Signal processing device and device for estimating biological information

Abstract

A signal processing device and a device for estimating biological information are disclosed. The signal processing device includes: an acquirer configured to acquire a signal; and a processor configured to obtain a frequency band spectrum by applying a fast Fourier transform (FFT) to the acquired signal, and to remove noise from the acquired spectrum by applying a first filter and a second filter different from each other to the acquired spectrum.

Description

[0001] This application claims priority to Korean Patent Application No. 10-2020-0010772, filed with the Korean Intellectual Property Office on January 30, 2020, the entire disclosure of which is incorporated herein by reference for all purposes. Technical Field

[0002] The following description relates to signal processing technology and technology for estimating biological information by using the signal processing technology. Background Art

[0003] Generally, methods for non-invasively measuring blood pressure without damaging the human body include a method of measuring blood pressure by measuring cuff-based pressure and a method of estimating blood pressure by measuring a pulse wave without using a cuff.

[0004] The Korotkoff-Sound method is one of the cuff-based blood pressure measurement methods. In the Korotkoff-Sound method, the pressure in the cuff wrapped around the upper arm is increased, and blood pressure is measured by listening to the sound generated in the blood vessel via a stethoscope while decreasing the pressure. Another cuff-based blood pressure measurement method is the oscillometric method using an automated machine. In the oscillometric method, the cuff is wrapped around the upper arm, the pressure in the cuff is increased, the pressure in the cuff is continuously measured while gradually decreasing the cuff pressure, and blood pressure is measured based on the point where the pressure signal changes significantly.

[0005] Cuffless blood pressure measurement methods generally include a method of estimating blood pressure by calculating the pulse transit time (PTT) and a pulse wave analysis (PWA) method of estimating blood pressure by analyzing the pulse wave shape. Summary of the Invention

[0006] In one general aspect, there is provided a signal processing device including: an acquirer configured to acquire a signal; and a processor configured to obtain a frequency band spectrum by applying a fast Fourier transform (FFT) to the acquired signal and remove noise from the acquired spectrum by applying a first filter and a second filter different from each other to the acquired spectrum.

[0007] In this case, the first filter may include a filter based on a Gaussian asymmetric window; and the second filter may include a filter based on a Gaussian symmetric window.

[0008] The processor may apply the first filter to the main frequency of the spectrum and apply the second filter to the harmonic frequencies of the spectrum.

[0009] When noise is removed from the spectrum, the processor may restore the signal by applying an inverse fast Fourier transform to the spectrum.

[0010] In another general aspect, there is provided a device for estimating biological information, the device including: a sensor unit configured to obtain a biological signal from an object; and a processor configured to obtain a frequency band spectrum by applying a fast Fourier transform (FFT) to the obtained biological signal, remove noise from the obtained spectrum by applying a first filter and a second filter different from each other to the obtained spectrum, and estimate biological information based on the spectrum from which the noise has been removed.

[0011] The biological signal may include one or more of photoplethysmography (PPG), impedance plethysmography (IPG), pressure wave, and video plethysmography (VPG).

[0012] In this case, the first filter may include a filter based on a Gaussian asymmetric window; and the second filter may include a filter based on a Gaussian symmetric window.

[0013] The processor may apply the first filter to the main frequency of the spectrum and may apply the second filter to the harmonic frequencies of the spectrum.

[0014] The processor may recover the biological signal by applying an inverse fast Fourier transform to the spectrum from which the noise has been removed.

[0015] The processor may extract oscillatory peaks from the recovered biological signal and may estimate biological information based on the extracted oscillatory peaks.

[0016] Additionally, the device for estimating biological information may further include: a contact pressure sensor configured to measure the contact pressure between the object and the sensor unit, wherein the processor may estimate biological information based on the oscillatory peaks and the contact pressure.

[0017] The biological information may include one or more of blood pressure, vascular age, arterial stiffness, aortic pressure waveform, vascular compliance, pressure index, and fatigue level.

[0018] In yet another general aspect, there is provided a method for estimating biological information, the method including: obtaining a biological signal from an object; obtaining a frequency band spectrum by applying a fast Fourier transform (FFT) to the obtained biological signal; removing noise from the obtained spectrum by applying a first filter and a second filter different from each other to the obtained spectrum; and estimating biological information based on the spectrum from which the noise has been removed.

