Mind-body state intervention apparatus, mind-body state intervention system, and mind-body state intervention method

Abstract

A mind-body state intervention apparatus (100) is provided with: a physiological amount acquisition unit (110) for acquiring a physiological amount of a subject detected by a physiological amount detection unit (160); and a quantification processing unit (120) for quantifying, on the basis of the physiological amount, two mind-body states including a mind-body state in which the subject is aware and a mind-body state in which the subject is not aware concerning a predetermined mind-body state; a first storage unit (130) that stores in advance relationship data indicating the relationship between each of a plurality of intervention control schemes and an intervention effect with respect to the two quantified mind-body states; and an intervention control unit (140) that refers to the relationship data to select one intervention control scheme from among the plurality of intervention control schemes on the basis of the intervention effect with respect to at least one of the two mind-body states quantified by the quantification processing unit (120), and performs intervention control for controlling an intervention device (150) by the selected intervention control scheme.

Description

Mental and physical condition intervention device, mental and physical condition intervention system, and mental and physical condition intervention method

[0001] The present disclosure relates to a mind-body condition intervention device, a mind-body condition intervention system, and a mind-body condition intervention method.

[0002] There are various mental and physical states, including a person's health and emotional state, such as fatigue, sleepiness, alertness, joy, anger, etc. Among these, particularly negative mental and physical states such as fatigue, sleepiness, and anger, are considered to be quantified and intervention control can be implemented according to the quantified mental and physical state in order to improve them.

[0003] For example, measuring the fatigue level of office workers is thought to prevent losses due to reduced productivity caused by fatigue, as well as pathological fatigue such as depression. Fatigue quantification technology is generally realized using autonomic nervous system measurement technology that analyzes heart rate variability. Interventions based on quantified fatigue include, for example, taking supplements containing ingredients that are said to have fatigue recovery effects, such as vitamin B1, GABA, and imidapeptides, forest bathing, which is said to regulate the autonomic nervous system and have a fatigue recovery effect, and listening to music that dominates the parasympathetic nervous system.

[0004] In addition, in the case of drowsiness, it is thought that measuring the drowsiness level of drivers can reduce accidents caused by drowsiness. Generally, drowsiness is quantified using autonomic nerve measurement technology that analyzes heart rate variability. Intervention control based on the quantified drowsiness level has been realized, for example, by issuing an alarm, vibrating stimulation, or stimulating with scents that have an awakening effect.

[0005] In the case of anger, measuring the anger levels of people working in the office is thought to prevent the deterioration of interpersonal relationships due to anger and improve the work environment. Generally, anger quantification technology is realized using autonomic nervous system measurement technology that analyzes heart rate variability. Intervention control based on quantified anger can be achieved, for example, by playing calming music, encouraging deep breathing, or stimulating with calming scents.

[0006] On the other hand, it is known that mental and physical states, for example, differ in the level of fatigue felt by the individual (hereinafter referred to as subjective fatigue) and the level of objective fatigue not perceived by the individual (hereinafter referred to as objective fatigue). Furthermore, subjective fatigue and objective fatigue do not necessarily correspond; psychological factors such as consuming energy drinks or motivation can mask objective fatigue, resulting in subjective fatigue being perceived as a state in which the objective fatigue is reduced or amplified. In response to this, Patent Document 1 discloses a technique for quantifying subjective fatigue using fatigue-related questions and objective fatigue using tremors, which are minute vibrations in specific parts of the body. Patent Document 2 also discloses a technique for determining whether objective fatigue is being masked by analyzing biological signals highly related to autonomic nervous function.

[0007] In addition, as with fatigue, it is known that there is a difference between the level of sleepiness one feels (hereafter referred to as subjective sleepiness) and the level of objective sleepiness (hereafter referred to as objective sleepiness). For example, it is said that when objective sleepiness is shallow, it is perceived as an insufficient level of subjective sleepiness. Subjective sleepiness and objective sleepiness do not necessarily correspond, and psychological effects such as consuming energy drinks or tension caused by driving a car can mask objective sleepiness and make it perceived as subjective sleepiness.

[0008] In addition, as with fatigue, it is known that there is a difference between the level of anger one feels (hereafter referred to as subjective anger) and the level of objective anger (hereafter referred to as objective anger). For example, when one is extremely angry, one may lose control and barely notice the subjective anger. Furthermore, subjective anger and objective anger do not necessarily correspond, and psychological factors such as one's personality and social position can mask objective anger and make it perceived as subjective anger.

[0009] JP 2002-191579 A JP 2018-93997 A JP 2008-305107 A

[0010] For example, in the intervention control method described in Patent Document 3, for example, an intervention control method with a low effect on fatigue is performed first, and if the intervention effect is insufficient, intervention control is performed in stages to increase the intervention effect in order to improve fatigue and suppress a decrease in work efficiency. However, if the intervention control method has four stages, and the user's fatigue level is high and the fourth stage of the intervention control method is most effective in obtaining the intervention effect, but because the intervention control method is performed in stages from the first stage, the user must wait until the fourth stage of the intervention control method is started. In other words, with this method, it is often not possible to select the optimal intervention control method initially, and it is not efficient.

[0011] Furthermore, the intervention control method disclosed in Patent Document 3 does not select an intervention control method that distinguishes between subjective fatigue and objective fatigue as described above. Therefore, it is possible that optimal intervention is not possible because the characteristics of the intervention control method for each type of fatigue are not taken into consideration, such as the intervention effect being large for subjective fatigue but small for objective fatigue.

[0012] The invention of this application primarily aims to solve the above problems, and aims to provide a mental and physical condition intervention device, a mental and physical condition intervention system, and a mental and physical condition intervention method for providing a more efficient and effective mental and physical condition intervention control method.

[0013] In order to achieve the above object, one embodiment of the mental and physical condition intervention device according to the present disclosure includes a physiological quantity acquisition unit that acquires the physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing unit that quantifies two mental and physical states, i.e., a mental and physical state that the subject is aware of for a predetermined mental and physical state, and a mental and physical state that the subject is not aware of, based on the physiological quantities; a storage unit that pre-stores relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effects on the two quantified mental and physical states; and an intervention control unit that, by referring to the relationship data, selects one intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical states quantified by the quantification processing unit, and performs intervention control to control an intervention device using the selected intervention control method.

[0014] Furthermore, in order to achieve the above object, one embodiment of the mental and physical condition intervention system according to the present disclosure comprises: a mental and physical condition intervention device including: a physiological quantity acquisition unit that acquires physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing unit that quantifies two mental and physical states, i.e., a mental and physical state of which the subject is aware and a mental and physical state of which the subject is not aware, based on the physiological quantities; a storage unit that pre-stores relationship data indicating the relationship between each of a plurality of intervention control methods and the quantified intervention effects for the two mental and physical states; and an intervention control unit that, by referring to the relationship data, selects one intervention control method from the plurality of intervention control methods based on the intervention effect for at least one of the two mental and physical states quantified by the quantification processing unit, and controls an intervention device with the selected intervention control method; the physiological quantity detection unit; and the intervention device.

[0015] Furthermore, in order to achieve the above-mentioned object, one embodiment of the mental and physical condition intervention method according to the present disclosure is an information processing method executed by a computer, and includes: a physiological quantity acquisition step of acquiring the physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing step of quantifying two mental and physical states, i.e., a mental and physical state of which the subject is aware and a mental and physical state of which the subject is not aware, based on the physiological quantities; and an intervention control step of selecting one intervention control method from among the plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical states quantified in the quantification processing step by referring to relationship data showing the relationship between each of the plurality of intervention control methods and the intervention effect on the two quantified mental and physical states, and performing intervention control to control an intervention device using the selected intervention control method.

[0016] According to the mental and physical condition intervention device of the present disclosure, by distinguishing between mental and physical states that a subject is aware of and mental and physical states that the subject is not aware of for a given mental and physical state, and selecting an intervention control method based on that, more efficient and effective intervention control can be performed.

[0017] FIG. 2B is a block diagram showing the characteristic functional configuration of the mental and physical state intervention device according to the first embodiment. FIG. 2C is a block diagram showing the configuration of the relationship data creation device according to the first embodiment. FIG. 2D is a flowchart showing the operation of the relationship data creation device according to the first embodiment. FIG. 2E is a flowchart showing the operation of the mental and physical state intervention device according to the first embodiment. FIG. 2F is a flowchart showing the detailed operation of step S5a in FIG. 2A. FIG. 1 is a block diagram showing the characteristic functional configuration of a mental and physical condition intervention device according to a second embodiment; FIG. 2 is a flowchart for explaining the operation of the mental and physical condition intervention device according to the second embodiment; FIG. 3 is a block diagram showing the characteristic functional configuration of a mental and physical condition intervention device according to a third embodiment; FIG. 4 is an example of a neural network in the optimization method according to the third embodiment; FIG. 5 is an example of a k-nearest neighbor method in the optimization method according to the third embodiment; FIG. 6 is an example of a random forest in the optimization method according to the third embodiment; FIG. 7 is a block diagram showing the characteristic functional configuration of a mental and physical condition intervention device according to a fourth embodiment;

[0018] Hereinafter, embodiments of the mind-body state intervention device according to the present invention will be described with reference to the drawings. Note that the same elements are designated by the same reference numerals, and their description may be omitted. The drawings mainly show each component in a schematic manner for ease of understanding. Note that each of the embodiments described below is a specific example of the present invention. The numerical values, shapes, components, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present invention. Furthermore, among the components in the following embodiments, components that are not recited in the independent claims representing the highest concepts are described as optional components. Furthermore, the contents of each embodiment can be combined. The same applies to the configurations of the respective variations described in each embodiment, and the configurations described in each variation may be combined.

[0019] (Embodiments) [First embodiment] A mind-body condition intervention device according to a first embodiment of the present invention will be described with reference to Figures 1A to 10. First, fatigue will be used as an example of a mind-body condition.

[0020] <Configuration> FIG. 1A is a block diagram showing the characteristic functional configuration of the mental and physical state intervention device according to the first embodiment. The mental and physical state intervention device 100 distinguishes between subjective and objective mental and physical states and selects an intervention control method based on the distinguished state, thereby providing a more efficient and effective intervention control method. The mental and physical state intervention device 100 includes a physiological value acquisition unit 110, a quantification processing unit 120, a first storage unit 130, an intervention control unit 140, and a mode acquisition unit 320, and communicates with an intervention device 150, a physiological value detection unit 160, and a mode selection unit 310. The mental and physical state intervention device 100 may also include other components. Note that intervention is a process that affects the mental and physical state of a subject. The subjective mental and physical state is a mental and physical state that the subject is aware of, while the objective mental and physical state is a mental and physical state that the subject is not aware of.

[0021] The physiological amount detection unit 160 is a device that acquires physiological amounts. Physiological amounts are information related to bodily features that differ from person to person, such as face, electrocardiogram, and voiceprint. The physiological amount detection unit 160 is, for example, a camera attached to a personal computer. The camera acquires an image of a subject (hereinafter simply referred to as a person) operating the personal computer. The physiological amount is acquired from the facial image of the person acquired by the camera. For example, if the physiological amount detection unit 160 is a camera, the information acquired may include the number of blinks within a certain period of time, pupil diameter, and skin color.

[0022] The number of blinks can be measured as follows. First, a facial image acquired by a camera is binarized using threshold processing, and the edges are extracted to detect the outline of the eyes and set the eye area. The eye area of ​​the facial image is scanned for each frame to determine its area. When a blink occurs, the area becomes 0, so the number of times the area becomes 0 can be measured to obtain the number of blinks. Pupil diameter is obtained by binarizing a facial image acquired by a camera using threshold processing, and the edges are extracted to detect the outline of the pupil, and measuring the width between the edges. Skin color is obtained using the RGB values ​​of a specified position or area in the facial image acquired by the camera.

