JP6836265B2 - Biological condition estimation device, biological condition estimation method, computer program and recording medium - Google Patents

Description

The present invention relates to a technique for estimating the state of a living body by using a biological signal obtained from the back of a person.

The present inventors have proposed techniques in Patent Documents 1 to 5 and the like for detecting a vibration generated on the body surface of the back in the upper body of a person by a biological signal measuring device and analyzing the state of the person. The sound / vibration information generated from the movement of the heart and aorta detected from the back of the human upper body is the pressure vibration generated from the movement of the heart and aorta, and the information on the systole and diastole of the ventricles and the auxiliary pump for circulation. It contains information on the elasticity of the blood vessel wall and information on the reflected wave. That is, the back body surface pulse wave (including Aartic Pulse Wave (APW)) near 1 Hz generated on the back surface from the movement of the heart and aorta, and the sound transmitted to the back side with the heartbeat (“pseudo heart sound” (this specification). In the book, the heart sound collected from the chest side is referred to as the "pseudo heart sound")), as opposed to the heart sound collected from the chest side. The signal waveform associated with the heart rate variability includes information on the nerve activity of the sympathetic nervous system and the parasympathetic nervous system, and the signal waveform associated with the rocking of the aorta contains information on the sympathetic nerve activity.

In Patent Document 1, a predetermined time width is applied to the time-series waveform of the back body surface pulse wave (APW) near 1 Hz extracted from the collected biological signal (sound / vibration information), and a slide calculation is performed to determine the frequency gradient. The time-series waveform is obtained, and the biological state is estimated from the tendency of the change, for example, whether the amplitude tends to be amplified or attenuated. In addition, the biological signal is frequency-analyzed, and the power spectrum of each frequency corresponding to the function adjustment signal, the fatigue acceptance signal, and the activity adjustment signal belonging to the predetermined ULF band (extremely low frequency band) to the VLF band (ultra-low frequency band). It is also disclosed that the state of a person is determined from the time-series changes of each power spectrum. Since the fatigue receptive signal indicates the degree of progression of fatigue in a normal active state, the human state (sympathetic nerve predominance) is obtained by comparing the degree of predominance of the power spectrum of the function adjustment signal and the activity adjustment signal. The state, parasympathetic predominant state, etc.) can be determined. In addition, the distribution rate of each frequency component when the sum of the power spectrum values of the frequency components corresponding to these three signals is set to 100 is obtained in a time series, and the time-series change of the distribution rate is used to determine the state of a person. Is also disclosed to determine.

Patent Document 2 proposes a technique for displaying a biological state as a physical condition map and a sensory map as a method for quantifying a biological state. In this method, the APW described above is frequency-analyzed, the analysis waveform is displayed on both logarithmic axes for the target analysis section, and the analysis waveform is divided into a low frequency band, a medium frequency band, and a high frequency band, and the analysis waveform is divided. The analysis waveform is scored based on a certain standard based on the inclination and the shape of the entire analysis waveform, and plotted on the coordinate axes. The physical condition map shows the state of control of the autonomic nervous system as the balance between the sympathetic nerve and the parasympathetic nerve, and the sensory map superimposes the state of changes in heart rate variability on the physical condition map.

Patent Documents 3 to 5 disclose means for determining the homeostasis function level. The means for determining the level of the constancy maintenance function are the positive / negative of the differential waveform of the frequency gradient time series waveform, the positive / negative of the integrated waveform obtained by integrating the frequency gradient time series waveform, and the frequency gradient time series waveform using the zero cross method and the peak detection method. The determination is made using at least one or more of the absolute values of the frequency gradient time-series waveforms obtained by processing each of the frequency-slope time-series waveforms. By combining these, it is determined which level of homeostasis function corresponds to. For example, using the frequency slope and the integrated value, it is judged as "homeostasis maintenance function level 1" when it is equal to or more than a predetermined value, or the differential value is equal to or less than a predetermined position and "of the two absolute values". In the case of "peak dominance", it can be set to determine "homeostasis maintenance function level 4". The combination of these, the threshold value at the time of determination, etc. are determined by statistically processing the data of a large number of subjects. In addition, the level of homeostasis maintenance function is divided into 5 to 7 stages, for example, from the case where the homeostasis maintenance function is excellent (when the degree of concentration is high, etc.) to the case where the homeostasis maintenance function is inferior (in the case of hypertension). Judgment (decrease in concentration due to inattentive driving, etc.). When displaying on the monitor, the level of 5 to 7 levels is displayed in characters, or if it is above the intermediate level (normal state), it is judged that the homeostasis maintenance function is excellent at once. It is also disclosed that if the value is lower than the above, it is collectively determined that the homeostasis maintenance function is inferior, and the monitor is set to display different colors for each case.

In Non-Patent Document 1, regarding fingertip plethysmogram information, a frequency gradient time-series waveform of a power value that reflects sympathetic nerve information is obtained, and the integrated value obtained by processing the absolute value is plotted in time-series as the fatigue degree. A technique for drawing a fatigue curve and capturing muscle fatigue is disclosed. Non-Patent Document 2 discloses a technique for capturing muscle fatigue by arithmetically processing a biological signal acquired from the back of a person using an air pack sensor by the same method to draw a fatigue curve. That is, the state of muscle fatigue can be grasped by using the frequency gradient time series waveform of the power value reflecting the information of the sympathetic nerve (in the case of APW, the frequency gradient time series waveform by the zero cross method).

Japanese Unexamined Patent Publication No. 2011-167362 Japanese Unexamined Patent Publication No. 2012-239480 WO2011 / 046178A Japanese Unexamined Patent Publication No. 2014-117425 Japanese Unexamined Patent Publication No. 2014-223271

Original work "Development of a simple evaluation method for long-term sitting fatigue using fingertip volume pulse wave information" Etsunori Fujita (8 outside), Ergonomics Vol. 40, No. 5 ('04) "Application of biological fluctuation signal measured by non-invasive sensor to fatigue and sleep onset prediction", Naoki Ochiai (6 outside), 39th Japan Ergonomics Society China-Shikoku Branch Conference Proceedings, November 2006 Published on May 25, Publisher: Japan Ergonomics Society Chugoku-Shikoku Branch Secretariat

All of the above-mentioned techniques analyze each element caused by fluctuations in the biological regulation function to determine the state of a person, but the arithmetic processing for biological signals is different from each other, and as a result of the output determination, falling asleep The timing of the sign is judged, the degree of fatigue is judged, the change in the homeostatic function level is judged, and the like is according to each purpose. However, they are all output separately. In Patent Document 4, time-series changes of a plurality of indicators related to each element caused by fluctuations in bioregulatory functions, such as a sign of falling asleep, an imminent sleep phenomenon, a low-sensitivity running state, a homeostatic function level, an initial fatigue state, and a mood judgment. Although the technique of determining the above with one device and outputting them to one monitor is also disclosed, in any case, the time-series change of each index is determined individually.

By the way, when a person is tired, he / she feels tired, but when he / she concentrates on things and his / her field of vision is narrowed, he / she may not realize that he / she is tired even though he / she is actually tired. In other words, the feeling of fatigue that a person feels does not always match the fatigue that occurs in the body. The sites where fatigue occurs are mainly the hypothalamus and anterior cingulate gyrus, which are the centers of the autonomic nerves, and the site where the fatigue is noticed is the orbitofrontal cortex, but the orbitofrontal cortex, which controls motivation and a sense of accomplishment, works. It is known that the feeling of fatigue generated in the frontal area is masked. Therefore, fatigue may accumulate without being aware of the feeling of fatigue, and it is desirable to be able to estimate a state in which the feeling of fatigue is masked.

The present invention has been made in view of the above, and an object of the present invention is to provide a technique capable of suppressing the accumulation of unconscious fatigue by making it possible to estimate a state in which masking of fatigue occurs.

