JP2001095769A - Display method and its apparatus for mental load degree using scattered wavelet transform - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide the mental load degree display method and its apparatus which can display the time transition of mental load degree by quantitatively and precisely. SOLUTION: The signal waveform concerning to the human organs governed by the autonomic nerve, is decomposed to the wavelet components having multi- resolution level by the multi-resolution analysis of scattered wavelet transform, and displayed for each wavelet component at each resolution level.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and an apparatus for displaying a degree of mental burden using a discrete wavelet transform. More specifically, the present invention is intended to prevent health disorders caused by stress, provide a comfortable road environment and
Visualization of the degree of mental burden using the discrete wavelet transform, which is especially useful for designing aircraft and railway vehicles, properly diagnosing elderly and young drivers, improving the quality of production lines and maintaining work efficiency for field workers It relates to a method and its device.

[0002]

2. Description of the Related Art The modern society has an aspect that the mental burden on people is extremely large as it is called a stressed society. Up to now, in the field of ergonomics, aircraft pilots and automobiles have been designed to prevent health problems caused by stress, design comfortable road environments and vehicles, properly diagnose elderly and young drivers, and improve the quality of production lines. Methods have been proposed for measuring the degree of mental burden for drivers such as buses, trucks and railways, office workers, factory workers, and the like. In particular, when considering measures to improve human quality and prevent human error, the degree of mental burden is important data, and it is required to establish a method of measuring the degree of mental burden.

Conventionally, methods for measuring the degree of mental burden include:
A beat-to-beat power spectrum method has been proposed and widely used. The beat-to-beat fluctuation power spectrum method is a method of expressing the degree of mental burden based on the state of excitement of the autonomic nervous system. Focusing on the high-frequency area (HFPA) of the inter-variation power spectrum, it was found that it is effective to express the degree of mental burden by increasing or decreasing the area.

More specifically, adjacent time intervals (typically, R-
R interval), and plotting on a plane with the number of data on the horizontal axis and the RR interval on the vertical axis, a beat interval variation, that is, an RR interval tachogram is obtained (FIG. 5). . Here, in the method using the fast Fourier transform in the conventional interbeat fluctuation power spectrum method,

[0005]

(Equation 1)

The RR interval tachogram is converted into time-series data using the time T shown in FIG. In this equation (1),

[0007]

(Equation 2)

[0008] a is, n is the number of data, T _a is the average R-
In the R interval, x _(i) is the individual RR interval. FIG. 6 shows a power spectrum diagram obtained as a result of Fourier transform of such an RR interval tachogram. The power spectrum diagram shows 0.05% by the frequency shown on the horizontal axis.
Hz or lower low frequency region (region of FIG. 6), 0.1 Hz
6 is divided into three regions: a low-frequency region around (the region in FIG. 6) and a high-frequency region around 0.3 Hz (the region in FIG. 6).
When the waveform has a peak in each region, it can be regarded that the waveform is affected by the body temperature regulation mechanism, the influence of a 10-second wave of blood pressure, and the effect of respiration, respectively.

Of the above classifications, the low frequency region and the high frequency region reflect the influence of the autonomic nervous system. The low frequency region is an antagonistic effect due to dual control of the sympathetic nerve and parasympathetic nerve, and the high frequency region is parasympathetic. It is said to be under nerve control. The area of each region is defined as a low-frequency area (LFPA) and a high-frequency area (HF), respectively.
P. A. ), And this area has been used as one indicator of the degree of mental burden.

However, in the inter-beat fluctuation power spectrum method, although the total mental burden after a certain period of time measurement can be ascertained, the time information is erased, so the mental burden due to the passage of time is lost. It is very difficult to comprehend the mental load, and in order to grasp the degree of mental burden that changes every moment over a long period of time, visualization display by a new means has been awaited.

[0011] The invention of this application has been made in view of the circumstances described above, and the time transition of the degree of mental burden is as follows.
It is an object of the present invention to provide a method and an apparatus for displaying a degree of mental burden that enables quantitative and detailed display.

[0012]

SUMMARY OF THE INVENTION The present invention solves the above-mentioned problem by providing a multi-resolution analysis of a signal waveform relating to a human body organ governed by the autonomic nervous system by a discrete wavelet transform. And a display method for displaying the degree of mental burden (claim 1), characterized by decomposing into wavelet components of each resolution level and displaying the wavelet components for each resolution level.

