JP4906967B1 - Automatic notification device - Google Patents

Abstract

An automatic notification device capable of automatically reporting to a monitoring center when a human body is in a “unusual psychosomatic state”.
Physiological data consisting of vibration or pulse wave accompanying respiration is extracted from a biological signal detected by a sensor. The physiological data is analyzed by the analyzing means 8 for power spectrum density. The determination means 18 compares the analysis data in the normal state where the human body is relaxed with the analysis data obtained by analyzing the physiological data as needed, and when the ratio matches a predetermined determination criterion, State ". At this time, the automatic alarm issuing means 22 automatically notifies the monitoring center. Thereby, dispatch of a security organization can be accelerated.
[Selection] Figure 2

Description

  The present invention relates to an automatic notification device.

2. Description of the Related Art Conventionally, a notification device installed to deal with an emergency such as a burglary is known at a convenience store or a financial institution. In general, when a victim who has faced an emergency activates an alarm button, the situation is reported to a monitoring center.
With this type of reporting device, victims who face an emergency situation may be surprised and terrified, making it harder and harder to activate the alarm button. Alternatively, it may be difficult to activate the alarm button if the victim is placed under criminal supervision.
Patent Document 1 describes a security system that detects and activates when a person's heart, pulse, and blood pressure change drastically.
Patent Document 2 describes a system that detects that a person suddenly felt fear and issues an alarm. In this system, a person's physiological condition is monitored, and a computer determines whether the person feels fear from the biological signal. The computer suddenly becomes afraid by processing the physiological signal with profile data based on the accumulation of data measuring the stressed state of the person and the statistical classification taking into account the combination of the person's characteristics. Determine if you feel.

JP-A-9-7073 Special table flat 10-506490

However, Patent Document 1 does not clearly describe how a person's heart, pulse, or blood pressure changes when it is identified as a severe change, so the reliability of the security system is low.
Since Patent Document 2 does not clearly describe how a biological signal detected from a person is processed by profile data or the like to determine that the person felt fear, the reliability of the alarm Is not only low, but also difficult to implement itself.
The present invention has been made in view of the above problems, and an object of the present invention is to provide an automatic notification device capable of automatically reporting to a monitoring center when a human body is in an “unusual mind and body state”.

In order to solve the above problems, according to the first aspect of the present invention, the automatic notification device includes a sensor, a biological signal detection unit, a data storage unit, a determination unit, and an automatic alarm notification unit.
The sensor detects a biological signal of the human body. The biological signal detection means extracts physiological data composed of vibration or pulse wave accompanying respiration from the biological signal detected by the sensor.
The analysis means analyzes the physiological data extracted by the biological signal detection means. The data accumulating means accumulates analysis data obtained by the analysis means analyzing physiological data when the human body is in a relaxed normal state.
The determination unit compares the analysis data stored in the data storage unit with the analysis data obtained by analyzing the physiological data extracted by the biological signal detection unit at a predetermined time interval, and a determination criterion whose ratio is set in advance. If it matches, it is determined that the human body is in a “non-normal mental and physical state”. Then, when the determination means determines that the human body is in an “unusual psychosomatic state”, the automatic alarm issuing means issues an automatic notification or alarm.

A psychological state in which the human body feels surprised or fears appears in the biological signal. By analyzing the physiological data extracted from the biological signal and comparing it with the analysis data in the normal state, it is possible to determine that the human body is in a “psychological body state different from normal”.
This makes it possible for a victim facing an emergency situation to become alarmed by surprise and fear, or if the victim is placed under criminal supervision, without causing the victim to activate an alarm button. Automatic notification to a monitoring center or an alarm at the monitoring center. Therefore, the dispatch of the security agency can be accelerated.

According to the invention of claim 2, the sensor is an optical sensor or a pressure sensor.
Fingertip pulse waves and earlobe pulse waves can be detected by the optical sensor. A person wearing the optical sensor can move freely. Therefore, for example, convenience is improved in a convenience store or a financial institution.
The pressure sensor can detect the vibration of the body surface due to the pulse wave and respiration as a change in pressure value. The pressure sensor can be used by being attached to a cushion or a seat cushion.

According to the invention of claim 3, the analysis means analyzes the physiological data of a certain time extracted by the biological signal detection means at predetermined time intervals.
If the predetermined time is lengthened, the reliability of the determination result of the determination means is increased. If the predetermined time interval is shortened, it becomes possible for the determination means to quickly determine that the human body is in a “non-normal psychosomatic state”. Accordingly, the automatic notification device can quickly and automatically notify highly reliable information to the monitoring center.

It is a block diagram which shows the structure of the automatic notification apparatus by 1st Embodiment of this invention. It is a block diagram which shows the structure of the automatic notification apparatus by 1st Embodiment of this invention. 6 is a power spectral density function graph of Verification Example 1. It is a continuation of FIG. It is the table | surface which displayed "the ratio with the normal time" about the power spectrum contained in the frequency component of the verification example 1. FIG. It is a continuation of FIG. It is a power spectrum density function graph of the verification example 2. FIG. 7 is a continuation of FIG. It is the table | surface which displayed "the ratio with normal time" about the power spectrum contained in the frequency component of the verification example 2. FIG. It is a continuation of FIG. It is a power spectral density function graph of the verification example 3. It is a continuation of FIG. It is the table | surface which displayed "ratio with normal time" about the power spectrum contained in the frequency component of the verification example 3. FIG. It is a continuation of FIG. 10 is a power spectral density function graph of verification example 4. It is a continuation of FIG. It is the table | surface which displayed "ratio with normal time" about the power spectrum contained in the frequency component of the verification example 4. FIG. FIG. 17 is a continuation of FIG. 10 is a power spectral density function graph of verification example 5. It is a continuation of FIG. It is the table | surface which displayed "ratio with normal time" about the power spectrum contained in the frequency component of the verification example 5. FIG. It is a continuation of FIG. It is a power spectrum density function graph of the verification example 6. It is a continuation of FIG. It is the table | surface which displayed "the ratio with the normal time" about the power spectrum contained in the frequency component of the verification example 6. FIG. It is a continuation of FIG. 14 is a power spectral density function graph of verification example 7. 27 is a continuation of FIG. It is a continuation of FIG. It is a continuation of FIG. It is the table | surface which displayed the power spectrum contained in the frequency component of the verification example 7, and "ratio with normal time". It is a continuation of FIG. It is a flowchart which shows the control method of the automatic notification apparatus by 1st Embodiment of this invention. It is a continuation of FIG. It is a continuation of FIG. It is a continuation of FIG. It is a continuation of FIG. FIG. 37 is a continuation of FIG. It is a continuation of FIG. It is a continuation of FIG. It is a block diagram which shows the structure of the biosignal detection means of the automatic notification apparatus by 2nd Embodiment of this invention.

