JP2018126422A - Electronic apparatus, method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To simply obtain stable information related to stress.SOLUTION: According to one embodiment, an electronic apparatus comprises: means for acquiring biological information related to a pulse or a heartbeat of a user measured by a sensor; means for determining a first stress index by using first biological information measured by the sensor in a first period after falling asleep and determining a second stress index by using second biological information measured by the sensor in a second period before awaking from sleep; and means outputting information related to stress of the user set by using a degree of a change of the stress indexes from the falling asleep to the awaking from sleep.SELECTED DRAWING: Figure 9

Description

  Embodiments of the invention relate to determining information regarding stress.

  There are two methods for measuring information about stress. One is based on blood or body fluids. Although it is possible to measure information related to stress accumulated in the user over a long period of time such as one week or one month (hereinafter also referred to as chronic stress), the measurement is not easy because the medical device is used. The burden of is great. The other is based on a pulse or heart rate measured using a wearable biosensor. In this case, it is possible to obtain information on short-term stress (hereinafter also referred to as instantaneous stress) such as 1 minute, 5 minutes, and 15 minutes, but it is not possible to easily obtain stable information on chronic stress. In order to obtain information on chronic stress, the user wears a wearable sensor and measures a pulse or a heartbeat. Then, it is necessary to calculate a measurement value, calculate an index related to autonomic nerve balance such as LF and HF, and examine the tendency of the index change.

  However, this method consumes much power because the index calculation is always performed. In addition, the pulse or heart rate is stable during sleep, but may fluctuate rapidly during waking. Therefore, when the autonomic nerve index is calculated using the average value of the daily measurement values of the pulse or heart rate or the representative value in the daytime measurement value group, the fluctuation of the pulse or heart rate at awakening becomes noise, and the index is Not stable.

US Patent Application Publication No. 2016/0331315 Japanese Patent Laid-Open No. 2006-10709

  Conventionally, stable information regarding stress cannot be easily obtained.

  The objective of this invention is providing the electronic device, method, and program which can obtain | require the stable information regarding stress easily.

  According to the embodiment, the electronic device uses the first biological information measured by the sensor within the first period after falling asleep and the means for acquiring biological information related to the user's pulse or heartbeat measured by the sensor. Means for determining one stress index and determining the second stress index using the second biological information measured by the sensor within the second period before falling asleep, and the degree of change of the stress index from falling asleep to falling asleep Output means for outputting information on the stress of the user determined by using.

FIG. 1 is a block diagram illustrating an example of a system configuration according to the embodiment. FIG. 2 is a block diagram illustrating an example of the configuration of the sensor terminal 10. FIG. 3 is a block diagram showing an example of the configuration of the mobile terminal 12. FIG. 4 shows an example of functional blocks of the stress determination application 66b. FIG. 5 shows an example of a procedure by which the body motion data processing module 112 calculates the body motion data fluctuation amount and the body motion amount. FIG. 6 shows an example of determination of wakefulness / sleep by the body motion detection module 114 and the wakefulness / sleep determination module 120. FIG. 7 shows an example of the operation in which the pulse interval data processing module 128 interpolates the pulse interval data. FIG. 8 shows an example of an operation in which the autonomic nerve index calculation module 132 calculates the autonomic nerve indices LF and HF. FIG. 9 shows an example of a general adult LF / HF fluctuation state. FIG. 10 is a flowchart showing an example of the operation of the sensor terminal 10. FIG. 11 is a flowchart showing the first half of an example of the operation of the mobile terminal 12. FIG. 12 is a flowchart showing the second half of an example of the operation of the mobile terminal 12. FIG. 13 shows an example of the stress index table. FIG. 14 is a flowchart showing an example of the operation of the server 14. FIG. 15 shows an example of changes in the stress index. FIG. 16 shows another example of changes in the stress index.

  Hereinafter, embodiments will be described with reference to the drawings.

(Relationship between stress and pulse or heart rate)
Events related to the movement of the heart include heartbeat and pulse. The number of times the heart beats per minute is the heart rate, and the number of times the limb artery beats per minute is the pulse rate. In the case of a healthy person, both coincide with each other, and hence the pulse rate and the heart rate are not distinguished from each other and are referred to as a pulse rate or a heart rate. The pulse rate or the reciprocal of the heart rate is the pulse interval or the heart rate interval.

  The movement of the heart is governed by the action of the autonomic nerve. Autonomic nerves include sympathetic nerves and parasympathetic nerves. When the sympathetic nerve becomes active (activates), the pulse rate or heart rate increases, and when the parasympathetic nerve becomes active, the pulse rate or heart rate decreases. Moving your body activates your sympathetic nerves, which increases your heart rate. When resting when the body is not moving, the parasympathetic nerve is activated, and the heart rate decreases. Therefore, when the resting pulse rate or heart rate is used, the balance of the autonomic nerve can be stably evaluated. The balance of autonomic nerves is also affected by stress. When tension is applied, sympathetic nerves are activated by instantaneous stress and the heart rate is increased (the chest is throbbing). Further, when chronic stress due to the influence of the workplace or human relations is accumulated, the sympathetic nerve is always activated, and the pulse rate or the heart rate at rest increases. Thus, by using information on the pulse or heart rate (for example, the pulse rate or heart rate or the pulse interval or heart rate interval), the stress index regarding how much the stress is received can be determined. The stress index only needs to indicate the strength of stress at a certain point in time such as a human being or an animal, and will be described in detail later. For example, it is an autonomic nerve balance and includes only LF / HF, SDNN, PNN50, and HF. , And LF only. The instantaneous stress is calculated using a stress index measured in a short period (1 minute, 5 minutes, 15 minutes, etc.). Chronic stress is calculated using a stress index measured over a long period (one week, one month or more).

(System configuration)
FIG. 1 is a block diagram illustrating an example of a system configuration according to the embodiment. The embodiment shows a system composed of three units of a sensor terminal 10, a mobile terminal 12, and a server 14 for convenience of explanation.

  The sensor terminal 10 and the mobile terminal 12 are connected between the mobile terminal 12 and the server 14 by wireless or wired. For example, the sensor terminal 10 and the mobile terminal 12 are connected using Bluetooth (registered trademark), and the mobile terminal 12 and the server 14 are connected via the network 16. The sensor terminal 10 may transmit the measurement value to the mobile terminal 12, and the sensor terminal 10 may receive a control signal or the like from the mobile terminal 12.

  The sensor terminal 10 includes a sensor that measures various biological information. The biological information may be information related to the pulse or heartbeat, and includes, for example, information related to the magnitude of the user's heartbeat or pulse interval and information related to the change in the user's heartbeat or pulse interval. In the present embodiment, the biological information is used to obtain information regarding stress. The sensor terminal 10 may have a sensor capable of detecting biological information, and includes, for example, a pulse wave sensor and an acceleration sensor. A heart rate sensor may be used instead of the pulse wave sensor. The sensor terminal 10 may be a wearable terminal or a terminal provided in a bed or the like.

