Body and mind state intervention device, body and mind state intervention system and body and mind state intervention method

Abstract

A physical and mental state intervention device (100) is provided with: a physiological quantity acquisition unit (110) that acquires a physiological quantity of a subject detected by a physiological quantity detection unit (160); a quantification processing unit (120) that quantifies, on the basis of the physiological quantity, two types of physical and mental states, including a physical and mental state sensed by the subject itself and a physical and mental state not sensed by the subject itself, among prescribed physical and mental states; a first storage unit (130) that stores relationship data in advance, said relationship data indicating the relationship between each of a plurality of intervention control methods and the intervention effect for the two types of physical and mental states after quantification; and an intervention control unit (140) that, by referring to the relationship data, selects one intervention control scheme from a plurality of intervention control schemes on the basis of the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit (120), and performs intervention control in the selected intervention control scheme to control the intervention device (150).

Description

Technical Field

[0001] This disclosure relates to devices, systems, and methods for intervening in physical and mental states. Background Technology

[0002] The physical and mental states, including a person's health and emotional state, include various emotions such as fatigue, drowsiness, alertness, joy, and anger. Among these, fatigue, drowsiness, and anger, which are particularly negative physical and mental states, should be quantified to improve their condition. Interventions and controls can then be implemented based on these quantified physical and mental states.

[0003] For example, regarding fatigue, measures can be taken to measure the fatigue levels of office workers in order to prevent losses caused by decreased productivity due to fatigue, as well as pathological fatigue such as depression. Generally, fatigue can be quantified by analyzing heart rate variability to measure the autonomic nervous system. Interventions to control fatigue based on this quantification can be implemented through methods such as: ingesting supplements containing ingredients that are said to have fatigue-relieving effects, such as vitamin B1, GABA, and imidazole dipeptide; or activities such as forest bathing or listening to music that supposedly regulates the autonomic nervous system and relieves fatigue.

[0004] Furthermore, regarding drowsiness, consideration is being given to reducing accidents caused by drowsiness by measuring the drowsiness level of drivers. Generally, drowsiness can be quantified by measuring autonomic nervous system activity through analysis of heart rate variability. Interventions based on this quantified drowsiness can then be implemented, for example, through audible warnings, vibrational stimulation, or olfactory stimuli with arousal effects.

[0005] Furthermore, regarding anger, it is considered to measure the anger levels of people working in offices to prevent the deterioration of interpersonal relationships caused by anger and to improve the work environment. Generally, anger has been quantified by measuring the autonomic nervous system through analysis of heart rate variability. Interventions based on quantified anger can be implemented, such as listening to soothing music, encouraging deep breathing, and using calming odor stimuli.

[0006] Regarding physical and mental states, such as fatigue, it is known that there is a difference in level between the fatigue one feels (hereinafter referred to as subjective fatigue) and the objective fatigue that one does not feel (hereinafter referred to as objective fatigue). Furthermore, subjective fatigue and objective fatigue are not necessarily corresponding; objective fatigue may be masked by psychological effects such as the consumption of energy drinks or motivation, thus leading to a feeling of subjective fatigue while simultaneously weakening or strengthening objective fatigue. In response, Patent Document 1 discloses a technique for quantifying subjective fatigue through questions about fatigue and objective fatigue through tremors of subtle vibrations in designated body parts. Additionally, Patent Document 2 discloses a technique for determining whether objective fatigue is being masked by analyzing physiological signals highly correlated with autonomic nervous system function.

[0007] Furthermore, similar to the example of fatigue, drowsiness can vary in level between what one perceives as drowsiness (hereinafter referred to as subjective drowsiness) and what one perceives as objective drowsiness (hereinafter referred to as objective drowsiness). For example, when objective drowsiness is mild, subjective drowsiness may be perceived at a very low level. Subjective drowsiness and objective drowsiness are not necessarily correlated; psychological effects such as the consumption of energy drinks or the stress of driving can mask objective drowsiness, leading to a perceived subjective drowsiness.

[0008] Furthermore, similar to the example of fatigue, there is a difference in the level of anger that one feels (hereinafter referred to as subjective anger) and objective anger (hereinafter referred to as objective anger). For example, in a state of intense anger, one may become so engrossed that one hardly perceives it as subjective anger. Additionally, subjective anger and objective anger are not necessarily corresponding; psychological factors such as personality traits or social status can mask objective anger, leading to the perception of subjective anger.

[0009] (Existing technical documents) (Patent Documents) Patent Document 1: Japanese Patent Application Publication No. 2002-191579 Patent Document 2: Japanese Patent Application Publication No. 2018-93997 Patent Document 3 Japanese Patent Application Publication No. 2008-305107 Summary of the Invention

[0010] The problem that the invention aims to solve For example, in the intervention control method described in Patent Document 3, for instance, regarding fatigue, the intervention control method with a lower effect on fatigue is implemented first. When the intervention effect is insufficient, further intervention control is implemented by progressively increasing the intervention effect, thereby expecting to improve fatigue and suppress the decline in work efficiency. However, some pre-control methods have four stages. When the user's fatigue level is high, and the fourth stage intervention control method is the most effective to achieve the intervention effect, since it is implemented progressively from the first stage, it is necessary to wait until the fourth stage intervention control method begins. In other words, in this method, it is often impossible to select the optimal intervention control method at the beginning, thus resulting in low efficiency.

[0011] Furthermore, the intervention control method disclosed in Patent Document 3 does not differentiate between subjective and objective fatigue as described above. Therefore, situations may arise where the intervention effect is greater on subjective fatigue but less effective on objective fatigue. Because the characteristics of intervention control methods specific to different fatigue types are not considered, optimal intervention may not be achieved.

[0012] The invention disclosed herein is primarily intended to address the aforementioned issues, and its purpose is to provide a mind-body state intervention device, mind-body state intervention system, and mind-body state intervention method for providing a more efficient and effective way to intervene and control mind-body states.

[0013] The means to solve the problem To achieve the above objectives, one embodiment of the physical and mental state intervention device disclosed herein includes: a physiological quantity acquisition unit for acquiring physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing unit for quantifying two physical and mental states of a specified physical and mental state based on the physiological quantities, the two physical and mental states including a physical and mental state felt by the subject and a physical and mental state not felt by the subject; a storage unit for pre-storing relational data representing the relationship between each of a plurality of intervention control methods and the intervention effect on the quantified two physical and mental states; and an intervention control unit for selecting an intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit, by referring to the relational data, and executing intervention control with the selected intervention control method to control the intervention device.

[0014] Furthermore, to achieve the above objectives, one embodiment of the physical and mental state intervention system disclosed herein includes a physical and mental state intervention device, a physiological quantity detection unit, and an intervention device. The physical and mental state intervention device includes: a physiological quantity acquisition unit for acquiring physiological quantities of the subject detected by the physiological quantity detection unit; a quantification processing unit for quantifying two physical and mental states of a specified physical and mental state based on the physiological quantities, the two physical and mental states including a physical and mental state felt by the subject and a physical and mental state not felt by the subject; a storage unit for pre-storing relational data, the relational data representing the relationship between each of a plurality of intervention control methods and the intervention effect on the two quantified physical and mental states; and an intervention control unit for selecting an intervention control method from the plurality of intervention control methods by referring to the relational data, based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit, and executing intervention control with the selected intervention control method to control the intervention device.

[0015] Furthermore, to achieve the above objectives, one embodiment of the physical and mental state intervention method disclosed herein is an information processing method executed by a computer, comprising: a physiological quantity acquisition step, acquiring physiological quantities of a subject detected by a physiological quantity detection unit; a quantification processing step, quantifying two physical and mental states of a specified physical and mental state based on the physiological quantities, the two physical and mental states including a physical and mental state felt by the subject and a physical and mental state not felt by the subject; and an intervention control step, selecting an intervention control method from the plurality of intervention control methods by referring to relational data representing the relationship between each of a plurality of intervention control methods and the intervention effect on the two quantified physical and mental states, based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing step, and executing intervention control with the selected intervention control method to control the intervention device.

[0016] Invention Effects According to the physical and mental state intervention device disclosed herein, by distinguishing between the physical and mental state that the subject perceives and the physical and mental state that he or she does not perceive, and by selecting an intervention control method based on this distinction, more efficient and effective intervention control can be implemented. Attached Figure Description

[0017] Figure 1A This is a block diagram illustrating the characteristic functional configuration of the mind-body state intervention device according to the first embodiment.

[0018] Figure 1B This is a block diagram showing the configuration of the relational data generation apparatus according to the first embodiment.

[0019] Figure 1C This is a flowchart illustrating the operation of the relational data generation apparatus according to the first embodiment.

[0020] Figure 2A This is a flowchart illustrating the operation of the mind-body state intervention device according to the first embodiment.

[0021] Figure 2B It is shown Figure 2A The flowchart shows the detailed actions of step S5a.

[0022] Figure 3 This is an example of a fatigue function.

[0023] Figure 4 This is an example of a physiological quantity-fatigue correspondence table.

[0024] Figure 5 This is an example of a neural network.

[0025] Figure 6 This is an example of the k-nearest neighbor algorithm.

[0026] Figure 7 This is an example of a random forest model.

[0027] Figure 8 This is an example of the choice of intervention and control methods.

[0028] Figure 9 It is an example of a drowsiness function.

[0029] Figure 10 This is an example of the anger function.

[0030] Figure 11 This is a block diagram illustrating the functional configuration of the mind-body state intervention device according to a variation of the first embodiment, Example 1.

[0031] Figure 12 This is an example of a display screen involved in a variation of the first embodiment.

[0032] Figure 13 This is a block diagram illustrating the functional configuration of the mind-body state intervention device according to a variation of the first embodiment, Example 2.

[0033] Figure 14 This is an example of a display screen involved in a variation of the first embodiment, Example 2.

[0034] Figure 15 This is a block diagram illustrating the characteristic functional configuration of the mind-body state intervention device according to the second embodiment.

[0035] Figure 16This is a flowchart illustrating the operation of the mind-body state intervention device according to the second embodiment.

[0036] Figure 17 This is an example of a change in the intervention control method involved in the second implementation.

[0037] Figure 18 This is an example of the phased intervention control involved in Variation 1 of the second embodiment.

[0038] Figure 19 This is a block diagram illustrating the characteristic functional configuration of the mind-body state intervention device according to the third embodiment.

[0039] Figure 20 This is an example of a neural network in the optimization method involved in the third embodiment.

[0040] Figure 21 This is an example of the k-nearest neighbor algorithm in the optimization method involved in the third embodiment.

[0041] Figure 22 This is an example of a random forest in the optimization method involved in the third implementation.

[0042] Figure 23 This is a block diagram illustrating the characteristic functional configuration of the mind-body state intervention device according to the fourth embodiment.

[0043] Figure 24 This is one example of a schedule.

[0044] Figure 25 This is a block diagram illustrating the characteristic functional configuration of the mind-body state intervention device according to the fifth embodiment.

[0045] Figure 26 This is an example of an analysis of a period when fatigue tends to accumulate.

[0046] Figure 27 This is an external diagram of a physical and mental state intervention system. Detailed Implementation

[0047] Hereinafter, embodiments of the mind-body state intervention device according to the present invention will be described with reference to the accompanying drawings. Furthermore, the same symbols will be used for the same elements, and descriptions may be omitted in some cases. The drawings are schematically illustrated, focusing primarily on each constituent element, for ease of understanding. Each embodiment described below represents a specific example of the present invention. The numerical values, shapes, constituent elements, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present invention. In addition, among the constituent elements in the following embodiments, those not described in the independent technical solution representing the highest-level concept will be described as arbitrary constituent elements. Furthermore, in all embodiments, the contents can be combined. Similarly, the configurations in the various modifications described in each embodiment can also be combined separately.