[0019] In this case, the first filter may include a filter based on a Gaussian asymmetric window; and the second filter may include a filter based on a Gaussian symmetric window.

[0020] The step of removing noise may include: applying the first filter to the main frequency of the spectrum; and applying the second filter to the harmonic frequencies of the spectrum.

[0021] The step of estimating biological information may include: recovering a biological signal by applying an inverse fast Fourier transform to a spectrum from which noise has been removed.

[0022] The step of estimating biological information may include: extracting oscillatory peaks from the recovered biological signal and estimating biological information based on the extracted oscillatory peaks.

[0023] In addition, the method of estimating biological information may further include: measuring a contact pressure applied to an object while obtaining the biological signal, wherein the step of estimating biological information may include: estimating biological information based on the oscillatory peaks and the contact pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a block diagram showing a signal processing device according to an embodiment of the present disclosure.

[0025] Figures 2A to 2F is a diagram explaining an example of filtering based on a Gaussian window.

[0026] Figure 3 is a flowchart showing a signal processing method according to an embodiment of the present disclosure.

[0027] Figure 4 is a block diagram showing a device for estimating biological information according to an embodiment of the present disclosure.

[0028] Figure 5 is a block diagram showing a device for estimating biological information according to another embodiment of the present disclosure.

[0029] Figure 6 is a flowchart showing a method for estimating biological information according to an embodiment of the present disclosure.

[0030] Figure 7 is a flowchart showing a method for estimating biological information according to another embodiment of the present disclosure.

[0031] Figure 8 is a diagram showing an example of a wearable device.

[0032] Figure 9 is a diagram showing an example of a smart device. DETAILED DESCRIPTION

[0033] Details of other embodiments are included in the following detailed description and the drawings. The advantages and features of the present invention and the method of implementing the present invention will be more clearly understood from the following embodiments described in detail with reference to the drawings. Throughout the drawings and the detailed description, unless otherwise described, the same reference numerals will be understood to represent the same elements, features, and structures.

[0034] It will be understood that although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. In addition, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. It will also be understood that unless explicitly described to the contrary, when an element is referred to as "including" another element, the element is not intended to exclude one or more other elements, but further includes one or more other elements. In the following description, terms such as "unit" and "module" indicate a unit for processing at least one function or operation, and they can be implemented by using hardware, software, or a combination of hardware and software.

[0035] Hereinafter, embodiments of a signal processing device, a signal processing method, and a device and method for estimating biological information will be described in detail with reference to the accompanying drawings.

[0036] Referring to Figure 1 , the signal processing device 100 includes an acquirer 110 and a processor 120. Figures 2A to 2F is a diagram for explaining an example of filtering based on a Gaussian window.

[0037] The acquirer 110 may receive signals from various sensors installed inside or outside the signal processing device 100. For example, the signals may include biological signals (such as, a photoplethysmogram (PPG) signal, an impedance plethysmogram (IPG) signal, a pressure wave signal, a video plethysmogram (VPG) signal, an electrocardiogram (ECG) signal, a ballistocardiogram (BCG) signal, etc.), but are not limited thereto.

[0038] In another example, the acquirer 110 may receive signals from an external device by controlling a communication module installed in the signal processing device 100. In this case, examples of the external device may include a smart phone, a tablet PC, a laptop computer, and a desktop computer that can be used to process various signals, as well as devices in a medical institution.

[0039] The processor 120 may remove noise by processing the signals acquired by the acquirer 110. The processor 120 may obtain a frequency band spectrum by applying a fast Fourier transform (FFT) to the signals.

[0040] The processor 120 can remove noise from the obtained frequency band spectrum by applying a first filter and a second filter, which are Gaussian window-based filters and different from each other, to the spectrum. In this case, the first filter can be a Gaussian-based asymmetric window filter (i.e., a filter using an asymmetric Gaussian window), and the second filter can be a Gaussian-based symmetric window filter (i.e., a filter using a symmetric Gaussian window). The processor 120 can apply the first filter to the main frequency of the frequency band spectrum and apply the second filter to the harmonics frequency of the spectrum.

