Signal processing device, electronic device, signal processing method, and program

Abstract

To provide a signal processing apparatus capable of emphasizing a signal having specific target characteristics included in a pulse wave.SOLUTION: The signal processing apparatus comprises: an input information acquisition part which acquires input information indicating a time-series change inside a skin of a living body; a pulse wave calculation part which calculates a pulse wave signal indicating a pulse wave from the input information; a noise adding part which generates a noise addition signal by adding a noise signal to the pulse wave signal; and a non-liner processing part which inputs the noise addition signal to a non-linear model with input / output characteristics having a hysteresis to generate an output signal, the input / output characteristics being adjusted such that the output signal may satisfy target characteristics.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present disclosure relates to a signal processing device, an electronic device, a signal processing method, and a program.

Background Art

[0002] Due to the pulsation of the heart, the blood flow undergoes periodic fluctuations, and the volume and blood flow velocity of the blood in the blood vessels change. Along with the change in blood volume and blood flow velocity, the degree of light absorption in hemoglobin in the blood also changes. By receiving the reflected light from the skin and imaging the skin, the temporal change in the absorbance of hemoglobin can be observed. Therefore, it is possible to calculate a pulse wave indicating the fluctuations in the volume and blood flow velocity of the blood in the blood vessels from the image of the imaged skin.

[0003] However, the light transmitted into the skin is highly scattered by tissues other than blood vessels and has a large loss. Therefore, the pulse wave calculated from the image of the imaged skin is a weak signal. Furthermore, the signal obtained from the image of the imaged skin includes disturbance noises such as ambient light, body movement, and noise of the imaging device in addition to the pulse wave. As a method for detecting a weak signal buried in the disturbance noises, a method using the stochastic resonance phenomenon has been proposed.

[0004] Patent Document 1 discloses a technique using the stochastic resonance phenomenon in which, for each of a plurality of pixel signals constituting read image data, a process of adding noise and performing binarization processing is performed in parallel, and the results are further combined. In the technique disclosed in Patent Document 1, by setting at least one of the noise intensity and the threshold value used for the binarization processing based on the pixel signal of the input image data, a specific part is extracted from a real image including various gradations.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] The pulse wave is composed of various periodic signals derived from vital activities such as heartbeat, autonomic nerve rhythm, and respiration. Therefore, in order to examine the periodicity derived from a specific vital activity from the pulse wave, it is required to emphasize and extract a signal with specific target characteristics from the pulse wave.

[0007] In the technique disclosed in Patent Document 1, since it only extracts specific parts from an image, there is a possibility that a signal with specific target characteristics cannot be emphasized and extracted. Therefore, an aspect of the present disclosure aims to provide a signal processing device, an electronic device, a signal processing method, and a program that can emphasize a signal having specific target characteristics included in a pulse wave.

Means for Solving the Problems

[0008] A signal processing device according to an aspect of the present disclosure includes an input information acquisition unit that acquires input information indicating a time-series change inside the skin of a living body, a pulse wave calculation unit that calculates a pulse wave signal indicating a pulse wave from the input information, a noise addition unit that adds a noise signal to the pulse wave signal to generate a noise-added signal, and a non-linear processing unit that inputs the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies target characteristics.

[0009] An electronic device according to an aspect of the present disclosure includes a sensor that measures a time-series change inside the skin and a signal processing device. The signal processing device includes an input information acquisition unit that acquires input information indicating a time-series change inside the skin of a living body, a pulse wave calculation unit that calculates a pulse wave signal indicating a pulse wave from the input information, a noise addition unit that adds a noise signal to the pulse wave signal to generate a noise-added signal, and a non-linear processing unit that inputs the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies target characteristics.

[0010] A signal processing method according to one embodiment of the present disclosure includes: a step of acquiring input information indicating a time-series change inside the skin of a living body; a step of calculating a pulse wave signal indicating a pulse wave from the input information; a step of adding a noise signal to the pulse wave signal to generate a noise-added signal; and a step of inputting the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, wherein the input-output characteristics are adjusted so that the output signal satisfies a target characteristic.

[0011] A program according to one embodiment of the present disclosure causes a computer to execute functions of: acquiring input information indicating a time-series change inside the skin of a living body; calculating a pulse wave signal indicating a pulse wave from the input information; adding a noise signal to the pulse wave signal to generate a noise-added signal; and inputting the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, wherein the input-output characteristics are adjusted so that the output signal satisfies a target characteristic.

Brief Description of the Drawings

[0012]

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Modes for Carrying Out the Invention

[0013] (First Embodiment) With reference to FIGS. 1 to 9, the first embodiment will be described. In the drawings, the same or similar elements are denoted by the same reference numerals, and redundant descriptions are omitted.

[0014] FIG. 1 is a diagram showing an example of a measurement system 100. The measurement system 100 includes a measurement device 101 and a signal processing device 102.

[0015] The measurement device 101 measures the time-series change inside the skin of the living body 103. The measurement device 101 is installed at a position where the time-series change inside the skin of the living body 103 can be imaged. Specifically, the measurement device 101 is installed at a position where a portion of the skin of the living body 103 that is exposed can be imaged. The portion of the skin of the living body 103 that is exposed is the forehead, cheeks, etc. of the living body 103.

[0016] The measuring device 101 is connected to and controlled by a signal processing device 102. The measuring device 101 includes a sensor that measures the time-series changes inside the skin of the living body 103. For example, the measuring device 101 includes a camera that includes an image sensor and a lens. For example, the image sensor is a CMOS (Complementary Metal-Oxide Semiconductor), a CCD (Charge-Coupled Device), or the like.

[0017] When the measuring device 101 includes a camera, the camera images the skin of the living body 103. For example, the measuring device 101 includes a color filter of an RGB Bayer array in order to detect minute changes in the color of the skin of the living body 103. Alternatively, the measuring device 101 includes a color filter such as RGBCy or RGBIR. Color filters such as RGBCy and RGBIR are suitable for observing increases and decreases in the blood volume indicated by the reflected light of the light transmitted through the skin.

[0018] Further, the measuring device 101 may include communication means and include a web camera that can be connected to the signal processing device 102 via a network. Alternatively, the measuring device 101 may be a smartphone including a camera, a monitoring robot including a camera, or the like.

[0019] In the present disclosure, when the measuring device 101 includes a camera, a continuous or discontinuous live recording showing the volume change of the blood vessels of the living body 103 imaged by the camera is referred to as a video. Also, a moving image or a still image cut out from the video is referred to as an image.

[0020] The signal processing device 102 generates a signal indicating a pulse wave from a signal indicating the time-series changes inside the skin of the living body 103 measured by the measuring device 101, using the stochastic resonance phenomenon. The signal indicating the time-series changes inside the skin indicates the time-series changes in the absorbance of hemoglobin. The stochastic resonance phenomenon is a phenomenon in which the amplitude of a signal having periodicity is amplified by inputting a signal generated by adding noise to a signal having periodicity into a system having a non-linear response.

[0021] FIG. 2 is a diagram showing an example of the configuration of the signal processing apparatus 102. The signal processing apparatus 102 includes a storage unit 201, an operation unit 202, a control unit 203, and the like.

[0022] The storage unit 201 is a recording medium capable of storing various data and programs. The storage unit 201 includes an area for storing various programs, an area for storing data used in the various programs, an area into which the various programs are loaded, and an area used when the various programs are executed. For example, the storage unit 201 can be configured by a semiconductor memory such as a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a programmable logic circuit. The storage unit 201 stores by associating the registered frequency band with the type of vital sign. In the present disclosure, the vital sign is a sign of vital activities such as heartbeat, autonomic nerve rhythm, and respiration. The registered frequency band is a registered frequency band.