[0023] Furthermore, the physiological amount detection unit 160 may be, for example, a heart rate sensor or a microphone other than a camera. The physiological amount detection unit 160 is not limited to a camera.

[0024] In the case of a heart rate sensor, physiological quantities include, for example, the LF and HF of heart rate fluctuations, heart rate, respiratory rate, etc. acquired by the heart rate sensor. First, heart rate interval fluctuation time series data (hereinafter referred to as RRI data) is acquired using the heart rate sensor. LF and HF are values ​​obtained by calculating the power spectrum of the acquired RRI data and integrating the power spectrum of LF: 0.05 Hz to 0.15 Hz and HF: 0.15 Hz to 0.40 Hz. The heart rate is the number of times peaks in the RRI data are detected per minute. The respiratory rate can be estimated using heart rate fluctuations due to respiratory sinus arrhythmia (hereinafter referred to as RSA). More specifically, RSA is a phenomenon in which the heart rate generally increases when one inhales and decreases when one exhales. In other words, if the interval between peaks in the RRI data is the heart rate, a fluctuation in the heart rate occurs such that the heart rate decreases when one inhales and increases when one exhales. Therefore, the respiratory rate can be estimated based on the number of peaks detected in the heart rate fluctuation.

[0025] In the case of a microphone, physiological quantities are, for example, the loudness, pitch, and formant frequency of the acquired voice. First, a Fourier transform is performed on the voice data acquired by the microphone to acquire a frequency spectrum. The loudness and pitch of the voice are represented by the amplitude and frequency of the acquired frequency spectrum. The formant frequency is calculated using the autocorrelation function of the voice data acquired by the microphone, and the lowest frequency at which a peak of the autocorrelation function is detected corresponds to the formant frequency. The formant frequency represents the timbre of the voice. Of course, other physiological quantities may also be used, and the configuration of the physiological quantity detection unit 160 is not limited here.

[0026] The physiological amount acquiring unit 110 communicates with the physiological amount detecting unit 160 and acquires the physiological amount detected by the physiological amount detecting unit 160. The physiological amount acquiring unit 110 may be, for example, a communication interface, or may be a program for acquiring the physiological amount and a processor for executing the program.

[0027] The quantification processing unit 120 quantifies subjective fatigue and objective fatigue based on the physiological quantities acquired by the physiological quantity acquisition unit 110 .

[0028] Here, a method for quantifying subjective fatigue and objective fatigue will be described. One example of a quantification method is a fatigue function. A fatigue function is a function that takes physiological quantities as input and outputs quantified subjective fatigue and objective fatigue. As an example, a fatigue function using the number of blinks shown in FIG. 3 will be described here. Functions for subjective fatigue, objective fatigue, and the number of blinks are created in advance through experiments. Methods for obtaining subjective fatigue include, for example, a visual analog scale (hereinafter referred to as VAS), a numerical rating scale (hereinafter referred to as NRS), and a face (rating) scale, and the value indicated by a person on a scale or a chart is obtained and used as a quantitative value. Objective fatigue is measured by obtaining physiological values, such as the amount of amylase, cortisol, or human herpes virus contained in saliva, and the quantitative value is obtained by replacing the physiological values ​​with the indicators used to obtain subjective fatigue.

[0029] Next, a fatigue function is created using the quantitative values ​​of subjective and objective fatigue obtained in advance through experiments and the physiological quantities obtained by the physiological quantity detection unit 160. Figure 3 shows, as an example, a fatigue function in which the levels of subjective and objective fatigue are quantified on a seven-level equidistant scale and the physiological quantities are expressed as blink counts. Using the equidistant scale, subjective and objective fatigue can be quantified using a predetermined number of levels. Here, the higher the level, the greater the degree of fatigue. For example, 1 indicates no fatigue at all, while 7 indicates physical and mental fatigue so severe that one does not want to work any more. While the above example describes the level of fatigue, the way in which fatigue is expressed is not limited. Subjective and objective fatigue can be quantified by inputting the blink count obtained by the physiological quantity detection unit as an input value for the fatigue function. For example, as shown in Figure 3, if the blink count obtained by the physiological quantity detection unit is 20, the objective fatigue level is 5. The subjective fatigue is not an integer value but is 2.8, but this is converted to 3 by rounding. In the above description, when the output value is not an integer, the conversion is performed by rounding off the value to the nearest integer. However, the conversion method may be any other method, such as rounding down the decimal point.

[0030] As an example, we have explained a method using values ​​quantifying the degree of subjective and objective fatigue on a seven-level equidistant scale and a fatigue function in which the physiological amount is the number of blinks, but this does not limit the methods for obtaining subjective and objective fatigue or the physiological amounts used as input values.

[0031] Methods for quantifying subjective fatigue and objective fatigue may be other than fatigue functions, such as a physiological value-fatigue correspondence table or machine learning, and are not limited to fatigue functions as methods for quantifying subjective fatigue and objective fatigue.

[0032] In the case of a physiological value-fatigue correspondence table, subjective fatigue and objective fatigue are quantified from the magnitude of the physiological value acquired by the physiological value detection unit based on a preset correspondence table between physiological values ​​and subjective and objective fatigue. Figure 4 shows an example of a physiological value-fatigue correspondence table in which fatigue level is expressed as a seven-level equal interval scale and physiological value is expressed as the number of blinks. The method of acquiring subjective fatigue and objective fatigue is the same as the example of a fatigue function, so a detailed explanation will be omitted here.

[0033] One example of machine learning is a neural network. In the case of a neural network, as shown in FIG. 5, at least one of the physiological quantities acquired by the physiological quantity detection unit is used as a feature, and a created learning model is used to quantify subjective fatigue and objective fatigue. In addition to neural networks, other machine learning techniques include k-nearest neighbor algorithms and random forests. As shown in FIGS. 6 and 7, in the k-nearest neighbor algorithms and random forests, as in the case of neural networks, at least one of the physiological quantities acquired by the physiological quantity detection unit is used as a feature, and a created learning model is used to quantify subjective fatigue and objective fatigue. The machine learning technique is not limited here. Furthermore, different physiological quantities may be used to quantify subjective fatigue and objective fatigue.

[0034] In the present embodiment, the physiological amount detection unit 160 calculates the physiological amount using data indirectly indicating the physiological amount (for example, a video showing the subject's eyes), and the calculated physiological amount is acquired by the physiological amount acquisition unit 110 and the quantification processing unit 120. However, the physiological amount acquired by the physiological amount acquisition unit 110 and the quantification processing unit 120 may be data indirectly indicating the physiological amount, and it may be the physiological amount acquisition unit 110 or the quantification processing unit 120 that calculates the physiological amount using the data indirectly indicating the physiological amount.

[0035] The first memory unit 130 stores pre-created relationship data. The relationship data is data showing the relationship between each of a plurality of intervention control methods and the intervention effect on quantified subjective fatigue and objective fatigue, as well as the relationship with work efficiency. The intervention effect refers to the amount of change in the subject's subjective fatigue and objective fatigue before and after intervention. Work efficiency refers to the amount of change per unit time between the amount of work before and after intervention control during the intervention period. Methods for calculating the intervention effect and work efficiency and procedures for creating the relationship data will be described later. The first memory unit 130 is a ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and the memory unit is not limited here.

[0036] The mode selection unit 310 allows the user to select a mode, which is an index for selecting an intervention control method. The mode may be, for example, an "intervention effect mode" that prioritizes intervention effect or a "work efficiency mode" that prioritizes work efficiency. The mode selection unit 310 is realized by a touch panel, a mouse, a keyboard, or the like.

[0037] The mode acquisition unit 320 acquires the mode selected by the user from the mode selection unit 310 .

[0038] The intervention control unit 140 refers to the relational data stored in the first memory unit 130 to select an appropriate intervention control method from among multiple intervention control methods based on the intervention effect on quantified subjective fatigue and objective fatigue, work efficiency, and the mode acquired by the mode acquisition unit 320.

[0039] The quantification processing unit 120, the intervention control unit 140, and the mode acquisition unit 320 are realized by a program and a processor that executes the program.

[0040] The intervention device 150 performs intervention based on the intervention control method selected by the intervention control unit 140. The intervention device 150 is, for example, an air conditioner or an electric fan. The air conditioner, which receives a signal from the intervention control unit 140, exposes the person to wind that is effective against fatigue. Wind that is effective against fatigue generally refers to wind that makes the parasympathetic nervous system dominant when exposed to it, and is characterized by having a calming effect. Examples of wind that is effective against fatigue include wind with 1 / f fluctuations, wind that simulates natural wind, and wind with temperature fluctuations. The wind that is effective against fatigue is not limited here. Furthermore, the intervention device 150 may be, for example, an audio device or a fragrance generating device other than an air conditioner. Although the intervention device 150 is given here as an example, it is not limited to an air conditioner.

[0041] In the case of audio equipment, sounds that are effective against fatigue are generated. Sounds that are effective against fatigue are generally sounds that stimulate the parasympathetic nervous system and have a calming effect. Examples include birdsong, forest ambient sounds, and hypersonic sounds.

[0042] Here, hypersonic sound is sound that contains high-frequency sounds that exceed the human audible range, and it has been confirmed that when it is received on the surface of the body at the same time as audible sounds, it increases alpha waves, which are observed when the parasympathetic nervous system is dominant. By exposing a person to sounds containing hypersonic sound using a speaker, it is possible to intervene and control fatigue. Note that, although birdsong and the like have been given here as an example of sounds that are effective against fatigue, effective sounds are not limited to these.

[0043] In the case of a fragrance generator, a person is exposed to a fragrance that is effective against fatigue. A fragrance that is effective against fatigue is generally a fragrance that makes the parasympathetic nervous system dominant when exposed to it, and is characterized by having a calming effect. Examples include grapefruit fragrance, cypress fragrance, and lavender fragrance. Here, fragrances that are effective against fatigue are not limited.

[0044] 1B is a block diagram showing the configuration of a relationship data creation device 10 according to the first embodiment. The relationship data creation device 10 is a device that creates in advance the relationship data to be included in the mind-body state intervention device 100 shown in FIG. 1A, and to this end, creates relationship data for multiple subjects and stores it in a first storage unit 130. The relationship data creation device 10 includes a physiological amount acquisition unit 11, a quantification processing unit 12, an intervention effect calculation unit 17, a workload acquisition unit 21, a work efficiency calculation unit 22, and the first storage unit 130, and communicates with a physiological amount detection unit 16 and a workload detection unit 23.

[0045] The physiological quantity acquisition unit 11, the quantification processing unit 12, and the physiological quantity detection unit 16 have the same functional configurations and operate in the same manner as the physiological quantity acquisition unit 110, the quantification processing unit 120, and the physiological quantity detection unit 160, respectively, provided in the mind-body state intervention device 100, and therefore will not be described here.

[0046] The intervention effect calculation unit 17 calculates the intervention effect from the subjective fatigue and objective fatigue after each intervention control for multiple intervention control methods quantified by the quantification processing unit 12, and stores the relationship between the intervention control method and the intervention effect as relationship data in the memory unit 130.

[0047] The workload acquisition unit 21 communicates with the workload detection unit 23 and acquires information about the workload of the subject detected by the workload detection unit 23. The workload acquisition unit 21 may be, for example, a communication interface, or a program for acquiring the workload and a processor for executing the program.