In order to solve the above problems, the present inventor has diligently studied and completed the present invention by paying attention to the following points. That is, the sound / vibration information generated from the movement of the heart and aorta detected from the back of the upper body of a person, especially the back body surface pulse wave (APW) near 1 Hz among them, is the elastic information of blood vessels and the reflected wave. Contains information, etc. Therefore, by analyzing the back body surface pulse wave (APW) and obtaining the frequency gradient time-series waveform described later, it is possible to obtain information on the comprehensive fluctuation of the living body, which is a bioregulatory functional element. If we grasp the state of fluctuation for each frequency in this fluctuation information, the distribution rate for each frequency will take a fluctuation method that reflects the fluctuation element of the brain wave (θ wave, α wave, β wave), so at that time By analyzing using the sequence waveform, it is possible to capture information on fluctuations such as which frequency band of the brain wave is the dominant fluctuation.

In addition, while the frequency gradient time series waveform by the zero cross method is dominated by the autonomic nervous system, the frequency gradient time series waveform by the zero cross method is frequency-analyzed in the place that reflects the fluctuation of the brain wave. It is shown by the power spectrum ratio of three frequency components of the function adjustment signal represented by 0.0017 Hz, the fatigue acceptance signal represented by 0.0035 Hz, and the activity adjustment signal represented by 0.0053 Hz. So to speak, the distribution rate of the three frequency components representing the shape of the power spectrum analyzed by frequency is considered to be regarded as the site showing the expression of the function of the endocrine system rather than the autonomic nerve reaction.

The frequency gradient time-series waveform obtained by the zero-cross method indicates the degree of sympathetic nerve expression by absolute value processing, and the frequency gradient time-series waveform obtained by the peak detection method indicates the degree of parasympathetic nerve expression. Therefore, by using these, it is possible to grasp the state of expression of the bioregulatory function in more detail. For example, the frequency gradient time-series waveform obtained by the zero-cross method is subjected to absolute value processing and integrated to obtain a fatigue curve showing the degree of human fatigue, and the state of muscle fatigue can be grasped.

In addition, homeostasis is achieved by using at least one of the positive and negative of the differential waveform of each frequency slope time series waveform using the zero cross method, the absolute value of each frequency slope time series waveform of the zero cross method or the peak detection method, and the like. It is possible to capture the state of fluctuations in the maintenance function level. The fatigue curve and the time-series waveform of the homeostatic function level are also derived from the frequency gradient time-series waveform, and serve as an index reflecting information on fluctuations in the autonomic nervous system, brain function, and the like. In addition to this, indicators that are highly relevant to physical and mental fatigue (physical condition map, sensory map) and indicators that are highly relevant to sensation (attention and warning of homeostatic function level) The frequency of consciousness or malaise as the required fatigue) is also required. These capture the fluctuations of the functions of the brain, autonomic nervous system, and endocrine system from multiple perspectives, and by combining these multiple types of fluctuation indicators, masking of fatigue can occur. We thought that it might be possible to estimate the presence or absence of sex.

That is, the biological state estimation device of the present invention
A biological state estimation device that estimates a biological state using a biological signal obtained from a biological signal measuring device that comes into contact with the back of a person.
A means for determining a bioregulatory function element that analyzes the biological signal to obtain a plurality of indices involved in the bioregulatory function, including an index highly related to autonomic nerve function, physical / mental fatigue, or sensation.
It is characterized by having a fatigue feeling estimating means for estimating whether or not a fatigue feeling masking is occurring as the biological state by combining a plurality of indexes obtained by the bioregulatory functional element determining means. And.

The fatigue estimation means is
The first estimation means for estimating that the masking of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. When,
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of the feeling of fatigue is caused by the first estimation means, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation means for presuming that the masking of fatigue has not occurred, and
With respect to the biological signal to be analyzed that is not presumed to be in a state where the fatigue feeling is not masked by the second estimation means, at least in a state where the fatigue feeling masking is occurring and in the fatigue feeling, based on a predetermined standard. It is preferable to have a third estimation means for classifying into any of the states in which masking has not occurred.

The biological state estimation method of the present invention is a biological state estimation method for estimating a biological state by using a biological signal obtained from a biological signal measuring device that is in contact with the back of a person.
By analyzing the biological signals, a plurality of indicators involved in the bioregulatory function were obtained, including indicators highly related to autonomic nervous function, physical / mental fatigue, or sensation.
It is characterized in that it is estimated whether or not the fatigue masking is occurring as the biological state by combining the plurality of indexes.

In the biological state estimation method of the present invention, masking of the feeling of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. Perform the first estimation procedure to estimate the state of being present,
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of fatigue is generated by the first estimation procedure, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation procedure for presuming that the masking of fatigue has not occurred is carried out.
With respect to the biological signal to be analyzed, which is not presumed to be in a state where masking of fatigue is not generated in the second estimation procedure, at least a state in which masking of fatigue is occurring and a state of fatigue, based on a predetermined standard. Perform a third estimation procedure to classify into one of the unmasked states,
It is preferable that the configuration is such that it is estimated whether or not the masking of the feeling of fatigue is occurring.

The computer program of the present invention is a computer program that causes a computer as a biological state estimation device to analyze a biological signal obtained from a biological signal measuring device that is in contact with the back of a person and execute a procedure for estimating a biological state. hand,
A procedure for analyzing the biological signals to obtain a plurality of indicators involved in the biological regulation function, including indicators highly related to autonomic nervous function, physical / mental fatigue, or sensation.
By combining the plurality of indexes, the procedure of masking the feeling of fatigue and estimating whether or not the masking of the feeling of fatigue is occurring is executed as the biological state.

The computer program of the present invention is a procedure for estimating whether or not the fatigue masking is occurring.
The first estimation procedure for estimating that the masking of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. To execute,
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of fatigue is generated by the first estimation procedure, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation procedure for presuming that the masking of fatigue is not occurring is executed.
With respect to the biological signal to be analyzed, which is not presumed to be in a state where masking of fatigue is not generated in the second estimation procedure, at least a state in which masking of fatigue is occurring and a state of fatigue, based on a predetermined standard. It is preferable that the configuration is such that the third estimation procedure for classifying into any of the states in which masking has not occurred is executed.

Further, according to the present invention, the computer program described above causes a computer as a biological state estimation device to analyze a biological signal obtained from a biological signal measuring device abutting on the back of a person and execute a procedure for estimating a biological state. Provided is a computer-readable recording medium on which a record is recorded.

According to the present invention, fatigue is caused by combining a plurality of indicators indicating changes in the state of bioregulatory functional elements, including indicators caused by fluctuations highly related to autonomic nervous function, physical / mental fatigue, or sensation. It is possible to grasp the possibility of masking of sensation.

FIG. 1 (a) is an exploded view showing an example of a biological signal measuring device for measuring a back body surface pulse wave used in one embodiment of the present invention, and FIG. 1 (b) is a cross section of a main part thereof. It is a figure. FIG. 2 is a diagram schematically showing a configuration of a biological state estimation device according to an embodiment of the present invention. FIG. 3A is a diagram showing an example of a frequency gradient time-series waveform obtained by a frequency gradient time-series waveform calculation means and a peak detection method, and FIG. 3B is a diagram showing an amplitude fluctuation. This is a waveform obtained by smoothing FIG. 3 (a). FIG. 4 is a diagram showing an example of a time-series waveform of the distribution rate obtained by the distribution rate calculation means. FIG. 5 is a diagram showing an example of a fatigue curve obtained by the fatigue curve calculation means. FIG. 6 is a diagram showing an example of a time-series waveform of the homeostasis maintenance function level obtained by the homeostasis maintenance function level calculation means. FIG. 7A is a diagram showing an example of a physical condition map obtained by the physical condition map calculation means, and FIG. 7B is a diagram showing an example of a sensory map obtained by the sensory map calculation means. FIG. 8 is a flowchart for explaining the estimation step by the fatigue feeling estimation means. 9 (a) and 9 (b) are diagrams showing the time-series data of the RR interval of the electrocardiogram in Experimental Example 1 and the inclination angle of the approximate line of the fluctuation waveform of APW. FIG. 10 is a diagram showing the inclination angle of the approximate line of the fluctuation waveform of APW in the actual vehicle running experiment of Experimental Example 1. FIG. 11 is a diagram showing the estimation results in the actual vehicle running experiment of Experimental Example 1. FIG. 12 is a diagram showing a transition of the estimation result in Experimental Example 2. FIG. 13 is a distribution diagram showing the traveling time (time spent driving a car) and the number of determinations for each operation in Experimental Example 2. FIG. 14 is a diagram showing the variation in the traveling time in Experimental Example 2 in units of two weeks. FIG. 15 is a diagram showing the number of times of frequent warnings for each day of the week by totaling the determination results on a monthly basis in Experimental Example 2.

