JPH1156827A - Motion intensity detector - Google Patents

Abstract

PROBLEM TO BE SOLVED: To detect motion intensity so as not to give a burden to an examinee. SOLUTION: A pulse wave detection part 10 detects pulse waveform MH. When a body motion detecting part 11 detects a body motion waveform TH showing body motion, a waveform processing part 12 gives waveform processing to the waveform TH to convert to a body motion component MHt. Next, by subtracting the component MHt from the waveform MH, a body motion removing part 13 generates a body motion removing pulse waveform MH'. After this, a breath component extracting part 14 gives FFT to the waveform MH' to execute frequency analyzing and calculates a breath frequency component based on the analyzing result. An evaluation part 15 calculates a distortion ratio K based on the breath frequency component and reads motion intensity X corresponding to this from ROM. A display part 16 displays the intensity X. Thereby, the subject is informed of the intensity X.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an exercise intensity detection device suitable for detecting exercise intensity from a pulse wave waveform.

[0002]

2. Description of the Related Art As a method of measuring exercise intensity such as running and weight training, a method of measuring the concentration of lactic acid in blood is known. In this measurement method, attention is paid to the fact that lactic acid is a fatigue substance, and it is determined that exercise intensity is high when the lactic acid concentration is high. Here, if the person training during exercise can know the exercise intensity by himself, it is convenient because he can perform health management and scientific training. However, to measure the lactate concentration,
Since it is necessary to collect blood and check the concentration, it is impossible to measure while continuing exercise. Therefore, the present inventors have repeatedly studied an index representing exercise intensity and found that there is a close relationship between a respiratory waveform and exercise intensity.

[0003]

Here, how to measure the respiratory waveform is an important problem. First, as a method of measuring the respiratory waveform of a subject who is at rest, such as a sick person, a band is wrapped around the chest or abdomen to measure the stretching state,
A general method is to insert a thermocouple into the nostril and count the change in the resistance value. However, it is extremely inconvenient and inconvenient for a subject performing daily health care and a subject performing training to wear such an object.

[0004] By the way, R of an electrocardiogram of a subject in a resting state
When analyzing the frequency component of the fluctuation of the -R cycle, there is a component corresponding to the respiratory rate. Since the pulse wave is synchronized with the electrocardiogram, a fluctuation frequency component of the pulse wave cycle (or pulse wave amplitude) includes a component corresponding to a respiratory waveform. Therefore,
A device that measures a respiratory waveform based on an electrocardiogram or a pulse wave by extracting such a component is known. For example, Japanese Patent Application Laid-Open No. 62-22627 discloses a technique in which a series of pulse intervals are measured, a change cycle of these pulse intervals is measured, and a respiratory rate is calculated based on a reciprocal of the change cycle.

In Japanese Utility Model Laid-Open No. 4-51912,
Detecting a first respiratory rate based on a fluctuation cycle of an RR interval of an electrocardiographic waveform or a fluctuation of an envelope of a peak value of a pulse wave waveform;
There is disclosed a technique of detecting a reciprocating motion of the abdomen surface of a subject to detect a second respiratory rate, and recording and displaying a lower one of the first and second respiratory rates.

In Japanese Utility Model Application Laid-Open No. 4-136207, the respiratory rate is estimated based on the period of the fluctuation of the amplitude of the pulse waveform, and the average value of the pulse waveform (swell of the low frequency component) is calculated. There is disclosed a technique for reducing the influence of undulations and noises by using data in the case where the slope of the average value is small.

Japanese Patent Application Laid-Open No. 6-142082 discloses a technique in which a pulse rate of a subject sequentially obtained is multiplied by a systolic blood pressure value, and a respiratory rate is calculated based on a pulsation cycle of the multiplied value. . In addition, actual fairness 6-22525
Discloses a technique for determining a respiratory rate of a living body based on a change period of a curve connecting peak values of pulse waves.

However, if the subject is exercising, a myoelectric waveform is superimposed on the electrocardiographic waveform, and a body motion component is superimposed on the pulse wave. Since the level of these components may be higher than the component corresponding to the respiratory waveform, there is a problem that the respiratory waveform is incorrectly calculated.

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and has as its object to provide an exercise intensity detecting device that extracts a respiratory component from a pulse wave waveform and easily detects exercise intensity based on the extracted respiratory component. I do.

[0010]

According to the first aspect of the present invention, there is provided a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, and a body movement of the living body. Body movement detecting means for detecting a body movement waveform indicating the body movement, generating a body movement component in the pulse wave waveform based on the body movement waveform, and removing the body movement component from the pulse wave waveform to remove the body movement A body motion removing unit that generates a pulse wave waveform, a respiratory component extracting unit that extracts a respiratory component based on the body motion removed pulse wave waveform, and a motion based on a respiratory component extracted by the respiratory component extracting unit. And a forcing intensity generating means for calculating the intensity.

Further, in the invention according to claim 2,
The respiratory component extraction means performs a wavelet transform on the body motion-removed pulse wave waveform to generate a body motion-removed pulse wave analysis data, and a pulse wave component from the body motion-removed pulse wave analysis data. A respiratory waveform generating unit that generates respiratory waveform analysis data by removing a corresponding frequency component, applies an inverse wavelet to the respiratory waveform analysis data, and generates a respiratory waveform as the respiratory component. .

Further, in the invention according to claim 3,
The mobility intensity generating means calculates the exercise intensity based on a ratio of frequency components obtained by performing frequency analysis on the respiratory component extracted by the respiratory component extracting means.

Further, in the invention according to claim 4,
The fortitude intensity generation means calculates a distortion rate from each frequency component obtained by performing frequency analysis on the respiration component extracted by the respiration component extraction means, and calculates the exercise intensity based on the distortion rate. It is characterized by the following.

Further, in the invention according to claim 5,
The mobility intensity generating means calculates a ratio between a fundamental frequency component and a third harmonic component obtained by performing frequency analysis on the respiratory component extracted by the respiratory component extracting means, and calculates the ratio based on the ratio. The exercise intensity is calculated.

In the invention according to claim 6,
The respiratory component extracting means extracts a respiratory waveform as the respiratory component, and the fortitude intensity generating means detects a duty ratio of the respiratory waveform extracted by the respiratory component extracting means, based on the duty ratio. The exercise intensity is generated.

In the invention according to claim 7,
The body motion removing unit includes a first frequency analysis unit that analyzes a frequency spectrum of the pulse wave waveform, a second frequency analysis unit that analyzes a frequency spectrum of the body motion waveform, and the second frequency analysis unit. A frequency spectrum having the same frequency as that of the frequency spectrum analyzed by the first frequency analyzer is removed from the frequency spectrum analyzed by the first frequency analyzer.
A body movement removal unit that generates a body movement removal spectrum from which body movement has been removed, wherein the respiratory component extracting unit extracts a frequency spectrum corresponding to a fundamental component of a respiratory component from the body movement removal spectrum, The exercise intensity generating means calculates the exercise intensity based on a frequency spectrum level corresponding to a fundamental component of the respiratory component and a frequency spectrum level corresponding to a harmonic component thereof.

Further, in the invention according to claim 8,
The respiratory component extracting means specifies a band determined according to a pulse rate from the body motion removal spectrum, and extracts a frequency spectrum corresponding to a fundamental component of a respiratory component from a frequency spectrum within this band. It is characterized by the following.

In the invention according to claim 9,
The mobility intensity generating means calculates a distortion rate of a respiratory waveform based on a spectrum level corresponding to a fundamental component of the respiration component and a spectrum level corresponding to a harmonic component thereof, and calculates the distortion rate based on the distortion rate. And calculating the exercise intensity.

Further, in the invention according to claim 10, the mobility intensity generating means includes a spectrum level corresponding to a fundamental component of the respiratory component and a spectrum corresponding to a third harmonic component thereof. Of the level of the
The exercise intensity is calculated based on the ratio.

According to the eleventh aspect of the present invention, there is provided a pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, a respiratory component extracting means for extracting a respiratory component from the pulse wave waveform, The present invention is characterized in that a fork intensity generating means for calculating exercise intensity based on the respiratory component extracted by the respiratory component extracting means is provided.

[0021] In the twelfth aspect of the present invention, the respiratory component extracting means performs a frequency analysis on the pulse wave waveform to generate pulse wave analysis data,
Refer to the pulse wave component removing unit that removes a pulse wave component from the pulse wave analysis data, a fundamental frequency table in which the relationship between the body motion fundamental wave frequency and the respiratory fundamental wave frequency is stored in advance and the fundamental wave frequency table Then, from the analysis data, a frequency specifying unit that specifies a respiratory fundamental frequency and a body motion fundamental frequency, and based on the respiratory fundamental frequency specified by the frequency specifying unit, calculates each harmonic frequency thereof. And an extractor for extracting a respiratory component.

Further, in the invention according to the thirteenth aspect, the mobility intensity generating means converts the level of a spectrum corresponding to a fundamental component of the respiratory component into a level of a spectrum corresponding to a harmonic component thereof. And calculating the exercise intensity based on the distortion rate based on the calculated distortion rate.

