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] In general, mental health disorders such as depression are caused by exposure to strong stress over a long period of time. Therefore, if we can continuously monitor our psychological state and understand the state of stress and its causes, it is highly likely that we will be able to take measures to prevent mental health disorders in advance.

[0003] As a method for grasping a human stress state, a method is known in which the stress state is detected using LF and HF as heart rate variability factors calculated from the subject's heart rate. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6550440 Summary of the Invention [Problem to be solved by the invention]

[0005] Conventional stress detection methods can assess the presence or absence of stress at the time of measurement and its level. However, it is difficult to assess the source of stress and its impact, which are related to mental health defense, using conventional stress detection methods.

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

[0007] The stress detection device includes a heart rate acquisition section, a factor calculation section, a first change detection section, a second change detection section, a stress detection section, an external stimulus acquisition section, and a stimulus identification section. The heart rate acquisition section acquires heart rate information of the subject. The factor calculation section calculates a heart rate variability factor from the heart rate information. The first change detection section detects a change in the sympathetic nerves of the subject based on the heart rate variability factor. The second change detection section detects a change in the parasympathetic nerves of the subject based on the heart rate variability factor. The stress detection section detects the presence or absence of a stressed state of the subject based on the time from when a change in the sympathetic nerves is detected to when a change in the parasympathetic nerves is detected. The external stimulus acquisition section acquires an external stimulus for the subject. The stimulus identification section identifies a stress source that caused the stressed state of the subject from the external stimuli acquired by the external stimulus acquisition section based on the timing at which a change in the sympathetic nerves is detected when a stressed state is detected. [Brief description of the drawings]

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

[0009] Whether or not a person feels stressed when exposed to a stress source varies from person to person. Also, the impact that a stress source has on a person varies from person to person. For such non-generalized subjects, grasping the stress sources felt by individuals and the degree of their impact is equivalent to providing a general answer to the question of what stress is.

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

[0011] Hereinafter, an embodiment will be described with reference to the drawings. Fig. 1 is a diagram showing a configuration of an example of a stress detection device according to an 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 heartbeat acquisition unit 11 acquires information on the subject's heartbeat. The information on the heartbeat can be heartbeat interval data. FIG. 2 shows a typical electrocardiogram waveform. Usually, a P wave occurs due to firing of the sinus node, which is the timekeeper of the heartbeat, followed by Q, R, and S waves, which represent the process of excitation from the atrium to the entire ventricle, as a complex wave. The heartbeat interval data is, for example, the time interval between an R wave and the next R wave (RR intervals: RRI). The heartbeat acquisition unit 11 acquires data on the interval between R waves as heartbeat interval data from electrocardiogram data acquired, for example, from an electrocardiograph. The heartbeat is constantly fluctuating. And the fluctuation of the heartbeat reflects the activity of the autonomic nerves. The activity of the autonomic nerves is influenced not only by external stimuli but also by various psychological factors such as internal factors.

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

[0014] An example of calculation of the heart rate variability factor by the factor calculation unit 12 will be described. For example, assume that the heart rate acquisition unit 11 has obtained RRI data as heart rate interval data as shown in FIG. 3A. As described above, the heart rate is constantly fluctuating, so the RRI value does not take a constant value. The RRI data is converted into a frequency spectrum by a Fast Fourier Transform (FFT), but usually, the number of measurement points of the RRI data is often insufficient for the number of samples to perform a highly accurate FFT. Therefore, the factor calculation unit 12 linearly interpolates the RRI data as shown in FIG. 3B to increase the apparent number of data points. Then, the factor calculation unit 12 performs a Fourier transform on the linearly interpolated RRI data to calculate the power spectrum density of the RRI data in the frequency space. FIG. 3C shows the power spectrum density of the RRI data. The sum of the power in the low frequency region (0.04Hz-0.15Hz) of the power spectrum density shown in FIG. 3C is LF, and the sum of the power in the high frequency region (0.15Hz-0.4Hz) is HF.

