Stress detection device, stress detection method, and stress detection program

Abstract

To detect stress related to mental health defense and evaluate a stress state.SOLUTION: A stress detection device includes: a heartbeat acquisition unit which acquires heartbeat information of a subject; a factor calculation unit which calculates heartbeat fluctuation factors from the heartbeat information; a first change detection unit which detects a change in the sympathetic nervous system of the subject on the basis of the heartbeat fluctuation factors; a first change detection unit which detects a change in the parasympathetic nervous system of the subject on the basis of the heartbeat fluctuation factors; a second change detection unit which detects a change in the parasympathetic nervous system of the subject on the basis of the heartbeat fluctuation factors; a stress detection unit which detects presence or absence of a stress state in the subject on the basis of the time between the detection of the change in the sympathetic nervous system and the detection of the change in the parasympathetic nervous system; an external stimulus acquisition unit which acquires an external stimulus applied to the subject; and a stimulus identification unit which identifies a stress source that gives the stress state to the subject from the external stimuli acquired by the external stimulus acquisition unit on the basis of timing at which the change in the sympathetic nervous system is detected in a case where the stress state is detected.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments relate to a stress detection device, a stress detection method, and a stress detection program. [Background technology]

[0002] Generally, mental health problems such as depression are caused by prolonged exposure to intense stress. Therefore, if we can continuously monitor psychological states and understand the state and causes of stress, there is a high probability that we can take preventative measures against mental health problems.

[0003] One known method for understanding a person's stress state is to detect stress using LF and HF, which are heart rate variability factors calculated from the subject's heart rate. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Patent No. 6550440 [Overview of the project] [Problems that the invention aims to solve]

[0005] Conventional stress detection methods can assess the presence and degree of stress at the time of measurement. However, these methods make it difficult to evaluate aspects related to protecting mental health, such as identifying stressors and assessing their impact.

[0006] The embodiment provides a stress detection device, a stress detection method, and a stress detection program that can detect stress related to mental health protection and evaluate the stress state. [Means for solving the problem]

[0007] The stress detection device includes a heartbeat acquisition unit, a factor calculation unit, a first change detection unit, a second change detection unit, a stress detection unit, an external stimulus acquisition unit, and a stimulus identification unit. The heartbeat acquisition unit acquires the heartbeat information of the subject. The factor calculation unit calculates from the heartbeat information The first heart rate variability factor, which is related to sympathetic nervous system activity, and the second heart rate variability factor, which is related to parasympathetic nervous system activity. to calculate. The first change detection unit First Based on the heartbeat fluctuation factor Time changes Detects the activity of the sympathetic nerve of the subject peak . The second change detection unit Second Based on the heartbeat fluctuation factor Time changes Detects the activity of the parasympathetic nerve of the subject peak . The stress detection unit determines that the time from when the activity of the sympathetic nerve Peak of activity is detected until the activity of the parasympathetic nerve peak is detected is Based on being longer than the threshold the stress state of the subject of to detect. The external stimulus acquisition unit Over time, acquires the external stimulus for the subject. The stimulus identification unit, when the stress state is detected, determines the timing Peak of activity when the activity of the sympathetic nerve The external stimulus detected immediately before that, identifies the stress source that gave the stress state as .

Brief Description of Drawings

[0008] [Figure 1] FIG. 1 is a diagram showing the configuration of an example of the stress detection device according to the embodiment. [Figure 2] FIG. 2 is a diagram showing a general electrocardiogram waveform. [Figure 3A] FIG. 3A is a diagram showing RRI data as heartbeat interval data. [Figure 3B] FIG. 3B is a diagram showing the interpolated RRI data. [Figure 3C] FIG. 3C is a diagram showing LF, RF, and LF / HF. [Figure 4] FIG. 4 is a diagram showing the autonomic nerve activity caused by external stimuli based on the autonomic nerve origin model of emotion y. [Figure 5]Figure 5 shows the power SN of sympathetic nervous system activity, the power PSN1 of parasympathetic nervous system activity, and the power PSN2 of parasympathetic nervous system activity, each colored separately. [Figure 6] Figure 6 shows an example of the hardware configuration of a stress detection device. [Figure 7] Figure 7 is a flowchart showing the operation of the stress detection device. [Figure 8] Figure 8 shows the time-dependent changes in RRI, HF, LF, and LF / HF as experimental results. [Modes for carrying out the invention]

[0009] Whether or not a person experiences stress when exposed to a stressor varies from person to person. Furthermore, the impact of a stressor on an individual also differs. To understand the stressors and the degree of their impact on such a non-generalizable subject is equivalent to providing a general answer to the question, "What is stress?"