[0048] (Implementation Method) [First Embodiment] Reference Figures 1A to 10 The mind-body state intervention device according to the first embodiment of the present invention will be described. Here, fatigue will be used as an example of a mind-body state for the first time.

[0049] <Composition> Figure 1A This is a block diagram illustrating the characteristic functional configuration of the physical and mental state intervention device according to the first embodiment. The physical and mental state intervention device 100 is a device that provides a more efficient and effective intervention control method by distinguishing between subjective and objective physical and mental states and selecting an intervention control method based on this distinction. The physical and mental state intervention device 100 includes a physiological quantity acquisition unit 110, a quantification processing unit 120, a first storage unit 130, an intervention control unit 140, and a pattern acquisition unit 320, and communicates with an intervention device 150, a physiological quantity detection unit 160, and a pattern selection unit 310. Furthermore, the physical and mental state intervention device 100 may also include other configurations besides these. It should be noted that intervention is a process that influences the physical and mental state of a subject. Furthermore, subjective physical and mental state is the physical and mental state that one perceives, while objective physical and mental state is the physical and mental state that one does not perceive.

[0050] The physiological measurement unit 160 is a device for acquiring physiological measurements. Physiological measurements refer to information related to the unique physical characteristics of each individual, such as facial features, electrocardiogram, and voiceprint. The physiological measurement unit 160 is, for example, a camera installed on a personal computer. The camera captures an image of the person operating the personal computer (hereinafter referred to as "person"). Physiological measurements are acquired based on the facial image of the person captured by the camera. For example, when the physiological measurement unit 160 is a camera, physiological measurements include the number of blinks, pupil diameter, and skin color within a certain time period.

[0051] Blink count can be measured as follows: First, the facial image captured by the camera is binarized through thresholding, and the eye contour is detected by extracting its edges, thus defining the eye region. The eye region of each frame of the facial image is scanned and its area is calculated. When a blink occurs, this area becomes 0; therefore, the blink count is obtained by counting the number of times the area becomes 0. Pupil diameter is obtained by thresholding the facial image captured by the camera to achieve binarization, extracting edges to detect the pupil contour, and then measuring the width between the edges. Skin color is obtained by using the RGB values ​​of a specified location or region in the facial image captured by the camera.

[0052] In addition to a camera, the physiological measurement unit 160 can also be a heart rate sensor or a microphone. The physiological measurement unit 160 is not limited to a camera.

[0053] In the case of a heart rate sensor, physiological quantities include, for example, the left lateral velocity (LF) and right lateral velocity (HF) of heart rate variability, heart rate, and respiratory rate, all acquired by the heart rate sensor. First, time-series data of heart rate interval variation (hereinafter referred to as RRI data) is acquired using a heart rate sensor. LF and HF are calculated by integrating the power spectra of the acquired RRI data over the ranges of LF (0.05Hz–0.15Hz) and HF (0.15Hz–0.40Hz). Heart rate is the number of times the RRI data peaks are detected within one minute. Respiratory rate can be estimated using heart rate variability caused by respiratory sinus arrhythmia (hereinafter referred to as RSA). Specifically, RSA typically refers to the phenomenon where heart rate increases during inspiration and decreases during expiration. That is, when the interval between peaks in the RRI data is taken as the heart rate interval, the following variation in heart rate interval occurs: the heart rate interval decreases during inspiration and increases during expiration. Therefore, respiratory rate can be estimated based on the number of times the heart rate variability peaks are detected.

[0054] In the case of a microphone, physiological quantities include, for example, the loudness, pitch, and formant frequencies of the acquired sound. First, a Fourier transform is performed on the sound data acquired by the microphone to obtain a frequency spectrum. The loudness and pitch of the sound are represented by the amplitude and frequency of the obtained frequency spectrum. The formant frequency is calculated using the autocorrelation function of the sound data acquired by the microphone; the lowest frequency corresponding to the peak of the autocorrelation function is the formant frequency. The formant frequency represents the timbre of the sound. Of course, other physiological quantities may also be used; the configuration of the physiological quantity detection unit 160 is not limited here.

[0055] The physiological quantity acquisition unit 110 communicates with the physiological quantity detection unit 160 and acquires the physiological quantities detected by the physiological quantity detection unit 160. The physiological quantity acquisition unit 110 may be, for example, a communication interface, or it may be a program for acquiring physiological quantities and a processor for executing the program.

[0056] The quantification processing unit 120 quantifies subjective fatigue and objective fatigue based on the physiological quantities acquired by the physiological quantity acquisition unit 110.

[0057] Here, we will explain the methods for quantifying subjective and objective fatigue. One example of this quantification method is the fatigue function. The fatigue function is a function that takes physiological quantities as input and outputs quantified subjective and objective fatigue. As an example, we will... Figure 3 The fatigue function using blink count is illustrated below. Functions relating subjective and objective fatigue to blink count were pre-constructed experimentally. Subjective fatigue was obtained using methods such as the Visual Analog Scale (VAS), Numerical Rating Scale (NRS), and Face (Rating) Scale, obtaining the position or value on the scale indicated by the person and using it as a quantitative value. Objective fatigue was obtained by acquiring physiological quantities, such as the amount of amylase, cortisol, or human herpes virus in saliva, and converting these physiological quantities into values ​​used to obtain the subjective fatigue index as a quantitative value.

[0058] Next, a fatigue function is constructed using the quantitative values ​​of subjective and objective fatigue obtained in the experiment beforehand and the physiological quantities obtained by the physiological quantity detection unit 160. Figure 3The fatigue function is illustrated as an example: it quantifies the degree of subjective and objective fatigue using a 7-level equally spaced scale, with the physiological quantity set as the fatigue function based on the number of blinks. The equally spaced scale allows for the quantification of subjective and objective fatigue using a specified number of level values. Here, the higher the level value, the greater the degree of fatigue. For example, 1 represents no fatigue at all, and 7 represents extreme fatigue, both physically and mentally. The above example illustrates the degree of fatigue, but the method of representing fatigue levels is not limited. By inputting the number of blinks obtained from the physiological quantity detection unit as the input value to the fatigue function, subjective and objective fatigue can be quantified. For example, as... Figure 3 As shown, when the number of blinks detected by the physiological measurement unit is 20, the objective fatigue value is 5. The subjective fatigue value is not an integer but 2.8, but it can be converted to 3 by rounding. In addition, although the example used is to convert the output value by rounding when it is not an integer, the decimal point can also be truncated, and the conversion method is not limited.

[0059] Furthermore, although this example illustrates the method of quantifying the degree of subjective and objective fatigue using a seven-level equally spaced scale and setting the physiological quantity as a fatigue function of the number of blinks, the methods for obtaining subjective and objective fatigue and the physiological quantities used as input values ​​are not limited to this.

[0060] In addition to fatigue functions, methods for quantifying subjective and objective fatigue can also include physiological quantity-fatigue correspondence tables or machine learning. It is not limited to using fatigue functions as the quantitative method for subjective and objective fatigue.

[0061] In the case of a physiological quantity-fatigue correspondence table, subjective fatigue and objective fatigue are quantified based on a pre-set correspondence table between physiological quantities and subjective and objective fatigue, according to the magnitude of the physiological quantities obtained by the physiological quantity detection unit. Figure 4 The following table illustrates a physiological quantity-fatigue correspondence as an example: fatigue level is measured on a 7-level equally spaced scale, and the physiological quantity is the number of blinks. The methods for obtaining subjective and objective fatigue are the same as in the example of the fatigue function, and therefore will not be repeated here.

[0062] In the context of machine learning, neural networks are one example. In the case of neural networks, such as... Figure 5 As shown, at least one of the physiological quantities acquired by the physiological quantity detection unit is used as a feature quantity, and the constructed learning model is used to quantify subjective fatigue and objective fatigue. Machine learning, besides neural networks, can also include, for example, the k-nearest neighbor algorithm or random forest. Similar to neural networks, the k-nearest neighbor algorithm and random forest are used in the following ways: Figure 6 , Figure 7As shown, at least one of the physiological quantities acquired by the physiological quantity detection unit is used as a feature quantity, and a learned model is used to quantify subjective fatigue and objective fatigue. The machine learning method is not limited here. Furthermore, the physiological quantities used to quantify subjective fatigue and objective fatigue can be different.

[0063] It should be noted that, in this embodiment, the physiological quantity detection unit 160 calculates the physiological quantity using data that indirectly represents the physiological quantity (e.g., a video showing the subject's eyes), and the physiological quantity acquisition unit 110 and the quantification processing unit 120 acquire the calculated physiological quantity. However, the physiological quantity acquired by the physiological quantity acquisition unit 110 and the quantification processing unit 120 may also be data that indirectly represents the physiological quantity, and either the physiological quantity acquisition unit 110 or the quantification processing unit 120 may use data that indirectly represents the physiological quantity to calculate the physiological quantity.

[0064] The first storage unit 130 pre-stores the prepared relational data. This relational data represents the relationship between each of the multiple intervention control methods and the intervention effects on quantified subjective and objective fatigue, as well as the relationship with work efficiency. Intervention effect refers to the change in the subjective and objective fatigue of the subject before and after the intervention. Work efficiency refers to the change in the amount of work done before and after the intervention control within the intervention period. The methods for calculating intervention effects and work efficiency, as well as the order in which the relational data is prepared, will be explained later. The first storage unit 130 may be, for example, ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and the storage unit is not limited here.

[0065] The mode selection unit 310 allows the user to select a mode as the selection indicator for the intervention control method. Modes may include, for example, an "intervention effect mode" that prioritizes intervention effectiveness or a "work efficiency mode" that prioritizes work efficiency. The mode selection unit 310 is implemented via a touch panel, mouse, or keyboard.

[0066] The pattern acquisition unit 320 acquires the pattern selected by the user from the pattern selection unit 310.

[0067] The intervention control unit 140 selects a suitable intervention control method from multiple intervention control methods by referring to the relationship data stored in the first storage unit 130, based on the intervention effect of quantified subjective fatigue and objective fatigue, work efficiency, and the pattern obtained by the pattern acquisition unit 320.

[0068] It should be noted that the quantification processing unit 120, the intervention control unit 140, and the pattern acquisition unit 320 are implemented by a program and a processor that executes the program.

[0069] 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, receiving a signal from the intervention control unit 140, directs a cooling breeze effective against fatigue to the person. Cooling breezes are generally those that induce parasympathetic nervous system dominance after exposure, exhibiting a calming effect. Examples of cooling breezes include those with 1 / f fluctuations, those simulating natural wind, and those causing temperature fluctuations. Cooling breezes are not limited to specific types. Furthermore, the intervention device 150 can be, for example, an audio device or an odor generator, in addition to an air conditioner. While the intervention device 150 is listed as an example, it is not limited to an air conditioner.

[0070] In the case of audio equipment, sounds effective for fatigue are produced. Sounds effective for fatigue are usually those that induce parasympathetic dominance and have a calming effect. Examples include bird calls, forest ambient sounds, and high-frequency ultrasound (hypersonic).

[0071] Here, high-frequency ultrasound refers to sound containing high frequencies beyond the range of human hearing, and it has been confirmed that when simultaneously received by the body surface along with sounds within the audible range, it increases alpha waves, which are typically associated with parasympathetic dominance. By exposing a person to sound containing high-frequency ultrasound through a loudspeaker, interventions targeting fatigue can be implemented. Furthermore, while bird calls are cited as an example of sounds effective for fatigue, effective sounds are not limited to these.