[0048] The workload detection unit 23 is, for example, a personal computer. In the case of document creation using a personal computer, the workload is the number of characters entered per minute. It can also be something other than document creation; for example, in the case of programming, it can be the number of input steps per minute, or in the case of document reading, it can be the number of pages turned per minute. Either of these can be detected from the work log of the personal computer.

[0049] Although a personal computer has been used as an example, other devices may be used, such as a camera or a weight sensor.

[0050] In the case of a camera, for example, in document creation, the amount of work is the number of characters written on a piece of paper within a specified time. The camera is positioned so that it captures the paper, and the amount of work can be detected from the increase in the amount of characters captured by the camera within a specified time. In addition to document creation, for example, it can also be the number of times a sample is measured within a specified time in evaluation or measurement, or the number of times a piece of luggage is moved within a specified time in luggage transportation.

[0051] If the number of times a sample is measured within a specified time is considered to be the workload, for example, if the sample is placed in a specific installation location and measured, the workload can be detected by placing a camera so that it captures the installation location of the sample and counting the number of times the sample is placed in the installation location for a certain period of time or more within a specified time span.

[0052] The number of times a person moves luggage within a specified period of time can be detected by placing a camera so that it captures the movement path and counting the number of times the person moves back and forth.

[0053] In the case of a weight sensor, for example, in the case of the transportation of goods, it is the amount of change in weight of stored luggage within a specified time. This can be detected by installing a weight sensor on the box in which the luggage is placed during transportation or on the truck bed and measuring the amount of change in weight within a specified time. As stored luggage is carried out and its weight decreases, the amount of work increases. In addition to the transportation of goods, for example, it can be the amount of change in weight of manufactured products at a manufacturing site, or the amount of change in weight of waste to be sorted at a waste sorting site.

[0054] The change in weight of manufactured products can be detected, for example, by storing manufactured products in a predetermined location within a predetermined time period and measuring the total weight of the stored products with a weight sensor.Similarly, the change in weight of waste to be separated can be detected, for example, by measuring the weight of the waste to be separated with a weight sensor.

[0055] Here, the indicator is not limited to the above, as long as it represents the amount of work.

[0056] Here, the configuration of the workload detection unit 23 is not limited.

[0057] The work efficiency calculation unit 22 calculates the work efficiency for each of the multiple intervention control methods based on the work amount acquired by the work amount acquisition unit 21, and stores the relationship between the intervention control method and the work efficiency as relationship data in the first memory unit 130.

[0058] The first memory unit 130 is also the first memory unit 130 provided in the mental and physical state intervention device 100 shown in FIG. 1A, and stores data indicating the relationship between each of the multiple intervention control methods and the intervention effect calculated by the intervention effect calculation unit 17 and the work efficiency calculated by the work efficiency calculation unit 22 as relational data.

[0059] <Operation> Next, the operation of the relationship data creation device according to this embodiment configured as above will be described with reference to the flowchart shown in FIG. 1C.

[0060] FIG. 1C is a flowchart showing the operation of the relationship data creation device according to the first embodiment.

[0061] First, the physiological amount acquisition unit 11 and the workload acquisition unit 21 acquire the physiological amount and workload of the subject detected by the physiological amount detection unit 16 and the workload detection unit 23, respectively (S11 in FIG. 1C ). The physiological amount acquisition unit 11 and the workload acquisition unit 21 may be the same device, for example, a computer camera. In the case of a computer camera, the acquired physiological amount is the number of blinks, and the workload is the number of characters written by the subject, etc.

[0062] The quantification processing unit 12 quantifies the subjective fatigue and objective fatigue of the subject based on the physiological quantities acquired by the physiological quantity acquisition unit 11 (S12 in Figure 1C), and then performs intervention control on the subject using a certain intervention control method (S13 in Figure 1C).

[0063] After the intervention control, the physiological amount acquisition unit 11 and the workload acquisition unit 21 again acquire the physiological amount and workload of the subject detected by the physiological amount detection unit 16 and the workload detection unit 23, respectively (S14 in Figure 1C), and the quantification processing unit 12 quantifies the subject's subjective fatigue and objective fatigue based on the physiological amount acquired by the physiological amount acquisition unit 11 (S15 in Figure 1C).

[0064] The intervention effect calculation unit 17 calculates the intervention effect based on the quantified subjective fatigue and objective fatigue of the subject obtained in S12 and S15 of FIG. 1C, and the work efficiency calculation unit 22 calculates the work efficiency based on the workload obtained in S11 and S14 of FIG. 1C (S16 of FIG. 1C).

[0065] Here, the intervention effect refers to the amount of change between subjective fatigue before and after the intervention control, or the amount of change between objective fatigue before and after the intervention control. That is, when A value is the intervention effect, B value is the subjective fatigue or objective fatigue before the intervention control, and C value is the subjective fatigue or objective fatigue after the intervention control, it is expressed as A value = B value - C value. In other words, the greater the amount of change in subjective fatigue or objective fatigue before and after the intervention control, the greater the intervention effect, that is, the greater the fatigue recovery effect.

[0066] The intervention effect has been described as the amount of change in fatigue between before and after intervention control, but it can also be, for example, the rate of change or the rate of change per unit time, and the intervention effect here is not limited to the amount of change in fatigue between before and after intervention control.

[0067] In the case of the rate of change, where A is the intervention effect, B is the fatigue before the intervention control, and C is the fatigue after the intervention control, it is expressed as A = C / B. In other words, the smaller the rate of change in fatigue before and after the intervention control, the greater the intervention effect, that is, the greater the fatigue recovery effect.

[0068] In the case of the amount of change per unit time, where A value is the intervention effect, B value is the fatigue before the intervention control, C value is the fatigue after the intervention control, and D value is the intervention time, it is expressed as A value = (B value - C value) / D value. In other words, the greater the rate of change per unit time in subjective fatigue or objective fatigue before and after the intervention control, the greater the intervention effect, that is, the greater the fatigue recovery effect. Note that fatigue here can refer to either subjective fatigue or objective fatigue.

[0069] Furthermore, work efficiency refers to the amount of change per unit time between the amount of work before and after intervention control during the intervention time. That is, where E value is work efficiency, F value is the amount of work before intervention control, G value is the amount of work after intervention control, and H value is the intervention time, it is expressed as E value = (G value - F value) / H value.

[0070] Although work efficiency has been described as the amount of change per hour between the amount of work before and after intervention control during the intervention time, it may be other than the amount of change per hour between the amount of work before and after intervention control during the intervention time, for example, the amount of change between the amount of work before and after intervention control, or the rate of change between the amount of work before and after intervention control, and work efficiency is not limited to the amount of change between the amount of work before and after intervention control during the intervention time.

[0071] In the case of the change in the workload before and after intervention control, the E value is expressed as E = G value - F value, where E value is the work efficiency, F value is the workload before intervention control, and G value is the workload after intervention control.

[0072] In the case of the rate of change between the workload before and after intervention control, the E value is expressed as E = G value / F value, where E value is the work efficiency, F value is the workload before intervention control, and G value is the workload after intervention control.

[0073] Finally, the intervention effect calculation unit 17 and the work efficiency calculation unit 22 store the relationship between the intervention control method and the intervention effects on subjective fatigue and objective fatigue, as well as the work efficiency, in the first storage unit 130 as relationship data (S17 in FIG. 1C).

[0074] These steps (S11 to S17 in FIG. 1C) are repeated while changing the intervention control method to create the relational data.

[0075] Next, the operation of the mind-body state intervention device according to this embodiment will be described with reference to Figures 2A and 2B. Here, a camera is used as an example of a physiological quantity detection unit, and the number of blinks, pupil diameter, and skin color are used as examples of physiological quantities.

[0076] 2A is a flowchart of the mind-body state intervention device according to the first embodiment. First, the physiological amount acquisition unit 110 communicates with the physiological amount detection unit 160 and acquires the physiological amount detected by the physiological amount detection unit 160 (S1 in FIG. 2A). Next, the quantification processing unit 120 quantifies the subjective fatigue and objective fatigue of the subject based on the physiological amount acquired from the physiological amount acquisition unit 110 (S2 in FIG. 2A).

[0077] Next, the mode acquisition unit 320 acquires the effect desired for the intervention control selected by the mode selection unit 310 by the user (S3 in FIG. 2A). When the mode selection unit 310 presents the effect desired for the intervention control to the user, the effect is prepared in advance as a choice, such as an "intervention effect mode" that prioritizes the intervention effect for fatigue and a "work efficiency mode" that prioritizes work efficiency. In addition to the "intervention effect mode" and "work efficiency mode," names that prioritize the intervention effect or work efficiency, such as a "fatigue recovery mode" and a "work efficiency mode," are not limited to these, and are not limited to these.

[0078] Next, the intervention control unit 140 selects an intervention control method based on the mode selected by the user (S4 in FIG. 2A).

[0079] The intervention control unit 140 refers to the relationship data stored in the first storage unit 130, acquires the intervention effect and work efficiency for each of the multiple intervention control methods, and selects an intervention control method based on the mode selected by the user acquired by the mode acquisition unit 320. That is, when the user selects the intervention effect priority mode (selecting the intervention effect priority mode in S4 of FIG. 2A), the intervention control method with the greatest intervention effect is selected from the multiple intervention control methods (S5a of FIG. 2A), and when the user selects the work efficiency priority mode (selecting the work efficiency priority mode in S4 of FIG. 2A), the intervention control method with the greatest work efficiency is selected from the multiple intervention control methods (S5b of FIG. 2A).

[0080] Here, Fig. 2B is a flowchart showing the detailed operation of step S5a in Fig. 2A. When the user selects the intervention effect priority mode in S4 in Fig. 2A, the intervention control unit 140 determines which of the quantified subjective fatigue and objective fatigue obtained from the quantification processing unit 120 is greater (S21 in Fig. 2B).

[0081] If the objective fatigue value is greater than the subjective fatigue value (Yes in S21 of FIG. 2B), the intervention control unit 140 selects, from among the multiple intervention control methods, the intervention control method with the greatest intervention effect on objective fatigue (S22a in FIG. 2B).If the subjective fatigue value is greater than the objective fatigue value or the objective fatigue value and the subjective fatigue value are equal (No in S21 of FIG. 2B), the intervention control unit 140 selects, from among the multiple intervention control methods, the intervention control method with the greatest intervention effect on subjective fatigue (S22b in FIG. 2B).

[0082] For example, FIG. 8 illustrates an example of selecting an intervention control method. As shown in FIG. 8 , fatigue level is represented by a 7-level equal interval scale, and work efficiency is represented by the number of characters entered per minute. Using the equal interval scale, the degree of subjective fatigue and objective fatigue can be expressed using a predetermined number of levels. Here, the level is expressed using 7 levels, with higher values ​​indicating greater fatigue. User A's fatigue level is subjective fatigue: 4, objective fatigue: 7, with the objective fatigue being greater (see FIG. 8 (a)). Therefore, if user A selects the intervention effect priority mode, the wind-based intervention control method, which has the greatest intervention effect on objective fatigue, is selected. If user A selects the work efficiency priority mode, the scent-based intervention control method is selected (see FIG. 8 (b)). Furthermore, user B's fatigue level is subjective fatigue: 7, objective fatigue: 4, with the subjective fatigue being greater (see FIG. 8 (c)). Therefore, if user B selects the intervention effect priority mode, the sound-based intervention control method, which has the greatest intervention effect on subjective fatigue, is selected. If user B selects the work efficiency priority mode, the scent-based intervention control method is selected (see FIG. 8 (d)).