Hereinafter, a plurality of embodiments of the present invention will be described with reference to the drawings.
(First embodiment)
Block diagrams of the automatic notification device according to the first embodiment of the present invention are shown in FIGS. The automatic notification device includes a biological signal detection unit 2 shown in FIG. 1 and a computer 8 shown in FIG.
The biological signal detection means 2 detects the biological signal of the human body 1 with a sensor. In this embodiment, the optical sensor 2 is illustrated as a sensor. As the optical sensor 2, a sensor using infrared light that is generally used can be used. By attaching the optical sensor 2 to a human fingertip or earlobe, a biological signal such as a fingertip pulse wave or an earlobe pulse wave can be detected.
The biological signal detection unit 2 removes unnecessary noise included in the biological signal by the noise filter circuit 4. Next, the voltage level of the biological signal is amplified by the signal amplification amplifier circuit 5. Subsequently, a pulse wave signal in a necessary frequency band is extracted as physiological data by the low-pass filter 6 serving as a physiological data extraction circuit. The physiological data signal is converted into a digital signal by the AD conversion circuit 7 and then transmitted to the computer 8.

The computer 8 first accumulates the data in the buffer 10 for 1 minute.
Next, the computer 8 analyzes the power spectrum density of the data accumulated for 1 minute by the power spectral density function analysis means 11 to obtain numerical data.
Here, the power spectrum density analysis used in the present embodiment is to obtain how the signal and time-series intensity (power spectrum) are distributed in frequency using Fourier transform.
In the power spectrum density analysis, first, in a first step, a coefficient of sin and a coefficient of cos are calculated for a specific frequency (period).
Next, in the second step, the two coefficients calculated in the first step are squared, the numbers squared are added together, and the square root of the added number is calculated.
In the third step, the numerical value calculated in the second step is a power spectrum included in the specific frequency. The observed frequency is represented on the horizontal axis, and the calculated power spectrum is represented on the vertical axis.
A power spectrum contained in a specific frequency component can be obtained by power spectrum density analysis. That is, the power spectral density indicates the magnitude of fluctuation for each frequency with respect to time series data of physiological data such as pulse wave and respiration. In other words, how the signal of physiological data is for each frequency. It shows whether it is distributed.

The power spectrum density analysis means 11 calculates analysis data for 1 minute every 5 seconds. One minute in this embodiment corresponds to the “certain time” described in the claims. Further, 5 seconds in this embodiment corresponds to a “predetermined time interval” described in the claims. The “certain time” and the “predetermined time interval” can be arbitrarily set.
The analysis data calculated by the power spectrum density analysis unit 11 is stored in the pre-analysis data memory by the pre-analysis unit 13 as a pre-power spectrum value. When 12 sets of analysis data for 1 minute sliding every 5 seconds are saved, then 0.0Hz, 0.2Hz, 0.4Hz, 0.6Hz, 0.8Hz, 1.0Hz, 1.2Hz, The average value of the power spectrum included in each frequency of 1.4 Hz is calculated. This is stored as analysis data at the normal time of the subject in the pre-data analyzed buffer 17 as data storage means.

  Subsequently, the computer 8 starts monitoring, and the determination means 18 sequentially stores the analysis data accumulated and analyzed in the normal time and the analysis data accumulated and analyzed in the length of one minute every 5 seconds. Compare. The determination means 18 determines that the subject is in “a psychosomatic state different from the normal state” when the predetermined determination criterion is met. Here, the digitized analysis data cannot be compared as an absolute value because there are individual differences and gender differences. Therefore, it is possible to make a comparative determination even when there is an individual difference or a gender difference by comparing with the analysis data of the subject at the normal time stored in advance. If it is determined that the condition is different from normal, the automatic alarm issuing means 22 is activated, and the information is automatically notified to the server 26 of the monitoring center 25 at a remote location.

  Generally, the average number of breaths is 15 to 20 times / minute, and is 0.25 to 0.33 Hz in the frequency domain. The average number of heartbeats is 50 to 70 times / minute, and is 0.83 to 1.17 Hz in the frequency domain. For this reason, in the analysis using the power spectral density, the change in the frequency range of 0.0 to 0.6 Hz, which is the change range of the respiratory component, and the frequency range of 0.8 to 1.4 Hz, which is the change range of the heart rate component. Observe changes.

  As a result of scrutinizing the data obtained by the power spectral density analysis for the normal time and the “body and mind state different from normal”, it was found that the data greatly changed at frequencies of 0.0 Hz, 0.2 Hz, and 0.4 Hz. In males, when the normal data is 100%, it has been found that the “normal psychosomatic state” decreases to 26% or less of the normal data in the “normal psychosomatic state”. On the other hand, it was found that, in the case of women, when the normal data is set to 100%, the data increases to 100% or more with respect to the normal data when “the psychosomatic state is different from normal”.

The following can be considered as the basis for the above.
When people are nervous or excited, they say “the heart is throbbing” or “appears to burst”, and when they are worried, the heart expresses “the cue and hurt”. From this, it can be fully recognized that the active movement of the heart and human emotions are greatly related.
In addition, when people are surprised, they say “breath away”, when they ’re relieved, “relieve”, or when they ’re focused on something or when they ’re looking at it, they say “sigh”. . From this, it can be said that human breathing is a sensitive reflection of psychological state.
When faced with an emergency of eating or being eaten by life and forced to choose between attacking or escaping, the anger is experienced when attacking and the fear is felt when escaping. The stress stimulation increases cardiovascular (circulatory) and respiratory responses such as blood pressure, heart rate, and respiration. This is a preparation for the expected increase in momentum and oxygen consumption, and when deciding whether to fight or escape, the pulsation rate and breathing rate temporarily decrease (stop) as a preparation stage. Then, blood pressure and heart rate increase. The increase in blood pressure and heart rate is to quickly carry oxygen and nutrient rich blood throughout the body. The increase in respiration is to increase the efficiency of gas exchange.
In general, men are likely to see lower numbers than usual in order to determine whether they are struggling or running away, whereas women are likely to go up in order to get ready for a breakaway.
Above, reference (excerpt)
“Edited by Kiyoshi Fujisawa, Shoji Kashiwagi, and Katsuo Yamazaki, supervised by Hiroshi Miyata: New Physiological Psychology, Volume 1: Basics of Physiological Psychology, Kitaoji Shobo”
“Shinji Hira, Makoto Nakayama, Masayuki Yagyu, Jihei Adachi: Discovery of a lie-looking for fragments of criminals and memories Kitaoji Shobo”