[0023] The operation unit 202 receives an input of a user's operation. For example, the operation unit 202 receives an input regarding the type of vital sign that is the measurement target. For example, the operation unit 202 is realized by a touch panel or the like.

[0024] The control unit 203 performs various controls on each unit included in the signal processing apparatus 102. For example, the control unit 203 is configured by a processor such as a CPU (Central Processing Unit) or a GPU (Graphic Processing Unit). When the control unit 203 is configured by a CPU, each functional block of the control unit 203 can be realized by the CPU reading a program from the storage unit 201 and executing it. In the present embodiment, the control unit 203 is illustrated and described as being built in a PC (Personal Computer), but the present invention is not limited thereto.

[0025] The control unit 203 includes a vital sign information acquisition unit 211, an input information acquisition unit 212, a pulse wave calculation unit 213, a noise generation unit 214, a noise addition unit 215, a non-linear model determination unit 216, a non-linear processing unit 217, a selection unit 218, etc.

[0026] The vital sign information acquisition unit 211 acquires vital sign information 221. The vital sign information 221 indicates the type of vital sign that is the measurement target.

[0027] The input information acquisition unit 212 acquires input information 222. The input information 222 indicates the time-series change inside the skin of the living body 103. For example, the input information 222 is an image including an image of the skin. Or, the input information 222 may be information indicating the vibration of a signal obtained from an image of the skin.

[0028] The pulse wave calculation unit 213 calculates a pulse wave signal S0 indicating a pulse wave from the input information 222.

[0029] The noise generation unit 214 generates a noise signal Sn. The noise signal Sn is a signal having a random signal intensity within a specified frequency band and a specified signal intensity range. That is, in the signal processing device 102 according to the present embodiment, the noise signal Sn is colored noise.

[0030] The noise addition unit 215 adds the noise signal Sn to the pulse wave signal S0 to generate a noise-added signal Sin. Specifically, the noise addition unit 215 adds a plurality of noise signals Sn having different noise intensities to the pulse wave signal S0 respectively to generate a plurality of noise-added signals Sin. The noise intensity is the difference between the maximum value and the minimum value of the signal intensity of the noise signal Sn within a predetermined time.

[0031] The non-linear model determination unit 216 determines a non-linear model 223. The non-linear model 223 is a non-linear system whose input-output characteristics have hysteresis. The input-output characteristics of the non-linear model 223 are adjusted so that the output signal Sout satisfies the target characteristics. The output signal Sout is a signal that is output by inputting the noise-added signal Sin to the non-linear model 223. The target characteristics are information regarding the characteristics of the output signal Sout. Specifically, the target characteristics indicate at least one selected from the group consisting of the signal-to-noise ratio of the output signal Sout, the signal intensity of the output signal Sout, and the frequency band of the output signal Sout.

[0032] The non-linear processing unit 217 inputs the noise-added signal Sin to the non-linear model 223 and generates the output signal Sout. Specifically, the non-linear processing unit 217 inputs each of the plurality of noise-added signals Sin to the non-linear model 223 and generates a plurality of output signals Sout. For example, the non-linear processing unit 217 is realized by using a circuit simulator. By being realized by using a circuit simulator, the non-linear processing unit 217 can easily calculate the output signal Sout.

[0033] The selection unit 218 selects the output signal Sout_Dm having the maximum signal-to-noise ratio among the plurality of generated output signals Sout.

[0034] FIG. 3 is a diagram for explaining the input / output characteristics of the non-linear model 223. In FIG. 3, the input signal strength is taken on the horizontal axis, and the output signal strength is taken on the vertical axis. When the input signal strength increases, the non-linear model 223 has input / output characteristics in which the output signal strength rapidly increases from the output signal strength Sl to the output signal strength Sh at the rising threshold Ath, as shown by the solid non-linear response A illustrated in FIG. 3. Further, when the input signal strength decreases, the non-linear model 223 has input / output characteristics in which the output signal strength rapidly decreases from the output signal strength Sh to the output signal strength Sl at the falling threshold Bth, as shown by the dashed non-linear response B illustrated in FIG. 3. That is, the non-linear model 223 has input / output characteristics in which the output signal strength varies between the output signal strength Sh when the input signal strength increases and exceeds the rising threshold Ath and the output signal strength Sl when the input signal strength decreases and becomes lower than the falling threshold Bth. The non-linear model 223 is realized, for example, as a Schmitt trigger circuit. Further, the shapes of the non-linear response A and the non-linear response B are changed by the resistance values of the circuit elements constituting the Schmitt trigger circuit. When the non-linear processing unit 217 is realized by a circuit simulator, the shapes of the non-linear response A and the non-linear response B may be changed by changing the resistance values in the circuit simulator.

[0035] FIG. 4 is a waveform diagram showing an example of the time waveform of the pulse wave signal S0. In FIG. 4, time is taken on the horizontal axis, and signal strength is taken on the vertical axis. Since the pulse wave is periodically repeated in response to heartbeat, breathing, etc., the pulse wave signal S0 is observed as a pulse-like signal that repeats increases and decreases. Therefore, the maximum value and the minimum value of the pulse wave signal S0 repeatedly appear. In the present disclosure, the maximum value of the pulse wave signal S0 is referred to as a peak, and the minimum value of the pulse wave signal S0 is referred to as a bottom. The peak of the pulse wave signal S0 is smaller than the rising threshold Ath, and the bottom of the pulse wave signal S0 is larger than the falling threshold Bth.

[0036] FIG. 5 is a waveform diagram showing an example of the time waveform of the noise signal Sn. In FIG. 5, time is taken on the horizontal axis and signal intensity is taken on the vertical axis. The noise signal Sn is colored noise having a frequency band. When the target characteristic indicates the frequency band of the output signal Sout, the frequency band of the noise signal Sn is the frequency band indicated by the target characteristic. ΔN is the noise intensity of the noise signal Sn. The noise intensity of the noise signal Sn is adjusted according to the amplitude of the pulse wave signal S0.

[0037] FIG. 6 is a waveform diagram showing an example of the time waveform of the noise-added signal Sin. In FIG. 6, time is taken on the horizontal axis and signal intensity is taken on the vertical axis. The noise-added signal Sin illustrated in FIG. 6 is a signal obtained by adding the noise signal Sn illustrated in FIG. 5 to the pulse wave signal S0 illustrated in FIG. 4. Since the noise intensity of the noise signal Sn is adjusted according to the amplitude of the pulse wave signal S0, as illustrated in FIG. 6, in the noise-added signal Sin, the shape having the peak and bottom of the pulse wave signal S0 is maintained. Further, by adding the noise signal Sn to the pulse wave signal S0, the noise-added signal Sin has a signal intensity exceeding the rising threshold value Ath and a signal intensity smaller than the falling threshold value Bth. That is, the noise-added signal Sin maintains the shape having the peak and bottom of the pulse wave signal S0, and the amplitude of the noise-added signal Sin is amplified more than the amplitude of the pulse wave signal S0.

[0038] FIG. 7 is a flowchart showing an example of the process executed by the signal processing device 102. When the signal processing device 102 receives an instruction to start the process by a user operation or the like, the control unit 203 starts the process of step S701 illustrated in FIG. 7.

[0039] In step S701, the vital sign information acquisition unit 211 acquires vital sign information 221. For example, the vital sign information acquisition unit 211 acquires the vital sign information 221 indicating the type of vital sign specified by a user operation or the like.

[0040] In step S702, the input information acquisition unit 212 acquires a video of imaging the skin. For example, the measuring device 101 transmits a video of imaging the skin at a predetermined time interval to the signal processing device 102. For example, the measuring device 101 transmits a video of imaging the skin with a frame rate of 60 fps (frames per second) to the signal processing device 102. The input information acquisition unit 212 acquires the video transmitted from the measuring device 101 to the signal processing device 102.

