Information Processing Apparatus, Control Method, and Program

Abstract

This information processing device 1X mainly has a stress value acquisition means 17X, a stress release behavior detection means 18X, and a notification means 19X. The stress value acquisition means 17X acquires a tress value representing the degree of stress of a subject. The stress release behavior detection means 18X detects stress release behavior, which is behavior for releasing stress, on the basis of the stress value. The notification means 19X performs notification regarding the result of the detection of the stress release behavior.

Description

Technical Field

[0001] The present disclosure relates to the technical field of information processing apparatuses, control methods, and storage media that perform processing related to stress states.

Background Art

[0002] An apparatus or system for determining the stress state of a subject based on data measured from the subject is known. For example, Patent Document 1 discloses a portable stress measurement device that determines the temporary stress level of a subject on each day based on the subject's examination data. Further, Patent Document 2 discloses a method for calculating momentum from acceleration information obtained by an acceleration sensor.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] For maintaining and improving mental health, it is essential not to accumulate stress and to cope with stress when stress has accumulated (i.e., stress dissipation). On the other hand, in the technologies such as Patent Document 1, the current stress state is determined and notified, and it is difficult for the user to clearly grasp whether stress can be dissipated only by being notified of the current stress state.

[0005] One object of the present disclosure is to provide an information processing apparatus, a stress estimation method, and a storage medium capable of suitably notifying information related to the stress state of a target person in view of the above-described problems.

Means for Solving the Problems

[0006] One aspect of the information processing apparatus is stress value acquisition means for acquiring a stress value representing the degree of stress of the target person, Momentum acquisition means for acquiring the momentum of the subject corresponding to the stress value Based on the stress value An index having a positive correlation with and a negative correlation with the momentum of the subject corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the momentum stress divergence behavior detection means for detecting a stress divergence behavior, which is a behavior for diverging the stress, notification means for notifying the result of the detection of the stress divergence behavior, and an information processing apparatus having the above.

[0007] One aspect of the control method is a computer acquires a stress value representing the degree of stress of the target person, Acquire the momentum of the subject corresponding to the stress value Based on the stress value An index having a positive correlation with and a negative correlation with the momentum of the subject corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the momentum detects a stress divergence behavior, which is a behavior for diverging the stress, and notifies the result of the detection of the stress divergence behavior. Note that "computer" includes any electronic device (which may be a processor included in the electronic device) and may be composed of a plurality of electronic devices.

[0008] One aspect of the program is to acquire a stress value representing the degree of stress of the target person, Acquire the momentum of the subject corresponding to the stress value Based on the stress value An index having a positive correlation with and a negative correlation with the momentum of the subject corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the momentum detects a stress divergence behavior, which is a behavior for diverging the stress, and is a program for causing a computer to execute a process of notifying the result of the detection of the stress divergence behavior.

Advantages of the Invention

[0009] According to the present disclosure, information regarding the stress state of the target person can be preferably notified.

Brief Description of the Drawings

[0010]

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Mode for Carrying Out the Invention

[0011] Hereinafter, embodiments of an information processing apparatus, a stress estimation method, and a storage medium will be described with reference to the drawings.

[0012] <First Embodiment> (1) System configuration FIG. 1 shows a schematic configuration of a stress divergence detection system 100 according to the first embodiment. The stress divergence detection system 100 detects an action that promotes the divergence of stress of a target person (also referred to as "stress divergence action"), and gives a notification regarding the detection result. Here, the "target person" may be an athlete or an employee whose stress state is managed by an organization, or may be an individual user. Note that the above-mentioned "organization" may be a family. In this case, the stress divergence detection system 100 detects the stress divergence level for each member of the family as the target person, and gives a notification regarding the detection result.

[0013] The stress divergence detection system 100 mainly includes an information processing device 1, an input device 2, an output device 3, a storage device 4, and a sensor 5.

[0014] The information processing device 1 detects the stress divergence action of the target person, and notifies the detection result to the target person who is the user or the administrator thereof. The information processing device 1 performs data communication with the input device 2, the output device 3, and the sensor 5 via a communication network or by direct communication via wireless or wired. For example, the information processing device 1 receives an input signal "S1" supplied from the input device 2, a sensor signal "S3" supplied from the sensor 5, and various information stored in the storage device 4. The input signal S1 and the sensor signal S3 are used for generating information obtained by subjectively or objectively observing (measuring) the target person (also referred to as "observation information").

[0015] In the present embodiment, the information processing device 1 estimates the stress state of the target person (specifically, a stress value representing the degree of stress) and calculates the amount of movement of the target person based on the observation information, and detects the stress divergence action based on these calculation results. The information processing device 1 generates an output control signal "S2" based on the detection result of the stress divergence action of the target person and the like, and supplies the generated output control signal S2 to the output device 3. Note that in the present embodiment, the stress refers to short-term stress, for example, stress in a relatively short period (about several seconds to several days).

[0016] The input device 2 is an interface that accepts user input (manual input) of information regarding each subject. Note that the user who inputs information using the input device 2 may be the subject himself / herself, or may be a person who manages the activities of the subject. The input device 2 may be various user input interfaces such as, for example, a touch panel, buttons, a keyboard, a mouse, a voice input device, etc. The input device 2 supplies an input signal S1 generated based on the user's input to the information processing device 1. The output device 3 displays and / or outputs sound a predetermined information based on an output control signal S2 supplied from the information processing device 1. The output device 3 includes, for example, a display device such as a display, a virtual (augmented) reality terminal or a projector, and a sound output device such as a speaker.

[0017] The sensor 5 measures the biological signals etc. of the subject, and supplies the measured biological signals etc. to the information processing device 1 as a sensor signal S3. In this case, the sensor signal S3 may be any biological signal (including vital information) such as the subject's heartbeat, electroencephalogram, pulse wave, sweating amount (skin electrical activity), hormone secretion amount, cerebral blood flow, blood pressure, body temperature, electromyogram, respiratory rate, acceleration, etc. Further, the sensor 5 may be a device that analyzes the blood collected from the subject and outputs a sensor signal S3 indicating the analysis result. Further, the sensor 5 may be a wearable terminal worn by the subject, may be a camera that photographs the subject, a microphone that generates a voice signal of the subject's speech, etc., or may be a terminal such as a personal computer or a smartphone operated by the subject. The above-mentioned wearable terminal includes, for example, a GNSS (global navigation satellite system) receiver, an acceleration sensor, etc., and outputs the output signals of these respective sensors as the sensor signal S3. Further, the sensor 5 may supply information corresponding to the amount of operation of a personal computer, a smartphone, etc. to the information processing device 1 as the sensor signal S3. Further, the sensor 5 may be one that outputs a sensor signal S3 representing biological data (including sleep time) from the subject during the subject's sleep.