[0083] In this embodiment, when the subjective fatigue value and the objective fatigue value are equal, the intervention control method with the greatest intervention effect on subjective fatigue is selected, but the intervention control method with the greatest intervention effect on objective fatigue may also be selected.

[0084] The intervention control unit 140 controls the intervention device 150 based on the selected intervention control method (S6 in FIG. 2A).

[0085] The mind-body state intervention device 100 according to the first embodiment includes the mode acquisition unit 320, and the mode selection unit 310 acquires the result of the user's selection of whether to implement a mode prioritizing intervention effect or a mode prioritizing work efficiency. However, the mode acquisition unit 320 may not be included. Furthermore, while the intervention control method is selected depending on the mode selected by the user, the mode may not be required, and intervention control may always be performed by prioritizing intervention effect, or intervention control may always be performed by prioritizing work efficiency. In this case, the relationship data may only include the relationship between each of the multiple intervention control methods and either the intervention effect or work efficiency, as needed.

[0086] Furthermore, although fatigue has been used as an example of a physical and mental state in the above description, other conditions such as drowsiness or anger may also be used.

[0087] In the case of drowsiness, the physiological quantity detection unit 160 is, for example, a camera attached to a personal computer. Physiological quantities are acquired from a person's facial image captured by the camera. Physiological quantities include, for example, the number of blinks, the rate at which the eyes are closed, and the pupil diameter. The method for acquiring the number of blinks and the pupil diameter has already been explained above, so a detailed explanation will be omitted here. The rate at which the eyes are closed is the percentage of time during one minute that the eyes are 20% or less open, assuming that the eye opening degree during wakefulness is 100%.

[0088] Other than a camera, for example, a heart rate sensor or an electroencephalograph may be used.

[0089] In the case of a heart rate sensor, physiological values ​​include, for example, the LF and HF of heart rate variability, the heart rate, and the respiratory rate acquired by the heart rate sensor. The methods for acquiring the LF, HF, heart rate, and respiratory rate have been explained above, so a detailed explanation will be omitted here.

[0090] In the case of an electroencephalograph, the physiological quantity is, for example, the relative alpha wave band power obtained. The relative alpha wave band power is obtained by performing a fast Fourier transform on the electroencephalogram at predetermined time intervals, and is the ratio of the power in the alpha wave band (8 to 13 Hz) to the power in the 0.5 to 50 Hz band. Alpha waves are generally known to be related to drowsiness, and when a person is in a state of high drowsiness, alpha waves become dominant in the electroencephalogram.

[0091] The quantification processing unit 120 quantifies the subjective sleepiness and the objective sleepiness using a preset quantification method based on the physiological amount acquired by the physiological amount acquisition unit 110 from the physiological amount detection unit 160.

[0092] Here, a method for quantifying subjective sleepiness and objective sleepiness will be described. One example of a quantification method is a sleepiness function. A sleepiness function is a function that takes physiological quantities as input and outputs quantified subjective sleepiness and objective sleepiness. Here, as an example, a sleepiness function using the eye closure rate shown in FIG. 9 will be described. A function for subjective sleepiness, objective sleepiness, and eye closure rate is created in advance through experiments. Methods for acquiring subjective sleepiness include, for example, a VAS, the Karolinska Sleepiness Scale, or the Stanford Sleepiness Scale, and the acquired value is used as a quantified value. A method for acquiring objective sleepiness is to acquire physiological quantities, such as relative alpha wave band power obtained by electroencephalography (EEG), oxygenated hemoglobin concentration obtained by functional near-infrared spectroscopy (fNIRS), or skin temperature at a specified site obtained by thermography. Even for quantities other than physiological quantities, a method has been used in which an experimenter objectively assesses a subject's sleepiness level from an image of the subject's face.

[0093] FIG. 9 shows, as an example, a sleepiness function in which the subjective and objective sleepiness levels are quantified using a nine-point equal-interval scale using the Karolinska Sleepiness Scale and the physiological values ​​are expressed as eye-closure rates. The sleepiness level is represented as 1 (very awake), 3 (awake), 5 (neutral), 7 (sleepy), and 9 (very sleepy). Subjective and objective sleepiness can be quantified by inputting the eye-closure rate acquired by the physiological value detection unit as an input value for the sleepiness function. Subjective sleepiness can be quantified by having the subject report it, while objective sleepiness can be quantified by objectively assessing it on a nine-point scale using the Karolinska Sleepiness Scale, for example, based on facial expressions. Note that, as an example, a method using a sleepiness function in which the subjective and objective sleepiness levels are quantified using a nine-point equal-interval scale and the physiological values ​​are expressed as eye-closure rates has been described. However, the method for acquiring subjective and objective sleepiness and the physiological values ​​used as input values ​​are not limited.

[0094] The method for quantifying subjective sleepiness and objective sleepiness may be, for example, a physiological amount-sleepiness correspondence table or machine learning, other than a drowsiness function, and the method for quantifying subjective sleepiness and objective sleepiness is not limited to a drowsiness function. In the case of a physiological amount-sleepiness correspondence table, subjective sleepiness and objective sleepiness are quantified from the magnitude of the physiological amount acquired by the physiological amount detection unit based on a preset correspondence table of physiological amounts and subjective sleepiness and objective sleepiness. The physiological amount-sleepiness correspondence table differs from the case of fatigue in that the preset physiological amount is the eye closure rate, but is otherwise the same, so a detailed explanation will be omitted.

[0095] In the case of machine learning, one example is a neural network. In the case of a neural network, a neural network can be used in the same way as in the case of fatigue. Quantification of fatigue using a neural network has already been explained above, and since it is the same except for the physiological quantities acquired by the physiological quantity detection unit used as feature quantities, a repeated explanation will be omitted.

[0096] In addition to neural networks, other machine learning techniques include multiple regression and random forests. The quantification of fatigue using multiple regression and random forests has already been explained above, and since they are the same except for the physiological quantities acquired by the physiological quantity detection unit used as feature quantities, a detailed explanation will be omitted. The machine learning technique is not limited here. Furthermore, the physiological quantities used to quantify subjective sleepiness and objective sleepiness may be different.

[0097] When the mental and physical state is sleepiness, the first storage unit 130 stores relationship data indicating the relationship between each of the multiple intervention control methods and the intervention effect on quantified subjective sleepiness and objective sleepiness, as well as the relationship with work efficiency. The methods for calculating the intervention effect and work efficiency and the methods for creating the relationship data are similar to those for the example of fatigue, and therefore will not be described again here.

[0098] The mode selection unit 310 inputs the items to be prioritized in the selection of the intervention control method from the user. The mode acquisition unit 320 acquires the mode selected by the user in the mode selection unit 310. As this is the same as the example of fatigue, a detailed explanation will be omitted here.

[0099] The intervention control unit 140 selects an intervention control method from among a plurality of intervention control methods based on the intervention effect on the quantified subjective and objective sleepiness, work efficiency, and the mode acquired by the mode acquisition unit 320 by referencing the relationship data stored in the first memory unit 130. The intervention control unit 140 controls the intervention device 150 based on the selected intervention control method. The intervention device 150 is, for example, an air conditioner. The air conditioner, upon receiving a signal from the intervention control unit 140, exposes a person to wind effective against drowsiness. Wind effective against drowsiness is generally wind that, when exposed to, dominates the sympathetic nervous system and has a stimulating effect. Examples of wind effective against drowsiness include wind with large fluctuations, wind with small diffusion that is exposed only to specific parts such as the face, and wind with large temperature fluctuations. However, wind effective against drowsiness is not limited here. Furthermore, the intervention device 150 may be, for example, an acoustic device or a vibration generator other than an air conditioner. The intervention device 150 is not limited here.

[0100] In the case of audio equipment, sounds that are effective against drowsiness are generated. Sounds that are effective against drowsiness are generally sounds that, when exposed to, activate the sympathetic nervous system and have the characteristic of having an awakening effect. For example, sounds that contain a lot of low frequencies, sounds with high sound pressure, and sounds that contain dissonances, etc., but the sounds that are effective against drowsiness here are not limited to the above.

[0101] In the case of a vibration generator, vibrations that are effective against drowsiness are applied to a person. Vibrations that are effective against drowsiness are generally vibrations that cause the sympathetic nervous system to become dominant when exposed to the vibration, and are characterized by having an awakening effect. For example, vibrations with large amplitude, large frequency, or irregular amplitude are included, but the vibrations that are effective against drowsiness here are not limited to the above.

[0102] In the case of anger, the physiological quantity detection unit 160 is, for example, a camera attached to a personal computer. Physiological quantities are acquired from a person's facial image captured by the camera. Examples of physiological quantities include pulse wave, pupil diameter, and the degree of rise of the corners of the mouth. The method for acquiring pupil diameter has been explained above, so a detailed explanation will not be given here. The pulse wave is acquired by calculating the average RGB luminance value for each video frame in a specified area of ​​the facial image acquired by the camera, thereby acquiring a time-varying waveform of the average luminance value. Independent component analysis is performed on the time-varying waveform of the average luminance value to extract three independent waveforms, and the pulse wave can be acquired by using the waveform with the highest peak in the power spectrum. The degree of rise of the corners of the mouth is acquired by setting a node in the facial image and deriving the amount of change in the coordinates of the node at the corners of the mouth.

[0103] Other than a camera, for example, a heart rate sensor or a skin potential meter may be used, and the physiological amount detector is not limited to a camera.

[0104] In the case of a heart rate sensor, physiological values ​​include, for example, the LF and HF of heart rate variability, the heart rate, and the respiratory rate acquired by the heart rate sensor. The methods for acquiring the LF, HF, heart rate, and respiratory rate have been explained above, so a detailed explanation will be omitted here.

[0105] In the case of a skin potential meter, the physiological value is, for example, the acquired skin potential. Skin potential is said to be related to the phenomenon of sweating, and generally, skin potential increases when one is angry.

[0106] The quantification processing unit 120 quantifies the subjective anger and objective anger using a preset quantification method based on the physiological amount acquired by the physiological amount acquisition unit from the physiological amount detection unit 160.

[0107] Here, a method for quantifying subjective anger and objective anger will be described. An example of a quantification method is an anger function. An anger function is a function that takes physiological quantities as input and outputs quantified subjective anger and objective anger. As an example, an anger function using pupil diameter shown in FIG. 10 will be described. A function for subjective anger, objective anger, and pupil diameter is created in advance through experiments. Methods for obtaining subjective anger include, for example, VAS, NRS, and face scale, and the obtained values ​​are used as quantitative values. Methods for obtaining objective anger include obtaining physiological quantities, and can be quantified from, for example, relative gamma wave band power obtained by electroencephalography, oxygenated hemoglobin concentration obtained by fNIRS, and saliva volume.

[0108] FIG. 10 shows an example of an anger function in which the values ​​quantified for the degree of subjective anger and objective anger on a 7-point scale with equal intervals and the physiological value are expressed as pupil diameter. Here, the higher the scale value, the greater the degree of anger. For example, 1 represents a mild state, and 7 represents the most intense state. While the above scale has been used as an example to explain the degree of anger, the way in which the anger stages are expressed is not limited. Subjective anger and objective anger can be quantified by inputting the pupil diameter acquired by the physiological value detection unit as an input value for the anger function. Note that, here, a method using an anger function in which the values ​​quantified for subjective anger and objective anger on a 7-point scale with equal intervals and the physiological value are expressed as pupil diameter is explained as an example, but the method for acquiring subjective anger and objective anger, and the physiological value used as the input value are not limited.