[0041] For example, the measuring device 101 may be installed in a display device such as a personal computer that the living body 103 faces directly. Alternatively, the measuring device 101 may be installed on the desk used by the living body 103. When the measuring device 101 is installed on a display device, a desk, etc. that the living body 103 faces and uses, it becomes easy for the measuring device 101 to image the skin of the face of the living body 103.

[0042] In step S703, the input information acquisition unit 212 acquires the input information 222 from the video acquired in step S702. For example, the input information 222 is an image including a region suitable for estimating the fluctuation of the pulse wave among a plurality of images included in the acquired video. The input information acquisition unit 212 can stably acquire the input information 222 from the video by acquiring a video of imaging the skin of the face or finger of the living body 103.

[0043] When the measuring device 101 is imaging the skin of the living body 103 and the living body 103 moves, the position of the skin image in the acquired video fluctuates. Therefore, it is preferable for the input information acquisition unit 212 to track the skin image with respect to the acquired video. For example, when the measuring device 101 images the skin of the face of the living body 103, the input information acquisition unit 212 tracks the face image by face detection technology. Note that when the fluctuation of the position of the skin image in the acquired video is within a predetermined range, the input information acquisition unit 212 may not track the skin image and acquire the input information 222 at a predetermined frame interval from the acquired video.

[0044] In step S704, the pulse wave calculation unit 304 calculates a pulse wave signal S0 from the input information 222. For example, the pixel values of the skin image included in the image indicated by the input information 222 are extracted. Alternatively, the pulse wave calculation unit 304 calculates the average value of the pixel values of the skin image. For example, the pixel value is a value indicated by RGB RAW data included in the image indicated by the input information 222, data processed by an image processing engine, or the like. Then, the pulse wave calculation unit 304 calculates the pulse wave signal S0 from the time-series change of the value calculated by substituting the extracted pixel value or the average value of the pixel values into a predetermined mathematical formula. Alternatively, the pulse wave calculation unit 304 may calculate the pulse wave signal S0 from the input information 222 using independent component analysis, pigment component separation method, or the like.

[0045] In step S705, the noise generation unit 214 calculates a cut-off frequency from the registered frequency band associated with the type indicated by the vital sign information 221 acquired in step S701.

[0046] For example, in the storage unit 201, a registered frequency band indicating 10 Hz or more and 15 Hz or less is associated with and stored for the type derived from the movement of the heart among the vital signs. When the type indicated by the vital sign information 221 indicates the type derived from the movement of the heart, the noise generation unit 214 determines the cut-off frequency to be 10 Hz or more and 15 Hz or less indicated by the registered frequency band associated with the type derived from the movement of the heart. This is because the frequency components of the pulse wave below 15 Hz tend to indicate the periodicity of the pulse wave derived from the movement of the heart.

[0047] Also, for example, when the pulse wave signal S0 contains a lot of external noise and the periodicity of the pulse wave derived from the movement of the heart in the pulse wave signal S0 is buried, the noise generation unit 214 may determine the cut-off frequency to be 5 Hz or less. As a result, the noise-added signal Sin to which the noise signal Sn with a cut-off frequency of 5 Hz or less is added contributes to reducing high-frequency noise.

[0048] For example, in the storage unit 201, a registered frequency band indicating 0.8 Hz or more and 1.7 Hz or less is associated with and stored in the type indicating heartbeat or pulse among the vital signs. Then, when the type of the vital sign indicated by the vital sign information 221 indicates heartbeat or pulse, the noise generation unit 214 determines the cut-off frequency such that the frequency band of the noise signal Sn is 0.8 Hz or more and 1.7 Hz or less indicated by the registered frequency band associated with the type indicating heartbeat or pulse. This is because the frequency components of 0.8 Hz or more and 1.7 Hz tend to indicate the fundamental frequency of the heartbeat or pulse.

[0049] Also, the fundamental frequency of the pulse wave tends to be around 1 Hz, and the pulse wave tends to include a peak spectrum at around 2 Hz, which is a frequency component of the harmonic of the fundamental frequency. Therefore, the noise generation unit 214 may determine a frequency within a predetermined range from 1 Hz or within a predetermined range from 2 Hz as the cut-off frequency.

[0050] For another example, in the storage unit 201, a registered frequency band indicating 0.16 Hz or more and 0.4 Hz or less is associated with and stored in the type indicating respiration among the vital signs. Then, when the type indicated by the vital sign information 221 indicates respiration, the noise generation unit 214 determines the cut-off frequency such that the frequency band of the noise signal Sn is 0.16 Hz or more and 0.4 Hz or less indicated by the registered frequency band associated with the type indicating respiration. This is because the frequency components of 0.16 Hz or more and 0.4 Hz or less in the pulse wave tend to be related to the respiration rate.

[0051] Also, for example, in the storage unit 201, a registered frequency band indicating 0.4 Hz or less is associated and stored with respect to the type related to the state of the autonomic nerve among the vital signs. And when the type indicated by the vital sign information 221 is the type related to the state of the autonomic nerve, the noise generation unit 214 determines the cut-off frequency to be 0.4 Hz or less indicated by the registered frequency band associated with the type related to the state of the autonomic nerve. This is because the frequency components of 0.4 Hz or less in the pulse wave tend to be related to the index indicating the state of the autonomic nerve.

[0052] Also, for example, in the storage unit 201, a registered frequency band indicating 0.05 Hz or more and 5 Hz or less is associated and stored with respect to the type related to blood pressure among the vital signs. And when the type indicated by the vital sign information 221 is the type related to blood pressure, the noise generation unit 214 determines the cut-off frequency so that the frequency band of the noise signal Sn is 0.05 Hz or more and 5 Hz or less indicated by the registered frequency band associated with the type related to blood pressure. This is because the frequency components of 0.05 Hz or more and 5 Hz or less in the pulse wave tend to be related to the hemodynamics.

[0053] Furthermore, the frequency components of 0.05 Hz or less in the pulse wave signal S0 tend to be due to disturbances unrelated to the pulse wave, such as body movement. Therefore, the noise generation unit 214 may determine the cut-off frequency to be 0.05 Hz or more.

[0054] In step S706, the noise generation unit 214 determines the noise intensity from the intensities within a predetermined range. For example, when the non-linear processing unit 207 is realized using a circuit simulator, the noise intensity may be set using the circuit simulator.

[0055] In step S707, the noise generation unit 214 generates a noise signal Sn having the frequency band determined from the cut-off frequency determined in step S705 and the noise intensity determined in step S706.

[0056] In step S708, the noise addition unit 215 adds the noise signal Sn generated in step S707 to the pulse wave signal S0 calculated in step S704 to generate a noise-added signal Sin. Vital signs such as heart rate, pulse rate, and respiratory rate are constantly fluctuating, and it is difficult to estimate their frequencies and the like. However, the noise addition unit 215 can generate a noise-added signal Sin having various frequency components by adding a noise signal Sn having random intensity changes within a frequency band to the pulse wave signal S0.

[0057] In step S709, the non-linear model determination unit 216 determines a non-linear model 223 whose input-output characteristics have a hysteresis characteristic. The input-output characteristics of the non-linear model 223 are adjusted so that the output signal Sout satisfies the target characteristics. For example, the target characteristics indicate at least one selected from the group consisting of a signal-to-noise ratio exceeding the signal-to-noise ratio of the pulse wave signal S0, an amplitude exceeding the amplitude of the pulse wave signal S0, and a frequency band of the output signal Sout. The frequency band indicated by the target characteristics is the frequency band of the noise signal Sn. That is, the frequency band indicated by the target characteristics is the associated registered frequency band indicated by the vital sign information.