[0072] In the case of an odor generating device, a person is exposed to odors effective against fatigue. Odors effective against fatigue are generally those that, upon exposure, induce a predominance of the parasympathetic nervous system, exhibiting a calming effect. Examples include the scents of grapefruit, cypress, and lavender. However, this description does not limit the specific odors effective against fatigue.

[0073] Figure 1B This is a block diagram illustrating the configuration of the relationship data generation apparatus 10 according to the first embodiment. The relationship data generation apparatus 10 is a device for pre-generating relationship data possessed by the physical and mental state intervention device 100. For this purpose, relationship data is generated for multiple subjects and stored in the first storage unit 130. The relationship data generation apparatus 10 includes a physiological quantity 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 a first storage unit 130, and communicates with the physiological quantity detection unit 16 and the workload detection unit 23.

[0074] Since the physiological quantity acquisition unit 11, the quantitative processing unit 12, and the physiological quantity detection unit 16 have the same functional configuration and perform the same actions as the physiological quantity acquisition unit 110, the quantitative processing unit 120, and the physiological quantity detection unit 160 of the physical and mental state intervention device 100, descriptions are omitted.

[0075] The intervention effect calculation unit 17 calculates the intervention effect based on the subjective fatigue and objective fatigue after each of the 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 relational data in the storage unit 130.

[0076] The workload acquisition unit 21 communicates with the workload detection unit 23 and acquires information about the workload of the target detected by the workload detection unit 23. The workload acquisition unit 21 may be, for example, a communication interface, or it may be a program for acquiring workload and a processor for executing the program.

[0077] The workload detection unit 23 is, for example, a personal computer. If the task involves creating data using a personal computer, the number of characters entered per minute is the workload. It can also be used for tasks other than data creation; for example, in the case of programming, it can be the number of steps entered per minute, or in the case of reading data, it can be the number of pages turned per minute. All of the above can be detected from the personal computer's workload log.

[0078] Although a personal computer is used as an example, other than a personal computer, it could also be a camera or a weight sensor.

[0079] In the case of a camera, for example in document production, the number of characters written on paper within a specified time is the workload. The camera is configured to capture images of the paper, and the workload can be detected based on the increase in the number of characters projected onto the camera within the specified time. Besides document production, this could also refer to, for example, the number of times a sample is measured within a specified time in evaluation or measurement, or the number of times goods are moved within a specified time in cargo handling.

[0080] When the workload is defined as the number of times a sample is measured within a specified time period, for example, if the sample is placed and measured at a specific location, the camera is configured to capture the location of the sample, and the detection is performed by counting the number of times the sample is placed at the location for a specified time or more within a specified time width.

[0081] With the workload defined as the number of times a person moves goods within a specified time, the camera is configured to capture the movement path, and the detection is performed by counting the number of times a person travels back and forth.

[0082] In the case of weight sensors, such as in goods transportation, the workload is the change in weight of goods held within a specified time. Weight sensors are installed on boxes or truck loading platforms used for loading goods, and the workload is detected by measuring the change in weight over a specified period. As the held goods are transported out, the smaller the weight, the greater the workload. Besides goods transportation, the workload can also be the change in weight of manufactured products in manufacturing sites, or the change in weight of waste materials awaiting sorting in waste sorting sites.

[0083] When the workload is based on the change in the weight of manufactured products, for example, the manufactured products can be stored in a designated location for a specified time, and the total weight of the stored products can be measured using a weight sensor. Similarly, when the workload is based on the change in the weight of waste to be sorted, for example, the weight of the waste to be sorted can also be measured using a weight sensor.

[0084] Here, any indicator representing workload is not limited to the above.

[0085] The composition of the workload detection unit 23 is not limited here.

[0086] The work efficiency calculation unit 22 calculates the work efficiency of each of the multiple intervention control methods based on the work quantity obtained by the work quantity acquisition unit 21, and stores the relationship between the intervention control method and the work efficiency as relational data in the first storage unit 130.

[0087] Storage Unit 130 is also for Figure 1A The first storage unit 130 of the physical and mental state intervention device 100 shown stores data representing 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.

[0088] <Action> Next, we will use Figure 1C The flowchart shown illustrates the operation of the relational data generation apparatus configured as described above in this embodiment.

[0089] Figure 1C This is a flowchart illustrating the operation of the relational data generation apparatus according to the first embodiment.

[0090] First, the physiological quantity acquisition unit 11 and the workload acquisition unit 21 acquire the physiological quantity and workload of the subject detected by the physiological quantity detection unit 16 and the workload detection unit 23, respectively. Figure 1C(S11). The physiological data acquisition unit 11 and the workload acquisition unit 21 can also be the same component, such as a personal computer camera. In the case of a personal computer camera, the acquired physiological data is the number of blinks, and the workload is the number of characters written by the subject, etc.

[0091] The quantification processing unit 12 quantifies the subjective and objective fatigue of the subject based on the physiological quantities acquired by the physiological quantity acquisition unit 11. Figure 1C (S12), then, an intervention control method is used to perform intervention control on the subject ( Figure 1C (S13).

[0092] After intervention and control, the physiological quantity acquisition unit 11 and the workload acquisition unit 21 again acquire the physiological quantity and workload of the subject detected by the physiological quantity detection unit 16 and the workload detection unit 23, respectively. Figure 1C (S14), and the quantification processing unit 12 quantifies the subjective and objective fatigue of the subject based on the physiological quantities acquired by the physiological quantity acquisition unit 11. Figure 1C (S15).

[0093] The intervention effect was calculated based on the following: Figure 1C The intervention effect is calculated based on the quantified subjective and objective fatigue of the subjects obtained in S12 and S15. The work efficiency calculation part 22 is based on... Figure 1C The work efficiency is calculated based on the workload obtained in S11 and S14. Figure 1C (S16).

[0094] Here, the intervention effect refers to the change between subjective fatigue before and after the intervention, or the change between objective fatigue before and after the intervention. That is, when A is the intervention effect, B is the subjective or objective fatigue before the intervention, and C is the subjective or objective fatigue after the intervention, A = B - C. In other words, the greater the change in subjective or objective fatigue before and after the intervention, the greater the intervention effect, which in turn indicates a greater fatigue recovery effect.

[0095] Although the intervention effect is described above as the change in fatigue before and after the intervention, it can also be a rate of change or a rate of change per unit time, in addition to the amount of change. The intervention effect is not limited to the change in fatigue before and after the intervention.

[0096] In the case of the rate of change, when A represents the intervention effect, B represents fatigue before intervention, and C represents fatigue after intervention, A = C / B. That is, the smaller the rate of change in fatigue before and after intervention, the greater the intervention effect, and consequently, the greater the fatigue recovery effect.

[0097] When considering the change in fatigue per unit time, with A representing the intervention effect, B representing fatigue before intervention, C representing fatigue after intervention, and D representing the intervention time, the formula is: A = (B - C) / D. That is, the greater the rate of change in subjective or objective fatigue per unit time before and after intervention, the greater the intervention effect, and consequently, the greater the fatigue recovery effect. It should be noted that fatigue here can be either subjective or objective fatigue.

[0098] In addition, operational efficiency refers to the change in the amount of work done before and after intervention within the intervention period. That is, when E is operational efficiency, F is the amount of work done before intervention, G is the amount of work done after intervention, and H is the intervention time, E = (G - F) / H.

[0099] Although the above describes work efficiency as the change in the amount of work before and after intervention within the intervention period, it can also be the change in the amount of work before and after intervention within the intervention period, or the rate of change of the amount of work before and after intervention. Work efficiency is not limited to the change in the amount of work before and after intervention within the intervention period.

[0100] Given the change in workload before and after intervention control, when E is the work efficiency, F is the workload before intervention control, and G is the workload after intervention control, E = G - F.

[0101] Given the rate of change between the workload before and after intervention control, when E is the work efficiency, F is the workload before intervention control, and G is the workload after intervention control, E = G / F.

[0102] 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 effect on subjective fatigue and objective fatigue, as well as work efficiency, as relational data in the first storage unit 130. Figure 1C (S17).

[0103] By repeating these steps while changing the intervention control method ( Figure 1C Use S11 to S17 to create relational data.

[0104] Next, refer to Figure 2A and Figure 2B The operation of the mind-body state intervention device according to this embodiment will be described. Here, a camera is used as an example of a physiological quantity detection unit, and blink rate, pupil diameter, and skin color are used as examples of physiological quantities.

[0105] Figure 2A This is a flowchart of the mind-body state intervention device according to the first embodiment. First, the physiological quantity acquisition unit 110 communicates with the physiological quantity detection unit 160 and acquires the physiological quantities detected by the physiological quantity detection unit 160. Figure 2A S1). Next, the quantification processing unit 120 quantifies the subjective and objective fatigue of the subject based on the physiological quantities obtained from the physiological quantity acquisition unit 110. Figure 2A S2).

[0106] Next, the pattern acquisition unit 320 acquires the desired effect of the intervention control selected by the user through the pattern selection unit 310. Figure 2A (S3). When the mode selection unit 310 prompts the user, the desired effect of the intervention control is prepared in advance as an option, such as an "intervention effect mode" that prioritizes fatigue intervention effect or a "work efficiency mode" that prioritizes work efficiency. In addition to "intervention effect mode" and "work efficiency mode", any name that prioritizes intervention effect or work efficiency, such as "fatigue recovery mode" or "work smooth mode", is not limited to the above names.

[0107] Next, the intervention control unit 140 selects the intervention control method based on the user-selected pattern. Figure 2A S4).

[0108] The intervention control unit 140 obtains the intervention effects and operational efficiency corresponding to each of the multiple intervention control methods by referring to the relational data stored in the first storage unit 130, and selects the intervention control method based on the mode selected by the user obtained by the mode acquisition unit 320. That is, when the user selects the intervention effect priority mode (in... Figure 2A In S4, selecting the intervention effect priority mode means choosing the intervention control method that maximizes the intervention effect from multiple intervention control methods. Figure 2A (S5a), when the user selects the job efficiency priority mode (in Figure 2A In S4, selecting the "Operation Efficiency Priority Mode" means choosing the intervention control method that maximizes operation efficiency from among multiple intervention control methods. Figure 2A (S5b).

[0109] Here, Figure 2B It is shown Figure 2AA detailed flowchart of the actions in step S5a. When the user... Figure 2A When selecting the intervention effect priority mode in S4, the intervention control unit 140 determines which of the two—quantified subjective fatigue and objective fatigue—is greater based on the data obtained from the quantification processing unit 120. Figure 2B (S21).

[0110] When the value of objective fatigue is greater than the value of subjective fatigue ( Figure 2B S21 is "Yes"), the intervention control unit 140 selects the intervention control method with the greatest effect on objective fatigue from multiple intervention control methods ( Figure 2B (S22a). When the value of subjective fatigue is greater than the value of objective fatigue, or when the value of objective fatigue is equal to the value of subjective fatigue ( Figure 2B S21 is "No"), the intervention control unit 140 selects the intervention control method with the greatest effect on subjective fatigue from multiple intervention control methods ( Figure 2B (S22b).