Further, in the invention according to claim 14, the mobility intensity generating means includes a spectrum level corresponding to a fundamental component of the respiratory component and a spectrum corresponding to a third harmonic component thereof. Of the level of the
The exercise intensity is calculated based on the ratio.

[0024] Further, the invention according to claim 15 is characterized in that there is provided a notifying means for notifying the exercise intensity generated by the fortitude intensity generating means.

[0025]

BEST MODE FOR CARRYING OUT THE INVENTION

A. Principle When exercise intensity increases, oxygen consumption of skeletal muscle increases, and respiratory rate increases. Here, the relationship between exercise intensity and respiratory waveform is shown in FIG. Note that, in FIG.
The direction indicates inhalation and the-direction indicates expulsion.
FIG. 7A shows a respiratory waveform at rest (exercise intensity X1), FIG. 8B shows a respiratory waveform at exercise intensity X2, FIG. 8C shows a respiratory waveform at exercise intensity X3, and FIG. ) Shows the respiration waveform at the exercise intensity X4, and the exercise intensity X1 to X4 has the following relationship. X1 <X2 <X3 <X4

From this figure, at rest, the inhalation time is longer than the discharge time, but as the exercise intensity increases, the difference between the inhalation time and the discharge time decreases, and the respiratory waveform gradually approaches a sine wave. It can be seen that the respiratory waveform is greatly disturbed as the value of becomes larger.

Here, the fact that the respiratory waveform approaches a sine wave means that a harmonic component with respect to the fundamental wave component is reduced. In particular, unless the exercise intensity exceeds a certain limit
Since the respiratory waveform changes from a sawtooth wave to a sine wave, the third harmonic component decreases as the exercise intensity increases. Therefore, an index of exercise intensity can be obtained by frequency-analyzing the respiratory waveform.

Focusing on this point, the applicant has determined that exercise intensity is detected by extracting a respiratory component from a pulse wave waveform and performing frequency analysis of the respiratory component.

B. Functional Configuration Next, the function of the exercise intensity detection device according to the present embodiment will be described. FIG. 2 is a functional block diagram of the exercise intensity detecting device according to the present embodiment. In the figure, f1 is a pulse wave detecting means, which detects a pulse wave waveform from a detection part of a living body.
For example, an optical pulse wave sensor, a pressure sensor, and the like correspond.
F2 is a body movement detecting means for detecting a body movement waveform indicating the body movement of the living body. For example, an acceleration sensor is applicable.

F3 is a body movement removing means which generates a body movement component in the pulse wave waveform based on the body movement waveform and removes the body movement component from the pulse wave waveform to remove the body movement. Generate Specifically, by performing appropriate waveform processing on the body motion waveform and subtracting this from the pulse waveform, or
Generate a body motion removal waveform by analyzing the frequency spectrum of the pulse wave waveform and the frequency spectrum of the body motion waveform, and removing the same frequency as the frequency spectrum of the body motion waveform from the frequency spectrum of the pulse wave waveform. can do.

F4 is a respiratory component extracting means,
A respiratory component is extracted based on the pulse waveform of the body motion removal. The respiratory component extracting unit f4 includes, for example, a wavelet transform unit that performs a wavelet transform on the body motion removal pulse wave waveform to generate body motion removal pulse wave analysis data, and a pulse wave component from the body movement removal pulse wave analysis data. And a respiratory waveform generating unit that generates respiratory waveform analysis data by removing a frequency component corresponding to the above, applies an inverse wavelet to the respiratory waveform analysis data, and generates a respiratory waveform as the respiratory component. Also,
f5 is a fortune intensity generating means for calculating exercise intensity based on the respiratory component extracted by the respiratory component extracting means. In this case, the exercise intensity can be calculated based on the ratio of the frequency component obtained by performing frequency analysis on the extracted respiratory component.

C. First Embodiment 1. First Embodiment Configuration of First Embodiment The configuration of the exercise intensity detecting device 1 according to the first embodiment of the present invention will be described with reference to the drawings. 1-1: External Configuration of First Embodiment FIG. 3 is a perspective view showing an external configuration of the exercise intensity detection device 1 according to the first embodiment. In FIG. 3, the exercise intensity detecting device 1 of the present embodiment is a device main body 110 having a wristwatch structure.
And a cable 120 connected to the apparatus main body 110.
And a pulse wave detection sensor unit 130 provided on the distal end side of the cable 120. A connector piece 80 is formed on the distal end side of the cable 120, and the connector piece 80 is detachable from a connector section 70 formed on the 6:00 side of the apparatus main body 10. The device main body 10 is provided with a wristband 60 which is wound around the wrist from the 12 o'clock direction and fixed at the 6 o'clock direction of the wristwatch. The pulse wave detection sensor unit 130 includes a sensor fixing band 140.
It is attached to the base of the index finger while being shielded from light.
As described above, when the pulse wave detection sensor unit 130 is attached to the base of the finger, the length of the cable 120 can be reduced, so that the cable 120 does not interfere with the running. Also, when the distribution of body temperature from the palm to the fingertip is measured, when the temperature is cold, the temperature at the fingertip drops significantly, whereas the temperature at the root of the finger does not drop relatively. Therefore, if the pulse wave detection sensor unit 130 is attached to the base of the finger, the pulse rate and the like can be accurately measured even when running outdoors on a cold day.

The main body 110 of the apparatus has a watch case 200 (body case) made of resin. The watch case 200 has a front surface on which the running time and walking pitch are added in addition to the current time and date. , A liquid crystal display device 210 with an EL backlight for displaying pulse rate, exercise intensity, respiratory rate and the like. The liquid crystal display device 210 includes a dot display area in addition to the segment display area, and various information can be graphically displayed in the dot display area.

Further, inside the watch case 200, changes in exercise intensity, pulse rate or respiratory rate, etc. are obtained based on the pulse wave waveform MH measured by the pulse wave detecting sensor unit 130, and the results are displayed on a liquid crystal display device. In order to display the data on the control unit 210, a control unit including a microcomputer for performing various controls and data processing is configured. The control unit also includes a timekeeping circuit, so that the normal time, lap time, split time, and the like can be displayed on the liquid crystal display device 210. Button switches 111 to 115 for performing external operations such as time adjustment and switching of display modes are configured on the outer peripheral portion of the watch case 200.

Next, the pulse wave detecting sensor unit 130
Is composed of an LED 32, a phototransistor 33 and the like as shown in FIG. When the switch SW is turned on and a power supply voltage is applied, light is emitted from the LED 32 and is reflected by blood vessels and tissues. Then, the light is received by the phototransistor 33 and the pulse wave signal M is detected. Here, the emission wavelength of the LED is selected near the absorption wavelength peak of hemoglobin in blood. Therefore, the light receiving level changes according to the blood flow. Therefore, the pulse wave waveform can be detected by detecting the light receiving level. Further, as the LED 32, an InGaN-based (indium-
Gallium-nitrogen based blue LEDs are preferred. Blue L
The emission spectrum of the ED has an emission peak at, for example, 450 nm, and the emission wavelength range is from 350 nm to 600 n.
m. In this case, a GaAsP-based (gallium-arsenic-phosphorus-based) phototransistor may be used as the phototransistor 33 corresponding to an LED having such light emission characteristics. The light-receiving wavelength region of the phototransistor 33 has, for example, a main sensitivity region of 300
In the range from nm to 600 nm, there is a sensitivity region even below 300 nm. When such a blue LED and the phototransistor 33 are combined, a pulse wave is detected in a wavelength region from 300 nm to 600 nm, which is an overlapping region thereof. In this case, there are the following advantages.

First, of the light included in the external light, light having a wavelength region of 700 nm or less tends to hardly penetrate the finger tissue, so that the external light is not covered with the sensor fixing band. , Does not reach the phototransistor 33 via the finger tissue, and only light in a wavelength region that does not affect detection reaches the phototransistor 33. On the other hand, since light in a wavelength region lower than 300 nm is almost absorbed by the skin surface, even if the light receiving wavelength region is set to 700 nm or less, the substantial light receiving wavelength region is 300 nm to 700 nm.
nm. Therefore, the influence of external light can be suppressed without covering the finger in a large scale. In addition, hemoglobin in blood has a large absorption coefficient for light having a wavelength of 300 nm to 700 nm, and is several times to about 100 times or more larger than the absorption coefficient for light having a wavelength of 880 nm. Therefore, as in this example, in accordance with the absorption characteristics of hemoglobin, the wavelength region where the absorption characteristics are large (300 n
When the light of (m to 700 nm) is used as the detection light, the detection value changes with high sensitivity according to the change in blood volume, so that the S / N ratio of the pulse wave waveform MH based on the change in blood volume can be increased.