[0058] For example, the non-linear model determination unit 216 determines a rising threshold Ath so as to exceed a predetermined value higher than the maximum value of the signal intensity of the pulse wave signal S0 at a specific time. For example, the specific time is the first 3 seconds of the pulse wave signal S0. Alternatively, the specific time may be a time when the displacement amount of the body movement of the living body 103 in the acquired video is relatively small. Similarly, the non-linear model determination unit 216 determines a falling threshold Bth so as to be lower than a second predetermined value lower than the minimum value of the signal intensity of the pulse wave signal S0 at a specific time. Further, the non-linear model determination unit 216 determines an output signal intensity Sh and an output signal intensity Sl so that the amplitude of the output signal Sout exceeds the amplitude of the pulse wave signal S0.

[0059] Alternatively, the non-linear model determination unit 216 may determine the rising threshold value Ath so as to exceed a predetermined value that is greater than the maximum value of the signal intensity of the pulse wave signal S0 at a specific time. Similarly, the non-linear model determination unit 216 may determine the falling threshold value Bth so as to be lower than a predetermined value that is less than the minimum value of the signal intensity of the pulse wave signal S0 at a specific time.

[0060] Alternatively, the non-linear model determination unit 216 may determine the rising threshold value Ath so as to exceed a predetermined value that is greater than the average value or the median value of the signal intensity of the pulse wave signal S0 at a specific time. Similarly, the non-linear model determination unit 216 may determine the falling threshold value Bth so as to be lower than a predetermined value that is less than the average value or the median value of the signal intensity of the pulse wave signal S0 at a specific time.

[0061] Also, for example, the non-linear model determination unit 216 calculates the signal-to-noise ratio of the pulse wave signal S0. Then, the non-linear model determination unit 216 may determine the shape of the input-output characteristics of the non-linear model 223 so that the signal-to-noise ratio of the output signal Sout exceeds the signal-to-noise ratio of the pulse wave signal S0. Thereby, even when fluctuations occur in the pulse wave signal S0, the non-linear model determination unit 216 can adjust the shape of the input-output characteristics indicated by the non-linear model 223 so as to emphasize the periodicity of the pulse wave and improve the signal-to-noise ratio.

[0062] Alternatively, the non-linear model determination unit 216 may determine the shape of the input-output characteristics indicated by the non-linear model 223 so that the frequency band of the output signal Sout is the same as the frequency band of the noise signal Sn.

[0063] In step S710, the non-linear processing unit 217 inputs the noise-added signal Sin to the determined non-linear model 223 to generate the output signal Sout.

[0064] For example, by adjusting the input / output characteristics of the non-linear model 223 by the non-linear model determination unit 216 so that the signal-to-noise ratio of the output signal Sout exceeds the signal-to-noise ratio of the pulse wave signal S0, the non-linear processing unit 217 can generate an output signal Sout with an improved signal-to-noise ratio of the pulse wave signal S0.

[0065] Also, for example, by adjusting the input / output characteristics of the non-linear model 223 by the non-linear model determination unit 216 so that the amplitude of the output signal Sout exceeds the signal-to-noise ratio of the pulse wave signal S0, the non-linear processing unit 217 can generate an output signal Sout with enhanced periodicity of the pulse wave signal S0.

[0066] Also, for example, when the input / output characteristics of the non-linear model 223 have a hysteresis characteristic corresponding to a registered frequency band associated with the type of vital sign, the non-linear processing unit 217 can generate an output signal Sout in which the frequency components of the registered frequency band associated with the type indicated by the vital sign information 221 are emphasized.

[0067] From the above, the non-linear processing unit 217 can reduce fluctuations due to external disturbance noise, perform waveform shaping of the pulse wave signal S0, and generate an output signal Sout with enhanced periodicity of the pulse wave signal S0. Thereby, the non-linear processing unit 217 can generate an output signal Sout in which the periodicity of a weak signal buried in external disturbance noise in the pulse wave signal S0 is emphasized.

[0068] Furthermore, the non-linear processing unit 217 can input a noise-added signal Sin having various frequency components to the non-linear model 223, suppress the influence of signal fluctuations, and generate an output signal Sout with a signal-to-noise ratio higher than the signal-to-noise ratio of the pulse wave signal S0.

[0069] In step S711, the non-linear processing unit 217 determines whether a plurality of noise-added signals Sin to which noise signals Sn having different signal strengths are added have been generated. If, in step S711, a plurality of noise-added signals Sin to which noise signals Sn having a plurality of intensities determined in advance are not added have not been generated, the control unit 203 returns the process to step S706. That is, the control unit 203 repeats the processes from step S706 to step S711 until a plurality of noise-added signals Sin are generated by adding noise signals Sn having different signal strengths to the pulse wave signal S0. On the other hand, if, in step S711, a plurality of noise-added signals Sin to which noise signals Sn having different signal strengths are added have been generated, the control unit 203 shifts the process to step S712.

[0070] In step S712, the selection unit 218 outputs an output signal Sout_Dm having the maximum signal-to-noise ratio among the plurality of generated output signals Sout. Thereby, the selection unit 218 can output an output signal Sout_Dm in which the periodicity of the pulse wave signal S0 is emphasized by using a noise signal Sn having a noise intensity at which the signal-to-noise ratio is maximum. Further, in the output signal Sout_Dm, since the frequency components in the frequency band corresponding to the type of vital sign are emphasized, the signal processing apparatus 102 according to the present embodiment can easily cause the user to analyze the periodicity of vital signs such as heartbeat, pulse, respiration, autonomic nerve, and blood dynamics by outputting the output signal Sout_Dm.

[0071] FIG. 8 is a diagram for explaining in detail the process of generating the output signal Sout. When the signal intensity of the noise-added signal Sin is increasing over time, the non-linear processing unit 217 determines the signal intensity of the output signal Sout according to the response indicated by the solid line of the non-linear model 223, i.e., the non-linear response A. Specifically, when the non-linear processing unit 217 determines the signal intensity of the output signal Sout according to the response indicated by the non-linear response A, when the signal intensity of the noise-added signal Sin increases and exceeds the rising threshold Ath, the non-linear processing unit 217 shifts the signal intensity of the output signal Sout from the lower state indicated by the non-linear response A to the upper state, and rapidly increases the signal intensity of the output signal Sout from the output signal intensity Sl indicated by the non-linear model 223 to the output signal intensity Sh. Note that the signal intensity of the pulse wave signal S0 is equal to or greater than the falling threshold Bth and equal to or less than the rising threshold Ath. Therefore, when the pulse wave signal S0 is input to the non-linear model 223, the signal intensity of the output signal Sout is maintained at the output signal intensity Sl and does not shift to the output signal intensity Sh.

[0072] On the other hand, when the signal intensity of the noise-added signal Sin is decreasing over time, the non-linear processing unit 217 determines the signal intensity of the output signal Sout according to the response indicated by the broken line of the non-linear model 223, i.e., the non-linear response B. Specifically, when the non-linear processing unit 217 determines the signal intensity of the output signal Sout according to the response indicated by the non-linear response B, when the signal intensity of the noise-added signal Sin reaches the rising threshold Ath from a state where it exceeds the rising threshold Ath, the non-linear processing unit 217 maintains the signal intensity of the output signal Sout at the output signal intensity Sh without shifting the signal intensity of the output signal Sout to the output signal intensity Sl. That is, when the non-linear processing unit 217 determines the signal intensity of the output signal Sout according to the response indicated by the non-linear response B, in a state where the signal intensity is equal to or greater than the falling threshold Bth, the non-linear processing unit 217 maintains the signal intensity of the output signal Sout at the output signal intensity Sh.

