Method for acquiring value of autonomic nerve index for evaluating mental condition of subject as well as method, system, and program for evaluating mental condition of subject

Abstract

To enable evaluation of a mental condition of a subject to be performed easily.SOLUTION: The present invention provides a method for acquiring a value of an autonomic nerve index for evaluating a mental condition of a subject. The method includes acquiring the value of the autonomic nerve index of the subject at a first time point and a second time point. The autonomic nerve index or an arithmetic value of the autonomic nerve index serves as an index representing the mental condition of the subject. A sleep condition of the subject is different between the first time point and the second time point.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for acquiring an autonomic nerve index value for evaluating a mental state of a subject, and further to a method, a system and a program for evaluating a mental state of a subject. [Background technology]

[0002] There is known a technique for checking the risk of developing a mental disorder by using an autonomic nerve index calculated in three states: a resting state, a task being performed, and a resting state after the task (for example, Patent Document 1). This technique requires the subject to perform a task, which places a large burden on the subject. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2013-233256 A Summary of the Invention [Problem to be solved by the invention]

[0004] One of the objects of the present invention is to enable the mental state of a subject to be evaluated simply and easily. [Means for solving the problem]

[0005] The present invention provides a method for acquiring an autonomic nerve index value to enable a simple evaluation of a mental state of a subject. The method includes acquiring values ​​of the autonomic nerve index of a subject at a first time point and a second time point, the autonomic nerve index or a calculated value thereof is an index representing the mental state of the subject, and the sleep state of the subject may be different between the first time point and the second time point.

[0006] The present invention provides, for example, the following items. (Item 1) 1. A method for obtaining an autonomic nervous index value for evaluating a mental state of a subject, comprising: obtaining values ​​of the autonomic index of the subject at a first time point and a second time point; the autonomic nerve index or its calculated value is an index representing the mental state of the subject, The method, wherein the subject's sleep state is different at the first time point and the second time point. (Item 2) The acquisition of the value of the autonomic nerve index includes: (1) acquiring values ​​of an autonomic nerve index for a predetermined period before the subject falls asleep and values ​​of an autonomic nerve index for a predetermined period after the subject falls asleep; (2) acquiring a value of an autonomic nerve index for a predetermined period before the subject wakes up and a value of an autonomic nerve index for a predetermined period after the subject wakes up; The method according to the above, comprising at least one of the following: (Item 3) The calculated value of the autonomic nerve index is (1) a ratio between a value of a parasympathetic nervous index during a predetermined period before the subject falls asleep and a value of a parasympathetic nervous index during a predetermined period after the subject falls asleep; (2) a ratio between a value of the parasympathetic nervous index for a predetermined period before the subject wakes up and a value of the parasympathetic nervous index for a predetermined period after the subject wakes up; (3) a ratio between a value of the sympathetic nervous index for a predetermined period before the subject falls asleep and a value of the sympathetic nervous index for a predetermined period after the subject falls asleep; (4) A ratio between a value of the sympathetic nerve index for a predetermined period before the subject wakes up and a value of the sympathetic nerve index for a predetermined period after the subject wakes up. The method according to any one of the preceding items, comprising at least one of the following: (Item 4) The method according to any one of the preceding items, further comprising determining the predetermined period based on a type of indicator representing the characteristics of the subject and / or the subject's external environment and / or the subject's mental state. (Item 5) A method according to any one of the preceding claims, wherein acquiring the value of the autonomic nerve index is performed using both a wearable device and a stationary device. (Item 6) 1. A method for assessing a mental state of a subject, comprising: Obtaining the value of the autonomic nerve index according to any one of the methods described above; a determiner deriving an index representing the mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof; The method includes: (Item 7) The method according to any one of the above items, wherein deriving the index includes deriving the index using only one of: (1) a ratio of a value of the parasympathetic nervous index for a predetermined period before the subject falls asleep to a value of the parasympathetic nervous index for a predetermined period after the subject falls asleep; (2) a ratio of a value of the parasympathetic nervous index for a predetermined period before the subject wakes up to a value of the parasympathetic nervous index for a predetermined period after the subject wakes up; (3) a ratio of a value of the sympathetic nervous index for a predetermined period before the subject falls asleep to a value of the sympathetic nervous index for a predetermined period after the subject wakes up; or (4) a ratio of a value of the sympathetic nervous index for a predetermined period before the subject wakes up to a value of the sympathetic nervous index for a predetermined period after the subject wakes up. (Item 8) obtaining the value of the autonomic nerve index includes obtaining a ratio of an average value of the parasympathetic nerve index for 60 minutes before the subject wakes up to an average value of the parasympathetic nerve index for 30 minutes after the subject wakes up as the value of the autonomic nerve index; The method of any one of the preceding claims, wherein the deriving step includes deriving the index using only the obtained ratio. (Item 9) 2. The method of claim 1, further comprising controlling an external device based on the derived indicator. (Item 10) Controlling the external device includes: determining an environment to be achieved by the external device based on the derived index; Controlling the external device so as to achieve the desired environment; The method according to any one of the above items, comprising: (Item 11) the derived index represents a mental health level of the subject; Determining the environment to be achieved by the external device includes determining the environment to be achieved such that the lower the mental health level is, the greater the fluctuation range from the current environment is. The method according to any one of the above items. (Item 12) 1. A system for assessing a mental state of a subject, comprising: means for acquiring values ​​of the subject's autonomic nerve index at a first time point and a second time point; A means for deriving an index representing a mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof; wherein a sleep state of the subject is different between the first time point and the second time point. (Item 12A) 13. A system according to claim 12, comprising the features according to any one of the preceding claims. (Item 13) 1. A program for assessing a mental state of a subject, the program being executed on a computer having a processor, the program comprising: receiving values ​​of the autonomic index of the subject at a first time point and a second time point; Deriving an index representing the mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof. A program that is executed by the processor and that causes the subject's sleep state to differ between the first time point and the second time point. (Item 13A) Item 14. The program according to item 13, comprising any one of the features according to the preceding items. (Item 13B) A computer-readable tangible storage medium storing the program according to item 13 or item 13A. Effect of the Invention

[0007] The present invention provides a method for acquiring an autonomic nerve index value, which allows a subject's mental state to be easily evaluated. Furthermore, the present invention also provides a method, a system, and a program for evaluating a subject's mental state, which also allows a subject's mental state to be easily evaluated. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 shows an example of a flow for assessing a subject's mental state using an embodiment of the present invention. [Diagram 2] FIG. 1 shows an example of the configuration of a system 100 for assessing the mental state of a subject. [Diagram 3] FIG. 1 shows an example of a system configuration including a server device 200 that implements the system 100. [Figure 4] FIG. 1 shows an example of the configuration of a server device 200 that implements the system 100. [Diagram 5] 5 is a flowchart showing a procedure 500 for deriving an index representative of a mental state of a subject using the system 100 for assessing a mental state of a subject. [Figure 6] A flowchart showing an example of a process 600 for controlling an external device based on an index representing a mental state of a subject. [Figure 7] A flowchart showing an example of a process 700 for controlling an external device based on an index representing a mental state of a subject. [Figure 8a] FIG. 1 shows HFnorm for a predetermined period before and after falling asleep obtained in an embodiment. [Figure 8b] FIG. 13 is a diagram showing an example of HFnorm for a predetermined period before and after waking up obtained in the embodiment. [Figure 9] FIG. 13 shows the results of evaluation using ROC curves of a classifier that makes a judgment based on sleep time, a classifier that makes a judgment based on HFnorm of the autonomic nerve index values, and a classifier that makes a judgment based on LFnorm of the autonomic nerve index values ​​in the embodiment. [Figure 10]FIG. 13 shows the results of an experiment using HFnorm for a predetermined period before and after waking up, using a classifier that performs a judgment based on HFnorm among the values ​​of autonomic nerve indices in the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] (definition) As used herein, "subject" refers to any person whose mental condition is to be assessed.

[0010] In this specification, the term "sleep state" refers to a periodically repeating state of physiological unconsciousness, including the state before the state of physiological unconsciousness and the state after the state of physiological unconsciousness. "Before the state of physiological unconsciousness" includes not only the state immediately before the state of physiological unconsciousness, but also a predetermined period before the state of physiological unconsciousness. "After the state of physiological unconsciousness" includes not only the state immediately after the state of physiological unconsciousness, but also a predetermined period after the state of physiological unconsciousness. The predetermined period is, for example, about 15 minutes to 300 minutes, typically 15 minutes to 120 minutes, and preferably 15 minutes to 60 minutes.