[0010] The technology described in the embodiment is one solution derived from the discovery of the mechanism by which human emotions are caused by autonomic nervous system activity, and the subsequent conclusion that emotions are psychological phenotypes that occur when the sympathetic nervous system is activated. As a result, the stress detection device according to the embodiment can identify the source of stress when a subject is detected to be under stress, and can also calculate the degree of impact that stress source has on the individual. By understanding the source of stress and its degree of impact, the stress detection device according to the embodiment can contribute to protecting people from stress disorders.

[0011] Embodiments will be described below with reference to the drawings. Figure 1 is a diagram showing the configuration of an example of a stress detection device according to the embodiment. The stress detection device 1 is a computer having a heart rate acquisition unit 11, a factor calculation unit 12, a first change detection unit 13, a second change detection unit 14, a stress detection unit 15, an external stimulus acquisition unit 16, a stimulus identification unit 17, and a control unit 18.

[0012] The heart rate acquisition unit 11 acquires information about the subject's heart rate. This heart rate information may be heart rate interval data. Figure 2 shows a typical electrocardiogram waveform. Normally, a P wave is generated by the firing of the sinoatrial node, which is the timekeeper of the heart rate, followed by a composite wave of Q, R, and S waves representing the excitation process from the atria to the ventricles. Heart rate interval data is, for example, the time interval between R waves (RR intervals: RRI). The heart rate acquisition unit 11 acquires data on the interval between R waves as heart rate interval data from electrocardiogram data acquired from, for example, an electrocardiograph. The heartbeat is constantly fluctuating. And the fluctuations in the heartbeat reflect the activity of the autonomic nervous system. The activity of the autonomic nervous system is influenced not only by external stimuli but also by various psychological factors such as internal factors.

[0013] The factor calculation unit 12 calculates heart rate variability factors from heart rate interval data. In this embodiment, the factor calculation unit 12 calculates LF (power of low frequency), which is the low-frequency component of the frequency domain of the heart rate interval data, HF (power of high frequency), which is the high-frequency component, and LF / HF, which is the quotient of LF and HF, as heart rate variability factors. LF is said to represent the total activity of sympathetic and parasympathetic nerve activity. HF is said to represent parasympathetic nerve activity. And LF / HF is said to represent the activity of sympathetic nerve activity only.

[0014] An example of calculating heart rate variability factors by the factor calculation unit 12 is described below. For example, suppose the heart rate acquisition unit 11 obtains RRI data as heart rate interval data as shown in Figure 3A. As mentioned above, since the heart rate is constantly fluctuating, the RRI value does not take a constant value. RRI data is converted into a frequency spectrum by Fourier transform (Fast Fourier Transform), but usually the number of measurement points in the RRI data is insufficient to perform a high-precision FFT. Therefore, as shown in Figure 3B, the factor calculation unit 12 increases the apparent number of data points by linearly interpolating the RRI data. Then, the factor calculation unit 12 performs a Fourier transform on the linearly interpolated RRI data to calculate the power spectral density of the RRI data in the frequency space. Figure 3C shows the power spectral density of the RRI data. The sum of power in the low frequency region (0.04Hz-0.15Hz) of the power spectral density shown in Figure 3C is LF, and the sum of power in the high frequency region (0.15Hz-0.4Hz) is HF.

[0015] Here, the heart rate variability factors used in heart rate variability assessment include time-domain factors and frequency-domain factors. Known time-domain factors include SDNN (standard deviation of normal-to-normal intervals), RMSSD (root mean square of successive difference of RR intervals), and pNN50. In addition, known frequency-domain factors include LF, HF, LF / HF, and total power, which is the sum of power across all frequency domains. Of these factors, RMSSD, pNN50, and HF are indices representing parasympathetic nervous system activity. SDNN, total power, and LF are indices representing the combined activity of the sympathetic and parasympathetic nervous systems. LF / HF is considered an index representing the activity of the sympathetic nervous system alone. As will be explained later, in this embodiment, stress is detected by separately evaluating the activity of the sympathetic and parasympathetic nervous systems. Therefore, the factor calculation unit 12 calculates, for example, LF, HF, and LF / HF as heart rate variability factors, outputs LF / HF to the first change detection unit 13 as an indicator representing sympathetic nerve activity, and outputs HF to the second change detection unit 14 as an indicator representing parasympathetic nerve activity.

[0016] Generally, LF (Low Frequency) contains information on both sympathetic and parasympathetic nervous system activity. For this reason, LF / HF, obtained by dividing LF by HF (High Frequency), which represents the power of parasympathetic nervous system activity, is widely used as an indicator of sympathetic nervous system activity. Here, we also use this example to indicate sympathetic nervous system activity, but when considering the power of sympathetic nervous system activity, observing LF provides a quicker, more intuitive understanding. Numerically, LF is obtained by multiplying LF / HF by HF, so its numerical significance is limited, but using LF is useful for intuitively observing sympathetic nervous system activity.