[0111] For example, Figure 8 This is an example of choosing an intervention and control method. For example... Figure 8 As shown, fatigue level is set to a 7-level equally spaced scale, and work efficiency is set to the number of characters typed per minute. Using an equally spaced scale allows for the representation of subjective and objective fatigue using a specified number of level values. Here, 7 levels are used, with higher values ​​indicating greater fatigue. User A's fatigue level is: subjective fatigue 4, objective fatigue 7, meaning objective fatigue is greater (…). Figure 8 (a)). Therefore, when user A selects the intervention effect priority mode, the intervention control method that has the greatest intervention effect on objective fatigue is selected through wind intervention; when user A selects the work efficiency priority mode, the intervention control method that intervenes through odor intervention is selected. Figure 8 (b)). Additionally, user B's fatigue level is: subjective fatigue 7, objective fatigue 4, meaning subjective fatigue is greater ( Figure 8 (c) Therefore, when user B selects the intervention effect priority mode, the intervention control method that has the greatest intervention effect on subjective fatigue is selected through sound intervention; when user B selects the work efficiency priority mode, the intervention control method that intervenes through smell intervention is selected. Figure 8 (d)

[0112] It should be noted that in this embodiment, when the value of subjective fatigue is equal to the value of objective fatigue, 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 can also be selected.

[0113] Intervention control unit 140 controls intervention device 150 based on the selected intervention control method. Figure 2A (S6).

[0114] Furthermore, in the physical and mental state intervention device 100 according to the first embodiment, although a mode acquisition unit 320 is provided, and a mode selection unit 310 allows the user to select a mode that prioritizes intervention effect or a mode that prioritizes work efficiency, and obtains the selection result, the mode acquisition unit 320 may not be provided. Moreover, although the intervention control method is selected based on the mode selected by the user, it is also possible to not set a mode and always prioritize intervention effect or always prioritize work efficiency in intervention control. In this case, the relational data only needs to represent the relationship between each of the multiple intervention control methods and any desired item among intervention effect and work efficiency.

[0115] In addition, although fatigue is used as an example of a physical and mental state in the above explanation, other states such as drowsiness or anger can also be present.

[0116] When the person is drowsy, the physiological measurement unit 160 is, for example, a camera installed on a personal computer. The camera acquires a facial image of the person, and physiological parameters are obtained from that image. These physiological parameters include, for example, the number of blinks, the eye-closing rate, and the pupil diameter. Since the methods for acquiring the number of blinks and the pupil diameter have already been described above, they will not be repeated here. The eye-closing rate refers to the percentage of time within one minute when the eye opening is set to 100% in a waking state, with the eye opening degree being less than 20%.

[0117] In addition to cameras, it could also be a heart rate sensor or an electroencephalogram (EEG).

[0118] In the case of a heart rate sensor, physiological quantities include, for example, the left basal body (LF) and right ventricle (HF) of heart rate variability, heart rate, and respiratory rate acquired by the heart rate sensor. Since the methods for acquiring LF, HF, heart rate, and respiratory rate have already been described above, a repetition is omitted here.

[0119] In the case of an EEG monitor, physiological quantities include, for example, the relative alpha wave band power. Relative alpha wave band power refers to the proportion of the alpha wave band (8–13 Hz) power in the 0.5–50 Hz band power obtained by performing a Fast Fourier Transform on the EEG signal at specified time intervals. Alpha waves are generally considered to be associated with drowsiness; when in a state of strong drowsiness, alpha waves dominate the EEG signal.

[0120] The quantification processing unit 120 quantifies subjective drowsiness and objective drowsiness based on the physiological quantities obtained by the physiological quantity acquisition unit 110 from the physiological quantity detection unit 160 using a pre-set quantification method.

[0121] Here, we will explain the methods for quantifying subjective and objective drowsiness. One example of a quantification method is the drowsiness function. The drowsiness function is a function that takes physiological quantities as input and outputs quantified subjective and objective drowsiness. As an example, we will... Figure 9 The drowsiness function using the eye-closing rate is illustrated below. Functions relating subjective and objective drowsiness to the eye-closing rate are established beforehand through experiments. Subjective drowsiness can be obtained using methods such as the VAS, Karolinska Somnolence Scale, or Stanford Somnolence Scale, and the obtained values ​​are used as quantitative values. Objective drowsiness is obtained by acquiring physiological quantities, such as relative alpha wave band power obtained through electroencephalography (EEG), oxyhemoglobin concentration obtained through functional near-infrared spectroscopy (fNIRS), or skin temperature at a specified location obtained through thermography, and then quantified. Even if it is not a physiological quantity, an objective assessment of the subject's drowsiness level by the experimenter based on facial images can be used.

[0122] Figure 9 An example of a drowsiness function is shown below: the degree of subjective and objective drowsiness is quantified using the Karolinska Sleepiness Scale on a 9-point equally spaced scale, with the physiological quantity set as the eye-closing rate. In the drowsiness level, 1 represents very alert, 3 represents alert, 5 represents moderate, 7 represents drowsy, and 9 represents very drowsy. By using the eye-closing rate obtained from the physiological quantity measurement unit as the input value of the drowsiness function, subjective and objective drowsiness can be quantified. Subjective drowsiness can be quantified through self-reporting, while objective drowsiness can be quantified, for example, by an assessor objectively evaluating facial expressions using a 9-point scale on the Karolinska Sleepiness Scale. It should be noted that although this example uses a drowsiness function quantified on a 9-point equally spaced scale with the physiological quantity set as the eye-closing rate, it does not limit the methods for obtaining subjective and objective drowsiness or the physiological quantities used as input values.

[0123] Besides the drowsiness function, methods for quantifying subjective and objective drowsiness can also include physiological quantity-drowsiness correspondence tables or machine learning; the quantification method is not limited to the drowsiness function. In the case of a physiological quantity-drowsiness correspondence table, subjective and objective drowsiness are quantified based on the magnitude of physiological quantities obtained by the physiological quantity detection unit, according to a pre-defined correspondence table between physiological quantities and subjective and objective drowsiness. This physiological quantity-drowsiness correspondence table differs from the correspondence table for fatigue conditions in that the pre-defined physiological quantity is the eye-closing rate; the rest are the same, so further explanation is omitted here.

[0124] In the context of machine learning, a neural network is used as an example. Similar to the case of fatigue, a neural network can also be used in the case of neural networks. Since the method for quantifying fatigue using a neural network has already been explained above, and the only difference is the physiological quantity obtained by the physiological quantity detection unit used as a feature quantity, the rest is the same, so the explanation is omitted here.

[0125] Besides neural networks, machine learning methods can also include, for example, multiple regression or random forests. Since the methods for quantifying fatigue using multiple regression and random forests have already been described above, and are identical except for the physiological quantities obtained by the physiological quantity detection unit used as features, they are omitted here. Machine learning methods are not limited here. Furthermore, the physiological quantities used to quantify subjective drowsiness and objective drowsiness can also be different.

[0126] When the physical and mental state is drowsy, the first storage unit 130 stores data showing the relationship between various intervention control methods and the intervention effects on quantified subjective and objective drowsiness, as well as the relationship with work efficiency. Since the methods for calculating the intervention effects and work efficiency, and the methods for creating the relationship data are the same as in the example of fatigue, they are omitted here.

[0127] The mode selection unit 310 allows the user to input the item they wish to prioritize when selecting an intervention control method. The mode acquisition unit 320 acquires the mode selected by the user through the mode selection unit 310. Since this is the same as an example of fatigue, a further explanation is omitted here.

[0128] The intervention control unit 140 selects an intervention control method from multiple intervention control methods by referring to the relational data stored in the first storage unit 130, based on the intervention effect on quantified subjective and objective drowsiness, work efficiency, and the pattern acquired by the pattern acquisition unit 320. 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, receiving a signal from the intervention control unit 140, directs drowsy air towards the person. Drowsy air is generally defined as air that induces sympathetic dominance and has a wake-up effect. Examples include wind with large fluctuations, wind that only affects specific areas such as the face and has limited diffusion, and wind with large temperature fluctuations, but this is not limited to wind effective for drowsiness. Furthermore, the intervention device 150 may be, for example, an audio device or a vibration generator, in addition to an air conditioner; this is not a limitation of the intervention device 150.

[0129] In the case of audio equipment, sounds that are effective in inducing drowsiness are produced. Sounds effective in inducing drowsiness are generally those that, upon hearing, cause the sympathetic nervous system to dominate and have an arousing effect. Examples include sounds containing a large number of low-frequency components, sounds with high sound pressure levels, and sounds containing dissonant sounds, but this is not limited to sounds effective in inducing drowsiness.

[0130] In the case of a vibration generating device, vibrations effective in inducing drowsiness are applied to a person. Vibrations effective in inducing drowsiness generally refer to vibrations that induce sympathetic dominance and have arousal characteristics by inducing arousal. Examples include vibrations with large amplitude, high frequency, and irregular amplitude, but this is not limited to vibrations effective in inducing drowsiness.

[0131] In cases of anger, the physiological quantity detection unit 160 is, for example, a camera installed on a personal computer. The camera acquires a facial image of the person, and physiological quantities are obtained from this image. Physiological quantities include, for example, pulse wave, pupil diameter, and the degree of upward turn of the corners of the mouth. Since the method for acquiring the pupil diameter has already been described above, it is omitted here. The pulse wave is obtained by calculating the average RGB brightness of a defined area in the facial image acquired by the camera on a video frame-by-frame basis, thereby obtaining a waveform showing the temporal variation of the average brightness value. Independent component analysis is performed on this temporal variation waveform to extract three independent waveforms, and the waveform containing the highest peak of the power spectrum is used to obtain the pulse wave. The degree of upward turn of the corners of the mouth is obtained by setting nodes on the facial image and based on the change in the coordinates of these nodes.

[0132] In addition to a camera, it could also be a heart rate sensor or a skin potential meter, so the physiological measurement unit is not limited to a camera.

[0133] In the case of a heart rate sensor, physiological quantities include, for example, the left basal body (LF) and right ventricular heart (HF) variability in heart rate, heart rate, and respiratory rate acquired by the heart rate sensor. Since the methods for acquiring LF, HF, heart rate, and respiratory rate have already been described above, they are omitted here.

[0134] In the case of a skin potential meter, physiological quantities include, for example, the acquired skin potential. Skin potential is thought to be related to sweating, and it typically increases during periods of anger.

[0135] The quantification processing unit 120 quantifies subjective anger and objective anger based on the physiological quantities obtained by the physiological quantity acquisition unit from the physiological quantity detection unit 160 through a pre-set quantification method.

[0136] Here, we will explain the methods for quantifying subjective and objective anger. One example of a quantitative method is the anger function. The anger function is a function that takes physiological quantities as input and outputs quantified subjective and objective anger. As an example, we will... Figure 10The diagram illustrates the use of pupil diameter as a function of anger. Functions relating subjective and objective anger to pupil diameter are established beforehand through experiments. Subjective anger is obtained using methods such as VAS, NRS, or facial scales, and the obtained values ​​are used as quantitative values. Objective anger is obtained by acquiring physiological quantities, such as relative gamma wave band power obtained through EEG measurements, oxyhemoglobin concentration obtained through fNIRS, or quantification through saliva volume.

[0137] Figure 10 An example of an anger function is shown below: the degree of subjective and objective anger is quantified on a 7-level equally spaced scale, with the physiological quantity set as pupil diameter. Here, a higher level value indicates a greater degree of anger. For example, 1 represents a calm state, and 7 represents the most intense state of anger. Although this example illustrates the degree of anger, it does not limit the way anger levels are represented. By using the pupil diameter obtained from the physiological quantity detection unit as the input value of the anger function, subjective and objective anger can be quantified. It should be noted that although this example uses an anger function that quantifies subjective and objective anger on a 7-level equally spaced scale and sets the physiological quantity as pupil diameter, it does not limit the method of obtaining subjective and objective anger or the physiological quantity used as the input value.

