph is the result obtained by removing the influence of the state before the fatigue task using covariance analysis.

[0221] Figure 16 The left picture and Figure 14 The same as the left picture. Figure 16 As shown, the change in the subjective evaluation of "difficulty concentrating" became a negative value and was the smallest when staying in the environment of Green B. In other words, when staying in the environment of Green B, the presence of plants with slender leaves in the environment successfully improved concentration.

[0222] Furthermore, if Figure 16 As shown in the middle figure, the result shows that the change in VLF2 is the largest in the green B environment. Next, as shown in the right figure, a correlation analysis was conducted between the change in the subjective evaluation of "difficulty in concentrating" and the change in VLF2. 2 ) was r=-0.34(p<0.1), and the change in subjective evaluation of “difficulty in concentrating attention” was related to ΔlnVLF2ln(ms 2 ) confirmed a related trend.

[0223] As described above, it is shown that there is a correlation between the amount of change in the subjective evaluation "difficulty in concentrating" indicating the state of concentration and VLF2, and therefore VLF2 can be used to estimate the state of concentration.

[0224] In addition, the present invention is not limited to the above-described embodiment, and can be appropriately modified within a scope not departing from the gist of the invention.

Claims

1. A physical and mental state estimation system comprising: a heart rate information acquisition unit that acquires heart rate information that is information related to the heart rate of the subject; a heart rate variability calculation unit that calculates the heart rate variability of a VLF (very low frequency) component based on the acquired heart rate information; and a physical and mental state estimating unit for estimating the concentration and effort state of the subject based on the calculated value of the heart rate variation; The heart rate variability calculation unit calculates the heart rate variability for each of VLF1, which is included in the VLF and indicates a fatigue state, and VLF2, which is lower in frequency than VLF1 and indicates a concentration / effort state. The physical and mental state estimating unit estimates the concentration / effort state of the subject based on the value of the VLF2, and estimates the fatigue state based on the value of the VLF1. The frequency band of the VLF is 0.0033-0.04 Hz, and the VLF1 and VLF2 are two frequency bands obtained by dividing the VLF with 0.015 Hz as the boundary value. When the value of VLF1 is greater than a threshold value and the value of VLF2 is greater than a threshold value, the physical and mental state estimating unit estimates that the subject is more focused and exerting effort than when at rest and is fatigued. When the value of VLF1 is greater than a threshold value and the value of VLF2 is less than a threshold value, the physical and mental state estimating unit estimates that the subject is not concentrating as compared to a resting state but is distracted and fatigued. When the value of VLF1 is smaller than a threshold value and the value of VLF2 is larger than a threshold value, the physical and mental state estimating unit estimates that the subject is concentrating / working hard and is not fatigued. When the value of the VLF1 is smaller than a threshold value and the value of the VLF2 is smaller than a threshold value, the physical and mental state estimating unit estimates that the subject is not concentrating but is distracted and not fatigued.

2. The physical and mental state estimation system according to claim 1, The heart rate variability calculation unit further calculates the heart rate variability of the high frequency component (HF) and the low frequency component (LF). The physical and mental state estimating unit estimates a fatigue state in addition to the concentration and effort state of the subject based on the VLF, the HF, and the LF.

3. The physical and mental state estimation system according to claim 1, The device further includes an environment control unit configured to control the environment of the subject based on the estimated physical and mental state.

4. The physical and mental state estimation system according to claim 3, The environment control unit performs control to improve concentration when the concentration / effort state estimated by the physical and mental state estimation unit is equal to or lower than a predetermined level.

5. The physical and mental state estimation system according to claim 1, further comprising an environment control unit configured to control the environment of the subject based on the estimated physical and mental state; The environment control unit performs control to alleviate fatigue when the fatigue state estimated by the physical and mental state estimating unit is equal to or higher than a predetermined level.

6. The physical and mental state estimation system according to claim 3, further comprising a goal setting unit for setting a goal for the physical and mental state; The environment control unit controls the environment surrounding the subject based on the estimated physical and mental state and the set physical and mental state target.

7. The physical and mental state estimation system according to claim 6, The goal setting unit sets a goal based on a predetermined schedule.

8. A method for estimating physical and mental state, comprising: a step of acquiring information related to the subject's heart rate, namely, heart rate information; A step of calculating the heart rate variation of the VLF component (very low frequency component) based on the obtained heart rate information; and a step of estimating the concentration or effort state of the subject based on the calculated heart rate variability value, In the calculation step, the heart rate variation is calculated for each of VLF1, which is included in the VLF and indicates a fatigue state, and VLF2, which is lower in frequency than VLF1 and indicates a concentration / effort state. In the estimating step, the concentration / effort state of the subject is estimated based on the value of the VLF2, and the fatigue state is estimated based on the value of the VLF1. The frequency band of the VLF is 0.0033-0.04 Hz, and the VLF1 and VLF2 are two frequency bands obtained by dividing the VLF with 0.015 Hz as the boundary value. If the value of the VLF1 is greater than a threshold value and the value of the VLF2 is greater than a threshold value, it is estimated in the estimating step that the subject is concentrating / exerting more than when at rest and is fatigued. If the value of VLF1 is greater than a threshold value and the value of VLF2 is less than a threshold value, the estimating step estimates that the subject is not concentrating as compared to a resting state but is distracted and fatigued. If the value of VLF1 is smaller than a threshold value and the value of VLF2 is larger than a threshold value, it is estimated in the estimating step that the subject is concentrating / working hard and is not fatigued. When the value of the VLF1 is smaller than a threshold value and the value of the VLF2 is smaller than a threshold value, it is estimated in the estimating step that the subject is not concentrating but is distracted and not fatigued.

9. A recording medium storing a program for causing a computer to execute the following steps: a step of acquiring information related to the subject's heart rate, namely, heart rate information; A step of calculating the heart rate variation of the VLF component (very low frequency component) based on the acquired heart rate information; and a step of estimating the concentration or effort state of the subject based on the calculated heart rate variability value, In the calculation step, the heart rate variation is calculated for each of VLF1, which is included in the VLF and indicates a fatigue state, and VLF2, which is lower in frequency than VLF1 and indicates a concentration / effort state. In the estimating step, the concentration / effort state of the subject is estimated based on the value of the VLF2, and the fatigue state is estimated based on the value of the VLF1. The frequency band of the VLF is 0.0033-0.04 Hz, and the VLF1 and VLF2 are two frequency bands obtained by dividing the VLF with 0.015 Hz as the boundary value. If the value of the VLF1 is greater than a threshold value and the value of the VLF2 is greater than a threshold value, it is estimated in the estimating step that the subject is concentrating / exerting more than when at rest and is fatigued. If the value of VLF1 is greater than a threshold value and the value of VLF2 is less than a threshold value, the estimating step estimates that the subject is not concentrating as compared to a resting state but is distracted and fatigued. If the value of VLF1 is smaller than a threshold value and the value of VLF2 is larger than a threshold value, it is estimated in the estimating step that the subject is concentrating / working hard and is not fatigued. When the value of the VLF1 is smaller than a threshold value and the value of the VLF2 is smaller than a threshold value, it is estimated in the estimating step that the subject is not concentrating but is distracted and not fatigued.

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