1-2: Electrical Configuration of First Embodiment Next, the electrical configuration of the first embodiment is shown in FIG. In the figure, reference numeral 10 denotes a pulse wave detection unit, which corresponds to the above-described pulse wave detection sensor unit 130. The pulse wave detection unit 10 detects a pulse wave waveform MH indicating the magnitude of the pulsation. Reference numeral 11 denotes a body movement detecting unit, which is constituted by, for example, an acceleration sensor, and is provided inside the watch case 200. As a result, a body motion waveform TH indicating a body motion caused by the swing of the arm during running or the like is detected.

Reference numeral 12 denotes a waveform processing unit for performing a predetermined waveform processing on the body motion waveform TH, and 13 denotes a body motion removing unit.
The reason why the waveform processing is performed is that the body motion removing unit 13 accurately removes the body motion component. Here, if the body motion component in the pulse wave waveform MH is represented by MHt and the true pulse wave component (body motion removal pulse wave waveform) is represented by MH ′, MH = MHt + M
H '. The body movement waveform TH is detected, for example, as the acceleration itself of the swing of the arm. However, since the blood flow is affected by blood vessels and tissues, the body movement component MHt is obtained by dulling the body movement waveform TH. For this reason, the waveform processing unit 12 is configured by an appropriate low-pass filter. Note that the type and coefficient of the low-pass filter are calculated from values obtained by experiments. Thereby, the body motion component MHt is calculated from the body motion waveform TH.
Can be requested. Further, the body movement removing unit 13 generates a body movement removed pulse wave waveform MH ′ by subtracting the body movement component MHt from the pulse wave waveform MH.

Next, reference numeral 14 denotes a respiratory component extracting unit,
It is composed of a PU (Central Processing Unit), an A / D converter and the like. In this example, the body motion removal pulse wave waveform MH ′ is
After being converted from an analog signal to a digital signal by an A / D converter, the CPU converts the signal into body motion removal pulse wave data MH '.
Has been taken into. Respiratory component extraction unit 14
Performs FFT processing on the body motion removal pulse wave data MH ′,
Perform frequency analysis.

FIG. 6 shows that FF is added to the body motion removal pulse wave data MH '.
It is a figure which shows the result of having performed T processing simplified and typically. In this figure, the maximum peak frequency in the low frequency range LF is the fundamental frequency Fv1 of the respiratory component,
The maximum peak frequency in the high frequency range HF is the fundamental frequency component Fm1 of the pulse wave. FIG. 7 is an enlarged view of the low frequency region LF in FIG. From this figure, the respiratory component has its fundamental frequency Fv1 and harmonic F
It can be seen that it consists of v2, Fv3, Fv4 ... In this example, the respiratory component extracting unit 14 firstly outputs the body motion removal pulse wave waveform M
H ′ is subjected to FFT processing to specify the maximum peak frequency.
Since the fundamental wave component of the pulse wave becomes maximum, Fm1 is specified as the maximum peak frequency. Thereafter, the maximum peak frequency is specified in a frequency range lower than Fm1. Since the frequency component lower than the pulse wave component corresponds to the respiratory component, the fundamental frequency Fv1 of the respiratory component is specified here. Thereafter, the respiratory component extraction unit 14
Is the level L1 of Fv1 and its harmonic frequencies Fv2, Fv3, F
The levels L2, L3, L4... of v4. In this example, the harmonic frequency is limited to a frequency lower than Fm1. This is because if Fm1 or more, a pulse wave component exists, and if Fm1 is an integral multiple of Fv1, the respiratory component cannot be separated.

Next, reference numeral 15 denotes an evaluation unit, which includes a CPU and an R.
OM and the like. The CPU determines the respiratory component extraction unit 14
The distortion factor K of the respiratory waveform is calculated based on L1, L2, L3, L4,. Specifically, the distortion factor K is calculated according to the following calculation formula. K = (L2 ² + L3 ² + L4 ² ...) ^1/2 / L1 As described above, at rest, the inhalation time is longer than the discharge time, but as exercise intensity X increases, the difference between the inhalation time and the discharge time. Becomes smaller, the respiratory waveform gradually approaches a sine wave, and when the exercise intensity X further increases, the respiratory waveform is greatly disturbed. That is, exercise intensity X
As long as the exercise intensity X does not exceed a certain limit, the ratio of the harmonic component to the fundamental component decreases.
When the exercise intensity X exceeds a certain limit, the ratio of the harmonic component to the fundamental component suddenly increases. This means
There is a certain relationship between the distortion rate K of the respiratory waveform and the fortune intensity X,
As the exercise intensity X increases, the distortion rate K decreases, and when the exercise intensity X exceeds a certain limit, the distortion rate K sharply increases. Therefore, if the relationship between the distortion rate K and the exercise intensity X is determined in advance, the exercise intensity X can be determined from the distortion rate K.

Next, the exercise intensity X is stored in the ROM in association with the distortion rate K. Therefore, the exercise intensity X can be calculated by accessing the ROM using the distortion rate K as an address. In this sense, the ROM functions as an exercise intensity table. Note that the fortune intensity X is 5
Grading such as three or three stages may be performed.
In this case, the exercise intensity X stored in the ROM may be represented by a predetermined number of steps.

A display unit 16 corresponds to the liquid crystal display device 210 described above. The display unit 16 may display the exercise intensity X as a numerical value as it is, or may display it as a bar graph or the like using a dot display area. Also,
When the evaluation unit 16 grades the exercise intensity X, characters or symbols corresponding to the grade may be displayed. For example, if the exercise intensity corresponding to walking is X1, the exercise intensity corresponding to jogging is X2, the exercise intensity of short-distance running is X3, and the exercise intensity that is too large and impairs health is X4. Light exercise. "X2 displays" moderate exercise. "X3 displays" strong exercise. "X4" Dangerous. " Further, the exercise intensity X may be displayed in association with a face chart as shown in FIG.

2. Operation of First Embodiment Next, the operation of the first embodiment will be described with reference to the drawings. FIG. 9 is a flowchart showing the operation of the first embodiment. Here, a case where the stopped subject starts running and gradually increases the speed will be described as an example. First, the examinee sets the button switches 111 to 1
15 (see FIG. 3) to set the exercise intensity measurement mode (step S1), the pulse wave detection unit 10 detects the pulse wave waveform MH (step S2).

Next, when the body movement detecting section 11 detects a body movement waveform TH indicating the body movement of the subject (step S3), the waveform processing section 12 performs a waveform process on the body movement waveform TH (step S4). This waveform processing is performed by the body motion waveform TH as described above.
Is converted into the body motion component MHt in the pulse wave waveform MH, the body motion removing unit 13 subtracts the body motion component MHt from the pulse wave waveform MH, and the
Is generated (step S5).

Next, the respiratory component extraction unit 13 performs an FFT on the body motion removal pulse wave waveform MH 'to perform frequency analysis. Then, based on the result of the analysis, the body motion removal pulse wave waveform MH ′
The maximum peak frequency is specified among the respective frequency components (step S6). In this case, the fundamental frequency F of the pulse wave component
m1 is specified. Thereafter, the respiratory component extraction unit 13 outputs Fm1
The fundamental frequency Fv1 of the respiratory component is detected by specifying the maximum peak frequency less than (Step S7).
Next, the respiratory component extraction unit 13 calculates a respiratory frequency component. Specifically, each harmonic frequency Fv2, Fv3, Fv4... Is detected by multiplying the fundamental frequency Fv1 by an integer, and the level L1 corresponding to each of the fundamental frequency Fv1 and each of the harmonic frequencies Fv2, Fv3, Fv4. , L2, L3, L4...

Next, the evaluation unit 15 calculates the respiratory frequency component L
The distortion factor K of the respiratory frequency component is calculated based on 1, L2, L3, L4,... (Step S9). As described above, since the relationship between the distortion factor K and the exercise intensity X is stored in the ROM in advance, the exercise intensity X is obtained by accessing the ROM based on the distortion factor K (step S10). After that, the exercise intensity X is displayed on the display unit 16, whereby
Exercise intensity X is notified to the subject.

As described above, in the first embodiment, the pulse wave waveform M
Since the body motion component MHt superimposed on H is generated and removed, the respiratory component extraction unit 14 can extract the respiratory component even during exercise. In addition, since the exercise intensity X is calculated based on the distortion factor K of the respiratory component, the exercise intensity X can be easily known without burdening the subject.

D. Second embodiment 1. Configuration of Second Embodiment The configuration of the exercise intensity detecting device 1 according to a second embodiment of the present invention will be described with reference to the drawings. The external configuration of the exercise intensity detection device 1 according to the second embodiment is the same as that of the first embodiment. In addition, the exercise intensity detection device 1 according to the second embodiment
The electrical configuration is the same as that of the exercise intensity detecting device 1 according to the first embodiment shown in FIG. Less than,
The respiratory component extraction unit 14 and the evaluation unit 15 will be described.

FIG. 10 is a block diagram showing the internal configuration of the respiratory component extraction unit 14 and the evaluation unit 15 according to the second embodiment. First, the respiratory component extractor 14 includes a wavelet converter 20, a respiratory component generator 21, and an inverse wavelet converter 22.