[0138] In the case of the physiological quantity-anger correspondence table, subjective and objective anger are quantified based on the magnitude of the physiological quantities obtained by the physiological quantity detection unit, according to a pre-set correspondence table between physiological quantities and subjective and objective anger. This physiological quantity-anger correspondence table differs from the one used in the fatigue condition in the pre-set physiological quantities; the rest is the same, so its explanation is omitted here. Furthermore, since the method for obtaining subjective and objective anger is the same as in one example of the anger function, a repetitive explanation is also omitted here.

[0139] In the context of machine learning, a neural network is used as an example. Similar to the case of fatigue, a neural network can also be used in the case of neural networks. Since the method for quantifying fatigue using a neural network has already been explained above, and the only difference is the physiological quantity obtained by the physiological quantity detection unit used as a feature quantity, the rest is the same, so the explanation is omitted here.

[0140] Besides neural networks, machine learning methods can also include, for example, multiple regression or random forests. Since the methods for quantifying fatigue using multiple regression and random forests have already been described above, they are the same except for the physiological quantities obtained by the physiological quantity detection unit used as features; therefore, further explanation is omitted here. The machine learning method is not limited here. Furthermore, the physiological quantities used to quantify subjective anger and objective anger can also be different.

[0141] When the mental and physical state is anger, the first storage unit 130 stores data showing the relationship between various intervention control methods and the intervention effects on quantified subjective and objective anger, as well as the relationship with work efficiency. Since the methods for calculating intervention effects and work efficiency, and the methods for creating the relationship data, are the same as in the example of fatigue, they are omitted here.

[0142] The mode selection unit 310 allows the user to input the preferred option when selecting an intervention control method. The mode acquisition unit 320 acquires the mode selected by the user through the mode selection unit 310. Since this is the same as an example of fatigue, a further explanation is omitted here.

[0143] The intervention control unit 140 selects a suitable intervention control method from multiple intervention control methods by referring to the relationship data stored in the first storage unit 130, based on the intervention effect on quantified subjective and objective anger, work efficiency, and the pattern obtained by the pattern acquisition unit 320.

[0144] 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. Upon receiving a signal from the intervention control unit 140, the air conditioner directs airflow effective against anger towards the person. Airflow effective against anger typically refers to airflow that, upon contact with the body, induces parasympathetic dominance and has a calming effect. Examples include airflow with 1 / f fluctuations, airflow with low and gentle fluctuations, and airflow with fluctuating temperature. The type of airflow effective against anger is not limited here. Furthermore, the intervention device 150 may be, for example, an audio device or an odor generator, in addition to a blower; the intervention device 150 is not limited here.

[0145] In the case of audio equipment, produce sounds that are effective against anger. Sounds effective against anger are generally those that, upon hearing, cause the parasympathetic nervous system to dominate and have a calming effect. Examples include sounds with 1 / f fluctuations, sounds with slow changes in sound pressure, and birdsong, but this is not limited to sounds effective against anger.

[0146] In the case of an odor-generating device, a person is made to smell an odor that is effective against anger. Odors effective against anger are generally those that, upon smelling, induce a predominance of the parasympathetic nervous system and have a calming effect. Examples include lavender, bergamot, and rose Otto, but this is not limited to odors effective against anger.

[0147] <Effect> As described above, by implementing intervention control based on the effect mode that the user chooses to prioritize in the intervention control, the following effects can be achieved.

[0148] Typically, fatigue intervention and control are implemented based on the degree and type of fatigue, that is, based on the degree of subjective and objective fatigue. However, the problem is that it sometimes fails to consider individual work situations. For example, in cases of a backlog of tasks that must be completed, if the system requires stopping business and implementing intervention controls, it becomes difficult for the user to use.

[0149] In this embodiment, the user can choose whether to prioritize operational efficiency or intervention effectiveness when performing intervention control. Therefore, in the case of business backlog, the mode of prioritizing operational efficiency can be selected, while in the case of excessive fatigue accumulation, the mode of prioritizing intervention effectiveness can be selected, thereby enabling the execution of effective interventions that meet the user's needs.

[0150] The same applies to situations of drowsiness. By allowing users to choose whether to prioritize operational efficiency or intervention effectiveness when implementing intervention controls, users can choose to prioritize operational efficiency when there is a backlog of tasks, and to prioritize intervention effectiveness when drowsiness is too high, thus enabling the implementation of effective interventions that meet user needs.

[0151] The same applies to situations of anger. By allowing users to choose whether to prioritize operational efficiency or intervention effectiveness when implementing intervention controls, a mode that prioritizes operational efficiency can be selected when there is a backlog of business, while a mode that prioritizes intervention effectiveness can be selected when the level of anger is too high, thus enabling the implementation of effective interventions that meet user needs.

[0152] <Summary> As described above, the physical and mental state intervention device 100 according to this embodiment includes: a physiological quantity acquisition unit 110, which acquires the physiological quantities of the subject detected by the physiological quantity detection unit 160; a quantification processing unit 120, which quantifies two physical and mental states of a specified physical and mental state based on the physiological quantities, the two physical and mental states including the physical and mental state felt by the subject and the physical and mental state not felt by the subject; a first storage unit 130, which pre-stores relational data, the relational data representing the relationship between each of a plurality of intervention control methods and the intervention effect on the quantified two physical and mental states; and an intervention control unit 140, which, by referring to the relational data, selects an intervention control method from a plurality of intervention control methods based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit 120, and performs intervention control with the selected intervention control method to control the intervention device 150.

[0153] Therefore, by distinguishing between the physical and mental states that the subject perceives and those that the subject does not perceive, and by selecting intervention and control methods based on this distinction, it is possible to choose more efficient and effective methods for intervening and controlling physical and mental states.

[0154] In addition, a pattern acquisition unit 320 is included, which acquires patterns from a pattern selection unit 310. The pattern selection unit 310 is used to enable the subject to select a pattern that represents either prioritizing the improvement of the intervention effect or the efficiency of the subject's work. The relationship data also represents the relationship between each of the multiple intervention control methods and the work efficiency. The intervention control unit 140 selects the intervention control method based on the intervention effect, the work efficiency, and the pattern.

[0155] Therefore, since the subjects can choose whether to focus on the intervention effect or the subjects' workload when intervening in their physical and mental state, they can choose a physical and mental state intervention and control method that better reflects the subjects' needs.

[0156] In addition, it includes a relational data creation device that creates relational data for multiple subjects and stores the relational data in a first storage unit 130.

[0157] Accordingly, it is possible to create and update the relational data that serves as a baseline when selecting intervention and control methods.

[0158] Furthermore, the relational data generation apparatus includes: a workload acquisition unit 21, which acquires the workload of the object being worked on by the workload detection unit 23; a workload efficiency calculation unit 22, which calculates the workload efficiency based on the workload; and an intervention effect calculation unit 17, which calculates the intervention effect and stores the calculated workload efficiency and intervention effect as relational data in the first storage unit 130.

[0159] Therefore, it is possible to obtain the information needed to create relational data for selecting intervention and control methods.

[0160] Furthermore, when prioritizing the improvement of intervention effect is selected in the mode selection unit 310, the intervention control unit 140 selects the intervention control method that makes the intervention effect the highest from multiple intervention control methods by referring to relationship data.

[0161] Therefore, when the target population wants to emphasize the effectiveness of the intervention, it can reflect this need and provide the intervention control method with the greatest intervention effect.

[0162] Furthermore, when prioritizing the improvement of work efficiency is selected in the mode selection unit 310, the intervention control unit 140 selects the intervention control method that makes the work efficiency the highest from multiple intervention control methods by referring to relationship data.

[0163] Therefore, when the target group wants to prioritize work efficiency, this need can be reflected and intervention and control methods that maximize work efficiency can be provided.

[0164] In addition, the intervention effect is the change in the physical and mental state of the subject before and after the intervention is implemented.

[0165] Therefore, intervention methods can be selected based on an emphasis on the changes in the physical and mental state of the subjects before and after intervention.

[0166] In addition, work efficiency is the rate of change in the amount of work before and after the intervention or control, which is the unit of time required for the subject to perform the intervention or control.

[0167] Therefore, intervention control methods can be selected based on the rate of change in workload before and after the intervention control, which is based on the unit time required to implement intervention control on the target.

[0168] Furthermore, the following will use fatigue as an example of a physical and mental state to further illustrate several variations.

[0169] (Modification 1 of the first embodiment) The physical and mental state intervention device 200 according to the first embodiment of the modified example 1 is used to prompt the user about subjective fatigue and objective fatigue before and after the intervention.

[0170] Figure 11 This is a block diagram showing the configuration of the mind-body state intervention device 200 according to a variation of the first embodiment. Based on 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 calculation unit 420, and a display unit 430.

[0171] In this modified example, the physiological quantity acquisition unit 110 and the quantification processing unit 120 will also operate again as described above after the intervention and control, to quantify the subjective and objective fatigue of the subject after the intervention and control.

[0172] The second storage unit 410 stores the quantified subjective and objective fatigue data before and after intervention control, sent by the quantification processing unit 120. The second storage unit 410 may be, for example, ROM (Read Only Memory), RAM (Random Access Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and is not limited to any particular storage unit here.

[0173] The change calculation unit 420 reads the subjective fatigue and objective fatigue before and after intervention control from the second storage unit 410, and calculates the change in each. For example, in objective fatigue, if the value is 5 before intervention control and 2 after intervention control, the change is -3. The change calculation unit 420 is implemented by a program and a processor that executes the program.

[0174] The display unit 430 obtains the changes in subjective fatigue and objective fatigue before and after intervention control from the change calculation unit 420, and then... Figure 12 This is what is shown to the user. Figure 12 This is just one example, and the method of displaying the changes in subjective and objective fatigue before and after intervention is not limited. The display unit 430 is, for example, a personal computer monitor.

[0175] <Effect> As mentioned above, by informing users about their subjective and objective fatigue before and after the intervention, the following effects can be achieved.

[0176] Typically, fatigue interventions only indicate the degree of either subjective or objective fatigue, or the effectiveness of the intervention. However, the problem is that the degree of either subjective or objective fatigue, or the effectiveness of the intervention, is not indicated, making it impossible to accurately understand one's own fatigue state and the effectiveness of the intervention.

[0177] In this embodiment, by prompting users with information about their subjective and objective fatigue before and after the intervention, users can gain a more accurate understanding of their own fatigue state and the effectiveness of the fatigue intervention.

[0178] The same applies to drowsiness. By prompting users with subjective and objective drowsiness before and after the intervention, users can gain a more accurate understanding of their drowsiness state and the effectiveness of the intervention.

[0179] The same applies to anger. By prompting users about their subjective and objective anger before and after the intervention, users can gain a more accurate understanding of their own anger state and the effectiveness of the intervention.

[0180] It should be noted that although a second storage unit 410 and a change calculation unit 420 have been added here as a means of prompting the user about fatigue before and after the intervention, the means of prompting the user about fatigue before and after the intervention are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0181] As described above, the intervention control unit 140 of this embodiment informs the subject of the changes in their physical and mental state before and after the intervention control.

[0182] Therefore, since the subjects are able to recognize the changes in their physical and mental state before and after the intervention, they can not only truly feel the effects of the intervention, but also grasp their own physical and mental state.

[0183] (Modification 2 of the first embodiment) The physical and mental state intervention device 300 in the modified example 2 of the first embodiment not only prompts the user about subjective fatigue and objective fatigue before and after the intervention, but also prompts the user about the intervention control method.

[0184] Figure 13 This is a block diagram showing the configuration of the mind-body state intervention device 300 according to a variation 2 of the first embodiment. Based on 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 calculation unit 420, and a display unit 430.