[0073] When the non-linear processing unit 217 determines the signal intensity of the output signal Sout based on the response indicated by the non-linear response B, when the signal intensity of the noise-added signal Sin becomes lower than the rising threshold Bth, the non-linear processing unit 217 shifts the signal intensity of the output signal Sout from the upper state indicated by the non-linear response B to the lower state, and rapidly decreases the signal intensity of the output signal Sout from the output signal intensity Sh indicated by the non-linear model 223 to the output signal intensity Sl.

[0074] Further, when the signal intensity of the noise-added signal Sin increases after the signal intensity of the noise-added signal Sin becomes lower than the rising threshold Bth, the non-linear processing unit 217 determines the signal intensity of the output signal Sout based on the response indicated by the non-linear response A.

[0075] When determining the signal intensity of the output signal Sout based on the response indicated by the non-linear response A, the non-linear processing unit 217 maintains the signal intensity of the output signal Sout at the output signal intensity Sl when the signal intensity of the noise-added signal Sin is in a state where it is less than or equal to the rising threshold Ath. From the above, the non-linear processing unit 217 can generate the output signal Sout by amplifying the amplitude of the output signal Sout more than the amplitude of the pulse wave signal S0 by inputting the noise-added signal Sin to the non-linear model 223 having the above input-output characteristics.

[0076] Furthermore, the non-linear processing unit 217 binarizes the signal intensity of the output signal Sout by inputting the noise-added signal Sin to the non-linear model 223 having the above input-output characteristics. As a result, even when the signal intensity of the noise-added signal Sin fluctuates in the vicinity of the rising threshold Ath due to external disturbance noise, the signal intensity of the output signal Sout can be maintained at the output signal intensity Sh. Similarly, even when the signal intensity of the noise-added signal Sin fluctuates in the vicinity of the falling threshold Bth due to external disturbance noise, the signal intensity of the output signal Sout can be maintained at the output signal intensity Sl. Therefore, the non-linear processing unit 217 can generate the output signal Sout having a stable waveform with the periodicity of the pulse wave signal S0 emphasized.

[0077] FIG. 9 is a graph showing an example of the signal-to-noise ratio of the output signal Sout according to the noise intensity of the noise signal Sn. In FIG. 9, the horizontal axis represents the noise intensity, and the vertical axis represents the signal-to-noise ratio. Curve 901 shows the signal-to-noise ratio according to the noise intensity. As shown by curve 901, the signal-to-noise ratio of the output signal Sout is maximized at the optimal noise intensity Dm.

[0078] If the noise intensity of the noise signal Sn is too low, the intensity of the noise-added signal Sin input to the non-linear model 223 cannot reach the rising threshold Ath. In that case, in step S710, the non-linear processing unit 217 cannot generate an output signal Sout having an amplitude of a magnitude necessary to emphasize periodicity. On the other hand, if the noise intensity of the noise signal Sn is too high, regardless of the waveform of the pulse wave signal S0, the intensity of the noise-added signal Sin input to the non-linear model 223 reaches the rising threshold Ath. In that case, the variation in the signal intensity of the output signal Sout generated in step S710 becomes random rather than periodic, and periodicity becomes difficult to appear in the output signal Sout. However, by adding the noise signal Sn with the optimal noise intensity Dm to the pulse wave signal S0, the signal-to-noise ratio of the output signal Sout can be improved compared to the signal-to-noise ratio of the pulse wave signal S0. Thereby, the signal processing device 102 according to the present embodiment can output an output signal Sout_Dm in which periodicity is appropriately emphasized.

[0079] (Modification 1) As a modification 1, the noise signal Sn may be a signal having frequency components in a frequency band wider than the registered frequency band associated with the type of vital sign. For example, the noise signal Sn may be white noise. Even when the noise signal Sn is white noise, since the amplitude of the noise-added signal Sin is amplified more than the amplitude of the pulse wave signal S0, the signal processing device 102 can generate an output signal Sout that emphasizes the periodicity of the pulse wave signal S0 by utilizing the stochastic resonance phenomenon.

[0080] (Modification 2) As a second modification example, the noise intensity may be associated with the cut-off frequency and stored in the storage unit 201 in advance. In that case, the noise generation unit 214 may generate a noise signal Sn having a frequency band determined from the determined cut-off frequency and a noise intensity associated with the cut-off frequency. Even when the noise intensity of the noise signal Sn is predetermined, since the amplitude of the noise-added signal Sin is amplified more than the amplitude of the pulse wave signal S0, the signal processing device 102 can generate an output signal Sout that emphasizes the periodicity of the pulse wave signal S0 by utilizing the stochastic resonance phenomenon.

[0081] (Third modification example) As a third modification example, the signal processing device 102 may convert the pulse wave signal S0 into current or voltage and input it to a circuit such as a hysteresis comparator. In this case, although there is a possibility that the cost, processing time, and power consumption for creating the circuit increase, the signal processing device 102 can generate an output signal Sout that emphasizes the periodicity of the pulse wave signal S0 by utilizing the stochastic resonance phenomenon. When using a circuit simulator, the cost, processing time, and power consumption for creating the circuit are suppressed, and the signal processing device 102 can generate an output signal Sout that emphasizes the periodicity of the pulse wave signal S0 on the circuit simulator. Also, when using a circuit simulator, the storage unit 201 stores the resistance value used in the circuit simulator. Thereby, the signal processing device 102 can easily adjust the shape of the non-linear model 223 by using the stored resistance value.

[0082] (Fourth modification example) As a fourth modification example, the signal processing device 102 may be connected to an external management server or the like through an Internet line or the like. In that case, the signal processing device 102 transmits the output signal Sout or the like to the management server. Then, the management server manages the output signal Sout transmitted from the signal processing device 102.

[0083] In addition, the management server may hold the type of vital sign and the registration frequency band in association with each other. In that case, the signal processing device 102 may acquire from the management server the registration frequency band associated with the type of vital sign.

[0084] In addition, the management server may hold update data of the program executed in the signal processing device 102. In that case, the signal processing device 102 may receive the update data held in the management server from the management server through the Internet line or the like and update the program. For example, the management server may hold a program for executing a process of determining the non-linear model 223. In that case, the signal processing device 102 can update the input / output characteristics of the non-linear model 223 to a more optimal shape.

[0085] (Second Embodiment) The second embodiment will be described. Configurations and processes having substantially the same functions as those in the first embodiment will be referred to by the same reference numerals and the description thereof will be omitted, and differences from the first embodiment will be described.

[0086] The storage unit 201 according to the present embodiment stores in advance, in association with the registration target characteristics, at least one selected from the group consisting of the acquisition condition of the pulse wave signal S0, the internal state of the skin, the environment of the space in which the living body 103 exists, and the attributes of the living body. The registration target characteristics are the target characteristics of the output signal Sout. That is, the registration target characteristics indicate at least one selected from the group consisting of the signal-to-noise ratio of the output signal Sout, the signal intensity of the output signal Sout, and the frequency band of the output signal Sout.

[0087] For example, the acquisition condition of the pulse wave signal S0 is the frame rate at which the measuring device 101 captures images, the intensity of ambient light, the magnitude of the body movement of the living body 103, and the like. The acquisition condition of the pulse wave signal S0 affects the brightness, contrast, etc. of the video obtained by imaging the skin. Therefore, by associating the registration target characteristics with the acquisition condition of the pulse wave signal S0, the signal processing device 102 can easily obtain the effect of emphasizing periodicity by the probabilistic resonance phenomenon using the registration target characteristics associated with the acquisition condition of the pulse wave signal S0.