[0109] In the case of the physiological level-anger correspondence table, subjective anger and objective anger are quantified from the magnitude of the physiological level acquired by the physiological level detection unit based on a preset correspondence table between physiological levels and subjective anger and objective anger. The physiological level-anger correspondence table differs from the case of fatigue in that the physiological levels are preset, but is otherwise the same, so a detailed explanation will be omitted. The method of acquiring subjective anger and objective anger is the same as the example of the anger function, so a detailed explanation will be omitted here.

[0110] In the case of machine learning, one example is a neural network. In the case of a neural network, a neural network can be used in the same way as in the case of fatigue. Quantification of fatigue using a neural network has already been explained above, and since it is the same except for the physiological quantities acquired by the physiological quantity detection unit used as feature quantities, a repeated explanation will be omitted.

[0111] In addition to neural networks, other machine learning techniques include multiple regression and random forests. The quantification of fatigue using multiple regression and random forests has already been explained above. The difference is that the feature values ​​are physiological values ​​acquired by a physiological value detection unit, but otherwise they are the same, so a detailed explanation will be omitted. The machine learning technique is not limited here. Furthermore, the physiological values ​​used to quantify subjective anger and objective anger may be different.

[0112] When the mental and physical state is anger, the first storage unit 130 stores relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effect on quantified subjective anger and objective anger, as well as the relationship with work efficiency. The methods for calculating the intervention effect and work efficiency and the methods for creating the relationship data are similar to those for the example of fatigue, and therefore will not be described again here.

[0113] The mode selection unit 310 inputs the items to be prioritized in the selection of the intervention control method from the user. The mode acquisition unit 320 acquires the mode selected by the user in the mode selection unit 310. As this is the same as the example of fatigue, a detailed explanation will be omitted here.

[0114] The intervention control unit 140 refers to the relationship data stored in the first memory unit 130 and selects an appropriate intervention control method from among multiple intervention control methods based on the intervention effect on quantified subjective anger and objective anger, work efficiency, and the mode selected by the mode acquisition unit 320.

[0115] The intervention control unit 140 controls the intervention device 150 based on the selected intervention control method. The intervention device 150 is, for example, an air conditioner. The air conditioner, which receives a signal from the intervention control unit 140, exposes the person to wind that is effective against anger. Wind that is effective against anger is generally wind that makes the parasympathetic nervous system dominant when exposed to it, and is characterized by having a calming effect. For example, wind with 1 / f fluctuations, wind with small wind speed fluctuations, or wind with temperature fluctuations. The wind that is effective against anger is not limited here. Furthermore, the intervention device 150 may be, for example, an audio device or a fragrance emitting device other than a fan. The intervention device 150 is not limited here.

[0116] In the case of audio equipment, sounds that are effective against anger are generated. Sounds that are effective against anger are generally sounds that, when exposed to, activate the parasympathetic nervous system and have the characteristic of having a calming effect. For example, sounds with 1 / f fluctuations, sounds with gradual changes in sound pressure, and the sound of birds chirping. Here, sounds that are effective against anger are not limited.

[0117] In the case of a scent generator, a person is exposed to a scent that is effective against anger. Scents that are effective against anger are generally scents that make the parasympathetic nervous system dominant when exposed to them, and are characterized by having a calming effect. Examples include lavender, bergamot, and rose otto. Scents that are effective against anger are not limited here.

[0118] <Effects> In this way, by performing intervention control based on the effect mode that the user wants to prioritize in intervention control selected by the user, the following effects are obtained.

[0119] Generally, intervention control for fatigue is performed according to the degree and type of fatigue, i.e., subjective and objective fatigue. However, one issue is that it cannot take into account the individual's work situation. For example, when there is a pile of work that needs to be done, a system that stops work and performs intervention control is difficult for users to use.

[0120] As in this embodiment, the user can select whether to prioritize work efficiency or intervention effect when performing intervention control, so that if there is a lot of work to do, a mode that prioritizes work efficiency can be selected, and if there is excessive fatigue, a mode that prioritizes intervention effect can be selected, thereby having the effect of enabling effective intervention to meet the user's needs.

[0121] Similarly, in the case of drowsiness, the user can select whether to prioritize work efficiency or intervention effect when performing intervention control.This allows the user to select a mode that prioritizes work efficiency when there is a backlog of work, and a mode that prioritizes intervention effect when the user is feeling too drowsy, thereby enabling effective intervention to be performed in response to the user's needs.

[0122] Similarly, in the case of anger, the user can select whether to prioritize work efficiency or intervention effect in the intervention control, so that if there is a backlog of work, a mode that prioritizes work efficiency can be selected, and if anger has become too great, a mode that prioritizes intervention effect can be selected, thereby having the effect of enabling effective intervention to meet the user's needs.

[0123] <Summary> As described above, the mental and physical state intervention device 100 according to this embodiment includes the physiological amount acquisition unit 110 that acquires the physiological amounts of the subject detected by the physiological amount detection unit 160, the quantification processing unit 120 that quantifies two mental and physical states, i.e., a mental and physical state of which the subject is aware and a mental and physical state of which the subject is not aware, based on the physiological amounts, the first storage unit 130 that pre-stores relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effect on the two quantified mental and physical states, and the intervention control unit 140 that selects one intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical states quantified by the quantification processing unit 120 by referring to the relationship data, and controls the intervention device 150 with the selected intervention control method.

[0124] This allows the system to distinguish between mental and physical states that the subject is aware of and mental and physical states that the subject is not aware of, and select an intervention control method based on that, thereby allowing the system to select a more efficient and effective mental and physical state intervention control method.

[0125] The system also includes a mode acquisition unit 320 that acquires a mode from a mode selection unit 310 that allows the subject to select a mode indicating whether to prioritize improvement of the intervention effect or work efficiency, which is the efficiency of the subject's work, and the relationship data further indicates the relationship between each of the multiple intervention control methods and work efficiency, and the intervention control unit 140 selects an intervention control method based on the intervention effect, work efficiency, and mode.

[0126] This allows the subject to choose whether to prioritize the intervention effect on the subject's mental and physical state or the subject's workload, making it possible to select a mental and physical state intervention control method that better reflects the subject's wishes.

[0127] Furthermore, a relational data creating device is provided that creates relational data for a plurality of subjects and stores the data in the first storage unit 130 .

[0128] This makes it possible to newly create and update relational data that serves as a criterion for selecting an intervention control method.

[0129] The relationship data creation device also includes a work amount acquisition unit 21 that acquires the amount of work performed by the subject detected by the work amount detection unit 23, a work efficiency calculation unit 22 that calculates the work efficiency based on the amount of work, and an intervention effect calculation unit 17 that calculates the intervention effect, and stores the calculated work efficiency and intervention effect as relationship data in the first memory unit 130.

[0130] This makes it possible to acquire information necessary for creating relational data used to select an intervention control method.

[0131] In addition, when the mode selection unit 310 selects to prioritize improving the intervention effect, the intervention control unit 140 refers to the relational data and selects the intervention control method from among the multiple intervention control methods that will maximize the intervention effect.

[0132] This makes it possible to provide an intervention control method that maximizes the intervention effect by reflecting the subject's request for emphasis on the intervention effect.

[0133] In addition, when the mode selection unit 310 selects to prioritize improving work efficiency, the intervention control unit 140 refers to the relational data and selects from among multiple intervention control methods the intervention control method that will result in the highest work efficiency.

[0134] This makes it possible to provide an intervention control method that maximizes work efficiency by reflecting the request of the subject who wishes to emphasize work efficiency.

[0135] The intervention effect is the amount of change in the subject's physical and mental state before and after the intervention control.

[0136] This makes it possible to select an intervention control method by focusing on changes in the subject's mental and physical state before and after intervention control.

[0137] Furthermore, the work efficiency is the rate of change in the amount of work per time required for intervention control on the subject before and after intervention control.

[0138] This makes it possible to select an intervention control method by placing emphasis on the rate of change in workload per unit time required for intervention control on the subject before and after intervention control.

[0139] Further, some further modifications will be described below using fatigue as an example of the physical and mental state.

[0140] (First Modification of First Embodiment) A mind-body state intervention device 200 according to a first modification of the first embodiment presents the user with subjective fatigue and objective fatigue before and after intervention.

[0141] 11 is a block diagram showing the configuration of a mind-body state intervention device 200 according to Modification 1 of the first embodiment. In addition to the configuration of the mind-body state intervention device 100, the mind-body state intervention device 200 further includes a second storage unit 410, a change amount calculation unit 420, and a display unit 430.

[0142] In this modified example, the physiological quantity acquisition unit 110 and the quantification processing unit 120 operate again as described above after the intervention control to quantify the subject's subjective fatigue and objective fatigue after the intervention control.

[0143] The second storage unit 410 stores the quantified subjective fatigue and objective fatigue before and after the intervention control transmitted from the quantification processing unit 120. The second storage unit 410 is a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid state drive (SSD), or the like, and the storage unit is not limited here.

[0144] The change amount calculation unit 420 reads the subjective fatigue and objective fatigue before and after the intervention control from the second storage unit 410 and calculates the amount of change in each. For example, if the objective fatigue was 5 before the intervention control and 2 after the intervention control, the amount of change is -3. The change amount calculation unit 420 is realized by a program and a processor that executes the program.

[0145] The display unit 430 acquires the amounts of change in subjective fatigue and objective fatigue before and after intervention control from the change amount calculation unit 420 and displays them to the user as shown in Fig. 12. Fig. 12 is an example and is not intended to limit the display of the amounts of change in subjective fatigue and objective fatigue before and after intervention control. The display unit 430 is, for example, a display monitor of a personal computer.

[0146] <Effects> By presenting the subjective fatigue and objective fatigue before and after the intervention to the user in this way, the following effects are achieved.

[0147] Generally, interventions for fatigue management involve displaying the degree of either subjective or objective fatigue, as well as the effects of the intervention. However, the problem is that neither the degree nor the effects of the intervention are displayed, which can make it difficult to correctly recognize one's own fatigue state and the effects of the intervention.

[0148] As in the present embodiment, by presenting the user with subjective and objective fatigue before and after the intervention, it is possible to recognize the user's own fatigue state with greater accuracy, and it has the effect of enabling the user to recognize the effects of the fatigue intervention with greater accuracy.

[0149] Similarly, with regard to drowsiness, by presenting the user with subjective and objective drowsiness before and after intervention, it is possible to recognize their own state of drowsiness with greater accuracy, and it has the effect of enabling them to recognize the effects of the drowsiness intervention with greater accuracy.

[0150] Similarly, with regard to anger, by presenting the user with subjective and objective anger before and after intervention, it is possible to recognize one's own anger state with greater accuracy, and it has the effect of enabling the user to recognize with greater accuracy the effects of anger intervention.

[0151] Here, the second memory unit 410 and the change amount calculation unit 420 have been added as means for presenting the user with fatigue before and after the intervention, but of course the means for presenting the user with fatigue before and after the intervention are not limited to these, and the configuration is not limited as long as it has similar functions.

[0152] As described above, the intervention control unit 140 according to this embodiment presents the subject with the amount of change in mental and physical state before and after intervention control.

[0153] This allows the subject to recognize the amount of change in their physical and mental state before and after the intervention control, allowing them to feel the effects of the intervention and understand their own physical and mental state.

[0154] (Second Modification of First Embodiment) A mind-body state intervention device 300 according to a second modification of the first embodiment presents the user with an intervention control method in addition to subjective fatigue and objective fatigue before and after intervention.

[0155] 13 is a block diagram showing the configuration of a mind-body state intervention device 300 according to Modification 2 of the first embodiment. In addition to the configuration of the mind-body state intervention device 100, the mind-body state intervention device 300 further includes a second storage unit 410, a change amount calculation unit 420, and a display unit 430.