[0041] Figure 2A is a diagram illustrating an example of applying a Gaussian symmetric window (i.e., a symmetric Gaussian window) 21. Figure 2A The illustration below shows the original signal (thin line) and the restored signal after filtering (thick line). In addition, Figure 2A the illustration above shows the frequency band spectrum (thin line in the upper diagram) obtained by applying FFT to the original signal (thin line in the lower diagram). As Figure 2A shown, by applying a Gaussian symmetric window 21 of equal size to the main frequency f1 and the harmonics frequencies f2, f3, f4, and f5 of the frequency band spectrum (thin line in the upper diagram), and by applying inverse FFT to the frequency band spectrum to restore the signal, as shown in the restored signal in the lower diagram (thick line in the lower diagram), the noise can be effectively removed.

[0042] Figure 2B is a diagram illustrating an example of applying FFT to the original signal (thin line in the lower diagram) and applying a symmetric window 21 of relatively small size to the main frequency f1 and the harmonics frequencies f2, f3, f4, and f5 of the frequency band spectrum (thin line in the upper diagram). As Figure 2B shown, when a Gaussian-based symmetric window with a relatively small window size is applied, the information of the original signal may not be retrieved properly, such that the original signal can be reduced while the noise is effectively removed.

[0043] Figure 2C and Figure 2D is a diagram illustrating an example of applying FFT to the original signal (thin line in the lower diagram) and applying a symmetric window 22 of relatively large size to the main frequency f1 and the harmonics frequencies f2, f3, f4, and f5 of the frequency band spectrum (thin line in the upper diagram). AsFigure 2C and Figure 2D As shown in Figure 2D , when a Gaussian-based symmetric window with a larger window size is applied, the problem of reducing the original signal can be solved. However, referring to Figure 2D , if the window size is increased, while the problem of reducing the original signal is solved, noise may not be effectively removed.

[0044] Figure 2E and Figure 2F show examples of applying two different filters 23 and filter 24 to the spectrum (the thin line in the upper figure) obtained from the original signal (the thin line in the lower figure) by applying FFT. In this case, one filter 23 is a filter based on a Gaussian asymmetric window, and the other filter 24 is a filter based on a Gaussian symmetric window. In this case, the degree of asymmetry can be predefined by considering the type of signal obtained, computational performance, accuracy of analysis, type of biological information to be estimated, etc. As Figure 2E and Figure 2F shown, the asymmetric window can be applied to the main frequency f1 of the spectrum, and the symmetric window can be applied to the harmonic frequencies f2, f3, f4, and f5 of the spectrum. By applying the asymmetric window to the spectrum, the signal (the thick line in the lower figure) can be restored, and noise can be effectively removed from the signal while maintaining the original signal.

[0045] Figure 3 is a flowchart showing a signal processing method according to an embodiment of the present disclosure. Figure 3 The method of Figure 1 is an example of a signal processing method executed by the signal processing device 100 of

[0046] and its detailed description will be omitted.

[0047] In operation 310, the signal processing device 100 may obtain the signal to be processed. The signal processing device 100 may receive the signal from a signal measurement sensor installed inside or outside the signal processing device 100, or receive the signal from a smart phone, a tablet PC, a device in a medical institution, etc.

[0047] Then, in operation 320, the signal processing device 100 may obtain a frequency band spectrum by applying FFT to the obtained signal, and in operation 330, may remove noise by applying two different filters to the obtained spectrum. In this case, the filters are filters based on Gaussian windows, where one filter may be a filter using an asymmetric window, and the other filter may be a filter using a symmetric window. The signal processing device 100 may apply the asymmetric window to the main frequency of the spectrum, and may apply the symmetric window to the harmonic frequencies of the spectrum.

[0048] Subsequently, at operation 340, the signal processing device 100 may restore the signal by applying an inverse FFT to the spectrum from which the noise has been removed.

[0049] Figure 4 is a block diagram showing a device for estimating biological information according to an embodiment of the present disclosure. Figure 5 is a block diagram showing a device for estimating biological information according to another embodiment of the present disclosure. The functions of the above signal processing device 100 may be embedded in devices 400 and 500 for estimating biological information.