Hereinafter, the present invention will be described in more detail based on the embodiments of the present invention shown in the drawings. The biological signal collected in the present invention is sound / vibration information (hereinafter, “back sound / vibration information”) collected from the back. As described above, the back sound / vibration information includes sound / vibration information generated from the movement of the heart and aorta detected from the back of the upper body of a person, and information on systole and diastole of the ventricles and blood circulation. It includes elastic information of the blood vessel wall serving as an auxiliary pump, elastic information due to blood pressure, and reflected wave information, that is, back body surface pulse wave (APW) and pseudo heart sound information. In addition, the signal waveform associated with heart rate variability includes nerve activity information of the sympathetic nervous system and the parasympathetic nervous system (activity information of the parasympathetic nervous system including the compensatory action of the sympathetic nerve), and the signal waveform associated with the rocking of the aorta is sympathetic. Since it contains information on nervous activity and information on the endocrine system, it is suitable for determining bioregulatory functional elements from different viewpoints.

As the biological signal measuring device for collecting biological signals, for example, a pressure sensor can be used, but it is preferably used in a sleep driving warning device (Sleep Buster (registered trademark)) manufactured by Delta Touring Co., Ltd. The biological signal measuring device 1 used is used. FIG. 1 shows a schematic configuration of the biological signal measuring device 1. This biological signal measuring device 1 can be used by being incorporated in the driver's seat of a vehicle, and can collect biological signals without restraining fingers.

Briefly explaining the biological signal measuring device 1, as shown in FIGS. 1A and 1B, the first layer 11, the second layer 12, and the third layer 13 are laminated in this order from the upper layer side. The first layer 11 having a layered structure and made of a three-dimensional three-dimensional knitted fabric or the like is used by being positioned on the human body side, which is a target for detecting a biological signal. Therefore, the sound / vibration information of the heart / vascular system including the biological signal from the back of the trunk of the human body, particularly the biological sound (direct trunk sound or bioacoustic signal) generated by the vibration of the ventricles, atrium, and large blood vessels. (Back body surface pulse wave (including APW)) is first propagated to the first layer 11 which is a biological signal input system. The second layer 12 functions as a resonance layer that emphasizes biological signals propagated from the first layer 11, particularly sounds and vibrations of the heart and vascular system by a resonance phenomenon or a beat phenomenon, and is a housing 121 made of bead foam or the like. , A three-dimensional three-dimensional knitted fabric 122 that functions as a natural oscillator, and a film 123 that causes membrane vibration. A microphone sensor 14 is arranged in the second layer 12 to detect sound / vibration information. The third layer 13 is laminated on the opposite side of the first layer 11 via the second layer 12, and reduces sound / vibration input from the outside.

Next, the configuration of the biological state estimation device 100 of the present embodiment will be described with reference to FIG. The biological state estimation device 100 includes a bioregulatory functional element determining means 200 and a fatigue feeling estimating means 300. The biological state estimation device 100 is composed of a computer (including a microcomputer and the like), and a computer program for executing a procedure for causing the computer to function as the bioregulatory functional element determination means 200, the fatigue feeling estimation means 300, and the like is stored in the storage unit. Has been done. Further, the biological state estimation device 100 uses the biological adjustment function element determination means 200, the fatigue feeling estimation means 300, and the like as a biological adjustment element determination circuit, a fatigue feeling estimation circuit, and the like, which are electronic circuits that operate in a predetermined procedure by a computer program. It can also be configured. In the following description, it goes without saying that a configuration expressed by adding a "means" other than the bioregulatory functional element determining means 200 and the fatigue feeling estimating means 300 can also be configured as an electronic circuit component. is there.

Further, the computer program may be stored in a recording medium. By using this recording medium, for example, the program can be installed on the computer. Here, the recording medium in which the above program is stored may be a non-transient recording medium. Non-transient recording media are not particularly limited, and examples thereof include recording media such as flexible disks, hard disks, CD-ROMs, MOs (magneto-optical disks), DVD-ROMs, and memory cards. It is also possible to transmit the program to the computer through a communication line and install it.

In the present embodiment, the bioregulatory functional element determining means 200 analyzes back sound / vibration information which is a biological signal measured by the biological signal measuring device 1 described above, and bioregulates used for estimating the basic physical condition of a person. A plurality of types of indexes related to the functional element are calculated for each predetermined determination time set in advance, and the time-series change is obtained.

The plurality of types of bioregulatory functional elements determined by the bioregulatory functional element determining means 200 are not limited, but at least an index highly related to brain function and autonomic nerve function, and related to physical / mental fatigue. It is preferable that the index contains a highly sexual index and an index having a high sensation relevance. These are indicators of fluctuations in brain wave fluctuations that affect a person's homeostatic function, or how bioregulatory functions such as thermoregulatory functions work, and depending on the physical condition, each regulatory function may be affected. This is because it has a large impact.

As indexes highly related to brain function and autonomic nerve function, for example, a time-series waveform of frequency gradient obtained by processing a collected biological signal, and a distribution rate of three signals described in the above-mentioned prior art section. Time-series waveforms, fatigue curves, and time-series fluctuations in determining the homeostatic function level can be mentioned. The time-series waveform of the frequency gradient shows fluctuations that are the basis of the regulatory action of the homeostatic function, and the fluctuations adjust the balance of the antinomy functions well and match with the human autonomic nervous function. The relevance is particularly high. This is supported by statistical methods. The time-series waveform of each frequency band obtained from the distribution rate corresponds to the types of brain waves (θ wave, α wave, β wave) indirectly involved in the rhythm of fluctuations, and corresponds to human brain function and autonomic nerve function. In addition, it is highly associated with the regulatory function of the endocrine system. Since homeostasis is maintained by various regulatory systems such as the endocrine system and the autonomic nervous system, fluctuations in its level are closely related to the regulatory performance due to fluctuations in the brain, autonomic nervous system and endocrine system.

The bioregulatory functional element determining means 200 is a frequency gradient time-series waveform calculation means for obtaining a time-series waveform of a frequency gradient as a calculation means for obtaining an index for capturing a fluctuation of the above-mentioned brain function, autonomic nerve function, and endocrine system fluctuation. It has 210, a distribution rate calculation means 220 for obtaining a distribution rate, a fatigue curve calculation means 230 for obtaining a fatigue curve, and a homeostasis maintenance function level calculation means 240 for obtaining a homeostasis maintenance function level.

The frequency gradient time-series waveform calculation means 210 is a time-series of frequencies from the time-series waveform of the back body surface pulse wave (APW) near 1 Hz obtained by filtering the back sound / vibration information obtained from the sensor 14 of the biological signal measuring device 1. After obtaining the waveform, the time-series waveform of the frequency is slide-calculated to obtain the frequency gradient time-series waveform (see FIGS. 3A and 3B). The frequency gradient time-series waveform calculation means 210 switches from positive to negative in the time-series waveform of the back body surface pulse wave (APW), as disclosed in Patent Document 1 and the like by the present inventors (zero cross). A method using points (zero cross method) and a method of smoothing and differentiating the time series waveform of the back body surface pulse wave (APW) and obtaining the time series waveform using the maximum value (peak point) (peak detection method). There are two methods.