[0018] The memory device 4 is a memory that stores various information necessary for estimating the stress state and the like. The memory device 4 may be an external storage device such as a hard disk connected to or built into the information processing device 1, or may be a storage medium such as a flash memory. Further, the memory device 4 may be a server device that performs data communication with the information processing device 1. Further, the memory device 4 may be composed of a plurality of devices.

[0019] The memory device 4 has an observation information storage unit 40, an attribute / life information storage unit 41, and a calculation result storage unit 42.

[0020] The observation information storage unit 40 stores observation information that is subjective information of the subject based on the input signal S1 or objective information of the subject based on the sensor signal S3. Here, the sensor signal S3 itself may be treated as observation information, or a feature amount calculated based on the sensor signal S3 (including indexes representing expressions, emotions, etc. analyzed from image or audio data) may be treated as observation information. Further, the observation information may include questionnaire information based on the input signal S1 or a diagnosis result such as personality based on the information. Note that the observation information is stored in the observation information storage unit 40 in association with, for example, identification information (subject ID) of the subject to be observed and observed date and time information.

[0021] The attribute / life information storage unit 41 stores at least one of attribute information regarding the attributes of the subject or life information regarding the life of the subject.

[0022] The attribute information is, for example, information regarding whether the subject likes or dislikes exercise (preference), the gender, age, personality, race, or cognitive tendency of the subject. The attribute information may be generated by the information processing device 1 and stored in the memory device 4, or may be generated in advance by a device other than the information processing device 1 and stored in the memory device 4. The attribute information is generated, for example, based on the answer results of a questionnaire by the subject (i.e., subjective measurement results).

[0023] The life information includes, for example, information regarding the daily exercise amount which is the average exercise amount that the subject performs daily, information regarding the subject's schedule (working days, working hours, days of travel, etc.), information regarding the physical condition (such as whether having a cold or not), and information regarding the environment where the subject is located (temperature, humidity, weather, noise level, etc.). The life information may be information supplied from various systems such as a management system that manages the subject's exercise amount, schedule, health, etc. to the storage device 4. In the attribute / life information storage unit 41, for example, the attribute information and / or life information for each subject is stored in association with the identification information of the subject (subject ID).

[0024] The calculation result storage unit 42 stores various calculation results calculated by the information processing device 1. The calculation result storage unit 42 stores, for example, the stress estimation value, exercise amount, and indexes related to stress dissipation (described later) of the subject calculated by the information processing device 1 in association with the identification information of the subject and the date and time information representing the target date and time. The above-mentioned "target date and time" may be the generation date and time of the signal used for the calculation, or may be the date and time when the calculation was performed.

[0025] Note that the storage device 4 is not limited to the above-described example, and may store various types of information necessary for the processes executed by the information processing device 1. For example, the storage device 4 may store parameters for configuring various calculation models. The above-described calculation models include, for example, a stress estimation model for the information processing device 1 to estimate a stress value from observation information, a momentum calculation model for calculating momentum from observation information, and an index calculation model for calculating an index related to stress divergence described later from the estimated stress value (also referred to as "stress estimation value") and momentum. Such models may be any machine learning models (including statistical models) such as neural networks and support vector machines, or may be predefined calculation formulas or lookup tables, etc. For example, when the above-described model is a model based on a neural network such as a convolutional neural network, the storage device 4 stores information on various parameters such as a layer structure, a neuron structure of each layer, the number of filters and filter sizes in each layer, and the weights of each element of each filter.

[0026] Note that the configuration of the stress divergence detection system 100 shown in FIG. 1 is an example, and various changes may be made to the configuration. For example, the input device 2 and the output device 3 may be integrally configured. In this case, the input device 2 and the output device 3 may be configured as a tablet-type terminal that is integrated with or separate from the information processing device 1. Also, the input device 2 and the sensor 5 may be integrally configured. Further, the information processing device 1 may be composed of a plurality of devices. In this case, the plurality of devices constituting the information processing device 1 exchange information necessary for executing the pre-assigned processes among these plurality of devices. In this case, the information processing device 1 functions as an information processing system.

[0027] (2) Hardware configuration of the information processing device FIG. 2 shows the hardware configuration of the information processing device 1. As hardware, the information processing device 1 includes a processor 11, a memory 12, and an interface 13. The processor 11, the memory 12, and the interface 13 are connected via a data bus 90.

[0028] By executing the program stored in the memory 12, the processor 11 functions as a controller (arithmetic unit) that controls the entire information processing apparatus 1. The processor 11 is, for example, a processor such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or a TPU (Tensor Processing Unit). The processor 11 may be composed of a plurality of processors. The processor 11 is an example of a computer.

[0029] The memory 12 is composed of various volatile memories and non-volatile memories such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory. Further, a program for executing the processing executed by the information processing apparatus 1 is stored in the memory 12. Note that a part of the information stored in the memory 12 may be stored by one or a plurality of external storage devices capable of communicating with the information processing apparatus 1, or may be stored by a storage medium detachable from the information processing apparatus 1.

[0030] The interface 13 is an interface for electrically connecting the information processing apparatus 1 and other devices. These interfaces may be wireless interfaces such as network adapters for wirelessly transmitting and receiving data with other devices, or may be hardware interfaces for connecting to other devices by a cable or the like.

[0031] Note that the hardware configuration of the information processing apparatus 1 is not limited to the configuration shown in FIG. 2. For example, the information processing apparatus 1 may include at least one of the input device 2 or the output device 3. Further, the information processing apparatus 1 may be connected to or incorporated with a sound output device such as a speaker.

[0032] (3) Stress dissipation behavior detection processing Next, the stress divergence behavior detection process executed by the information processing apparatus 1 will be described. Generally speaking, the information processing apparatus 1 calculates an index (also referred to as the "stress divergence index SR") for determining stress divergence behavior based on the stress estimation value and momentum of the target person, and detects stress divergence behavior based on the stress divergence index SR. Thereby, the information processing apparatus 1 detects the stress divergence behavior of the target person with high accuracy and presents the detection result.

[0033] (3-1) Functional Block Figure 3 is an example of the functional block of the information processing apparatus 1. Functionally, the processor 11 of the information processing apparatus 1 includes an observation information acquisition unit 15, a momentum calculation unit 16, a stress estimation unit 17, a stress divergence behavior detection unit 18, and a notification unit 19. In FIG. 3, blocks between which data is exchanged are connected by solid lines, but the combinations of blocks between which data is exchanged are not limited to those in FIG. 3. The same applies to the diagrams of other functional blocks described later.

[0034] The observation information acquisition unit 15 acquires the observation information of the target person based on the input signal S1 and the sensor signal S3, and stores the acquired observation information in the observation information storage unit 40. In this case, as described above, the observation information acquisition unit 15 may acquire the sensor signal S3 as the observation information, or may acquire a feature amount calculated based on the sensor signal S3 (including an index representing an expression, emotion, etc. analyzed from image or audio data) as the observation information. Further, the observation information acquisition unit 15 may acquire questionnaire information based on the input signal S1 or a diagnosis result such as a personality based on the information as the observation information.