[0050] Referring to Figure 4 , the device 400 for estimating biological information includes a sensor unit 410, a processor 420, an output interface 430, a storage device 440, and a communication interface 450.

[0051] The sensor unit 410 may obtain a biological signal from an object. For example, the sensor unit 410 may include a pulse wave sensor for obtaining a photoplethysmogram (PPG) signal from an object. However, the sensor unit 410 is not limited thereto, and may include sensors for obtaining various biological signals such as impedance plethysmogram (IPG) signals, pressure wave signals, video plethysmogram (VPG) signals, electrocardiogram (ECG) signals, ballistocardiogram (BCG) signals, etc. In this case, the object may be a skin tissue of a human body, and may be a body part where veins or capillaries are located, such as the back of the hand, the wrist, the finger, etc. However, the object is not limited thereto, and may be a body part where an artery (such as the radial artery) is located.

[0052] The pulse wave sensor includes a light source that emits light onto the object and a detector that detects the light scattered or reflected from the object. In this case, the light source may include a light emitting diode (LED), a laser diode, a phosphor, etc. In addition, the detector may include a photodiode, an image sensor, etc., but is not limited thereto. The light source and / or the detector may be formed as an array of two or more light sources and / or detectors, and each light source may emit light of a different wavelength.

[0053] The processor 420 may be electrically connected to the sensor unit 410. In response to a request for estimating biological information, the processor 420 may control the sensor unit 410 and may obtain biological information by using the biological signal received from the sensor unit 410. In this case, the biological information may include blood pressure, vascular compliance, cardiac output, total peripheral resistance, vascular age, arterial stiffness, aortic pressure waveform, pressure index, and fatigue level, etc.

[0054] When receiving a biological signal, the processor 420 may perform preprocessing to remove noise. The processor 420 may obtain a frequency band spectrum by applying an FFT to the biological signal. In addition, the processor 420 may remove noise from the spectrum by applying two or more different Gaussian window-based filters to the spectrum, and may restore the biological signal by applying an inverse FFT. For example, one of the different filters may be a filter using an asymmetric window, and the other filter may be a filter using a symmetric window. In this case, the processor 420 may apply the asymmetric window to the main frequency of the frequency band spectrum, and may apply the symmetric window to the harmonic frequencies of the spectrum.

[0055] When restoring the pulse wave signal, the processor 420 may extract an oscillometric peak from the restored signal. When extracting the oscillometric peak, the processor 420 may extract the time and / or amplitude value of the oscillometric peak, and the time and / or amplitude value corresponding to a predetermined ratio (e.g., 0.5 to 0.7) of the amplitude value of the oscillometric peak, or the time and / or amplitude value at the points before and after the peak point where the slope is the maximum and / or minimum point, etc. as additional features. The processor 420 may estimate blood pressure by applying a blood pressure estimation model that defines the correlation between the extracted features and blood pressure. Optionally, the processor 420 may estimate blood pressure based on the extracted features and the contact pressure between the object and the sensor unit 410.

[0056] For example, the processor 420 may obtain the contact pressure based on the amplitude value at each time of the pulse wave signal by applying a contact pressure conversion model that defines the correlation between the amplitude and the contact pressure. The processor 420 may independently estimate the mean arterial pressure, systolic blood pressure, and diastolic blood pressure based on the contact pressure value corresponding to the time point of the oscillometric peak, and the contact pressure values at the right time point and the left time point corresponding to a predetermined ratio of the amplitude value of the oscillometric peak. In this case, the processor 420 may estimate blood pressure by using a blood pressure estimation model that defines the correlation between the contact pressure and blood pressure.

[0057] The output interface 430 may provide various information related to the estimated biological information to the user by using various output modules. In this case, the output module may include a visual output module (such as a display, etc.), a voice output module (such as a speaker, etc.), or a tactile module using vibration, touch, etc., but is not limited thereto. The output interface 430 may output information (such as the estimated blood pressure value and / or the user's health condition determined based on the estimated blood pressure value, actions in response to the determined health condition, etc.). In addition, the output interface 430 may output the blood pressure estimation history in the form of a curve graph, and may provide detailed information related to the blood pressure estimated at the corresponding time point selected by the user.