[0011] The "sleep state" includes, for example, a state before a physiological loss of consciousness (also called "before falling asleep"), a state after a physiological loss of consciousness (also called "after falling asleep"), a state of physiological loss of consciousness accompanied by rapid eye movement (also called "REM sleep"), a state of physiological loss of consciousness not accompanied by rapid eye movement (also called "non-REM sleep"), a state before an awakening state (also called "before waking up"), and a state after an awakening state (also called "after waking up"). For example, "non-REM sleep" can be subdivided into several states according to the depth of sleep, for example, into four stages, S1 to S4.

[0012] The "sleep state" may be determined, for example, based on electroencephalograms, based on the amount of activity, or based on the subject's subjective report.

[0013] In this specification, the term "autonomic nerve index" refers to an index that captures the function or activity state of the autonomic nerve, and this index makes it possible to evaluate the function or activity state of the autonomic nerve. The "autonomic nerve index" can be obtained, for example, from a heart rate variability analysis based on pulse waves, electrocardiograms, skin color changes, etc.

[0014] As used herein, "about" means ±10% of the preceding numerical value.

[0015] Hereinafter, preferred embodiments of the present disclosure will be described with reference to the drawings.

[0016] 1. Flow for assessing the subject's mental state As a result of intensive research, the inventors of the present invention have developed a method for easily evaluating the mental state of a subject in daily life. In particular, the inventors of the present invention have found that the values ​​of autonomic nerve indices obtained from a subject at multiple time points in different sleep states are useful for evaluating the mental state of the subject, and have developed this method by utilizing this fact.

[0017] 1 shows an example of a flow for evaluating a mental state of a subject using an embodiment of the present invention. In this flow, values ​​of an autonomic nerve index are acquired from a subject at multiple time points, and the mental state of the subject is evaluated based on the acquired values ​​of the autonomic nerve index. In this example, the subject may be active while wearing a wearable device (e.g., a smart watch, an activity meter, etc.).

[0018] For example, the value of the autonomic nerve index is acquired from a subject in a first state T1, i.e., a pre-sleep state, and input to the system 100 of the present invention in step S1. The value of the autonomic nerve index may be measured, for example, by a wearable device worn by the subject. Preferably, the wearable device has a communication function, whereby the measured value of the autonomic nerve index may be transmitted to the system 100 of the present invention via a network.

[0019] For example, after the subject goes to bed, the value of the autonomic nerve index is acquired from the subject in the second state T2, i.e., the post-sleep state, and input to the system 100 of the present invention in step S2. The value of the autonomic nerve index can be measured, for example, by a stationary device (e.g., a bed mat sensor, a non-contact sensor, etc.) installed near the subject's bed. Preferably, the stationary device has a communication function, whereby the measured value of the autonomic nerve index can be transmitted to the system 100 of the present invention via a network.

[0020] For example, after the subject wakes up from sleep, the value of the autonomic nerve index is obtained from the subject in the third state T3, i.e., the post-wake-up state, and input to the system 100 of the present invention in step S3. The value of the autonomic nerve index may be measured, for example, by a wearable device worn by the subject.

[0021] In the above example, the value of the autonomic nerve index is transmitted from the wearable device or the stationary device via a network, but the manner in which the value of the autonomic nerve index is input to the system 100 of the present invention is not limited to this. For example, the value of the autonomic nerve index may be input from the wearable device or the stationary device to the system 100 of the present invention via another terminal device such as a smartphone, tablet, or personal computer, or the value of the autonomic nerve index may be stored in a storage medium built into or external to the wearable device or the stationary device and input to the system 100 of the present invention via the storage medium.

[0022] When the value of the subject's autonomic nerve index is input, the system 100 of the present invention derives an index representing the subject's mental state. The index representing the subject's mental state may represent the mental state in two stages, such as healthy-unhealthy or good-bad, or may represent the mental state in more than two stages, such as good-normal-bad or healthy-pre-illness-already ill. The index representing the subject's mental state expressed in multiple stages may represent, for example, the probability of being pre-ill in multiple stages.

[0023] The index derived by the system 100 is output to the outside of the system 100 in step S4.

[0024] For example, when the system 100 derives an index indicating whether the mental state of the subject is healthy or not as an index representing the mental state of the subject, the index is output to enable the subject himself or another person (e.g., a doctor, etc.) to evaluate whether the mental state of the subject is healthy or not (e.g., not yet ill, already has a disease, etc.). Alternatively, when the system 100 derives an index indicating which of a plurality of levels the mental state of the subject is at as an index representing the mental state of the subject, the index is output to enable the subject himself or another person (e.g., a doctor, etc.) to evaluate how healthy the mental state of the subject is or how deteriorated it has become.

[0025] The indicators output by the system 100 can also be used for any other purpose. Typically, the indicators representing the mental state of the subject can be used to control the environment around the subject. The environment around the subject is controlled by an environmental control device, which can be any device capable of adjusting the environment around the subject. In one example, the environmental control device controls, for example, a thermal and humid environment (e.g., temperature, humidity, radiation, airflow, etc.), air components (e.g., CO 2 The environmental control device is a device capable of adjusting the temperature and humidity environment (e.g., oxygen concentration, hydrogen concentration, fragrance, etc.), light environment (e.g., lighting), and / or acoustic environment (e.g., music, noise, etc.). Typically, the environmental control device can be an air conditioner or other air conditioning device capable of adjusting the temperature and humidity environment.

[0026] In a preferred example, the indicator representing the mental state of the subject can be used to control an environmental control device to achieve an environment for improving the mental state of the subject. The environment for improving the mental state of the subject can be, for example, at least one of a specific temperature and humidity environment, a specific air component, a specific light environment, and a specific acoustic environment.

[0027] In one example, when the environmental conditioning device is an air conditioner, the indicator representing the mental state of the subject can be used to control the air conditioner to achieve a temperature and humidity environment (e.g., temperature, humidity) for improving the mental state of the subject. In another example, when the environmental conditioning device is an acoustic device capable of adjusting an acoustic environment and an aroma diffuser capable of adjusting an air component, the indicator representing the mental state of the subject can be used to control the acoustic device and the aroma diffuser to achieve an acoustic environment (e.g., music) and an air component (e.g., fragrance) for improving the mental state of the subject. In yet another example, when the environmental conditioning device is an oxygen concentrator capable of adjusting an oxygen concentration, the indicator representing the mental state of the subject can be used to control the oxygen concentrator to achieve an oxygen concentration for improving the mental state of the subject.

[0028] As described above, the subject of the present invention may be the acquisition of values ​​of autonomic nerve indices from a subject at multiple points in time when the subject is in a different sleep state. Furthermore, the subject of the present invention may be a system 100 having a configuration for acquiring values ​​of autonomic nerve indices and evaluating the mental state of a subject.

[0029] In the above example, the wearable device worn by the subject and the stationary device near the subject are described as being outside of system 100, but the wearable device and the stationary device may also be included in system 100.

[0030] The configuration of the system 100 of the present invention will be described below.

[0031] 2. Configuration of a system for assessing the mental state of a subject FIG. 2 shows an example configuration of a system 100 for assessing a mental state of a subject.

[0032] The system 100 comprises an obtaining means 110 and a deriving means 120 .

[0033] The acquisition means 110 is configured to acquire values ​​of the autonomic nerve index of the subject. The acquisition means 110 can acquire values ​​of the autonomic nerve index of the subject at a plurality of time points including at least a first time point and a second time point. The sleep state of the subject may be different at the first time point and the second time point. Therefore, the acquisition means 110 acquires values ​​of the autonomic nerve index of the subject in different sleep states.

[0034] For example, the acquiring means 110 acquires the subject's autonomic nerve index in at least two states among before falling asleep, after falling asleep, REM sleep, non-REM sleep, before waking up, and after waking up. For example, when non-REM sleep is subdivided into several states according to the depth of sleep, the acquiring means 110 may acquire values ​​of the subject's autonomic nerve index in at least two states among the several subdivided states.

[0035] In a preferred embodiment, the acquisition means 110 may acquire the value of the subject's autonomic nerve index before falling asleep and the value of the subject's autonomic nerve index after falling asleep, or the value of the subject's autonomic nerve index before waking up and the value of the subject's autonomic nerve index after waking up, or the value of the subject's autonomic nerve index before falling asleep and the value of the subject's autonomic nerve index after falling asleep, the value of the subject's autonomic nerve index before waking up and the value of the subject's autonomic nerve index after waking up.