[0035] The momentum calculation unit 16 calculates the momentum of the subject (specifically, the momentum per unit time) based on the observation information stored in the observation information storage unit 40. In this case, as the observation information used for momentum calculation, the momentum calculation unit 16 may use biological information (for example, the change amount of acceleration, the increase amount of heart rate, body temperature, and / or the increase amount of skin temperature, etc.) obtained from a wearable terminal or the like worn by the subject during the target period of momentum calculation, or may use device information (for example, the change amount of acceleration, the change amount of the positioning position by GNSS, etc.) obtained from a smartphone or the like possessed by the subject. In this case, for example, the momentum calculation unit 16 inputs the above-mentioned observation information to a momentum calculation model whose parameters are stored in the storage device 4 in advance, and acquires the momentum output by the model. In this case, the momentum calculation model is a model that outputs momentum when a predetermined type of observation information is input.

[0036] Note that the momentum calculation model is not limited to a learned model. For example, the momentum calculation model may output the average value per unit time of the norm of the acceleration vector when an acceleration vector output by a three-axis acceleration sensor in time series is given as an input. In another example, the momentum calculation model may classify the motion state such as the walking state and the running state based on the periodic change of the acceleration output by the acceleration sensor, etc., and output the momentum corresponding to the classification result. The momentum calculation unit 16 supplies the calculated momentum to the stress dissipation behavior detection unit 18 and stores it in the calculation result storage unit 42.

[0037] The stress estimation unit 17 calculates the stress estimation value of the subject based on the observation information stored in the observation information storage unit 40. In this case, the stress estimation unit 17 may use any information having a correlation with stress (for example, biological information such as heartbeat, sweating, skin temperature, expression / affection information recognized from an image or voice, questionnaire results, personality diagnosis results, operation logs, etc.) as the observation information used for calculating the stress estimation value. In this case, the stress estimation unit 17, for example, inputs the above-described observation information to a stress estimation model whose parameters are stored in the storage device 4 in advance, and acquires the stress value output by the model as the stress estimation value. In this case, the stress estimation model is a model that outputs a stress value when observation information is input.

[0038] Note that the stress estimation model is not limited to a learned model. For example, the stress estimation model may be an equation for deriving a stress estimation value from the degree of fluctuation of the heartbeat per unit time, the number of peaks of the sweating amount per unit time, etc. The stress estimation unit 17 supplies the calculated stress estimation value to the stress divergence behavior detection unit 18 and stores it in the calculation result storage unit 42.

[0039] The stress divergence behavior detection unit 18 calculates a stress divergence index SR based on the amount of movement of the subject supplied from the amount of movement calculation unit 16 and the stress estimation value of the subject supplied from the stress estimation unit 17, and detects a stress divergence behavior based on the stress divergence index SR. Hereinafter, for convenience of explanation, the stress divergence index SR is, as an example, an index that becomes higher as the degree of stress divergence is higher. Note that, instead of this, the stress divergence behavior detection unit 18 may calculate an index that becomes lower as the degree of stress divergence is higher.

[0040] The stress divergence behavior detection unit 18 calculates the stress divergence index SR as an index that increases as the momentum increases (i.e., has a positive correlation with the momentum) and decreases as the stress estimation value increases (i.e., has a negative correlation with the stress estimation value). As a representative example, when the momentum is "M" and the stress estimation value is "S", the stress divergence behavior detection unit 18 calculates the stress divergence index SR according to the following formula (1). SR = M / S (1) Then, for example, when the stress divergence index SR is equal to or greater than a predetermined lower threshold value (also referred to as the "lower threshold value Th1"), the stress divergence behavior detection unit 18 determines that there has been a stress divergence behavior.

[0041] In a preferred example, in addition to the lower threshold value Th1, the stress divergence behavior detection unit 18 provides an upper threshold value (also referred to as the "upper threshold value Th2") for the stress divergence index SR, and when the stress divergence index SR is equal to or greater than the lower threshold value Th1 and less than the upper threshold value Th2, it is determined that there has been a stress divergence behavior. As will be described later, when the stress divergence index SR is extremely high, the subject is in a state of being moved by a vehicle or the like, and there is a high possibility that the stress divergence index SR does not represent a value that conforms to the actual situation of the subject's stress divergence. Considering the above, when the stress divergence index SR is equal to or greater than the upper threshold value Th2, the stress divergence behavior detection unit 18 does not determine that there has been a stress divergence behavior. The lower threshold value Th1 and the upper threshold value Th2 are set to appropriate values pre-stored in, for example, the storage device 4 or the like.

[0042] Also, in another preferred example, the stress divergence behavior detection unit 18 may refer to at least one of the attribute information or life information of the subject stored in the attribute and life information storage unit 41, and further consider these information to determine the stress divergence index SR. This specific example will be described later. The stress divergence behavior detection unit 18 supplies the detection result of the stress divergence behavior to the notification unit 19. Also, the stress divergence behavior detection unit 18 stores the calculated stress divergence index SR and the like in the calculation result storage unit 42.

[0043] The notification unit 19 performs control to cause the output device 3 to output information regarding the stress divergence behavior based on the detection result of the stress divergence behavior supplied from the stress divergence behavior detection unit 18 and the information stored in the calculation result storage unit 42. An example of the output control by the notification unit 19 will be specifically described in the section of "(3-4) Notification example".

[0044] In addition, each component of the observation information acquisition unit 15, the momentum calculation unit 16, the stress estimation unit 17, the stress divergence behavior detection unit 18, and the notification unit 19 described in FIG. 3 can be realized, for example, by the processor 11 executing a program. Also, by recording the necessary program in an arbitrary non-volatile storage medium and installing it as needed, each component may be realized. Note that at least a part of these components is not limited to being realized by software by a program, and may be realized by any combination of hardware, firmware, and software. Also, at least a part of these components may be realized using a user-programmable integrated circuit such as an FPGA (Field-Programmable Gate Array) or a microcontroller. In this case, a program composed of the above components may be realized using this integrated circuit. Also, at least a part of each component may be composed of an ASSP (Application Specific Standard Produce), an ASIC (Application Specific Integrated Circuit), or a quantum processor (quantum computer control chip). Thus, each component may be realized by various hardware. The above is the same in other embodiments described later. Furthermore, these components may be realized by the cooperation of a plurality of computers using, for example, cloud computing technology.

[0045] (3-2) Relationship between stress divergence behavior and index Next, a supplementary explanation will be given regarding the relationship between stress divergence behavior and the stress divergence index SR. FIG. 4 shows a two-dimensional map with the vertical axis representing the activity level of the sympathetic nerve and the horizontal axis representing the activity level of the parasympathetic nerve. In the two-dimensional map shown in FIG. 4, the region corresponding to the stress divergence state and the region corresponding to the sleep state are respectively indicated by a dashed-line ellipse and a solid-line circle.