  In the case of a wearable terminal, the mode may be any of a wristband type, a wristwatch type, a ring type, a glasses type, an earring type, a pendant type, a pasting type, a garment built-in type, a lower sternum, etc. attached with a belt.

  As a sensor terminal provided in the bed, there is a so-called mat sensor. The mat sensor is a pressure sensor that is installed on the surface of the bed mat and detects heartbeat and body movement by measuring vibrations of the chest or abdomen of the user. The pressure sensor only needs to be able to detect vibration so as to be able to measure the absence of the user, bed presence, body movement, etc. For example, a polymer piezoelectric material such as polyvinylidene fluoride is formed into a thin film and flexible electrode films on both sides It is comprised by the piezoelectric element formed in tape shape by adhering.

  The mobile terminal 12 receives the biological information transmitted from the sensor terminal 10, calculates the received biological information, and obtains a stress index. The calculation for obtaining the stress index may be performed by the sensor terminal 10. In that case, a stress index is transmitted from the sensor terminal 14 to the mobile terminal 12. The mobile terminal 12 transmits the stress index to the server 14, the server 14 determines the degree of stress based on the stress index, and outputs the determination result from itself, or transmits the determination result to the mobile terminal 12, and the mobile terminal 12 to output. The determination is not limited to the server 14 and may be performed by the mobile terminal 12. Further, when the sensor terminal 10 performs an operation for obtaining a stress index, the determination may be performed by the sensor terminal 10. Examples of the determination include stress relief and stress accumulation. In the latter case, a warning may be generated from the sensor terminal 10, the mobile terminal 12, or the server 14. The mobile terminal 12 may be, for example, a mobile phone, a smartphone, a PC, a tablet terminal, or a dedicated terminal.

  The server 14 can be connected to many mobile terminals 12. For example, a server of a corporate health care organization receives a stress index from the mobile terminal 12 of the employee of the company, and accumulates information useful for determining the stress index and the stress state in the storage. An industrial physician or the like can access the server 14 using the client terminal 18, browse information related to employee stress, etc., determine the degree of stress, and perform health management accordingly.

  Although FIG. 1 shows a system composed of three units, the number of units is not limited to three. All the functions of the three units may be implemented in one unit, for example, the sensor terminal 10, and the functions of the three units may be implemented in two units, for example, the sensor terminal 10 and the mobile terminal 12 or the sensor terminal 10 and the server 14. You may divide and implement. For example, in FIG. 1, pulse wave data is transmitted from the sensor terminal 10 to the mobile terminal 12, a stress index is calculated from the pulse wave data at the mobile terminal 12, a stress index is transmitted to the server 14, and a stress index is transmitted to the server 14. Although it is accumulated, the calculation for obtaining the stress index by the sensor terminal 10, the accumulation of the obtained stress index may be performed, the server 14 may be omitted, the stress index may be accumulated by the mobile terminal 12, and the mobile terminal The terminal 12 may be omitted, and the server 14 may calculate the stress index and accumulate the calculated stress index. The sensor terminal 10 and the mobile terminal 12 are terminals for each user, and information from a large number of sensor terminals 10 can be uploaded to the server 14 via the large number of mobile terminals 12.

(Sensor terminal 12)
FIG. 2 is a block diagram illustrating an example of the configuration of the sensor terminal 10. The sensor terminal 10 includes an acceleration sensor 22, a pulse wave sensor 24, a Bluetooth module 26, a CPU 28, a memory 30, a flash memory 34, an embedded controller (EC) 36, a secondary battery (here, a lithium ion battery) 38, a charging terminal 40, A system controller 42, a display 43, a speaker 45, and the like are included. The system controller 42 is a bridge device that connects between the CPU 28 and each component.

  The acceleration sensor 22 measures, for example, −2G to 2G acceleration in three axis directions. Analog acceleration data output from the acceleration sensor 22 is input to the system controller 42 via the A / D converter 44. The A / D converter 44 adjusts the gain and offset of the analog data and then converts the analog data into 10-bit acceleration data. The acceleration sensor 22 constantly measures acceleration, and the A / D converter 44 supplies acceleration data to the system controller 42 at a constant cycle, for example, every 50 ms.

  The pulse wave sensor 24 is for measuring a volume change of a blood vessel that occurs when the heart pumps blood as a pulse wave, and is a light emitting element (for example, a green LED) 24a that is a light source and a light receiving unit. A photodiode 24b is included. The LED 24a and the photodiode 24b are mounted on the same plane. A transparent window is provided on the front surface of the pulse wave sensor 24, light from the LED 24a is applied to the skin surface through the window, and reflected light from the skin surface is incident on the photodiode 24b through the window. The photodiode 24b measures the fluctuation of the reflected light that changes due to the change in blood flow in the capillary. Analog measurement data output from the photodiode 24 b is converted into digital data by the A / D converter 46 and input to the system controller 42. The A / D converter 46 converts the output current from the photodiode 24b of the pulse wave sensor 24 into a voltage and amplifies the voltage, for example, a high-pass filter (cut-off frequency: 0.1 Hz) and a low-pass filter (cut-off frequency). : 50 Hz), and then converted into 10-bit pulse wave data. When the sensor terminal 12 is a ring-type wearable terminal, the photodiode may receive light that has passed through the blood vessel instead of reflected light.

  The Bluetooth module 26 is used for communication with the mobile terminal 12. The system controller 42 transmits acceleration data and pulse wave data to the mobile terminal 12 using the Bluetooth module 26. The system controller 42 receives a control signal from the mobile terminal 12 using the Bluetooth module 26.

  The CPU 28 is a processor that controls the operation of each component of the sensor terminal 10. The CPU 28 controls the operation of the sensor terminal 10 by executing an application program (hereinafter sometimes simply referred to as an application or an application) stored in the flash memory 34. The application can be updated. The operation of the sensor terminal 10 is not controlled by an application, but may be controlled by dedicated hardware such as a custom LSI, a semi-custom LSI, or a programmable DSP.

  The embedded controller 36 is a power management controller for executing power management of the sensor terminal 10 and controls charging of the lithium ion battery 38. When the sensor terminal 10 is attached to the charger 48, the charging current from the charger 48 is supplied to the sensor terminal 10 via the charging terminal 40, and the lithium ion battery 38 is charged. The embedded controller 36 supplies operating power to each component based on the power from the lithium ion battery 38.

  A device ID is assigned to the sensor terminal 10. When the sensor terminal 10 transmits acceleration data and pulse wave data to the mobile terminal 12, the sensor terminal 10 includes the device ID in the transmission data. For this reason, the mobile terminal 12 can identify the data from the sensor terminal 10 based on the device ID.

(Mobile terminal 12)
FIG. 3 is a block diagram showing an example of the configuration of the mobile terminal 12. The mobile terminal 12 includes a Bluetooth module 60, a wireless communication module 62, a CPU 64, a memory 66, an SSD (or HDD) 68, a display 70, a speaker 72, an embedded controller (EC) 74, and a secondary battery (here, a lithium ion battery) 76. A charging terminal 78, a system controller 82, and the like. The system controller 82 is a bridge device that connects between the CPU 64 and each component.