[0017] Here, an example is shown in which the factor calculation unit 12 calculates LF, HF, and LF / HF. In this embodiment, heart rate variability factors other than LF, HF, and LF / HF that can evaluate the activity of the sympathetic and parasympathetic nervous systems may be used.

[0018] The first change detection unit 13 detects an increase in the subject's sympathetic nervous system activity from the time-dependent changes in a first heart rate variability factor representing sympathetic nervous system activity. For example, the first change detection unit 13 detects an increase in the subject's sympathetic nervous system activity by detecting when the time-series LF / HF reaches a peak value. The time-series LF / HF reaching a peak value can be detected, for example, by detecting when LF / HF increases above a rising threshold and then begins to decrease.

[0019] The second change detection unit 14 detects an increase in the subject's parasympathetic nervous system activity from the time-dependent changes in a second heart rate variability factor representing parasympathetic nervous system activity. For example, the second change detection unit 14 detects an increase in the subject's parasympathetic nervous system activity by detecting when the time-series HF reaches a peak value. The time-series HF reaching a peak value can be detected, for example, by detecting when HF increases above a rising threshold and then begins to decrease.

[0020] The stress detection unit 15 detects whether or not the subject is experiencing stress based on the comparison results from the first change detection unit 13 and the second change detection unit 14.

[0021] The principle of stress detection by the stress detection unit 15 will be explained below. First, the assessment of autonomic nervous system activity for stress assessment will be explained. The inventor has been studying emotions that are factors in human behavior and decision-making, and in the process, noticed the similarity between emotional responses and physical responses when the sympathetic nervous system is activated.

[0022] Emotions themselves are instinct-based self-preservation responses and always exhibit negative reactions. Taking the fear response of rats as an example, in response to stimuli that evoke fear, the rat's pupils dilate, heart rate increases, blood pressure rises, and its fur stands on end. The standing fur is a known threat behavior in many mammals. On the other hand, human sympathetic nervous system responses also include increased heart rate, vasoconstriction, and contraction of the arrector pili muscles. In other words, these human sympathetic nervous system responses are similar to the fear response of rats. The autonomic nervous system operates completely independently of human will and primarily functions as a means of maintaining homeostasis in the body, such as in the internal organs including the heart, respiratory system, digestive system, and kidneys. The autonomic nervous system is divided into the sympathetic and parasympathetic nervous systems. The sympathetic nervous system works to maintain and improve the motor functions necessary for avoiding danger, often referred to as "fight or flight." The purpose of the fear response of rats is also to avoid danger. From this, the inventor came to the conclusion that the fear emotional response and the sympathetic nervous system response might be exactly the same phenomenon.

[0023] External stimuli stimulate the sympathetic nervous system, while the parasympathetic nervous system stimulates the parasympathetic nervous system, suppressing sympathetic activity. This is the core of autonomic nervous system activity. Maintaining this balance (homeostasis) between the sympathetic and parasympathetic nervous systems is essential for the survival of living organisms.

[0024] Normally, the sympathetic nervous system is activated in response to environmental stimuli received through the sensory organs, but this is suppressed without particular problems by the activity of the parasympathetic nervous system, maintaining a low-stress state. However, in response to stimuli that deviate from the normal routine, the sympathetic nervous system remains excited. The parasympathetic nervous system is not activated until the danger of the situation disappears. This is a high-stress state. The similarity between such emotional responses and physical responses during sympathetic nervous system activation will be explained below as an autonomic nervous system-driven model of emotion.

[0025] In the autonomic nervous system-driven model of emotion, all stimuli received from the external environment are transmitted as emotions to the limbic system in the thalamus, stimulating the sympathetic nervous system. The body then automatically exhibits a response to this sympathetic nervous system activation. Whether consciously or unconsciously, stimuli from the external environment are constantly being input, and therefore, sympathetic nervous system activation is always occurring.

[0026] When a stimulus reaches the thalamus, it is simultaneously transmitted to the cortical pathway. In the cortex, the stimulus is analyzed. If the analysis determines that the stimulus is an ordinary, non-attentional one, the parasympathetic nervous system is activated and sympathetic nervous system activity is suppressed. For example, everyday stimuli such as ambient sounds and ambient light like lamps are considered ordinary stimuli.

[0027] On the other hand, in the case of unusual stimuli, the parasympathetic nervous system is not activated. Instead, the sympathetic nervous system remains activated to continue responding to the perceived danger. This state of being subjected to unusual stimuli is a stress state. Mental health problems such as depression are caused by prolonged exposure to intense stress.