In the zero-cross method, if the zero-cross point is obtained, it is divided into, for example, every 5 seconds, the reciprocal of the time interval between the zero-cross points of the time-series waveform included in the 5 seconds is obtained as the individual frequency f, and the zero-cross point is obtained in that 5 seconds. The average value of the individual frequencies f is adopted as the value of the frequency F for the 5 seconds. Then, by plotting the frequency F obtained every 5 seconds in a time series, the time-series waveform of the frequency fluctuation is obtained.

In the peak detection method, the maximum value of the time-series waveform of the back body surface pulse wave (APW) is obtained by, for example, a smoothing differential method using Savitzky and Golay. Next, for example, the maximum value is divided every 5 seconds, the reciprocal of the time interval between the maximum values of the time series waveform included in the 5 seconds is obtained as the individual frequency f, and the average value of the individual frequencies f in the 5 seconds is the relevant value. It is adopted as the value of frequency F for 5 seconds. Then, by plotting the frequency F obtained every 5 seconds in a time series, the time-series waveform of the frequency fluctuation is obtained.

The frequency gradient time series waveform calculation means 210 has a predetermined overlap time (for example, 18 seconds) and a predetermined time width (for example, 180 seconds) from the time series waveform of the frequency fluctuation obtained by the zero cross method or the peak detection method. A time window is set, the frequency gradient is obtained for each time window by the minimum square method, and the time series waveform of the gradient is output. This slide calculation is sequentially repeated, and the time-series change of the frequency slope of the APW is output as the frequency slope time-series waveform.

The dorsal body surface waveform (APW) is a biological signal that mainly includes the state of control of the heart, which is the central system, that is, the state of sympathetic innervation of the artery, and the appearance information of the sympathetic nervous system and the parasympathetic nervous system. It is a signal, and the frequency gradient time-series waveform obtained by the zero-cross method (the waveform displayed as "0x" in FIGS. 3A and 3B) is more related to the state of control of the heart, and the appearance of sympathetic nerves. Although the state is reflected, the frequency gradient time-series waveform obtained by the peak detection method (the waveform displayed as "Peek" in FIGS. 3A and 3B) is more related to the heart rate variability. Therefore, in order to grasp the state of autonomic nerve function more clearly, it is preferable to use the frequency gradient time series waveform obtained by the zero cross method.

The activity of the sympathetic nerve affects the vascular elasticity and the vascular diameter, and the influence of the reflected wave from the vascular wall is the pseudo heart sound information (detected from the back) included in the sound / vibration information detected from the back of a person. Therefore, between the pseudo I sound (corresponding to the heart sound I sound) and the pseudo II sound (corresponding to the heart sound II sound) of the muscle, skin, etc. between the heart and the back surface as a signal near 20 Hz. It is superimposed on the waveform component. This is the reason why the width between the zero cross points in the zero cross method and the width between the peak points in the peak detection method are different, and in the zero cross method, the period is affected by the reflected wave. Therefore, sympathetic nerve information can be captured by observing the frequency gradient time-series waveform by the zero-cross method.
On the other hand, the peak value reflects the heart rate variability information as described above, but the heart rate variability is mainly controlled by the parasympathetic nerve. Therefore, the parasympathetic nerve information can be captured by looking at the peak value.

Frequency gradient time series waveform The frequency gradient time series waveform obtained by the zero cross method obtained by the calculation means 210 expands in amplitude with a temporary increase in sympathetic nerve activity that occurs as resistance to drowsiness at a predetermined timing before sleep, and has a long period. It is known that when it shows a tendency to become sleepy, it can be regarded as an index of a sign of falling asleep (see Patent Document 4). In addition, after the appearance of a waveform showing a sign of falling asleep, the waveform shows a tendency to converge, and then when a large fluctuation with a longer cycle is shown, the point at which the fluctuation of the long cycle begins to be shown is the imminent sleep immediately before falling asleep. It is known that it can be regarded as an index showing a phenomenon.

The distribution rate calculation means 220 first frequency-analyzes each of the frequency gradient time-series waveforms obtained from the frequency gradient time-series waveform calculation means 210, and starts from the above 0.0033 Hz, which is the frequency at which the characteristics of the fluctuation of the cardiovascular system are switched. Each frequency component belonging to the VLF band is extracted from the ULF band corresponding to the function adjustment signal having a low frequency, the fatigue reception signal having a frequency higher than the function adjustment signal, and the activity adjustment signal having a frequency higher than the fatigue reception signal. Next, the distribution ratio of each of these frequency components is obtained in time series. That is, the ratio of each frequency component when the sum of the power spectrum values of the three frequency components is set to 1 is obtained in time series as the distribution rate (see FIG. 4).

In the present embodiment, as shown in FIG. 4, a frequency component of 0.0017 Hz is used as the function adjustment signal, a frequency component of 0.0035 Hz is used as the fatigue acceptance signal, and a frequency component of 0.0053 Hz is used as the activity adjustment signal. I am using it. In atrial fibrillation, which is one of the heart diseases, the frequency at which the characteristics of fluctuations in the heart and circulatory system are switched is said to be 0.0033 Hz, and by capturing changes in fluctuations near 0.0033 Hz, the autonomic nerves Information on activities and homeostasis can be obtained. Further, it is said that the frequency bands below 0.0033 Hz and around 0.0053 Hz are mainly related to thermoregulation, and the frequency bands 0.01 to 0.04 Hz are related to the control of autonomic nerves. There is. Then, the present inventors actually obtained a frequency gradient time-series waveform for calculating these low-frequency fluctuations inherent in the biological signal, and when the frequency analysis was performed, the frequency was 0.0017 Hz, which is lower than 0.0033 Hz. It was confirmed that there are fluctuations in the frequency band centered on 0.0035 Hz in the vicinity of 0.0033 Hz, and in addition to these two, there are fluctuations in the frequency band centered on 0.0053 Hz. However, the frequency component of each signal can be adjusted according to individual differences, etc., the function adjustment signal is in the range of less than 0.0033 Hz, preferably in the range of 0.001 to 0.0027 Hz, and the fatigue acceptance signal is 0. The activity adjustment signal can be adjusted and used in the range of 002 to 0.0052 Hz and the range of 0.004 to 0.007 Hz.

As for the time-series change of the distribution rate obtained by the distribution rate calculation means 220, for example, the distribution rate of 0.0017 Hz drops sharply and the distribution rate of 0.0053 Hz rises sharply, as shown in Patent Document 2. The time point of change can be regarded as the time point of the imminent sleep phenomenon.

The fatigue curve calculation means 230 is a means disclosed in Japanese Patent Application Laid-Open No. 2009-22610 of the present inventors, and calculates an integrated value by performing absolute value processing on a frequency gradient time-series waveform obtained by the zero-cross method. This is a means for obtaining the integrated value as the degree of fatigue for each predetermined determination time, plotting it corresponding to the time, and obtaining the fatigue curve as shown in FIG. Muscle activity is muscle contraction or relaxation, and the fatigue curve, which is the integral information of the frequency gradient time series waveform by the zero cross method that reflects the information of the sympathetic nerve, has a high correlation with muscle activity (Non-Patent Document 1). reference). Therefore, in the fatigue curve, points where the slope fluctuates by a predetermined value or more indicate singular points, and each singular point indicates that muscle activity has occurred and blood flow has increased in response to the increased fatigue. It shows the point.

The constancy maintenance function level determining means 240 is based on the technique disclosed in Patent Document 3, and the positive and negative of the differential waveform of each frequency slope time series waveform using the zero cross method obtained by the frequency slope time series waveform calculation means 210. , Positive / negative of the integrated waveform obtained by integrating the frequency slope time series waveform, frequency slope time series waveform using the zero cross method and frequency slope time series waveform using the peak detection method are processed by absolute values, respectively. Judgment is made using at least one or more of the absolute values of the series waveforms. By combining these, it is determined which level of homeostasis function corresponds to. For example, using the frequency slope and the integrated value, it is judged as "homeostasis maintenance function level 1" when it is equal to or more than a predetermined value, or the differential value is equal to or less than a predetermined position and "of the two absolute values". In the case of "peak dominance", it can be set to determine "homeostasis maintenance function level 4". Then, for example, by combining the above conditions in various ways and correlating with the human condition, attention should be paid to the case where it is determined to be level 1 to 3 and the case where it is determined to be from normal to good condition and level 4 to 6. Judged as a necessary state. In addition, when it is determined that a sign of falling asleep or a sign of imminent sleep is occurring, an index such as level 7 to 11 is given as a level requiring immediate warning, depending on each state. In the product name "Sleep Buster" manufactured by Delta Touring Co., Ltd., the determination result by the homeostasis maintenance function level determination means 240 is set to be displayed as shown in FIG. 6, for example.