[0058] The storage device 440 can store reference information related to estimated biological information, pulse wave signals, estimated biological information values, extracted feature information, etc. In this case, the reference information can include information such as user characteristic information including the user's age, gender, health condition, etc., reference blood pressure, biological information estimation model, contact pressure conversion model, etc.

[0059] The storage device 440 can include at least one storage medium such as a flash memory type memory, a hard disk type memory, a multimedia card micro memory, a card type memory (e.g., SD memory, XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk, etc., but is not limited thereto.

[0060] The communication interface 450 can communicate with an external device by using the communication module as described above, and can send various information to the connected external device and receive various information from the connected external device. In this case, examples of the external device can include a blood pressure measuring device (such as a cuff blood pressure monitor), a medical device related to measuring biological information, and an information processing device (such as a smart phone, a tablet PC, a desktop computer, a laptop computer, etc.). For example, the communication interface 450 can receive cuff blood pressure, a biological information estimation model, a contact pressure conversion model, etc. from an external device. In addition, the communication interface 450 can send information (such as the pulse wave signal measured by the sensor unit 410, the features extracted by the processor 520, the estimated biological information value, etc.) to an external device.

[0061] The communication interface 450 can communicate with an external device by using various wired communication technologies or wireless communication technologies (such as Bluetooth communication, Bluetooth low energy (BLE) communication, near field communication (NFC), WLAN communication, Zigbee communication, infrared data association (IrDA) communication, Wi-Fi direct (WFD) communication, ultra-wideband (UWB) communication, Ant+ communication, WIFI communication, radio frequency identification (RFID) communication, 3G communication, 4G communication, and 5G communication, etc.). However, this is only exemplary and is not intended to limit.

[0062] Refer to Figure 5 , the device 500 for estimating biological information includes a sensor unit 410, a processor 420, an output interface 430, a storage device 440, a communication interface 450, and a contact pressure sensor 510. Refer to the above Figure 4 The sensor unit 410, the processor 420, the output interface 430, the storage device 440, and the communication interface 450 have been described such that their detailed descriptions will be omitted.

[0063] When receiving a request for estimating biological information, the processor 420 may provide guidance information regarding the contact state to the user through the output interface 430. For example, the processor 420 may provide guidance information regarding the contact position of the sensor unit 410 to be contacted by the subject and / or the contact pressure that will be changed while the biological signal is being measured.

[0064] The contact pressure sensor 510 may measure the change in the contact pressure applied by the subject to the sensor unit 410 while the biological signal is being measured.

[0065] Based on the pulse wave signal obtained by the sensor unit 410 and the contact pressure obtained by the contact pressure sensor 510, the processor 420 may obtain an oscillometric envelope that indicates the correlation between the amplitude at each time of the pulse wave signal and the contact pressure. In addition, the processor 420 may obtain the above features from the oscillometric envelope and may estimate the biological information by using the obtained features.

[0066] Generally, when the pulse wave signal is being measured, if noise is included in the pulse wave signal due to the movement noise of the subject, the oscillometric peak may be erroneously detected from the pulse wave signal. However, in an embodiment of the present disclosure, the optimal signal with noise removed may be restored by applying a Gaussian-based asymmetric window to the frequency band spectrum, such that the oscillometric peak may be accurately detected.

[0067] Figure 6 is a flowchart showing a method for estimating biological information according to an embodiment of the present disclosure. Figure 6 The method of may be an example of a method for estimating biological information executed by the device 400 for estimating biological information according to an embodiment of Figure 4 and will be briefly described below.

[0068] When receiving a request for estimating biological information, at operation 610, the device 400 for estimating biological information may obtain a biological signal.

[0069] Then, at operation 620, the device 400 for estimating biological information may obtain a frequency band spectrum by applying an FFT to the acquired biological signal, and at operation 630, noise may be removed by applying a first filter and a second filter, which are Gaussian window-based filters and different from each other, to the spectrum. In this case, the first filter may be a filter based on a Gaussian asymmetric window, and the second filter may be a filter based on a Gaussian symmetric window. For example, the device 400 for estimating biological information may apply an asymmetric window to the main frequency of the spectrum and may apply a symmetric window to the harmonic frequencies of the spectrum. In this way, the device 400 for estimating biological information may appropriately recover the biological signal while effectively removing noise.