[0185] In this modified example, the physiological quantity acquisition unit 110 and the quantification processing unit 120 will also operate again as described above after the intervention and control, to quantify the subjective and objective fatigue of the subject after the intervention and control.

[0186] The second storage unit 410 differs from the variation 1 of the first embodiment in that it not only stores the quantified subjective fatigue and objective fatigue before and after intervention control sent by the quantification processing unit 120, but also stores the intervention control mode sent by the intervention control unit 140.

[0187] In the change calculation unit 420, similar to Modification 1 of the first embodiment, the changes in subjective fatigue and objective fatigue before and after intervention control, read from the second storage unit 410, are calculated respectively. For example, in objective fatigue, if the value before intervention control is 5 and the value after intervention control is 2, then the change is -3.

[0188] The display unit 430 obtains the changes in subjective fatigue and objective fatigue before and after intervention control from the change calculation unit 420, and combines this with the intervention control methods stored in the second storage unit 410, such as... Figure 14 This is what is shown to the user. Figure 14 This is just one example and does not limit the changes in subjective and objective fatigue before and after intervention or control, or the display format of the intervention or control method implemented.

[0189] <Effect> As mentioned above, by prompting users about their subjective and objective fatigue before and after the intervention, as well as the intervention control method, the following effects can be achieved.

[0190] Typically, fatigue interventions only indicate the degree or effectiveness of either subjective or objective fatigue. However, the problem is that users cannot recognize what kind of intervention they have received, leading to questions about the intervention itself.

[0191] In this embodiment, by prompting the user about subjective and objective fatigue before and after the intervention, as well as the intervention control method, the user can understand the intervention control method and its effect, thus eliminating any doubts about the intervention control.

[0192] The same applies to drowsiness. By informing users of their subjective and objective drowsiness before and after the intervention, as well as the intervention control methods, users can understand the intervention control methods implemented and their effects, thus eliminating any doubts about the intervention control.

[0193] The same applies to anger. By informing users about their subjective and objective anger before and after the intervention, as well as the intervention control methods, users can understand the intervention control methods implemented and their effects, thus eliminating any doubts about the intervention control.

[0194] It should be noted that although a second storage unit 410 and a change calculation unit 420 have been added here as a means of prompting the user about the intervention control method, the means of prompting the user about the intervention control method are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0195] As described above, the intervention control unit 140 of this embodiment prompts the subject with the intervention control method for intervention control.

[0196] Therefore, since the subjects are able to recognize the intervention and control methods and their effects, they can eliminate any doubts about the intervention and control.

[0197] [Second Embodiment] <Composition> Figure 15 This is a block diagram illustrating the characteristic functional configuration of the mental and physical state intervention device 400 according to the second embodiment. The mental and physical state intervention device 400 has the function of changing the previously used intervention control method when the intervention effect is lower than 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 it adds a second storage unit 410, a change calculation unit 420, and an intervention effect determination unit 440. Its operation and effects will be explained below with reference to these parts. Furthermore, fatigue will be used as an example of a mental and physical state for explanation.

[0198] The second storage unit 410 stores the quantified subjective and objective fatigue data before and after the intervention control, sent by the quantification processing unit 120, along with the date and time of the intervention control. Since the configuration of the second storage unit 410 has already been described in Variation 1 of the first embodiment, it will not be described here.

[0199] The change calculation unit 420 reads subjective fatigue, objective fatigue, and date and time from the second storage unit 410, and calculates the change in subjective fatigue and objective fatigue over a specified period of time. That is, it calculates the change in subjective fatigue and objective fatigue at the start of intervention control and the change in subjective fatigue and objective fatigue after a specified period of time.

[0200] Here, since the calculation requires subjective and objective fatigue after a specified time for quantification, in this embodiment, the physiological quantity detection unit 160, the physiological quantity acquisition unit 110, and the quantification processing unit 120 repeatedly perform a series of actions to quantify the physiological quantity of the subject at a certain time interval required for implementation. Figure 2A (S1, S2).

[0201] The intervention effect determination unit 440 compares the changes in subjective and objective fatigue within a specified time, obtained from the change calculation unit 420, with preset thresholds and determines which is greater. When the changes in subjective and objective fatigue are lower than the thresholds, the intervention control unit 140 changes the currently executed intervention control. The intervention effect determination unit 440 is implemented by a program and a processor that executes the program.

[0202] <Action> Figure 16 This is a flowchart illustrating 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 that of the mind-body state intervention device 100 up to the middle of the process; specifically, from... Figure 2A The actions following step S6 are different. Therefore, the following is combined with... Figure 16 right Figure 2A The content following step S6 will be explained in detail.

[0203] First, the intervention control unit 140 confirms the pattern obtained by the pattern acquisition unit when selecting the current intervention control method. Figure 16 S31). The subsequent actions differ depending on the pattern confirmed by the intervention control unit 140. Figure 16 S32). When the mode is the job efficiency priority mode ( Figure 16 S32 is the work efficiency priority mode), and the intervention control unit 140 continues the current intervention control mode. Figure 16 S35b). When the mode is the intervention effect priority mode ( Figure 16 S32 is the intervention effect priority mode), and the intervention effect determination unit 440 determines the intervention effect by comparing the changes in subjective fatigue and objective fatigue within a specified time with the magnitude of a pre-set threshold. Figure 16 (S33).

[0204] When the intervention effect is above the threshold, that is, when the intervention effect is above the baseline value ( Figure 16 If S34 is "No", the intervention control unit 140 continues the current intervention control mode. Figure 16 (S35b).

[0205] When the intervention effect is less than the threshold, that is, when the intervention effect is lower than the baseline value ( Figure 16 If S34 is "Yes", the intervention control unit 140 changes the intervention control method. At this time, in addition to the currently executed intervention control method, the intervention control method with the greatest intervention effect is selected from multiple intervention control methods. Figure 16 (S35a).

[0206] It should be noted that in this embodiment, the intervention effect is determined by judging whether it is above or below the threshold, but it can also be determined by judging whether it exceeds or falls below the threshold.

[0207] For example, Figure 17 This is an example of a change in the intervention control method involved in the second implementation method. For example... Figure 17 As shown, user C's fatigue level is: subjective fatigue 4, objective fatigue 7, meaning objective fatigue is greater ( Figure 17 (a)). When selecting the intervention effect priority mode, the intervention control method with the greatest intervention effect on objective fatigue is selected through wind intervention ( ). Figure 17 (c) However, due to adaptation, the effectiveness of the intervention decreases over time. Figure 17 (b)). When the intervention effect is below the threshold, the intervention control method of odor intervention, which has the second-highest effect on objective fatigue, is selected (after wind). Figure 17 (c)).

[0208] <Effect> As mentioned above, when the changes in subjective and objective physical and mental states are below a threshold, the intervention and control methods are changed, thereby achieving the following effects.

[0209] Typically, the effectiveness of interventions to control fatigue is not constant but varies over time. For example, when using odor as an intervention, even if the intervention is initially effective, the effect gradually diminishes due to adaptation over time. Therefore, after a certain intervention period, the effect almost disappears, leading to the problem of ineffective intervention.

[0210] In this embodiment, when the changes in subjective fatigue and objective fatigue are below a threshold, the intervention control method is changed, thereby suppressing the decline in intervention effect due to adaptation and enabling effective intervention control.

[0211] Furthermore, the magnitude of the effect of intervention on fatigue and its variation over time can vary due to individual differences. For example, when temperature is used as an intervention, the effect is greater for women, but the decline over time is also greater, while the effect is smaller for men, but the decline over time is also smaller. Therefore, even if intervention is carried out for the same duration, it may be effective for women but ineffective for men.

[0212] In this embodiment, when the changes in subjective and objective fatigue are below a threshold, the intervention control method is changed, thereby suppressing the decline in intervention effectiveness due to individual differences. For example, when using temperature as an intervention, the temperature intervention time for women can be shortened and the next intervention control method can be switched, while for men, the temperature intervention time can be extended before switching to the next intervention control method, thereby achieving effective intervention control.

[0213] The same applies to drowsiness. When the changes in subjective and objective drowsiness are below the threshold, the intervention control method is changed, thereby suppressing the decline in intervention effect due to individual differences.

[0214] The same applies to anger. When the changes in subjective and objective anger fall below a threshold, the intervention and control methods are changed, thereby suppressing the decline in intervention effectiveness due to individual differences.

[0215] It should be noted that although a second storage unit 410, a change calculation unit 420, and an intervention effect determination unit 440 have been added here as means of changing intervention control methods, the means of changing intervention control methods are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0216] (Modification 1 of the second embodiment) A variation of the second embodiment is to use a specific intervention device to perform intervention control and gradually increase the intensity of intervention control over a certain period of time.

[0217] The configuration of Modification Example 1 is the same as that of the mental and physical state intervention device 400 in the second embodiment, but there are differences in the operation of the intervention control unit 140. The following description will mainly focus on this point.

[0218] The intervention control unit 140 refers to the relational data stored in the storage unit 130 and selects an intervention control method based on the pattern acquired by the pattern acquisition unit 320. Here, the intervention control unit 140 controls the intervention device 150 to perform the intervention in a phased manner. That is, when the intervention control unit 140 controls the intervention device 150 according to the selected intervention control method, it controls the intensity of the intervention, gradually increasing it over time.

[0219] For example, such as Figure 18 As shown, taking odor as an intervention control method as an example, the intervention control with weak odor intensity and small intervention effect is implemented in the first 10 minutes of the intervention control, the intervention control with normal odor intensity and moderate intervention effect is implemented in the next 20 minutes, and the intervention control with strong odor intensity and strong intervention effect is implemented in the next 30 minutes.

[0220] Although the above example uses smell, intervention and control methods can also be sound or wind, and are not limited to phased intervention and control methods. For sound, this could involve increasing the fluctuation of the sound, increasing the volume, or raising the frequency at regular intervals. For wind, this could involve increasing the wind force, increasing the fluctuation, or changing the amount of air acting on the human body.

[0221] <Effect> As mentioned above, by intervening and controlling the subjective and objective physical and mental states in stages over a certain period of time, the following effects can be achieved.

[0222] Typically, the effectiveness of interventions in fatigue control is not constant but varies as individuals adapt to the intervention. For example, when using odor intervention, even if the effect is significant at the beginning, it diminishes over time due to adaptation. Therefore, after a certain intervention period, the effect becomes almost negligible, leading to the problem of ineffective intervention.

[0223] In this embodiment, by implementing intervention control in a phased manner, it is possible to suppress the reduction in intervention effectiveness due to adaptation and to implement effective intervention control.

[0224] The same applies to drowsiness; by implementing intervention control in a phased manner, it is possible to suppress the reduction in intervention effectiveness due to adaptation and to implement effective intervention control.

[0225] The same applies to anger; by implementing interventions and controls in a phased manner, it is possible to suppress the reduced effectiveness of interventions due to adaptation and to implement effective interventions and controls.

[0226] It should be noted that although a second storage unit 410, a change calculation unit 420, and an intervention effect determination unit 440 have been added here as means of changing the intervention control method, the means of changing the intervention control method are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0227] [Third Embodiment] <Composition> Figure 19This is a block diagram illustrating the characteristic functional configuration of the physical and mental state intervention device 500 according to the third embodiment.

[0228] The physical and mental state intervention device 500 has the function of selecting an intervention control method suitable for the individual subject. The physical and mental state intervention device 500 differs from the physical and mental state intervention device 100 of the first embodiment in that it adds a second storage unit 410, a change calculation unit 420, and an optimization processing unit 450. This will be explained below. Furthermore, fatigue will be used as an example of a physical and mental state for explanation.