  The CPU 64 is a processor that controls the operation of each component mounted on the mobile terminal 12. The CPU 64 executes various software loaded into the memory 66 from the SSD 68 that is a nonvolatile storage device. This software includes an operating system (OS) 66a, a stress determination application program 66b, and the like. The memory 66 also includes a working memory 66c. The stress determination application program 66b obtains a stress index at a certain time of the user based on the acceleration data and the pulse wave data.

  Various examples of the stress index exist, but in the embodiment, an index indicating the balance of the autonomic nerve is used. An example of an index indicating the balance of the autonomic nerve will be described later. Based on the degree of change of the stress index, etc., it can be determined whether the user has accumulated stress and is in a chronic stress state or whether the stress has been eliminated. The stress index and the degree of change may be calculated by any of the sensor terminal 10, the mobile terminal 12, and the server 14. The stress index itself or the degree of change of the index may be displayed on the display 70 of the mobile terminal 12 or the client terminal 18 accessing the server 14. When the degree of change is equal to or greater than a predetermined threshold, a warning in which stress is accumulated may be issued from the display 70, the speaker 72, or the like of the mobile terminal 12. The application can be updated. The calculation for obtaining the stress index, the calculation for determining the degree of change of the index, and the process for determining the stress state based on the degree of change are not performed by the application, but dedicated hardware such as custom LSI, semi-custom It may be executed by an LSI or a programmable DSP.

  The display 70 may be a touch panel, and may display the stress index itself or the degree of change of the index based on the display signal from the system controller 82 under the control of the CPU 64, or the stress state is not good. May be displayed. Information and warning regarding stress may be output from the speaker 72.

  The Bluetooth module 60 communicates with the Bluetooth module 26 of the sensor terminal 10, receives biological information output from the sensor terminal 10, and transmits a control signal to the sensor terminal 10.

  The wireless communication module 62 is a module configured to execute wireless communication such as wireless LAN and 3G mobile communication or near field wireless communication such as NFC (Near Field Communication). The mobile terminal 12 is connected to the network 16 via the wireless communication device 62.

  The embedded controller 74 is a power management controller for executing power management of the mobile terminal 12 in the same manner as the embedded controller 36 of the sensor terminal 10, and controls charging of the lithium ion battery 76. When the mobile terminal 12 is attached to the charger 80, the charging current from the charger 80 is supplied to the mobile terminal 12 via the charging terminal 78, and the lithium ion battery 76 is charged. The embedded controller 74 supplies operating power to each component based on the power from the lithium ion battery 76.

(Stress index calculation)
FIG. 4 shows an example of functional blocks of the stress determination application program 66b. The stress determination application program 66b inputs acceleration data and pulse wave data transmitted from the sensor terminal 10. The stress determination application program 66b includes a body motion data processing module 112, a body motion detection module 114, a wake / sleep determination module 120, a midway wake detection module 122, a pulse wave data processing module 126, a pulse interval data processing module 128, and a frequency spectrum conversion. A module 130, an autonomic nerve index calculation module 132, a sleep state determination module 136, and the like are included.

  The body motion data processing module 112 obtains a body motion amount that is an average of the body motion data variation amount and the body motion data variation amount within the pulse interval from the triaxial acceleration data transmitted from the sensor terminal 10.

  An example of a procedure in which the body motion data processing module 112 calculates the body motion data fluctuation amount and the body motion amount is shown in FIG. Although the acceleration data is composed of three axis components of the X axis, the Y axis, and the Z axis, FIG. 5 shows the X axis component for convenience of explanation. The other axes (Y axis, Z axis) are the same as the X axis, and are not shown. The body motion data processing module 112 calculates a differential coefficient of acceleration data in the X-axis direction shown in FIG. FIG. 5B shows a one-minute change in the differential coefficient of acceleration in the X-axis direction. Next, the body motion data processing module 112 calculates the square root of the sum of squares of the differential coefficient of acceleration in the X-axis direction, and obtains the fluctuation amount of the body motion data shown in FIG. The body motion data processing module 112 calculates a body motion amount that is an average of the body motion data variation amount within the pulse interval from the body motion data variation amount. The fluctuation amount of the body movement data is, for example, the fluctuation amount of the user's body movement every 50 ms. The average value of the fluctuation amount of the body motion data for 1 minute is defined as the body motion amount. The body motion data processing module 112 provides the body motion detection module 114 with the fluctuation amount and the body motion amount of the body motion data as data for body motion detection. The body motion detection module 114 detects a user's body motion.

  The awake / sleep determination module 120 determines whether the user is sleeping or awake from the occurrence frequency of the body motion detected by the body motion detection module 114. An example of determination of arousal / sleep determination by the body motion detection module 114 and the arousal / sleep determination module 120 is shown in FIG. The body motion detection module 114 determines whether or not the fluctuation amount of the body motion data acquired from the body motion data processing module 112 is equal to or greater than a first threshold (for example, 0.01 G). Detect body movement.

  The awakening / sleep determination module 120 measures the frequency at which the body motion detection module 114 detects body motion in a set section, for example, 1 minute, and the body motion occurrence frequency is a second threshold value, for example, 5 times / minute or more. The user determines that the user is in an awake state. On the other hand, when the body motion occurrence frequency is less than the second threshold, the user determines that the user is in a sleep state. Alternatively, the arousal / sleep determination module 120 has a body motion occurrence frequency of a third threshold, for example, 20 times / minute or more, and pulse interval data obtained by processing to be described later is the pulse interval data during past sleep. If it is shorter than the average value, the user may determine that he is awake.

  The midway awakening detection module 122 acquires the number of midway awakenings and the total time from the determination result of the awakening / sleep determination module 120. Midway awakening refers to awakening between sleep and wake-up, and includes instantaneous awakening. Note that the midway arousal detection module 122 may acquire either one as a value indicating midway awakening without acquiring both the number of midway awakenings and the total time. The detection result of the midway awakening detection module 122 is fed back to the awakening / sleep determination module 120.

  Based on this feedback, the awake / sleep determination module 120 does not regard a short-term awakening of, for example, 30 minutes or 1 hour or less detected by the midway awakening detection module 122 as awakening, and also considers it to be a sleep during that time. Hesitate. For example, after detecting the first sleep, the first sleep may be detected, then the second sleep may be detected, and the second sleep may be detected. However, when the period from the first sleep detection timing to the second sleep detection timing is equal to or shorter than the threshold period, this period is regarded as sleeping. That is, the first sleep fall and the second fall asleep are ignored, and the sleep period is from the first fall asleep to the second fall asleep.