<Verification experiment>
Next, the result of the verification experiment is shown.
In the experiment, the time-series change indicating the magnitude of the fluctuation for each frequency was obtained from the power spectrum density for the time-series data of physiological data such as pulse wave and respiration.
The experiment used an optical sensor. The normal condition was measured for 6 minutes by the same subject, and the fear and surprise were measured for 6 minutes. The normal state and the state of fear and surprise were produced by playing the video and sound source stored in the notebook computer and allowing the subject to view it.
In the experiment, when measuring the normal state, video and music that can be relaxed were played on a notebook computer and viewed by the subjects. On the other hand, when measuring fear and surprise, a pseudo “panic” is played by playing a video and a loud sound that a suspicious person appears and attacks suddenly while playing a relaxing video and music. "The environment was created and the subjects watched it, and the changes at that time were measured."
Based on this, power spectrum density analysis was performed on time-series data of physiological data such as pulse waves and respiration extracted from a biological signal for 1 minute every 5 seconds, and the magnitude of fluctuation for each frequency was obtained.
The following shows the temporal flow of the video and music composition used to produce the “panic” environment.
Opening 0 to 99 seconds: Relax video and music Panic 100 to 130 seconds: A dubious person appears and suddenly attacks (high volume)
130-280 seconds: Other panic video Ending 280-360 seconds: Relax video and music

Hereinafter, the state of the normal state is defined as “normal”, and the state of change of the living body when the user is surprised or feels fear is defined as “panic”.
(Description of analysis)
1. Physiological data for 60 seconds is used for power spectral density analysis.
Therefore, the first power spectrum density analysis is 60 seconds later.
2. This is slid every 5 seconds to analyze the power spectral density. That is, the first time analyzes data of 0 to 60 seconds. The second time starts analysis of data for 5 to 65 seconds after 5 seconds from the start of the first analysis. The third time starts analysis of data for 10 to 70 seconds after 5 seconds from the start of the second analysis. In the Nth time, analysis is started after N × 5-5 seconds, and physiological data from (N × 5-5 seconds) to (N × 5-5 seconds) +60 seconds is analyzed.
By this power spectrum density analysis, it was verified whether or not the subject is in “an unusual psychosomatic state”, that is, whether or not the subject can be determined to be “panic”.

The method for obtaining the judgment reference value in the power spectrum density analysis is as follows. ~ 4. Shown in
1. The analysis data measured from the start of measurement for 120 seconds is used as a reference value at normal time.
2. Above 1. First, the power spectrum included in the predetermined frequency of the physiological data for 60 seconds from 0 to 60 seconds is calculated, and then slides every 5 seconds, for 12 sets (5 seconds × 12 = 60 seconds) ) Is calculated for each power spectrum included in the predetermined frequency.
3. 2. The average value is obtained for each frequency from 0.0 Hz to 1.4 Hz.
4). Of the average value 0.8 Hz or 1.0 Hz of the normal heart rate component, the frequency with the higher number of appearances of the high power spectrum is set as the reference heart rate.

Hereinafter, conditions for determining “panic” for males are referred to as determination criterion details 1 and are shown as conditions 1 to 5 below.
<Criteria details 1>
Condition 1: In the frequency band of 0.0 to 0.6 Hz, there is a frequency band in which the ratio to the reference value at normal time is less than 30%. Condition 2: In the frequency band of 0.0 to 0.6 Hz, normal time Condition 3: There is a frequency band in which the ratio to the reference value exceeds 105%. Condition 3: In the frequency band of 0.8 to 1.4 Hz, there is a frequency band in which the ratio to the reference value in the normal state exceeds 120%. 4: Condition 1, Condition 2, or Condition 3 continues continuously 5 times or more Condition 5: How to obtain the above-described determination reference value in Condition 3 Therefore, if it is recognized as the frequency band of the reference heartbeat, it is determined as “normal”. For this reason, condition 3 is recognized in a frequency band other than the frequency band of the reference heartbeat.

Hereinafter, the subject is a male from Verification Example 1 to Verification Example 3. From Verification Example 4 to Verification Example 7, the test subject is a woman.
(Verification example 1)
Subject A (gender: male, age: 30s, health: good) Reference heart rate frequency band = 0.8 Hz
The power spectrum density function graph of the verification example 1 is shown in FIGS. Moreover, the ratio when the analysis data by power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds is compared is shown in the tables of FIGS.
In FIG. 3 and FIG. 4, the result of measuring the above-mentioned surprising effect is displayed as “panic” and described in A1 to A4 in the upper stage. The power spectral density function graph shows only 80 seconds to 155 seconds (from 110 seconds to 125 seconds in the center) where there is a positive response for “panic”.
On the other hand, among the results of measuring the normal state, those that appear to have similar waveforms compared to the above-mentioned “panic” are displayed as similar types at the time of “normal” and are described in A5 to A8 in the lower row. .
3 and 4, it can be read that the wave height of 0.0 Hz to 0.4 Hz at “panic” is “lower” than the wave height of 0.0 Hz to 0.4 Hz of the similar type at “normal”. .