[0070] Then, when removing noise from the frequency band spectrum, at operation 640, the device 400 for estimating biological information may recover the biological signal by applying an inverse FFT, and at operation 650, biological information may be estimated by using the biological signal. For example, the device 400 for estimating biological information may obtain oscillatory peaks from the recovered biological signal, may obtain additional features based on the peak amplitudes, and may obtain biological information by applying a biological information estimation model that defines the mutual relationship between the features and the biological information.

[0071] Subsequently, at operation 660, the device 400 for estimating biological information may output a biological information estimation result. For example, the device 400 for estimating biological information may visually output the biological information estimation result, the health condition monitoring result based on the biological information estimation result, etc. on a display. Optionally, the device 400 for estimating biological information may estimate biological information by various methods such as using a voice output module (such as a speaker, etc.) or a tactile module that uses vibration, touch, etc.

[0072] Figure 7 is a flowchart showing a method for estimating biological information according to another embodiment of the present disclosure. Figure 7 The method may be an example of a method for estimating biological information executed by a device 500 for estimating biological information according to an Figure 5 embodiment of the present disclosure, and thus will be briefly described below.

[0073] When a request for estimating biological information is received, at operation 710, the device 500 for estimating biological information may obtain a biological signal by using a sensor.

[0074] In this case, at operation 720, the device 500 for estimating biological information may measure the contact pressure applied to an object by using a contact pressure sensor while the biological signal is being measured.

[0075] Then, at operation 730, the device 500 for estimating biological information may obtain a frequency band spectrum by applying FFT to the acquired biological signal, and at operation 740, noise may be removed by applying a Gaussian-based asymmetric window and a symmetric window to the spectrum. For example, the device 500 for estimating biological information may apply the asymmetric window to the main frequency of the spectrum and may apply the symmetric window to the harmonic frequencies of the spectrum. In this way, the device 500 for estimating biological information may appropriately recover the biological signal while effectively removing noise.

[0076] Subsequently, when removing noise, at operation 750, the device 500 for estimating biological information may recover the biological signal by applying inverse FFT, at operation 760, may estimate the biological information by using the biological signal, and at operation 770, may output the biological information estimation result. For example, the device 500 for estimating biological information may obtain an oscillatory peak from the recovered biological signal, may obtain additional features based on the peak amplitude, and may obtain the biological information by applying a biological information estimation model that defines the mutual relationship between the features and the biological information.

[0077] Figure 8 is a diagram showing an example of a wearable device.

[0078] Figure 8 is a diagram showing a wearable device worn on an object. The above-described embodiments of the devices 400 and 500 for estimating biological information may be installed in a smart watch or a smart band-type wearable device worn on a user's wrist, but are not limited thereto.

[0079] Referring to Figure 8 , the wearable device 800 includes a main body 810 and a band 830.

[0080] The main body 810 may be formed in various shapes and may include various modules installed inside or outside the main body 810 to perform the above-described functions of extracting features and estimating biological information as well as various other functions (e.g., timekeeping, alerting, etc.). A battery may be embedded in the main body 810 or the band 830 to supply power to the various modules of the wearable device 800.

[0081] The band 830 may be connected to the main body 810. The band 830 may be flexible so as to wrap around the user's wrist. The band 830 may be formed as a detachable or non-detachable band from the user's wrist. Air may be injected into the band 830 or an airbag may be included in the band 830 such that the band 830 may be elastic according to a change in the pressure applied to the wrist and may send the change in the pressure of the wrist to the main body 810.

[0082] The main body 810 may include a sensor 820 for measuring a biological signal. The sensor 820 may be mounted on one surface of the main body 810, and when the main body 810 is worn on the user's wrist, this surface contacts the user's wrist. For example, the sensor 820 may include a light source for emitting light onto the wrist and a detector for detecting light scattered or reflected from body tissues (such as the skin surface, blood vessels, etc.). However, the sensor 820 is not limited thereto.

[0083] In addition, a processor may be mounted in the main body 810 and may be electrically connected to various modules of the wearable device 800 to control the operation of the wearable device 800.

