Biological information measuring device

Abstract

To provide a biological information measurement device capable of suitably measuring biological information.SOLUTION: A biological information measurement device comprises: a photoelectric pulse wave sensor for detecting a plethysmogram; and an estimation unit which on the basis of a first peak being a point of taking a maximum value or a minimum value within a period of an acceleration pulse wave obtained by second-order differentiating the plethysmogram, estimates biological information concerning a blood vessel.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] This embodiment relates to a biological information measuring device.

Background Art

[0002] A pulse wave represents the volume change of blood vessels over time. There are various methods for measuring a pulse wave, such as a method using a photoelectric sensor or a piezoelectric sensor, and a method for directly measuring arterial pressure.

[0003] This pulse wave is also referred to as a volume pulse wave. Information obtained by second-order differentiating the volume pulse wave is referred to as an acceleration pulse wave. In the waveform of the acceleration pulse wave, the maximum peak in one cycle appears during the systolic period of the heart, that is, the period when the heart strongly pumps out blood. Various biological information related to blood vessels can be estimated from information including at least the information of this maximum peak.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] An object of the present invention is to provide a biological information measuring device capable of more suitably measuring biological information.

Means for Solving the Problems

[0006] According to the present invention, a biological information measuring device includes a photoelectric pulse wave sensor that detects a volume pulse wave, and an estimation unit that estimates biological information related to blood vessels based on a first peak that is a point taking the maximum value or the minimum value within the period of an acceleration pulse wave obtained by second-order differentiating the volume pulse wave.

Effects of the Invention

[0007] The present invention provides a biological information measuring device that can more effectively measure biological information. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a diagram illustrating how the biological information measuring device of the embodiment acquires biological information from blood vessels and explains its characteristic points. [Figure 2] Figure 2 is an external view of the wearable device, which is a biometric information measuring device according to the embodiment, when it is worn on the human body. [Figure 3] Figure 3 is a cross-sectional view of the wearable device and the arm in the YZ plane, as shown in Figure 2. [Figure 4] Figure 4 is a schematic diagram showing an example of the internal configuration of the wearable device according to the embodiment. [Figure 5] Figure 5 is a schematic diagram showing an example of the functional configuration realized by the processor, which serves as the estimation unit, in the wearable device of the embodiment. [Figure 6] Figure 6 is a flowchart showing an example of the operation of the biological information measuring device according to the embodiment. [Figure 7] Figure 7 shows examples of volume pulse waves before and after filtering by the filter unit of the embodiment. [Figure 8] Figure 8 is a graph showing an example of the acceleration waveform in the embodiment. [Figure 9] Figure 9 is a schematic diagram illustrating an example of a feature calculation method by the feature calculation unit of the embodiment. [Figure 10] Figure 10 is a schematic diagram showing an example of the functional configuration realized by the processor in the wearable device of the first modified example. [Figure 11] Figure 11 is a flowchart showing an example of the operation of a wearable device in the first modified example. [Figure 12] Figure 12 is a flowchart showing an example of the operation of a wearable device in a second modified example. [Figure 13] FIG. 13 is a flowchart showing an example of the operation of the wearable device according to the third modification. [Figure 14] FIG. 14 is a diagram for explaining the processes of S401 and S402 shown in FIG. 13.

Embodiment for Carrying Out the Invention

[0009] FIG. 1 is a diagram for explaining the living body information measuring device of the embodiment acquiring living body information from a blood vessel and its feature points. In this figure, waveforms of a volume pulse wave, a velocity pulse wave obtained by differentiating the volume pulse wave by the first order, and an acceleration pulse wave obtained by differentiating the volume pulse wave by the second order are drawn from top to bottom.

[0010] The volume pulse wave is a waveform representing the transition of the volume of the blood vessel where the value increases when the volume of the blood vessel at the part where the sensor is applied increases according to the pulse and the value decreases when the volume of the blood vessel decreases. The velocity pulse wave is a waveform representing the transition of the velocity of the volume of the blood vessel that takes a positive value when the volume of the blood vessel increases and a negative value when the volume of the blood vessel decreases. The acceleration pulse wave is a waveform representing the change rate of the acceleration of increase of the volume of the blood vessel that takes a positive value when the acceleration of increase of the volume of the blood vessel rises and a negative value when the acceleration of increase of the volume of the blood vessel falls.

