Patent Grant
9986922
The embodiments discussed herein are directed to a pulse wave detection method, a pulse wave detection apparatus, and a pulse wave detection program.
There are known methods of detecting fluctuations of the volume of the blood, that is, pulse waves, from an image obtained by imaging a subject. Generally, improvement in detection accuracy is attempted by photographing an image using a light source such as infrared light or photographing an image with an imaging device in close contact with the living body of the subject. However, such a case has demerits such as providing hardware such as a light source and bringing a measurement tool into contact with a living body.
For this reason, it is desired to detect pulse waves without contact between the measurement tool and the living body under environmental light such as sunlight and indoor light. However, the measurement of pulse waves without infrared light or the like incurs large influence of noise, which can possibly decrease the accuracy of detection of pulse waves.
For example, the following signal processor has been presented as an example of a technique for reducing noise. The signal processor is provided with a light-emitting diode that emits red wavelength light and a light-emitting diode that emits infrared wavelength light. With this structure, the signal processor determines a coefficient that minimizes correlation between respective signals obtained by transmission rays of the two light-emitting diodes, and removes a noise component from one signal of the signals using the other signal multiplied by the coefficient. In this processing, the signal processor comprehensively calculates correlation for each of n assumed values, to use the assumed value with the least correlation as a coefficient.
Patent Literature 1: Japanese Laid-open Patent Publication No. 2003-135434
Patent Literature 2: Japanese Laid-open Patent Publication No. 2005-185834
Patent Literature 3: Japanese Laid-open Patent Publication No. 2005-218507
However, the above conventional art increases the processing load because calculation is performed n times to derive a coefficient for reducing noise. In addition, when the number n of the assumed values is reduced to prevent an increase in processing load, the coefficient is diverged from a proper value, which reduces the accuracy of detection of pulse waves.
According to an aspect of the embodiment of the invention, a pulse wave detection method includes obtaining an image obtained by photographing a subject with an imaging device, extracting intensities representative of signal components of a specific frequency band for respective wavelength components among signals of a plurality of wavelength components included in the image, the specific frequency band having a section having a predetermined length or less overlapping a frequency band that pulse waves are enabled to take, calculating, using the intensities extracted for the respective wavelength components, a weight coefficient by which a signal is multiplied when the signals are calculated between the wavelength components to minimize an arithmetic value of the signal components in the specific frequency band after multiplication, multiplying at least one of the signals of the respective wavelength components by the weight coefficient, performing arithmetic operation on the signals between the wavelength components after multiplication by the weight coefficient, and detecting pulse waves of the subject using a signal after the arithmetic operation.
The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention.
Preferred embodiments of the pulse wave detection method, the pulse wave detection apparatus, and the pulse wave detection program disclosed in the present application will be explained in detail with reference to the accompanying drawings. The embodiments do not restrict the disclosed technique. The embodiments may be properly combined within the range in which the details of the processes do not conflict with each other.
First Embodiment
A form of the server apparatus 10 can be mounted by installing an electronic medical chart program that provides an electronic medical chart service as packaged software or on-line software in a desired computer. For example, the server apparatus 10 may be mounted as a Web server that provides the above pulse wave detection service, or a cloud that provides the above pulse wave detection service by outsourcing.
As illustrated in
Configuration of Client Terminal 30
The client terminal 30 is a terminal device that is provided with the pulse wave detection service provided by the server apparatus 10. A form of the client terminal 30 is a fixed terminal such as a personal computer, or a mobile terminal such as a mobile phone, a personal handyphone system (PHS), and personal digital assistants (PDA).
The client terminal 30 includes a communication interface (I/F) unit 31, a camera 32, and a display unit 33, as illustrated in
Among the functional units, the communication I/F unit 31 is an interface that controls communication with another device, such as the server apparatus 10. A network interface card such as a LAN card may be adopted as a form of the communication I/F unit 31. For example, the communication I/F unit 31 transmits an image obtained by photographing the subject's face with the camera 32 to the server apparatus 10, and receives a pulse wave detection result from the server apparatus 10.