[0046] As shown in FIG. 4, the stress divergence state occurs when the activity level of the parasympathetic nerve is proportional to the activity level of the sympathetic nerve. Also, the stress divergence state generally has two types: a venting type and a relaxation type. Here, the venting type is stress divergence accompanied by physical activity, which is caused by actions such as exercise, karaoke, taking a stroll in a theme park or on a trip, etc. Also, the relaxation type is divergence without physical activity, which is caused by actions such as listening to music, practicing zazen, meditation, forest bathing, natural bathing, aromatherapy, deep breathing, etc. And in the present embodiment, the information processing apparatus 1 detects a stress divergence state including these.

[0047] FIG. 5 shows a two-dimensional map with the vertical axis representing the stress value and the horizontal axis representing the amount of exercise. In the two-dimensional map shown in FIG. 5, regions corresponding to the "stress state" in which the subject feels stress, the "relaxed state" in which the subject is relaxed, the "vented state" in which the subject can relieve stress, and the "passive state" in which the subject is moved by riding in a vehicle or the like are respectively indicated. Roughly speaking, the "stress state" is a state when the stress value is a high stress value higher than the threshold and the amount of exercise is a low amount of exercise below the threshold, and the "relaxed state" is a state when the stress value is a low stress value below the threshold and the amount of exercise is a low amount of exercise. Also, the "vented state" is a state when the stress value is a high stress value and the amount of exercise is a high amount of exercise higher than the threshold, and the "passive state" is a state when the stress value is a low stress value and the amount of exercise is a high amount of exercise (in other words, a state in which the amount of exercise has increased due to the subject's passive activity). Note that the stress value is proportional to the activity level of the sympathetic nerve adopted as the vertical axis in the two-dimensional map shown in FIG. 4.

[0048] As shown in FIG. 5, the "relaxed state" and the "stress-relieved state" corresponding to the stress divergence state correspond to states where both the stress value and the momentum are low or both are high. Therefore, in the case of the stress divergence index SR based on the definition of Equation (1), the stress divergence state corresponds to the intermediate value range excluding the range where the stress divergence index SR is too high (i.e., the range of values equal to or higher than the upper threshold value Th2) and the range where it is too low (i.e., the range of values less than the lower threshold value Th1).

[0049] In general, the higher the momentum of the subject, the higher the sympathetic nerve (i.e., the stress value) of the subject becomes. Therefore, the "passive state" shown in FIG. 5 basically does not occur. On the other hand, when the subject is being moved by riding in a vehicle or the like, the apparent value of the acceleration of the subject detected by the sensor 5 increases, and the momentum is calculated to be higher than the actual value. As a result, a state where the momentum is high and the stress value is low (i.e., the passive state) occurs. This state occurs due to the sensor 5 not correctly measuring the acceleration of the subject itself (i.e., mismeasurement).

[0050] Taking the above into consideration, when the stress divergence behavior detection unit 18 according to this embodiment corresponds to such a passive state (i.e., a state where the stress divergence index SR is equal to or higher than the upper threshold value Th2), only the stress value is used, and if the stress value is equal to or lower than the threshold value "Th_s", it is determined that there is stress divergence behavior. The threshold value Th_s is set to an appropriate value pre-stored in, for example, the storage device 4 or the like. For example, in the case of a driver who is riding in a car, when the stress divergence index SR becomes equal to or higher than the upper threshold value Th2 due to a high momentum, the stress divergence behavior detection unit 18 determines that it is a passive state, and when the stress value of the subject is equal to or lower than the threshold value Th_s, it determines that there has been stress divergence behavior. The same applies to a person riding in the passenger seat or a person traveling by train.

[0051] FIG. 6 schematically visualizes the stress divergence index SR, the lower threshold value Th1, and the upper threshold value Th2 based on Equation (1) in a two-dimensional map with the stress value on the vertical axis and the momentum on the horizontal axis.

[0052] As shown in FIG. 6, the stress divergence index SR decreases as the stress value increases and the momentum decreases, and increases as the stress value decreases and the momentum increases. In this embodiment, the stress divergence behavior detection unit 18 determines that there is a stress divergence behavior when the stress divergence index SR is equal to or greater than a lower threshold Th1 and less than an upper threshold Th2. In this case, the value range recognized as the stress divergence behavior determined by the lower threshold Th1 and the upper threshold Th2 is the value range to which the "relaxed state" and the "relaxed state" shown in FIG. 5 belong. Thereby, the stress divergence behavior detection unit 18 can suitably detect the stress divergence behavior corresponding to the "relaxed state" or the "relaxed state".

[0053] According to the method for detecting stress divergence behavior using the stress divergence index SR described above, the stress divergence behavior detection unit 18 can suitably detect whether stress has been dissipated by stress divergence behaviors such as music listening and exercise in daily life. In addition, the stress divergence behavior detection unit 18 can also perform stress divergence detection in stress divergence methods (such as exercise and karaoke) that can also lead to stress divergence with physical activity and an excited state.

[0054] (3-3) Index calculation using attribute information and life information The stress divergence behavior detection unit 18 may determine the stress divergence index SR based on at least one of the attribute information and the life information of the subject.

[0055] First, the case of using attribute information will be described. The stress divergence behavior detection unit 18 switches, for example, an index calculation model used for calculating the stress divergence index SR based on the attribute information of the subject. For example, when the attribute information includes information regarding the preference for exercise, the stress divergence behavior detection unit 18 considers that an increase in momentum is particularly effective for stress divergence when the subject likes exercise. Therefore, in this case, the stress divergence behavior detection unit 18 calculates the stress divergence index SR by the following formula (2) (M = momentum, S = stress estimated value) using a correction coefficient α greater than 1 as the exponential coefficient of momentum. SR = M α / S (2)

[0056] On the other hand, when the attribute information indicates that the subject dislikes exercise, the stress divergence behavior detection unit 18 regards an increase in the amount of exercise as less effective for stress divergence, and calculates the stress divergence index SR by the above formula (2) in which the correction coefficient α is set to a value smaller than 1. By doing so, in the case of a subject who likes exercise, an increase in the amount of exercise can be easily reflected in the increase in the stress divergence index SR, and in the case of a subject who dislikes exercise, an increase in the amount of exercise can be made less likely to lead to an increase in the stress divergence index SR.

[0057] Similarly, even when the attribute information indicates other attributes such as age or gender, the stress divergence behavior detection unit 18 prepares an index calculation model capable of calculating an appropriate stress divergence index SR for each category of the attribute (for example, a category for each age group, a category of male or female), and switches the index calculation model according to the attribute of the subject. Note that the parameters (the value of α in formula (2)) for configuring each index calculation model are associated with each category of the attribute to be segmented and stored in advance in the storage device 4 or the like. Thereby, the stress divergence behavior detection unit 18 can calculate the stress divergence index SR more accurately in consideration of the attribute of the subject.