1-1: Wavelet Transformation Unit The wavelet transformation unit 20 performs a well-known wavelet transformation on the body movement removal pulse wave waveform MH ′ output from the body movement removal unit 13 to remove body movement. Pulse wave analysis data MKD is generated.

Generally, in time-frequency analysis in which a signal is simultaneously captured from both the time and frequency sides, a wavelet is a unit for cutting out a signal portion. The wavelet transform indicates the size of each part of the signal extracted in this unit. In order to define the wavelet transform, a function ψ (x) localized in time and frequency is introduced as a mother wavelet as a basis function. Here, the wavelet transform of the function f (x) by the mother wavelet ψ (x) is defined as follows.

(Equation 1)

In Equation 1, b is a parameter used when translating (translating) the mother wavelet ψ (x), while a is a parameter when scaling (expanding or contracting). Therefore, in Equation 1, the wavelet ψ ((xb) / a) is obtained by translating the mother wavelet ψ (x) by b and expanding and contracting by a. In this case, since the width of the mother wavelet ψ (x) is expanded in accordance with the scale parameter a, 1 / a corresponds to the frequency.

Here, a detailed configuration of the wavelet conversion unit 20 will be described. FIG. 11 is a block diagram showing a detailed configuration of the wavelet conversion unit 20. The wavelet conversion unit 20 is configured to perform the above-described arithmetic processing of Equation 1, and is supplied with a clock CK and performs the arithmetic processing in a clock cycle. As shown in the figure, the wavelet conversion unit 20 includes a basis function storage unit W1 that stores a mother wavelet ψ (x), a scale conversion unit W2 that converts a scale parameter a, a buffer memory W3, and a translation unit that performs translation. W4
And a multiplication unit W5. As the mother wavelet ψ (x) stored in the basis function storage unit W1, a Mexican hat, a Haar wavelet, a Meyer wavelet, a Shannon wavelet, or the like can be applied in addition to the Gabor wavelet.

First, when the mother wavelet ψ (x) is read from the basis function storage unit W1, the scale conversion unit W2 converts the scale parameter a. here,
Since the scale parameter a corresponds to the period, as a becomes larger, the mother wavelet ψ
(X) is extended on the time axis. In this case, the mother wavelet ψ (x) stored in the basis function storage unit W1
Is constant, the data amount per unit time decreases as a increases. The scale conversion unit W2 performs an interpolation process to compensate for this, and a
Becomes smaller, a thinning process is performed, and the function ψ (x / a)
Generate This data is temporarily stored in the buffer memory W3.

Next, the parallel moving section W4 is connected to the buffer memory W
3 by reading the function ψ (x / a) at the timing corresponding to the translation parameter b, the function ψ (x / a)
/ A) to generate a function ψ (x−b / a).

Next, the body movement removal pulse wave data MH 'obtained by A / D converting the body movement removal pulse wave waveform MH' via an A / D converter (not shown) is supplied to the multiplying unit W5. Is done. Multiplication unit W
4 multiplies the variable 1 / a ^1/2 , the function ψ (x−b / a), and the body motion removal pulse wave data MH ′ to perform a wavelet transform to generate body movement removal pulse wave analysis data MKD. . In this example, the body motion removal pulse wave analysis data MKD is 0H
z ~ 0.5Hz, 0.5Hz ~ 1.0Hz, 1.0Hz
~ 1.5Hz, 1.5Hz ~ 2.0Hz, 2.0Hz ~
2.5 Hz, 2.5 Hz to 3.0 Hz, 3.0 Hz to
It is divided into frequency ranges of 3.5 Hz, 3.5 Hz to 4.0 Hz and output.

1-2: Respiratory component generator Next, the respiratory component generator 21 compares the body motion elimination pulse wave analysis data MKD in each frequency region, specifies the region having the largest energy component, and Is removed to generate respiratory waveform analysis data VKD. The reason why the frequency region having the maximum energy component or more is removed is that the fundamental frequency component of the pulse wave component exists in the frequency region having the maximum energy component.

Now, the body motion removal pulse wave analysis data MK
Assuming that D is as shown in FIG. 12, each of the periods t1 to t
In FIG. 8, an area indicated by oblique lines in FIG. 13 is specified as the maximum energy component. In this case, the high frequency region beyond the hatched region is replaced with “0”, and the data shown in FIG. 14 is generated as the respiratory waveform analysis data VKD.

1-3: Inverse Wavelet Transform Unit Next, the inverse wavelet transform unit 22 has a complementary relationship with the wavelet transform unit 20, and calculates the following equation 2 to convert the respiratory waveform data VD. A respiratory waveform VH is output by performing A / D conversion.

(Equation 2)

As described above, the respiratory component extracting unit 14
The respiratory waveform VH is extracted based on the body motion removal pulse wave data MH '. Next, the evaluation unit 15 includes a zero cross comparator 23, a duty ratio detection unit 24, and an exercise intensity table 25.

1-4: Zero Cross Comparator First, the zero cross comparator 23 is, for example, as shown in FIG.
As shown in the figure, the circuit comprises a capacitor C and an operational amplifier OP. The value of the capacitor C is set so that the respiratory waveform VH sufficiently passes. The operational amplifier OP has a respiratory waveform VH
Is compared with the zero level to generate a square wave S. However, since the respiratory waveform VH is supplied to the operational amplifier OP via the capacitor C, the rectangular wave S is shaped using the average value level of the respiratory waveform VH as a threshold. Is going.

By the way, as described above, the difference between the inhalation time and the discharge time disappears as the exercise intensity X increases in human breathing. Therefore, as the exercise intensity X increases, the duty ratio of the rectangular wave S approaches 50%.

1-5: Duty Ratio Detector Next, FIG. 16 is a circuit diagram of the duty ratio detector 24, and FIG. 17 is a timing chart thereof. The clock signal CK (see FIG. 17A) is applied to the gates 241 and 241 respectively.
242 is provided to one input. Also, the gate 241
The other input of the gate 242 is supplied with a rectangular wave S (see FIG. 17B).
Supplies the inverted rectangular wave S. Here, since the clock signal CK is restricted by the gates 241 and 242, the output signal of the gate 241 is as shown in FIG.
As shown in (1), the clock signal CK passes only during the period when the rectangular wave S is at the high level. On the other hand, the output signal of the gate 242 allows the clock signal CK to pass only during the period when the rectangular wave S is at the low level as shown in FIG.

The output signals of the gates 241 and 242 are supplied to the counters 243 and 244, respectively.
The count value C1 of 43 indicates the high level period of the rectangular wave S,
The count value C2 of the counter 244 indicates the low level period of the rectangular wave S.

Then, the divider 245 calculates C1 / C2 and outputs this as a duty ratio. FIG.
At time T shown in FIG. 7, a division operation is performed, and the counters 243 and 244 are reset immediately thereafter.

Since the division result DR in this example is the high-level period C1 / the low-level period C2, as the exercise intensity X increases, the operation result DR approaches "1". However, when the exercise intensity X increases beyond a certain limit, the respiratory waveform is greatly disturbed, so that the calculation result DR changes drastically at such exercise intensity X. Conversely, in the region of the normal fork intensity X, the duty ratio of the respiratory waveform does not suddenly change. The configuration described below specifies the limit value Xmax of the exercise intensity X by detecting the continuity of the calculation result DR, in other words, the continuity of the duty ratio.

The operation result DR is supplied to and stored in the memory 246. The storage contents are updated each time the next calculation result DR is output. Subtractor 24
7 subtracts the immediately preceding operation result DR ′ from the current operation result DR, the comparator 248 determines whether the subtraction result ΔDR is within a predetermined range. Specifically, it is determined whether or not the following equation is satisfied. + K>ΔDR> −K Here, + K and −K are determined so that it can be determined whether the exercise intensity X exceeds the limit value Xmax and the continuity of the duty ratio of the respiratory waveform is lost.

If the above equation is satisfied, it is determined that the exercise intensity is normal, and the output signal of the comparator 248 goes high. On the other hand, if the above equation is not satisfied,
It is determined that the exercise intensity X exceeds the limit value Xmax, and the output signal of the comparator 248 goes low.

When the output signal of the comparator 248 is at a high level, the combiner 249 outputs the operation result DR.
When the output signal of the comparator 248 is at a low level, a value that cannot be obtained by the operation result DR, for example, “0” is output.

1-6: Exercise Intensity Table Next, the exercise intensity table 25 (see FIG. 9) is composed of a ROM or the like, in which the exercise intensity X is stored in association with the calculation result DR. ing. Therefore, by accessing the exercise intensity table 25 with reference to the calculation result DR, the exercise intensity X can be obtained. When a value that cannot be obtained from the operation result DR is input, for example,
In the case of "0", the limit value Xmax is output. As a result, the respiratory waveform is extracted from the pulse wave waveform, and the exercise intensity X can be obtained from the duty ratio.