[0028] Figure 4 is a diagram illustrating autonomic nervous system activity triggered by external stimuli, based on an autonomic nervous system-driven model of emotion. The horizontal axis in Figure 4 represents time, and the vertical axis represents the power of nervous system activity. The power is based on baseline B, which is, for example, the power value of a hypothetical subject's daily nervous system activity. Curve SN in Figure 4 shows the time evolution of the power value of sympathetic nervous system activity. On the other hand, curves PSN1 and PSN2 in Figure 4 show the time evolution of the power value of parasympathetic nervous system activity. Curves PSN1 and PSN2 show the parasympathetic nervous system's response to different unusual stimuli.

[0029] According to the autonomic nervous system-driven model of emotion, the sympathetic nervous system is always activated when stimulated by the external environment. This sympathetic activation is then suppressed by an increase in the parasympathetic nervous system.

[0030] For example, if a stimulus S is applied at time t0, the power SN of sympathetic nerve activity will be at time t SN At the peak value PSN Take the peak value P of the power SN of sympathetic nerve activity. SN This can vary depending on the intensity of the given stimulus. If the intensity of the stimulus S is a simple physical stimulus, it is proportional to that physical quantity. Also, the intensity of the stimulus S depends on the subject even for the same stimulus, and becomes stronger for stimuli that the subject dislikes. If the intensity of the stimulus is strong, the peak value P SN becomes higher. Therefore, the amount of work of sympathetic nerve activity, that is, the integrated value of the power SN of sympathetic nerve activity also becomes larger. On the other hand, in the case of sympathetic nerve activity, the peak time Δt 0-S = t SN - t0 becomes a value according to the unique reaction time (latency) of the sensory organ independent of the stimulus.

[0031] On the other hand, since the parasympathetic nerve is not enhanced until the danger of the situation in which the subject is placed disappears, the peak time until the power of the parasympathetic nerve activity reaches its peak is not determined only by the time when the stimulus is given, but also depends on the strength of the stimulus. That is, when a weak stimulus is given, as shown by the curve PSN1 in Fig. 4, the peak time Δt PN until the parasympathetic nerve activity reaches the peak value P S-P = t PN - t0 is short, and when a strong stimulus is given, as shown by the curve PSN2 in Fig. 4, the peak time Δt' PN until the parasympathetic nerve activity reaches the peak value P' S-P = t' PN - t0 becomes long. Also, contrary to sympathetic nerve activity, the peak value of the power of parasympathetic nerve activity is high for weak stimuli and low for strong stimuli. That is, for strong stimuli, the degree of its enhancement is small or not enhanced until the danger disappears. Or, it is difficult to be enhanced.

[0032] As mentioned above, a stressed state is a condition in which a subject is subjected to an unusual and strong stimulus. In other words, a stressed state is a condition in which the time between the detection of sympathetic nervous system activation and the detection of parasympathetic nervous system activation is longer than the time between the detection of sympathetic nervous system activation and parasympathetic nervous system activation when subjected to an ordinary stimulus. (Time between detection of sympathetic nervous system activation and detection of parasympathetic nervous system activation Δt) S-P This can be measured as the time difference between the time when sympathetic nervous system activity reaches its peak and the time when parasympathetic nervous system activity reaches its peak.

[0033] Based on the above considerations, the stress detection unit 15 determines time Δt S-P The stress state is detected by comparing it with a threshold. Alternatively, the stress detection unit 15 detects the time t when the power of sympathetic nerve activity reaches its peak. SN Stress is detected by detecting when the elapsed time since the stressor exceeds a threshold. Here, the threshold is the time Δt when a normal stimulus is applied. S-P The threshold is set based on a time of approximately 200ms-300ms. The threshold is the time Δt when a normal stimulus is applied. S-P This may be given by measuring it for each subject.

[0034] Here, the time used as an indicator for detecting stress is not limited to the time difference between the time when sympathetic nervous system activity reaches its peak and the time when parasympathetic nervous system activity reaches its peak. The time used as an indicator for detecting stress may, for example, be the time difference between the rise time of sympathetic nervous system activity and the rise time of parasympathetic nervous system activity. The rise time is the time t when the power of each nerve activity reaches a predetermined rise threshold Th. SE and t PE Alternatively, the inflection points of each power may be time tss and t PS That's fine.