The invention of this application also decomposes a signal waveform relating to a human body organ controlled by the autonomic nervous system into wavelet components of multiple resolution levels by multi-resolution analysis of discrete wavelet transform, and obtains wavelet components of each resolution level. A mental load degree characterized by selecting wavelet components of a plurality of resolution levels from among them, constructing a new signal waveform by adding the wavelet components, and displaying the newly constructed signal waveform. In addition to the display method (Claim 2), the signal waveforms relating to the human body organs controlled by the autonomic nervous system are decomposed into wavelet components of multiple resolution levels by multi-resolution analysis of discrete wavelet transform, and wavelet components of each resolution level are obtained. From among the two resolution level wavelet components -Option and provides a display method of mental burden degree of and displaying a vector by associating each of the wavelet components to each component of the orthogonal coordinate system (claim 3).

Further, the invention of this application is characterized in that a signal waveform relating to a human body organ governed by the autonomic nervous system is a time interval in which R waves in a heartbeat interbeat fluctuation waveform are adjacent to each other. Is provided (claim 4).

The invention of this application is an apparatus for realizing the above-mentioned method of visualizing the degree of mental burden, comprising a waveform measuring device, a calculation processing device, and a display device. An apparatus (claim 5) is also provided.

[0016]

DESCRIPTION OF THE PREFERRED EMBODIMENTS In the invention of this application, a multi-resolution analysis by a discrete wavelet transform is performed on a signal waveform related to a human body organ controlled by an autonomic nervous system including a degree of mental burden, and a wavelet of a multi-resolution level is performed. There is a great feature in displaying the degree of mental burden that changes every moment without separating time components without erasing time information for each resolution level. Hereinafter, the resolution levels of the wavelet components are set such that the higher the level, the finer the resolution, in order from level 0.

In the invention of this application, if necessary, wavelet components of a plurality of resolution levels are selected, and by adding them, only a specific resolution level is extracted, whereby the degree of mental burden can be displayed. . By selecting only the wavelet component of a specific resolution level, it is possible to display a targeted mental burden degree by excluding information included in other resolution levels. The resolution level to be selected depends on the type of waveform used as the input waveform, the sampling interval, the number of samples, and the like, and an empirically optimum resolution level is selected. The wavelet components at each resolution level can be added because the discrete wavelet transform is an orthogonal transform and the wavelet components at each resolution level are orthogonal.

In the invention of this application, an electrocardiogram waveform,
Any waveform can be used as an input waveform as long as it is a signal waveform relating to a human body organ governed by the autonomic nervous system, such as a brain waveform, a respiratory waveform, and an arterial waveform. When the input waveform is a heartbeat waveform, it may be obtained directly from a waveform measuring device such as a heartbeat electrocardiograph, or a time interval in which R waves seen in the waveform are adjacent to each other, That is, it may be RR tachogram data. further,
The input waveform may be obtained from any waveform measuring device. In the invention of this application, a plurality of simultaneously measured signal waveforms can be used for measuring the input waveform.

A mental burden degree display device for realizing the mental burden degree display method according to the invention of this application includes, for example, a waveform measuring device such as a heart rate electrocardiograph, an arithmetic processing device, and a display device. You.

The waveform measuring device and the arithmetic processing device transmit data by transmission means such as a digital data logger. For the purpose, the waveform measuring device may use, for example, a stationary heart rate electrocardiograph or a portable type that only records the heartbeat once. It is desirable to be able to input. Further, the arithmetic processing device may be embedded in the electrocardiograph by reducing the size, or a general-purpose arithmetic processing device such as a personal computer may be used.

The invention of this application has the above-mentioned features, and will be described in more detail with reference to examples below.

[0022]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the conventional method using Fourier transform, the method of displaying the degree of mental burden, which is the invention of this application, is applied to discontinuous task fluctuation in which fluctuation with time cannot be specified.

As a discontinuous task fluctuation, heart rate data is measured when a regular passenger car starts running at a constant speed of 140 km / h on a dedicated high-speed orbital road from the time of sitting and resting while the vehicle is stationary. Distribution data was obtained. For measurement, a test subject (a 22-year-old man) was equipped with a measurement electrode, and an electrocardiogram was collected with a long-time measurement holder electrocardiograph.

FIG. 1 is a graph showing R-values obtained from the heart rate data.
It is an R tachogram, and there is a slight difference in the interbeat variation between when the vehicle is stationary and the vehicle is stationary and the interpulse variation data number is 588 or less and when the vehicle travels at the designated speed on the dedicated high-speed circuit thereafter. It is difficult to specifically determine whether it is a mental burden. Also, in this embodiment, since the time at which the discontinuous task variation is applied is indicated, it is easy to find a portion where the waveform in the interbeat variation data which is the input waveform is changed, at first glance. In general, it is often difficult to know the time when a discontinuous task change is applied, and the measurement time is also long. Therefore, it is very difficult to determine the change in the waveform with time.

Also in the method using the Fourier transform, since a change in high frequency exists over the entire measurement time irrespective of the presence or absence of a discontinuous task change, it is necessary to capture a change in waveform due to local time. Is impossible.