The camera 32 is an imaging device using an imaging device such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS). For example, the camera 32 may be provided with light-receiving elements of three type or more, such as red (R), green (G), and blue (B). As an example of mounting the camera 32, a digital camera or a Web camera may be connected via an external terminal, or a camera mounted on a device such as a mobile terminal in shipping may be used. Although the example illustrates the case where the client terminal 30 includes the camera 32, the client terminal 30 does not necessarily include the camera 32 when an image can be obtained via the network or a storage device.
The display unit 33 is a display device that displays various pieces of information, such as a pulse wave detection result transmitted from the server apparatus 10. As a form of the display unit 33, a monitor or a display can be adopted, or the display unit 33 may be mounted as a touch panel formed as one unitary piece with the input unit. The display unit 33 may be omitted, when no information is displayed through the client terminal 30. The information may be displayed on a display unit of another client terminal 30 or the like.
The client terminal 30 includes a pre-installed or installed application program that is provided with the pulse wave detection service from the server apparatus 10 in cooperation with the server apparatus 10. The above client application program may be referred to as “client application” hereinafter.
When the client application is started up via an input device that is not illustrated, the client application starts up the camera 32. In response to the startup, the camera 32 starts photographing the subject contained in the photographing range of the camera 32. In photographing, the client application can display a target position reflecting the subject's nose as a sight, while displaying the image photographed by the camera 32 on the display unit 33. This display enables the photographing of an image in which the subject's nose is contained in the central part of the photographing range among the subject's facial parts such as the eyes, ears, nose, and mouth. The client application transmits the image obtained by photographing the subject's face with the camera 32 to the server apparatus 10 via the communication I/F unit 31. Next, when the client application receives a pulse wave detection result from the server apparatus 10, such as the subject's heart rate and heart beat waveform, the client application displays the subject's heart rate and heart beat waveform on the display unit 33.
Configuration of Server Apparatus 10
By contrast, the server apparatus 10 includes a communication I/F unit 11, an obtaining unit 12, a converting unit 13, an extracting unit 14, a calculator 15, a multiplier 16, an arithmetic unit 17, and a detector 18, as illustrated in
Among the functional units, the communication I/F unit 11 is an interface that controls communication with another device, such as the client terminal 30. A network interface card such as a LAN card may be adopted as a form of the communication I/F unit 11. For example, the communication I/F unit 11 receives an image obtained by photographing the subject's face from the client terminal 30, and transmits a pulse wave detection result to the client terminal 30.
The obtaining unit 12 is a processor that obtains an image obtained by photographing the subject. As a form, the obtaining unit 12 obtains an image photographed with the camera 32 of the client terminal 30. As another form, the obtaining unit 12 is capable of obtaining an image from an auxiliary storage device such as a hard disk and an optical disk storing therein images obtained by photographing the subject, or a removable medium such as a memory card and a universal serial bus (USB) memory. The image obtained by the obtaining unit 12 as described above is output to the extracting unit 14. The obtaining unit 12 can intermittently or continuously obtain still images including the subject, or obtain a stream of video encoded data encoded by a predetermined compression encoding method. Although the example illustrates the case where the obtaining unit 12 executes processing using image data such as two-dimensional bitmap data or vector data obtained from an output of an imaging device such as a CCD and a CMOS, a signal that is output from a detector may be obtained as it is to execute the subsequent processing.
In addition, the obtaining unit 12 extracts a partial image based on a predetermined facial part from the image obtained by photographing the subject's face. As a form, the obtaining unit 12 executes image processing such as template matching on the image including the subject's face, to detect a specific facial part, that is, the subject's nose among the subject's facial parts such as the eyes, ears, nose, and mouth. Next, the obtaining unit 12 extracts a partial part included in a predetermined range including the center with the subject's nose serving as the center. This operation extracts a partial image including the facial center part including the subject's nose and part of the cheeks located around the nose, as the image used for detection of pulse waves. Thereafter, the obtaining unit 12 outputs the partial image extracted from the original image to the converting unit 13.