[0035] The external stimulus acquisition unit 16 sequentially acquires information on external stimuli to the subject. The stimuli are not particularly limited to visual stimuli or auditory stimuli. When acquiring visual stimuli, the external stimulus acquisition unit 16 is equipped with a camera. Here, the camera may be set up to film the subject himself. Alternatively, the camera may be attached to the subject's head, for example, to film the subject's field of vision. Furthermore, the camera may be a panoramic camera capable of filming not only the subject's field of vision but also its surroundings. The camera may also be a time-lapse camera rather than a video camera. When acquiring auditory stimuli, the external stimulus acquisition unit 16 is equipped with a microphone. When various events are divided into artificial and non-artificial ones, it is clear that many of the events that cause stress are artificial. In particular, it is thought that many of the events that cause artificial stress are related to interpersonal relationships. Such events that cause artificial stress can be determined by acquiring the audio recording at the time. With audio containing a timestamp and location information, it is possible to obtain information such as when, where, with whom, and what kind of conversation took place. Furthermore, accidental major events such as traffic accidents themselves generate loud, recordable sounds. Such events can also be determined by sound. In addition, screen displays of information devices that the subject is viewing can also be treated as external stimuli. In this case, the external stimulus acquisition unit 16 acquires data of the currently displayed screen from the display device that is displaying the screen. Alternatively, the external stimulus acquisition unit 16 acquires data of the displayed screen by identifying the location the user is looking at, for example, by gaze detection using a camera. In addition, information related to the application the subject is using, such as what operations were performed or what emails were received, can also be treated as external stimuli. In this case, the external stimulus acquisition unit 16 acquires the necessary information from the application in question. Furthermore, the external stimulus acquisition unit 16 may be configured to acquire various situations in which the subject is placed and various events occurring around the subject.

[0036] The stimulus identification unit 17 identifies the stimulus that is likely causing stress to the subject when a stress state is detected by the stress detection unit 15 as the stress source. The stimulus identification unit 17 also calculates the degree of stress that the stress source is having on the subject.

[0037] The stimulus that likely caused stress to the subject was detected when the stress state was detected, i.e., at time Δt. S-P This is the stimulus acquired by the external stimulus acquisition unit 16 at time t0, just before the sympathetic nervous system becomes activated when the threshold is exceeded.

[0038] Furthermore, the degree of influence of a stimulus can be calculated from the work done by sympathetic and parasympathetic nervous system activity, i.e., the integral values ​​of the power SN of sympathetic nervous system activity and the PSN of parasympathetic nervous system activity. This is because the amount of work expended to respond to a stimulus is proportional to the intensity of the stimulus as a stressor. Figure 5 is a diagram showing the power SN of sympathetic nervous system activity, the power PSN1 of parasympathetic nervous system activity, and the power PSN2 of parasympathetic nervous system activity, each colored separately.

[0039] The control unit 18 controls the presentation of stress-related information to the subject. For example, the control unit 18 displays information such as whether the subject is currently stressed, the name of the stress source, and the degree of influence of the stress source on the display device. The presentation of information does not necessarily have to be done by display. Information may be presented by other methods such as audio output. In addition, the control unit 18 may also perform controls such as memorizing for learning.

[0040] Figure 6 shows an example of the hardware configuration of the stress detection device 1. The stress detection device 1 may be a computer having, for example, a processor 101, memory 102, storage 103, input device 104, display device 105, camera 106, microphone 107, and communication device 108 as hardware. The processor 101, memory 102, storage 103, input device 104, display device 105, camera 106, microphone 107, and communication device 108 are connected to a bus 109. The stress detection device 1 can be installed in terminal devices such as personal computers (PCs), smartphones, and tablet terminals.

[0041] The processor 101 is a processor that controls the overall operation of the stress detection device 1. The processor 101 operates as the heart rate acquisition unit 11, the factor calculation unit 12, the first change detection unit 13, the second change detection unit 14, the stress detection unit 15, the external stimulus acquisition unit 16, the stimulus identification unit 17, and the control unit 18 by executing a stress detection program stored in the storage 103, for example. The processor 101 is, for example, a CPU. The processor 101 may also be an MPU, GPU, ASIC, FPGA, etc. The processor 101 may be a single CPU, etc., or multiple CPUs, etc.

[0042] Memory 102 includes ROM and RAM. ROM is non-volatile memory. ROM stores the startup program for the stress detection device 1, etc. RAM is volatile memory. RAM is used, for example, as working memory during processing in the processor 101.

[0043] The storage 103 is, for example, a flash memory, a hard disk drive, or a solid-state drive. The storage 103 stores various programs executed by the processor 101, such as the stress detection program 1031. The storage 103 may also function as a memory unit and store stress data 1032. The stress data 1032 is stress data for each subject and includes information such as the date and time the stress occurred, the name of the stress source, and the degree of influence of the stress source.

[0044] The input device 104 is an input device such as a touch panel, keyboard, or mouse. When the input device 104 is operated, a signal corresponding to the operation is input to the processor 101 via the bus 109. The processor 101 performs various processes in response to this signal.

[0045] The display device 105 is a display device such as a liquid crystal display or an organic EL display. The display device 105 displays various images.