[0229] The second storage unit 410 stores the quantified subjective fatigue and objective fatigue, as well as the date and time, sent by the quantification processing unit 120; the changes in subjective fatigue and objective fatigue within a specified time period, sent by the change calculation unit 420; and the intervention control method sent by the intervention control unit 140.

[0230] Since the change calculation unit 420 is the same as the change calculation unit 420 in the second embodiment, its description is omitted here.

[0231] The optimization processing unit 450 selects the optimal intervention control method for an individual based on the subjective and objective fatigue data read from the second storage unit 410, the date and time, the changes in subjective and objective fatigue within a specified time, and the intervention control method. The optimization processing unit 450 is implemented by a program and a processor that executes the program.

[0232] Optimization methods include, for example, neural networks. In the case of neural networks, such as... Figure 20 As shown, at least one of the following is taken as input: subjective fatigue and objective fatigue versus date and time, and the change in subjective fatigue and objective fatigue within a specified time. The intervention control method is taken as the output. Although the above example uses a neural network as the optimization method, the k-nearest neighbor algorithm or random forest can also be used; the optimization method is not limited.

[0233] In the case of k-nearest neighbors algorithm or random forest, such as Figure 21 and Figure 22 As shown, at least one of the following is taken as input: subjective fatigue and objective fatigue versus date and time, and the change in subjective fatigue and objective fatigue within a specified time. The intervention control method is taken as output.

[0234] The intervention control unit 140 refers to the relational data stored in the storage unit 130 and selects the intervention control method based on the pattern acquired by the pattern acquisition unit 320 and the processing of the optimization processing unit 450. For example, when the user selects the intervention effect priority mode, the intervention control method with the greatest intervention effect is selected from multiple intervention control methods. However, when a signal from the optimization processing unit 450 is present, the intervention control method selected by the optimization processing unit 450 is used preferentially. In addition, when the user selects the work efficiency mode, the intervention control method with the greatest work efficiency is selected from multiple intervention control methods.

[0235] The intervention device 150 is controlled based on the intervention control mode selected by the intervention control unit 140. Since the control of the intervention device 150 is the same as that in the first embodiment, a description is omitted here.

[0236] <Effect> As mentioned above, by optimizing an individual based on changes in subjective and objective fatigue, date and time, and intervention control methods, the following effects can be achieved.

[0237] The magnitude of the effect of intervention on fatigue and how that effect changes over time can vary depending on individual differences. For example, when using temperature as an intervention, the effect is greater for women, but the decline over time is also greater, while for men the effect is smaller, but the decline over time is also smaller. Therefore, even if intervention is carried out for the same duration, it may be effective for women but ineffective for men.

[0238] In this embodiment, by changing the intervention control method when the changes in subjective and objective fatigue are below a threshold, the decline in intervention effectiveness due to individual differences can be suppressed. For example, when using temperature as an intervention, the temperature intervention time for women can be shortened and the next intervention control method can be changed, while the temperature intervention time for men can be extended before changing the next intervention control method, thereby enabling effective intervention control.

[0239] The same applies to drowsiness. By changing the intervention control method when the changes in subjective and objective drowsiness are below the threshold, it is possible to suppress the decline in intervention effectiveness due to individual differences.

[0240] The same applies to anger. By changing the intervention control method when the changes in subjective and objective anger are below the threshold, it is possible to suppress the decline in intervention effectiveness due to individual differences.

[0241] It should be noted that although a second storage unit 410, a change calculation unit 420, and an optimization processing unit 450 have been added here as a means to suppress the decline in intervention effect due to individual differences, the means to suppress the decline in intervention effect due to individual differences are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0242] [Fourth Embodiment] <Composition> Figure 23 This is a block diagram illustrating the characteristic functional configuration of the mental and physical state intervention device 600 according to the fourth embodiment. The mental and physical state intervention device 600 has the function of creating a work schedule for the subject based on the subject's subjective and objective fatigue. The difference between the mental and physical state intervention device 600 and the mental and physical state intervention device 100 of Embodiment 1 is the addition of a schedule creation unit 510, a work terminal control unit 520, and a work terminal 530. This will be explained below. Furthermore, fatigue will be used as an example of a mental and physical state in this explanation.

[0243] The schedule creation department 510 creates user schedules based on subjective and objective fatigue data obtained from the quantitative processing department 120. For example, in the case of a schedule based on a one-day unit, such as... Figure 24 As shown, when users experience significant subjective and objective fatigue at the start of a business activity, tasks requiring high concentration should be avoided in the first half of the activity. Instead, a schedule primarily focused on operational tasks should be created. Intervention and control measures should be implemented in the middle of the activity to reduce subjective and objective fatigue. In the latter half of the activity, a schedule primarily focused on tasks requiring concentration should be created. The schedule can be based on a monthly or yearly timeframe, without any restrictions on the time frame.

[0244] The work terminal control unit 520 controls the user's work terminal 530 based on the schedule sent by the schedule creation unit 510. For example, when an intervention begins, a notification such as "Intervention is about to begin" can be displayed on the screen of the user's work terminal 530. Although the above is explained using a notification as an example, if business continues beyond the scheduled time, the power to the work terminal 530 can be turned off, or a warning sound can be issued. There are no restrictions on the control method of the user's work terminal 530.

[0245] It should be noted that the schedule creation unit 510 and the work terminal control unit 520 are implemented by a program and a processor that executes the program.

[0246] The work terminal 530 is the terminal on which the user actually performs the work, such as a personal computer or a smartphone.

[0247] The intervention control unit 140 refers to the relationship data stored in the storage unit 130 and selects the intervention control method based on the pattern obtained by the pattern acquisition unit 320. At this time, the intervention control is started and ended based on the schedule obtained from the schedule production unit 510.

[0248] The intervention device 150 is controlled based on the intervention control method and schedule selected by the intervention control unit 140. Since the control of the intervention device 150 is the same as that in the first embodiment, a description is omitted here.

[0249] <Effect> As described above, by creating schedules based on subjective and objective fatigue and controlling users' work terminals, the following effects can be achieved.

[0250] Fatigue can usually be reduced through intervention and control, but the problem is that the business is being conducted under a schedule that is prone to accumulating fatigue, and doing business in a state of accumulated fatigue will lead to decreased concentration, which in turn will lead to errors.

[0251] In this embodiment, by formulating a schedule based on subjective and objective fatigue, it is possible to prevent the accumulation of fatigue and errors in business operations.

[0252] The same applies to drowsiness. By creating a schedule based on subjective and objective drowsiness, it is possible to prevent the accumulation of drowsiness and to prevent errors in business operations.

[0253] The same applies to anger. By scheduling based on subjective and objective anger, we can prevent the accumulation of anger and avoid errors in business operations.

[0254] It should be noted that although the schedule creation unit 510, the work terminal control unit 520, and the work terminal 530 have been added here as means of intervention control based on the created schedule, the means of intervention control based on the created schedule are of course not limited to these. As long as they have the same function, their configuration is not limited.

[0255] [Fifth Embodiment] <Composition> Figure 25 This is a block diagram illustrating the characteristic functional configuration of the mental and physical state intervention device 700 according to the fifth embodiment of the present invention. The mental and physical state intervention device 700 has the function of analyzing the time periods during which subjective fatigue and objective fatigue easily accumulate. The difference between the mental and physical state intervention device 700 and the mental and physical state intervention device 100 of the first embodiment is the addition of a second storage unit 410 and a control time analysis unit 540. This will be explained below. Furthermore, fatigue will be used as an example of a mental and physical state in the explanation.

[0256] The second storage unit 410 stores the subjective and objective fatigue of the subjects before and after intervention control, as well as the date and time, after being quantified by the quantification processing unit 120.

[0257] The control time analysis unit 540 reads subjective fatigue, objective fatigue, and date and time from the second storage unit 410, and analyzes the time periods when subjective fatigue and objective fatigue are likely to increase. For example, ... Figure 26 As shown, the average subjective and objective fatigue levels for each hour throughout a week's workday are compared, and the time period with the highest levels is derived. Intervention control unit 140 performs intervention control to reduce the average subjective and objective fatigue levels during this highest time period. Figure 26 The period from 17:00 to 18:00 was the time when the average subjective and objective fatigue levels were highest. Therefore, the timing of intervention control should be determined to minimize the average subjective and objective fatigue levels during the 17:00 to 18:00 period.

[0258] It should be noted that although the above explanation uses the average of subjective and objective fatigue per hour as an example, the analysis can also be conducted monthly or seasonally; there is no limitation on the analysis method. The time control analysis unit 540 is implemented by a program and a processor that executes the program.

[0259] The intervention control unit 140 refers to the relational data stored in the storage unit 130 and selects the intervention control method based on the pattern obtained by the pattern acquisition unit 320. At the same time, it starts and ends the intervention control according to the time period for executing the intervention control obtained from the control time analysis unit 540.

[0260] For example, when intervention control is implemented at 4 PM, the intervention control method is selected based on the intervention effect, work efficiency, and pattern at 4 PM. The method for selecting the intervention control method is the same as that of the intervention control unit 140 in the first embodiment, so it is omitted here.

[0261] The intervention device 150 controls the intervention according to the intervention control method selected by the intervention control unit 140 and the time period for performing the intervention control. Since the control of the intervention device 150 is the same as that in the first embodiment, a description is omitted here.

[0262] <Effect> As mentioned above, by analyzing the time periods during which subjective and objective fatigue tend to accumulate, and implementing intervention controls based on this analysis, the following effects can be achieved.

[0263] A problem with implementing intervention controls is that if the user's fatigue level is low, the intervention effect will be minimal, and the user will hardly perceive the effect. In this embodiment, intervention controls are implemented based on periods of subjective and objective fatigue accumulation, thereby preventing excessive increases in fatigue levels and making the intervention effect more readily perceived by the user.

[0264] The same applies to drowsiness. By implementing intervention controls based on the time periods when subjective and objective drowsiness tend to accumulate, it is possible to prevent drowsiness from becoming excessive and to make it easier for users to perceive the effects of the intervention.

[0265] The same applies to anger. By implementing interventions based on the time periods when subjective and objective anger tend to accumulate, it is possible to prevent anger from escalating excessively and make it easier for users to perceive the effects of the interventions.

[0266] It should be noted that although a second storage unit 410 and a control time analysis unit 540 have been added here as a means of performing intervention control based on the relationship between physical and mental state and time period, the means of performing intervention control based on the relationship between physical and mental state and time period are of course not limited to this. As long as they have the same function, their configuration is not limited.

[0267] <Mind-Body State Intervention System> Figure 27 This is an external view of the mind-body intervention system. The mind-body intervention system includes a desktop computer that executes the mind-body intervention device 100, a physiological measurement unit 160, intervention devices 150 (150a-150c), and a mode selection unit 310. The desktop computer can control the intervention devices 150 by communicating with them.

[0268] The physiological measurement unit 160 is implemented, for example, by a built-in camera of a desktop computer. The intervention device 150 is implemented, for example, by an air conditioner 150a, a speaker 150b, an aroma diffuser 150c, etc. The mode selection unit 310 is implemented, for example, by a mouse, keyboard, or touch panel. Communication between the intervention device 150 and the physical and mental state intervention device 100 can be implemented either by wired communication or by wireless communication such as BLUETOOTH (registered trademark).

[0269] It should be noted that the mode selection unit 310 may not be set, and the intervention device 150 only needs to have one or more of these features.

[0270] In addition, the relationship data creation system includes a desktop computer that executes the relationship data creation device 10, a physiological quantity detection unit 16, a workload detection unit 23, and an intervention device 150. The desktop computer can control the intervention device 150 by communicating with it.