[0036] For example, the value of the autonomic nerve index of the subject before falling asleep may be the value of the autonomic nerve index for a predetermined period before the subject falls asleep. For example, the acquisition means 110 may continuously acquire values ​​of the autonomic nerve index from the subject before falling asleep, and acquire the value of the autonomic nerve index for the predetermined period before falling asleep from the continuously acquired values ​​of the autonomic nerve index, or the acquisition means 110 may be activated when the predetermined period before falling asleep is reached, and acquire the value of the autonomic nerve index for the predetermined period before falling asleep.

[0037] For example, the value of the subject's autonomic nerve index after falling asleep may be the value of the autonomic nerve index for a predetermined period after the subject falls asleep. For example, the acquisition means 110 may continuously acquire values ​​of the autonomic nerve index from the subject after falling asleep, and acquire the value of the autonomic nerve index for the predetermined period after falling asleep from among the continuously acquired values ​​of the autonomic nerve index, or the acquisition means 110 may be activated when the predetermined period after falling asleep has arrived, and acquire the value of the autonomic nerve index for the predetermined period after falling asleep.

[0038] For example, the value of the autonomic nerve index of the subject before waking up may be the value of the autonomic nerve index for a predetermined period before the subject wakes up. For example, the acquisition means 110 may continuously acquire values ​​of the autonomic nerve index from the subject before waking up, and acquire the value of the autonomic nerve index for the predetermined period before waking up from the continuously acquired values ​​of the autonomic nerve index, or the acquisition means 110 may be activated when the predetermined period before waking up is reached, and acquire the value of the autonomic nerve index for the predetermined period before waking up.

[0039] For example, the value of the subject's autonomic nerve index after waking up may be the value of the autonomic nerve index for a predetermined period after the subject wakes up. For example, the acquisition means 110 may continuously acquire values ​​of the autonomic nerve index from the subject after waking up, and acquire the value of the autonomic nerve index for the predetermined period after waking up from among the continuously acquired values ​​of the autonomic nerve index, or the acquisition means 110 may be activated when the predetermined period after waking up arrives, and acquire the value of the autonomic nerve index for the predetermined period after waking up.

[0040] In this case, the value of the autonomic nerve index for a specified period of time may be the average, maximum, minimum, or median of the values ​​of the autonomic nerve index for the specified period of time, or it may be a normalized value normalized by a specified value.

[0041] The predetermined period may be a fixed period set in advance, or may be a variable period. When the predetermined period is a variable period, the predetermined period may be set by a user, or may be determined by the system 100. The system 100 can preferably determine the predetermined period based on at least one of the characteristics of the subject, the external environment of the subject, and the type of indicator representing the mental state of the subject.

[0042] In one example, the predetermined period may be determined based on the characteristics of the subject. The characteristics of the subject include, for example, invariant characteristics of the subject and variable characteristics of the subject, and the invariant characteristics of the subject include, for example, but are not limited to, the gender, age, medical history, etc. of the subject, and the variable characteristics of the subject include, but are not limited to, the value of the autonomic nerve index measured over time, the behavior of the subject, the state of the subject, etc. The value of the autonomic nerve index measured over time does not need to be used by the derivation means 120 described later, and the value of the autonomic nerve index measured from a subject not in a sleeping state or the value of the autonomic nerve index measured outside the predetermined period may be used. For example, when the value of the autonomic nerve index measured over time shows a steep change, the predetermined period can be set to a time earlier or later than the specified value. For example, when the value of the autonomic nerve index before sleep differs by a certain amount or more from the average value of the reference period, it is expected that the sleep state thereafter will also be different, and therefore the predetermined period can be set to a time earlier or later than the specified value in accordance with the change in the sleep state. For example, if the subject looks at the smartphone within one hour before falling asleep, the predetermined period can be set to be earlier or later than the specified value. For example, if the subject consumes more calories than the average calorie consumption, the predetermined period can be set to be earlier or later than the specified value. For example, if the subject's condition is expressed by a feeling of fatigue, if the feeling of fatigue is higher or lower than normal, the predetermined period can be set to be earlier or later than the specified value.

[0043] In another example, the predetermined period of time may be determined based on the external environment of the subject. The external environment of the subject may be, for example, a temperature and humidity environment (e.g., temperature, humidity, radiation, air flow, etc.), air components (e.g., CO2 The predetermined period may include, but is not limited to, the following: concentration, oxygen concentration, hydrogen concentration, scent, etc., light environment (e.g., lighting), and / or acoustic environment (e.g., music, noise, etc.), etc. For example, if the temperature is higher than the monthly average temperature, the predetermined period may be earlier or later than the specified value.

[0044] In yet another example, the predetermined period of time may be determined based on the type of specific mental state represented by the index derived by the system 100. For example, if the index derived by the system 100 represents whether the subject has depression, the predetermined period of time may be earlier or later than a specified value. For example, if the index derived by the system 100 represents whether the subject has schizophrenia, the predetermined period of time may be earlier or later than a specified value.

[0045] In yet another example, the predetermined period may be determined based on the expected sleep state of the subject. For example, when it is expected that the subject's sleep onset time will be later than the average sleep onset time, the predetermined period for sleep onset may be set to a time later than a specified value. For example, when it is expected that the subject's sleep onset time will be earlier than the average sleep onset time, the predetermined period for sleep onset may be set to a time earlier than a specified value. For example, when it is expected that the subject's sleep time will be shorter than the average sleep time due to mid-sleep awakening or the like, the predetermined period for waking up may be set to a time later than a specified value or a time that matches the mid-sleep awakening. For example, when it is expected that the subject's sleep onset time will be earlier than the average sleep onset time, the predetermined period for sleep onset may be set to a time earlier than a specified value.

[0046] For example, the sleep state may change depending on the subject's behavior, the subject's schedule, the subject's external environment, and the like. For example, the sleep state may change from normal on a day when the subject is exhausted, on a day when the subject is in a state of tension due to an event the next day, when the room temperature is higher than the appropriate temperature, or when there is noise. It is preferable to capture these changes and set the predetermined period appropriately. For example, when the room temperature is higher than the appropriate temperature, it is assumed that the subject will not be able to fall asleep, and therefore the predetermined time for falling asleep may be set to a later time. For example, when there is noise, it is assumed that the subject will wake up more often during sleep, and therefore the predetermined time for waking up may be set to an earlier time to accommodate the wake up in the middle of the night.

[0047] The autonomic nerve index includes, for example, HR (Heart Rate), HF (High Frequency), LF (Low Frequency), and TP (Total Power; HF+LF). The autonomic nerve index may be, for example, any index that can be measured by an electrocardiograph. In a preferred embodiment, the autonomic nerve index includes at least one of a parasympathetic nerve index, which is an index related to the parasympathetic nerve, and a sympathetic nerve index, which is an index related to the sympathetic nerve. The parasympathetic nerve index may be, for example, HF. The sympathetic nerve index may be, for example, LF. Preferably, the acquisition means 110 can acquire at least one of the value of the parasympathetic nerve index and the value of the sympathetic nerve index as the value of the autonomic nerve index.

[0048] Thus, in a preferred embodiment, the acquisition means 110 (1) acquiring a value of a parasympathetic nervous index for a predetermined period before the subject falls asleep and a value of a parasympathetic nervous index for a predetermined period after the subject falls asleep; (2) acquiring a value of a parasympathetic nervous index for a predetermined period before the subject wakes up and a value of a parasympathetic nervous index for a predetermined period after the subject wakes up; (3) acquiring a value of the sympathetic nervous index for a predetermined period before the subject falls asleep and a value of the sympathetic nervous index for a predetermined period after the subject falls asleep; (4) Obtaining a value of the sympathetic nervous index for a predetermined period before the subject wakes up and a value of the sympathetic nervous index for a predetermined period after the subject wakes up. At least one of the following may be performed.

[0049] The derivation means 120 is configured to derive an index representing the mental state of the subject based on the acquired value of the autonomic nerve index or its calculated value. The derivation means 120 may derive an index representing the mental state of the subject by directly using the acquired value of the autonomic nerve index, but preferably derives an index representing the mental state of the subject based on a calculated value of the acquired value of the autonomic nerve index.