[0046] Camera 106 collects video as an external stimulus to the subject. Microphone 107 collects audio and ambient sounds as external stimuli to the subject.

[0047] The communication device 108 is a communication device for the stress detection device 1 to communicate with external devices. The communication device 108 may have a communication device for wired communication or a communication device for wireless communication. The communication device 108 receives electrocardiogram data from the electrocardiograph 201 by communicating with the electrocardiograph 201, for example. The electrocardiograph 201 is worn on the subject's wrist, chest, etc., and collects the subject's electrocardiogram data over time. The configuration of the electrocardiograph 201 is not limited to a specific configuration. For example, the electrocardiograph 201 may be integrated into a smartwatch.

[0048] Figure 7 is a flowchart showing the operation of the stress detection device 1. The process shown in Figure 7 is performed by the processor 101.

[0049] In step S1, the processor 101 acquires heart rate data and information on external stimuli. For heart rate data, the processor 101 converts the electrocardiogram data collected from the electrocardiograph 201 via the communication device 108 into RRI data as heart rate data and stores it in the memory 102. For information on external stimuli, the processor 101 stores the video collected by the camera 106 and the audio collected by the microphone 107 in the memory 102.

[0050] In step S2, the processor 101 calculates heart rate variability factor data from heart rate data. For example, the processor 101 calculates HF as the second factor, and LF and LF / HF as the first factors, respectively, from the RRI data.

[0051] In step S3, the processor 101 detects a peak in sympathetic activity by comparing the sympathetic activity as the first factor with the sympathetic activity immediately preceding it. Specifically, the processor 101 compares the current LF / HF with the LF / HF immediately preceding it.

[0052] In step S4, the processor 101 detects a peak in parasympathetic activity by comparing the parasympathetic activity as a second factor with the parasympathetic activity immediately preceding. Specifically, the processor 101 compares the current HF with the HF immediately preceding.

[0053] In step S5, the processor 101 determines whether or not a stress state has been detected. As mentioned above, the processor 101 determines whether or not time Δt S-P The stress state is detected by comparing it with a threshold. In step S5, the stress state is detected, i.e., time Δt S-P If it is determined that the time Δt is longer than the threshold, the process proceeds to step S6. In step S5, if no stress state is detected, i.e., time Δt S-P If it is determined that the time is not longer than the threshold, the process proceeds to step S9.

[0054] In step S6, the processor 101 determines the time when the stimulus immediately preceding the time when the stress state was detected was acquired, i.e., the peak time of sympathetic nerve activity when the stress state was detected. SN The peak time of sympathetic nerve activity Δt 0-S External stimuli acquired at a time point earlier are identified as the source of stress. (Sympathetic nervous system activity peak time Δt) 0-S For this purpose, the reaction time (latency) of a unique sensory organ may be used.

[0055] Here, further stress sources may be identified based on the content of the external stimulus. For example, if the identified stimulus is an image, the objects depicted in the image may be identified using methods such as object recognition within the image, thereby analyzing the objects that could be sources of stress.

[0056] In step S7, the processor 101 calculates the degree of influence of the stressor. As mentioned above, the degree of influence of the stressor may be the integral value of the power of sympathetic and parasympathetic nervous system activity.

[0057] In step S8, the processor 101 displays stress-related information on the display device 105. Stress-related information includes whether or not the user, who is the current subject, is experiencing stress, the name of the stress source, and the degree of influence of the stress source. In step S8, the processor 101 may also store the stress-related information as stress data 1032 in the storage 103.

[0058] In step S9, the processor 101 determines whether or not to terminate the process. For example, if the power to the stress detection device 1 is turned off, or if the user (the subject) instructs the process to end, the processor determines to terminate the process. If it is determined in step S9 to terminate the process, the process shown in Figure 7 ends. If it is not determined in step S9 to terminate the process, the process returns to step S1. In this case, the detection of the stress state continues.

[0059] As described above, according to the embodiment, based on an autonomic nervous system-driven model of emotion, stress is treated as an unusual stimulus, allowing the presence or absence of a stressed state to be detected from the temporal changes in sympathetic and parasympathetic nervous system activity in response to the unusual stimulus. Furthermore, by detecting the presence or absence of a stressed state through the temporal changes in sympathetic and parasympathetic nervous system activity, the stress source that triggered the stressed state can also be identified based on the peak time of sympathetic nervous system activity. Moreover, the degree of influence of the stress source can also be calculated as the workload of sympathetic and parasympathetic nervous system activity. In this way, according to the embodiment, stress can be detected and evaluations related to the protection of mental health can be performed.

[0060] The above explanation is based on the idea that stress can be viewed as an unusual stimulus, based on the autonomic nervous system-driven model of emotion. The following shows the experimental results by the inventor.