[0271] The physiological measurement unit 16 and the workload measurement unit 23 are implemented, for example, by a built-in camera equipped in a desktop computer. Communication between the intervention device 150 and the relational data generation device 10 can be achieved through wired communication or wireless communication such as Bluetooth.

[0272] (Other implementation methods) The above description, based on embodiments, outlines the mind-body state intervention device, mind-body state intervention system, and mind-body state intervention method according to the present invention. It should be noted that the configurations shown in the above embodiments are merely examples, and various modifications can be made without departing from the spirit of the present invention. Furthermore, the above embodiments or their modifications can be combined for use.

[0273] For example, in the above embodiments, the mind-body state intervention system can also be implemented by one or more server devices. Thus, the "system" referred to in this specification can be composed of a single device or multiple devices distributed among them. When the system is composed of multiple devices, the constituent elements (especially functional constituent elements) of the system can be distributed among the multiple devices in any manner.

[0274] Furthermore, the communication method between the devices in the above embodiments is not particularly limited. In the communication between devices, relay devices (such as broadband routers, not shown) may also be present.

[0275] Furthermore, in the above embodiments, the processing performed by a specific processing unit can also be performed by other processing units. The order of multiple processes can also be changed, or multiple processes can be executed in parallel.

[0276] Furthermore, in the above embodiments, each component can also be implemented by executing a software program suitable for each component. Each component can also be implemented by a program execution unit (e.g., a CPU or processor) reading and executing a software program recorded on a recording medium (e.g., a hard disk or semiconductor memory).

[0277] Furthermore, each component can also be implemented using hardware. For example, each component can be a circuit (or integrated circuit). These circuits can be configured as a whole into a single circuit or as separate circuits. Additionally, these circuits can be general-purpose circuits or application-specific circuits.

[0278] Furthermore, the present invention in its entirety or in specific form can also be implemented by a system, apparatus, method, integrated circuit, computer program, or computer-readable recording medium (e.g., CD-ROM). Additionally, it can be implemented by any combination of the system, apparatus, method, integrated circuit, computer program, and recording medium.

[0279] For example, the present invention can also be implemented as a method executed by a computer system such as an image information providing system or a face recognition system, or as a program that causes a computer system to execute the method. Alternatively, the present invention can also be implemented as a computer-readable, non-transitory recording medium containing the program.

[0280] Furthermore, for each embodiment, any modifications that can be conceived by those skilled in the art, or any form that is achieved by arbitrarily combining the constituent elements and functions of each embodiment without departing from the spirit of the invention, are all included in the present invention.

[0281] (Postscript) The features of the mind-body state intervention device described in the above embodiments are shown below.

[0282] <Technology 1> A mind-body state intervention device, comprising: The physiological quantity acquisition unit acquires the physiological quantities of the subject detected by the physiological quantity detection unit. The quantification processing unit quantifies two physical and mental states based on the physiological quantities, including the physical and mental states that the subject himself feels and the physical and mental states that the subject himself does not feel. The storage unit pre-stores relational data, which represents the relationship between each of multiple intervention control methods and the intervention effect on the quantified two physical and mental states; and The intervention control unit, by referring to the relationship data, selects an intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit, and executes intervention control with the selected intervention control method to control the intervention device.

[0283] <Technology 2> The mind-body state intervention device as described in Technique 1 The physical and mental state intervention device includes a pattern acquisition unit that acquires a pattern from a pattern selection unit. This pattern is selected by the subject through the pattern selection unit, and the pattern indicates whether the improvement of the intervention effect or the subject's work efficiency is prioritized. The relational data also represents the relationship between each of the multiple intervention and control methods and the operational efficiency. The intervention control unit selects the intervention control method based on the intervention effect, the work efficiency, and the mode.

[0284] <Technology 3> The mind-body state intervention device as described in technique 1 or 2 The physical and mental state intervention device also includes a relationship data creation device, which creates relationship data for multiple subjects and stores the relationship data in the storage unit.

[0285] <Technology 4> The mind-body state intervention device as described in Technique 3 The relational data generation device includes: The workload acquisition unit acquires the workload of the object being performed, as detected by the workload detection unit. The work efficiency calculation unit calculates the work efficiency based on the said work volume; and The intervention effect calculation unit calculates the intervention effect. The relational data generation device stores the calculated work efficiency and the intervention effect as relational data in the storage unit.

[0286] <Technology 5> The mind-body state intervention device as described in any one of techniques 2 to 4, When the mode selection unit selects the mode that prioritizes improving the intervention effect, the intervention control unit selects the intervention control mode that achieves the highest intervention effect from among the multiple intervention control modes by referring to the relationship data.

[0287] <Technology 6> The mind-body state intervention device as described in any one of techniques 2 to 5, When the mode selection unit selects the mode that prioritizes improving work efficiency, the intervention control unit selects the intervention control mode that maximizes work efficiency from among the multiple intervention control modes by referring to the relationship data.

[0288] <Technology 7> The mind-body state intervention device as described in any one of techniques 1 to 6, The intervention effect refers to the change in the physical and mental state of the subject before and after the intervention control is implemented.

[0289] <Technology 8> The mind-body state intervention device as described in any one of techniques 2 to 7, The work efficiency is the rate of change of the amount of work per unit time before and after the intervention control, and the unit time is the time required to perform the intervention control on the subject.

[0290] <Technology 9> The mind-body state intervention device as described in any one of techniques 1 to 8, The intervention control unit informs the subject of the changes in their physical and mental state before and after the intervention control.

[0291] <Technology 10> The mind-body state intervention device as described in any one of techniques 1 to 9, The intervention control unit prompts the subject with the intervention control method for intervention control.

[0292] <Technology 11> The mind-body state intervention device as described in any one of techniques 1 to 10, The physical and mental state referred to is the fatigue of the subject.

[0293] <Technology 12> A physical and mental state intervention system, comprising: The physical and mental state intervention device described in any one of techniques 1 to 11; The physiological quantity detection unit; and The intervention device.

[0294] <Technology 13> One method for intervening in the mind-body state is an information processing method executed by a computer. The methods for intervening in the physical and mental state include: The physiological measurement acquisition step involves acquiring the physiological measurements of the subject detected by the physiological measurement detection unit. The quantitative processing step, based on the physiological quantities, quantifies two physical and mental states of a defined physical and mental condition. These two states include the physical and mental state perceived by the subject and the physical and mental state not perceived by the subject. The intervention control step involves selecting one intervention control method from the plurality of intervention control methods by referring to relational data representing the relationship between each of the plurality of intervention control methods and the intervention effect on the two quantified physical and mental states, based on the intervention effect on at least one of the two physical and mental states quantified in the quantification process step, and executing the intervention control with the selected intervention control method to control the intervention device.

[0295] Explanation of reference numerals in the attached figures 100: The mind-body state intervention device according to the first embodiment (mind-body state intervention device) 200: The mind-body state intervention device (mind-body state intervention device) involved in the first embodiment of the modified example 1. 300: The mind-body state intervention device (mind-body state intervention device) involved in the second variation of the first embodiment. 400: The mind-body state intervention device according to the second embodiment (mind-body state intervention device) 500: The mind-body state intervention device according to the third embodiment (mind-body state intervention device) 600: The mind-body state intervention device according to the fourth embodiment (mind-body state intervention device) 700: The mind-body state intervention device according to the fifth embodiment (mind-body state intervention device) 110: Physiological Measurement Acquisition Department 11: Physiological Measurement Acquisition Department 120: Quantitative Processing Department 12: Quantitative Processing Department 130: First Storage Unit (Storage Unit) 140: Intervention and Control Department 150: Intervention equipment 160: Physiological Measurement Department 16: Physiological Measurement Department 17: Intervention Effect Calculation Department 21: Workload Acquisition Department 22: Work Efficiency Calculation Department 310: Mode Selection Department 320: Pattern Acquisition Department.

Claims

1. A device for intervening in physical and mental states, comprising: The physiological quantity acquisition unit acquires the physiological quantities of the subject detected by the physiological quantity detection unit. The quantification processing unit quantifies two physical and mental states based on the physiological quantities, including the physical and mental states that the subject himself feels and the physical and mental states that the subject himself does not feel. The storage unit pre-stores relational data, which represents the relationship between each of multiple intervention control methods and the intervention effect on the quantified two physical and mental states; as well as The intervention control unit, by referring to the relationship data, selects an intervention control method from the plurality of intervention control methods based on the intervention effect on at least one of the two physical and mental states quantified by the quantification processing unit, and executes intervention control with the selected intervention control method to control the intervention device.

2. The mind-body state intervention device as described in claim 1, The physical and mental state intervention device includes a pattern acquisition unit, which acquires a pattern from a pattern selection unit. This pattern is selected by the subject through the pattern selection unit, and the pattern indicates whether the improvement of the intervention effect or the subject's work efficiency is prioritized. The relational data also represents the relationship between each of the multiple intervention and control methods and the operational efficiency. The intervention control unit selects the intervention control method based on the intervention effect, the work efficiency, and the mode.

3. The physical and mental state intervention device as described in claim 1 or 2, The physical and mental state intervention device also includes a relationship data creation device, which creates relationship data for multiple subjects and stores the relationship data in the storage unit.

4. The mind-body state intervention device as described in claim 3, The relational data generation device includes: The workload acquisition unit acquires the workload of the object being performed, as detected by the workload detection unit. The work efficiency calculation unit calculates the work efficiency based on the work volume; as well as The intervention effect calculation unit calculates the intervention effect. The relational data generation device stores the calculated work efficiency and the intervention effect as relational data in the storage unit.

5. The mind-body state intervention device as described in claim 2, When the mode selection unit selects the mode that prioritizes improving the intervention effect, the intervention control unit selects the intervention control mode that achieves the highest intervention effect from among the multiple intervention control modes by referring to the relationship data.

6. The mind-body state intervention device as described in claim 2, When the mode selection unit selects the mode that prioritizes improving work efficiency, the intervention control unit selects the intervention control mode that maximizes work efficiency from among the multiple intervention control modes by referring to the relationship data.

7. The physical and mental state intervention device as described in claim 1 or 2, The intervention effect refers to the change in the physical and mental state of the subject before and after the intervention control is implemented.

8. The mind-body state intervention device as described in claim 2, The work efficiency is the rate of change of the amount of work per unit time before and after the intervention control, and the unit time is the time required to perform the intervention control on the subject.

9. The physical and mental state intervention device as described in claim 1 or 2, The intervention control unit informs the subject of the changes in their physical and mental state before and after the intervention control.

10. The mind-body state intervention device as described in claim 1 or 2, The intervention control unit prompts the subject with the intervention control method for intervention control.

11. The mind-body state intervention device as described in claim 1 or 2, The physical and mental state referred to is the fatigue of the subject.

12. A mind-body state intervention system, comprising: The physical and mental state intervention device as described in claim 1; The physiological quantity detection unit; as well as The intervention device.

13. A method for intervening in a mind-body state, which is an information processing method executed by a computer. The methods for intervening in the physical and mental state include: The physiological measurement acquisition step involves acquiring the physiological measurements of the subject detected by the physiological measurement detection unit. The quantitative processing step involves quantifying two physical and mental states based on the physiological quantities. These two physical and mental states include the physical and mental states that the subject himself perceives and the physical and mental states that the subject himself does not perceive. as well as The intervention control step involves selecting one intervention control method from the plurality of intervention control methods by referring to relational data representing the relationship between each of the plurality of intervention control methods and the intervention effect on the two quantified physical and mental states, based on the intervention effect on at least one of the two quantified physical and mental states in the quantification process step, and executing the intervention control with the selected intervention control method to control the intervention device.

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