[0015] Here, the heart rate variability factors used in the heart rate variability evaluation include factors in the time domain and factors in the frequency domain. Known factors in the time domain include SDNN (standard deviation of normal-to-normal intervals), RMSSD (root mean square of successive difference of RR intervals), and pNN50. Known factors in the frequency domain include LF, HF, LF / HF, and total power, which is the sum of power in the entire frequency domain. Among these factors, RMSSD, pNN50, and HF are indexes representing parasympathetic nerve activity. SDNN, total power, and LF are indexes representing the overall activity of the sympathetic nerve and the parasympathetic nerve. LF / HF is said to be an index representing the activity of the sympathetic nerve alone. As will be described later, in the embodiment, stress is detected by separately evaluating the activities of the sympathetic nerve and the parasympathetic nerve. Therefore, factor calculation unit 12 calculates, for example, LF, HF, and LF / HF as heart rate variability factors, and outputs LF / HF to first change detection unit 13 as an index representing sympathetic nerve activity, and outputs HF to second change detection unit 14 as an index representing parasympathetic nerve activity.

[0016] Generally, LF contains information on sympathetic and parasympathetic nervous activity. For this reason, it is common to use LF / HF, which is the value obtained by dividing LF by HF, which represents the power of parasympathetic nervous activity, as an index of sympathetic nervous activity. This example is also used here as an index of sympathetic nervous activity, but when considering the power of sympathetic nervous activity, observing LF provides a quicker intuitive understanding. Numerically, LF is obtained by multiplying LF / HF by HF, so the numerical significance is weak, but using LF is also useful for intuitively observing sympathetic nervous activity.

[0017] Here, an example is shown in which the factor calculation unit 12 calculates LF, HF, and LF / HF. In the embodiment, a heart rate variability factor capable of evaluating the activities of the sympathetic nerves and the parasympathetic nerves other than LF, HF, and LF / HF may be used.

[0018] The first change detection unit 13 detects that the subject's sympathetic nerves have been enhanced from a time-dependent change in the first heart rate variability factor that represents sympathetic nerve activity. For example, the first change detection unit 13 detects that the subject's sympathetic nerves have been enhanced by detecting that the time-series LF / HF has reached a peak value. The time-series LF / HF reaching a peak value can be detected, for example, by detecting that the LF / HF has increased beyond a rising threshold and then started to decrease.

[0019] The second change detection unit 14 detects the enhancement of the subject's parasympathetic nerves from the change over time of the second heart rate variability factor representing the parasympathetic nerve activity. For example, the second change detection unit 14 detects the enhancement of the subject's parasympathetic nerves by detecting that the time series of HF has reached a peak value. The fact that the time series of HF has reached a peak value can be detected, for example, by detecting that the HF has increased beyond a rising threshold and then started to decrease.

[0020] Stress detection section 15 detects whether the subject is under stress based on the comparison results from first change detection section 13 and second change detection section 14.

[0021] The principle of stress detection by the stress detection unit 15 will be described below. First, the understanding of autonomic nerve activity for understanding stress will be described. The inventor has been studying emotions that are factors in human behavior and decision-making, and in the process, noticed a similarity between emotional reactions and physical reactions during sympathetic nervous activation.

[0022] Emotions themselves are instinctual self-preservation responses, and always show negative reactions. For example, in rats, when they receive a stimulus that they expect to be frightening, their pupils dilate, their heart rate increases, their blood pressure rises, and their fur stands on end. The act of standing on end is known as a threatening behavior in many mammals. On the other hand, the sympathetic nervous system in humans also increases their heart rate, constricts blood vessels, and contracts the arrector pili muscles. In other words, these sympathetic nervous system responses in humans are similar to the fearful emotional responses in rats. The autonomic nervous system works completely independent of human will, and mainly functions to maintain homeostasis in the body, including the heart, respiratory system, digestive system, and kidneys, and other internal organs. The autonomic nervous system is divided into the sympathetic nervous system and the parasympathetic nervous system. The sympathetic nervous system works to maintain and improve the motor functions necessary for avoiding danger, which is called "fight or flight." The purpose of the fearful emotional response in rats is also to avoid danger. This led the inventor to consider whether the fear emotional response and the sympathetic nervous response are one and the same phenomenon.

[0023] External stimuli stimulate the sympathetic nervous system, while parasympathetic activity suppresses it. This is the core of autonomic nervous activity. The survival of living organisms is maintained by maintaining a balance (homeostasis) between the sympathetic and parasympathetic nervous systems.