5 and 6 show the “ratio with normal time” for the power spectrum at each measurement time. Here, the “ratio with the normal time” is a percentage display of the ratio between the power spectrum for each frequency at each measurement time and the average value of the power spectrum from the start of measurement to 120 seconds.
A9 in the upper part of FIG. 5 shows a “ratio with the normal time” obtained by analyzing “70 to 130 seconds” to “125 to 185 seconds” in “panic”.
A10 to A14 in FIG. 5 and FIG. 6 show “ratio with normal time” in which “normal” is analyzed from “0 to 60 seconds” to “295 to 355 seconds”.
“Table 1” shown below is a result of verifying the above-described determination criterion details 1 for A9: “panic” in the upper part of FIG. 5.
(1) Between the analysis time zone of 70 to 130 seconds and the analysis time zone of 95 to 155 seconds, the ratio with the normal time in the frequency band of 0.2 to 0.6 Hz falls below 30% six times in succession. ing. Therefore, Condition 1 and Condition 4 are satisfied.
(2) During the same analysis time zone, the ratio with the normal time exceeds 105% continuously 6 times in the same frequency band of 0.6 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) During the same analysis time zone, the ratio of normal times exceeds 120% six times in the same frequency band of 1.2 Hz, and 1.2 Hz is not the reference heart rate frequency band of the subject. Therefore, the conditions 3, 4 and 5 are satisfied.
Therefore, conditions 1-5 are satisfied. Therefore, it is possible to determine the “panic” of the subject A according to the conditions 1 to 5 described in the determination criterion details 1.

Table 2 shows the results of searching for patterns having conditions 1 to 5 from “normal” of the subject A. “Table 2” verifies A12: “normal” in the upper part of FIG.
(1) Between the analysis time zone of 110 to 170 seconds and the analysis time zone of 135 to 195 seconds, the ratio to the normal time is continuously lower than 30% six times in the frequency band of 0.2 Hz. Therefore, Condition 1 and Condition 4 are satisfied.
(2) During the same analysis time zone, the ratio of normal times exceeds 105% for 6 times in the same frequency band of 0.4 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) However, the ratio of the normal time to the same time during the same analysis time zone exceeds 120% is three consecutive times from the analysis time zone of 110 to 170 seconds to the analysis time zone of 120 to 180 seconds, and Three consecutive times from the analysis time zone of 125 to 185 seconds to the analysis time zone of 135 to 195 seconds. Therefore, Condition 4, which is determined to continue five times in the same frequency band, is not satisfied.
Similarly, in other time zones, none of the conditions 1 to 5 is satisfied in “normal”.

(Verification example 2)
Subject B (gender: male, age: 50s, health: good) Reference heart rate frequency band = 1.0 Hz
The power spectrum density function graph of the verification example 2 is shown in FIGS. Moreover, the ratio when the analysis data by power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds are compared is shown in the tables of FIGS.
7 and 8 is the same as the description of the graphs of FIGS.
In the graphs of FIGS. 7 and 8, the wave height of 0.0 Hz to 0.4 Hz at the time of “panic” of B1 to B4 is similar to the wave height of 0.0 Hz to 0.4 Hz of the similar type at the time of “normal” of B5 to B8. It can be read that it is “lower”.

The description regarding the tables of FIGS. 9 and 10 is the same as the description of the tables of FIGS.
“Table 3” shown below is obtained by verifying the above-described determination criterion details 1 for B9: “panic” in the upper part of FIG.
(1) Between the analysis time zone of 100 to 160 seconds and the analysis time zone of 120 to 180 seconds, the ratio with the normal time in the frequency band of 0.0 to 0.6 Hz falls below 30% five times in succession. ing. Therefore, Condition 1 and Condition 4 are satisfied.
(2) During the same analysis time zone, the ratio with the normal time exceeds 100% continuously 5 times in the same frequency band of 0.4 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) During the same analysis time zone, the ratio to the normal time exceeds 5% continuously in the same frequency band of 1.2 Hz five times and is not the reference heart rate frequency band. Therefore, the conditions 3, 4 and 5 are satisfied.
Therefore, conditions 1-5 are satisfied. Therefore, it is possible to determine the “panic” of the subject B according to the conditions 1 to 5 described in the determination criterion details 1.

Table 4 shows the results of searching for patterns having conditions 1 to 5 from “normal” of the subject B. “Table 4” verifies B12: “normal” in the upper part of FIG.
(1) Between the analysis time zone of 125 to 185 seconds and the analysis time zone of 145 to 205 seconds, the ratio with the normal time in the frequency band of 0.0 to 0.6 Hz falls below 30% five times in succession. ing. Therefore, the condition 1 is not satisfied.
(2) During the same analysis time zone, the ratio with the normal time exceeds 105% continuously for 5 times or more in the same frequency band of 0.4 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) During the same analysis time zone, the ratio to the normal time is continuously over 5 times in the same frequency band of 0.8 Hz, which is not the reference heart rate frequency band. Therefore, the conditions 3, 4 and 5 are satisfied.
Therefore, not all conditions 1 to 5 are satisfied.
Similarly, in other time zones, none of the conditions 1 to 5 is satisfied in “normal”.

(Verification Example 3)
Subject C (gender: male, age: 50s, health: good) Reference heart rate frequency band = 1.0 Hz
The power spectrum density function graph of the verification example 3 is shown in FIGS. Moreover, the ratio when the analysis data by power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds is compared is shown in the tables of FIGS.
11 and 12 is the same as the description of the graphs of FIGS.
In the graphs of FIGS. 11 and 12, the wave height of 0.0 Hz to 0.4 Hz at the time of “panic” of C1 to C4 is similar to the wave height of 0.0 Hz to 0.4 Hz of the similar type at the time of “normal” of C5 to C8. It can be read that it is “lower”.

The description regarding the tables of FIGS. 13 and 14 is the same as the description of the tables of FIGS.
“Table 5” shown below is obtained by verifying the above-described determination criterion details 1 for C9: “panic” in the upper part of FIG.
(1) The ratio from the normal time in the frequency band of 0.0 to 0.6 Hz is 9 times lower than 9% continuously from the analysis time zone of 70 to 130 seconds to the analysis time zone of 110 to 170 seconds. ing. Therefore, Condition 1 and Condition 4 are satisfied.
(2) During the same analysis time zone, the ratio of normal times exceeds 105% for 9 times in the same frequency band of 0.6 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) The ratio between the normal time and the normal time is 9 times continuously in the same frequency band of 1.4 Hz for the same analysis time zone, exceeding 120%, and is not the reference heart rate frequency band. Therefore, the conditions 3, 4 and 5 are satisfied.
Therefore, conditions 1-5 are satisfied. Therefore, the “panic” of the subject C can be determined according to the conditions 1 to 5 described in the determination criterion details 1.