The converting unit 13 is a processor that converts each two or more wavelength components included in the partial image into frequency components. The present embodiment illustrates the case where pulse waves are detected using signals of two wavelength components formed of R component and G component among the R component, the G component, and the B component. Specifically, a G signal having a light wavelength of 525 nm band has a higher light absorption sensitivity than that of the other components. In the present embodiment, such a G component is used as a basis and used together with signals of other light wavelengths, such as a signal that has passed through a band stop filter, as well as an R signal and a B signal, to cancel the noise component.
As a form, whenever a partial image is input from the obtaining unit 12, the converting unit 13 calculates a mean value of pixel values of the pixels included in the partial image for each of the R component and the G component included in the partial image. Next, when the mean value of each component of the partial image is sampled in a time-series manner for a predetermined time such as one second and one minute, the converting unit 13 performs discrete Fourier transform (DFT) on the signals of the sampled R component and the sampled G component. By performing such DFT, the R signal and the G signal are converted into respective frequency spectrums. The respective frequency spectrums obtained for the R signal and the G signal by application of DFT are output to the extracting unit 14. Although this example illustrates the case of applying discrete Fourier transform, another method may be applied as long as the method is capable of developing a signal into frequency components. For example, the disclosed device can use Fourier transform, fast Fourier transform (FFT), or discrete cosine transform (DCT), as well as discrete Fourier transform.
The extracting unit 14 is a processor that extracts a signal intensity representative of a signal component of a specific frequency band having a section of a predetermined length or less that overlaps the frequency band that pulse waves can take, for each wavelength component, from the frequency spectrum of each wavelength component.
The term “specific frequency band” indicates a frequency band in which a noise component markedly appears in comparison with other frequency bands. For example, a specific frequency band can be defined by comparing the frequency band with a frequency band that pulse waves can take. An example of the frequency band that pulse waves can take is a frequency band equal to or larger than 0.7 Hz and less than 4 Hz, that is, a frequency band equal to or larger than 42 bpm and equal to or less than 240 bpm when it is converted into a frequency band per minute. In view of the above, an example of the specific frequency band can be a frequency band less than 0.7 Hz and equal to or larger than 4 Hz, which may not be measured as pulse waves. Part of the specific frequency band may overlap the frequency band that pulse waves can take. For example, the specific frequency band may be allowed to overlap the frequency band that pulse waves can take in a section of 0.7 Hz to 1 Hz that is hardly supposed to be measured as pulse waves that can take a frequency band less than 1 Hz and equal to or larger than 4 Hz as the specific frequency band.
Such specific frequency band may be narrowed to a frequency band in which noise appears more markedly and having a frequency band less than 1 Hz and equal to or larger than 4 Hz as an outer edge. For example, noise appears more markedly in a low frequency band lower than the frequency band that pulse waves can take, than a high frequency band higher than the frequency band that pulse waves can take. For this reason, the specific frequency band can be narrowed to a frequency band less than 1 Hz. In addition, the specific frequency band may be narrowed to a frequency band equal to or larger than 3 bpm and less than 1 Hz because most difference in sensitivity between the imaging devices of the respective components is included in the vicinity of a direct-current component having a zero spatial frequency. The specific frequency band may also be narrowed to a frequency band equal to or larger than 3 bpm and less than 20 bpm, in which noise easily occurs, such as the movement of a human body such as blinks and body shake, and flicker of the environmental light.
As a form, the extracting unit 14 extracts a signal intensity representative of the signal component in the specific frequency band for each of the R component and the G component. As an example, the extracting unit 14 is capable of extracting a signal intensity corresponding to a preset frequency in the frequency band equal to or larger than 3 bpm and less than 20 bpm. As another example, the extracting unit 14 is capable of extracting a mean value of the signal intensities by executing averaging such as arithmetic mean, weighted mean, and moving average on the signal intensities in the frequency band equal to or larger than 3 bpm and less than 20 bpm, and extracting an integrated value of the signal intensities by integrating the signal intensities. In the following explanation, the signal intensity representative of the signal component being the R component in the specific frequency band may be referred to as “Rnoise”, and the signal intensity representative of the signal component being the G component in the specific frequency band may be referred to as “Gnoise”.