[0024] In everyday life, the sympathetic nervous system is activated in response to stimuli from the environment that constantly enter through the sensory organs, but this is suppressed without any particular problems by the activity of the parasympathetic nervous system, maintaining a low stress state. However, in response to stimuli that deviate from everyday life, the sympathetic nervous system remains in an excited state. The parasympathetic nervous system is not activated until the danger of the situation is eliminated. This is a high-stress state. Below, the similarity between such emotional reactions and physical reactions when the sympathetic nervous system is activated will be explained as an autonomic nervous system model of emotions.

[0025] In the autonomic nervous system model of emotion, all stimuli from the outside world are transmitted to the limbic system as emotions by the thalamus, which activates the sympathetic nervous system. The body automatically responds when the sympathetic nervous system is activated. Whether we are aware of it or not, stimuli from the outside world are always being input, and sympathetic nervous activation is always occurring.

[0026] The stimuli that reach the thalamus are simultaneously transmitted to the cortical pathway. The cortex analyzes the stimuli. If the analysis determines that the stimuli are not part of everyday life and require special attention, parasympathetic nerve activity is increased and sympathetic nerve activity is suppressed. For example, stimuli such as environmental sounds and ambient light from lighting that fill our daily lives are everyday stimuli.

[0027] On the other hand, in the case of unusual stimuli, the parasympathetic nervous system is not activated. In order to continue responding to danger, the sympathetic nervous system continues to be activated. This state of being subjected to unusual stimuli is a stressful state. Mental health disorders such as depression are caused by exposure to strong stress for a long period of time.

[0028] FIG. 4 is a diagram showing autonomic nerve activity caused by external stimuli based on an autonomic nerve origin model of emotion. The horizontal axis of FIG. 4 indicates time, and the vertical axis indicates the power of nerve activity. The power standard is baseline B. Baseline B is, for example, the power value of everyday nerve activity of a hypothetical subject. Furthermore, curve SN in FIG. 4 indicates the change over time in the power value of sympathetic nerve activity. Meanwhile, curves PSN1 and PSN2 in FIG. 4 indicate the change over time in the power value of parasympathetic nerve activity. Curve PSN1 and curve PSN2 indicate parasympathetic nerve responses to different non-everyday stimuli.

[0029] According to the autonomic nervous system model of emotion, when an external stimulus is given, the sympathetic nervous system is always activated. The activated sympathetic nervous system is suppressed by the activation of the parasympathetic nervous system.

[0030] For example, when a stimulus S is given at time t0, the power of sympathetic nerve activity SN is SN At the peak value PSN The peak value of the sympathetic nerve activity power SN, P SN can change depending on the intensity of the given stimulus. If the stimulus is a simple physical stimulus, the intensity of the stimulus S is proportional to its physical quantity. In addition, the intensity of the stimulus S depends on the subject even if it is the same stimulus, and if the subject dislikes the stimulus, it will be stronger. If the intensity of the stimulus is strong, the peak value P SN Therefore, the amount of work done by the sympathetic nervous activity, i.e., the integral value of the power SN of the sympathetic nervous activity, also increases. On the other hand, in the case of sympathetic nervous activity, the peak time Δt 0-S =t SN -t0 is a value that corresponds to the reaction time (latency) of a unique sensory organ, regardless of the stimulus.

[0031] On the other hand, since the parasympathetic nervous system is not stimulated until the danger of the subject's situation is eliminated, the time until the power of parasympathetic nervous activity reaches its peak is not determined only by the time the stimulus is applied, but also by the strength of the stimulus. In other words, when a weak stimulus is applied, the parasympathetic nervous activity reaches its peak value PSN1, as shown by the curve PSN1 in Figure 4. PN Time to peak Δt S-P =t PN -t0 is short, and when a strong stimulus is applied, the parasympathetic nerve activity reaches a peak value P' as shown by the curve PSN2 in Figure 4. PN Time to peak Δt' S-P =t' PN -t0 becomes longer. Also, contrary to sympathetic nerve activity, the peak value of the power of parasympathetic nerve activity becomes high with weak stimulation and low with strong stimulation. In other words, the degree of enhancement is small or not enhanced until the danger is gone with strong stimulation. Or, it is difficult to enhance.