2: Operation of Second Embodiment Next, the operation of the second embodiment will be described with reference to the drawings.
FIG. 18 is a flowchart of the exercise intensity detection device 1 according to the second embodiment. In the figure, the processing from step S1 to step S5 is the same as the operation of the first embodiment shown in FIG. 9, and the body motion waveform is removed from the pulse wave waveform to generate the body motion removed pulse wave waveform MH '. Is done.

After that, the wavelet conversion processing is performed on the body movement removal pulse wave data MH ′ by the wavelet conversion unit 20, and the body movement removal pulse wave analysis data MKD is generated. The body motion removal pulse wave analysis data MKD includes a pulse wave component and a respiratory component. The pulse wave component exists in a frequency region higher than the respiratory component, and the energy of the pulse wave component is the energy of the respiratory component. Large compared to. For this reason, the respiratory component generation unit 21 generates respiratory waveform data VKD by substituting “0” for the maximum energy frequency region or more of the body motion removal pulse wave analysis data MKD (Step S21).

Next, when the inverse wavelet transform unit 22 performs the inverse wavelet transform on the respiratory waveform data VKD to generate the respiratory waveform VH, the zero-cross comparator 23
Compares the respiratory waveform VH with its average value level and
Generate Thereafter, the duty ratio detector 24 detects the duty ratio of the rectangular wave S (Step S23).

Next, when the exercise intensity table 25 obtains the exercise intensity X with reference to the output data of the duty ratio detector 24 (step S24), the display unit 16 displays the exercise intensity X (step S25). Thereby, the exercise intensity X
Is announced to the subject.

As described above, in the second embodiment, the pulse wave waveform M
Since the body motion component MHt superimposed on H is generated and removed, the respiratory component extraction unit 14 can extract the respiratory waveform using the wavelet transform even during exercise. . Further, since the exercise intensity X is calculated based on the duty ratio of the respiratory waveform, the exercise intensity X can be easily known without burdening the subject.

E. Third embodiment 1. Configuration of Third Embodiment The configuration of an exercise intensity detection device 1 according to a third embodiment of the present invention will be described with reference to the drawings. The external configuration of the exercise intensity detection device 1 according to the third embodiment is the same as that of the first embodiment. In addition, the exercise intensity detection device 1 according to the third embodiment
The electrical configuration is the same as that of the exercise intensity detection device 1 according to the first embodiment shown in FIG. 5 except that the body motion is removed after the FFT processing.

FIG. 19 is a block diagram showing the configuration of the exercise intensity detecting device 1 according to the third embodiment. In the figure,
Reference numerals 30 and 31 denote a first FFT processing unit and a second FFT processing unit, which are constituted by a CPU or the like. First FFT
The processing unit 30 performs an FFT process on the pulse wave waveform MH to generate pulse wave analysis data MFD. Further, the second FFT processing unit 31 performs FFT processing on the body movement waveform TH to generate body movement analysis data TFD.

Next, the body motion removing unit 13 removes the spectrum frequency component corresponding to each spectrum frequency of the body motion analysis data TFD from among the spectrum frequency components of the pulse wave analysis data MFD, and removes the body motion. Pulse wave analysis data MKD is generated. This body motion removal pulse wave analysis data MKD
, The maximum peak frequency in the low frequency region is the fundamental frequency Fv1 of the respiratory component, and the maximum peak frequency in the high frequency region is the fundamental frequency Fm1 of the pulse wave.

Next, the respiratory component extraction unit 14, the evaluation unit 15, and the display unit 16 are the same as those in the first embodiment, and the description is omitted here.

2. Operation of Third Embodiment Next, the operation of the exercise intensity detecting device 1 according to the third embodiment will be described with reference to the drawings. FIG. 20 is a flowchart showing the operation of the exercise intensity detection device 1 according to the third embodiment. First, when the apparatus main body is set to the exercise intensity measurement mode (step S1), the pulse wave detection unit 10
H is detected. Thereafter, the first FFT processing unit 30 performs FFT processing on the pulse wave waveform MH to generate pulse wave analysis data MFD (step S32). On the other hand, the body motion detection unit 11
However, when detecting the body movement waveform TH indicating the body movement of the subject, the second FFT processing unit 31 performs FFT processing on the body movement waveform TH to generate body movement analysis data TFD.

Next, the body movement removing section 13 removes the body movement component from the pulse wave analysis data MFD to generate the body movement removed pulse wave analysis data MKD. FIG. 21 shows pulse wave analysis data MFD,
Body motion analysis data TFD and body motion removal pulse wave analysis data M
It is a figure which shows an example about the relationship of KD. The operation of removing the body motion will be described with reference to FIG. First, FIG.
(A) shows the contents of the pulse wave analysis data MFD, and FIG. 21 (b) shows the contents of the body motion analysis data TFD. In this example, the body motion removing unit 13 specifies each of the spectrum frequencies Ft1 to Ft6 shown in FIG. 21B based on the body motion analysis data TFD. Thereafter, the body movement removing unit 1
3 removes the spectrum frequency components corresponding to the spectrum frequencies Ft1 to Ft6 from among the spectrum frequency components of the pulse wave analysis data MFD, and generates the body motion removal pulse wave analysis data MKD shown in FIG. . By the way, the body movement waveform TH is detected, for example, as the acceleration itself of the swing of the arm, but since the blood flow is affected by blood vessels and tissues,
Body motion component of pulse wave analysis data MFD and body motion analysis data TF
D does not match. Specifically, FIG. 21B and FIG.
As shown in (a), each spectrum frequency component corresponding to the spectrum frequencies Ft1 to Ft6 is the pulse wave analysis data M
The FD differs from the body motion analysis data TFD. For this reason, in this example, the body motion analysis data TF
Instead of subtracting D, the spectral frequencies Ft1 to Ft6
Is removed. Thereby, it is possible to generate a body movement component that has been sufficiently removed.

Next, the respiratory component extraction unit 13 specifies the maximum peak frequency among the respective spectral frequency components based on the body motion removal pulse wave analysis data MKD (step S35). In this case, the fundamental frequency Fm1 of the pulse wave component is specified. Thereafter, the processing of steps S7 to S11 described in the first embodiment with reference to FIG. 9 is performed, and the exercise intensity X is displayed on the display unit 16.

As described above, in the third embodiment, the pulse wave waveform MH and the body movement waveform TH are each subjected to the FFT processing to remove the body movement component, and thus are described in the first embodiment. The waveform processing unit 12 can be omitted. Thereby, the respiratory component extraction unit 14 can extract the respiratory component even during exercise. In addition, since the exercise intensity X is calculated by the evaluation unit 15 based on the distortion rate K of the respiratory component, the exercise intensity X can be easily known without burdening the subject.

F. Fourth Embodiment In the above-described first to third embodiments, in order to remove a body motion, a body motion waveform is detected using the body motion detection unit 10, and the frequency of the pulse wave waveform MH is determined based on the detected waveform. Although the body movement component is removed from the component, the fourth embodiment
This removes the body motion component without using the.

1. Overall Configuration of Fourth Embodiment A configuration of an exercise intensity detection device 1 according to a fourth embodiment of the present invention will be described with reference to the drawings. The external configuration of the exercise intensity detection device 1 according to the fourth embodiment is the same as that of the first embodiment. In addition, the exercise intensity detecting device 1 according to the fourth embodiment
FIG. 22 shows the electrical configuration of the first embodiment. The same components as those shown in FIG. 19 are denoted by the same reference numerals.

FIG. 22 differs from the exercise intensity detecting apparatus 1 of the third embodiment shown in FIG. 19 in that the body movement detecting section 11 and the second FFT processing section 31 are not provided. Pulse wave component removing unit 14 instead of the motion removing unit 13
Is provided, and a respiratory component extracting unit 13 ′ in which the internal configuration of the respiratory component extracting unit 13 is changed is provided. Hereinafter, the differences will be described.

2. Pulse wave component removing unit Next, the pulse wave component removing unit 13 'is configured by a low-pass filter, removes a pulse wave component from the pulse wave analysis data MFD, and generates pulse wave component removal analysis data MD'. Here, the cutoff frequency of the low-pass filter is selected to be slightly lower than the fundamental wave frequency of the pulse wave component. The reason is,
This is because the fundamental frequency of the body motion component and the fundamental frequency of the respiration component are lower than the fundamental frequency of the pulse wave component. Specifically, the cutoff frequency is set slightly lower than the fundamental frequency of the pulse wave component measured at rest. For example, assuming that the pulse wave analysis data MFD and the cutoff frequency fc of the low-pass filter have the relationship shown in FIG. 23, the pulse wave component removal analysis data MD 'is as shown in FIG.

3. Respiratory component extractor Next, a respiratory component extractor 13 'extracts a respiratory component from the pulse wave component removal analysis data MD', and is constituted by a CPU or the like. FIG. 25 is a block diagram showing a detailed functional configuration of the respiratory component extraction unit 13 '.