[0050] The calculated value of the autonomic nerve index may be, for example, a calculated value of the value of the autonomic nerve index acquired at the first time point and the value of the autonomic nerve index acquired at the second time point. The calculated value of the autonomic nerve index may be, for example, a sum, a difference, a product, a quotient, or a combination of these between the value of the autonomic nerve index acquired at the first time point and the value of the autonomic nerve index acquired at the second time point, or may be a calculated value by any other function. As described above, the value of the autonomic nerve index acquired at the first time point and the value of the autonomic nerve index acquired at the second time point may be the average value, maximum value, minimum value, median value, or normalized value of the values ​​of the autonomic nerve index for a predetermined period of time, and therefore, the calculated value of the autonomic nerve index may be a calculated value of the average value, maximum value, minimum value, median value, or normalized value of the values ​​of the autonomic nerve index acquired at the first time point and the average value, maximum value, minimum value, median value, or normalized value of the values ​​of the autonomic nerve index acquired at the second time point.

[0051] In a preferred example, the calculated value of the autonomic nerve index may be, for example, a calculated value of the average value of the normalized values ​​of the autonomic nerve index values ​​acquired at a first time point and the average value of the normalized values ​​of the autonomic nerve index values ​​acquired at a second time point. For example, the normalized value of the autonomic nerve index may be a normalized value of the parasympathetic nerve index (HFnorm: the proportion of the activity of the parasympathetic nerve in the activity of the autonomic nerve index) or a normalized value of the sympathetic nerve index (LFnorm: the proportion of the activity of the sympathetic nerve in the activity of the autonomic nerve index), and the calculated value of the autonomic nerve index may be a ratio of the average value of the HFnorm at the first time point to the average value of the HFnorm at the second time point, or a ratio of the average value of the LFnorm at the first time point to the average value of the LFnorm at the second time point.

[0052] In the above-mentioned preferred embodiment, the calculated value of the autonomic nerve index is (1) A ratio of a parasympathetic nervous index value for a predetermined period before the subject falls asleep to a parasympathetic nervous index value for a predetermined period after the subject falls asleep; (2) the ratio of the value of the parasympathetic nervous index for a predetermined period before the subject wakes up to the value of the parasympathetic nervous index for a predetermined period after the subject wakes up; (3) the ratio of the value of the sympathetic nervous index for a predetermined period before the subject falls asleep to the value of the sympathetic nervous index for a predetermined period after the subject falls asleep; (4) A ratio between the value of the sympathetic nervous index during a predetermined period before the subject wakes up and the value of the sympathetic nervous index during a predetermined period after the subject wakes up. More specifically, the calculated value of the autonomic nerve index may include at least one of: (1) The ratio of the average HFnorm for a specified period before the subject falls asleep to the average HFnorm for a specified period after the subject falls asleep, (2) The ratio of the average HFnorm for a specified period before the subject wakes up to the average HFnorm for a specified period after the subject wakes up, (3) The ratio of the average LFnorm for a predetermined period before the subject falls asleep to the average LFnorm for a predetermined period after the subject falls asleep, (4) The ratio of the average LFnorm for a specified period before the subject wakes up to the average LFnorm for a specified period after the subject wakes up may include at least one of:

[0053] The inventors of the present invention focused on HFnorm, a parasympathetic nerve index, based on the hypothesis that autonomic nerve switching is not done well in a state where the mental state is not healthy, for example, in a pre-illness state. Experiments conducted by the inventors of the present invention revealed that the change in HFnorm before and after falling asleep in subjects with a mental state that is not healthy (i.e., the pre-illness group) is smaller than that in subjects with a mental state that is healthy (i.e., the healthy group), and that the change in HFnorm before and after waking up in subjects in the healthy group changes more slowly than that in subjects in the pre-illness group. In the present invention, by using the change in HFnorm before and after falling asleep and / or before and after waking up as the value of the autonomic nerve index, it is possible to more accurately distinguish between the healthy group and the pre-illness group, and in a more preferred embodiment, the derivation means 120 can derive an index representing the subject's mental state (e.g., an index indicating whether the subject is healthy or pre-illness) based on the ratio of the average value of HFnorm for a predetermined period before the subject falls asleep to the average value of HFnorm for a predetermined period after the subject falls asleep and / or the ratio of the average value of HFnorm for a predetermined period before the subject wakes up to the average value of HFnorm for a predetermined period after the subject wakes up.

[0054] The derivation means 120 can derive an index representing the mental state of the target by, for example, statistical processing. The derivation means 120 can derive an index representing the mental state of the target by using a classifier constructed with the values ​​of the autonomic nerve indexes of the multiple subjects previously obtained as explanatory variables and the indexes representing the respective mental states of the multiple subjects as objective variables.

[0055] When the index representing the mental state is a two-classification index of whether or not it is healthy, the objective variable is a binary value of a value indicating whether it is healthy and a value indicating whether it is not healthy, and the output from the determiner is also a binary value. For example, for a plurality of subjects, a determiner can be constructed using the ratio of the average value of HFnorm for a predetermined period before falling asleep to the average value of HFnorm for a predetermined period after falling asleep as an explanatory variable, and whether or not the mental state of each subject is healthy as an objective variable. This determiner has, for example, a specific threshold value, and can output whether or not the mental state is healthy by determining whether or not the ratio of the input HFnorm exceeds the threshold value. When the ratio of the average value of HFnorm for a predetermined period before falling asleep to the average value of HFnorm for a predetermined period after falling asleep of the subject is input into the determiner constructed in this way, the input ratio is compared with the threshold value, and whether or not the mental state of the subject is healthy is output.

[0056] In the present invention, even with such a simple classifier, it is possible to predict the mental state of a subject with a certain degree of accuracy by using the values ​​of the autonomic nerve index acquired in a plurality of sleep states. In particular, by appropriately selecting the autonomic nerve index to be acquired and appropriately setting the predetermined period for acquiring the value of the autonomic nerve index, it is possible to predict the mental state with higher accuracy than, for example, the accuracy of predicting the mental state from only the sleep time. For example, by using the ratio of the average value of HFnorm before and after falling asleep as the autonomic nerve index and setting the predetermined period to about 30 minutes to about 210 minutes before waking up and about 15 minutes to about 90 minutes after waking up, it is possible to achieve prediction of the mental state with practical accuracy.

[0057] When the index representing the mental state represents the mental state in multiple stages, the objective variable is a multi-value corresponding to each stage of the mental state, and the output from the determiner is also a multi-value. Each stage can be classified by, for example, the probability of being non-ill, and can be, for example, a stage with a high probability of being non-ill, a medium stage, a low stage, etc. For example, for multiple subjects, a determiner can be constructed using the ratio of the average value of HFnorm for each subject in a predetermined period before falling asleep to the average value of HFnorm for a predetermined period after falling asleep as an explanatory variable, and the stage of each subject's mental state among the multiple stages as an objective variable. This determiner has, for example, multiple thresholds, and can output which stage of the mental state among the multiple stages by determining which threshold value the input HFnorm ratio exceeds among the multiple thresholds. When the ratio of the average value of HFnorm for a predetermined period before falling asleep to the target's average value of HFnorm for a predetermined period after falling asleep is input into the determiner constructed in this way, the input ratio is compared with at least one of the multiple thresholds, and the stage of the target's mental state among the multiple stages is output.

[0058] The derivation means 120 can derive an index representing the mental state of the target by, for example, machine learning. The derivation means 120 can derive an index representing the mental state of the target by using a machine learning model constructed by learning the values ​​of the autonomic nerve indexes of multiple subjects acquired in the past as input teacher data and the indexes representing the respective mental states of the multiple subjects as output teacher data.

[0059] When the index representing the mental state is a two-classification index of whether or not it is healthy, the output teacher data is a scalar indicating whether or not it is healthy, and whether or not the subject is healthy can be output depending on whether or not the output node of the machine learning model fires. For example, for a plurality of subjects, a machine learning model can be constructed using the ratio of the average value of HFnorm for a predetermined period before falling asleep to the average value of HFnorm for a predetermined period after falling asleep as input teacher data, and a scalar indicating whether or not the mental state of each subject is healthy as output teacher data. When the ratio of the average value of HFnorm for a predetermined period before falling asleep to the average value of HFnorm for a predetermined period after falling asleep is input into the machine learning model constructed in this way, whether or not the mental state of the subject is healthy is output.