The calculator 15 is a processor that calculates, using the signal intensities extracted for the respective wavelength components by the extracting unit 14, a weight coefficient by which one signal is multiplied when the signals are calculated between the wavelength components. The weight coefficient minimizes the arithmetic value of the signal component of the specific frequency band after multiplication.
As a form, the calculator 15 calculates a weight coefficient that minimizes the arithmetic value of the signal intensities in the specific frequency band between the R component and the G component. For example, the calculator 15 calculates coefficients a1 and a2 that satisfy the derivation expression “a1*Rnoise+a2*Gnoise=0”. These coefficients a1 and a2 cancel the signal intensities in the specific frequency band corresponding to noise among signal intensities that are different between the respective components, to make the signal intensities uniform, without attenuating the difference therebetween in signal intensity around the frequency in which pulse waves strongly appear so much as the components corresponding to noise in the specific frequency band. Either of the values of the coefficients a1 and a2 takes a negative value. Next, the calculator 15 calculates a weight coefficient a1/a2 for the spectrum of the R signal, and a weight coefficient a2/a2 for the spectrum of the G signal.
The multiplier 16 is a processor that multiplies at least one signal component of the signals of the wavelength components by the weight coefficient. As a form, the multiplier 16 multiplies the spectrum of each signal of the R component and the G component by the weight coefficient. In the above example, the multiplier 16 multiplies the spectrum Rall of the R signal by the weight coefficient a1/a2, and multiplies the spectrum Gall of the G signal by the weight coefficient a2/a2.
The arithmetic unit 17 is a processor that performs arithmetic operation on signals between the wavelength components after multiplication by the weight coefficients. As a form, the arithmetic unit 17 performs arithmetic operation between a multiplication result of the spectrum Rall of the R signal and the weight coefficient a1/a2, and a multiplication result of the spectrum Gall of the G signal and the weight coefficient a2/a2. In this case, because the weight coefficient a1/a2 is negative, the spectrum of the R signal after multiplication by the weight coefficient is subtracted from the spectrum of the G signal after multiplication by the weight coefficient.
The detector 18 is a processor that detects pulse waves of the subject using the spectrum after arithmetic operation. As a form, the detector 18 detects the subject's heart rate from the maximum peak of the spectrum after arithmetic operation in a frequency section corresponding to the section having the lower limit value of 42 bpm and the upper limit value of 240 bpm. For example, in the example of
The detection result detected as described above, such as the heart rate and the heart beat waveform, can be output to the client terminal 30, for example. In output, the detector 18 outputs the subject's heart rate to a diagnostic program that diagnoses whether the subject suffers from a heart disease, for example, a Web application mounted on the server apparatus 10. The detector 18 may also output a diagnostic result obtained by causing the diagnostic program to diagnose the subject's heart diagnose to the client terminal 30 together with the heart rate. For example, the diagnostic program diagnoses that the subject is suspected to suffer from angina pectoris or myocardinal infarction when the subject with high blood pressure has tachycardia of, for example, 100 bpm or more. The diagnostic program also diagnoses arrhysmia and mental diseases, such as strains and stresses, using the heart rate. Output of such diagnostic result together enables monitoring services outside the hospital, such as those at home and at desk.
Various integrated circuits or electronic circuits may be adopted as the obtaining unit 12, the converting unit 13, the extracting unit 14, the calculator 15, the multiplier 16, the arithmetic unit 17, and the detector 18. For example, examples of the integrated circuits are an application specific integrated circuit (ASIC) and a field programmable gate array (FGPA). Examples of the electronic circuits are a central processing unit (CPU) and a micro processing unit (MPU).
Flow of Process
Next, the flow of the process executed by the server apparatus 10 according to the present embodiment will be explained hereinafter.
As illustrated in
Next, the converting unit 13 applies discrete Fourier transform to each signal of the R component and the G component to convert them into frequency components (Step S103). In this manner, the R signal and the G signal are converted into frequency spectrums.