[0011] Part or all of the volume pulse wave, the velocity pulse wave, and the acceleration pulse wave may be acquired in an inverted state depending on the design. In this specification, each pulse wave in the non-inverted state will be referred to. For a system in which part or all of the volume pulse wave, the velocity pulse wave, and the acceleration pulse wave are acquired in an inverted state, the technology described in this specification can be applied by re-reading the inverted pulse wave as a waveform further inverted, that is, the waveform before inversion.

[0012] The biological information measurement device acquires the volume pulse wave in the upper part of FIG. 1 by means of a sensor. The same waveform repeatedly appears in the volume pulse wave. Therefore, for example, in the volume pulse wave, the period from one minimum point to the next minimum point can be regarded as the period of the pulse wave. When the period of the pulse wave is defined in this way, among the periods of the pulse wave, the first half period corresponds to the contraction period of the heart, and the second half period corresponds to the relaxation period of the heart.

[0013] Since the contraction period is the period during which the heart strongly pumps out blood, the waveform in the contraction period reflects biological information related to blood vessels, such as blood pressure and the flexibility of blood vessels. Therefore, by analyzing the waveform in the contraction period, it is possible to estimate the degree of blood pressure and the progression of arteriosclerosis.

[0014] This biological information measurement device identifies a peak that takes the maximum value and four peaks following the peak that takes the maximum value among the waveforms of one period of the acceleration wave pulse wave. Here, the peak that takes the maximum value among the waveforms of one period of the acceleration wave pulse wave is referred to as point a. That is, point a is the point where the acceleration of the increase in the volume of the blood vessel is the largest during the contraction period of the heart. The four peaks following point a are sequentially referred to as point b, point c, point d, and point e. Point b is also the point that takes the minimum value among the waveforms of one period of the acceleration wave pulse wave. The biological information measurement device of the embodiment estimates biological information related to blood vessels based on the information of points a to e. In this specification, as can be read from FIG. 1, not only the maximum points but also the minimum points are treated as peaks.

[0015] Conventionally, in order to identify points a to e from the acceleration pulse wave, a waveform obtained by further differentiating the acceleration pulse wave is used. According to this conventional method, when a volume pulse wave with less noise is obtained by a high-sensitivity sensor, points a to e can be accurately identified, and thus biological information can be accurately obtained.

[0016] However, when using sensors susceptible to disturbances, such as photoelectric pulse wave sensors, the more the pulse wave is differentiated, the more pronounced the effects of vibration, amplitude fluctuations, and noise components from ambient light become, making it difficult to accurately pinpoint points a to e. Furthermore, while pulse waves are normally formed by the signal fluctuating up and down from the central position (also called the baseline), when using sensors susceptible to disturbances as described above, the central position may fluctuate from the zero point. In such cases, differentiation of the pulse wave is also affected by the baseline fluctuation, worsening the accuracy of pinpointing points a to e.

[0017] Furthermore, when using this photoplethysmography sensor, in order to minimize noise components caused by ambient light and other factors, it was conventionally necessary to press the sensor firmly against the skin. For example, if the photoplethysmography sensor were implemented in a wristwatch-type wearable device, the user would need to tighten the band of the wristwatch-type wearable device, compromising wearing comfort.

[0018] Therefore, in order to suitably detect biological information even if the output from the photoplethysmography sensor contains many noise components, in this embodiment, points a to e are identified using the acceleration pulse wave without differentiating the acceleration pulse wave. The biological information measuring device of this embodiment will be described in detail below.

[0019] Here, as an example, we will use a wristwatch-type wearable device (e.g., a smartwatch) as the biometric information measurement device and describe its configuration. The biometric information measurement device will measure blood pressure as an example of biometric information related to blood vessels. Furthermore, the biometric information measurement device will be equipped with a photoplethysmography sensor to acquire volume pulse waves.

[0020] Furthermore, the biometric information measuring device may be configured as a wearable device different from a wristwatch-type wearable device, such as smart glasses, a smart ring, or wireless earphones. The biometric information measuring device may also be configured to measure the degree of progression of arteriosclerosis as biometric information related to blood vessels.

[0021] Figure 2 is an external view of the wearable device 1, which is a biometric information measuring device according to the embodiment, when it is worn on the human body.

[0022] The wearable device 1 comprises a flat-shaped housing 10, a display device 11 attached to the surface of the housing, and a band 12 attached to the side of the housing. The housing 10 consists of a top surface, a bottom surface, and sides connecting the periphery of the top surface and the periphery of the bottom surface. In the figure, the housing is approximately a rectangular parallelepiped and consists of four sides.