As an index highly related to physical / mental fatigue, a physical condition map and a sensory map, which are indexes disclosed in Patent Document 2, can be used. These are graphs of how fluctuations fluctuate, and are highly related to human physical and mental fatigue.

Therefore, the bioregulatory functional element determination means 200 of the present embodiment further includes a physical condition map calculation means 250 and a sensory map calculation means 260. The back body surface pulse wave (APW) obtained from the back sound / vibration information acquired from the biological signal measuring device 1 is frequency-analyzed, and the analysis waveform is displayed on both logarithmic axes for the target analysis section, and the analysis waveform is displayed. The analysis waveform is scored based on a certain standard based on the inclination of the divided analysis waveform and the shape of the entire analysis waveform divided into the low frequency band, medium frequency band, and high frequency band, and plotted on the coordinate axes. Is. The physical condition map shows the state of control of the autonomic nervous system as the balance between the sympathetic nerve and the parasympathetic nerve, and the sensory map superimposes the state of changes in heart rate variability on the physical condition map.

Specifically, the physical condition map calculation means 250 first obtains a regression line for each predetermined periodic region for the analysis waveform obtained by frequency-analyzing the back body surface pulse wave. Next, each regression line obtained for each periodic region is given a region score based on its slope, and the difference in power spectral density values between the regression lines in the adjacent frequency region and the difference in the slope between the regression lines. Based on, the number of break points indicating the branching phenomenon in each regression line is obtained, the shape score based on the number of break points is given, and at least one of the region score and the shape score is used to determine the judgment reference point for each analysis waveform. Ask. As the area score, the slope of each regression line in each area is divided into three, substantially horizontal, upward and downward. For example, the score is increased or decreased depending on whether the score is upward or downward based on the score in the substantially horizontal state. And give points. As for the shape score, the smaller the number of breaks, the higher the score.

When obtaining the judgment reference point, the first judgment reference point is obtained using the frequency gradient time series waveform obtained by the zero cross method, and the second judgment reference point is obtained using the frequency slope time series waveform obtained by the peak detection method. Find the point. Then, the coordinate points are plotted with the index based on the first judgment reference point on one axis and the index based on the second judgment reference point on the other axis, and the physical condition map as illustrated in FIG. 7A is plotted. Is created. In the physical condition map, if it is determined that the coordinate time-series change line connecting the coordinate points has a change tendency close to the slope of 1 / f, it is determined to be comfortable, and it is determined that the coordinate points are changing in the vertical direction. If it is done, it is judged to be unpleasant. In FIG. 7A, a plurality of coordinate points are connected without being aligned with the coordinate origin, but when looking at the change tendency of two points that differ in time, the first point is aligned with the coordinate origin and the second point is When plotted in the fourth quadrant, this bioregulatory functional element is "good", which makes it easier to judge.

The sensory map calculation means 260 performs a movement calculation for obtaining an average value of frequencies for each predetermined time window set at a predetermined overlap time in a time series waveform of frequencies using a peak detection method related to heart rate variability. The time-series change of the average value of the frequencies obtained for each time window is obtained as a frequency fluctuation time-series waveform, and the index corresponding to the functional point obtained from the time-series waveform of the frequency using the zero-cross method is taken on one axis. At the same time, the index corresponding to the amount of change in the above-mentioned frequency fluctuation time-series waveform obtained by the peak detection method in a predetermined time width is taken as the other axis, and the time-series change of the coordinates obtained from the functional point and the amount of change is obtained. It's a way to go. FIG. 7B is an example of the sensory map obtained in this way. In FIG. 7B, a plurality of coordinate points are connected without being aligned with the coordinate origin. However, when looking at the change tendency of two points that differ in time, the first point is aligned with the coordinate origin and two points are connected. Plotting the eyes makes it easier to determine the distance and direction of separation between the two.

The function points are expressed by the following equation between the judgment reference points of the analysis waveform in the two time ranges before and after the comparison target.
Function point = Judgment reference point in the back time range + (Judgment reference point in the back time range-Judgment reference point in the front time range) x n, (where n is the correction coefficient)
Demanded by.

As an index highly related to the senses, among the time-series changes of the homeostasis maintenance function level obtained by the above-mentioned homeostasis maintenance function level determination means 240, for example, using the frequency slope and the integrated value, it is normal to good. It can be judged by using the index of the level that can be said to be (levels 1 to 3 in the above example) and the index of the level requiring attention (levels 4 to 6 in the above example) and the frequency of occurrence thereof. The level of homeostatic function is highly associated with the state of autonomic nervous function as described above, but when physical condition, basic physical strength, or motivation causes sympathetic nerve compensatory action against fatigue, fatigue Sometimes I feel it and sometimes I don't. Therefore, the sympathetic compensatory effect on fatigue and the basic physical condition are highly related to the sensation of feeling fatigue. The sensation referred to here is a sensation similar to a feeling of loss accompanied by fatigue or a state of low sensation.

The fatigue estimation means 300 is based on the time-series changes related to the fluctuation performance of each bioregulatory functional element required by the bioregulatory functional element determining means 200, and is based on the basic physical condition of the person to be analyzed according to a predetermined standard. It is a means to estimate (basic physical condition). As described above, the bioregulatory functional element determining means 200 is set so that a plurality of types of time-series changes relating to the fluctuation performance of the bioregulatory functional elements can be obtained. Obtained every predetermined determination time. For example, the frequency gradient time-series waveform calculation means 210 takes several minutes to output the first calculation result after acquiring the data from the biological signal measuring device 1, but after that, it is obtained, for example, every 18 seconds. , Therefore, time series change is required. The distribution rate obtained by the distribution rate calculation means 220, the fatigue degree obtained by the fatigue curve calculation means 230, and the homeostasis maintenance function level obtained by the homeostasis maintenance function level are also a number until the first calculation result is output. It takes minutes and is then obtained, for example, every 18 seconds, each of which is required to change over time. The calculation results obtained by the physical condition map calculation means 250 and the sensory map calculation means 260 each take 20 to 30 minutes at first, but the second point is obtained about ten and several minutes later, and the third and subsequent points are obtained every few minutes. .. On the other hand, the basic physical condition estimation means 300 estimates the basic physical condition for a time longer than each of these determination times (basic physical condition estimation time) in each bioregulatory functional element.

The fatigue feeling estimation means 300 combines the determination results of each bioregulatory functional element to estimate whether or not the fatigue feeling is masked. The fatigue estimation means 300 includes a first estimation means 310, a second estimation means 320, and a third estimation means 330. FIG. 8 is a flowchart showing an estimation procedure by the fatigue feeling estimation means 300. As shown in this figure, the first estimation means 310 masks the feeling of fatigue when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed meets a predetermined criterion. Is presumed to occur (category 1) (S110). The second estimation means 320 is a time series of indicators having a high relevance to physical and mental fatigue for the biological signals to be analyzed that are not presumed to be in a state where the fatigue feeling is masked by the first estimation means 310 (category 1). When the change satisfies a predetermined criterion, it is presumed that the masking of fatigue is not generated (category 3) (S120). The third estimation means 330 causes at least masking of fatigue on the biological signal to be analyzed that is not presumed to be in a state where masking of fatigue has not occurred (category 3) in the second estimation means 320, based on a predetermined standard. It is classified into either a state of being tired (category 1) or a state of not masking fatigue (category 3) (S130).