Table 6 shows the results of searching for patterns having conditions 1 to 5 from “normal” of the subject C. “Table 6” is a verification of C10: “Normal” in the middle of FIG.
(1) Between the analysis time zone of 210 to 270 seconds and the analysis time zone of 230 to 290 seconds, the ratio with the normal time is continuously lower than 30% five times in the 0.0 Hz frequency band. Therefore, Condition 1 is satisfied.
(2) During the same analysis time zone, the ratio with the normal time exceeds 105% continuously for 5 times or more in the same frequency band of 0.4 Hz. Therefore, conditions 2 and 4 are satisfied.
(3) The ratio of the normal time does not exceed 120% five times continuously in the same frequency band of 1.4 Hz during the same analysis time zone. Therefore, Condition 3 and Condition 4 are not satisfied.
Therefore, not all conditions 1 to 5 are satisfied.
Similarly, in other time zones, none of the conditions 1 to 5 is satisfied in “normal”.

Next, the condition for determining “panic” for a woman is referred to as determination criterion detail 2 and is shown as conditions 6 to 9.
<Criteria details 2>
Condition 6: In the frequency band of 0.0 to 0.6 Hz, there is a frequency band in which the ratio with the normal time exceeds 400%. Condition 7: In the frequency band of 0.8 to 1.4 Hz, the ratio with the normal time If there is a frequency band exceeding 120%, Condition 8: The above-mentioned 1-2 are continuously continued five times or more. Condition 9: When the frequency band of the above-mentioned reference heartbeat is recognized in Condition 7, “Normal” to decide. Therefore, condition 7 is a frequency band other than the frequency band of the reference heartbeat.

(Verification Example 4)
Subject D (gender: female, age: 20s, health: good) Reference heart rate frequency band = 0.8 Hz
The power spectrum density function graph of the verification example 4 is shown in FIGS. Moreover, the ratio when the analysis data by power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds are compared is shown in the tables of FIGS.
The description regarding the graphs of FIGS. 15 and 16 is the same as the description of the graphs of FIGS.
In the graphs of FIGS. 15 and 16, the wave height of 0.0 Hz to 0.4 Hz at the time of “panic” of D1 to D4 is the wave height of 0.0 Hz to 0.4 Hz of the similar type at the time of “normal” of D5 to D8. It can be read that it is “high”.

The description regarding the tables of FIGS. 17 and 18 is the same as the description of the tables of FIGS.
“Table 7” shown below is a result of verifying the above-described determination criterion details 2 for D9: “panic” in the upper part of FIG. 17.
(1) Between the analysis time zone of 70 to 130 seconds and the analysis time zone of 95 to 155 seconds, the ratio with the normal time is over 400% six times in the same frequency band of 0.6 Hz. Therefore, the condition 6 is satisfied.
(2) During the same analysis time zone, the ratio to the normal time continuously exceeds 6 times in the same frequency band of 1.4 Hz six times or more, and is not the reference heart rate frequency band. Therefore, conditions 7, 8 and 9 are satisfied.
Therefore, conditions 6-9 are satisfied. Therefore, it is possible to determine the “panic” of the subject D according to the conditions 1 to 4 described in the determination criterion details 2.

Table 8 shows a result of searching for patterns having conditions 6 to 9 from “normal” of the subject D. “Table 8” verifies D14: “normal” in the lower part of FIG.
(1) Between the analysis time zone of 250 to 310 seconds and the analysis time zone of 275 to 335 seconds, the ratio with the normal time is over 400% six times in the same frequency band of 0.6 Hz. Therefore, the condition 6 is satisfied.
(2) There is no case in which the ratio from the normal time exceeds 120% in the frequency band of 0.8 to 1.4 Hz during the same analysis time zone. Therefore, the condition 7 is not satisfied.
Therefore, not all the conditions 6 to 9 are satisfied.
Similarly, in other time zones, none of the conditions 6 to 9 is satisfied at the time of “normal”.

(Verification Example 5)
Subject E (gender: female, age: 20s, health: good) Reference heart rate frequency band = 0.8 Hz
The power spectrum density function graph of the verification example 5 is shown in FIGS. In addition, the ratios when the analysis data obtained by the power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds are compared are shown in the tables of FIGS.
The description regarding the graphs of FIGS. 19 and 20 is the same as the description of the graphs of FIGS.
In the graphs of FIGS. 19 and 20, the wave height of 0.0 Hz to 0.4 Hz at the time of “panic” of E1 to E4 is the wave height of 0.0 Hz to 0.4 Hz of the similar type at the time of “normal” of E5 to E8. It can be read that it is “high”.

The description regarding the tables of FIGS. 21 and 22 is the same as the description of the tables of FIGS.
“Table 9” shown below is obtained by verifying the above-described determination criterion details 2 for E9: “panic” in the upper part of FIG. 21.
(1) Between the analysis time zone of 90 to 150 seconds and the analysis time zone of 110 to 170 seconds, the ratio of the normal time is over 400% in the same frequency band of 0.2 Hz five times. Therefore, the condition 6 is satisfied.
(2) During the same analysis time zone, the ratio with the normal time exceeds 120% continuously in the same frequency band of 1.2 Hz five times or more, and is not the reference heart rate frequency band. Therefore, conditions 7, 8 and 9 are satisfied.
Therefore, conditions 6-9 are satisfied. Therefore, it is possible to determine the “panic” of the subject E according to the conditions 6 to 9 described in the determination criterion details 2.

Table 10 shows a result obtained by searching for a pattern having conditions 6 to 9 from “normal” of the subject E. “Table 10” verifies E11: “normal” in the lower part of FIG. 21.
(1) No ratio in the frequency range of 0.0 to 0.6 Hz exceeds 400% in the frequency band of 0.0 to 0.6 Hz from the analysis time zone of 75 to 135 seconds to the analysis time zone of 100 to 160 seconds. . Therefore, the condition 6 is not satisfied.
(2) During the same analysis time zone, the ratio to the normal time exceeds 120% continuously in the same frequency band of 1.2 Hz five times or more, and is not the reference heart rate frequency band. Therefore, conditions 7, 8 and 9 are satisfied.
Therefore, not all the conditions 6 to 9 are satisfied.
Similarly, in other time zones, none of the conditions 6 to 9 is satisfied at the time of “normal”.