[0088] Further, for example, information indicating the internal state of the skin includes the color of the skin, the moisture content of the skin, the roughness of the skin surface, and the like. For example, in the storage unit 201, the registration target characteristics may be associated in advance with an ID for identifying the color of the skin, the moisture content of the skin, the roughness of the skin surface, and the like. The internal state of the skin may affect the reflected light that enters the skin and is reflected inside the skin. Therefore, by associating the registration target characteristics with the internal state of the skin, the signal processing device 102 can easily obtain the effect of emphasizing the periodicity by the stochastic resonance phenomenon by using the registration target characteristics corresponding to the internal state of the skin.

[0089] Further, for example, information indicating the environment of the space in which the living body 103 exists includes the temperature, humidity, etc. of the environment imaged by the measuring device 101. For example, in the storage unit 201, the registration target characteristics may be associated in advance with an ID for identifying the range to which the temperature, humidity, etc. of the environment imaged by the measuring device 101 belong.

[0090] Further, information indicating the attributes of the living body 103 includes age, gender, etc. For example, in the storage unit 201, the registration target characteristics may be associated in advance with the information indicating the attributes of the living body 103.

[0091] The environment of the space in which the living body 103 exists, the age, gender, etc. of the living body 103 may affect the state of the skin. Therefore, by associating the registration target characteristics with the environment of the space in which the living body 103 exists and the attributes of the living body 103, the signal processing device 102 can easily obtain the effect of emphasizing the periodicity by the stochastic resonance phenomenon by using the registration target characteristics corresponding to the environment of the space in which the living body 103 exists and the attributes of the living body 103.

[0092] Further, the signal processing device 102 may identify at least one selected from the group consisting of the acquisition conditions of the pulse wave signal S0, information indicating the internal state of the skin, information indicating the environment of the space where the living body 103 exists, and information indicating the attributes of the living body, from the image indicated by the input information 222. Thereby, it is not necessary for the user to input the acquisition conditions of the pulse wave signal S0, the internal state of the skin, the environment of the space where the living body 103 exists, the attributes of the living body, etc., and the signal processing device 102 can reduce the burden on the user.

[0093] Also, for example, when the measuring device 101 includes a temperature sensor, a humidity sensor, etc., the storage unit 201 may store the air temperature, humidity, etc. measured by the humidity sensor, etc. as information indicating the environment of the space where the living body 103 exists.

[0094] When the storage unit 201 stores information indicating at least one selected from the group consisting of the acquisition conditions of the pulse wave signal S0, the internal state of the skin, the environment of the space where the living body 103 exists, and the attributes of the living body, the noise generation unit 214 determines at least one selected from the group consisting of the frequency band and intensity of the noise signal Sn from the registered target characteristics associated with the stored information.

[0095] Also, when the storage unit 201 stores information indicating at least one selected from the group consisting of the acquisition conditions of the pulse wave signal S0, the internal state of the skin, the environment of the space where the living body 103 exists, and the attributes of the living body, the non-linear model determination unit 216 may determine the input / output characteristics of the non-linear model 223 from the registered target characteristics associated with the stored information.

[0096] (Modification Example 5) When the signal processing device 102 is connected to an external management server or the like through an Internet line or the like, the management server may associate and hold the registered target characteristics with an ID for identifying the skin color, skin moisture rate, roughness of the skin surface, etc. In that case, the signal processing device 102 may acquire the registered target characteristics associated with the ID for identifying the skin color, skin moisture rate, roughness of the skin surface, etc. from the management server.

[0097] (Third Embodiment) With reference to FIGS. 10 to 11, the third embodiment will be described. Regarding the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant descriptions are omitted. Configurations and processes having substantially the same functions as those in the first embodiment are referred to by the same reference numerals, and the description thereof is omitted, and differences from the first embodiment will be described.

[0098] FIG. 10 is a diagram showing an example of the configuration of the signal processing apparatus 102 according to the present embodiment. The difference between the signal processing apparatus 102 illustrated in FIG. 10 and the signal processing apparatus 102 illustrated in FIG. 2 is that the signal processing apparatus 102 illustrated in FIG. 10 includes a frequency calculation unit 1001.

[0099] The frequency calculation unit 1001 calculates the fundamental frequency 1011 of the pulse wave signal S0. When the type of the vital sign indicated by the vital sign information 221 is a heartbeat or a pulse in the noise generation unit 214 according to the present embodiment, the cut-off frequency is determined from the fundamental frequency 1011, and the frequency band of the noise signal Sn is determined so as to be below the determined cut-off frequency.

[0100] FIG. 11 is a flowchart showing an example of the process executed by the signal processing apparatus 102 according to the present embodiment. When the pulse wave calculation unit 304 calculates the pulse wave signal S0 from the input information 222 in step S704 illustrated in FIG. 7, the control unit 203 starts the process of step S1101 illustrated in FIG. 11.

[0101] In step S1101, the frequency calculation unit 1001 determines whether the type of the vital sign indicated by the vital sign information 221 is a heartbeat or a pulse. If the type of the vital sign indicated by the vital sign information 221 is not a heartbeat or a pulse in step S1101, the control unit 203 shifts the process to step S705 illustrated in FIG. 7. On the other hand, if the type of the vital sign indicated by the vital sign information 221 is a heartbeat or a pulse in step S1101, the control unit 203 shifts the process to step S1102.

[0102] In step S1102, the frequency calculation unit 1001 calculates the fundamental frequency 1011 of the pulse wave signal S0. Specifically, the frequency calculation unit 1001 calculates the fundamental frequency 1011 of the pulse wave signal S0 by performing Fourier analysis on the pulse wave signal S0.

[0103] In step S1103, the noise generation unit 214 determines the cut-off frequency from the calculated fundamental frequency 1011. For example, the noise generation unit 214 determines a frequency that is 1.5 times the fundamental frequency or 2 times the fundamental frequency as the cut-off frequency. This is because the spectral peaks of the heartbeat and pulse tend to be included in the frequency components of the harmonics of the fundamental frequency of the pulse wave. Then, the control unit 203 proceeds to step S706 illustrated in FIG. 7 for processing.

[0104] As described above, when the type of vital sign indicated by the vital sign information 221 is a heartbeat or a pulse, the signal processing device 102 according to the present embodiment determines the frequency band of the noise signal Sn according to the fundamental frequency 1011 of the pulse wave signal S0. Therefore, when it is selected to emphasize the periodicity of the heartbeat or pulse by a user operation, the signal processing device 102 according to the present embodiment can add a noise signal Sn in a frequency band corresponding to the calculated fundamental frequency 1011 of the pulse wave signal S0 to the pulse wave signal S0. Thereby, the signal processing device 102 according to the present embodiment can generate an output signal Sout that emphasizes the periodicity indicating the heartbeat or pulse according to the pulse wave signal S0.

[0105] (Fourth Embodiment) With reference to FIGS. 12 to 14, the fourth embodiment will be described. Regarding the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant descriptions are omitted. Configurations and processes having substantially the same functions as those of the first embodiment are referred to by the same reference numerals, and the description thereof is omitted, and differences from the first embodiment will be described.

[0106] FIG. 12 is a diagram showing an example of the configuration of the signal processing apparatus 102 according to the present embodiment. The difference between the signal processing apparatus 102 illustrated in FIG. 12 and the signal processing apparatus 102 illustrated in FIG. 2 is that the signal processing apparatus 102 illustrated in FIG. 12 includes a determination unit 1201 and a notification unit 1202.

[0107] The determination unit 1201 determines whether or not the output signal Sout satisfies the quality standard. When the output signal Sout satisfies the quality standard, the determination unit 1201 outputs the output signal Sout. For example, the quality standard is a standard related to the signal-to-noise ratio of the output signal Sout and a standard related to the periodicity of the output signal Sout.