That is, a state in which the biological signal to be analyzed is discriminated using at least three types of bioregulatory functional elements, and masking of a feeling of fatigue is performed, in which the field of view is narrow and it is difficult to be aware of fatigue (category 1). Estimate a state in which masking of one's own fatigue is not occurring in a resting state (category 3). It is also preferable to estimate a vague state (category 2) at an intermediate level between the two.

More specifically, in the first estimation means 310, the frequency gradient time series waveform, distribution rate, and fatigue, which are indexes related to the homeostatic function based on the fluctuation of the brain function / autonomic nerve function or the regulation function of the endocrine system, are used. Use a curve (fatigue). The homeostatic function level is an index that makes it easy to discriminate signs that result from the accumulation of fatigue, such as signs of falling asleep, imminent sleep, and low-sensitivity running. In addition, the fluctuation of the homeostatic function regulated by the brain function differs from the regulated system controlled by the endocrine system due to the difference in the frequency band. Among the above, the distribution rate is the regulation system of these. It is an index that well reflects the time of sudden change, decay, and increase of. Therefore, by using these, it is possible to capture situations that require caution or warning when performing work such as driving, and when the frequency of sudden changes in the distribution rate exceeds a certain level, the field of view is narrowed for things. It is presumed that masking of fatigue is occurring (category 1), which makes it difficult to be aware of fatigue.

The physical condition map / sensory map, which is an index highly related to physical / mental fatigue, makes it easy to remarkably distinguish the index when feeling comfortable and comfortable. Therefore, the second estimation means 320 uses this index to extract the condition in which the feeling of fatigue is not masked in the resting state (category 3) under the condition of showing good condition and comfort. Regarding whether to preferentially use an index that is highly related to brain function, autonomic nervous function, and endocrine system regulatory function, or an index that is highly related to physical / mental fatigue, the modulation of physical / mental fatigue Since there is basically a relationship with the autonomic nerve, the first estimation means 310 is used to perform estimation using an index highly related to brain function, autonomic nerve function, etc., as in the present embodiment. In addition, it is preferable that the second estimation means 320 is used for estimation using an index highly related to physical and mental fatigue.

In this embodiment, the indexes highly related to the brain function, the autonomic nerve function, and the hormone secretion regulatory function of the endocrine system are the frequency gradient time-series waveform, the distribution rate, the fatigue curve (fatigue degree), and the homeostasis, as described above. There are four levels of sexual maintenance function. Of these, it is possible to set the presumption to be "Category 1" when a sign such as a sign of falling asleep is detected in one of them, but when a predetermined sign is detected in a plurality of indicators. , "Category 1" is preferable because it enhances reliability.

Therefore, for example, in the first estimation means 310, the frequency gradient time series waveform obtained from the frequency gradient time series waveform calculation means 210, the time series waveform of the distribution rate obtained from the distribution rate calculation means 220, and the fatigue curve calculation means 230 are obtained. When three or more indexes satisfy a predetermined criterion among the fatigue curve (time-series waveform of the degree of fatigue) and the constancy maintenance function level obtained from the constancy maintenance function level calculation means 240 (in S110 of FIG. 8). When it is determined to be "Yes"), it can be set to estimate "Category 1" (S111 in FIG. 8).

The predetermined criteria for presuming "Category 1" in the present embodiment are set as follows.
(A) Index obtained from the frequency gradient time-series waveform calculation means 210 In the frequency gradient time-series waveform using the zero-cross method, the amplitude changes are compared and 1 in succession (usually set in the range of 2 to 4 times). When a convergence point that changes to less than 90 to 60% of the previous amplitude occurs (an index that estimates that sympathetic nerve activity has decreased and the patient has fallen into a state where he / she cannot resist drowsiness).
(B) Index obtained from the distribution rate calculation means 220 In the time series change of the distribution rate of the frequency gradient time series waveform using the zero cross method, 0 is set in the predetermined time range (usually set in the range of 60 to 120 seconds). When the distribution rate of 0017 Hz suddenly decreases (usually set at a decrease rate of 15% or more) and the distribution rate of 0.0053 Hz suddenly increases (usually set at an increase rate of 15% or more) (the appearance of a sleep onset sign phenomenon) Estimating index)
(C) Index obtained from the fatigue curve calculation means 230 Fatigue curve using the peak detection method (usually set in the range of 3 to 10 minutes) (frequency slope time series waveform using the peak detection method) The slope of the fatigue curve (time-series waveform of the integration of the absolute value of the absolute value) changes more than the slope of the fatigue curve (time-series waveform of the integration of the absolute value of the time-series waveform using the zero-cross method). When there is one or more locations and the fatigue curve using the peak detection method reaches a predetermined value or more after a lapse of a predetermined time (an index for estimating that parasympathetic nerve activity is in an extremely dominant state).
(D) Index obtained from the homeostasis maintenance function level calculation means 240 Among the homeostasis maintenance function levels obtained every 18 seconds, a level lower than the normal level, in the above example, a caution judgment of levels 4 to 6 is made several times. When it appears more than a dozen times (an index that appears when parasympathetic nerve activity is presumed to be dominant), or a level that requires a warning, in the above example, level 7 to 11 warning judgments appear several times or more. (Indicator that appears when a rapid increase or extreme decrease in sympathetic nerve activity is estimated)

The second estimation means 320 relates to data that was not estimated to be “Category 1” in three or more of the above indicators (a) to (d) (data determined to be “No” in S110 of FIG. 8). , The index of the physical condition map calculation means 250 and the sensory map calculation means 260 is used to determine whether or not the predetermined criteria are satisfied (S120 in FIG. 8), and if the predetermined criteria are met, it is estimated to correspond to "Category 3". (S121 in FIG. 8).

(E) Index when it is determined to be "Category 3" In the present embodiment, the time-series change obtained from the physical condition map calculation means 250 is the point at which the calculation result immediately before is output (as described above, 1). The next point is plotted in the 4th quadrant when the points (the second point is output after a predetermined time, but the third and subsequent points are output every few minutes) are aligned with the coordinate origin. In addition, when the time-series change obtained from the sensory map calculation means 260 is plotted with a predetermined distance or more in the X-axis direction when the point immediately before is aligned with the coordinate origin, it is referred to as "category 3". It is set to estimate (S121 in the case of "Yes" in S120 of FIG. 8).

The criteria of (a) to (d) presumed to be "category 1" and the criteria of (e) presumed to be "category 3" are based on statistical analysis of a large number of cases. , Not limited to this. For example, data may be accumulated for each individual and statistical conditions may be set for each individual.

In the third estimation means 330, when the data to be estimated does not satisfy any of the criteria of "category 1" in the first estimation means 310 and "category 3" in the second estimation means ("No" in S110 of FIG. 8). Is determined, and is determined to be "No" in S120) (S130 in FIG. 8). The third estimation means 330 is an index of a level in which sympathetic nerve activity is dominant and can be said to be normal to good among the time-series changes in the homeostasis maintenance function level obtained by the homeostasis maintenance function level determination means 240 (in the above example, The frequency of occurrence of levels near the boundary between levels 1 to 3) and indicators of levels where parasympathetic nerve activity is predominant and requires attention (levels 4 to 6 in the above example) is compared. However, since the difference in state is small when there is a difference in one level, it is preferable to compare at two or more levels. In this embodiment, the ratio of the appearance frequency of the level 2 index in the middle of the normal to good level indicators and the level 4 index that needs attention should be compared. Basically, if the frequency of appearance of level 2 in which sympathetic nerve activity is dominant and shows a good state is high, and the frequency of appearance of level 4 in which parasympathetic nerve activity is dominant and shows a state of attention is low, it can be estimated as "category 3". , When the frequency of appearance is opposite, it can be estimated as "Category 1", but the data to be analyzed by the third estimation means 330 is clearly "Category 1" in the first estimation means 310 and the second estimation means 320. , And because it was not presumed to be "Category 3", data that is difficult to classify into any of them is assumed. Therefore, in the present invention, a large number of cases are analyzed, and a criterion for classifying them into "Category 1" and "Category 3" and their intermediate state "Category 2" is set by a Bayesian estimation method (Fig.). 8 S131).