Next, the extracting unit 14 extracts signal intensities Rnoise and Gnoise representative of the signal components of the specific frequency band from the frequency spectrums of the respective wavelength components (Step S104). The calculator 15 calculates the weight coefficients a1/a2 and a2/a2 that minimize the arithmetic values of the signal intensities Rnoise and Gnoise in the specific frequency band between the R component and the G component (Step S105).
Thereafter, the multiplier 16 multiplies the spectrum Rall of the R signal by the weight coefficient a1/a2, and multiplies the spectrum Gall of the G signal by the weight coefficient a2/a2 (step S106). Next, the arithmetic unit 17 performs arithmetic operation between the multiplication result of the spectrum Rall of the R signal and the weight coefficient a1/a2, and the multiplication result of the spectrum Gall of the G signal and the weight coefficient a2/a2 (Step S107).
Next, the detector 18 detects pulse waves such as the subject's heart rate and heart rate waveform using the spectrum after multiplication (Step S108), thereafter outputs a pulse wave detection result to the client terminal 30 (Step S109), and ends the process.
Effect of First Embodiment
As described above, the server apparatus 10 according to the present embodiment calculates a noise intensity of a frequency component that does not substantially include any pulse waves between signals of a plurality of wavelength components, and detects pulse waves from a signal calculated by multiplying the signals of the respective wavelength components by the respective weight coefficients that minimize the arithmetic value of the noise intensity. With this structure, the server apparatus 10 according to the present embodiment enables reduction in the calculation quantity of the weight coefficients. For this reason, the server apparatus 10 according to the present embodiment enables the suppression of an increase in processing load or a decrease in accuracy when noise is reduced.
Second Embodiment
Although the first embodiment described above illustrates the example in which the noise component is canceled in the frequency space to detect pulse waves, the disclosed apparatus can cancel the noise component to detect pulse waves, without necessarily converting the signals of the respective wavelength components into frequency components. Accordingly, the present embodiment illustrates the case where the noise component is canceled in a time-series space to detect pulse waves.
Among the functional units, the obtaining unit 51 calculates a mean value of pixel values of pixels included in a partial image for each of the R component and the G component included in the partial image, whenever a partial image is extracted. The obtaining unit 51 also samples a mean value of each of the R signal and the G signal included in the partial image in time series for a predetermined time, such as one second and one minute, to output time-series data of the sampled R signal and the sampled G signal to the following functional unit. For example, the obtaining unit 51 outputs the time-series data of the R signal to the BPF 52A and the BPF 56A, and outputs the time-series data of the G signal to the BPF 52B and the BPF 56B.
Each of the BPF 52A, the BPF 52B, the BPF 56A, and the BPF 56B is a band-pass filter that passes only a signal component of a predetermined frequency band therethrough, and removes signal components of frequency bands other than the predetermined frequency band. The BPF 52A, the BPF 52B, the BPF 56A, and the BPF 56B may be mounted with hardware or software.
The following is explanation of difference in frequency band signals of which the BPFs pass therethrough. The BPF 52A and BPF 52B pass signal components of the specific frequency band therethrough, for example, a frequency band equal to or larger than 3 bpm and less than 20 bpm. Although this explanation illustrates the case of using band-pass filters to extract signal components of the specific frequency band, low-pass filters may be used in the case of extracting a signal component in a frequency band less than 20 bpm. By contrast, the BPF 56A and the BPF 56B pass signal components of the frequency band that pulse waves can take, for example, the frequency band equal to or larger than 42 bpm and less than 240 bpm. In the following explanation, the frequency band that pulse waves can take may be referred to as “pulse wave frequency band”.
The extracting unit 53A extracts the absolute intensity value of the signal component of the R signal in the specific frequency band. For example, the extracting unit 53A extracts the absolute intensity value of the signal component of the specific frequency band, by executing a multiplication process of exponentiating the signal component of the R component in the specific frequency band. The extracting unit 53B extracts the absolute intensity value of the signal component of the G signal in the specific frequency band. For example, the extracting unit 53B extracts the absolute intensity value of the signal component of the specific frequency band, by executing a multiplication process of exponentiating the signal component of the G component in the specific frequency band.