[0023] A band 12 is attached to one side of the housing 10 and the other side. The band 12 is wrapped around the wearer's arm 200, fixing the bottom surface of the housing 10 to the arm 200. A display device 11 is provided on the top surface of the housing 10, and the wearable device 1 outputs various image information to the display device 11. The wearer can visually confirm the various image information output to the display device 11. This image information includes blood pressure and pulse rate values, the waveform shown in Figure 1, and biometric information after analysis.

[0024] In the following explanation, the direction in which the arm extends will be referred to as the +X axis, the direction from the bottom surface to the top surface of the housing 10 will be referred to as the +Z axis, and the axis perpendicular to both the X and Z axes will be referred to as the Y axis.

[0025] Figure 3 is a cross-sectional view of the wearable device 1 and arm 200 in the YZ plane, as shown in Figure 2.

[0026] Inside the arm 200, the radius 211 and ulna 212 extend in the X-axis direction. The radial artery 213 passes below the radius 211, and the ulnar artery 214 passes below the ulna 212. A tendon 215 passes subcutaneously at the lowest part of the cross-section of the arm 200.

[0027] A photoelectric pulse wave sensor 13 is provided on the lower surface of the housing 10. The photoelectric pulse wave sensor 13 comprises a sensor substrate 130, one or more light-receiving elements 131, and one or more light-emitting elements 132. Here, as an example, when the wearable device 1 is worn, one light-receiving element 131 is provided in the center of the surface of the sensor substrate 130 that faces the skin 201, and one light-emitting element 132 is provided at a position spaced apart from the light-receiving element 131 in the positive Y-axis direction and at a position spaced apart in the negative Y-axis direction. In other words, two light-emitting elements 132 are provided in positions surrounding the light-receiving element 131.

[0028] The number of light-receiving elements 131 and light-emitting elements 132 provided on the sensor substrate 130, and the arrangement of each element, are not limited to the above example. In addition, in the example shown in Figure 3, the light-receiving element 131 and the two light-emitting elements 132 are configured to come into contact with the skin 201 when worn, but the light-receiving element 131 and the two light-emitting elements 132 may be covered by a light-transmitting member, a glass substrate, or a transparent resin substrate so as not to come into direct contact with the skin 201.

[0029] Each light-emitting element 132 emits light selected from a wavelength range that is easily absorbed by hemoglobin in the blood, for example, from green to near-infrared. Each light-emitting element 132 emits light of the same wavelength. The light-receiving element 131 can detect the light emitted by the light-emitting elements 132.

[0030] When the wearable device 1 is worn by the wearer, the two light-emitting elements 132 emit light toward the skin 201. The light-receiving element 131 detects the light incident on it, including light that penetrates beneath the skin 201 and is reflected or scattered by the subcutaneous tissue. Since blood vessels, including the radial artery 213, exist beneath the skin, the amount of light received by the light-receiving element 131 increases or decreases due to the influence of the volume pulse wave of the blood vessels. The photoplethysmography sensor 13 acquires the time change in the amount of light received by the light-receiving element 131 as a volume pulse wave. In other words, the photoplethysmography sensor 13 detects a volume pulse wave.

[0031] Figure 4 is a schematic diagram showing an example of the internal configuration of the wearable device 1 of the embodiment.

[0032] The wearable device 1 includes a display device 11 and a photoplethysmography sensor 13, as well as a processor 14 and memory 15. The processor 14, memory 15, photoplethysmography sensor 13, and display device 11 are electrically connected to a bus 16.

[0033] The photoelectric pulse wave sensor 13 comprises a light-receiving element 131, two light-emitting elements 132, a microcomputer unit 133, and a gain circuit 134.

[0034] The gain circuit 134 is a circuit that amplifies the signal from the photodetector 131.

[0035] The microcomputer unit 133 lights up the light-emitting element 132 and stores the time progression of the signal from the light-receiving element 131 via the gain circuit 134 and outputs it as a volume pulse wave.

[0036] Memory 15 stores computer programs and data, and functions as a workspace for the processor 14. Memory 15 is, for example, a combination of volatile and non-volatile memory. Volatile memory is, for example, RAM (Random Access Memory), which can function as a workspace for the processor 14. Non-volatile memory is, for example, storage memory such as an SSD (Solid State Drive) or HDD (Hard Disk Drive), which can store computer programs and data non-volatilely. Note that the configuration of memory 15 is not limited to this example.