According to the present embodiment, for example, when analyzing a human condition, a plurality of bioregulatory functional elements are combined and analyzed by the fatigue estimation means 300. Since the criteria for multiple factors are used, the field of vision is narrowed for things. It is difficult to be aware of fatigue. Masking of fatigue occurs (category 1), and masking of one's own fatigue occurs in a resting state. It is possible to properly estimate the absence state (category 3) and the vague state (category 2) at an intermediate level between the two.

Further, the fatigue estimation means 300 of the present embodiment first extracts the state of "Category 1" by the first estimation means 310 for the biological signal data to be analyzed, and only the data not extracted by the first estimation means 310. In the second estimation means 320, the state of "category 3" is extracted, and only the data not extracted by either the first estimation means 310 or the second estimation means 320 is determined by the third estimation means 330. Be the target. That is, the first estimation means 310 and the second estimation means 320 first extract only those clearly corresponding to "category 1" and "category 3", and then only the remaining analysis target data are extracted by the third estimation means. It is processed at 330. Since a plurality of bioregulatory functional elements are used as the determination elements, the arithmetic processing unit of the computer that processes each data by narrowing down the extracted data in each of the estimation means 310 to 330 and making a stepwise determination in this way. , Data exchange and calculation with the storage unit that stores the data received from the biological signal measuring device can be simplified, and the processing of the calculation processing device can be made more efficient. When each estimation means 310 to 330 analyzes at once based on the respective criteria, further processing is required according to the difference in the analysis results, and there is a concern that the calculation processing becomes complicated and the calculation speed becomes slow. ..

(Experimental Example 1)
A: EEG measurement experiment (1) Experimental method The frontal pole brain wave is measured to confirm the function of the prefrontal cortex that forms emotions such as a sense of accomplishment. The measurement sites are Fp1, Fp2, A1, A2 and eye movements based on the International 10-20 Law. This was measured under two conditions: a state in which the subject was watching a movie that he / she wanted to watch again among the movies he had watched in the past, and a state in which he / she was sitting at rest in a stress-free state. All of them were seated on an automobile seat in the laboratory and measured for 60 minutes. In addition to the electroencephalogram, the measurement items are an electrocardiogram, a back body surface pulse wave (APW: a waveform near 1 Hz obtained by filtering back sound / vibration information obtained from the sensor 14 of the biological signal measuring device 1), and a fingertip volume pulse wave. .. The subjects were three healthy Japanese men aged 24, 25, and 30 years.

(2) Experimental results Frequency analysis of the time series data of the RR interval of the electrocardiogram and the frequency gradient time series waveform of APW was performed to obtain a fluctuation waveform showing the relationship between the logarithmic power spectral density and the logarithmic frequency. It was pulled and its tilt angle was calculated. The results are shown in FIGS. 9 (a) and 9 (b). From this figure, it can be seen that the fluctuation of the RR interval of the electrocardiogram and the tendency of the fluctuation of the frequency gradient time series waveform of APW match. That is, at rest, the inclination of both the electrocardiogram and APW is close to -1, and at the time of watching a movie, the inclination is steeper than -1.
Therefore, from this premise, it can be seen that the measurement using APW is effective in estimating the human condition. The frequency band used for the frequency analysis was in the range of 0.01 to 0.03 Hz. It is said that the degree of appearance of sympathetic nerve activity and parasympathetic nerve activity appears in the frequency band of 0.01 to 0.04 Hz, but in order to avoid noise contamination as much as possible and eliminate variations, data up to 0.03 Hz is used. It is preferable to use it.

Next, the biological state is estimated by the biological state estimation device 100. The waveforms of the APW analysis target sections of each subject were estimated in the above categories 1, category 2, and category 3. In addition, the β-wave content of EEG at the timing of the analysis target section where each category was estimated was measured. In the electroencephalogram analysis, in order to remove artifacts such as blinking and body movement, sections in which the amplitude of Fp1 or Fp2 exceeds a certain value are excluded from the analysis target. The frequency bands of 14 to 30 Hz of Fp1 and Fp2 were obtained for each analysis target section as frontal frontal β waves, the average value of the content rate of the frontal frontal β waves of Fp1 and Fp2 was calculated, and the number of analysis target sections higher than the average value. , The number of analysis target sections below the average value was calculated for each category. The results are shown in the following table. Fisher's exact test showed p = 0.017, and a correlation was found.

From Table 1, it can be seen that when the β wave content is high, the prefrontal cortex is activated, and this is highly correlated with category 1.

B: Actual vehicle running experiment (1) Experimental method Astaxanthin-supported fabric (AX-supported fabric) and non-astaxanthin-supported fabric (AX-supported fabric), which are known to exhibit physiologically active effects such as blood flow improvement and muscle fatigue improvement. AX-supported fabric) is prepared in advance. The actual vehicle running experiment is carried out for three types: when the AX-supported fabric is attached to both shoulders of the subject, when the AX-supported fabric is attached to both shoulders of the subject, and when nothing is attached. However, the experiment was conducted by telling the subject that both the AX-supported fabric and the AX-supported fabric were astaxanthin-supported fabrics without distinguishing between them. The subject is a truck driver, a 35-year-old, 43-year-old, and 45-year-old Japanese male. The measurement item is a back body surface pulse wave (APW: a waveform in the vicinity of 1 Hz obtained by filtering back sound / vibration information obtained from the sensor 14 of the biological signal measuring device 1).

(2) Experimental results The frequency gradient time series waveform of APW was frequency-analyzed to obtain a fluctuation waveform showing the relationship between the logarithmic power spectral density and the logarithmic frequency, and an approximate line was drawn for this fluctuation waveform to obtain the tilt angle. The frequency band of the frequency gradient time-series waveform of APW used in the frequency analysis was in the range of 0.01 to 0.03 Hz as described above. The result is shown in FIG. As shown in FIG. 10, the average value of the inclination when the AX-supported fabric is attached is -1.61, and the average value of the inclination when the AX-supported fabric is attached is -0.97. The average value of the inclination when no was attached was -1.00. When the AX-supported fabric is attached, it can be said that the effect of astaxanthin tends to increase the concentration and narrow the field of view, as in the case of watching a movie described above. On the other hand, when the cloth without AX support was attached and when nothing was attached, it is considered that the subject was a professional driver and thus ran relaxedly without feeling great stress. Therefore, astaxanthin is considered to have an effect of enhancing the action of masking fatigue.

FIG. 11 shows the number of times of determination of category 1 by the biological state estimation device 100 of the present invention when the AX-supported fabric is attached, the AX-supported fabric is attached, and nothing is attached. ..

In each case, the number of judgments of category 1 increased as the operation time became longer, but in ascending order of the number of judgments, the AX-supported fabric was attached, the AX-supported fabric was attached, and nothing was attached. It has become a case. That is, the change in the number of judgments in category 1 indicates the degree of accumulation in the process of actually fatigue, but as shown in Table 1, the judgment in category 1 correlates with the content rate of prefrontal cortex β waves. Therefore, it is considered that the smaller the degree of increase with the passage of time, the higher the degree of masking due to the action of the prefrontal cortex so as not to make a feeling of fatigue. Therefore, the degree of masking when the AX-supported fabric is attached is the highest. It is considered that the reason why the degree of masking was judged to be higher when the AX-supported cloth was attached than when nothing was attached was due to the effect of the placebo effect due to the fact that the cloth was previously supported with astaxanthin. Be done. Therefore, it can be seen that the fatigue feeling estimation means 300 by the biological state estimation device 100 of the present invention can estimate the possibility of masking the fatigue feeling.