[0156] In this modified example, the physiological quantity acquisition unit 110 and the quantification processing unit 120 operate again as described above after the intervention control to quantify the subject's subjective fatigue and objective fatigue after the intervention control.

[0157] Unlike variant example 1 of the first embodiment, the second memory unit 410 not only stores the quantified subjective fatigue and objective fatigue before and after the intervention control transmitted from the quantification processing unit 120, but also stores the intervention control method transmitted from the intervention control unit 140.

[0158] As in the first modification of the first embodiment, the change amount calculation unit 420 calculates the amount of change in each of the subjective fatigue and objective fatigue before and after the intervention control read from the second storage unit 410. For example, if the objective fatigue level was 5 before the intervention control and 2 after the intervention control, the amount of change is −3.

[0159] The display unit 430 acquires the amount of change in each of the subjective fatigue and objective fatigue before and after the intervention control from the change amount calculation unit 420, and displays this together with the intervention control method stored in the second storage unit 410 to the user as shown in Fig. 14. Fig. 14 is an example, and does not limit the display of the amount of change in each of the subjective fatigue and objective fatigue before and after the intervention control or the intervention control method performed.

[0160] <Effects> In this way, presenting the intervention control method to the user in addition to the subjective fatigue and objective fatigue before and after the intervention has the following effects.

[0161] Generally, interventions for fatigue only present the degree of either subjective or objective fatigue, or the effects of the intervention. However, the problem is that people are unable to recognize what kind of intervention method they have received, which can lead to doubts about the intervention method.

[0162] As in this embodiment, by presenting the user with the intervention control method in addition to the subjective and objective fatigue before and after the intervention, the user can recognize the intervention control method used and the effects of the intervention, which has the effect of eliminating any doubts about the intervention control.

[0163] Similarly, with regard to drowsiness, by presenting the user with the intervention control method in addition to the subjective and objective drowsiness before and after the intervention, the user can recognize the intervention control method used and the effects of the intervention, which has the effect of eliminating any doubts about the intervention control.

[0164] Similarly, with regard to anger, by presenting the user with the intervention control method in addition to the subjective and objective anger before and after the intervention, the user can recognize the intervention control method used and the effects of the intervention, which has the effect of eliminating any doubts about the intervention control.

[0165] Here, the second memory unit 410 and the change amount calculation unit 420 have been added as means for presenting the intervention control method to the user, but of course the means for presenting the intervention control method to the user are not limited to these, and the configuration is not limited as long as it has similar functions.

[0166] As described above, the intervention control unit 140 according to this embodiment presents the intervention control method used for the intervention control to the subject.

[0167] This will enable the subjects to understand the intervention control method and its effects, thereby eliminating any doubts they may have about the intervention control.

[0168] [Second Embodiment] <Configuration> Fig. 15 is a block diagram showing the characteristic functional configuration of a mental and physical state intervention device 400 according to the second embodiment. The mental and physical state intervention device 400 has a function of changing the intervention control method that has been adopted up until that point when the intervention effect falls below a predetermined threshold. The mental and physical state intervention device 400 differs from the mental and physical state intervention device 100 of the first embodiment in that a second storage unit 410, a change amount calculation unit 420, and an intervention effect assessment unit 440 are added. The operation and effects will be described below, focusing on this point. Furthermore, fatigue will be used as an example of a mental and physical state.

[0169] The second storage unit 410 stores the quantified subjective fatigue and objective fatigue before and after the intervention control, together with the date and time of the intervention control, transmitted from the quantification processing unit 120. The configuration of the second storage unit 410 has already been explained in Modification 1 of the first embodiment, and therefore will not be explained again here.

[0170] The change amount calculation unit 420 reads the subjective fatigue, objective fatigue, and date and time from the second storage unit 410, and calculates the amount of change in each of the subjective fatigue and objective fatigue over a predetermined time based on this. In other words, it calculates the amount of change in each of the subjective fatigue and objective fatigue at the start of intervention control and after the predetermined time has passed.

[0171] Here, the calculation requires quantified subjective fatigue and objective fatigue after a predetermined time has elapsed, so in this embodiment, the physiological amount detection unit 160, physiological amount acquisition unit 110, and quantification processing unit 120 repeat a series of operations (S1 and S2 in FIG. 2A) to quantify the physiological amount of the subject at regular intervals that are necessary and sufficient for implementation.

[0172] The intervention effect assessment unit 440 compares the amount of change in subjective fatigue and objective fatigue over a predetermined time period obtained from the change amount calculation unit 420 with a preset threshold value to determine which is greater. If the amount of change in subjective fatigue and objective fatigue is below the threshold value, the intervention control unit 140 changes the intervention control currently being performed. The intervention effect assessment unit 440 is realized by a program and a processor that executes the program.

[0173] <Operation> Figure 16 is a flowchart for explaining the operation of the mind-body state intervention device 400 according to the second embodiment. The operation of the mind-body state intervention device 400 is the same as the operation of the mind-body state intervention device 100 up to a certain point, but specifically, the operation from step S6 onwards in Figure 2A is different. Therefore, step S6 onwards in Figure 2A will be described in detail using Figure 16.

[0174] First, the intervention control unit 140 checks the mode acquired by the mode acquisition unit when selecting the current intervention control method (S31 in FIG. 16 ). The subsequent operation differs depending on the mode acquired by the intervention control unit 140 (S32 in FIG. 16 ). If the mode is the work efficiency priority mode (work efficiency priority mode in S32 in FIG. 16 ), the intervention control unit 140 continues the current intervention control method (S35b in FIG. 16 ). If the mode is the intervention effect priority mode (intervention effect priority mode in S32 in FIG. 16 ), the intervention effect assessment unit 440 assesses the intervention effect by comparing the amount of change in subjective fatigue and objective fatigue over a specified time period with a predetermined threshold (S33 in FIG. 16 ).

[0175] If the intervention effect is equal to or greater than the threshold, that is, if the intervention effect is equal to or greater than the reference value (No in S34 of FIG. 16), the intervention control unit 140 continues the current intervention control method (S35b of FIG. 16).

[0176] If the intervention effect is less than the threshold, that is, if the intervention effect is below the reference value (Yes in S34 in FIG. 16), the intervention controller 140 changes the intervention control method. At this time, the intervention control method with the greatest intervention effect is selected, excluding the currently implemented intervention control method (S35a in FIG. 16).

[0177] In this embodiment, the intervention effect is determined based on whether the value is equal to or greater than the threshold value or less than the threshold value, but it may also be determined based on whether the value is above the threshold value or less than the threshold value.

[0178] For example, Fig. 17 shows an example of a change in the intervention control method according to the second embodiment. As shown in Fig. 17, the fatigue level of user C is subjective fatigue: 4, objective fatigue: 7, with objective fatigue being greater (Fig. 17(a)). When the intervention effect priority mode is selected, the wind intervention control method, which has the greatest intervention effect on objective fatigue, is selected (Fig. 17(c)). However, the intervention effect decreases over time due to familiarity (Fig. 17(b)). When the intervention effect falls below a threshold, the scent intervention control method, which has the second greatest intervention effect on objective fatigue after wind, is selected (Fig. 17(c)).

[0179] <Effects> In this way, by changing the intervention control method when the amount of change in the subjective mental and physical state and the objective mental and physical state falls below the threshold, the following effects are achieved.

[0180] In general, the effect of intervention on fatigue through intervention control is not constant and fluctuates over time. For example, in the case of intervention using fragrance, even if the intervention effect is large at the start of the intervention control, it decreases over time as the person becomes accustomed to it, so there is an issue that the intervention effect becomes almost zero after a certain intervention time, making it no longer an effective intervention.

[0181] As in this embodiment, by changing the intervention control method when the change in subjective fatigue and objective fatigue falls below a threshold, it is possible to suppress the reduction in intervention effectiveness due to habituation and perform effective intervention control.

[0182] Furthermore, there are individual differences in the magnitude of the intervention effect on fatigue through intervention control and the fluctuation of the intervention effect over time. For example, in the case of an intervention using temperature, the intervention effect is large for women but the reduction in the intervention effect over time is also large, while the intervention effect is small for men but the reduction in the intervention effect over time is also small. This means that even if the same intervention control is performed over a certain period of time, it may be effective for women but not for men.

[0183] As in this embodiment, by changing the intervention control method when the change in subjective fatigue and objective fatigue falls below a threshold, it is possible to suppress a reduction in the intervention effect due to individual differences. For example, in the case of intervention using temperature, effective intervention control can be performed by shortening the temperature intervention control time for women and switching to the next intervention control method, and by lengthening the temperature intervention control time for men and switching to the next intervention control method.

[0184] Similarly, with regard to drowsiness, by changing the intervention control method when the amount of change in subjective drowsiness and objective drowsiness falls below a threshold, it is possible to suppress the reduction in the intervention effect due to individual differences.

[0185] Similarly, with regard to anger, by changing the intervention control method when the amount of change in subjective anger and objective anger falls below a threshold, it is possible to suppress the reduction in the intervention effect due to individual differences.

[0186] Here, the second memory unit 410, the change amount calculation unit 420, and the intervention effect judgment unit 440 have been added as means for changing the intervention control means, but of course the means for the user to change the intervention control means are not limited to these, and the configuration is not limited as long as it has similar functions.

[0187] (Modification 1 of Second Embodiment) In Modification 1 of the second embodiment, the strength of intervention control performed using a specific interventional device is increased stepwise over a certain period of time.

[0188] The first modification has the same configuration as the mind-body state intervention device 400 of the second embodiment, but differs in the operation of the intervention controller 140. This difference will be mainly described below.

[0189] The intervention control unit 140 refers to the relationship data stored in the storage unit 130 and selects an intervention control method based on the mode acquired by the mode acquisition unit 320, and at this time controls the intervention device 150 to perform the intervention in stages. In other words, when controlling the intervention device 150 according to the selected intervention control method, the intervention control unit 140 controls so that the strength of the intervention increases over time.

[0190] For example, as shown in Figure 18, taking fragrance as an example of an intervention control method, intervention control with a weak fragrance strength and small intervention effect is performed for the first 10 minutes of intervention control, followed by intervention control with a normal fragrance strength and medium intervention effect for the next 20 minutes, and then intervention control with a strong fragrance strength and large intervention effect for the next 30 minutes.

[0191] Although scent has been used as an example, the intervention control method can be sound or wind, and is not limited to a step-by-step intervention control method. In the case of sound, the intervention control method can be to increase the fluctuation of the sound at regular intervals, to make the sound louder, or to increase the frequency. In the case of wind, the intervention control method can be to increase the wind, to increase the fluctuation, or to change the amount of wind hitting the person.

[0192] <Effects> In this way, by carrying out intervention control on the subjective mental and physical state and the objective mental and physical state in stages over a certain period of time, the following effects are achieved.

[0193] In general, the intervention effect on fatigue through intervention control is not constant and fluctuates depending on the person's habituation to the intervention control. For example, in the case of intervention using fragrance, even if the intervention effect is large at the start of the intervention control, it decreases over time as the person becomes accustomed to the control. Therefore, there is an issue that the intervention effect becomes almost zero after a certain intervention time, making it no longer an effective intervention.

[0194] By performing intervention control in stages as in this embodiment, it is possible to suppress a reduction in the intervention effect due to habituation, and to perform effective intervention control.

[0195] Similarly, with regard to drowsiness, by performing intervention control in stages, it is possible to suppress a reduction in the intervention effect due to habituation, and to perform effective intervention control.