[0060] When the index representing the mental state represents the mental state in multiple stages, the output teacher data is a vector representing which stage of the mental state is, and which output node of the machine learning model is fired can output which stage of the mental state of the target. For example, for multiple subjects, the ratio of the average value of HFnorm of each subject for a predetermined period before falling asleep to the average value of HFnorm of a predetermined period after falling asleep is used as input teacher data, and a vector representing which stage of the mental state of each subject is used as output teacher data, so that a machine learning model can be constructed. When the ratio of the average value of HFnorm of the subject for a predetermined period before falling asleep to the average value of HFnorm of a predetermined period after falling asleep is input into the machine learning model constructed in this way, which stage of the mental state of the target is output.

[0061] The index representing the mental state of the subject may be, for example, an index represented by a position related to mental health on a health positioning map described in International Publication No. 2020 / 091053 and the like. In the health positioning map, the magnitude of the value of an axis (e.g., the vertical axis) representing mental health represents the level of mental health risk, and when the value of the axis representing mental health exceeds a predetermined threshold, the subject may be clustered as a mental health disease risk group. Therefore, whether or not the subject is healthy can be represented by whether or not the subject belongs to a cluster of a mental health disease risk group in the health positioning map, and the level of the subject's mental state can be represented by a mental health level based on the value of the axis representing mental health.

[0062] The index representing the mental state of the subject derived by the derivation means 120 can be used to evaluate the mental state of the subject. For example, the index representing the mental state of the subject is notified to the subject, thereby enabling the subject to grasp his / her own mental state. For example, the index representing the mental state of the subject is notified to a doctor who diagnoses the subject, thereby enabling the doctor to use the index as an index when diagnosing the subject.

[0063] The index representing the mental state of the subject derived by the derivation means 120 can be used for any other purpose.

[0064] In one embodiment, the indicators representing the mental state of the subject can be used to control an external device, and to this end, the system 100 may comprise a control means (not shown).

[0065] The control means is configured to control the external device based on at least the derived index representing the mental state. The external device may be an environmental adjustment device capable of adjusting the environment around the subject. In one example, the environmental adjustment device adjusts, for example, a temperature and humidity environment (e.g., temperature, humidity, radiation, airflow, etc.), air components (e.g., CO 2The environmental control device is a device capable of adjusting the temperature and humidity environment (e.g., oxygen concentration, hydrogen concentration, fragrance, etc.), light environment (e.g., lighting), and / or acoustic environment (e.g., music, noise, etc.). Typically, the environmental control device can be an air conditioner or other air conditioning device capable of adjusting the temperature and humidity environment.

[0066] The control means can determine an environment to be achieved based on the derived index representing the mental state, and control the environmental adjustment device so as to achieve the determined environment.

[0067] In a preferred embodiment, when the derived index representing the mental state indicates that the mental health level is not healthy or indicates that the mental health level is low, the control means can determine an environment that can bring about a healthy mental state (for example, an environment that stabilizes the mind) and control the environmental adjustment device to achieve the determined environment. The environment that can bring about a healthy mental state can be, for example, at least one of a specific temperature and humidity environment, a specific air component, a specific light environment, and a specific acoustic environment. Specifically, for example, the temperature environment can be a temperature environment that increases / decreases the current air temperature by ±1 to 15°C, a humidity environment that increases / decreases the current humidity by ±10 to 30%, an air component that increases / decreases the current oxygen concentration by 1 to 15% / decreases by 1 to 5%, a light environment that increases / decreases the current illuminance by ±10 to 50%, an acoustic environment that plays classical music, and the like. For example, the specific temperature and humidity environment can be an environment that keeps the temperature at 25°C. For example, the specific temperature and humidity environment can be an environment that increases / decreases the current air temperature by ±1°C, and even such a small temperature difference can have a sufficient effect.

[0068] In one example, when the environmental control device is an air conditioner, the control means can determine a temperature and humidity environment (e.g., temperature, humidity) that can bring about a healthy mental state when the derived indicator representing the mental state indicates an unhealthy state or when the derived indicator representing the mental state indicates a low mental health level, and control the air conditioner to achieve that temperature and humidity environment.

[0069] In another example, when the environmental conditioning device is an acoustic device capable of adjusting the acoustic environment and a fragrance device capable of adjusting the air components, the control means can determine an acoustic environment (e.g., music) and air components (e.g., fragrance) that can bring about a healthy mental state when the indicator representing the derived mental state indicates an unhealthy state or when the indicator representing the derived mental state indicates a low mental health level, and control the acoustic device and the fragrance device to achieve that acoustic environment and air components.

[0070] In yet another example, when the environmental conditioning equipment is an oxygen concentrator capable of adjusting the oxygen concentration, the control means can determine an oxygen concentration that can result in a healthy mental state when the derived indicator representing the mental state indicates an unhealthy state or when the derived indicator representing the mental state indicates a low mental health level, and control the oxygen concentrator to achieve that oxygen concentration.

[0071] The environment that can bring about a healthy mental state may be changed according to the mental health level. For example, when the mental health level is at its lowest, the environmental control device can be controlled to bring about a larger change in the environment than when the mental health level is high.

[0072] The above-described system 100 can be implemented as a system including a server device 200 and a terminal device 300, for example.

[0073] FIG. 3 shows an example of the configuration of a system including a server device 200 implementing the system 100. As shown in FIG.

[0074] The system 100 includes a server device 200 , a database unit 250 connected to the server device 200 , and at least one terminal device 300 connected to the server device 200 via a network 500 .

[0075] The server device 200 can communicate with at least one terminal device 300 via the network 500. The server device 200 can also communicate with a database unit 250 connected to the server device 200.

[0076] For example, the database unit 250 connected to the server device 200 may store previously acquired values ​​of autonomic nerve indices of a plurality of subjects together with indices representing the mental states of the respective subjects.

[0077] The terminal device 300 may be any terminal device as long as it can acquire the value of the autonomic nerve index from the subject. The terminal device 300 may be, for example, a wearable device (e.g., a smart watch, an activity meter) worn by the subject, an information terminal (e.g., a smartphone, a tablet) used by the subject, or a stationary device (e.g., a bed mat sensor, a non-contact sensor) installed near the subject, but is not limited thereto. The bed mat sensor is a sensor installed under a bed mat or a futon, etc., and can acquire data when the subject is sleeping. The non-contact sensor is a sensor that can detect the activity of the subject without contact, and can detect the pulse and / or breathing of the subject by the Doppler effect of microwaves, etc., for example. The terminal device 300 can communicate with the server device 200 via a network 500. Here, the type of the network 500 does not matter. For example, the terminal device 300 may communicate with the server device 200 via the Internet, or may communicate with the server device 200 via a LAN. Although three terminal devices 300 are depicted in FIG. 3, the number of the terminal devices 300 is not limited thereto. The number of terminal devices 300 may be any number equal to or greater than one.

[0078] The acquisition means 110 of the system 100 can be implemented by the terminal device 300. Also, the control means of the system 100 can be implemented by the terminal device 300. In this case, the terminal device 300 can directly or indirectly communicate with an external device.

[0079] FIG. 4 shows an example of the configuration of a server device 200 that implements the system 100. As shown in FIG.

[0080] The server device 200 includes an interface unit 210, a memory unit 230, and a processor unit 220. The server device 200 is connected to a database unit 250.

[0081] For example, the database unit 250 connected to the server device 200 may store previously acquired values ​​of autonomic nerve indices of a plurality of subjects together with indices representing the mental states of the respective subjects.

[0082] The interface unit 210 controls communication via the network. The interface unit 210 also controls communication with the database unit 250. The processor unit 220 of the server device 200 can receive information from outside the server device 200 via the interface unit 210, and can transmit information to outside the server device 200. The processor unit 220 of the server device 200 can receive information from the terminal device 300 via the interface unit 210, and can transmit information to the terminal device 300. The interface unit 210 can control communication in any manner.

[0083] The processor unit 220 controls the overall operation of the server device 200. The processor unit 220 reads out a program stored in the memory unit 230 and executes the program. This makes it possible to cause the server device 200 to function as a device that executes desired steps. The processor unit 220 may be implemented by a single processor or by multiple processors.

[0084] The memory unit 230 stores a program required for executing the processing of the server device 200, data required for executing the program, and the like. For example, a part or all of a program for making the processor unit 220 perform a process for evaluating the mental state of a target (for example, a program for realizing the process shown in FIG. 6 or FIG. 7 described later) is stored. Here, how the program is stored in the memory unit 230 does not matter. For example, the program may be pre-installed in the memory unit 230. Alternatively, the program may be installed in the memory unit 230 by being downloaded via a network. The program may be stored on a computer-readable tangible storage medium. The memory unit 230 may be implemented by any storage means.