  The pulse wave data processing module 126 samples the pulse wave data transmitted from the sensor terminal 10, obtains a DC fluctuation component of the series of pulse wave data by time differentiation of the sampled series of pulse wave data, and obtains a series of pulse wave data. Remove DC fluctuation components from wave data. Then, the pulse wave data processing module 126 acquires the maximum value and the minimum value of the pulse wave data about 1 second before and after centering on the processing points of the series of pulse wave data from which the DC fluctuation component has been removed. A predetermined value between the minimum value is set as the fourth threshold value. For example, the fourth threshold value is 90% of the amplitude from the minimum value with the difference between the maximum value and the minimum value as the amplitude. Further, the pulse wave data processing module 112 calculates a time at which a series of pulse wave data values matching the fourth threshold appears from the series of pulse wave data from which the DC fluctuation component has been removed, and the calculated time interval. To obtain pulse interval data.

  The pulse interval data processing module 128 generates a series of pulse interval data, for example, a one-minute data set from the pulse interval data acquired by the pulse wave data processing module 126, and interpolates the series of pulse interval data with a high-order polynomial. To do. Here, an example in which the pulse interval data processing module 113 interpolates a series of pulse interval data will be described.

  An example in which the pulse interval data processing module 128 interpolates a series of pulse interval data is shown in FIG. The pulse interval data processing module 128 interpolates and resamples the irregularly spaced pulse interval data to generate equally spaced pulse interval data. For example, the pulse interval data processing module 128 generates equidistant pulse interval data using three sampling points before and after the point to be interpolated by a cubic polynomial interpolation method.

  The frequency spectrum conversion module 130 converts a series of equally spaced pulse interval data processed by the pulse interval data processing module 128 into a frequency spectrum distribution by a frequency analysis method such as FFT (Fast Fourier Transform) method. As shown in FIG. 8, the autonomic nerve index calculation module 132 acquires autonomic nerve indices LF and HF as stress indices from the value of the power spectrum. For example, the autonomic nerve index calculation module 132 takes the arithmetic average of the total value of three points of the peak value of a plurality of power spectra and one point at regular intervals around the peak value as LF and HF. The use of the FFT method as the frequency analysis method has an advantage that the burden of data processing is light, but any other method such as an AR model method, a maximum entropy method, and a wavelet method may be used. LF is an index in the low frequency region (near 0.05 to 0.1 Hz) of the power spectrum, and HF is an index in the high frequency region (near 0.15 to 0.4 Hz). HF reflects parasympathetic activity and LF reflects sympathetic activity. LF / HF, which is the ratio of LF and HF power, represents the overall balance of sympathetic and parasympathetic nerves. A large ratio indicates sympathetic dominance, and a small ratio indicates parasympathetic dominance. Therefore, an autonomic nerve index can be used as a stress index. Although the stress index includes an index other than the autonomic nerve index, the autonomic nerve index is synonymous with the stress index in the following description.

  Although the information LF and HF related to the pulse interval or the heart beat interval has been described as the autonomic nerve index, the index related to the autonomic nerve balance is not limited to LF and HF, and other indexes may be used as the stress index. The autonomic nerve index is classified into a time domain index and a frequency domain index. LF and HF are autonomic nerve indices in the frequency domain, and other examples of autonomic nerve indices in the frequency domain include VLF and total power. VLF is an index in the very low frequency region (around 0 to 0.05 Hz). The total power is a calculated value of the total power of the power spectrum at a frequency of 0 to 0.4 Hz (VLF, LF, HF) in a short time (for example, 5 minutes) test. This value reflects the overall autonomic nervous system activity, which is mainly occupied by sympathetic nerve activity.

  Autonomic indices in the time domain are HRT (heart rate), MeanNN (NN interval average), SDNN (NN interval standard deviation value), RMS-SD (adjacent NN interval standard deviation), PNN50 (rate of heart rate interval), TI (Tension index). HRT (heart rate) is the average value of all heart rates during the test period and is measured in 1 minute heart rate (BPM). MeanNN (NN interval average) is an average of all pulsation sense values during the test period. SDNN (NN interval standard deviation value) is the standard deviation between NNs and is the square root of the variance of NN intervals. RMS-SD (adjacent NN interval standard deviation) is the standard deviation of adjacent NN intervals, and is the average square root of the variance of adjacent NN intervals. PNN50 (rate of heart rate interval) is the average value of all heart rates during the test period and HRT is measured in 1 minute heart rate (BPM). TI (tension index) is a functional tension index of an autonomic nervous control mechanism that basically occurs due to mental stress.

  In the embodiment, the ratio LF of the power of the autonomic nerve index LF and HF is utilized by utilizing the influence of the fluctuation wave of HF and the fluctuation wave of LF on the change of the pulse rate depending on the balance of the active state of the sympathetic nerve and the parasympathetic nerve. / HF is used as a stress index.

  FIG. 9 shows an example of a variation of a general adult stress index (LF / HF). During awakening such as during the day, the stress index fluctuates greatly from a very large value to a very small value, but during sleep, the stress index fluctuates around a small value and the fluctuation range is small. Therefore, stable information about stress can be obtained by using the stress index during sleep instead of the stress index during the awakening period.

  The stress index may be constantly calculated during sleep and the stress state may be determined based on a number of stress indices, but it is known that the stress index decreases when the stress is eliminated. Therefore, the embodiment determines the stress state based on the change of the stress index during sleep. As two stress indices for obtaining changes, the stress index of T1 immediately after falling asleep (also referred to as a first stress index) and the stress index of T2 immediately before falling asleep (also referred to as a second stress index), which are the stress indices having the longest time interval, are used. ).

  Thus, since the stress index is not always calculated during sleep, the power consumption can be reduced. Further, since pulse wave data for obtaining a stress index is not constantly transmitted from the sensor terminal 10 to the mobile terminal 12, power consumption is still small.

  Immediately after falling asleep is a period of a first predetermined time from the time of falling asleep (change from awakening to sleep), and the first predetermined time may be several minutes (for example, 5 minutes) or several hours (for example, one hour or 2 to 3 hours). Immediately before falling asleep is a period from the time of falling asleep (change from sleep to awakening) to a second predetermined time before, and the second predetermined time may be several minutes (for example, 5 minutes) or several hours (for example, 1 hour or 2 to 3 hours). The first and second predetermined times may be different.

  The first and second stress indices may be obtained from pulse wave data at two times after a first predetermined time after falling asleep and before a second predetermined time before falling asleep. May be obtained from the calculated values (for example, average value, maximum value, minimum value, median value) of a plurality of pulse wave data at regular intervals. In the latter case, the plurality of pulse wave data may not be calculated by the sensor terminal 10 but may be transmitted from the sensor terminal 10 to the mobile terminal 12 and calculated by the mobile terminal 12.

  Whether the sign of the change value of the stress index, which is the difference between the first stress index and the second stress index (second-first), is positive (indicator increases), negative (indicator decreases), or ± 0 (no increase or decrease) Based on this, it can be determined that the stress is accumulated or eliminated. It can be determined from the absolute value of the change value how much stress is accumulated or eliminated. Furthermore, it is possible to determine how much stress is accumulated or eliminated based on the rate of change, which is the rate of change per unit time, rather than just a change value. The change rate is obtained by dividing the change value by the difference between the times when the first stress index and the second stress index are obtained. Furthermore, the stress state can also be determined based on the value of the second stress index itself during sleep. The determination example described above is a determination example based on the overnight change of the stress index. However, when the tendency of the change of the stress index over a long period of time (for example, one week or one month) is examined, chronic stress Health status can be determined.