FIG. 2 shows the result of decomposing the inter-beat variation data shown in FIG. 1 by multi-resolution analysis of discrete wavelet transform. In FIG. 2, the horizontal axis represents the number of interbeat data, which is a kind of time axis, and the vertical axis represents the resolution level. The resolution levels are 0 to 6 in order from the bottom to the top of the figure, and the higher the resolution level, the finer the resolution. When the wavelet components of each resolution level are converted into the center frequency, the resolution level 0 is 0.0093375 Hz, the resolution level 1 is 0.01875 Hz, and the resolution level 2 is 0.0375H.
z, resolution level 3 is 0.075 Hz, resolution level 4
Is 0.15 Hz, resolution level 5 is 0.3 Hz, and resolution level 6 is 0.6 Hz. Here, resolution level 7
Are determined to be noise, and description thereof is omitted. Then, at each resolution level, the amplitude value of the fluctuation increases as the color approaches white, and the amplitude value increases as the color approaches black.

Next, based on the result of the discrete wavelet transform shown in FIG. 2, attention is paid to resolution levels 2 and 3, and a specific threshold value is set for each of these two resolution levels. FIG. 3 is a diagram in which waveforms of two resolution levels are binarized and a high value is represented by black and a low value is represented by white. According to the results of research by the inventors of the present invention, for example, since a resolution level indicating a coarse resolution corresponds to a low frequency, a large amplitude in the waveform of the resolution level 2 has a high mental burden, that is, Since a resolution level indicating a stress state and a fine resolution corresponds to a high frequency, a large amplitude in the waveform of the resolution level 4 indicates a low mental burden state, that is, a relaxed state. In FIG. 3, the horizontal axis represents a kind of time by the number of inter-beat variation data, the lower part of the vertical axis represents the resolution level 2, and the upper part represents the resolution level 4. From this FIG.
Before the course in, many high levels of black appear on level 4 as a whole, and after the course in, many high levels of black appear on level 2. That is, from FIG. 3, it can be clearly recognized that the user is in a relaxed state before entering the course, but is in a stressed state immediately before entering the course.

As described above, in the present invention, it is possible to determine whether the subject is in a high mental load state or a low mental load state with respect to time by analyzing the multiple resolution by the wavelet transform. .

Since the wavelet components of each resolution level obtained as a result of the multi-resolution decomposition by the discrete wavelet transform have the property of being orthogonal, the number of interbeat variation data is assigned to each of the three coordinate axes constituting the three-dimensional orthogonal coordinate system. , The amplitude of the waveform of the resolution level 2 and the amplitude of the waveform of the resolution level 4 are set, and a vector diagram of the resolution level 2 and the resolution level 4 is drawn at the position of each interbeat variation data number, and the vector diagram is analyzed. Thereby, a more detailed mental burden degree can be grasped.

FIG. 4 is a diagram showing, from above, a vector having the amplitude of the waveform of the resolution level 2 and the amplitude of the waveform of the resolution level 4 as components based on FIG. This figure 4
In the graph, the horizontal axis represents the number of inter-beat fluctuation data, the vertical axis represents the amplitude of the waveform at the resolution level 4, and the axis on the front side of the drawing sheet the amplitude of the waveform at the resolution level 2 is set. The amplitude of the waveform of level 2 shows a larger value as the portion at the tip of the vector (the portion indicated by the diamond) is larger. The number of interbeat variation data on the horizontal axis was extracted from the 531st to the 600th.

For example, in FIG. 4, it can be divided into four stages from A to D. In the area A, the amplitude of the waveform of the resolution level 4 is large and the amplitude of the waveform of the resolution level 2 is small, that is, a relaxed state is shown. In the area A, the degree of relaxation is large because the time is much before the time of the course-in. In the area B, both the amplitude of the waveform of the resolution level 4 and the amplitude of the waveform of the resolution level 2 are large, indicating a transition from the relaxed state to the stress state. In other words, the region B shows a state in which the user is getting more and more nervous as the course-in time approaches. The region C indicates a stress state because the amplitude of the waveform of the resolution level 4 is small and the amplitude of the waveform of the resolution level 2 is large. In the area C, it can be recognized that the degree of mental burden is extremely high because the course is approaching. D area is resolution level 4
It can be said that both the amplitude of the waveform and the amplitude of the waveform of the resolution level 2 are small, and it is a kind of an active state that is neither a relaxed state nor a stress state. In this D region, it is recognized that high-speed operation has been started and the vehicle has been released from a strong stress state.

That is, in the present invention, by taking the two levels in the rectangular coordinate system, not only a simple mental load is recognized, but also a more detailed mental load such as a transient state or an active state. Can also be recognized.