[0084] The processor may control the sensor 820 in response to a request for estimating biological information. The request for estimating biological information may be generated in response to a user's command input through the manipulator 840 or the touch screen of the display, or may be generated at a predetermined biological information estimation interval or by monitoring the biological information estimation result.

[0085] Once the sensor 820 measures a biological signal, the processor may obtain a frequency band spectrum by applying an FFT to the obtained biological signal, and may remove noise by applying a Gaussian-based asymmetric window and a symmetric window to the frequency band spectrum. In this case, the processor may apply the asymmetric window to the main frequency of the spectrum and may apply the symmetric window to the harmonic frequencies of the spectrum. In addition, the processor may restore the biological signal by applying an inverse FFT to the spectrum from which the noise has been removed.

[0086] The processor may detect an oscillatory peak from the restored biological signal. In addition, the processor may obtain biological information by using the contact pressure applied to the object while measuring the oscillatory peak and the biological signal.

[0087] The display may be mounted on the front surface of the main body 810 and may be a touch panel having a touch screen for sensing a touch input. The display may receive a user's touch input, may send the received touch input to the processor, and may display the processing result of the processor. For example, the display may display the biological information estimation result and may display additional information (such as the biological information estimation history, changes in health status, warning information, etc.) together with the estimation result.

[0088] A storage device for storing the processing result of the processor and various information may be mounted in the main body 810. In this case, the various information may include information related to estimating biological information and information related to other functions of the wearable device 800.

[0089] In addition, the main body 810 may include a manipulator 840 that receives a user's command and sends the received command to the processor. The manipulator 840 may include a power button for inputting a command to turn on / off the wearable device 800.

[0090] Furthermore, a communication interface for communicating with an external device may be installed in the main body 810. The communication interface may send the biological information estimation result to the external device so as to output the estimation result to the external device (e.g., the output module of the user's mobile terminal), or store the estimation result in the storage module of the external device. In addition, the communication interface may receive information and the like for supporting various other functions of the wearable device from the external device.

[0091] Figure 9 is a diagram showing an example of a smart device. Figure 9 shows a smart device to which the above-described devices 400 and 500 for estimating biological information are applied. In this case, the smart device may be a smart phone and a tablet PC, but is not limited thereto.

[0092] Referring to Figure 9 , the smart device 900 may include a main body 910 and a sensor 930 mounted on one surface of the main body 910. The sensor 930 may include one or more light sources 931 and detectors 932. However, the sensor 930 is not limited thereto, and may include an impedance-based sensor or a pressure-based sensor. As Figure 9 shown, the sensor 930 may be mounted on the rear surface of the main body 910, but is not limited thereto, and may be configured in combination with a fingerprint sensor or a touch panel mounted on the front surface of the main body 910.

[0093] A display may be mounted on the front surface of the main body 910. The display may visually display the biological information estimation result and the like. The display may include a touch panel, and may receive information input through the touch panel and send the received information to the processor.

[0094] In addition, an image sensor 920 may be installed in the main body 910. When the user's finger approaches the sensor 930 to measure a biological signal, the image sensor 920 may capture an image of the finger and send the captured image to the processor. In this case, based on the image of the finger, the processor may identify the relative position of the finger with respect to the actual position of the sensor 930, and may provide the relative position of the finger to the user through the display in order to guide the user to accurately contact the sensor 930 with the finger.

[0095] The processor can obtain a spectrum by applying an FFT to the biological signal measured by the sensor 930, and can remove noise by applying a Gaussian-based asymmetric window and a symmetric window to the obtained spectrum. The processor can restore the biological signal by applying an inverse FFT to the spectrum from which the noise has been removed, and can estimate biological information by detecting peaks of the oscilloscopic envelope from the restored biological signal.

[0096] The processor can estimate biological information and output the estimation result through a display.

[0097] The present invention can be implemented as computer-readable code written on a computer-readable recording medium. The computer-readable recording medium can be any type of recording device that stores data in a computer-readable manner.

[0098] Examples of the computer-readable recording medium include: ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, and a carrier wave (e.g., data transmission via the Internet). The computer-readable recording medium can be distributed among multiple computer systems connected to a network such that the computer-readable code is written in the computer-readable recording medium and executed in a distributed manner from the computer-readable recording medium. Those of ordinary skill in the art can easily derive the functional programs, code, and code segments required to implement the present invention.