[0196] Similarly, with regard to anger, by carrying out intervention control in stages, it is possible to suppress the reduction in the intervention effect due to habituation, and to carry out effective intervention control.

[0197] Here, the second memory unit 410, the change amount calculation unit 420, and the intervention effect judgment unit 440 have been added as means for changing the intervention control means, but of course the means for the user to change the intervention control means are not limited to these, and the configuration is not limited as long as it has similar functions.

[0198] Third Embodiment <Configuration> FIG. 19 is a block diagram showing the characteristic functional configuration of a mind-body state intervention device 500 according to the third embodiment.

[0199] The mental and physical state intervention device 500 has a function of selecting an intervention control method suitable for an individual subject. The mental and physical state intervention device 500 differs from the mental and physical state intervention device 100 of the first embodiment in that a second storage unit 410, a change amount calculation unit 420, and an optimization processing unit 450 are added. This point will be mainly described below. In addition, fatigue will be used as an example of a mental and physical state.

[0200] The second memory unit 410 stores the quantified subjective fatigue and objective fatigue and the date and time sent from the quantification processing unit 120, the amount of change in subjective fatigue and objective fatigue at a specified time sent from the change amount calculation unit 420, and the intervention control method sent from the intervention control unit 140.

[0201] The change amount calculation unit 420 is the same as the change amount calculation unit 420 in the second embodiment, and therefore will not be described again here.

[0202] The optimization processing unit 450 selects the optimal intervention control method for an individual based on the subjective fatigue and objective fatigue, the date and time, the amount of change in each of the subjective fatigue and objective fatigue over a predetermined time period, and the intervention control method read from the second storage unit 410. The optimization processing unit 450 is realized by a program and a processor that executes the program.

[0203] The optimization method is, for example, a neural network. In the case of a neural network, as shown in Figure 20, at least one of subjective fatigue, objective fatigue, date and time, and the amount of change in each of subjective fatigue and objective fatigue over a predetermined time is used as input, and an intervention control method is used as output. Although a neural network has been used as an example of the optimization method, other methods such as a k-nearest neighbor method or a random forest may also be used, and the optimization method is not limited thereto.

[0204] In the case of the k-nearest neighbor method and random forest, as shown in Figures 21 and 22, at least one of subjective fatigue, objective fatigue, date and time, and the amount of change in each of subjective fatigue and objective fatigue over a specified time is used as input, and an intervention control method is used as output.

[0205] The intervention control unit 140 refers to the relationship data stored in the memory unit 130 and selects an intervention control method based on the mode acquired by the mode acquisition unit 320 and the processing of the optimization processing unit 450. For example, if the user selects the intervention effect priority mode, the intervention control method with the greatest intervention effect is selected from among multiple intervention control methods. However, if there is a signal from the optimization processing unit 450, the intervention control method selected by the optimization processing unit 450 is given priority. Furthermore, if the user selects the work efficiency mode, the intervention control method with the greatest work efficiency is selected from among multiple intervention control methods.

[0206] The interventional device 150 controls the interventional device 150 based on the intervention control method selected by the intervention control unit 140. The control of the interventional device 150 is similar to that of the interventional device 150 of the first embodiment, and therefore will not be described again here.

[0207] <Effects> In this way, by optimizing for an individual based on the amount of change in subjective fatigue and objective fatigue, the date and time, and the intervention control method, the following effects are obtained.

[0208] There are individual differences in the magnitude of intervention effects on fatigue through intervention control and the fluctuation of intervention effects over time. For example, in the case of intervention using temperature, the intervention effect is large for women but the reduction in the intervention effect over time is also large, while the intervention effect is small for men but the reduction in the intervention effect over time is also small. Therefore, there is an issue that even if the same intervention control over time is effective for women, it may not be effective for men.

[0209] As in this embodiment, by changing the intervention control method when the change in subjective fatigue and objective fatigue falls below a threshold, it is possible to suppress a reduction in the intervention effect due to individual differences. For example, in the case of intervention using temperature, effective intervention control can be performed by shortening the temperature intervention control time for women and switching to the next intervention control method, and by lengthening the temperature intervention control time for men and switching to the next intervention control method.

[0210] Similarly, for drowsiness, by changing the intervention control method when the amount of change in subjective drowsiness and objective drowsiness falls below a threshold, it is possible to suppress a reduction in the intervention effect due to individual differences.

[0211] Similarly, for anger, by changing the intervention control method when the amount of change in subjective anger and objective anger falls below a threshold, it is possible to suppress a reduction in the intervention effect due to individual differences.

[0212] Here, the second memory unit 410, the change amount calculation unit 420, and the optimization processing unit 450 have been added as means for suppressing the reduction in the intervention effect due to individual differences, but of course the means for suppressing the reduction in the intervention effect due to individual differences are not limited to these, and the configuration is not limited as long as it has similar functions.

[0213] [Fourth Embodiment] <Configuration> Fig. 23 is a block diagram showing the characteristic functional configuration of a mental and physical state intervention device 600 according to the fourth embodiment. The mental and physical state intervention device 600 has a function of creating a work schedule for a subject based on the subject's subjective fatigue and objective fatigue. The mental and physical state intervention device 600 differs from the mental and physical state intervention device 100 of the first embodiment in that it additionally includes a schedule creation unit 510, a work terminal control unit 520, and a work terminal 530. This point will be mainly described below. Furthermore, fatigue will be used as an example of a mental and physical state.

[0214] The schedule creation unit 510 creates a schedule for the user based on the subjective fatigue and objective fatigue acquired from the quantification processing unit 120. For example, in the case of a daily schedule, as shown in Fig. 24, if the user's subjective fatigue and objective fatigue are high at the start of work, a schedule is created in which tasks requiring concentration are avoided in the first half and tasks that are primarily focused on work, intervention control is performed in the middle to reduce subjective fatigue and objective fatigue, and a schedule in which tasks requiring concentration and tasks are primarily focused in the second half is created. The schedule may be a monthly schedule or a yearly schedule, and there is no limit to the time span of the schedule.

[0215] The work terminal control unit 520 controls the user's work terminal 530 based on the schedule transmitted from the schedule creation unit 510. For example, when an intervention begins, a notification such as "Intervention is beginning" may be displayed on the screen of the user's work terminal 530. Although a notification has been used as an example, if work is still being performed after the schedule has passed, the work terminal 530 may be powered off or an alarm may sound, and this does not restrict the control of the user's work terminal 530.

[0216] The schedule creation unit 510 and the work terminal control unit 520 are realized by a program and a processor that executes the program.

[0217] The work terminal 530 is a terminal on which the subject person actually performs work, and is, for example, a personal computer or a smartphone.

[0218] The intervention control unit 140 refers to the relationship data stored in the memory unit 130 and selects an intervention control method based on the mode acquired by the mode acquisition unit 320, and at this time starts and ends intervention control based on the schedule acquired from the schedule creation unit 510.

[0219] The intervention device 150 is controlled based on the intervention control method and schedule selected by the intervention control unit 140. The control of the intervention device 150 is similar to that of the intervention device 150 of the first embodiment, and therefore will not be described again here.

[0220] <Effects> In this way, creating a schedule based on subjective fatigue and objective fatigue and controlling the user's work terminal has the following effects.

[0221] Generally, it is possible to reduce fatigue through intervention control, but the problem is that work schedules tend to make workers more likely to become fatigued in the first place. Another problem is that working while fatigued can reduce concentration and lead to mistakes.

[0222] By creating a schedule based on subjective fatigue and objective fatigue as in this embodiment, it is possible to prevent fatigue from accumulating and to prevent mistakes in work.

[0223] Similarly, with regard to drowsiness, by creating a schedule based on subjective and objective drowsiness, it is possible to prevent drowsiness from building up and to prevent mistakes at work.

[0224] Similarly, with regard to anger, creating a schedule based on subjective and objective anger can prevent anger from building up and prevent mistakes at work.

[0225] Here, a schedule creation unit 510, a work terminal control unit 520, and a work terminal 530 have been added as means for implementing intervention control based on the created schedule, but of course the means for implementing intervention control based on the created schedule are not limited to these, and the configuration is not limited as long as it has similar functions.

[0226] [Fifth Embodiment] <Configuration> Fig. 25 is a block diagram showing the characteristic functional configuration of a mental and physical state intervention device 700 according to a fifth embodiment of the present invention. The mental and physical state intervention device 700 has a function of analyzing time periods in which subjective fatigue and objective fatigue tend to accumulate. The mental and physical state intervention device 700 differs from the mental and physical state intervention device 100 of the first embodiment in that a second storage unit 410 and a control time analysis unit 540 are added. This point will be mainly described below. Furthermore, fatigue will be used as an example of a mental and physical state.

[0227] The second storage unit 410 stores the subjective fatigue and objective fatigue of the subject before and after the intervention control quantified by the quantification processing unit 120, as well as the date and time.

[0228] The control time analysis unit 540 reads the subjective fatigue and objective fatigue and the date and time from the second storage unit 410 and analyzes the time periods during which subjective fatigue and objective fatigue are high. For example, as shown in FIG. 26 , the average values ​​of subjective fatigue and objective fatigue for each hour of a work week are compared to determine the time period during which they are highest. The intervention control unit 140 performs intervention control to reduce the average values ​​of subjective fatigue and objective fatigue during this highest time period. In FIG. 26 , the time period during which the average values ​​of subjective fatigue and objective fatigue are highest is 17:00 to 18:00. Therefore, the time during which intervention control is performed is determined to reduce the average values ​​of subjective fatigue and objective fatigue between 17:00 and 18:00.

[0229] Although the example described above uses hourly average values ​​of subjective and objective fatigue, monthly or seasonal average values ​​may also be used, and the analysis method is not limited thereto. The control time analysis unit 540 is realized by a program and a processor that executes the program.

[0230] The intervention control unit 140 refers to the relationship data stored in the memory unit 130 and selects an intervention control method based on the mode acquired by the mode acquisition unit 320, and at this time starts and ends the intervention control according to the time period for performing the intervention control acquired from the control time analysis unit 540.

[0231] For example, if intervention control is to be started at 4:00 p.m., an intervention control method is selected based on the intervention effect, work efficiency, and mode at 4:00 p.m. The method for selecting the intervention control method is the same as that of the intervention control unit 140 in the first embodiment, and therefore will not be described again here.

[0232] The intervention device 150 is controlled according to the intervention control method and the time period for performing the intervention control selected by the intervention control unit 140. The control of the intervention device 150 is the same as that of the intervention device 150 of the first embodiment, so a detailed description thereof will be omitted here.

[0233] <Effects> In this way, analyzing the time periods when subjective fatigue and objective fatigue tend to accumulate and performing intervention control based on the results has the following effects.

[0234] When performing intervention control, there is a problem that if the user's fatigue level is low, the intervention effect is small and the user does not easily feel the effect. By performing intervention control based on time periods when subjective fatigue and objective fatigue tend to accumulate, as in this embodiment, it is possible to perform intervention control so that the fatigue level does not become too high, and it is possible to have the effect of making it easier for the user to feel the effect of the intervention.

[0235] Similarly, with regard to drowsiness, by performing intervention control based on time periods when subjective and objective drowsiness tend to accumulate, intervention control can be performed to prevent drowsiness levels from becoming too high, which has the effect of making it easier for users to feel the effects of the intervention.

[0236] Similarly, with regard to anger, by performing intervention control based on time periods when subjective and objective anger tend to accumulate, intervention control can be performed to prevent the level of anger from becoming too high, which has the effect of making it easier for users to feel the effects of the intervention.