[0058] Next, the case of using life information will be described. The stress divergence behavior detection unit 18 switches, for example, an index calculation model used for calculating the stress divergence index SR based on the life information of the target person. For example, when the life information is schedule information indicating whether the target person is at work or not, the stress divergence behavior detection unit 18 recognizes whether the target person is at work at the date and time of determination of the presence or absence of stress divergence behavior, and calculates the stress divergence index SR using the index calculation model selected according to the recognition result. In this case, an index calculation model used for determining the stress divergence behavior during the period when the target person is at work and an index calculation model used for determining the stress divergence behavior during the period when the target person is not at work are each prepared in advance. Similarly, for other life information, an index calculation model is prepared in advance for each life pattern specified by the life information to be referred to. The stress divergence behavior detection unit 18 selects an index calculation model corresponding to the life pattern of the target person in the target period specified by referring to the life information of the target person, and calculates the stress divergence index SR.

[0059] Note that when the target person is at work, since the noise during work (for example, the sound of tools or heavy machinery, etc.) also affects the presence or absence of stress divergence, the stress divergence behavior detection unit 18 may further refer to information indicating the noise level at the workplace in the calculation of the stress divergence index SR used for determining the stress divergence behavior during the period when the target person is at work. Such a noise level, etc. may be specified, for example, by the sensor signal S3 output by the sensor 5 such as a noise sensor. Further, the stress divergence behavior detection unit 18 refers to the occupation information included in the attribute information. When the target person is engaged in a job mainly involving physical movement such as a sports player, the stress divergence behavior detection unit 18 may regard the working hours as outside the determination target period of stress divergence behavior, and it may not be necessary to calculate the stress divergence index SR of the target person during work.

[0060] In addition, when information on the daily exercise amount, which is the average exercise amount that the subject performs daily, is further included in the lifestyle information, the stress dissipation behavior detection unit 18 may normalize the exercise amount calculated by the exercise amount calculation unit 16 using the daily exercise amount. In this case, for example, the stress dissipation behavior detection unit 18 divides the exercise amount (calculated exercise amount) calculated by the exercise amount calculation unit 16 by the ratio of the daily exercise amount to the average exercise amount of the general population (i.e., daily exercise amount / average exercise amount) to obtain the exercise amount (i.e., calculated exercise amount × average exercise amount / daily exercise amount), and calculates it as the normalized exercise amount. Then, the stress dissipation behavior detection unit 18 can calculate the stress dissipation index SR that preferably takes into account the differences in the daily exercise amounts of individuals by calculating the stress dissipation index SR using the exercise amount normalized in this way.

[0061] In addition to or instead of the process of normalizing the exercise amount calculated by the exercise amount calculation unit 16 based on the daily exercise amount, the stress dissipation behavior detection unit 18 may change at least one of the lower threshold Th1 or the upper threshold Th2 based on the daily exercise amount. In this case, for example, the stress dissipation behavior detection unit 18 increases the lower threshold Th1 as the daily exercise amount increases. Thereby, the stress dissipation behavior detection unit 18 can accurately determine the presence or absence of stress dissipation behavior even for those with a high daily exercise amount, such as athletes or manual laborers.

[0062] (3-4) Notification example FIG. 7 is an example of a stress dissipation confirmation screen that the notification unit 19 causes the output device 3 to display. Here, as an example, the notification unit 19 provides an index graph display area 51 representing the transition of the stress dissipation index SR of the subject on the date specified by the user (here, today) and a text information display area 52 representing a text passage regarding the time period in which stress dissipation behavior was detected on the stress dissipation confirmation screen. The notification unit 19 generates an output control signal S2 for displaying the stress dissipation confirmation screen and supplies the output control signal S2 to the output device 3 via the interface 13, thereby causing the output device 3 to display the stress dissipation confirmation screen.

[0063] In the example of Fig. 7, the exercise amount calculation unit 16 and the stress estimation unit 17 calculate the exercise amount and stress estimated value in a time series based on the observation information generated in a time series on the target day. The exercise amount calculation unit 16 and the stress estimation unit 17 may sequentially calculate the exercise amount and stress estimated value from the observation information every time the observation information acquisition unit 15 acquires the observation information, or may collectively calculate the exercise amount and stress estimated value from the target observation information when a display request for a stress release confirmation screen is made. Then, the stress release behavior detection unit 18 calculates the stress release index SR for the time series of the target day from the calculated exercise amount and stress estimated value for the time series of the target day.

[0064] The notification unit 19 displays a transition graph of the stress release index SR of the subject on a two-dimensional coordinate system with time on the horizontal axis and the stress release index SR on the vertical axis in the index graph display area 51. Moreover, the notification unit 19 indicates the range determined as a stress release behavior on the index graph display area 51 by indicating the lower limit threshold Th1 and the upper limit threshold Th2 in the above-mentioned graph with dashed dotted lines.

[0065] Furthermore, based on the detection result of the stress-relieving behavior detection unit 18, the notification unit 19 recognizes the time periods when the stress-relieving behavior was detected (around 14:00 and 19:00 in this case) and the time period during which the stress-relieving behavior is a diversion-type behavior (around 14:00 in this case) and displays information about these time periods in the text information display area 52. As shown in FIG. 5, the stress value and the amount of exercise differ between the diversion state and the relaxed state. Therefore, the stress-relieving behavior detection unit 18 or the notification unit 19 determines that a diversion-type behavior has been performed, for example, when a stress-relieving behavior is detected and at least one of the corresponding amount of exercise or the estimated stress value is equal to or greater than a predetermined threshold. The above-mentioned threshold is, for example, stored in advance in the storage device 4.

[0066] As described above, according to the display example of FIG. 7, the notification unit 19 can preferably notify the subject or the administrator of the timing when the stress divergence behavior is performed, the timing when there is a stress-relieving behavior, and the like. Then, the subject or the administrator can utilize the notified information for stress management of the subject and maintaining motivation for continuous stress management. Further, when the subject is an employee, the administrator can also use it for mental health management of employees in the workplace, work assignment, and the like.

[0067] Note that the mode of notification of the stress divergence behavior is not limited to the display example shown in FIG. 7. In the first example, the notification unit 19 may output the detection result of the stress divergence behavior of the subject on a weekly or monthly basis (i.e., in an arbitrary predetermined period specified by the user). In this case, for example, the notification unit 19 may output information regarding the statistical tendency of the time zone in which the stress divergence behavior (which may be a stress-relieving behavior, the same applies hereinafter) occurs. In another example, the notification unit 19 may output information regarding the statistical tendency regarding the type of stress divergence behavior (stress-relieving type, relaxation type). Thereby, the notification unit 19 can preferably notify the user of what kind of divergence behavior the subject is likely to perform (for example, whether there are many stress-relieving type behaviors or many relaxation type behaviors).