[0032] As described above, the stress state is a state in which the subject is subjected to strong, non-routine stimuli. In other words, the stress state is a state in which the time from when sympathetic nerve activation is detected to when parasympathetic nerve activation is detected is longer than the time from when sympathetic nerve activation is detected to when parasympathetic nerve activation is detected when routine stimuli are applied. The time Δt S-P can be measured as the time difference between the time when the power of sympathetic nerve activity reaches its peak and the time when the power of parasympathetic nerve activity reaches its peak.

[0033] Based on the above concept, the stress detection unit 15 determines whether or not the time Δt S-P The stress detection unit 15 detects a stress state by comparing the time t SN The stress state is detected by detecting whether the time elapsed since the daily stimulus was given exceeds a threshold value. Here, the threshold value is the time Δt S-P The threshold is set based on a time of about 200ms-300ms. The threshold is the time Δt S-P may be given by measuring for each subject.

[0034] Here, the time used as an index for detecting a stressed state is not limited to the time difference between the time when the power of sympathetic nerve activity reaches a peak and the time when the power of parasympathetic nerve activity reaches a peak. The time used as an index for detecting a stressed state may be, for example, the time difference between the rise time of the power of sympathetic nerve activity and the rise time of the power of parasympathetic nerve 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 The times tss and t PS may be also possible.

[0035] The external stimulus acquisition unit 16 sequentially acquires information on external stimuli for the subject. The stimuli may be video stimuli, audio stimuli, etc., and are not particularly limited. When acquiring video stimuli, the external stimulus acquisition unit 16 is provided with a camera. Here, the camera may be installed so as to capture the subject himself. Also, the camera may be attached to the subject's head, for example, so as to capture an image of the subject's field of vision. Furthermore, the camera may be a 360° camera that can capture not only the subject's field of vision but also the surroundings. Also, the camera may be a camera that does not capture video, but performs time-lapse photography. Also, when acquiring audio stimuli, the external stimulus acquisition unit 16 is provided with a microphone. If various events are divided into artificial and non-artificial, events that become stress sources are clearly artificial. In particular, it is thought that events that become artificial stress sources include many related to interpersonal relationships. Such events that become artificial stress sources can be determined by acquiring the voice at that time. By using the voice with a timestamp and location information, information such as when, where, with whom, and what kind of conversation was held can be obtained. Also, in a large accidental event such as a traffic accident, a large sound that can be recorded is generated. Such an event can also be determined by sound. In addition, the screen display of an information device or the like that the subject is looking at can also be treated as an external stimulus. In this case, the external stimulus acquisition unit 16 acquires data of the currently displayed screen from the display device that displays the screen. Alternatively, the external stimulus acquisition unit 16 acquires data of the currently displayed screen by identifying the location where the user is looking by gaze detection using a camera or the like. In addition, information related to the application used by the subject, such as information on what operation was performed and what email was received, can also be treated as an external stimulus. In this case, the external stimulus acquisition unit 16 acquires necessary information from the corresponding application. In addition, 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 identifying unit 17 identifies, as a stress source, a stimulus that is likely to be causing stress to the subject when a stressed state is detected by the stress detection unit 15. In addition, the stimulus identifying unit 17 calculates the degree of influence of the stress that the stress source is causing to the subject.

[0037] The stimulus that may be stressful for the subject is the time when the stress state is detected, i.e., the time Δt S-P This is a stimulus acquired by the external stimulus acquisition unit 16 just before the sympathetic nerves become activated when the threshold value is exceeded, that is, at time t0.

[0038] The degree of influence of the stimulation can be calculated from the amount of work of the sympathetic nerve activity and the parasympathetic nerve activity, that is, the integral value of the power of the sympathetic nerve activity SN and the parasympathetic nerve activity PSN. This is because the amount of work spent to respond to the stimulation is proportional to the strength of the stimulation as stress. Figure 5 shows the power of the sympathetic nerve activity SN, the power of the parasympathetic nerve activity PSN1, and the power of the parasympathetic nerve activity PSN2, each of which is shaded differently.

[0039] The control unit 18 controls the presentation of information related to stress 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 a display device. Presentation of information does not necessarily have to be performed by display. Presentation of information may be performed by other methods such as audio output. In addition, the control unit 18 may perform control such as storing information for learning purposes.