In the figure, the spectrum extracting unit 40 calculates the spectrum frequency of the pulse wave component removal analysis data MD 'from each spectrum frequency.
Two spectral frequencies are extracted as one set, the lower spectral frequency is output to the basic frequency table 41, and the higher spectral frequency is detected by the difference detector 42.
Output to

For example, if the pulse wave component removal analysis data MD 'is as shown in FIG.
An arbitrary spectrum frequency is extracted as one set from f14. In this case, there are ₁₄ C ₂ sets as the set of spectral frequencies to be extracted. If the set of spectral frequencies is f1 and f3, f1 is the basic frequency table 41.
Then, f3 is output to the difference detection unit 42.

Next, the fundamental frequency table 41 is stored in the ROM
In this case, the fundamental frequency Ft1 of the body motion component is stored in advance in association with the fundamental frequency fm1 of the respiratory component. The contents of the fundamental frequency table 41 are as follows:
Consists of measured values. In setting the data of the fundamental frequency table 41, the present inventors measured the relationship between the running pitch and the respiratory rate by gradually changing the running speed of the subject. FIG. 26 shows the result of the experiment. The running pitch is the number of steps per unit time. In this example, the pulse wave detection unit 10 (pulse wave detection sensor unit 130) is attached to the base of the finger as shown in FIG. 3, and therefore exists in the pulse wave waveform MH detected thereby. The moving body motion component depends on the swing of the arm. The relationship between the arm swing and the running pitch differs depending on whether the player swings vigorously or smoothly, but it is usual that the arm swings once for two pitches. Further, one arm swing cycle corresponds to one cycle of the body motion waveform. Therefore, assuming that the running pitch (times / minute) is P and the respiratory rate (times / minute) is V, the basic frequency Ft1 of the body motion component and the basic frequency Fv1 of the respiratory component use the running pitch P and the respiratory rate V. And is given by the following equation: Ft1 = P / (60 · 2), Fv1 = V / 60 By using the above equation and converting the graph shown in FIG. 26, the relationship between the fundamental frequency Ft1 of the body motion component and the fundamental frequency Fv1 of the respiratory component is obtained. . This is shown in FIG. The contents of the fundamental frequency table 41 are, for example, as shown in FIG.

Next, the difference detector 42 detects a difference between the other spectrum frequency output from the spectrum extractor 40 and the frequency output from the fundamental frequency table 41. If the set of spectral frequencies extracted by the spectrum extracting unit 40 is the fundamental frequency Ft1 of the body motion component and the fundamental frequency Fv1 of the respiratory component, Fv1 is supplied to the fundamental frequency table 41 and Ft1 is output. Therefore, the output of the difference detection unit 42 is “0”. On the other hand, if the set of spectrum frequencies extracted by the spectrum extraction unit 40 is Fv1 and F (where Fv1 <F), the output of the difference detection unit 42 is “| F−Ft1 |”. Therefore,
The set of spectral frequencies at which the output of the difference detection unit 42 becomes the smallest is Ft1, Fv1.

Next, the comparing section 43 is connected to the spectrum extracting section 4.
For each set of spectrum frequencies output from 0, the output of the difference detection unit 42 is compared, the set whose value is the smallest is specified, and the lower one of the spectrum frequencies constituting the set is output. In this case, the specified pair is Ft1, Fv1, and since there is a relationship of Ft1> Fv1, the comparison unit 43 outputs the fundamental frequency Fv1 of the respiratory component.

Next, the harmonic frequency generation unit 44 multiplies the fundamental frequency Fv1 of the respiratory component by an integer to obtain Fv2, Fv3, Fv4.
Are generated, and the corresponding levels L1, L2, L
3, L4... Are output as respiratory components. The respiratory component thus generated is supplied to the evaluation unit 15 described in the first embodiment, and the exercise intensity X is generated based on the distortion rate K, and this is displayed on the display unit 16.

As described above, according to the present embodiment, focusing on the relationship between the fundamental frequency Ft1 of the body movement component and the fundamental frequency Fv1 of the respiration component, the body movement component and the respiration component are extracted by the respiration component extraction unit 14 '.
, The body motion detection unit 11 and the second F
The exercise intensity X can be obtained based on the respiratory component without using the FT processing unit 31 or the like. This allows for downsizing,
The exercise intensity detecting device 1 can be reduced in weight and can be used more easily by the subject.

G. Modifications The present invention is not limited to the above-described embodiment, and various modifications described below are possible. (1) When the exercise intensity X increases, the oxygen consumption of the skeletal muscle increases, so that the respiration rate and the pulse rate increase. here,
There is a certain relationship between respiratory rate and pulse rate. FIG. 28 is a diagram illustrating an example of a respiratory rate and a pulse rate during running. First
-In the respiratory component extraction unit 14 in the fourth embodiment,
Filtering processing may be performed in relation to the pulse rate.

Specifically, a table in which the relationship between the respiratory rate and the pulse rate is stored in advance is provided. First, the respiratory rate (6 / fm1) estimated from the pulse rate (60 / fm1) using this table.
0 / Fv1). Then, the fundamental frequency Fv1 of the respiratory component is extracted by using a bandpass filter having the center frequency of the estimated fundamental frequency of the respiratory component. Note that the filtering process in this case may be performed digitally. This makes it possible to extract the respiratory component more accurately.

(2) In the above-described first embodiment, since the third harmonic component Fv3 is considered to best represent the characteristic of the respiratory waveform, the third harmonic component Fv3 of the fundamental frequency Fv1 is used as the third harmonic component Fv3. Attention may be paid to determine the exercise intensity X. In this case, the respiratory component extraction unit 14 extracts the fundamental frequency Fv1 and its third harmonic Fv3. Evaluation unit 15
Are the levels L1, L3 to L3 / L1 corresponding to these.
Is calculated, and the exercise intensity X is obtained by referring to an exercise intensity table in which the relationship between L3 / L1 and the exercise intensity X is stored in advance.
As a result, it is not necessary to calculate the distortion factor K, so that the arithmetic processing can be simplified, and as a result, the processing can be performed at a high speed and the load on the CPU can be reduced.

(3) In the second embodiment described above,
Although frequency analysis was performed using FFT, the present invention is not limited to this, and if frequency analysis is performed,
Any method may be used. For example, wavelet transform can be used. In the wavelet transform,
Although the frequency analysis can be performed in a short time, the frequency analysis becomes coarse when the time is shortened. Therefore, by taking one unit (time resolution) of the analysis time to some extent longer, the frequency domain can be made finer.

(4) In the above-described second embodiment, the wavelet transform unit 20 implements the wavelet transform by performing basis function expansion. However, the present invention is not limited to this. It may be realized by a filter bank. Fig. 2 shows a configuration example of the filter bank.
It is shown in FIG. In the figure, the filter bank is composed of three stages, the basic units of which are a high-pass filter 1A and a decimation filter 1C, and a low-pass filter 1B and a decimation filter 1C. High-pass filter 1A
And the low-pass filter 1B divides a predetermined frequency band,
A high frequency component and a low frequency component are output. In this example, since the frequency band of the pulse wave data MD is assumed to be 0 Hz to 4 Hz, the pass band of the first-stage high-pass filter 1A is set to 2 Hz to 4 Hz, while the first-stage low-pass filter is set. 1B passband is 0
Hz to 2 Hz. The decimation filter 1C thins out data every other sample. When the data generated in this way is supplied to the next stage, the division of the frequency band and the thinning of the data are repeated, and finally, the 0
Data M1 to M obtained by dividing the frequency band of Hz to 4 Hz into eight
8 is obtained.

The high-pass filter 1A and the low-pass filter 1
B may be constituted by a transversal filter including a delay element (D flip-flop) therein. By the way, the pulse rate of a person is in the range of 40 to 200, and the fundamental frequency of the pulse wave waveform MH fluctuates every moment according to the state of the living body. In this case, if the band to be divided can be changed in synchronization with the fundamental frequency, information that follows a dynamic biological state can be obtained. Therefore, the clock to be supplied to the transversal filter may have a pulse wave waveform MH to adaptively change the band to be divided.

(5) In the above-described second embodiment, when the wavelet transform unit 20 is constituted by the filter bank shown in FIG. 29, the inverse wavelet transform unit 22 is constituted by the inverse filter bank shown in FIG. You may. In the figure, an inverse filter bank is composed of three stages, and its basic unit is a high-pass filter 2A and an interpolation filter 2C.
, A low-pass filter 1B, an interpolation filter 2C, and an adder 2D. High-pass filter 2A and low-pass filter 2B
Is configured to divide a predetermined frequency band and output a high frequency component and a low frequency component, respectively. Also,
The interpolation filter 2C interpolates one sample every two samples.

Here, in order to reproduce the waveform, FIG.
It is necessary to use a completely reconstructed filter bank for the filter bank shown in FIG. In this case, the high-pass filters 1A, 2A and the low-pass filters 1B,
The characteristics of 2B need to have the following relationship. H0 (-Z) F0 (Z) + H1 (-Z) F1 (Z) = 0 H0 (Z) F0 (Z) + H1 (-Z) F1 (Z) = 2Z- ^L

The high-pass filter 2A and the low-pass filter 2A
B may be constituted by a transversal filter including a delay element (D flip-flop) therein.