[0068] In the second example, the notification unit 19 may output the detection result of the stress divergence behavior or the like corresponding to the current state of the subject in real time. In this case, the information processing apparatus 1 determines the presence or absence of the stress divergence behavior using the observation information based on the sensor signal S3 obtained in real time, and immediately performs an output for notifying that the stress divergence behavior has been performed by the output apparatus 3 when the stress divergence behavior is detected. In this case, the momentum calculation unit 16 and the stress estimation unit 17 calculate the momentum and the stress estimation value with respect to the observation information representing the current state of the subject acquired by the observation information acquisition unit 15, and the stress divergence behavior detection unit 18 calculates the stress divergence index SR and determines the stress divergence behavior based on these momentum and stress estimation values. According to the second example, the notification unit 19 can preferably notify the subject of the occurrence of the stress divergence behavior and encourage the behavior leading to stress relief.

[0069] In the third example, the notification unit 19 may output the detection result of the stress divergence behavior or the like at any time after it is determined that there is a stress divergence behavior. In the fourth example, instead of notifying the user of the degree of stress divergence as in the example of FIG. 7, the notification unit 19 may notify the user of the presence or absence of the stress divergence behavior. In this case, the notification unit 19 may notify that there is a stress divergence behavior by sound (including voice), or may notify that there is a stress divergence behavior by display.

[0070] (3-5) Processing flow FIG. 8 is an example of a flowchart executed by the information processing apparatus 1 in the first embodiment. The information processing apparatus 1 executes the processing of the flowchart shown in FIG. 8, for example, when it is determined that a predetermined stress estimation timing has arrived.

[0071] First, the information processing apparatus 1 acquires observation information based on the input signal S1 supplied from the input device 2 or / and the sensor signal S3 supplied from the sensor 5, and stores the acquired observation information in the observation information storage unit 40 (step S11).

[0072] Then, the information processing apparatus 1 determines whether it is the detection timing of the stress divergence behavior (step S12). If the information processing apparatus 1 determines that it is the detection timing of the stress divergence behavior (step S12; Yes), the process proceeds to step S13. On the other hand, if the information processing apparatus 1 determines that it is not the detection timing of the stress divergence behavior (step S12; No), the process returns to step S11.

[0073] Next, in step S13, the information processing apparatus 1 calculates the amount of movement and the stress estimation value of the target person based on the observation information during the target period for detecting stress divergence behavior (step S13). Then, the information processing apparatus 1 calculates a stress divergence index SR based on the amount of movement and the stress estimation value calculated in step S13 (step S14). Then, the information processing apparatus 1 performs a detection process for the stress divergence behavior of the target person based on the calculated stress divergence index SR (step S15). In this case, for example, the information processing apparatus 1 determines the presence or absence of stress divergence behavior based on the stress divergence index SR, the lower threshold Th1, and the upper threshold Th2. Then, the information processing apparatus 1 performs a process of notifying the detection result of the stress divergence behavior in step S15 (step S16).

[0074] (4) Modification example Next, a modification of the above-described embodiment will be described. The following modifications may be arbitrarily combined and applied to the above-described embodiment.

[0075] (Modification 1) The stress divergence behavior detection unit 18 may perform detection processing specialized for stress relief-type behaviors among stress divergence behaviors.

[0076] In this case, when the stress divergence behavior detection unit 18 determines that the stress divergence behavior is detected and at least one of the corresponding amount of movement or stress estimation value is equal to or greater than a predetermined threshold, it determines that a stress relief-type behavior has been performed. Then, the notification unit 19 performs a process of notifying the detection result of the stress relief-type behavior based on the detection result by the stress divergence behavior detection unit 18. Thereby, the information processing apparatus 1 can preferably notify the target person or the administrator of the timing when there was a stress relief-type behavior and the like.

[0077] (Modification 2) When determining the presence or absence of stress divergence behavior, the stress divergence behavior detection unit 18 may further consider the stress value after a predetermined time has elapsed in addition to the stress divergence index SR and its threshold value to determine the presence or absence of stress divergence behavior.

[0078] For example, in the example of FIG. 7, the case of determining the presence or absence of stress divergence behavior at 13:00 will be specifically described. In this case, when the stress divergence behavior detection unit 18 determines that the stress divergence index SR at 13:00 is equal to or greater than the lower threshold Th1 and less than the upper threshold Th2, and the stress estimated value after a predetermined time (for example, several minutes or several tens of minutes) has elapsed since 13:00 has decreased by a predetermined value or a predetermined rate or more compared to the stress estimated value at 13:00, it is determined that there was stress divergence behavior at 13:00. The above-mentioned predetermined value or predetermined rate is stored in advance in the storage device 4 or the like, for example.

[0079] According to this modification example, the stress divergence behavior detection unit 18 can more accurately determine the presence or absence of stress divergence behavior by considering the change in the actual stress value after the timing for which the presence or absence of stress divergence behavior is to be determined.

[0080] (Modification Example 3) The stress divergence behavior detection unit 18 may determine that there was stress divergence behavior when the criteria regarding the stress divergence index SR are continuously satisfied.

[0081] In this case, for example, the stress dissipation behavior detection unit 18 aggregates the determination results of the presence or absence of stress dissipation behavior based on the stress dissipation index SR calculated at each calculation timing for each period (time window) of a predetermined time length, and determines the presence or absence of stress dissipation behavior for each time window based on the aggregation result. For example, when the time window is 10 minutes and the stress dissipation index SR is calculated every 30 seconds, the stress dissipation behavior detection unit 18 determines whether each of the 20 stress dissipation indices SR calculated in the target time window satisfies the criteria for stress dissipation behavior (i.e., the criteria using the lower threshold Th1 and the upper threshold Th2). Then, when a predetermined number or more (for example, more than half) of the 20 stress dissipation indices SR satisfy the criteria for stress dissipation behavior, the stress dissipation behavior detection unit 18 determines that there is stress dissipation behavior in the target time window. Note that the stress dissipation behavior detection unit 18 determines whether each of the stress dissipation indices SR calculated in the target time window satisfies the criteria for stress dissipation behavior (i.e., the criteria using the lower threshold Th1 and the upper threshold Th2), and when the duration for which the criteria for stress dissipation behavior are satisfied exceeds a predetermined determination duration, determines that the stress dissipation behavior is continuing. In this case, for example, when the stress dissipation behavior is continuously detected for 5 minutes, the stress dissipation behavior detection unit 18 determines that the stress dissipation behavior is continuing.

[0082] According to this modification example, the information processing apparatus 1 can more stably detect the presence or absence of stress dissipation behavior.

[0083] (Modification Example 4) The stress dissipation behavior detection unit 18 may detect the stress dissipation behavior by further considering the temporal change of the stress dissipation index SR.