[0037] The processor 14 is an arithmetic unit that performs various functions according to a computer program, and is, for example, a CPU (Central Processing Unit). The processor 14 controls the wearable device 1 by executing a computer program that is pre-stored in the memory 15. In this embodiment, as part of the control of the wearable device 1, the processor 14 estimates blood pressure based on the volume pulse wave detected by the photoplethysmography sensor 13.

[0038] The processor 14 is an example of an estimation unit in this embodiment. The estimation unit estimates biological information about blood vessels based on point a, which is the peak where the maximum value is taken or point b, which is the peak where the minimum value is taken, within the period of the acceleration pulse wave obtained by taking the second derivative of the volume pulse wave detected by the photoplethysmography sensor 13. Some or all of the functions of the estimation unit may be implemented by an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or any hardware circuit. In addition, some or all of the functions of the estimation unit may be implemented by a microcomputer unit provided in the photoplethysmography sensor 13.

[0039] Figure 5 is a schematic diagram showing an example of the functional configuration realized by the processor 14, which serves as the estimation unit in the wearable device 1 of this embodiment. Here, each functional component is described in general terms, and details will be explained when describing the operation.

[0040] The processor 14 functions as an acquisition unit 140, a filter unit 141, a differentiation unit 142, a peak detection unit 143, a feature point identification unit 144, a feature quantity calculation unit 145, a blood pressure estimation unit 146, and an output unit 147.

[0041] The acquisition unit 140 acquires volume pulse wave data from the photoelectric pulse wave sensor 13. The acquisition unit 140 acquires data that includes volume pulse waves for one or more periods that are detected continuously over time.

[0042] The filter unit 141 performs filtering on the volume pulse wave data using a bandpass filter. The volume pulse wave data acquired from the photoplethysmography sensor 13 may contain baseline fluctuations corresponding to the wearer's breathing and body movements, as well as noise due to ambient light. The filter unit 141 removes baseline fluctuations and noise using a bandpass filter.

[0043] The differential unit 142 obtains acceleration pulse wave data by performing a second derivative on the volume pulse wave data after it has been subjected to a bandpass filter.

[0044] The peak detection unit 143 detects the peaks of the acceleration pulse wave from the acceleration pulse wave data. The peak detection unit 143 detects the maximum and minimum points as peaks.

[0045] The feature point identification unit 144 identifies points a to e from among the detected peaks. Hereafter, points a to e may be referred to as feature points. If the volume pulse wave data contains multiple periods, the feature point identification unit 144 identifies a set of feature points, i.e., a set of points a to e, for each period.

[0046] The feature calculation unit 145 calculates the ratio of the values ​​of two feature points, the time interval between the appearance of two feature points, or both of these as features. The feature calculation unit 145 calculates one or more sets of features per period. If the volume pulse wave data includes multiple periods, the feature calculation unit 145 calculates a set of features from each period and then takes the average of the features for each type over all periods. Note that the feature calculation unit 145 does not necessarily have to take the average of the features over all periods. The feature calculation unit 145 may also pass the set of features obtained from all periods as a set of features to the blood pressure estimation unit 146, which will be described next.

[0047] The blood pressure estimation unit 146 estimates blood pressure based on a set of features. If the mean of each type of feature is calculated, the blood pressure estimation unit 146 uses the set of mean values ​​of the features to estimate blood pressure. In one example, the blood pressure estimation unit 146 is a pre-trained neural network model configured to output an estimated blood pressure when a set of features is input.

[0048] The method for outputting an estimated blood pressure value based on the set of features can be configured arbitrarily. The blood pressure estimation unit 146 estimates blood pressure based on the set of features and any known or future-developed algorithm.

[0049] The output unit 147 outputs an estimated blood pressure value. The method of outputting the estimated blood pressure value is not limited to a specific method. In one example, the output unit 147 outputs the estimated blood pressure value as image information to the display device 11. In another example, the output unit 147 stores the data containing the estimated blood pressure value in the memory 15.

[0050] Next, the operation of the wearable device 1 as a biological information measurement device in this embodiment will be described.

[0051] Figure 6 is a flowchart illustrating an example of the operation of the wearable device 1 according to the embodiment. Here, we will describe the operation after the detection of the volume pulse wave by the photoplethysmography sensor 13 is completed. We will also describe a method in which the wearable device 1 first detects point a and then identifies points b to e based on the detected point a. Alternatively, the wearable device 1 may first detect point b and then identify points a, c to e based on the detected point b.

[0052] First, the acquisition unit 140 acquires volume pulse wave data from the photoelectric pulse wave sensor 13 (S101).