(Verification Example 6)
Subject F (gender: female, age: 20s, health: good) Reference heart rate frequency band = 1.0 Hz
The power spectrum density function graph of the verification example 6 is shown in FIGS. Moreover, the ratio when the analysis data by power spectrum density analysis and the reference value at the normal time from the start of measurement to 120 seconds is compared is shown in the tables of FIGS.
The description regarding the graphs of FIGS. 23 and 24 is the same as the description of the graphs of FIGS.
In the graphs of FIGS. 23 and 24, the wave height of 0.0 Hz to 0.4 Hz at the time of “panic” of F1 to F4 is similar to the wave height of 0.0 Hz to 0.4 Hz of the similar type at the time of “normal” of F5 to F8. It can be read that it is “high”.

The description regarding the tables of FIGS. 25 and 26 is the same as the description of the tables of FIGS.
“Table 11” shown below is a result of verifying the above-described determination criterion details 2 for “panic” in the upper stage F9 of FIG.
(1) From the analysis time zone of 85 to 145 seconds to the analysis time zone of 120 to 180 seconds, the ratio with the normal time is continuously over 400% in the same frequency band of 0.2 Hz, exceeding 400%. Therefore, the condition 6 is satisfied.
(2) During the same analysis time zone, the ratio with the normal time continuously exceeds 8 times in the same frequency band of 1.2 Hz for 8 times or more, and is not the reference heart rate frequency band. Therefore, conditions 7, 8 and 9 are satisfied.
Therefore, conditions 6-9 are satisfied. Therefore, it is possible to determine the “panic” of the subject F according to the conditions 6 to 9 described in the determination criterion details 2.

Table 12 shows the results of searching for patterns having the conditions 6 to 9 from the “normal” of the subject F. “Table 12” verifies F14: “normal” in the lower part of FIG.
(1) Between the analysis time zone of 240 to 300 seconds and the analysis time zone of 265 to 325 seconds, the ratio with the normal time is over 400% six times in the same frequency band of 0.6 Hz. Therefore, the condition 6 is satisfied.
(2) The ratio of the normal time in the same analysis time zone exceeds 110% is two consecutive times from the analysis time zone of 240 to 300 seconds to the analysis time zone of 245 to 305 seconds, and 250 to 310. 4 consecutive times from the analysis time zone of seconds to the analysis time zone of 265 to 325 seconds. Therefore, the condition 9 that is continued five times in the same frequency band is not satisfied.
Therefore, not all the conditions 6 to 9 are satisfied.
Similarly, in other time zones, none of the conditions 6 to 9 is satisfied at the time of “normal”.

(Verification example 7)
The power spectrum density function graph of the verification example 7 is shown in FIGS.
Test subject E (same person as test subject 5)
This Verification Example 7 is a test example when a person other than the subject quietly approached the subject during the measurement and surprised the subject from behind.

  FIG. 27 to FIG. 30 are power spectrum density function graphs of E15 “normal time” → E16 “starting surprise” → E17 to E27 “being surprised” → E28 “starting calm” → E29, E30 “being calm”.

Tables in which the power spectral density function graphs of FIGS. 27 to 30 and subsequent figures are digitized are shown in FIGS. 31 and 32.
31 and 32, the center measurement time indicates the numerical value at the center of the measurement time. In each measurement time, the left column indicates the power spectrum, and the right column indicates “ratio with normal time”.
(1) When paying attention to “ratio with normal time” of 0.0 Hz, “ratio with normal time” of 0.2 Hz, and “ratio with normal time” of 0.4 Hz, the measurement time is 100 to 160 seconds. The numerical value increases rapidly, and in the measurement time of 110 to 170 seconds, it is far over 1000% and can be seen in the 2000% or 3000% range. The “ratio with normal time” of 0.2 Hz after the measurement time of 150 to 210 seconds exceeds 7000%, and shows a numerical value that can clearly identify an abnormal situation.

  (2) When paying attention to “ratio with normal time” of 0.0 Hz, “ratio with normal time” of 0.2 Hz, and “ratio with normal time” of 0.4 Hz, numerical values of measurement time of 160 to 220 seconds It can be read that the peak gradually approaches 100%. This indicates that the subject E is gradually regaining calmness.

Next, a control method of the biological signal detection means 2 and the computer 8 based on the above-described determination criterion details 1 and determination criterion details 2 will be described with reference to the flowcharts of FIGS.
First, as shown in FIG. 33, in step 1 (steps are expressed as “S” in FIGS. 33 to 40), information such as the gender of the subject is input to the computer 8, and then the process is started.
In step 2, the biological signal detection unit 2 collects physiological data from the biological signal detected by the sensor 3. The sampling time at this time is 5 ms. The biological signal detection means 2 extracts a pulse wave signal in a necessary frequency band as physiological data through the noise filter circuit 4, the signal amplification amplifier circuit 5, and the low-pass filter 6. The physiological data signal is AD converted by the AD conversion circuit 7 in step 3, and then transmitted to the computer 8 by the data transmission means in step 4.

In step 5, the data receiving means of the computer 8 receives the physiological data signal transmitted from the biological signal detecting means 2. In step 6, before the start of monitoring, the received physiological data is stored in the buffer by the prior data storage means.
When data for 1 minute is stored in the buffer (step 7: YES), storage processing is sequentially performed every 5 ms until the analysis timer elapses for 5 seconds (step 8: NO). At this time, the buffer slides for 1 ms for 5 ms, and the save timer and the analysis timer are added by 5 ms.
When the analysis timer has elapsed for 5 seconds (step 8: YES), after clearing the analysis timer in step 10, the process first proceeds to pre-analysis (step 11: NO).

In the pre-analysis, as shown in FIG. 34, in step 12, the data accumulated for 1 minute is analyzed by power spectrum density analysis. At this time, the analysis data of each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, 0.6 Hz, 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz are digitized. Subsequently, in step 13, the digitized data is stored in the pre-analysis data memory as analysis data of the subject at normal time.
The above steps 5 to 13 are repeated (step 14: NO), and when 12 sets of analysis data for 1 minute each slide every 5 seconds are stored (step 14: YES), the process proceeds to step 15.
In step 15, the average value of the power spectral density of each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, 0.6 Hz, 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz is calculated. Further, a reference frequency at 0.8 Hz or 1.0 Hz is calculated. In step 16, this is stored in a pre-data analyzed buffer. Subsequently, a flag “normal analysis is possible” is set in step 17, and the process proceeds to step 5.
Steps 5 to 11 are as described above. When the flag “normal analysis is possible” is detected in step 11, the process proceeds to normal analysis.