(6) In each of the above-described embodiments, the display unit 16 has been described as an example of a notifying unit. However, as a unit for notifying a human from the apparatus, the following unit may be used. No. It is considered appropriate to classify these means based on the five senses. Of course, these means may be used alone or in combination with a plurality of means. And, as described below, for example, if a means that appeals other than visual is used, even a visually impaired person can understand the contents of the notification, and similarly, if a means that appeals other than hearing is used, Can be notified, and a device friendly to a user with a disability can be configured.

First, as a means for notifying the sense of hearing, there is a means for notifying a result of analysis and diagnosis of a pulse or a warning. For example, in addition to a buzzer, a piezoelectric element and a speaker correspond. Further, as a special example, it is conceivable that a portable radio paging receiver is provided to a person to be notified, and the portable radio paging receiver is called from the device side when performing the notification. In addition, when making a notification using these devices, there are many cases where it is desired not only to make a notification but also to transmit some information together. In such a case, the level of the information such as the volume shown below may be changed according to the content of the information to be transmitted. For example, pitch, volume, tone, voice, and music type (such as a song).

Next, the visual notification means is used for the purpose of notifying various messages and measurement results from the apparatus or for giving a warning. The following devices can be considered as means for that. For example, a display device, a CRT (cathode ray tube display),
There are an LCD (Liquid Crystal Display), a printer, an XY plotter, a lamp, and the like. Note that there is a spectacle-type projector as a special display device. Further, in the notification, the following variations are conceivable. For example, there are digital display and analog display in notification of numerical values, graphical display, display color shading, bar graph display in the case of notifying numerical values as they are or by grading numerical values, pie graphs, face charts, and the like.

Next, it is considered that the tactile notification means may be used for a warning purpose. There are the following means for that purpose. First, there is an electric stimulus for providing a shape memory alloy protruding from the back surface of a portable device such as a wristwatch and energizing the shape memory alloy. Further, there is a mechanical stimulus that is stimulated by the projection as a structure that allows a projection (for example, a needle that is not sharp) to be inserted into and removed from the back of a portable device such as a wristwatch.

Next, the notifying means for appealing to the sense of smell is configured such that the device to be provided with a discharge mechanism of fragrance or the like, the contents to be notified correspond to the fragrance, and the fragrance is discharged in accordance with the content of the notification. You may. By the way, a micropump or the like is most suitable for a mechanism for discharging a fragrance or the like.

(7) In each of the embodiments described above, the pulse wave detecting sensor unit 130 has been described as an example of the pulse wave detecting means f1, but the present invention is not limited to this. Anything that can be used may be used.

For example, the pulse wave detection sensor unit 130
Uses reflected light, but may use transmitted light. By the way, the wavelength region is 700 nm
The following light tends to hardly penetrate the finger tissue. For this reason, when using transmitted light, a wavelength of 60
Light of 0 nm to 1000 nm is irradiated, the irradiated light is transmitted in the order of tissue → blood vessel → tissue, and a change in the amount of transmitted light is detected. Since the transmitted light is absorbed by hemoglobin in blood, a pulse wave waveform can be detected by detecting a change in the amount of transmitted light.

In this case, an InGaAs-based (indium-gallium-arsenic) or GaAs-based (gallium-arsenic) laser light emitting diode is suitable for the light emitting section. By the way, since external light having a wavelength of 600 nm to 1000 nm easily passes through the tissue, when external light enters the light receiving portion, the S / N of the pulse wave signal deteriorates. Therefore, the light emitting unit may emit polarized laser light, and the transmitted light may be received by the light receiving unit via the polarizing filter. Thus, the pulse wave signal can be detected with a good S / N ratio without being affected by external light.

In this case, as shown in FIG. 31A, the light emitting section 200 is provided on the fastening side of the fastener 145,
A light receiving unit 201 is provided on the main body of the timepiece. in this case,
The light emitted from the light emitting unit 200 passes through the blood vessel 143 and then passes between the radius 202 and the ulna 203, and passes through the light receiving unit 2
Reach 01. In the case where transmitted light is used, the irradiation light needs to pass through the tissue. Therefore, the wavelength is preferably 600 nm to 1000 nm in consideration of the absorption of the tissue.

FIG. 14B shows an example in which the detection site is an earlobe. The holding member 204 and the holding member 205
07, and can rotate about the shaft 206. The light emitting unit 200 and the light receiving unit 201 are provided on the holding members 204 and 205. When using this pulse wave detector, the earlobe is
The pulse wave is detected by grasping with the grasping member 205. When using reflected light, the pulse wave waveform MH may be detected from the fingertip as shown in FIG.

Next, a description will be given of a use mode in which the photoelectric pulse wave sensor is combined with eyeglasses. In addition, in the form of these glasses,
A display device as a means for notifying the user is also incorporated. Therefore, functions as a display device in addition to the pulse wave detection unit will also be described. FIG. 32 is a perspective view illustrating a state in which the device to which the pulse wave detection unit is connected is attached to eyeglasses. As shown in the figure, the apparatus main body is divided into a main body 75a and a main body 75b, each of which is separately attached to the vine 76 of the glasses, and these main bodies are electrically connected to each other through lead wires embedded inside the vine 76. ing.

The main body 75a has a built-in display control circuit. A liquid crystal panel 78 is mounted on the entire surface of the main body 75a on the side of the lens 77, and a mirror 79 is provided at one end of the side at a predetermined angle. Fixed. Further body 75
In a, a driving circuit of the liquid crystal panel 78 including a light source (not shown) and a circuit for creating display data are incorporated. The light emitted from this light source is reflected by a mirror 79 via a liquid crystal panel 78 and is projected on a lens 77 of spectacles. The main part of the apparatus is incorporated in the main body 75b, and various buttons are provided on the upper surface thereof. The functions of these buttons 80 and 81 differ for each device. Also. The LED 32 and the phototransistor 33 (see FIG. 4) constituting the photoelectric pulse wave sensor are connected to the pad 8.
2 and 83, and the pads 82 and 83 are fixed to the earlobe. These pads 82, 8
3 are lead wires 84, 84 drawn from the main body 75b.
Are electrically connected by

Next, an example in which the pulse wave waveform MH is detected by the pressure sensor will be described. FIG. 33A is a perspective view showing an external configuration of a pulse wave diagnostic device using a pressure sensor. As shown in this figure, a pair of bands 1
44, 144, one of the fasteners 14 is provided.
5, the elastic rubber 1 of the pressure sensor 130 '
A projection 31 is provided. The band 144 having the fastener 145 has a structure in which an FPC (Flexible Printed Circuit) substrate is covered with a soft plastic to supply a detection signal from the pressure sensor 130 (details are not shown).

At the time of use, as shown in FIG. 33B, the elastic rubber 131 provided on the fastener 145 is used.
Is placed in the vicinity of the radial artery 143 so that the watch 146
Is wound around the left arm 147 of the subject. For this reason, a pulse wave can be constantly detected. Note that this winding does not differ from the usual use state of the wristwatch. In this way, the elastic rubber 131 is applied to the radial artery 14 of the subject.
When pressed near 3, the blood flow fluctuation (ie, pulse wave) of the artery is transmitted to the pressure sensor 130 'via the elastic rubber 131, and the pressure sensor 130' detects this as blood pressure.

[0120]

As described above, according to the present invention, the exercise intensity is calculated by extracting the respiratory component from the pulse wave waveform and frequency-analyzing the extracted respiratory component. Exercise intensity can be calculated with a simple configuration.

[Brief description of the drawings]

FIG. 1 is a diagram showing a relationship between exercise intensity and a respiratory waveform.

FIG. 2 is a functional block diagram of the exercise intensity detecting device according to the embodiment.

FIG. 3 is a perspective view showing an external configuration of the exercise intensity detection device according to the first embodiment.

FIG. 4 is a circuit diagram of a pulse wave detection sensor unit according to the embodiment.

FIG. 5 is a block diagram showing an electrical configuration of the exercise intensity detection device according to the embodiment.

FIG. 6 is a diagram showing a result of performing FFT processing on body motion removal pulse wave data MH ′.

FIG. 7 is an enlarged view of a low frequency region LF in FIG. 6;

FIG. 8 is a face chart showing one mode of the display unit of the embodiment.

FIG. 9 is a flowchart showing an operation of the exercise intensity detection device according to the embodiment.

FIG. 10 is a block diagram illustrating an internal configuration of a respiratory component extraction unit and an evaluation unit according to the second embodiment.

FIG. 11 is a block diagram showing a configuration of a wavelet conversion unit according to the embodiment.

FIG. 12 is a diagram showing an example of body motion removal pulse wave analysis data MKD according to the embodiment.

13 is a pulse wave analysis data M for removing body motion shown in FIG.
It is a figure showing the maximum energy area of KD.