  The sleep state determination module 136 in FIG. 4 determines the user's sleep state from the LF / HF calculated by the autonomic nerve index calculation module 132. For example, the sleep state determination module 136 determines that the sleep state is deep sleep (non-REM sleep) when LF / HF is smaller than the fifth threshold and HF is larger than the sixth threshold, and When LF / HF is larger than the seventh threshold, HF is smaller than the eighth threshold, and the sum of standard deviations of LF and HF is larger than the ninth threshold, it is determined that the sleep state is REM sleep. In cases other than deep sleep (non-REM sleep) and REM sleep, light sleep (non-REM sleep) is determined.

(Operation example)
Various operation modes of the embodiment will be described.

  As shown in FIG. 9, two stress indexes immediately after falling asleep and immediately before falling asleep are calculated. In order to calculate the stress index immediately after falling asleep and immediately before falling asleep, pulse wave data immediately after falling asleep and immediately before falling asleep are required. For this purpose, the sensor terminal 10 or the mobile terminal 12 needs to know whether the user wearing the sensor terminal 10 is currently sleeping or awake. When the sleep time is determined in advance, the sleep time, sleep time, first predetermined time, and second predetermined time are input to the sensor terminal 10 in advance, and the time or period that the sensor terminal 10 measures can be set in advance. . Thereby, the sensor terminal 10 can turn on the pulse wave sensor 24 at a preset time or period, and transmit pulse wave data for calculating the first and second stress indices to the mobile terminal 12. . In this case, the acceleration sensor 22 is not necessary.

  Although it is rare that the sleeping time is fixed, the wake-up (sleeping) time may be fixed. In this case, the sleep time and the second predetermined time may be input to the center terminal 10 in advance, and the time or time measured by the sensor terminal 10 may be set in advance. Thereby, the sensor terminal 10 can turn on the pulse wave sensor 24 at a preset time or period, and transmit pulse wave data for calculating the second stress index to the mobile terminal 12. The sleep time is determined by the awake / sleep determination module 120 based on the output of the acceleration sensor 22, and the pulse wave sensor 24 is turned on after the first predetermined time from the sleep time or after the first predetermined time from the sleep time, and the first stress Pulse wave data for calculating the index is transmitted to the mobile terminal 12.

  When the sleeping time (sleeping time and sleeping time) has not been determined, the sleep / sleep time is determined by the awake / sleep determination module 120 based on the output of the acceleration sensor 22, and the sensor terminal 10 determines the pulse according to the determination result. The wave sensor 24 and the like are controlled. The sensor terminal 10 collects pulse wave data periodically during sleep, and stores pulse wave data for a certain period in the memory 30 at all times. The mobile terminal 12 requests pulse wave data from the sensor terminal 10 after the sleep / wakefulness determination by the awakening / sleep determination module 120. In response to this request, the sensor terminal 10 can read past pulse wave data for calculating the second stress index from the memory 30 and transmit it to the mobile terminal 12. In addition, since the sleep time may be determined somewhat later than the sleep time instead of the moment, the sensor terminal 10 always collects the pulse wave data regularly and stores the pulse wave data for a certain period in the memory 30 at all times. Store it. The mobile terminal 12 requests pulse wave data from the sensor terminal 10 after the sleep / sleep determination by the awakening / sleep determination module 120 or the sleep / sleep determination. In response to this request, the sensor terminal 10 can read the pulse wave data for calculating the first or second stress index from the memory 30 and transmit the pulse wave data to the mobile terminal 12.

(Operation of sensor terminal 10)
FIG. 10 is a flowchart showing the operation of the sensor terminal 10. In block 202, the system controller 42 writes the pulse wave data measured by the pulse wave sensor 24 into the memory 30. In block 204, the system controller 42 transmits the acceleration data measured by the acceleration sensor 22 to the mobile terminal 12 through the Bluetooth module 26. Since the acceleration sensor 22 measures acceleration data at regular intervals, for example, every 50 ms, the block 204 is executed at regular intervals. In block 206, the system controller 42 determines whether or not the mobile terminal 12 has requested transmission of pulse wave data.

  If not, the process returns to block 202 and the writing of the pulse wave data to the memory 30 is repeated. The storage capacity of the pulse wave data in the memory 30 is constant, and when there is no more free capacity, the temporally old data is sequentially overwritten with new data. For this reason, the pulse wave data of the latest predetermined period, for example, 1 hour is stored in the memory 30.

  In block 206, when transmission of pulse wave data is requested from the mobile terminal 12, the system controller 42 reads the requested pulse wave data from the memory 30 and transmits it to the mobile terminal 12.

(Operation of mobile terminal 12)
11 and 12 are flowcharts showing the operation of the mobile terminal 12. In block 222, the system controller 82 receives acceleration data transmitted from the sensor terminal 10 by the Bluetooth module 60. Since the acceleration data is transmitted from the sensor terminal 10 at regular intervals, for example, every 50 ms, the mobile terminal 12 also receives the acceleration data at block 222 at regular intervals. The acceleration data is processed by the stress determination application 66b.

  In block 224, the body motion data processing module 112 obtains the amount of fluctuation of the body motion data from the acceleration data as shown in FIG.

  In block 226, the body motion detection module 114 detects the body motion from the fluctuation amount of the body motion data as shown in FIG. At block 228, the awake / sleep determination module 120 measures body motion detection frequency, as shown in FIG. When the detection frequency of body movement is obtained, in block 232, the awakening / sleep determination module 120 awakens the user wearing the sensor terminal 10 based on the detection frequency of body movement as shown in FIG. Whether you are sleeping or sleeping.

  That is, the body motion detection module 114 detects the user's body motion when the fluctuation amount of the body motion data acquired from the body motion data processing module 112 is equal to or greater than the first threshold. The awakening / sleep determination module 120 obtains the number (frequency) of body movements detected in a set section, for example, 1 minute, and determines that the user is awake if the body movement detection frequency is equal to or greater than the second threshold. In other cases, it is determined that the user is sleeping. The determination of awakening / sleep is not limited to the above example, and any determination may be made.

  If it is determined in block 232 that the user is sleeping, in block 233, the awakening / sleep determination module 120 further determines whether the previous determination result is awakening. If the previous determination result is sleeping, processing returns to block 222 and acceleration data reception continues.

  If the previous determination result is awakening, that is, if the current state is immediately after falling asleep, in block 234, the system controller 82 transmits pulse wave data necessary for calculating the first stress index immediately after falling asleep. A request is made to the sensor terminal 10 by the Bluetooth module 60. In response to this request, the sensor terminal 10 transmits the pulse wave data immediately after falling asleep to the mobile terminal 12 as indicated by block 208 in FIG.