[0084] When the stress divergence behavior detection unit 18 meets the criteria regarding the magnitude of the stress divergence index SR (i.e., the criteria using the lower threshold Th1 and the upper threshold Th2), or / and when the rate of increase of the stress divergence index SR (the increase ratio with respect to the stress divergence index SR calculated immediately before) is equal to or higher than a predetermined rate, it determines that the stress divergence behavior has started. Then, when the criteria regarding the magnitude of the stress divergence index SR (i.e., the criteria using the lower threshold Th1 and the upper threshold Th2) are no longer met, or / and when the rate of decrease of the stress divergence index SR (the decrease ratio with respect to the stress divergence index SR calculated immediately before) is equal to or higher than a predetermined rate, the stress divergence behavior detection unit 18 determines that the stress divergence behavior has ended.

[0085] According to this modification example, the stress divergence behavior detection unit 18 can accurately detect the period during which the stress divergence behavior is performed based on the change in the stress divergence index SR.

[0086] (Modification Example 5) Instead of determining the stress divergence index SR based on the ratio of the momentum and the stress estimation value as in Formula (1) or Formula (2), the stress divergence behavior detection unit 18 may determine it based on the subtraction process using the stress estimation value and the momentum. For example, the stress divergence behavior detection unit 18 normalizes the stress estimation value and the momentum so that they are in the value range from 0 to 1, then multiplies at least one of the stress estimation value or the momentum by a predetermined weight, and after normalization and multiplication of the weight, calculates the value obtained by subtracting the stress estimation value from the momentum as the stress divergence index SR. In this way, the stress divergence index SR may be determined by various calculation methods using the stress estimation value and the momentum.

[0087] (Modification Example 6) The information processing apparatus 1 may detect the stress divergence behavior without relying on the momentum and notify the detection result to the subject or the administrator.

[0088] In this case, when the stress estimation value calculated by the stress estimation unit 17 is equal to or less than a predetermined threshold value, or when the rate of decrease of the stress estimation value is equal to or more than a predetermined rate, etc., the information processing apparatus 1 determines that there has been a stress divergence behavior, and notifies the subject or the administrator of the detection result of the stress divergence behavior. Also by this, the information processing apparatus 1 can suitably provide the subject or the administrator with information that can be used for stress management of the subject and maintaining motivation for continuous stress management.

[0089] <Second Embodiment> FIG. 9 shows a schematic configuration of a stress divergence detection system 100A according to the second embodiment. The stress divergence detection system 100A according to the second embodiment is a server-client model system, and an information processing apparatus 1A that functions as a server apparatus performs the processing of the information processing apparatus 1 in the first embodiment. Hereinafter, for the same components as those in the first embodiment, the same reference numerals are appropriately given, and the description thereof is omitted.

[0090] As shown in FIG. 9, the stress divergence detection system 100A mainly includes an information processing apparatus 1A that functions as a server, a storage device 4, and a terminal device 8 that functions as a client. The information processing apparatus 1A and the terminal device 8 perform data communication via a network 7.

[0091] The terminal device 8 is a terminal used by a user who is a subject, and has an input function, a display function, and a communication function, and functions as the input device 2 and the output device 3 shown in FIG. 1. The terminal device 8 may be, for example, a personal computer, a tablet-type terminal such as a smartphone, a PDA (Personal Digital Assistant), or the like. The terminal device 8 is electrically connected to a sensor 5 such as a wearable sensor worn by the user, and transmits biometric signals of the subject output by the sensor 5 (that is, information corresponding to the sensor signal S3 in FIG. 1) to the information processing apparatus 1A. Further, the terminal device 8 receives user input regarding the answer to the questionnaire and transmits information generated by the user input (information corresponding to the input signal S1 in FIG. 1) to the information processing apparatus 1A.

[0092] The information processing apparatus 1A has the same hardware configuration as that of the information processing apparatus 1 shown in FIG. 2, and the processor 11 of the information processing apparatus 1A has the functional blocks shown in FIG. 3. Then, the information processing apparatus 1A receives information corresponding to the input signal S1 and the sensor signal S3 in FIG. 1 from the terminal device 8 via the network 7, and executes stress estimation processing. Further, the information processing apparatus 1A transmits an output signal for outputting a stress estimation result to the terminal device 8 via the network 7 based on an output request from the terminal device 8.

[0093] According to the second embodiment, it is possible to detect the stress divergence behavior of the subject based on the biological signal or the like of the subject received from the terminal used by the subject, and to preferably notify the subject of the detection result.

[0094] <Third Embodiment> FIG. 10 is a block diagram of the information processing apparatus 1X in the third embodiment. The information processing apparatus 1X mainly includes a stress value acquisition means 17X, a stress divergence behavior detection means 18X, and a notification means 19X. Note that the information processing apparatus 1X may be configured by a plurality of devices.

[0095] The stress value acquisition means 17X acquires a stress value representing the degree of stress of the subject. The stress value acquisition means 17X may acquire the stress value by estimating the stress value from the biological signal or the like of the subject, or may acquire the stress value of the subject stored in a storage device or the like or calculated by another device. In the former case, the stress value acquisition means 17X can be, for example, the stress estimation unit 17 in the first embodiment (including modifications, the same applies hereinafter) or the second embodiment.

[0096] The stress divergence behavior detection means 18X detects a stress divergence behavior, which is a behavior for diverging stress, based on the stress value. The stress divergence behavior detection means 18X can be, for example, the stress divergence behavior detection unit 18 in the first embodiment or the second embodiment.

[0097] The notification means 19X notifies the result of the detection of the stress divergence behavior. In this case, the notification means 19X may notify the detection result of the stress divergence behavior by sound (including voice), or may notify the detection result of the stress divergence behavior by display. The notification means 19X can be, for example, the notification unit 19 in the first embodiment or the second embodiment.

[0098] FIG. 11 is an example of a flowchart executed by the information processing apparatus 1X in the third embodiment. First, the stress value acquisition means 17X acquires a stress value representing the degree of stress of the subject (step S21). Next, the stress divergence behavior detection means 18X detects a stress divergence behavior, which is a behavior for diverging stress, based on the stress value (step S22). The notification means 19X notifies the result of the detection of the stress divergence behavior (step S23).

[0099] According to the third embodiment, the information processing apparatus 1X detects the stress divergence behavior and notifies the user of the detection result. Thereby, it is possible to suitably provide information that can be used for stress management of the subject and maintaining motivation for continuous stress management.

[0100] In addition, in each of the above-described embodiments, the program can be stored using various types of non-transitory computer readable media and supplied to a processor or the like which is a computer. The non-transitory computer readable media include various types of tangible storage media. Examples of the non-transitory computer readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memories (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)). Further, the program may be supplied to the computer by various types of transitory computer readable media. Examples of the transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to the computer via a wired communication path such as electric wires and optical fibers, or a wireless communication path.

[0101] In addition, some or all of the above embodiments may be described as follows in the appended claims, but are not limited thereto.