[0237] Here, the second memory unit 410 and the control time analysis unit 540 have been added as means for implementing intervention control based on the relationship between the mental and physical state and the time period, but of course the means for implementing intervention control based on the relationship between the mental and physical state and the time period are not limited to these, and the configuration is not limited as long as it has similar functions.

[0238] <Mental and physical state intervention system> Fig. 27 is an external view of the mental and physical state intervention system. The mental and physical state intervention system includes a desktop computer that runs the mental and physical state intervention device 100, a physiological quantity detection unit 160, intervention devices 150 (150a to 150c), and a mode selection unit 310. The desktop computer can control the intervention device 150 by communicating with it.

[0239] The physiological quantity detection unit 160 is realized, for example, by an in-camera provided in a desktop personal computer. The interventional device 150 is realized by an air conditioner 150a, a speaker 150b, an aroma diffuser 150c, etc. The mode selection unit 310 is realized by a mouse, a keyboard, a touch panel, etc. Communication between the interventional device 150 and the mind-body state intervention device 100 may be performed by wired communication or wireless communication such as BLUETOOTH (registered trademark).

[0240] The mode selection unit 310 may not be provided, and it is sufficient that there is at least one interventional device 150.

[0241] The relationship data creation system also includes a desktop computer that executes the relationship data creation device 10, a physiological amount detection unit 16, a workload detection unit 23, and an interventional device 150. The desktop computer can control the interventional device 150 by communicating with it.

[0242] The physiological amount detection unit 16 and the workload detection unit 23 are realized, for example, by an in-camera provided in a desktop computer. Communication between the interventional device 150 and the relationship data creation device 10 may be performed by wired communication or wireless communication such as Bluetooth.

[0243] (Other Embodiments) The above has described the mind-body state intervention device, mind-body state intervention system, and mind-body state intervention method according to the present invention based on the embodiments. Note that the configurations shown in the above-described embodiments are merely examples, and it goes without saying that various modifications can be made without departing from the spirit of the invention. Furthermore, it is of course possible to combine the above-described embodiments and modifications thereof.

[0244] For example, in the above-described embodiment, the mind-body state intervention system may be realized by one or more server devices. As such, the "system" in this specification may be configured by a single device or may be configured in a distributed manner by multiple devices. When the system is configured by multiple devices, the components (especially functional components) of the system may be distributed among the multiple devices in any manner.

[0245] Furthermore, the method of communication between the devices in the above-described embodiment is not particularly limited. Furthermore, a relay device (such as a broadband router, not shown) may be involved in the communication between the devices.

[0246] In the above-described embodiment, the processing performed by a specific processing unit may be performed by another processing unit. The order of multiple processing operations may be changed, or multiple processing operations may be performed in parallel.

[0247] In the above-described embodiments, each component may be realized by executing a software program suitable for that component, or by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0248] Furthermore, each component may be realized by hardware. For example, each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or each may be a separate circuit. Furthermore, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0249] Furthermore, the general or specific aspects of the present invention may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0250] For example, the present invention may be realized as a method executed by a computer system such as an image information providing system or a face authentication system, or as a program for causing a computer system to execute the method. Furthermore, the present invention may be realized as a computer-readable non-transitory recording medium on which such a program is recorded.

[0251] In addition, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art would think of, or forms realized by arbitrarily combining the components and functions of each embodiment within the scope that does not deviate from the spirit of the present invention.

[0252] (Additional Notes) The features of the mind and body state intervention devices described based on the above embodiments will be described below.

[0253] <Technology 1> A mental and physical condition intervention device comprising: a physiological quantity acquisition unit that acquires physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing unit that quantifies two mental and physical conditions, one of which the subject is aware and one of which the subject is not aware, based on the physiological quantities; a storage unit that pre-stores relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effects on the two quantified mental and physical conditions; and an intervention control unit that, by referring to the relationship data, selects one intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical conditions quantified by the quantification processing unit, and performs intervention control to control an intervention device using the selected intervention control method.

[0254] <Technology 2> A mind and body condition intervention device according to Technology 1, comprising: a mode acquisition unit that acquires a mode from a mode selection unit that allows the subject to select a mode indicating whether improvement of the intervention effect or work efficiency, which is the efficiency of the work performed by the subject, is to be prioritized; the relationship data further indicates a relationship between each of a plurality of intervention control methods and the work efficiency; and the intervention control unit selects an intervention control method based on the intervention effect, the work efficiency, and the mode.

[0255] <Technology 3> The mind-body state intervention device according to Technology 1 or 2, further comprising a relationship data creation device that creates the relationship data for a plurality of subjects and stores the relationship data in the storage unit.

[0256] <Technology 4> The mind and body condition intervention device according to Technology 3, wherein the relationship data creation device includes: a workload acquisition unit that acquires an amount of work performed by the subject detected by a workload detection unit; a work efficiency calculation unit that calculates work efficiency based on the amount of work; and an intervention effect calculation unit that calculates the intervention effect, and the calculated work efficiency and the intervention effect are stored in the storage unit as the relationship data.

[0257] <Technology 5> The mind and body condition intervention device according to any one of Technologies 2 to 4, wherein when the mode selection unit selects to prioritize improvement of the intervention effect, the intervention control unit selects, by referring to the relationship data, an intervention control method that maximizes the intervention effect from among the plurality of intervention control methods.

[0258] <Technology 6> The mind and body state intervention device according to any one of Technologies 2 to 5, wherein when the mode selection unit selects to prioritize improvement of the work efficiency, the intervention control unit selects an intervention control method that maximizes the work efficiency from among the plurality of intervention control methods by referring to the relationship data.

[0259] <Technology 7> The mind and body condition intervention device according to any one of Technologies 1 to 6, wherein the intervention effect is an amount of change in the mind and body condition of the subject before and after intervention control.

[0260] <Technology 8> The mind and body condition intervention device according to any one of Technologies 2 to 7, wherein the work efficiency is a rate of change in the amount of work before and after intervention control per time required for the intervention control on the subject.

[0261] <Technology 9> The mental and physical state intervention device according to any one of Technologies 1 to 8, wherein the intervention control unit presents to the subject an amount of change in the mental and physical state before and after intervention control.

[0262] <Technology 10> The mind and body state intervention device according to any one of Technologies 1 to 9, wherein the intervention control unit presents to the subject an intervention control method used for the intervention control.

[0263] <Technology 11> The mental and physical condition intervention device according to any one of Technologies 1 to 10, wherein the mental and physical condition is fatigue of the subject.

[0264] <Technology 12> A mind-body state intervention system comprising: the mind-body state intervention device according to any one of technologies 1 to 11; the physiological quantity detection unit; and the intervention device.

[0265] <Technology 13> An information processing method executed by a computer, comprising: a physiological quantity acquisition step of acquiring physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing step of quantifying two mental and physical states, i.e., a mental and physical state of which the subject is aware and a mental and physical state of which the subject is not aware, based on the physiological quantities; and an intervention control step of selecting one intervention control method from a plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical states quantified in the quantification processing step by referring to relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effect on the two quantified mental and physical states, and performing intervention control to control an intervention device with the selected intervention control method.

[0266] 100 Mental and physical state intervention device according to first embodiment (mental and physical state intervention device) 200 Mental and physical state intervention device according to modification 1 of first embodiment (mental and physical state intervention device) 300 Mental and physical state intervention device according to modification 2 of first embodiment (mental and physical state intervention device) 400 Mental and physical state intervention device according to second embodiment (mental and physical state intervention device) 500 Mental and physical state intervention device according to third embodiment (mental and physical state intervention device) 600 Mental and physical state intervention device according to fourth embodiment (mental and physical state intervention device) 700 Mental and physical state intervention device according to fifth embodiment (mental and physical state intervention device) 110 Physiological amount acquisition unit 11 Physiological amount acquisition unit 120 Quantification processing unit 12 Quantification processing unit 130 First memory unit (memory unit) 140 Intervention control unit 150 Intervention device 160 Physiological amount detection unit 16 Physiological amount detection unit 17 Intervention effect calculation unit 21 Work amount acquisition unit 22 Work efficiency calculation unit 310 Mode selection unit 320 Mode acquisition unit

Claims

1. A mental and physical condition intervention device comprising: a physiological quantity acquisition unit that acquires physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing unit that quantifies two mental and physical conditions, a mental and physical condition of which the subject is aware and a mental and physical condition of which the subject is not aware, based on the physiological quantities; a memory unit that pre-stores relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effects on the two quantified mental and physical conditions; and an intervention control unit that, by referring to the relationship data, selects one intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical conditions quantified by the quantification processing unit, and performs intervention control to control an intervention device using the selected intervention control method.

2. A mind and body condition intervention device as described in claim 1, further comprising a mode acquisition unit that acquires the mode from a mode selection unit that allows the subject to select a mode indicating whether improvement of the intervention effect or work efficiency, which is the efficiency of the work performed by the subject, is to be prioritized, wherein the relationship data further indicates the relationship between each of a plurality of intervention control methods and the work efficiency, and the intervention control unit selects the intervention control method based on the intervention effect, the work efficiency and the mode.

3. The mind-body condition intervention device according to claim 1 or 2, further comprising a relationship data creation device which creates the relationship data for a plurality of subjects and stores the relationship data in the storage unit.

4. The mind-body condition intervention device as described in claim 3, wherein the relationship data creation device comprises a workload acquisition unit that acquires an amount of work performed by the subject detected by a workload detection unit, a work efficiency calculation unit that calculates work efficiency based on the amount of work, and an intervention effect calculation unit that calculates the intervention effect, and the calculated work efficiency and intervention effect are stored in the memory unit as the relationship data.

5. The mind-body condition intervention device according to claim 2, wherein when the mode selection unit selects to prioritize improvement of the intervention effect, the intervention control unit refers to the relationship data to select from the plurality of intervention control methods an intervention control method that maximizes the intervention effect.

6. The mind-body condition intervention device according to claim 2, wherein when the mode selection unit selects to prioritize improvement of the work efficiency, the intervention control unit refers to the relationship data and selects from the plurality of intervention control methods an intervention control method that maximizes the work efficiency.

7. The mind-body condition intervention device according to claim 1 or 2, wherein the intervention effect is an amount of change in the mind-body condition of the subject before and after intervention control.

8. The mind-body condition intervention device according to claim 2, wherein the work efficiency is a rate of change in the amount of work per unit time required for the intervention control on the subject before and after the intervention control.

9. The mind-body condition intervention device according to claim 1 or 2, wherein the intervention control unit presents to the subject an amount of change in the mind-body condition before and after intervention control.

10. The mind-body condition intervention device according to claim 1 or 2, wherein the intervention control unit presents to the subject the intervention control method used for the intervention control.

11. The mind-body condition intervention device according to claim 1 or 2, wherein the mind-body condition is fatigue of the subject.

12. A mind-body condition intervention system comprising: the mind-body condition intervention device according to claim 1; the physiological quantity detection unit; and the intervention device.

13. An information processing method executed by a computer, comprising: a physiological quantity acquisition step of acquiring physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing step of quantifying two mental and physical conditions, a mental and physical condition of which the subject is aware and a mental and physical condition of which the subject is not aware, based on the physiological quantities; and an intervention control step of selecting one intervention control method from among a plurality of intervention control methods based on the intervention effect on at least one of the two mental and physical conditions quantified in the quantification processing step by referring to relationship data showing the relationship between each of a plurality of intervention control methods and the intervention effect on the two quantified mental and physical conditions, and performing intervention control to control an intervention device with the selected intervention control method.