  At block 236, when the pulse wave data is received by the Bluetooth module 60, a first stress index is calculated. For example, in block 236, the pulse wave data processing module 126 samples the pulse wave data transmitted from the sensor terminal 10, and time-differentiates the series of sampled pulse wave data to obtain pulse interval data from the series of pulse wave data. get. The pulse interval data processing module 128 generates a series of pulse interval data from the pulse interval data, and interpolates the series of pulse interval data with a high-order polynomial. The frequency spectrum conversion module 130 converts the interpolated pulse interval data into a frequency spectrum distribution by a frequency analysis method such as an FFT (Fast Fourier Transform) method. The autonomic nerve index calculation module 132 takes the arithmetic average of the total value of a total of three points including the peak value of the plurality of power spectra and the peak value at the center and two points at equal intervals before and after that as LF and HF. The LF / HF at this time is the first stress index immediately after falling asleep.

  At block 238, the system controller 82 writes the first stress index along with the sleep time, index measurement time, etc., into the stress index table in the memory 66.

  An example of the stress index table is shown in FIG. The stress index table manages the stress index for each user. The sensor terminal 10 that is a wearable terminal is generally a user-specific terminal, but it may happen that another person's wearable terminal is used by mistake. A plurality of users often use the sensor terminal 10 provided in the bed instead of the wearable terminal. Therefore, the stress index table also has a user ID in addition to the device ID. When a user uses the sensor terminal 10 for the first time, the mobile terminal 12 registers the user of the sensor terminal 10 and associates the stress index table with the device ID / user ID. When the sensor terminal 10 is remounted after being removed from the user, the user ID is input, and the stress index table associated with the user ID is specified. Thereby, even when a plurality of users use the same sensor terminal 10, the stress index calculated based on the pulse wave data from the sensor terminal can be written in the stress index table of the same user.

  The index change value is (second stress index immediately before falling asleep) − (first stress index immediately after falling asleep). When stress is eliminated by sleep, the value of the stress index decreases and the change value becomes negative. When the value of the second stress index is larger than the value of the first stress index, the change value is positive and stress is accumulated. When the stress state does not change, the change value is ± 0.

  The index change rate is a change value per unit time, and is a value obtained by dividing the index change value by the period from the first time related to the first stress index to the second time related to the second stress index. The absolute value of the rate of change indicates the degree of elimination (when negative) / the degree of accumulation (when positive). When the rate of change is negative and the value is large, it can be determined that the force against the stress is strong. When the rate of change is positive and the value is large, the force against the stress is weak and it can be determined that the stress tends to accumulate.

  The number of midway awakenings and the midway awakening time are acquired by the midway awakening detection module 122 of FIG.

  After execution of block 238, processing returns to block 222 to continue receiving acceleration data.

  If it is determined in block 232 that the user is awake, in block 243 (FIG. 12), the awake / sleep determination module 120 further determines whether or not the previous determination result is sleeping. If the previous determination result is awakening, the process returns to block 222 (FIG. 11) and acceleration data reception continues.

  If the previous determination result is sleeping, that is, if the current state is immediately after falling asleep, in block 244, the system controller 82 calculates pulse wave data necessary for calculating the second stress index immediately before falling asleep. Is transmitted to the sensor terminal 10 by the Bluetooth module 60. In response to this request, the sensor terminal 10 transmits the pulse wave data immediately before falling asleep to the mobile terminal 12 as indicated by block 208 in FIG.

  When the pulse wave data is received by the Bluetooth module 60 at block 246, the autonomic nerve index is calculated as in block 236. The LF / HF at this time is the second stress index immediately before falling asleep.

  In block 248, the system controller 82 writes the second stress index into the stress index table in the memory 66 together with the sleep time, the index measurement time, and the like. Since the stress index table manages data for each sleep, the date is the date before going to bed.

  In block 250, the system controller 82 writes the index change value, the index change rate, the midway awakening time, and the number of midway awakenings in the stress index table, and completes the table for one day shown in FIG. The daily stress index table is stored in the memory 66 of the mobile terminal 12, uploaded to the server 14, and stored in the storage of the server 14 (not shown). The server 14 can store a stress index table for all past dates of all users.

  It can be determined from the stress index table for one day whether or not the stress due to overnight sleep has been eliminated. For example, if the stress is eliminated by sleep, the change value becomes a large negative value. Therefore, the system controller 82 determines in block 252 whether the change value is negative and the absolute value is equal to or greater than a threshold value. If yes, at block 254, the system controller 82 notifies that the stress has been removed. For example, a recovery message may be displayed on the display 70 of the client terminal 12 and a recovery sound may be output from the speaker 72. Alternatively, a recovery message may be displayed on the display 43 of the sensor terminal 10 and a recovery sound may be output from the speaker 45. If no, at block 253, the system controller 82 warns that the stress has not been removed. For example, a warning message may be displayed on the display 70 of the client terminal 12 or a warning sound may be output from the speaker 72. Alternatively, a warning message may be displayed on the display 43 of the sensor terminal 10 or a warning sound may be output from the speaker 45.

  Instead of the change value, it is possible to determine the overnight stress relieving status by the second stress index alone immediately before falling asleep. The system controller 82 may determine at block 252 whether the second stress index is greater than or equal to a threshold value. If the second stress index is greater than or equal to the threshold, block 253 is executed, and if the second stress index is not greater than or equal to the threshold, block 254 is executed.

  In block 256, the system controller 82 determines the difference between the measurement value of the previous day and the current measurement value, and determines whether the difference from the previous day is equal to or greater than a threshold value. If it is determined that the threshold value is exceeded, the user may have attached a sensor terminal different from yesterday, or may have registered as a user by mistake. Therefore, if the difference from the previous day is greater than or equal to the threshold, at block 258, the system controller 82 sends a message requesting confirmation from the mobile terminal 12 or sensor terminal 10 that the user of the sensor terminal 10 is the same as yesterday. As output. Thereby, the misjudgment based on a comparison with another person's stress parameter | index can be prevented. After execution of block 258 or if it is determined at block 256 that the difference is not greater than or equal to the threshold, processing returns to block 222 to continue receiving acceleration data.

  In FIG. 9, sleep from sleep to sleep is defined as one sleep, but sleep is classified into REM sleep with shallow sleep that is close to awake state with rapid eye movement and non-REM sleep with deep sleep. The Non-REM sleep is divided into four stages according to the depth of sleep. When falling asleep, it first becomes REM sleep, then becomes the shallowest sleep level 1 non-REM sleep, gradually increases the depth of sleep, reaches level 4 and gradually decreases the sleep depth, level 1 non-REM Return to sleep and even REM sleep. The cycle of returning from REM sleep to non-REM sleep to REM sleep is about 90 minutes, and this cycle is repeated. In most cases, the sleep state immediately after falling asleep is REM sleep, but the sleep state immediately before falling asleep differs depending on the sleep time. Since the wakefulness of the brain differs between REM sleep and non-REM sleep (including shallow sleep states of levels 1 and 2 and deep sleep states of levels 3 and 4), the first state when the sleep state immediately before going to sleep is non-REM sleep The stress index and the second stress index may be values in different sleep states.