[0033]

As described above in detail, according to the invention of this application, a signal waveform relating to a human body organ controlled by the autonomic nervous system including a degree of mental burden is subjected to multi-resolution analysis by discrete wavelet transform. It is possible to quantitatively display the temporal change of the degree of mental burden in detail. The invention of this application makes it easy to grasp the degree of mental burden in various situations, prevent health disorders due to stress, design comfortable road environments and automobiles, airplanes and railway vehicles, and properly diagnose elderly people and young drivers It contributes to improving the quality of production lines and improving the work efficiency of field workers.

[Brief description of the drawings]

FIG. 1 is an example of the present invention and is a relationship diagram showing a heartbeat state with respect to time of a driver performing high-speed driving.

FIG. 2 is a relationship diagram showing a result of performing a multi-resolution analysis by a discrete wavelet transform on the waveform shown in FIG. 1 according to the embodiment of the present invention.

FIG. 3 is a diagram showing a result of binarizing the waveforms of the resolution level 2 and the resolution level 4 in FIG. 2 according to the embodiment of the present invention.

FIG. 4 is an embodiment of the present invention, which forms a vector having components of the amplitude of the waveform of the resolution level 2 and the amplitude of the waveform of the resolution level 4 shown in FIG. 2; FIG. 3 is a relationship diagram showing a three-dimensional orthogonal coordinate system.

FIG. 5 is a relationship diagram showing a typical number of inter-beat variation data and inter-beat variation.

FIG. 6 is a relationship diagram showing a relationship between a frequency and a power spectrum density obtained by a conventional method.

 ──────────────────────────────────────────────────続 き Continuation of the front page (71) Applicant 599137286 Michiharu Okano 1-8-14 Kanda Surugadai, Chiyoda-ku, Tokyo Department of Mechanical Engineering, Faculty of Science and Technology, Nihon University (71) Applicant 599137297 Hiroyasu Nagae Kanda Surugadai, Chiyoda-ku, Tokyo 1-8-14 Nihon University Faculty of Science and Technology, Department of Mechanical Engineering (71) Applicant 591114216 Saito Saito 778-12 Yamada, Akiruno-shi, Tokyo (71) Applicant 390031853 Kiyoyuki Horii 5-815 Kamiguro, Meguro-ku, Tokyo No.-501 (72) Inventor Taro Sekine 1-8-14 Kanda Surugadai, Chiyoda-ku, Tokyo Department of Mechanical Engineering, Faculty of Science and Technology, Nihon University (72) Inventor Masahiro Takei 1-8-14 Kanda Surugadai, Chiyoda-ku, Tokyo Nihon University Department of Mechanical Engineering, Faculty of Science and Technology (72) Inventor Michiharu Okano 1-8-14, Kanda Surugadai, Chiyoda-ku, Tokyo Department of Mechanical Engineering, Faculty of Science and Technology, Nihon University (72) Inventor Hiroyasu Nagae 1-8-14 Kanda Surugadai, Chiyoda-ku, Tokyo Department of Mechanical Engineering, Faculty of Science and Technology, Nihon University (72) Inventor Choko Saito 778-12 Yamada, Akiruno-shi, Tokyo (72) Inventor Kiyoyuki Horii Tokyo 5-8-15-501, Kamimeguro, Meguro-ku F-term (reference) 4C027 AA02 AA03 FF00 GG05 GG11 GG15 HH11 HH12 KK03

Claims (5)

[Claims] 1. A method of decomposing a signal waveform relating to a human body organ governed by the autonomic nervous system into wavelet components of multiple resolution levels by multi-resolution analysis of discrete wavelet transform and displaying each of the wavelet components of each resolution level. How to display the characteristic mental burden degree. 2. A signal waveform relating to a human body organ controlled by the autonomic nervous system is decomposed into wavelet components of multiple resolution levels by multi-resolution analysis of discrete wavelet transform, and a plurality of wavelet components of each resolution level are selected. A method of displaying a degree of mental burden, comprising selecting a wavelet component at a resolution level, adding up the wavelet components, constructing a new signal waveform, and displaying the newly constructed signal waveform. 3. A signal waveform relating to a human organ governed by the autonomic nervous system is decomposed into wavelet components of multiple resolution levels by multi-resolution analysis of discrete wavelet transform, and two wavelet components of each resolution level are selected.
A method for displaying a degree of mental burden, wherein a wavelet component of one resolution level is selected, and each wavelet component is displayed as a vector by associating the wavelet component with each component of a rectangular coordinate system. 4. A signal waveform relating to a human body organ governed by the autonomic nervous system is a time interval in which R waves in an interbeat fluctuation waveform of a heartbeat are adjacent to each other. How to display the degree of mental burden described. 5. An apparatus for realizing the mental burden degree visualization method according to any one of claims 1 to 4, comprising a waveform measuring apparatus, a calculation processing apparatus, and a display apparatus. A display device for the degree of mental burden.