(Experimental Example 2)
The biological signal measuring device 1 of the above embodiment (sleep driving warning device (Sleep Buster (registered trademark)) manufactured by Delta Touring Co., Ltd.) was attached to a truck seat, and biological signals were measured for a plurality of professional drivers. Of these, the data of the biological signal measuring device 1 was extracted from the vehicle of a male driver in his 40s who caused the accident for about 2 years, for a total of 539 operations. The data is from 2 months before the accident to 21 months. The driver was working at night, and the operation route was almost the same for each operation. From the extracted data, the biological condition estimation device 100 of the above embodiment was used, and the fatigue feeling estimation means 300 was used. Three categories (a state in which it is difficult to notice fatigue due to the masking action of fatigue (category 1), a state in which the masking action of fatigue is not occurring and fatigue can be noticed (category 3), and an intermediate state between them (category 2). )) Estimated results were calculated every 5 minutes.

FIG. 12 is a matrix of estimation results from 45 days before the accident occurrence date (“0 day” in the figure, occurrence time “6 o'clock”) to the 8th day after the accident occurrence. In the normal state before the accident, the judgment changed moderately, and it was found that the change in physical condition fluctuated. On the other hand, it was found that in the week of the accident occurrence day, there were many states (category 1) in which it was difficult to notice fatigue, and there were few changes in the judgment, and fluctuations were reduced. The week after the accident occurred, the changes were fluctuating again. Therefore, it was shown that the body was not able to respond to changes in physical condition such as fatigue and drowsiness on the day of the accident. Therefore, in the case of the above pattern, it was suggested that the physical condition may have caused an accident.

FIG. 13 shows the running time (time spent driving a car) and the number of judgments for each operation in a distribution map, and summarizes the data two months before the accident and the two weeks immediately before the accident. is there. Two months before the accident, the running time during one operation remained unchanged, and while there were fluctuations in the number of judgments for each operation, the running time varied during the two weeks immediately before the accident. Although there were many cases, the number of judgments tended to be close to a positive correlation. In other words, the driver immediately before the accident was in a state where he could not cope with the change, as shown by the rapid increase in the number of judgments. In addition, it can be seen from FIG. 14 that the variation in the traveling time during one operation tended to increase immediately before the accident. In FIG. 12, since the fluctuation tended to decrease when the accident occurred, it was suggested that there was a possibility that some kind of physical condition had occurred due to the variation in the running time immediately before the accident.

For reference, the results of giving advice to the driver in an interview once a week will be explained. In the product name "Sleep Buster" manufactured by Delta Touring Co., Ltd., the judgment result by the homeostasis maintenance function level judgment means 240 is set to be displayed as shown in FIG. 6, for example, and therefore at the time of an interview. The result determined by the homeostasis maintenance function level determining means 240 was used. In FIG. 15, the determination results are aggregated on a monthly basis, and the average value of the number of frequent occurrences of warning determination for each day of the week is calculated. In the figure, the bar graph on the left shows the average number of frequent warnings, the line graph shows the number of operating days, and the right figure shows the actual value of each operation. In the month following the accident, the number of warnings on Sunday or Monday after the holiday was high, and tended to be the same or less from the middle to the latter half of the week. From these facts, it is considered that the way of spending holidays is important in the work style mainly for night shifts. It seems that the number of warnings after the holidays increased because it became difficult for the body to switch from the holiday mode to the work mode during the night shift after the holidays. Therefore, I advised him to pay attention to how he spends his holidays. As a result, after 13 months and 21 months, the number of days off tended to be less than on other days. It is probable that this interview made him feel better for work after the holidays. As a result, it is considered that the effect of reducing accident prevention was obtained by the interview once a week, and as a result, the accident-free situation could be continued for two years or more.

The physical condition estimation method calculated from the back body surface pulse wave was applied to a professional driver, and the tendency of the physical condition change by the interview immediately before the accident and for the following two years was observed, and the effect was verified. As a result, it is presumed that the influence of variation in running time and the change of vacation and work, such as night shift after holidays, affect the physical condition, and it is useful to pay attention and improve by interviewing to reduce accidents. I can say.

1 Biometric signal measuring device 11 Core pad 12 Spacer pad 14 Sensor 100 Biological condition estimation device 200 Bioregulatory function element determination means 210 Frequency tilt time series waveform calculation means 220 Distribution rate calculation means 230 Fatigue curve calculation means 240 Homeostasis maintenance function level calculation means 250 Physical condition map calculation means 260 Sensory map calculation means 300 Fatigue estimation means 310 First estimation means 320 Second estimation means 330 Third estimation means

Claims (4)

A biological state estimation device that estimates a biological state using a biological signal obtained from a biological signal measuring device that comes into contact with the back of a person.
A means for determining a bioregulatory function element that analyzes the biological signal to obtain a plurality of indices involved in the bioregulatory function, including an index highly related to autonomic nerve function, physical / mental fatigue, or sensation.
A fatigue feeling estimating means for estimating whether or not fatigue masking is occurring as the biological state by combining a plurality of indexes obtained by the bioregulatory functional element determining means.
Have,
The fatigue estimation means is
The first estimation means for estimating that the masking of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. When,
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of the feeling of fatigue is caused by the first estimation means, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation means for presuming that the masking of fatigue has not occurred, and
With respect to the biological signal to be analyzed that is not presumed to be in a state where the fatigue feeling is not masked by the second estimation means, at least, based on whether or not an index highly related to the sensation satisfies a predetermined criterion, at least. A biological state estimation device comprising a third estimation means for classifying into either a state in which masking of fatigue is generated and a state in which masking of fatigue is not occurring. It is a biological state estimation method that estimates a biological state using a biological signal obtained from a biological signal measuring device that comes into contact with the back of a person.
By analyzing the biological signals, a plurality of indicators involved in the bioregulatory function were obtained, including indicators highly related to autonomic nervous function, physical / mental fatigue, or sensation.
By combining the plurality of indexes, it is estimated whether or not the fatigue masking is occurring as the biological state.
Have a procedure
Whether or not the fatigue masking is occurring
The first estimation procedure for estimating that the masking of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. And carry out
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of fatigue is generated by the first estimation procedure, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation procedure for presuming that the masking of fatigue has not occurred is carried out.
At least, based on whether or not an index highly related to the sensation satisfies a predetermined criterion for the biological signal to be analyzed that is not presumed to be in a state where the masking of the feeling of fatigue has not occurred in the second estimation procedure. A third estimation procedure for classifying into either a state in which the fatigue feeling is masked or a state in which the fatigue feeling masking is not occurring is carried out.
Biological state estimation method and estimating. It is a computer program that causes a computer as a biological state estimation device to analyze a biological signal obtained from a biological signal measuring device that is in contact with the back of a person and execute a procedure for estimating a biological state.
A procedure for analyzing the biological signals to obtain a plurality of indicators involved in the biological regulation function, including indicators highly related to autonomic nervous function, physical / mental fatigue, or sensation.
By combining the plurality of indexes and performing the procedure of masking the feeling of fatigue and estimating whether or not the masking of the feeling of fatigue is occurring as the biological state, the procedure is executed .
In the procedure for estimating whether or not the fatigue feeling is masked,
The first estimation procedure for estimating that the masking of fatigue occurs when the time-series change of the index highly related to the autonomic nerve function of the biological signal to be analyzed satisfies a predetermined criterion. To execute,
With respect to the biological signal to be analyzed that is not presumed to be in a state where masking of fatigue is generated by the first estimation procedure, the time-series change of the index highly related to physical / mental fatigue satisfies a predetermined criterion. In this case, the second estimation procedure for presuming that the masking of fatigue is not occurring is executed.
At least, based on whether or not an index highly related to the sensation satisfies a predetermined criterion for the biological signal to be analyzed that is not presumed to be in a state where the masking of the feeling of fatigue has not occurred in the second estimation procedure. A computer program that executes a third estimation procedure for classifying into either a state in which the fatigue feeling is masked or a state in which the fatigue feeling masking is not occurring. The computer program according to claim 3 is recorded, which causes a computer as a biological state estimation device to analyze a biological signal obtained from a biological signal measuring device abutting on the back of a person and execute a procedure for estimating a biological state. A computer-readable recording medium.