[0085] The derivation means 120 of the system 100 can be implemented by the processor unit 220 of the server device 200. In addition, the control means of the system 100 can also be implemented by the processor unit 220. At this time, the processor unit 220 can communicate directly or indirectly with an external device via the interface unit 210.

[0086] In the example shown in FIG. 4, each component of the server device 200 is provided in the server device 200, but the present invention is not limited to this. Any of the components of the server device 200 can also be provided outside the server device 200. For example, when the memory unit 230 and the processor unit 220 are each composed of separate hardware components, the hardware components may be connected via any network. In this case, the type of network does not matter. The hardware components may be connected, for example, via a LAN, wirelessly, or wired. The server device 200 is not limited to a specific hardware configuration. For example, it is also within the scope of the present invention to configure the processor unit 220 by an analog circuit rather than a digital circuit. The configuration of the server device 200 is not limited to the above-mentioned one as long as it can realize its functions.

[0087] In the example shown in FIG. 3 and FIG. 4, the database unit 250 is provided outside the server device 200, but the present invention is not limited to this. The database unit 250 can also be provided inside the server device 200. In this case, the database unit 300 may be implemented by the same storage means as the storage means implementing the memory unit 230, or may be implemented by a storage means different from the storage means implementing the memory unit 230. In any case, the database unit 250 is configured as a storage unit for the server device 200. The configuration of the database unit 250 is not limited to a specific hardware configuration. For example, the database unit 250 may be configured as a single hardware part, or may be configured as a plurality of hardware parts. For example, the database unit 250 may be configured as an external hard disk device of the server device 200, or may be configured as a cloud storage connected via a network.

[0088] 3. Procedures in the system for assessing the mental state of a subject FIG. 5 is a flow chart illustrating a procedure 500 for deriving an index representative of a mental state of a subject using the system 100 for assessing a mental state of a subject.

[0089] In step S501, the acquisition means 110 of the system 100 acquires values ​​of the autonomic nerve index of the subject at a first time point and a second time point. The sleep state of the subject may be different at the first time point and the second time point. Therefore, in step S501, values ​​of the autonomic nerve index in the different sleep states of the subject are acquired.

[0090] The acquisition means 110 may acquire the value of the subject's autonomic nerve index before falling asleep and the value of the subject's autonomic nerve index after falling asleep, or the value of the subject's autonomic nerve index before waking up and the value of the subject's autonomic nerve index after waking up, or the value of the subject's autonomic nerve index before falling asleep and the value of the subject's autonomic nerve index after falling asleep, the value of the subject's autonomic nerve index before waking up and the value of the subject's autonomic nerve index after waking up.

[0091] Preferably, the acquiring means 110 acquires values ​​of the autonomic nerve indexes for a predetermined period before and after falling asleep and / or values ​​of the autonomic nerve indexes for a predetermined period before and after waking up. The predetermined period may be a fixed period set in advance or may be a variable period.

[0092] When the predetermined period is determined by the system 100, a step of determining the predetermined period is performed before performing step S501. The step of determining the predetermined period may be performed, for example, in the target terminal device 300 or in the server device 200.

[0093] The predetermined period can be determined, for example, based on at least one of the characteristics of the subject, the subject's external environment, the subject's expected sleep state, and the type of indicator representing the subject's mental state.

[0094] In one example, the predetermined period may be determined based on the characteristics of the subject. The characteristics of the subject may include, for example, invariant characteristics of the subject and variable characteristics of the subject, and the invariant characteristics of the subject may include, but are not limited to, the subject's gender, age, medical history, etc., and the variable characteristics of the subject may include, but are not limited to, the value of the autonomic nerve index measured over time, the subject's behavior, the subject's condition, etc. For example, when the variable characteristics of the subject differ from the normal or average value by a predetermined value or more, the predetermined period may be set to be earlier or later than the specified value.

[0095] In another example, the predetermined period of time may be determined based on the external environment of the subject. The external environment of the subject may be, for example, a temperature and humidity environment (e.g., temperature, humidity, radiation, air flow, etc.), air components (e.g., CO 2 These include, but are not limited to, environmental factors (e.g., concentration, oxygen concentration, hydrogen concentration, scent, etc.), light environment (e.g., lighting), and / or acoustic environment (e.g., music, noise, etc.).

[0096] In yet another example, the predetermined period of time may be determined based on the type of specific mental state that the indicator derived by the system 100 represents.

[0097] In yet another example, the predetermined period may be determined based on the expected sleep state of the subject. For example, when it is expected that the subject's sleep onset time will be later than the average sleep onset time, the predetermined period regarding sleep onset may be set to a time later than a specified value. For example, when it is expected that the subject's sleep onset time will be earlier than the average sleep onset time, the predetermined period regarding sleep onset may be set to a time earlier than a specified value. For example, when it is expected that the subject's sleep time will be shorter than the average sleep time due to mid-sleep awakening or the like, the predetermined period regarding waking up may be set to a time later than a specified value or a time that matches the mid-sleep awakening. For example, when it is expected that the subject's sleep onset time will be earlier than the average sleep onset time, the predetermined period regarding sleep onset may be set to a time earlier than a specified value. The sleep state may change depending on, for example, the subject's behavior, the subject's schedule, the subject's external environment, and the like.

[0098] The acquisition means 110 may be a wearable device worn by the subject and a stationary device installed near the subject, and step S501 may be performed using both the wearable device and the stationary device. For example, when the subject is awake and active, the value of the autonomic nerve index may be acquired by the wearable device worn by the subject, and when the subject is asleep, the value of the autonomic nerve index may be acquired by the stationary device installed near the bed. This allows the subject to remove the wearable device when sleeping, which is preferable in that the subject's sleep is not disturbed by wearing the wearable device.

[0099] The value of the autonomic nerve index acquired by the acquisition means 110 is passed to the derivation means 120, which receives it. For example, when the system 100 is implemented as shown in Fig. 3, the value of the autonomic nerve index acquired by the terminal device 300, which is the acquisition means 110, is transmitted to the server device 200 via the network 500, and the processor unit 220 of the server device 200, which is the derivation means 120, receives the value of the autonomic nerve index via the interface unit 210.

[0100] In step S502, the derivation means 120 of the system 100 derives an index representing the mental state of the subject based on the value of the autonomic nerve index acquired in step S501 or a calculated value thereof.

[0101] The derivation means 120 can derive an index representing the mental state of the target by, for example, statistical processing. The derivation means 120 can derive an index representing the mental state of the target by using a classifier constructed with the values ​​of the autonomic nerve indexes of the multiple subjects previously obtained as explanatory variables and the indexes representing the respective mental states of the multiple subjects as objective variables.

[0102] The derivation means 120 can derive an index representing the mental state of the target by, for example, machine learning. The derivation means 120 can derive an index representing the mental state of the target by using a machine learning model constructed by learning the values ​​of the autonomic nerve indexes of multiple subjects acquired in the past as input teacher data and the indexes representing the respective mental states of the multiple subjects as output teacher data.

[0103] The derived index may be a binary index indicating whether the subject is healthy or not, or may be a multi-stage index expressing the mental state on a multi-stage basis.

[0104] It is preferable that the derivation means 120 derives the index using only one of (1) the ratio of the parasympathetic nerve index value for a predetermined period before the subject falls asleep to the parasympathetic nerve index value for a predetermined period after the subject falls asleep, (2) the ratio of the parasympathetic nerve index value for a predetermined period before the subject wakes up to the parasympathetic nerve index value for a predetermined period after the subject wakes up, (3) the ratio of the sympathetic nerve index value for a predetermined period before the subject falls asleep to the sympathetic nerve index value for a predetermined period after the subject falls asleep, and (4) the ratio of the sympathetic nerve index value for a predetermined period before the subject wakes up to the sympathetic nerve index value for a predetermined period after the subject wakes up. This has the advantage of making it possible to evaluate the mental state of the subject by simple measurement without imposing a burden on the subject.

[0105] The index representing the mental state of the subject derived in step S502 can be used to evaluate the mental state of the subject. For example, the index representing the mental state is transmitted from the server device 200 to the terminal device 300 of the subject, thereby enabling the subject to grasp his / her own mental state. For example, the index representing the mental state is transmitted from the server device 200 to the terminal device of a doctor who diagnoses the subject, thereby enabling the doctor to use the index as an index when diagnosing the subject.