[0053] The filter unit 141 performs filtering on the volume pulse wave data using a bandpass filter (S102).

[0054] Figure 7 shows an example of volume pulse wave before and after filtering by the filter unit 141 of the embodiment. In this example, the volume pulse wave data is data of the sensor output value against time. The sensor output value is, for example, the absorbance calculated by the microcomputer unit 133 based on the amount of light received by the photodetector 131, which is amplified by the gain circuit 134 and then acquired by the microcomputer unit 133, and corresponds to the amount of light absorbed by the blood vessels, i.e., the volume of the blood vessels.

[0055] Before filtering, as shown in Figure 7(A), the volume pulse wave contains baseline fluctuations and high-frequency noise. Filtering removes the baseline fluctuations and high-frequency noise, as shown in Figure 7(B), resulting in a smooth waveform.

[0056] If, for example, an acceleration pulse wave is calculated from the volume pulse wave shown in Figure 7(A), and then peak detection is performed, many peaks may be falsely detected due to the influence of high-frequency noise, or the peak position may be shifted due to baseline fluctuations. The filtering process of the filter unit 141 removes baseline fluctuations and high-frequency noise, thereby improving the accuracy of the peak detection performed later.

[0057] Let's return to the explanation in Figure 6. Following S102, the differentiation unit 142 performs the first differentiation on the volume pulse wave data after filtering to obtain the velocity pulse wave data (S103). Furthermore, the differentiation unit 142 performs the second differentiation on the velocity pulse wave data to obtain the acceleration pulse wave data (S104).

[0058] The peak detection unit 143 performs peak detection on the acceleration pulse wave data (S105). The peak detection unit 143 detects either the point where the value takes a maximum value, that is, the point where the value changes from rising to falling over time, or the point where the value takes a minimum value, that is, the point where the value changes from falling to rising over time, as a peak. Hereafter, the point where the value takes a maximum value will also be called the positive peak, and the point where the value takes a minimum value will also be called the negative peak. At this peak detection, both the positive peak and the negative peak may be detected as peaks.

[0059] Following S105, the feature point identification unit 144 identifies point a from among the multiple peaks detected by peak detection. Point a is a feature point that represents a characteristic where the rise velocity accelerates during the rise period of the volume pulse wave, and appears as a positive value in the acceleration pulse wave. Here, point a is identified from among the multiple positive peaks.

[0060] When the feature point identification unit 144 is configured to identify point a, the peak detection unit 143 detects the point where the value takes its maximum value, i.e., the positive peak, as a peak. In this way, the feature point identification unit 144 identifies point a from among multiple positive peaks.

[0061] The feature point identification unit 144 may be configured to identify point b from among a plurality of negative peaks. In such a case, the peak detection unit 143 detects the point where the value takes a minimum value, i.e., the negative peak, as a peak.

[0062] The feature point identification unit 144 selects one peak from the detected positive peaks (S106). The peak selected in S106 is referred to as the target peak.

[0063] The feature point identification unit 144 determines whether the target peak corresponds to point a in steps S107 to S109.

[0064] Specifically, the feature point identification unit 144 first compares the ratio of the value of the target peak to the maximum value of the acceleration pulse wave data with a predetermined first threshold, and determines whether the ratio exceeds the first threshold (S107).

[0065] Point a is the point where the maximum value is obtained within one period of the acceleration pulse wave. Therefore, if the target peak corresponds to point a, the ratio of the value of the target peak to the maximum value of the acceleration pulse wave data is expected to be close to 1. Thus, by appropriately setting the first threshold, it is possible to suppress the false detection of extremely small peaks as point a.

[0066] If it is determined that the ratio of the value of the target peak to the maximum positive value of the acceleration pulse wave data exceeds the first threshold (S107: Yes), the feature point identification unit 144 determines whether the ratio of the value of the target peak to the positive peak immediately preceding the target peak and the ratio of the value of the target peak to the positive peak immediately following the target peak both exceed the second threshold (S108).

[0067] Point a is not only the point where the maximum value is obtained within one cycle of the acceleration pulse wave, but also, if we consider only the positive side peaks, the value at point a is significantly higher than that of the other peaks. Therefore, if we consider only the positive side peaks, the ratio of the value at point a to the peak immediately preceding and immediately following point a is likely to be well over 1. Thus, by appropriately setting the second threshold, it is possible to determine whether or not the target peak corresponds to point a.