[0040] 6 is a diagram showing an example of a hardware configuration of the stress detection device 1. The stress detection device 1 may be a computer having, for example, a processor 101, a memory 102, a storage 103, an input device 104, a display device 105, a camera 106, a microphone 107, and a communication device 108 as hardware. The processor 101, the memory 102, the storage 103, the input device 104, the display device 105, the camera 106, the microphone 107, and the communication device 108 are connected to a bus 109. The stress detection device 1 may be mounted on a terminal device such as a personal computer (PC), a smartphone, or a tablet terminal.

[0041] The processor 101 is a processor that controls the overall operation of the stress detection device 1. The processor 101 executes a stress detection program stored in the storage 103, for example, to operate as 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. The processor 101 is, for example, a CPU. The processor 101 may be an MPU, a GPU, an ASIC, an FPGA, or the like. The processor 101 may be a single CPU, or the like, or may be multiple CPUs, or the like.

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

[0043] The storage 103 is, for example, a storage such as 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 a stress detection program 1031. The storage 103 may also operate as a memory unit and store stress data 1032. The stress data 1032 is data on stress for each subject, and includes, for example, information such as the date and time when the subject experienced stress, 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, a keyboard, a mouse, etc. When the input device 104 is operated, a signal corresponding to the operation content 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, an organic EL display, etc. The display device 105 displays various images.

[0046] The camera 106 collects video as an external stimulus to the subject. The microphone 107 collects audio and environmental sounds as an external stimulus to the subject.

[0047] The communication device 108 is a communication device for the stress detection device 1 to communicate with an external device. The communication device 108 may have a communication device for wired communication or may have a communication device for wireless communication. The communication device 108 receives electrocardiogram data from the electrocardiogram 201, for example, by communicating with the electrocardiogram 201. The electrocardiogram 201 is worn on the wrist, chest, or the like of the subject, and collects electrocardiogram data of the subject over time. The configuration of the electrocardiogram 201 is not limited to a specific configuration. For example, the electrocardiogram 201 may be mounted on a smart watch.

[0048] 7 is a flowchart showing the operation of the stress detection device 1. The processing in FIG.

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

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

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

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

[0053] In step S5, the processor 101 determines whether or not a stress state is detected. As described above, the processor 101 determines whether or not a stress state is detected. S-P In step S5, a 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. S-P If it is determined that 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 before the time when it is determined that the stress state is detected is acquired, that is, the peak time t SN Sympathetic nerve activity peak time Δt 0-S The external stimulus acquired at the time Δt before is identified as the stress source stimulus. 0-S The reaction time (latency) of a unique sensory organ may be used as the sensory response time.

[0055] Here, further stress sources may be identified depending on the content of the external stimulus. For example, if the identified stimulus is an image, an object shown in the image may be identified by a method such as object recognition in the image, and an analysis of objects that may be stress sources may be performed.

[0056] In step S7, the processor 101 calculates the degree of influence of the stress source. As described above, the degree of influence of the stress source can be the integral value of the power of the sympathetic nerve activity and the parasympathetic nerve activity.

[0057] In step S8, the processor 101 displays information related to stress on the display device 105. The information related to stress is information such as whether or not the user who is the current subject is stressed, the name of the stress source, and the degree of influence of the stress source. Note that in step S8, the processor 101 may store the information related to stress in the storage 103 as stress data 1032.

[0058] In step S9, the processor 101 determines whether or not to end the process. For example, it is determined that the process is to be ended when the power supply of the stress detection device 1 is turned off, or when an instruction to end the process is given by the user who is the subject. If it is determined that the process is to be ended in step S9, the process of FIG. 7 ends. If it is not determined that the process is to be ended in step S9, the process returns to step S1. In this case, the detection of the stress state continues.

[0059] As described above, according to the embodiment, by regarding stress as an unusual stimulus based on the autonomic nerve-attributed model of emotion, the presence or absence of a stressful state can be detected from the time-dependent changes in sympathetic nerve activity and parasympathetic nerve activity in response to the unusual stimulus. Furthermore, by detecting the presence or absence of a stressful state based on the time-dependent changes in sympathetic nerve activity and parasympathetic nerve activity, the stress source that triggered the stressful state can be identified based on the peak time of sympathetic nerve activity. Furthermore, the degree of influence of the stress source can be calculated as the workload of sympathetic nerve activity and parasympathetic nerve activity. In this way, according to the embodiment, stress can be detected and an evaluation related to mental health defense can be performed.