[0106] The index representing the mental state of the subject derived by the derivation means 120 can be used for any other purpose.

[0107] For example, an indicator of a subject's mental state can be utilized to control an external device.

[0108] 6 is a flow chart showing an example of a process 600 for controlling an external device based on an index representing a mental state of a subject. The process 600 can be executed in the processor unit 220 of the server device 200.

[0109] In step S601, the processor unit 220 receives a value of an autonomic nerve index. The processor unit 220 can receive the value of the autonomic nerve index acquired by the terminal device 300, which is the acquisition means 110, from the terminal device 300 via the interface unit 210.

[0110] In step S602, the processor unit 220 derives an index representing the mental state of the subject based on the value of the autonomic nerve index received in step S601 or its calculated value. Step S602 is the same process as step S502.

[0111] In step S603, the processor unit 220 determines whether or not the subject's mental state is healthy based on the index derived in step S602.

[0112] If the subject's mental state is normal, there is no need to control the external device in a special way, so the process proceeds to step S607, and the external device is normally controlled. For example, the processor unit 220 can communicate with the external device via the interface unit 210 and control it normally.

[0113] If the subject's mental state is not healthy, the process proceeds to step S604 to control external devices to achieve an environment that can make the subject's mental state healthy.

[0114] In step S604, the processor unit 220 determines N. N is a number for determining the time to start controlling the external device. The external device is controlled based on the mental state level, for example, N minutes before waking up. N can be any value between 30 and 120, for example, and is typically any value between 30 and 60. This is because, before waking up, the parasympathetic nerve value between 0 and 120 minutes before has the greatest effect on the mental state determination result.

[0115] The processor unit 220 may determine N based on a user input, or may determine N based on the value of the autonomic nerve index acquired from the subject. For example, when the value of the autonomic nerve index of the subject starts to vary m minutes before waking up based on the value of the autonomic nerve index acquired from the subject in the past, it can be determined that N=m.

[0116] In step S605, the processor unit 220 controls the external device normally until N minutes before the target wakes up. For example, the processor unit 220 can communicate with the external device via the interface unit 210 and perform normal control.

[0117] The processor unit 220 can, for example, obtain a sleep onset flag and an average sleep time in advance, predict the wake-up timing of the subject from these, and normally control the external device up to N minutes before the wake-up.

[0118] In step S606, the processor unit 220 performs special control of the external device starting from N minutes before waking up. The special control is to control the external device so as to achieve an environment that can improve the mental state of the subject.

[0119] For example, if the external device is an air conditioner, the air conditioner can be controlled to achieve a predetermined temperature Ta. Ta can be expressed, for example, as follows: · Ta = Reference temperature + 2°C (fixed value), or · Ta = Reference temperature + individual setting It can be expressed as:

[0120] When the target wakes up, the processor unit 220 can end the special control of the external device. The processor unit 220 may, for example, acquire a sleep onset flag and an average sleep time in advance and determine the wake-up timing of the target from these, or may determine the wake-up timing of the target from a user's advance notification of a planned wake-up time, or may determine the wake-up timing by waking up the user in conjunction with an alarm, or may determine the wake-up timing from a user's notification after waking up, or may determine the wake-up timing from behavior detection.

[0121] In this manner, the process 600 can achieve control of an external device based on the mental state of the subject. This is done to achieve an environment that can improve the mental state of the subject, for example, when the mental state of the subject is determined to be unhealthy, such a determination is not made the next morning.

[0122] 7 is a flow chart showing an example of a process 700 for controlling an external device based on an index representing a mental state of a subject. The process 700 can be executed in the processor unit 220 of the server device 200. The process 700 differs from the process 600 in that the index representing the mental state of the subject is a multi-stage index rather than a two-class index.

[0123] In step S701, the processor unit 220 receives the value of the autonomic nerve index. Step S701 is the same process as step S601.

[0124] In step S702, the processor unit 220 derives an index representing the mental state of the subject based on the value of the autonomic nerve index received in step S701 or its calculated value. Step S702 is the same process as step S602.

[0125] In step S703, the processor unit 220 determines the mental state level of the subject based on the index derived in step S702. The processor unit 220 may determine the mental state level of the subject according to the output from the determiner that outputs multiple values. The mental state level of the subject may be classified into levels according to the degree of probability of being non-ill, for example.

[0126] In step S704, the processor unit 220 determines N. Step S704 is the same process as step S604.

[0127] In step S705, the processor unit 220 controls the external device in the normal manner until N minutes before the target wakes up. Step S705 is the same process as step S605.

[0128] In step S706, the processor unit 220 performs special control of the external device starting from N minutes before waking up, based on the mental state level of the subject.

[0129] For example, if the external device is an air conditioner, the air conditioner can be controlled to achieve a predetermined temperature Ta. Ta can be expressed, for example, as follows: · Ta = reference temperature + fixed value, or Ta = Reference temperature + Mental state level * Temperature change factor, or ·Ta = Reference temperature + Mental state level * Temperature change coefficient * Personal coefficient It can be expressed as:

[0130] For example, if the reference temperature is 25°C and the temperature change coefficient is 0.5, and the mental state level is 1 and the probability of not being ill is low, Ta = Reference temperature + Mental state level * Temperature change coefficient Using this formula, Ta = 25 + 1 * 0.5 = 25.5°C, whereas if the mental state level is 5 and the probability of non-illness is high, Ta = 25 + 5 * 0.5 = 27.5°C, and the processor unit 220 will control the air conditioner to achieve these temperatures.

[0131] When the target wakes up, the processor unit 220 can end the special control of the external device. The processor unit 220 may, for example, acquire a sleep onset flag and an average sleep time in advance and determine the wake-up timing of the target from these, or may determine the wake-up timing of the target from a user's advance notification of a planned wake-up time, or may determine the wake-up timing by waking up the user in conjunction with an alarm, or may determine the wake-up timing from a user's notification after waking up, or may determine the wake-up timing from behavior detection.

[0132] In this manner, the process 700 can effect control of an external device based on the mental state level of the subject, which is advantageous in that control can be tailored to the mental state level of the subject.

[0133] 5 to 7, the steps are described as being executed in a specific order, but the order shown is only an example, and the order in which the steps are executed is not limited thereto. The steps may be executed in any order that is logically possible.

[0134] In the examples described above with reference to Figures 6 and 7, the processing of each step shown in Figures 6 and 7 has been described as being realized by the processor unit 220 and the program stored in the memory unit 230, but the present invention is not limited to this. At least one of the processing of each step shown in Figures 6 and 7 may be realized by a hardware configuration such as a control circuit.

[0135] As described above, in the present invention, the method of acquiring the value of the autonomic nerve index, which includes step S501, can be the subject of the present invention, and does not necessarily need to include step S502.

[0136] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. EXAMPLES

[0137] To verify whether it is possible to estimate indicators of mental state from vital signs that can be measured on a daily basis, we calculated autonomic nervous index values ​​from data obtained from a 24-hour test and a nap test, and from the calculated autonomic nervous index values, we estimated the pre-illness group of mental health and verified the accuracy of the estimation.

[0138] (24 hour test) Measurements were taken over a test period of approximately three months on 57 subjects (31 of whom were women) aged 30-50, recruited from the general public. Electrocardiograms were taken every three weeks over the three-month period, for three weekdays and during the sleep time before and after. Health measurements were taken at the beginning and end of the three-month period to determine the mental state of the subjects.

[0139] (Nap test) Fourteen male subjects aged 20-59 were recruited from the RIKEN Institute and had their electrocardiograms recorded over five weekday days over a period of about one and a half months, as well as during naps before and after. Health measurements were taken twice during the month and a half period to determine the mental state of the subjects.

[0140] (Electrocardiogram) Electrocardiogram measurements were performed using the heart rate sensor "MyBeat." The data specifications were: data format: CSV format, sampling rate: 1000Hz, data update cycle: every heart rate acquisition.

[0141] Noise removal processing was performed on the measured heart rate data. Specifically, data with a heart rate of 180 ppm or more and data with a heart rate of 40 ppm or less were cut off as outliers. In addition, sections with an outlier content rate of 10% or more in the 100 points before and after the heart rate were also cut off. After removing outliers, the heart rates were converted to HFnorm. The HFnorm was resampled at 1 / 60 Hz using linear interpolation. Resampling ensured that the data acquisition period was constant, which ensured that the number of data points was consistent across subjects.