[0061] In the experiment, subjects wore electrocardiograms on their chests and viewed a display showing a stimulus as a stressor. The stimulus consisted of images of everyday scenes in which occult icons suddenly appeared. The subjects had not seen this image before the experiment. Additionally, a second display was provided, showing the current time in an enlarged format. The duration of the stimulus presentation was measured by recording the image on this display with a camera.

[0062] Furthermore, during the experiment, electrocardiogram data collected by the electrocardiograph was transferred to a smartphone. After the experiment, LF, HF, and LF / HF were calculated using FFT with the 1-minute RRI value from the electrocardiogram data to determine the heart rate variability during the experiment.

[0063] Figure 8 shows the time-dependent changes in RRI, HF, LF, and LF / HF as experimental results. The horizontal axis of Figure 8 represents time. Time is expressed in units of day and time. In Figure 8, the stimulus image was presented at 9:38 on the 21st. The graph in Figure 8 shows, from bottom to top, RRI (msec) and LF power (msec).2 (Hz), HF power (msec 2 (Hz), LF / HF.

[0064] As shown in Figure 8, at the time the stimulus image was presented, sympathetic nervous system activation, i.e., a peak in LF / HF, was measured, as indicated by T01_1_SN+. Following T01_1_SN+, a sharp increase in parasympathetic nervous system activation was expected, but as indicated by T01_1_PN+, a gradual increase in parasympathetic nervous system activation, i.e., a peak in HF, was observed in the experiment. This is thought to be parasympathetic nervous system activity that suppresses the sympathetic nervous system activation caused by the stimulus.

[0065] Thus, the experiment also observed that a stimulus triggers an increase in the sympathetic nervous system, followed by an increase in the parasympathetic nervous system to suppress the sympathetic nervous system's activation. This confirms that it is possible to detect stress states based on an autonomic nervous system-driven model of emotion.

[0066] (modified version) In this embodiment, the stress detection device 1 detects the presence or absence of a stressed state in real time, regardless of the degree of external stimulation. Alternatively, the stress detection device 1 may only detect the presence or absence of a stressed state when there is a certain level of external stimulation that could trigger stress.

[0067] In this embodiment, the stress detection device 1 determines the presence or absence of a stress state, identifies the stress source, and calculates the degree of influence of the stress source based on the temporal changes in sympathetic and parasympathetic nerve activity. However, past detection results may be used to detect future stress states. For example, the stress detection unit 15 of the stress detection device 1 learns the relationship between the stress source and its degree of influence, and if a stimulus similar to the past is acquired by the external stimulus acquisition unit 16, it may determine that a stress state exists without determining the presence or absence of a stress state based on the temporal changes in sympathetic and parasympathetic nerve activity. In this case, the stimulus identification unit 17 may determine that there is a stress source with a similar degree of influence to the past.

[0068] Furthermore, the learning of stress sources and their impact does not need to be performed within a single stress detection device 1, but may be performed in a system upstream of the stress detection device 1. In this case, the upstream system can perform a more statistical analysis based on the stress source and impact information transferred from multiple stress detection devices 1.

[0069] Furthermore, in this embodiment, the control unit 18 is said to present stress-related information to the user, who is the subject. However, the recipient of this stress-related information does not necessarily have to be the subject themselves; it could be someone related to the subject. This allows the subject's relatives to take measures against the subject's stress state at an early stage.

[0070] Furthermore, the instructions shown in the processing procedure described in the above-described embodiments can be executed based on a software program. A general-purpose computer system can store this program in advance and, by reading this program, can obtain effects similar to those of the stress detection device described above. The instructions described in the above-described embodiments are recorded as a program that can be executed by a computer on a magnetic disk (flexible disk, hard disk, etc.), optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, Blu-ray® Disc, etc.), semiconductor memory, or similar recording medium. Any storage format is acceptable as long as it is a recording medium that can be read by a computer or embedded system. The computer can read the program from this recording medium and, based on this program, have the CPU execute the instructions described in the program, thereby achieving operation similar to that of the stress detection device in the above-described embodiments. Of course, when the computer acquires or reads the program, it may do so via a network. Furthermore, an operating system (OS) running on a computer, a database management software, a network, or other middleware (MW) operating on a computer, based on instructions from a program installed on a computer or embedded system from a recording medium, may execute some of the processes necessary to realize this embodiment. Furthermore, the recording medium in this embodiment is not limited to a medium independent of the computer or embedded system, but also includes recording media that store or temporarily store programs downloaded via LAN, the Internet, etc. Furthermore, the recording medium is not limited to one; even when the processing in this embodiment is performed from multiple media, these are also included as recording media in this embodiment, and the configuration of the media may be any configuration.