  The stress state may be more accurately determined based on a comparison between the first stress index immediately after falling asleep and the second stress index immediately before falling asleep in the same sleep state. Therefore, the first stress index may be obtained during the first REM sleep or non-REM sleep during sleep, and the second stress index may be obtained during the last REM sleep or non-REM sleep during sleep.

  In order to cope with this, in the flowchart of FIG. 11, after detecting immediately after falling asleep in block 233, a block for determining whether or not the first REM sleep or non-REM sleep is added, and the first REM sleep or non-REM sleep is detected. In such a case, the block 234 may be modified to be executed. Similarly, in the flowchart of FIG. 12, the pulse wave data at the time of the last REM sleep or non-REM sleep (last REM sleep or non-REM sleep during the sleep period) is read from the memory 30 in block 244 after sleep detection is detected in block 243. What is necessary is just to deform | transform so that it may transmit to the mobile terminal 12. Thereby, the first stress index immediately after falling asleep in the same sleep state and the second stress index immediately before falling asleep can be compared, and the degree of stress relief can be determined more accurately.

(Operation of server 14)
FIG. 14 is a flowchart showing an example of the operation of the server 14. Here, an industrial physician or the like who manages the health of employees of a company accesses the server 14 using the client terminal 18 and browses information related to the stress of the employee (stress index change value, rate of change, etc.) An example of diagnosing As a premise, the employee always wears the wearable sensor terminal 10 and the mobile terminal 12 which is the employee's smartphone collects pulse wave data from the sensor terminal 10. The mobile terminal 12 calculates the first and second stress indicators, the change value / change rate of the index, and the like from the pulse wave data immediately after falling asleep and immediately before falling asleep, and uploads the calculation results to the server 14. The server 14 accumulates information on stress for each user in the storage.

At block 272, the user's ID is entered. At block 274, the user's relatively long-term (eg, one week, one month) stress index table is read from the storage of the server 14 and displayed on the client terminal 18. An example of the display is shown in FIG. A graph showing the change (broken line) of the first and second stress indicators and a numerical value showing the change value and the change rate are displayed. The subscripts of t1 ₁ , t1 ₂ , t1 ₃ , t1 ₄ immediately after falling asleep, which is the timing of the first stress index, and t2 ₁ , t2 ₂ , t2 ₃ , t2 ₄ immediately before falling asleep, which is the timing of the second stress index, are dates. Indicates. FIGS. 15 and 16 are display examples showing the first stress index and the second stress index for four days, but in actuality, a reduced number of days is displayed on one screen. Alternatively, the size is left as it is, and the part that is not displayed is displayed by scrolling. FIG. 16 shows an example of another user's display. At the timing of block 274, the change mark and change rate are not displayed with a ☆ mark or ◎ mark. The display of the stress index is not limited to the modes shown in FIGS. 15 and 16, but may be a table as shown in FIG.

  The human can diagnose the stress state by looking at the display of FIGS. 15 and 16, but displaying the result determined by the server 14 from the stress index together may be useful for human diagnosis. Examples of information used in the determination of the server 14 are the second stress index and the change value / change rate of the stress index during sleep.

  The server 14 makes a determination based on the second stress index in block 276, and makes a determination based on the change value / change rate of the stress index in block 278. Examples of determination using the second stress index are as follows.

  If the second stress index of the newest date is equal to or greater than the threshold, it is determined that the stress has accumulated, and otherwise, it is determined that the stress has been eliminated.

  When the average value of the second stress index in the past is equal to or greater than the threshold value, it is determined that the stress has accumulated, and otherwise, it is determined that the stress has been eliminated.

  When it is determined that the change in the past second stress index is increasing, it is determined that the stress has accumulated, and in other cases, it is determined that the stress has been eliminated.

  The server 14 makes the above three determinations at block 276, and outputs a chronic stress warning at block 282 if any of the determination results indicate that stress has accumulated. An example of the warning is to add a ☆ mark to the change value or change rate value on the stress indicator display screen of FIG. Other examples of warnings include outputting warnings by characters or voice / sound from the client terminal 18 to the industrial physician, and outputting warnings by letters or voices / sound from the mobile terminal 12 or the sensor terminal 10 to the user. Including doing. The warning mode may be changed according to the severity of chronic stress. For example, the number of ☆ marks may be increased or the size may be increased according to the severity.

  When all the determination results in block 276 have been relieved of stress, the server 14 performs determination based on the change value / change rate of the stress index in block 278. Examples of determination based on the change value or rate of change of the stress index are as follows. The following is an example for determining that stress has accumulated.

  -The change value of plus or ± 0 continues for a predetermined number of days such as 3 days or 1 week.

  -The average value of change values for a given number of days is plus or ± 0.

  -The rate of change is positive, and the value is larger than the value of the rate of change before the predetermined number of days in the past.

  If the above determination is yes, it can be determined that the subject has been exposed to chronic stress, and if the determination is no, it can be determined that the stress has been eliminated.

  The server 14 makes the above three determinations at block 278 and outputs a chronic stress warning at block 282 if any determination result is yes. If all three determinations are no, block 280 notifies the patient of chronic stress relief. An example of the notification is to add a mark to the numerical value of the change value or the change rate of the stress index display screen in FIG. Another example of the notification is to notify from the client terminal 18 or the mobile terminal 12 by text or voice / sound as in the case of the chronic stress warning. The notification form may also be changed according to the degree of cancellation. For example, the number of marks may be increased or the size may be increased according to the resolution.

  Note that the display of the determination result (including warning) is not limited to the server 14 or the mobile terminal 12 but may be performed by the sensor terminal 10.

  Access to the server 14 is not limited to the client terminal 18, and the mobile terminal 12 may access and the user himself / herself may view the displays of FIGS. 15 and 16.

(Summary of embodiment)
According to the embodiment, the following electronic device is realized.

(1) acquisition means for acquiring biological information relating to a user's pulse or heartbeat measured by a sensor;
The first stress index is determined using the first biological information measured by the sensor during the first period after falling asleep, and the second biological information measured by the sensor during the second period before falling asleep is used to determine the first stress index. 2 a determination means for determining a stress index;
A determination means for determining the degree of change of the stress index during the period from falling asleep to falling asleep using at least the first stress index and the second stress index;
An electronic device comprising: output means for outputting information relating to a user's stress using the degree of change of the stress index determined by the determination means.

  According to this electronic device, the state of chronic stress can be stably determined by determining two stress indices immediately after falling asleep and immediately before falling asleep. In addition, since the stress index is not always determined, less power is consumed.

  (2) In the electronic device of (1), the sensor is a wearable device or a sensor device attached to the bed.

  (3) In the electronic device of (1), the biological information relates to the pulse interval or the magnitude of the heartbeat interval.

  (4) The electronic device of (1) is connected to the sensor by wire or wireless, and acquires biological information measured by the sensor. An example of wireless is Bluetooth. According to this electronic device, there is no need to constantly transmit biological information from the sensor to the electronic device, and power consumption for transmission can be reduced.