In the normal analysis, as shown in FIG. 35, data for one minute is analyzed in step 18. Specifically, the power spectral density of each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, 0.6 Hz, 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz is calculated.
Next, in step 19, the average value of the power spectral density of each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, 0.6 Hz, 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz is calculated. To do. Further, a reference frequency at 0.8 Hz or 1.0 Hz is calculated. This is stored in a normal data analyzed buffer in step 20.
If the subject is a woman based on the information input to the computer 8 in step 1 (step 21: NO), the processing shifts to female analysis.

In the female analysis, it is detected whether the ratio with the normal time exceeds 400% at each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz. If it does not exceed 400% at all frequencies (step 22: NO), the process proceeds to step 5. On the other hand, if it exceeds 400% at any frequency (step 22: YES), the process goes to step 26 via step 23.
As shown in FIG. 39, in step 26, it is detected whether the ratio to the normal time exceeds 5000% at each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz. When the frequency exceeds 5000% at any frequency (step 26: YES), an abnormal situation numerical value 1 counter is added once (step 27). Then, via step 28, the panic counter is incremented once.
Next, it is detected whether the ratio with the normal time exceeds 120% at each frequency of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz. If the frequency exceeds 120% at any frequency (step 31: YES), the process goes to step 33 via step 32.
In step 33, it is detected whether or not the ratio to the normal time exceeds 2800% at each frequency of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz. When the frequency exceeds 2800% at any frequency (step 33: YES), as shown in FIG. 37, the counter of the abnormal situation numerical value 2 is added once (step 34). Then, the process proceeds to step 5 via step 35 and step 37.

  Here, when the counter of the abnormal situation numerical value 1 becomes 5 times or more in Step 28 (Step 28: YES), when the counter of the abnormal situation numerical value 2 becomes 5 times or more in Step 35 (Step 35: YES). And when a panic counter becomes 5 times or more by step 37 (step 37: YES), an automatic alarm device act | operates (step 38). In step 39, the automatic alarm device 21 is activated and automatically notified to a monitoring center at a remote location. This automatic notification continues until it is canceled (step 40: NO). For example, when the notification is canceled by the subject or the like (step 40: YES), the process is terminated.

However, in step 23 described above, among the frequencies of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz, the frequency whose ratio to the normal time exceeds 400% is not the same frequency as the previous time. If this is the case (step 23: NO), the abnormal situation numerical value 1 counter is cleared, the panic counter is cleared (step 24 and step 25), and the process proceeds to step 5.
Moreover, in the above-mentioned step 26, when the ratio with the normal time does not exceed 5000% at all frequencies of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz (step 26: NO), an abnormal situation The counter of numerical value 1 is cleared (step 29). In this case, the panic counter is incremented once.
Further, in step 31, when the ratio to the normal time does not exceed 120% at all frequencies of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz (step 31: NO), and step 32 In the case of the frequency of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz, the frequency whose ratio with the normal time exceeds 120% is not the same as the previous frequency (step 32: NO) ), The panic counter is cleared (step 25), and the process proceeds to step 5.
In step 33, when the ratio of normal frequency does not exceed 2800% at all frequencies of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz (step 33: NO), the abnormal situation numerical value 2 Is cleared (step 36). Then, the process proceeds to step 5 via step 37.

Next, in step 21 in the above-described normal analysis, a process when the subject is a male (step 21: YES) based on the information input to the computer 8 in step 1 will be described.
In this case, as shown in FIG. 38, in step 41, it is detected whether the ratio with the normal time exceeds 105% at each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz. . If it does not exceed 105% at all frequencies (step 41: NO), the process proceeds to step 5. On the other hand, if it exceeds 105% at any frequency (step 41: YES), the process goes to step 45 via step 42.
In step 45, it is detected whether the ratio with the normal time exceeds 5000% at each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz. When the frequency exceeds 5000% at any frequency (step 45: YES), the abnormal situation numerical value 1 counter is added once (step 46). Then, the process proceeds to step 49 via step 47.
As shown in FIG. 39, in step 49, it is detected whether the ratio with the normal time is within 30% at each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz. If it is within 30% at any frequency (step 49: YES), the panic counter is incremented once via step 50 (step 51). Then, the process proceeds to step 52.
In step 52, it is detected whether the ratio with the normal time exceeds 120% at each frequency of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz. If it exceeds 120% at any frequency (step 52: YES), the process proceeds to step 54 via step 54.
In step 54, it is detected whether the ratio with the normal time exceeds 2800% at each frequency of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz. If the frequency exceeds 2800% at any frequency (step 54: YES), the abnormal situation numerical value 2 counter is added once (step 55). Then, the process proceeds to step 5 via step 56 and step 58.

  Here, when the counter of the abnormal situation value 1 becomes 5 times or more at Step 47 (Step 47: YES), when the counter of the abnormal situation value 2 becomes 5 times or more at Step 56 (Step 56: YES). And when a panic counter becomes 5 times or more by step 58 (step 58: YES), an automatic alarm device act | operates (step 59). Step 60 and step 61 shown in FIG. 40 are the same as step 39 and step 40 described above.

However, in the above-described step 42, among the frequencies of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz, the frequency whose ratio with the normal time exceeds 105% is not the same frequency as the previous time. If this is the case (step 41: NO), the abnormal situation numerical value 1 counter is cleared, the panic counter is cleared (step 43 and step 44), and the process proceeds to step 5.
In Step 45, if the ratio of each frequency of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz to the normal time does not exceed 5000% (Step 45: NO), the abnormal situation numerical value is obtained. 1 is cleared (step 48).
Further, when the ratio to the normal time is not within 30% in all frequencies of 0.0 Hz, 0.2 Hz, 0.4 Hz, and 0.6 Hz in Step 49 (Step 49: NO), 0.0 Hz in Step 50 If the frequency within 30% of the frequencies of 0.2 Hz, 0.4 Hz, and 0.6 Hz is not the same frequency as the previous time (step 50: NO), 0 in step 52 When the ratio of all frequencies of 8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz to the normal time does not exceed 120% (step 52: NO), and in step 53, 0.8 Hz, 1. Of the frequencies of 0 Hz, 1.2 Hz, and 1.4 Hz, the frequency whose ratio to the normal time exceeds 120% is not the same as the previous frequency (step 53: N ), Clears the panic counter (step 44), the flow goes to step 5.
In step 54, when the ratio of normal frequency does not exceed 2800% at all frequencies of 0.8 Hz, 1.0 Hz, 1.2 Hz, and 1.4 Hz (step 54: NO), the abnormal situation numerical value 2 Is cleared (step 3). Then, the process proceeds to step 5 via step 58.