FIG. 14 shows respiratory waveform analysis data V according to the embodiment.
It is a figure showing an example of KD.

FIG. 15 is a circuit diagram of a zero-cross comparator according to the same embodiment.

FIG. 16 is a circuit diagram of a duty ratio detection unit according to the same embodiment.

FIG. 17 is a timing chart of the duty ratio detection unit according to the embodiment.

FIG. 18 is a flowchart showing an operation of the exercise intensity detection device according to the embodiment.

FIG. 19 is a block diagram illustrating an electrical configuration of an exercise intensity detection device according to a third embodiment.

FIG. 20 is a flowchart showing an operation of the exercise intensity detection device according to the embodiment.

FIG. 21 is pulse wave analysis data MF according to the embodiment.
D is a diagram showing an example of the relationship between body motion analysis data TFD and body motion removal pulse wave analysis data MKD.

FIG. 22 is a block diagram illustrating an electrical configuration of an exercise intensity detection device according to a fourth embodiment.

FIG. 23 is a pulse wave analysis data MFD according to the embodiment.
FIG. 5 is a diagram showing an example of the relationship between the cut-off frequency fc and the frequency.

FIG. 24 is a diagram showing an example of pulse wave component removal analysis data MD 'according to the embodiment.

FIG. 25 is a respiratory component extraction unit 13 ′ according to the same embodiment.
FIG. 3 is a block diagram showing a detailed configuration.

FIG. 26 is a diagram showing a result of actually measuring a relationship between a running pitch and a respiratory rate in the embodiment.

FIG. 27 is a graph showing a relationship between a fundamental frequency Ft1 of a body motion component and a fundamental frequency Fv1 of a respiratory component in the embodiment.

FIG. 28 is a diagram showing a relationship between a respiratory rate and a pulse rate.

FIG. 29 is a block diagram illustrating a configuration example of a filter bank.

FIG. 30 is a block diagram illustrating a configuration example of an inverse filter bank.

FIG. 31 is a diagram showing an example of a photoelectric pulse wave sensor according to a modification.

FIG. 32 is a diagram showing an example in which a photoelectric pulse wave sensor is applied to eyeglasses in a modified example.

FIG. 33 is a perspective view showing an external configuration of a pulse wave diagnostic device using a pressure sensor in a modified example.

[Explanation of symbols]

MH pulse wave waveform 10 pulse wave detection unit (pulse wave detection means) 130 pulse wave detection sensor unit (pulse wave detection means) 11 body movement detection unit (body movement detection means) TH notational movement waveform MH 'body movement removal pulse Wave waveform, body movement removal pulse wave data 13 Body movement removal unit (body movement removal unit) 14 Respiratory component extraction hand unit (respiration component extraction unit) 15 Evaluation unit (fortune intensity generation unit) MKD Body movement removal pulse wave analysis data Reference Signs List 20 wavelet conversion unit VKD respiration waveform analysis data 21 respiration component generation unit (respiration waveform generation unit) 22 inverse wavelet conversion unit (respiration waveform generation unit) VH respiration waveform 30 first FFT processing unit (first frequency analysis unit) , Frequency analyzing unit) 31 second FFT processing unit (second frequency analyzing unit) 13 ′ pulse wave component removing unit, 41 basic frequency table 42 difference detecting unit (frequency specifying unit) 43 comparing unit (frequency specifying unit) 44 harmonic frequency generator (frequency identification unit, extraction unit) 16 display unit (notifying means)

Claims (15)

[Claims] 1. A pulse wave detecting means for detecting a pulse wave waveform from a detection part of a living body, a body movement detecting means for detecting a body movement waveform indicating the body movement of the living body, and the pulse based on the body movement waveform. A body movement removing unit that generates a body movement component in the wave waveform, removes the body movement component from the pulse wave waveform to generate a body movement removal pulse wave waveform, based on the body movement removal pulse wave waveform, An exercise intensity detection device comprising: a respiratory component extracting unit that extracts a respiratory component; and a fortitude intensity generating unit that calculates exercise intensity based on the respiratory component extracted by the respiratory component extracting unit. 2. A wavelet conversion unit that performs a wavelet transform on the body motion removal pulse wave waveform to generate body motion removal pulse wave analysis data, and the body motion removal pulse wave analysis data. A respiratory waveform generating unit that generates a respiratory waveform analysis data by removing a frequency component corresponding to a pulse wave component from the data, applies an inverse wavelet to the respiratory waveform analysis data, and generates a respiratory waveform as the respiratory component. The exercise intensity detecting device according to claim 1, wherein: 3. The exercise intensity generating means calculates the exercise intensity based on a ratio of a frequency component obtained by performing a frequency analysis on the respiratory component extracted by the respiratory component extracting means. The exercise intensity detecting device according to claim 1 or 2, wherein: 4. The fortitude intensity generating means calculates a distortion rate from each frequency component obtained by performing a frequency analysis on the respiratory component extracted by the respiratory component extracting means, and calculates the distortion rate based on the distortion rate. The exercise intensity detection device according to claim 3, wherein the exercise intensity is calculated. 5. The fortitude intensity generating means calculates a ratio between a fundamental frequency component and a third harmonic component obtained by performing frequency analysis on the respiratory component extracted by the respiratory component extracting means, The exercise intensity detection device according to claim 3, wherein the exercise intensity is calculated based on the ratio. 6. The respiratory component extracting means extracts a respiratory waveform as the respiratory component, and the fortitude intensity generating means detects a duty ratio of the respiratory waveform extracted by the respiratory component extracting means. The exercise intensity detection device according to claim 1, wherein the exercise intensity is generated based on a duty ratio. 7. The body movement removing unit, wherein: a first frequency analysis unit for analyzing a frequency spectrum of the pulse wave waveform; a second frequency analysis unit for analyzing a frequency spectrum of the body movement waveform; A frequency spectrum having the same frequency as that of the frequency spectrum analyzed by the second frequency analysis unit is removed from the frequency spectrum analyzed by the first frequency analysis unit to generate a body motion removal spectrum from which the body motion has been removed. A motion removing unit, wherein the respiratory component extracting unit extracts a frequency spectrum corresponding to a fundamental component of the respiratory component from the body motion removing spectrum, and the exercise intensity generating unit includes a fundamental component of the respiratory component. The exercise intensity is calculated based on the frequency spectrum level corresponding to the frequency spectrum level and the frequency spectrum level corresponding to the harmonic component thereof. Exercise intensity detecting device according to claim 1, wherein the. 8. The respiratory component extracting means specifies a band determined according to a pulse rate from the body motion removal spectrum, and corresponds to a fundamental component of a respiratory component from a frequency spectrum within the band. The exercise intensity detection device according to claim 7, wherein a frequency spectrum is extracted. 9. The mobility intensity generating means, based on a spectrum level corresponding to a fundamental component of the respiratory component and a spectrum level corresponding to a harmonic component thereof,
The exercise intensity detecting device according to claim 7 or 8, wherein a distortion rate of a respiratory waveform is calculated, and the exercise intensity is calculated based on the distortion rate. 10. The mobility intensity generating means obtains a ratio between a spectrum level corresponding to a fundamental wave component of the respiratory component and a spectrum level corresponding to a third harmonic component thereof, and based on the ratio. The exercise intensity detecting device according to claim 7 or 8, wherein the exercise intensity is calculated by using the following method. 11. A pulse wave detecting means for detecting a pulse waveform from a detection part of a living body, a respiratory component extracting means for extracting a respiratory component from the pulse waveform, and a respiratory component extracted by the respiratory component extracting means. An exercise intensity detecting device comprising: a fortitude intensity generating means for calculating exercise intensity based on the exercise intensity. 12. A respiratory component extracting means for performing a frequency analysis on the pulse wave waveform to generate pulse wave analysis data, and a pulse wave component removing unit for removing a pulse wave component from the pulse wave analysis data. And a fundamental frequency table in which the relationship between the fundamental frequency of the body motion and the fundamental frequency of the respiration is stored in association with each other in advance, and by referring to the fundamental frequency table, the fundamental frequency of the respiratory A frequency specifying unit that specifies a fundamental frequency, and an extracting unit that calculates each harmonic frequency thereof based on the respiratory basic frequency specified by the frequency specifying unit and extracts a respiratory component. Exercise intensity detection device. 13. The fortitude intensity generating means calculates a distortion rate of a respiratory waveform based on a spectrum level corresponding to a fundamental component of the respiratory component and a spectrum level corresponding to a harmonic component thereof, 13. The exercise intensity detection device according to claim 11, wherein the exercise intensity is calculated based on the distortion rate. 14. The fortitude intensity generating means obtains a ratio between a spectrum level corresponding to a fundamental component of the respiratory component and a spectrum level corresponding to a third harmonic component thereof, and based on the ratio. The exercise intensity detection device according to claim 11, wherein the exercise intensity is calculated by using the following method. 15. The exercise intensity detection device according to claim 1, further comprising a notification unit that notifies the exercise intensity generated by the fortitude intensity generation unit.