  (5) In the electronic device of (1), examples of the stress index are LF / HF, LF, HF, SDNN, and PNN50.

  (6) In the electronic device of (1), an example of the length of the first and second periods is several minutes or 1 to 3 hours.

  (7) In the electronic device of (1), whether the determination unit is in a first state where the second stress index is smaller than the first stress index, or is the second state where the second stress index is equal to the first stress index Or whether the second stress index is in a third state greater than the first stress index.

(8) The electronic device of (1) further includes second determination means for determining whether the user is sleeping or awake based on the acceleration measured by the acceleration sensor. According to this electronic device, the first period after falling asleep and the second period before falling asleep can be obtained more accurately.
(9) In the electronic device of (8), the second determination means determines that the awakening for a predetermined period or less is a mid-wake awakening and is sleeping. According to this electronic device, the second period before falling asleep can be obtained more accurately.

  (10) In the electronic device of (1), both the first and second periods are included in the non-REM sleep period or the REM sleep period. According to this electronic apparatus, since the degree of change of the stress index in the same sleep state is determined, more correct information regarding stress can be obtained.

  (11) In the electronic device of (1), information on the user's stress is output from the electronic device itself or a sensor.

(Modification)
Although the pulse has been described as the biological information, an independent nerve index can be calculated in the same manner from the heartbeat. An electrocardiographic sensor may be used instead of the pulse wave sensor of the above-described embodiment. If an electrocardiographic signal is acquired from an electrocardiographic sensor, a heartbeat for each beat is detected, and a heartbeat is used instead of the pulse, the same effects as those of the above-described embodiment can be obtained.

  Although an example in which a sensor is attached to a human body to detect a pulse or a heartbeat has been described, a camera that captures a user may be used instead of the sensor. The camera only needs to be able to capture an image that can detect the user's movement, and for example, captures an image of a futon hung on the user. A difference for each frame in the time direction is calculated from an image captured by the camera, and a difference value for each pixel is summed to obtain a value corresponding to the amount of displacement. Since the futon hung on the user is slightly displaced in synchronization with the user's breathing and heartbeat, the value corresponding to the displacement obtained from the difference between the images of the imaged futon is based on the user's breathing and heartbeat. It depends on the indicated value. Therefore, the heart rate can be measured by analyzing a value corresponding to the amount of displacement.

  Since the processing of the present embodiment can be realized by a computer program, the same effect as that of the present embodiment can be obtained simply by installing and executing the computer program on a computer through a computer-readable storage medium storing the computer program. Can be easily realized.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Further, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, you may combine suitably the component covering different embodiment.

DESCRIPTION OF SYMBOLS 10 ... Center terminal, 12 ... Mobile terminal, 14 ... Server, 16 ... Network, 22 ... Acceleration sensor, 24 ... Pulse wave sensor, 26, 60 ... Bluetooth module, 114 ... Body motion detection module, 120 ... Awakening / sleep determination module 128 ... Pulse interval data processing module, 130 ... Frequency spectrum conversion module, 132 ... Autonomic nerve index calculation module, 136 ... Sleep state determination module.

Claims (12)

Acquisition means for acquiring biological information relating to a user's pulse or heartbeat measured by a sensor;
The first stress index is determined using the first biological information measured by the sensor within the first period after falling asleep, and the second biological information measured by the sensor is used during the second period before falling asleep. Determining means for determining the second stress index;
Output means for outputting information on the user's stress determined using the degree of change in the stress index from falling asleep to falling asleep;
An electronic device comprising:   The degree of change in the stress index from falling asleep to falling is based on the first state in which the value of the second stress index is smaller than the value of the first stress index, and the value of the second stress index is the first stress index. 2. The electronic device according to claim 1, wherein the electronic device is in any one of a second state that is equal to the value of the second stress and a third state in which the value of the second stress index is greater than the value of the first stress index.   The electronic device according to claim 1, further comprising: a determination unit that determines whether the user is sleeping or awake based on an acceleration measured by an acceleration sensor. The determination means determines whether the user's sleep period is a REM sleep period or a non-REM sleep period using the biological information related to the user's pulse or heartbeat measured by the sensor,
The determining means determines the first stress index using the first biological information measured by the sensor within the first period from the start of the first REM sleep period or the non-REM sleep period of the sleep period, The electronic device according to claim 3, wherein the second stress index is determined using second biological information measured by the sensor within a second period until the end of the last REM sleep period or the non-REM sleep period. Obtaining biological information about the user's pulse or heartbeat measured by the sensor;
The first stress index is determined using the first biological information measured by the sensor within the first period after falling asleep, and the second biological information measured by the sensor is used during the second period before falling asleep. Determining a second stress index,
Outputting information on the user's stress determined using the degree of change in the stress index from falling asleep to falling asleep;
A method comprising:   The degree of change in the stress index from falling asleep to falling is based on the first state in which the value of the second stress index is smaller than the value of the first stress index, and the value of the second stress index is the first stress index. The method according to claim 5, wherein the second state is equal to the value of the second stress index, and the third stress index is greater than the first stress index value.   6. The method of claim 5, further comprising determining whether the user is sleeping or awake based on acceleration measured by an acceleration sensor. Determining whether the user is sleeping or awakening is based on the biometric information about the user's pulse or heart rate measured by the sensor, and the user's sleep period is a REM sleep period or a non-REM sleep period. Determining whether there is,
The determination of the first stress index and the second stress index is performed using first biological information measured by the sensor within a first period from the start of a first REM sleep period or a non-REM sleep period of a sleep period. The first stress index is determined, and the second stress index is determined using the second biological information measured by the sensor during the second period until the end of the last REM sleep period or the non-REM sleep period of the sleep period. 8. The method of claim 7, comprising: A program executed by a computer, the program being stored in the computer,
Obtaining biological information about the user's pulse or heartbeat measured by the sensor;
The first stress index is determined using the first biological information measured by the sensor within the first period after falling asleep, and the second biological information measured by the sensor is used during the second period before falling asleep. Determining a second stress index,
Outputting information on the user's stress determined using the degree of change in the stress index from falling asleep to falling asleep;
A program for running   The degree of change in the stress index from falling asleep to falling is based on the first state in which the value of the second stress index is smaller than the value of the first stress index, and the value of the second stress index is the first stress index. The program according to claim 9, wherein the second state is equal to the value of the second stress index and the third state is greater than the value of the first stress index.   The program according to claim 9, further comprising determining whether the user is sleeping or awake based on an acceleration measured by an acceleration sensor. Determining whether the user is sleeping or awakening is based on the biometric information about the user's pulse or heart rate measured by the sensor, and the user's sleep period is a REM sleep period or a non-REM sleep period. Determining whether there is,
The determination of the first stress index and the second stress index is performed using first biological information measured by the sensor within a first period from the start of a first REM sleep period or a non-REM sleep period of a sleep period. The first stress index is determined, and the second stress index is determined using the second biological information measured by the sensor during the second period until the end of the last REM sleep period or the non-REM sleep period of the sleep period. The program according to claim 11, further comprising:

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