[0060] The above explanation is based on the idea that stress can be considered as an extraordinary stimulus based on the autonomic nervous system model of emotion. The following are the results of an experiment conducted by the inventor.

[0061] In the experiment, the subjects wore an electrocardiogram around their chest and watched a display that presented stimuli as a source of stress. The image given as a stimulus was an image of an occult icon suddenly appearing in an everyday landscape. Note that the subject did not see this image until the experiment. Also, in the experiment, another display was prepared, which showed an enlarged view of the current time. The image of this display was recorded by a camera to measure the presentation time of the stimuli.

[0062] In addition, the electrocardiogram data collected during the experiment was transferred to a smartphone. After the experiment was completed, the LF, HF, and LF / HF were calculated using the RRI value for one minute from the electrocardiogram data by FFT to obtain the heart rate variability during the experiment.

[0063] FIG. 8 shows the time-dependent changes in RRI, HF, LF, and LF / HF as experimental results. The horizontal axis in FIG. 8 is time. Time is expressed in units of date and time. In FIG. 8, the stimulus video was presented at 9:38 on the 21st. The graph in FIG. 8 shows, from the bottom, RRI (msec), LF power (msec2 / Hz), HF power (msec 2 / Hz), LF / HF.

[0064] As shown in Figure 8, at the timing when the stimulus image was presented, sympathetic nerve activation, i.e., the peak of LF / HF, was measured as shown by T01_1_SN+. Following T01_1_SN+, we expected to see a sharp parasympathetic nerve activation, but in the experiment, a gentle parasympathetic nerve activation, i.e., a peak of HF, was observed as shown by T01_1_PN+. This is thought to be parasympathetic nerve activity that suppresses sympathetic nerve activation due to the stimulus.

[0065] In this way, the sympathetic nervous system is activated by a stimulus, and then the parasympathetic nervous system is activated to suppress the sympathetic nervous system activation. This result confirmed that it is possible to detect stress states based on the autonomic nervous system-based emotion model.

[0066] (Modification) In the embodiment, the stress detection device 1 detects the presence or absence of a stress state in real time regardless of the level of external stimuli. In contrast, the stress detection device 1 may detect the presence or absence of a stress state only when there is a certain level of external stimuli that may be a stress trigger.

[0067] In the embodiment, the stress detection device 1 determines whether or not a stress state exists, identifies the stress source, and calculates the influence of the stress source based on the time-dependent changes in sympathetic and parasympathetic activity. In contrast, past detection results may be used to detect a future stress state. For example, the stress detection unit 15 of the stress detection device 1 may learn the relationship between the stress source and the influence, and when a stimulus similar to that in the past is acquired by the external stimulus acquisition unit 16, may determine that the device is in a stress state without determining whether or not a stress state exists based on the time-dependent changes in sympathetic and parasympathetic activity. In this case, the stimulus identification unit 17 may determine that a stress source with the same influence as that in the past exists.

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

[0069] In the embodiment, the control unit 18 presents information about stress to the user who is the subject. In contrast, the person to whom the information about stress is presented does not necessarily have to be the subject himself / herself, but may be a person related to the subject. This allows the person related to the subject to take measures against the subject's stress state at an early stage.

[0070] Moreover, the instructions shown in the processing procedure shown in the above-mentioned embodiment can be executed based on a program, which is software. A general-purpose computer system can store this program in advance and obtain the same effect as that of the above-mentioned stress detection device by reading this program. The instructions described in the above-mentioned embodiment are recorded as a program that can be executed by a computer on a magnetic disk (flexible disk, hard disk, etc.), an optical disk (CD-ROM, CD-R, CD-RW, DVD-ROM, DVD±R, DVD±RW, Blu-ray (registered trademark) Disc, etc.), a semiconductor memory, or a recording medium similar thereto. The recording medium may be in any storage format as long as it is readable by a computer or an embedded system. The computer can realize the same operation as the stress detection device of the above-mentioned embodiment by reading the program from the recording medium and executing the instructions described in the program based on this program with a CPU. Of course, when the computer acquires or reads the program, it may acquire or read it through a network. In addition, an OS (operating system), database management software, network, or other MW (middleware) running on a computer may execute some of the processes required to realize this embodiment based on instructions from a program installed on the computer or embedded system from a recording medium. Furthermore, the recording medium in this embodiment is not limited to a medium independent of a computer or an embedded system, but also includes a recording medium that stores or temporarily stores a program downloaded via a LAN, the Internet, or the like. Furthermore, the number of recording media is not limited to one, and a case where the processing in this embodiment is executed from a plurality of media is also included in the recording media in this embodiment, and the media may have any configuration.