[0108] When the output signal Sout that satisfies the quality standard is not generated, the notification unit 1202 outputs notification information 1211 for notifying to acquire new input information 222.

[0109] FIG. 13 is a flowchart showing an example of the processing executed by the signal processing apparatus 102 according to the present embodiment. When the signal processing apparatus 102 receives an instruction to start processing by a user operation or the like, the control unit 203 starts the processing of step S1301 illustrated in FIG. 13. Assume that the quality standard is stored in the storage unit 201 when the control unit 203 starts the processing of step 1301. Since the processing of steps S1301 to S1310 is the same as the processing of steps S701 to S710 illustrated in FIG. 7, detailed description thereof is omitted.

[0110] In step S1310, when the non-linear processing unit 217 generates the output signal Sout by inputting the noise-added signal Sin to the non-linear model 223, the control unit 203 shifts the processing to step S1311.

[0111] In step S1311, the determination unit 1201 determines whether the output signal Sout satisfies the quality standard. For example, when the quality standard is a standard related to the signal-to-noise ratio of the output signal Sout, the determination unit 1201 determines whether the signal-to-noise ratio of the output signal Sout satisfies the signal-to-noise ratio of the quality standard. Also, for example, when the quality standard is a standard related to the periodicity of the output signal Sout, the determination unit 1201 analyzes the periodicity of the output signal Sout by performing a Fourier analysis on the frequency components of the output signal Sout, and determines whether the periodicity of the output signal Sout satisfies the quality standard.

[0112] If the output signal Sout satisfies the quality standard in step S1311, in step S1312, the determination unit 1201 outputs the output signal Sout. Then, the control unit 203 ends the process of generating the output signal Sout with enhanced periodicity of the pulse wave signal S0. On the other hand, if the output signal Sout does not satisfy the quality standard in step S1311, the control unit 203 proceeds to step S1401 illustrated in FIG. 14.

[0113] Next, with reference to FIG. 14, the process executed by the signal processing apparatus 102 according to the present embodiment will be continuously described.

[0114] In step S1401, the determination unit 1201 determines whether the output signal Sout has been generated for the first time. That is, the determination unit 1201 determines whether non-linear processing has been performed on the pulse wave signal S0 using the non-linear model 223 having mutually different input-output characteristics for the first time.

[0115] In step S1401, if the output signal Sout has been generated for the first time, the process of generating the output signal Sout with enhanced periodicity of the pulse wave signal S0 is ended. Thereby, when the signal processing apparatus 102 according to the present embodiment generates the output signal Sout for the first time, it can suppress the measurement apparatus 101 from repeatedly imaging the skin when an output signal Sout that satisfies the quality standard is not generated.

[0116] On the one hand, in step S1401, when the output signal Sout has not been generated for the first number of times, in step S1402, the determination unit 1201 determines whether the output signal Sout has been generated with a predetermined noise intensity and input-output characteristics. In step S1402, when the output signal Sout has been generated with the predetermined noise intensity and input-output characteristics, in step S1403, the determination unit 1201 determines whether the output signal Sout has been generated for a second number of times less than the first number of times for the same pulse wave signal S0. In step S1403, when the output signal has not been generated for the second number of times, the control unit 203 shifts the process to step S1405.

[0117] On the other hand, in step S1403, when the output signal has been generated for the second number of times, in step S1404, the notification unit 1202 notifies notification information 1211 indicating that new input information 222 is to be acquired. For example, the notification unit 1202 outputs the notification information 1211 indicating a message such as "Since the appropriate shooting was not possible, reshooting will be performed" in characters or voice. Further, in order for the non-linear processing unit 217 to generate an output signal Sout that satisfies the quality standard, the notification unit 1202 may output the notification information 1211 indicating a message that prompts the person who is the imaging target to move so that the skin can be appropriately imaged, such as "Please get closer to the camera", in characters or voice. Thereby, the notification unit 1202 can make the person who is the imaging target recognize that the measuring device 101 images the skin again. Then, the control unit 203 returns the process to step S1302 illustrated in FIG. 13.

[0118] When the output signal Sout that satisfies the quality standard is not generated in a state where the output signal Sout has been generated a predetermined number of times, in step S1302, the input information acquisition unit 212 acquires a new video of the skin being imaged. Then, in step S1303, the input information acquisition unit 212 acquires new input information 222 from the acquired new video. That is, when the output signal that satisfies the quality standard is not generated, the input information acquisition unit 212 acquires new input information 222.

[0119] On the one hand, in step S1402, if the output signal Sout has not been generated with a predetermined noise intensity and input-output characteristics, or in step S1403, if the output signal has not been generated for the second time, in step S1405, the noise generation unit 214 generates a new noise signal Sn with the noise intensity changed to a predetermined value. The frequency band of the generated new noise signal Sn is a predetermined frequency band.

[0120] In step S1406, the noise addition unit 215 adds the new noise signal Sn to the pulse signal S0 to generate a new noise-added signal Sin. That is, when the output signal Sout generated in step S1310 does not meet the quality standard, the noise addition unit 215 adds the noise signal Sn with the intensity and frequency band changed to a predetermined value to the pulse signal S0 to generate a new noise-added signal Sin.

[0121] In step S1407, the non-linear model determination unit 216 generates a new non-linear model 223 with the input-output characteristics changed. Then, in step S1408, the non-linear processing unit 217 inputs the new noise-added signal Sin generated in step S1406 to the new non-linear model 223 determined in step S1407 to generate a new output signal Sout. That is, in the process of step S1408, when the output signal Sout generated in step S1310 illustrated in FIG. 13 does not meet the quality standard, the non-linear processing unit 217 inputs the new noise-added signal Sin to the non-linear model 223 with the input-output characteristics changed to generate a new output signal Sout. Then, the control unit 203 returns the process to step S1311 illustrated in FIG. 13. That is, when the output signal Sout generated in step S1310 does not meet the quality standard, in step S1311, the determination unit 1201 determines whether the new output signal Sout generated in step S1407 meets the quality standard.

[0122] As described above, the signal processing apparatus 102 according to the present embodiment determines whether the output signal Sout satisfies the quality standard. When the output signal Sout does not satisfy the quality standard, the non-linear processing unit 217 inputs a noise-added signal Sin, in which a noise signal Sn with a new noise intensity is added to the pulse signal S0, to a non-linear model 223 of new input-output characteristics to generate a new output signal Sout.

[0123] (Modification Example 6) FIG. 15 is a flowchart showing an example of the process executed in Modification Example 6. As illustrated in FIG. 15, when the output signal Sout has been generated a first number of times in step S1501 illustrated in FIG. 15, the notification unit 1202 may notify notification information 1211 indicating that new input information 222 is to be acquired in step S1504. Then, the control unit 203 may return the process to step S1302 illustrated in FIG. 13.

[0124] (Modification Example 7) As a modification example 7, the signal processing apparatus 102 according to the present embodiment may further include a frequency calculation unit 1001 illustrated in FIG. 10.

[0125] (Fifth Embodiment) With reference to FIG. 16, the fifth embodiment will be described. Regarding the drawings, the same or equivalent elements are denoted by the same reference numerals, and redundant descriptions are omitted. Configurations and processes having substantially the same functions as those of the first embodiment are referred to by the same reference numerals, and the descriptions thereof are omitted, and differences from the first embodiment will be described.

[0126] FIG. 16 is a diagram showing an example of the configuration of an electronic device 1601 according to the present embodiment. The electronic device 1601 includes a sensor 1602, a signal processing apparatus 1603, a storage unit 1604, an operation unit 1605, and the like. Since the configuration of the signal processing apparatus 1603 is the same as the configuration illustrated in FIG. 2, a detailed description thereof is omitted.