[0142] From the resampled HFnorms, HFnorms for a specified period before and after falling asleep and HFnorms for a specified period before and after waking up were obtained.

[0143] Fig. 8 shows an example of the acquired HFnorm for a predetermined period before and after falling asleep and the HFnorm for a predetermined period before and after waking up. Fig. 8(a) shows the HFnorm for a predetermined period before and after falling asleep, and Fig. 8(b) shows the HFnorm before and after waking up.

[0144] As described above, in subjects with an unhealthy mental state (i.e., the pre-illness group), the change in HFnorm before and after falling asleep is smaller than in subjects with a healthy mental state (i.e., the healthy group), and in subjects in the healthy group, the change in HFnorm before and after waking up is more gradual than in subjects in the pre-illness group.

[0145] (Estimation of mental state) The results of the health measurements for each subject were input into a health function described in WO 2020 / 091053 etc. to obtain the position of each subject on a health positioning map. Based on the position on the health positioning map, each of the multiple subjects was classified into a mental health pre-disease group and a healthy group.

[0146] A classifier was constructed to determine whether the subject is in the mental health non-disease group or the healthy group using the sleep time of each subject, and further, a classifier was constructed to determine whether the subject is in the mental health non-disease group or the healthy group using the value of the autonomic nerve index of each subject. Specifically, the cutoff value that borders the mental health non-disease group or the healthy group was varied between the maximum and minimum values ​​of the sleep time or the autonomic nerve index, and the true positive rate and the false positive rate were calculated for each cutoff value, and an ROC curve (Receiver Operating Characteristic Curve) was drawn. A classifier was constructed that uses a value corresponding to the cutoff value that gives the point closest to the upper left in the drawn ROC curve as a threshold. Note that the cutoff value for determining the threshold is not limited to the cutoff value that gives the point closest to the upper left, and can be selected according to the purpose of the classifier. For example, a value corresponding to the cutoff value that gives the point with the highest true positive rate may be used as the threshold, or a value corresponding to the cutoff value that gives the point with the lowest false positive rate may be used as the threshold.

[0147] The estimation accuracy of each constructed classifier was evaluated using the AUC (Area Under Curve: the area under the ROC curve created by the true positive rate and false positive rate) of the ROC curve.

[0148] FIG. 9 shows the results of evaluation using ROC curves of a classifier that makes a judgment from the sleep time, a classifier that makes a judgment from the HFnorm of the autonomic nerve index values, and a classifier that makes a judgment from the LFnorm of the autonomic nerve index values.

[0149] The classifier that judges from sleep time had an AUC of 0.42, which is close to 0.5, and the estimation performance was low. In contrast, the classifier that judges from HFnorm before and after waking up had an AUC of 0.78, which shows that it is possible to judge with relatively high accuracy. This suggests that it is possible to monitor mental health from heart rate indicators, which are easy to measure. This is advantageous in that it does not require the subject to perform tasks, making it possible to monitor mental health without placing a burden on the subject.

[0150] Figure 10 shows the results of an experiment using HFnorm for a predetermined period before and after waking up, using a classifier that judges from HFnorm among the values ​​of the autonomic nerve index. In this experiment, the predetermined period was varied from 30 minutes to 300 minutes before waking up and from 15 minutes to 270 minutes after waking up. The values ​​shown in Figure 10 represent the AUC for each combination of the predetermined period.

[0151] As can be seen from Figure 10, when the specified period was 60 minutes before waking up and 30 minutes after waking up, the AUC was reconsidered. In addition, even with a relatively short specified period of 30 minutes before waking up and 15 minutes after waking up, a practically sufficient accuracy of AUC = 0.75 was achieved. This suggests the possibility of evaluating the subject's mental state even during a short period of sleep. [Industrial Applicability]

[0152] INDUSTRIAL APPLICABILITY The present invention is useful in that it provides a method for acquiring values ​​of autonomic nerve indices, thereby making it possible to easily evaluate the mental state of a subject. [Explanation of symbols]

[0153] 100 Systems 110 Acquisition means 120 Derivation means 200 Server device 300 Terminal Equipment

Claims

1. 1. A method for obtaining an autonomic nervous index value for evaluating a mental state of a subject, comprising: obtaining values ​​of the autonomic index of the subject at a first time point and a second time point; the autonomic nerve index or its calculated value is an index representing the mental state of the subject, The method, wherein the subject's sleep state is different at the first time point and at the second time point.

2. The acquisition of the value of the autonomic nerve index includes: (1) acquiring values ​​of an autonomic nerve index for a predetermined period before the subject falls asleep and values ​​of an autonomic nerve index for a predetermined period after the subject falls asleep; (2) acquiring a value of an autonomic nerve index for a predetermined period before the subject wakes up and a value of an autonomic nerve index for a predetermined period after the subject wakes up; The method of claim 1 , comprising at least one of:

3. The calculated value of the autonomic nerve index is (1) a ratio between a value of a parasympathetic nervous index during a predetermined period before the subject falls asleep and a value of a parasympathetic nervous index during a predetermined period after the subject falls asleep; (2) a ratio between a value of the parasympathetic nervous index for a predetermined period before the subject wakes up and a value of the parasympathetic nervous index for a predetermined period after the subject wakes up; (3) a ratio between a value of the sympathetic nerve index for a predetermined period before the subject falls asleep and a value of the sympathetic nerve index for a predetermined period after the subject falls asleep; (4) A ratio between a value of the sympathetic nerve index for a predetermined period before the subject wakes up and a value of the sympathetic nerve index for a predetermined period after the subject wakes up. The method of claim 2 , comprising at least one of:

4. The method of claim 2 , further comprising determining the predetermined period based on a type of indicator representative of the subject's characteristics and / or the subject's external environment and / or the subject's mental state.

5. The method according to claim 1 , wherein the acquisition of the value of the autonomic nerve index is performed using both a wearable device and a stationary device.

6. 1. A method for assessing a mental state of a subject, comprising: Obtaining the value of the autonomic nerve index according to the method of any one of claims 1 to 5; a determiner deriving an index representing the mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof; The method includes:

7. The method of claim 6, wherein deriving the index includes deriving the index using only one of: (1) a ratio of a value of the parasympathetic nerve index for a predetermined period before the subject falls asleep to a value of the parasympathetic nerve index for a predetermined period after the subject falls asleep; (2) a ratio of a value of the parasympathetic nerve index for a predetermined period before the subject wakes up to a value of the parasympathetic nerve index for a predetermined period after the subject wakes up; (3) a ratio of a value of the sympathetic nerve index for a predetermined period before the subject falls asleep to a value of the sympathetic nerve index for a predetermined period after the subject wakes up; or (4) a ratio of a value of the sympathetic nerve index for a predetermined period before the subject wakes up to a value of the sympathetic nerve index for a predetermined period after the subject wakes up.

8. obtaining the value of the autonomic nerve index includes obtaining a ratio of an average value of the parasympathetic nerve index for 60 minutes before the subject wakes up to an average value of the parasympathetic nerve index for 30 minutes after the subject wakes up as the value of the autonomic nerve index; The method of claim 6 , wherein the deriving step comprises deriving the index using only the obtained ratio.

9. The method of claim 6 , further comprising controlling an external device based on the derived indicator.

10. Controlling the external device includes: determining an environment to be achieved by the external device based on the derived index; Controlling the external device so as to achieve the desired environment; 10. The method of claim 9, comprising:

11. the derived index represents a mental health level of the subject; Determining the environment to be achieved by the external device includes determining the environment to be achieved such that the lower the mental health level is, the greater the fluctuation range from the current environment is. The method of claim 10.

12. 1. A system for assessing a mental state of a subject, comprising: means for acquiring values ​​of the subject's autonomic nerve index at a first time point and a second time point; A means for deriving an index representing a mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof; wherein a sleep state of the subject is different between the first time point and the second time point.

13. 1. A program for assessing a mental state of a subject, the program being executed on a computer having a processor, the program comprising: receiving values ​​of the autonomic index for the subject at a first time point and a second time point; Deriving an index representing the mental state of the subject based on the value of the autonomic nerve index or a calculated value thereof. A program that causes the processor to execute the program, the program being characterized in that the sleep state of the subject is different between the first time point and the second time point.

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