[0099] The present invention has been described with respect to preferred embodiments. However, it is apparent to those skilled in the art that various changes and modifications can be made without departing from the technical idea and essential features of the present disclosure. Therefore, it is clear that the embodiments described above are illustrative in all respects and are not intended to limit the present disclosure.

Claims

1. A signal processing device, comprising: An acquirer configured to acquire a signal; And A processor configured to obtain a frequency band spectrum by applying a fast Fourier transform to the acquired signal, and to remove noise from the acquired frequency band spectrum by applying a first filter and a second filter different from each other to the acquired frequency band spectrum, Wherein the first filter includes a filter using an asymmetric Gaussian window; and the second filter includes a filter using a symmetric Gaussian window.

2. The signal processing device according to claim 1, wherein, The processor: Applies the first filter to the main frequency of the frequency band spectrum; and Applies the second filter to the harmonic frequencies of the frequency band spectrum.

3. The signal processing device according to claim 1, wherein, When noise is removed from the frequency band spectrum, the processor restores the signal by applying an inverse fast Fourier transform to the frequency band spectrum.

4. A device for estimating biological information, the device comprising: A sensor unit configured to acquire a biological signal from an object; And A processor configured to obtain a frequency band spectrum by applying a fast Fourier transform to the acquired biological signal, to remove noise from the acquired frequency band spectrum by applying a first filter and a second filter different from each other to the acquired frequency band spectrum, and to estimate biological information based on the frequency band spectrum from which noise has been removed, Wherein the first filter includes a filter using an asymmetric Gaussian window; and the second filter includes a filter using a symmetric Gaussian window.

5. The device according to claim 4, wherein The biological signal includes one or more of a photoplethysmogram signal, an impedance plethysmogram signal, a pressure wave signal, and a video plethysmogram signal.

6. The device according to claim 4 or claim 5, wherein, The processor: Applies the first filter to the main frequency of the acquired frequency band spectrum; and Applies the second filter to the harmonic frequencies of the acquired frequency band spectrum.

7. The device according to claim 4 or claim 5, wherein The processor restores the biological signal by applying an inverse fast Fourier transform to the frequency band spectrum from which noise has been removed.

8. The apparatus according to claim 7, wherein The processor extracts oscillatory peaks from the restored biological signal and estimates biological information based on the extracted oscillatory peaks.

9. The device according to claim 8, further comprising: A contact pressure sensor configured to measure the contact pressure between the object and the sensor unit, Wherein the processor estimates biological information based on the oscillatory peaks and the contact pressure.

10. The apparatus according to claim 4, wherein, The biological information includes one or more of blood pressure, vascular age, arterial stiffness, aortic pressure waveform, vascular compliance, pressure index, and fatigue level.

11. A computer-readable recording medium storing a program, wherein, When executed by the processor, the program causes the processor to execute a method for estimating biological information, the method including: Acquiring a biological signal from an object; Obtaining a frequency band spectrum by applying a fast Fourier transform to the acquired biological signal; Removing noise from the acquired frequency band spectrum by applying a first filter and a second filter different from each other to the acquired frequency band spectrum; and Estimating biological information based on the frequency band spectrum from which noise has been removed, Wherein the first filter includes a filter using an asymmetric Gaussian window; and the second filter includes a filter using a symmetric Gaussian window.

12. The computer-readable recording medium according to claim 11, wherein, The step of removing noise includes: Applying the first filter to the main frequency of the acquired frequency band spectrum; and Applying the second filter to the harmonic frequencies of the acquired frequency band spectrum.

13. The computer-readable recording medium according to claim 11, wherein, The step of estimating biological information includes: restoring the biological signal by applying an inverse fast Fourier transform to the frequency band spectrum from which noise has been removed.

14. The computer-readable recording medium according to claim 13, wherein, The step of estimating biological information includes: extracting oscillatory peaks from the restored biological signal and estimating biological information based on the extracted oscillatory peaks.

15. The computer-readable recording medium according to claim 14, further comprising: Measuring the contact pressure applied to an object while obtaining a biological signal, wherein the step of estimating biological information includes: estimating biological information based on an oscillatory peak and contact pressure.

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