Each of the LPF 54A and the LPF 54B is a low-pass filter that executes smoothing on time-series data of the absolute intensity value in the specific frequency band to respond to time change. The LPF 54A and the LPF 54B have no difference between them except that the signal that is input to the LPF 54A is an R signal and the signal that is input to the LPF 54B is a G signal. Such smoothing produces absolute value intensities R′noise and G′noise in the specific frequency band.
The calculator 55 calculates a weight coefficient a by executing division “G′noise/R′noise” in which the absolute value intensity G′noise of the G signal in the specific frequency band that is output by the LPF 54B is divided by the absolute value intensity R′noise of the R signal in the specific frequency band that is output by the LPF 54A.
The multiplier 57 multiplies the signal component of the R signal in the pulse wave frequency band that is output from the BPF 56A by the weight coefficient a calculated by the calculator 55.
The arithmetic unit 58 executes arithmetic operation “a*Rsignal−Gsignal” in which the signal component of the G signal in the pulse wave frequency band that is output from the BPF 56B is subtracted from the signal component of the R signal in the pulse wave frequency band that is multiplied by the weight coefficient a by the multiplier 57. The time-series data of the signal obtained by the arithmetic operation corresponds to the heart beat waveform.
The detector 59 detects the subject's pulse waves using the signal after the arithmetic operation. As a form, the detector 59 outputs the time-series data of the signal as a pulse wave detection result. As another form, the detector 59 may detect the heart rate by applying Fourier transform to the time-series data of the signal.
Next, the obtaining unit 51 outputs time-series data of the R signal to the BPF 52A and the BPF 56A, and outputs time-series data of the G signal to the BPF 52B and the BPF 56B (Step S303).
Next, the BPF 52A extracts a signal component of the R signal in the specific frequency band, for example, the signal component in the frequency band equal to or larger than 3 bpm and less than 20 bpm and the BPF 52B extracts the signal component of the G signal in the specific frequency band (Step S304A).
Thereafter, the extracting unit 53A extracts the absolute intensity value of the signal component of the R signal in the specific frequency band, and the extracting unit 53B extracts the absolute intensity value of the signal component of the G signal in the specific frequency band (Step S305).
Thereafter, the LPF 54A removes a steep frequency component from the time-series data of the absolute intensity value of the R signal in the specific frequency band, and the LPF 54B removes a steep frequency component from the time-series data of the absolute intensity value of the G signal in the specific frequency band (Step S306).
Next, the calculator 55 calculates the weight coefficient a by executing the division “G′noise/R′noise” in which the absolute value intensity G′noise of the G signal in the specific frequency band that is output by the LPF 54B is divided by the absolute value intensity R′noise of the R signal in the specific frequency band that is output by the LPF 54A (Step S307).
In parallel with the processing at the above step S304A, the BPF 56A extracts a signal component of the R signal in the pulse wave frequency band, for example, the frequency band equal to or larger than 42 bpm and less than 240 bpm, and the BPF 56B extracts a signal component of the G signal in the pulse wave frequency band (Step S304B).
Thereafter, the multiplier 57 multiplies the signal component of the R signal in the pulse wave frequency band that is extracted at Step S304B by the weight coefficient a calculated at step S307 (Step S308). Next, the arithmetic unit 58 executes the arithmetic operation “a*Rsignal−Gsignal” in which the signal component of the G signal in the pulse wave frequency band that has been extracted at Step S304B is subtracted from the signal component of the R signal in the pulse wave frequency band that has been multiplied by the weight coefficient a at Step S308 (Step S309).
Next, the detector 59 detects the subject's pulse waves, such as the heart rate and the heart beat waveform, using the time-series data of the signal after the arithmetic operation (Step S310), outputs the pulse wave detection result to the client terminal 30 (Step S311), and ends the process.
Effects of Second Embodiment
As described above, the server apparatus 50 according to the present embodiment cancels the noise component in the time-series space, to detect pulse waves. This case also enables reduction in the calculation quantity of the weight coefficient like the first embodiment described above; hence this case suppresses an increase in processing load or a decrease in accuracy when the noise is reduced. In addition, because the server apparatus 50 according to the present embodiment enables obtaining of the heart beat waveform serving as a form of pulse waves without Fourier transform, in comparison with the first embodiment described above, the server apparatus 50 more effectively suppresses an increase in processing load or a decrease in accuracy.