The first embodiment described above has the following effects.
In the present embodiment, the physiological data extracted from the biological signal is subjected to power spectral density analysis, and compared with the analysis data in the normal state, thereby determining that the human body is in a “body and body state different from normal”. When it is determined that the state is different from the normal state of mind, the automatic alarm issuing means automatically notifies the monitoring center. This makes it possible for a victim facing an emergency situation to become alarmed by surprise and fear, or if the victim is placed under criminal supervision, without causing the victim to activate an alarm button. Automatically notified to the monitoring center. Therefore, the dispatch of the security agency can be accelerated.

In the present embodiment, finger plethysmogram waves or earlobe pulse waves are detected by an optical sensor. For this reason, in a convenience store or the like, a person wearing an optical sensor can freely move, and convenience is improved.
In this embodiment, since the power spectrum analysis means analyzes the power spectrum density of 60 seconds of physiological data, the reliability of the determination result is high. In addition, since the power spectrum analysis means analyzes the power spectrum density at intervals of 5 seconds, it can be quickly determined that the human body is in a “psychological and physical state different from normal”.

  In the present embodiment, when the human body is a male, it is determined based on the above-described determination criterion details 1 that the “body and body state different from normal” has been reached. Further, when the human body is a woman, it is determined according to the above-described determination criterion details 2 that the “body and mind state is different from normal”. For this reason, a highly reliable determination result can be obtained in both cases of men and women.

In the present embodiment, it may be determined that a “psychological and physical condition different from normal” has been reached based on the following determination criteria.
In the case of males, when the ratio of normal analysis data to 26% or less continues at a frequency of 0.0 Hz, 0.2 Hz, or 0.4 Hz for a certain period of time, State ".
On the other hand, in the case of a woman, when a state in which the ratio to the normal data is 100% or more at any frequency of 0.0 Hz, 0.2 Hz, and 0.4 Hz continues for a certain period of time, Is determined.
For example, when the physiological data for 60 seconds is subjected to power spectrum density analysis at intervals of 5 seconds, it is preferable that the fixed time is continuous 5 times or more.

(Second Embodiment)
Next, FIG. 41 shows a block diagram of the biological signal detection means 30 according to the second embodiment of the present invention. In the present embodiment, the same reference numerals are given to substantially the same configurations as those in the first embodiment described above, and the description thereof is omitted.
In the present embodiment, the pressure sensor 33 is used as the sensor.
For example, when detecting a biological signal of a person sitting on a chair in a bank, the pressure sensor 33 can be used by being attached to a cushion or a seat cushion. On the other hand, when detecting a biological signal of a person in a standing posture such as a convenience store, it can be attached to a floor mat layer. Thereby, the vibration of the body surface accompanying a pulse wave and respiration can be easily detected as a biological signal.
The biological signal detection means 30 includes two bandpasses 36 and 37 as a physiological data extraction circuit. After the biological signal detected by the pressure sensor 33 passes through the noise filter circuit 34 and the signal amplification amplifier circuit 35, the respiratory component is extracted by one band pass 36 and the heart rate component is extracted by the other band pass 37. . These signals are combined and converted into a digital signal, and then transmitted to the computer 8 by data transmission means. Note that the processing by the computer 8 is the same as in the first embodiment.
Also in this embodiment, the same operational effects as those of the first embodiment described above can be achieved.

(Other embodiments)
In the above-described embodiment, the analysis data included in the specific frequency component is obtained by performing power spectrum density analysis on the physiological data extracted by the biological signal detection unit. On the other hand, according to the present invention, analysis data included in a specific frequency component can be obtained by various analysis methods. As analysis methods, for example, FFT (Fast Fourier transform), MEM (Principle of maximum entropy), AR (Autoregressive) model, ARMA (Autoregressive moving average model: Autoregressive moving average model) ) Model, STFT (Short Time Fourier Transform), wavelet transformation (Wavelet Transformation), frequency analysis such as Wigner distribution. Also by these analyses, it is possible to detect the magnitude of the fluctuation of the physiological data signal for each frequency. In addition to frequency analysis, other analysis methods can be used as long as analysis data included in a specific frequency component can be obtained from physiological data extracted by the biological signal detection means.
In the above-described embodiment, the computer determines that the human body is in a “non-normal mental and physical state” and automatically notifies the monitoring center server. In contrast, the present invention transmits the biological signal of the human body detected by the sensor to the server of the monitoring center, determines whether or not the human body is in an “unusual mental and physical state”, and A warning may be issued directly to a member or the like.

2, 30 ... biological signal detection means 3 ... optical sensor (sensor)
11 ・ ・ ・ Power spectrum density analysis means (analysis means)
17... Pre-analyzed buffer (data storage means)
18 ・ ・ ・ Determining means 22 ・ ・ ・ Automatic alarm issuing means 23 ・ ・ ・ Pressure sensor 25 ・ ・ ・ Server

Claims (3)

A sensor for detecting a biological signal of the human body;
Biological signal detection means for extracting physiological data consisting of vibration or pulse wave accompanying breathing from the biological signal detected by the sensor;
Analyzing means for analyzing physiological data extracted by the biological signal detecting means;
Data storage means for storing analysis data obtained by the analysis means analyzing physiological data when the human body is in a relaxed normal state;
The analysis data stored in the data storage means and the analysis data analyzed by the analysis means at a predetermined time interval with the physiological data extracted by the biological signal detection means are compared, and the ratio matches a predetermined criterion. In the case, the determination means for determining that the human body is in a “psychological and physical state different from normal”,
An automatic notification device, comprising: an automatic alarm issuing unit that issues an automatic notification or an alarm when the determination unit determines that the human body is in a “unusual psychosomatic state”.   The automatic notification device according to claim 1, wherein the sensor is an optical sensor or a pressure sensor.   The automatic notification device according to claim 1 or 2, wherein the analysis unit analyzes physiological data of a predetermined time extracted by the biological signal detection unit at the predetermined time interval.