[0071] The computer or embedded system in this embodiment is for executing each process in this embodiment based on a program stored in a recording medium, and may be configured as any one of a device such as a personal computer or a microcomputer, or a system in which multiple devices are connected to a network. In addition, the computer in this embodiment is not limited to a personal computer but also includes an arithmetic processing device, a microcomputer, etc. included in information processing equipment, and is a general term for equipment or devices that can realize the functions in this embodiment by a program.

[0072] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [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 for acquiring heart rate information of a subject; a factor calculation unit for calculating a heart rate variability factor from the heart rate information; a first change detection unit that detects a change in sympathetic nerve activity of the subject based on the heart rate variability factor; a second change detection unit that detects a change in parasympathetic activity of the subject based on the heart rate variability factor; a stress detection unit that detects whether or not the subject is in a stressed state based on a time period from when a change in the sympathetic nerve activity is detected to when a change in the parasympathetic nerve activity is detected; An external stimulus acquisition unit that acquires an external stimulus for the subject; a stimulus identification unit that identifies a stress source that caused the stress state to the subject from among the external stimuli acquired by the external stimulus acquisition unit based on a timing at which a change in the sympathetic nerve when the stress state is detected; A stress detection device comprising:

2. the stress detection unit detects that the subject is in the stress state when a time period from when the sympathetic nerve change is detected to when the parasympathetic nerve change is detected is longer than a threshold value.

2. The stress detection device according to claim 1.

3. The stimulus specifying unit further calculates the degree of influence of the stress source on the subject based on the workload of the sympathetic nerve and the parasympathetic nerve.

2. The stress detection device according to claim 1.

4. Further comprising a memory unit for storing information regarding the stress given to the subject.

2. The stress detection device according to claim 1.

5. The information about the stress includes information about 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 and the information on the stress source acquired by the external stimulus acquisition unit.

5. The stress detection device according to claim 4.

6. Further comprising a control unit that presents information regarding the stress applied to the subject.

2. The stress detection device according to claim 1.

7. the first change detection unit detects a change in the sympathetic nerves of the subject based on a change over time in a ratio of a low frequency region component of a frequency domain of a heartbeat interval calculated from the heartbeat information to a high frequency region component of the frequency domain; the second change detection unit detects a change in the parasympathetic nerve activity of the subject based on a time-dependent change in a high-frequency region component of a frequency domain of a heartbeat interval calculated from the heartbeat information.

2. The stress detection device according to claim 1.

8. Obtaining heart rate information of a subject; Calculating a heart rate variability factor from the heart rate information; detecting a change in the subject's sympathetic nervous system based on the heart rate variability factor; detecting a change in the subject's parasympathetic nervous system based on the heart rate variability factor; detecting whether or not the subject is in a stressed state based on a time period from when the change in the sympathetic nerves is detected to when the change in the parasympathetic nerves is detected; Obtaining an external stimulus for the subject; Identifying a stress source that caused the stress state in the subject from among the acquired external stimuli based on a timing at which a change in the sympathetic nerve was detected when the stress state was detected; 13. A stress detection method comprising:

9. Obtaining heart rate information of a subject; Calculating a heart rate variability factor from the heart rate information; detecting a change in the subject's sympathetic nervous system based on the heart rate variability factor; detecting a change in the subject's parasympathetic nervous system based on the heart rate variability factor; detecting whether or not the subject is in a stressed state based on a time period from when the change in the sympathetic nerves is detected to when the change in the parasympathetic nerves is detected; Obtaining an external stimulus for the subject; Identifying a stress source that caused the stress state in the subject from among the acquired external stimuli based on a timing at which a change in the sympathetic nerve was detected when the stress state was detected; A stress detection program for causing a computer to execute the following.

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