[0071] In this embodiment, the computer or embedded system is used to execute each process in this embodiment based on a program stored on a recording medium, and may be configured as any of the following: a single device such as a personal computer or microcomputer, or a system in which multiple devices are connected via a network. Furthermore, the term "computer" in this embodiment is not limited to personal computers, but also includes arithmetic processing units, microcontrollers, and the like included in information processing equipment, and refers collectively to any equipment or device capable of realizing the functions of this embodiment through a program.

[0072] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0073] 1 Stress detection device, 11 Heart rate acquisition unit, 12 Factor calculation unit, 13 First change detection unit, 14 Second change detection unit, 15 Stress detection unit, 16 External stimulus acquisition unit, 17 Stimulus identification unit, 18 Control unit, 101 Processor, 102 Memory, 103 Storage, 104 Input device, 105 Display device, 106 Camera, 107 Microphone, 108 Communication device, 109 Bus, 201 Electrocardiograph, 1031 Stress detection program, 1032 Stress data.

Claims

1. A heart rate acquisition unit that acquires the subject's heart rate information, A factor calculation unit that calculates a first heart rate variability factor related to sympathetic nerve activity and a second heart rate variability factor related to parasympathetic nerve activity from the aforementioned heart rate information, A first change detection unit detects the peak of the subject's sympathetic nerve activity based on the time change of the first heart rate variability factor, A second change detection unit detects the peak of parasympathetic nerve activity in the subject based on the time change of the second heart rate variability factor, A stress detection unit that detects the subject's stress state based on the fact that the time from the detection of the peak of sympathetic nerve activity to the detection of the peak of parasympathetic nerve activity is longer than a threshold, An external stimulus acquisition unit acquires external stimuli to the subject over time, A stimulus identification unit identifies the external stimulus detected immediately before the timing at which the peak of sympathetic nerve activity is detected when the stress state is detected as the stress source that caused the stress state, A stress detection device equipped with the following features.

2. The stimulus identification unit further calculates the degree of influence the stress source has on the subject based on the workload of the sympathetic and parasympathetic nerves. The stress detection device according to claim 1.

3. The system further comprises a memory unit that stores information regarding the stress given to the subject. The stress detection device according to claim 1.

4. The information relating to the stress includes information relating to the stress source given to the subject, The stress detection unit detects the presence or absence of the stress state based on the external stimulus acquired by the external stimulus acquisition unit and the information of the stress source. The stress detection device according to claim 3.

5. The system further comprises a control unit that presents information regarding the stress given to the subject, The stress detection device according to claim 1.

6. The first change detection unit detects changes in the subject's sympathetic nervous system based on the time-dependent change in the ratio of the low-frequency component and the high-frequency component of the frequency domain of the heart rate interval calculated from the heart rate information. The second change detection unit detects changes in the parasympathetic nervous system of the subject based on the time-dependent changes in the high-frequency component of the frequency domain of the heart rate interval calculated from the heart rate information. The stress detection device according to claim 1.

7. The stress detection device acquires the heart rate information of the subject, The stress detection device calculates a first heart rate variability factor related to sympathetic nerve activity and a second heart rate variability factor related to parasympathetic nerve activity from the heart rate information. The stress detection device detects the peak of the subject's sympathetic nerve activity based on the time change of the first heart rate variability factor using the first change detection unit, The stress detection device detects the peak of the subject's parasympathetic nerve activity based on the time change of the second heart rate variability factor, The stress detection device detects the subject's stress state based on the fact that the time between the detection of the peak of sympathetic nerve activity and the detection of the peak of parasympathetic nerve activity is longer than a threshold, The stress detection device acquires external stimuli to the subject over time, The stress detection device identifies the external stimulus detected immediately before the timing at which the peak of sympathetic nerve activity is detected when the stress state is detected as the stress source that caused the stress state. A stress detection method comprising the following features.

8. To obtain the subject's heart rate information, From the aforementioned heart rate information, a first heart rate variability factor related to sympathetic nerve activity and a second heart rate variability factor related to parasympathetic nerve activity are calculated. The first change detection unit detects the peak of the subject's sympathetic nerve activity based on the time change of the first heart rate variability factor, Based on the temporal changes of the second heart rate variability factor, the peak of parasympathetic nerve activity in the subject is detected, The stress state of the subject is detected based on the fact that the time from the detection of the peak of sympathetic nerve activity to the detection of the peak of parasympathetic nerve activity is longer than a threshold, Over time, the external stimuli of the subject are acquired, Identifying the external stimulus detected immediately before the peak of sympathetic nerve activity detected when the stress state is detected as the stress source that caused the stress state, A stress detection program that is run on a computer.

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