[0102] [Appended Claim 1] A stress value acquisition means for acquiring a stress value representing the degree of stress of a target person; A stress divergence behavior detection means for detecting a stress divergence behavior which is a behavior for diverging the stress based on the stress value; A notification means for notifying a result of the detection of the stress divergence behavior; An information processing apparatus having the above. [Appended Claim 2] Further having a momentum acquisition means for acquiring the momentum of the target person corresponding to the stress value; The stress divergence behavior detection means detects the stress divergence behavior based on the stress value and the momentum, according to the information processing apparatus described in Supplementary Note 1. [Supplementary Note 3] The information processing apparatus further includes a life information acquisition means for acquiring life information regarding the life of the subject. The stress divergence behavior detection means detects the stress divergence behavior based on the stress value and the life information, according to the information processing apparatus described in Supplementary Note 1 or 2. [Supplementary Note 4] The life information indicates at least the daily exercise amount of the subject. The stress divergence behavior detection means detects the stress divergence behavior based on the momentum obtained by normalizing the momentum of the subject corresponding to the stress value by the daily exercise amount and the stress value, according to the information processing apparatus described in Supplementary Note 3. [Supplementary Note 5] The stress divergence behavior detection means detects the stress divergence behavior based on an index having a positive correlation with the stress value and a negative correlation with the momentum of the subject corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the momentum, according to the information processing apparatus described in any one of Supplementary Notes 1 to 4. [Supplementary Note 6] The stress divergence behavior detection means detects the stress divergence behavior based on the index, a lower limit threshold value and an upper limit threshold value for the index, according to the information processing apparatus described in Supplementary Note 5. [Supplementary Note 7] The stress divergence behavior detection means detects the stress divergence behavior based on the index and the stress value after a lapse of a predetermined time, according to the information processing apparatus described in Supplementary Note 5 or 6. [Supplementary Note 8] The stress divergence behavior detection means detects the stress divergence behavior based on a change over time of the index, according to the information processing apparatus described in Supplementary Note 5 or 6. [Supplementary Note 9] When the stress divergence behavior detection means determines, based on the index, that the subject is in a passive state in which the amount of movement has increased due to passive activities, the stress divergence behavior is detected based on the stress value. The information processing apparatus according to any one of Appendices 5 to 8. [Appendix 10] Further comprising attribute information acquisition means for acquiring attribute information regarding the attributes of the subject, The stress divergence behavior detection means detects the stress divergence behavior based on the stress value and the attribute information. The information processing apparatus according to any one of Appendices 1 to 9. [Appendix 11] The attribute information includes at least information regarding the subject's preference for movement, The stress divergence behavior detection means detects the stress divergence behavior based on the amount of movement of the subject corresponding to the stress value, the information regarding the preference, and the stress value. The information processing apparatus according to Appendix 10. [Appendix 12] The stress divergence behavior detection means detects a stress divergence behavior of the stress relief type among the stress divergence behaviors, The notification means performs a notification regarding the result of the detection of the stress divergence behavior of the stress relief type. The information processing apparatus according to any one of Appendices 1 to 11. [Appendix 13] A computer, Obtains a stress value representing the degree of stress of the subject, Based on the stress value, detects a stress divergence behavior, which is a behavior for diverging the stress, A control method for performing a notification regarding the result of the detection of the stress divergence behavior. [Appendix 14] Obtains a stress value representing the degree of stress of the subject, Based on the stress value, detects a stress divergence behavior, which is a behavior for diverging the stress, A storage medium storing a program for causing a computer to execute a process of performing a notification regarding the result of the detection of the stress divergence behavior.

[0103] The present invention has been described with reference to the embodiments above, but the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. That is, the present invention naturally includes various variations and modifications that those skilled in the art could make in accordance with the entire disclosure including the claims and the technical idea. In addition, each disclosure of the above-cited patent documents and the like is incorporated herein by reference.

Explanation of Reference Numerals

[0104] 1, 1A, 1X Information processing apparatus 2 Input device 3 Output device 4 Storage device 5 Sensor 8 Terminal device 100, 100A Stress divergence detection system

Claims

1. Stress value acquisition means for acquiring a stress value representing the degree of stress of a target person; Momentum acquisition means for acquiring the momentum of the target person corresponding to the stress value; Based on an index having a positive correlation with the stress value and a negative correlation with the momentum of the target person corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the momentum, stress divergence behavior detection means for detecting stress divergence behavior, which is behavior for diverging stress; Notification means for notifying the result of detection of the stress divergence behavior; An information processing apparatus having the above.

2. Further having life information acquisition means for acquiring life information regarding the life of the target person; The stress divergence behavior detection means detects the stress divergence behavior based on the index based on the life information, the information processing apparatus according to claim 1.

3. The life information at least indicates the daily exercise amount of the target person; The stress divergence behavior detection means detects the stress divergence behavior based on the index based on the momentum obtained by normalizing the momentum of the target person corresponding to the stress value by the daily exercise amount, the information processing apparatus according to claim 2.

4. The stress divergence behavior detection means detects the stress divergence behavior based on the index and a lower limit threshold value and an upper limit threshold value for the index, the information processing apparatus according to any one of claims 1 to 3.

5. The stress divergence behavior detection means detects the stress divergence behavior based on the index and the stress value after a lapse of a predetermined time, the information processing apparatus according to any one of claims 1 to 4.

6. The stress divergence behavior detection means detects the stress divergence behavior based on the time change of the index, the information processing apparatus according to any one of claims 1 to 5.

7. When the stress divergence behavior detection means determines, based on the index, that the target person is in a passive state in which the momentum has increased due to a passive activity, the stress divergence behavior detection means detects the stress divergence behavior based on the stress value, the information processing apparatus according to any one of claims 1 to 6.

8. Further having attribute information acquisition means for acquiring attribute information regarding the attributes of the target person; The stress divergence behavior detection means detects the stress divergence behavior based on the index based on the attribute information, the information processing apparatus according to any one of claims 1 to 7.

9. The attribute information includes at least information regarding the preferences of the target person's exercise. The stress relief behavior detection means detects the stress relief behavior based on the index based on the information regarding the preferences, for the information processing apparatus according to claim 8.

10. The stress relief behavior detection means detects a stress relief behavior of a catharsis type among the stress relief behaviors. The notification means gives a notification regarding the result of the detection of the stress relief behavior of the catharsis type, for the information processing apparatus according to any one of claims 1 to 9.

11. A computer acquires a stress value representing the degree of stress of a target person, acquires the amount of exercise of the target person corresponding to the stress value, detects a stress relief behavior, which is a behavior for relieving the stress, based on an index having a positive correlation with the stress value and a negative correlation with the amount of exercise of the target person corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the amount of exercise, and gives a notification regarding the result of the detection of the stress relief behavior, a control method.

12. acquires a stress value representing the degree of stress of a target person, acquires the amount of exercise of the target person corresponding to the stress value, detects a stress relief behavior, which is a behavior for relieving the stress, based on an index having a positive correlation with the stress value and a negative correlation with the amount of exercise of the target person corresponding to the stress value, or an index having a negative correlation with the stress value and a positive correlation with the amount of exercise, and a program for causing a computer to execute a process of giving a notification regarding the result of the detection of the stress relief behavior.

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