[0127] The electronic device 1601 is configured such that the sensor 1602 and the signal processing device 1603 are integrated. As a result, the electronic device 1601 can be configured to be smaller than the measurement system 100. For example, the electronic device 1601 may be attached to a ceiling or the like and driven as a monitoring camera or the like that photographs the living body 103 passing through the photographable area.

[0128] The sensor 1602 measures the time-series changes inside the skin. For example, the sensor 1602 is constituted by an image sensor. The image sensor is a CMOS, a CCD, or the like.

[0129] The signal processing device 1603 includes the control unit 203 of the signal processing device 102 illustrated in FIG. 2.

[0130] The storage unit 1604 stores the same information as the storage unit 201 illustrated in FIG. 2, and is constituted by a semiconductor memory such as a ROM, a RAM, or the like.

[0131] The operation unit 1605 receives the input of the user's operation in the same manner as the operation unit 202 illustrated in FIG. 2. For example, the operation unit 1605 is realized by a touch panel or the like.

[0132] (Modification Example 8) As a modification example 8, the control unit 203 included in the electronic device 1601 according to the present embodiment may have the same configuration as the control unit 203 illustrated in FIG. 10 or FIG. 12.

[0133] Each process executed in the above embodiment is not limited to the processing modes exemplified in each embodiment. The above-described functional blocks may be realized by either a logic circuit (hardware) formed in an integrated circuit or the like, or software using a CPU. Each process executed in the above embodiment may be executed by a plurality of computers. For example, the processes executed by each functional block of the control unit 203 of the signal processing device 102 may be partially executed by another computer, or all the processes may be executed in a distributed manner by a plurality of computers.

[0134] The present disclosure is not limited to the above-described embodiments, and may be replaced with a configuration that is substantially the same as the configuration shown in the above-described embodiments, a configuration that exhibits the same operational effects, or a configuration that can achieve the same object. The present disclosure also includes embodiments obtained by appropriately combining the technical means disclosed in different embodiments. Furthermore, new technical features can be formed by combining the technical means disclosed in each embodiment.

Explanation of Reference Numerals

[0135] 100 Measurement system, 101 Measuring device, 102 Signal processing device, 103 Living body, 201 Storage unit, 202 Operation unit, 203 Control unit, 211 Vital sign information acquisition unit, 212 Input information acquisition unit, 213 Pulse wave calculation unit, 214 Noise generation unit, 215 Noise addition unit, 216 Nonlinear model determination unit, 217 Nonlinear processing unit, 218 Selection unit, 221 Vital sign information, 222 Input information, 223 Nonlinear model, 304 Pulse wave calculation unit, 1001 Frequency calculation unit, 1011 Fundamental frequency, 1201 Determination unit, 1202 Notification unit, 1211 Notification information, 1601 Electronic device, 1602 Sensor, 1603 Signal processing device, 1604 Storage unit, 1605 Operation unit, S0 Pulse wave signal, Sin Noise-added signal, Sn Noise signal, Sout Output signal, Sout_Dm Output signal

Claims

1. An input information acquisition unit that acquires input information indicating temporal changes inside the skin of a living body; A pulse wave calculation unit that calculates a pulse wave signal indicating a pulse wave from the input information; A noise addition unit that adds a noise signal to the pulse wave signal to generate a noise-added signal; A non-linear processing unit that inputs the noise-added signal into a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies a target characteristic; A signal processing apparatus comprising the above, wherein the input information is an image including an image of the skin.

2. The signal processing apparatus according to claim 1, wherein the input information indicates vibrations of a signal obtained from the image of the skin.

3. The noise addition unit adds a plurality of noise signals having different noise intensities to the pulse wave signal respectively to generate a plurality of noise-added signals, The non-linear processing unit inputs each of the plurality of noise-added signals into the non-linear model to generate a plurality of output signals, The signal processing apparatus according to claim 1 or 2, further comprising a selection unit that selects an output signal having the maximum signal-to-noise ratio among the plurality of output signals.

4. The target characteristic indicates at least one selected from the group consisting of a signal-to-noise ratio exceeding the signal-to-noise ratio of the pulse wave signal, an amplitude exceeding the amplitude of the pulse wave signal, and a frequency band of the output signal. The signal processing apparatus according to any one of claims 1 to 3.

5. The target characteristic indicates a frequency band of the output signal, The frequency band of the noise signal is the frequency band indicated by the target characteristic. The signal processing apparatus according to any one of claims 1 to 4.

6. A storage unit that stores by associating a registered frequency band with a type of vital sign; A vital sign information acquisition unit that acquires vital sign information indicating a type of vital sign that is a measurement purpose; Further comprising, The signal processing apparatus according to claim 5, wherein the frequency band indicated by the target characteristic is a registered frequency band associated with the type indicated by the vital sign information.

7. Further comprising a frequency calculation unit that calculates a fundamental frequency of the pulse wave signal, When the type indicated by the vital sign information indicates a heartbeat or a pulse, the frequency band of the noise signal is equal to or lower than a cut-off frequency determined from the fundamental frequency. The signal processing apparatus according to claim 6.

8. The memory unit stores in advance, in association with registration target characteristics, at least one selected from the group consisting of acquisition conditions of the pulse wave signal, the internal state of the skin, the environment of the space where the living body exists, and the attributes of the living body. The signal processing device according to claim 6 or 7, wherein the target characteristics are registration target characteristics associated with at least one selected from the group consisting of the acquisition conditions, the internal state of the skin, the environment of the space where the living body exists, and the attributes of the living body.

9. The signal processing device further includes a determination unit that determines whether or not the output signal satisfies a quality standard. When the output signal satisfies the quality standard, the determination unit outputs the output signal. The signal processing device according to any one of claims 1 to 8.

10. When the output signal does not satisfy the quality standard, the noise addition unit adds a noise signal changed to a predetermined noise intensity and frequency band to the pulse wave signal to generate a new noise-added signal. The non-linear processing unit inputs the new noise-added signal to a non-linear model with changed input-output characteristics to generate a new output signal. The signal processing device according to claim 9, wherein the determination unit determines whether or not the new output signal satisfies the quality standard.

11. When the input information acquisition unit does not generate an output signal that satisfies the quality standard, the input information acquisition unit acquires new input information. The signal processing device according to claim 10, further including a notification unit that outputs notification information notifying that new input information is to be acquired when an output signal that satisfies the quality standard is not generated.

12. An electronic device including a sensor that measures a time-series change inside the skin and the signal processing device according to any one of claims 1 to 11.

13. A step of acquiring input information indicating a time-series change inside the skin of a living body, a step of calculating a pulse wave signal indicating a pulse wave from the input information, a step of adding a noise signal to the pulse wave signal to generate a noise-added signal, a step of inputting the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies target characteristics. The signal processing method includes the above steps, and the input information is an image including an image of the skin.

14. On a computer, a function of acquiring input information indicating a time-series change inside the skin of a living body, a function of calculating a pulse wave signal indicating a pulse wave from the input information, A function of adding a noise signal to the pulse signal to generate a noise-added signal; A function of inputting the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies a target characteristic; A program that causes the above to be executed, and the input information is an image including an image of the skin.

15. An input information acquisition unit that acquires input information indicating a time-series change inside the skin of a living body; A pulse wave calculation unit that calculates a pulse wave signal indicating a pulse wave from the input information; A noise addition unit that adds a noise signal to the pulse wave signal to generate a noise-added signal; A non-linear processing unit that inputs the noise-added signal to a non-linear model having input-output characteristics with hysteresis to generate an output signal, and the input-output characteristics are adjusted so that the output signal satisfies a target characteristic; Comprising; The target characteristic indicates a frequency band of the output signal; A signal processing device, wherein a frequency band of the noise signal is a frequency band indicated by the target characteristic.

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