Third Embodiment
The present invention may be carried out in various different forms as well as the embodiments described above relating to the disclosed apparatus. The following is explanation of other embodiments included in the present invention.
Although the first embodiment and the second embodiment described above illustrate the case of using two types of input signals, that is, the R signal and the G signal, signals of desired types and a desired number may be used as the input signals, as long as the signals have different light wavelength components. For example, a combination of two signals may be used among signals of different light wavelength components, such as R, G, B, IR, and NIR, or a combination of three or more signals may be used.
Distribution and Integration
In addition, it is noted that the components of each device illustrated in the description of the foregoing embodiments may not necessarily be physically configured as illustrated in the drawings. That is, specific manners of distribution and integration of the devices are not limited to those illustrated in the drawings and the whole or part thereof may be distributed or integrated functionally or physically in any units depending on various loads and use conditions. For example, the client terminal 30 may be operated in a stand-alone manner by causing the client terminal 30 to execute a pulse wave detection program that executes processing corresponding to that executed by the functional units of the server apparatus 10, such as the obtaining unit 12, the converting unit 13, the extracting unit 14, the calculator 15, the multiplier 16, the arithmetic unit 17, and the detector 18. In addition, among the obtaining unit 12, the converting unit 13, the extracting unit 14, the calculator 15, the multiplier 16, the arithmetic unit 17, and the detector 18, part of the functional units may be connected via a network as an external device of the server apparatus 10. For example, because arithmetic operation such as DFT incurs high processing load, the converting unit 13 may be mounted on the client terminal 30, and the other functional units may be mounted on the server apparatus 10, from the viewpoint of causing the server apparatus 10 having high specifications between the client and the server to perform the process. Besides, the function of the above server apparatus 10 may be achieved by separate devices including part of the functional units among the obtaining unit 12, the converting unit 13, the extracting unit 14, the calculator 15, the multiplier 16, the arithmetic unit 17, and the detector 18 and connected via a network to cooperate with each other.
Pulse Wave Detection Program
The above processes explained in the above embodiments can be implemented by executing a computer program prepared in advance by a computer such as a personal computer and a workstation. The following is explanation of an example of a computer that executes a pulse wave detection program having the same function as that in the above embodiments, with reference to
As illustrated in
Next, the CPU 150 reads out the pulse wave detection program 170a from the HDD 170, to expand the pulse wave detection program 170a in the RAM 180. In this manner, the pulse wave detection program 170a functions as a pulse wave detection process 180a, as illustrated in
The above pulse wave detection program 170a is not necessarily stored in the HDD 170 or the ROM 160 initially. For example, each program may be stored in a “portable physical medium” that is inserted into the computer 100, such as a flexible disk (FD), a compact disc read only memory (CD-ROM), a digital versatile disc (DVD), a magneto-optical disc, and an integrated circuit (IC) card. The computer 100 may obtain and execute each program from the portable physical medium. Otherwise, each program may be stored in another computer or a server apparatus that is connected to the computer 100 via a public line, the Internet, a LAN, or a wide area network (WAN), and the computer 100 may obtain and execute each program therefrom.
The pulse wave detection method disclosed in the present application produces the effect of suppressing an increase in processing load or a decrease in accuracy in reduction of noise.
All examples and conditional language recited herein are intended for pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.
This application is a continuation application of International Application PCT/JP2012/072990 filed on Sep. 7, 2012 and designating the U.S., the entire contents of which are incorporated herein by reference.
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| Poh et al.,“Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.”, Optics Express, May 10, 2010, pp. 10763-10775, vol. 18 No. 10, Optical Society of America. [14 pages] [Cited in the above listed CNOA]. |
| International Search Report, mailed in connection with PCT/JP2012/072990 dated Oct. 16, 2012. |
| Number | Date | Country | |
|---|---|---|---|
| 20150173630 A1 | Jun 2015 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2012/072990 | Sep 2012 | US |
| Child | 14638570 | US |