[0068] Figure 8 is a graph showing an example of the acceleration waveform in the embodiment.

[0069] In the example acceleration waveform shown in this figure, the peak p1 value is 95 points, the peak p2 value is 98 points, the peak p3 value is 205 points, the peak p4 value is 110 points, and the peak p5 value is 230 points. The peak p5 value of 230 points is considered to be the maximum value of the acceleration waveform data.

[0070] The ratio of the value of the acceleration waveform data to the maximum value is approximately 0.41 for peak p1, approximately 0.42 for peak p2, approximately 0.89 for peak p3, approximately 0.47 for peak p4, and 1 for peak p5. For example, if the first threshold is set to 0.4, the feature point identification unit 144 determines that the ratio of the value of the target peak to the maximum value of the acceleration waveform data exceeds the first threshold, regardless of which of peaks p1 to p5 is the target peak. In other words, regardless of which of peaks p1 to p5 is the target peak, the determination process in S107 is determined to be Yes.

[0071] For example, if peak p2 is the target peak, the ratio of the value of the target peak to peak p1, which is the positive peak immediately preceding the target peak, is approximately 1.0, and the ratio of the value of the target peak to p3, which is the positive peak immediately following the target peak, is approximately 0.48.

[0072] For example, if peak p3 is the target peak, the ratio of the value of the target peak to peak p2, which is the positive peak immediately preceding the target peak, is approximately 2.1, and the ratio of the value of the target peak to peak p4, which is the positive peak immediately following the target peak, is approximately 1.9.

[0073] Therefore, in the case where the second threshold is set to, for example, 1.5, if peak p2 is the target peak, the determination in S108 will be No. If peak p3 is the target peak, the determination in S108 will be Yes.

[0074] Point b is the point where the minimum value is obtained within one cycle of the acceleration pulse wave. Also, the negative peak is significantly lower compared to the other peaks.

[0075] Therefore, when detecting point b instead of point a, in S107, the feature point identification unit 144 compares the ratio of the value of the target peak to the minimum value of the acceleration pulse wave data with a predetermined first threshold, and determines whether the ratio exceeds the first threshold. Also, in S108, the feature point identification unit 144 identifies the peak immediately preceding the target peak. negative The ratio of the value of the target peak to the peak on the side, and the value one peak after the target peak. negative The ratio of the value of the target peak to the peak on the side is determined, and it is determined whether both exceed the second threshold.

[0076] Let's return to the explanation in Figure 6. If the determination process in S108 is determined to be Yes, the feature point identification unit 144 determines the target peak as point a (S109).

[0077] If the result is No after S109, or in the determination process of S107, or if the result is No in the determination process of S108, the feature point identification unit 144 determines whether or not there are any unselected peaks remaining among the positive peaks (S110).

[0078] If there are still unselected peaks (S110: Yes), the feature point identification unit 144 selects one peak from among the unselected peaks (S111), and repeats the process from S107 using the selected peak as the new target peak.

[0079] If there are no unselected peaks remaining (S110: No), the feature point identification unit 144 identifies the four peaks that appear after each point a as points b, c, d, and e, in the order in which they appear (S112).

[0080] Furthermore, if point b is determined instead of point a in S109, the feature point characteristic unit 144 identifies the peak immediately preceding point b as point a, and identifies the three peaks that appear immediately after point b as points c, d, and e, in the order in which they appear.

[0081] The processing up to S112 identifies points a to e for each period. The feature calculation unit 145 calculates a set of features for each period (S113). The period from point a to just before the next point a roughly corresponds to one period. Therefore, the feature calculation unit 145 can consider the period from point a to just before the next point a as one period and calculate a set of features for each period.

[0082] Figure 9 is a schematic diagram illustrating an example of a feature calculation method by the feature calculation unit 145 of the embodiment.

[0083] The feature calculation unit 145 first calculates the following as features within a certain period: time interval bx from point a to point b, time interval cx from point a to point c, time interval dx from point a to point d, and time interval ex from point a to point e.

[0084] Furthermore, the feature calculation unit 145 calculates the ratio of the value of point b to point a by / ay, the ratio of the value of point c to point a cy / ay, the ratio of the value of point d to point a dy / ay, and the ratio of the value of point e to point a ey / ay as features.

[0085] The feature calculation unit 145 then uses bx, cx, dx, ex, by / ay, cy / ay, dy / ay, and ey / ay as the set of features for this period.

[0086] Note that the feature calculation method explained using Figure 9 is merely one example. Designers can use any numerical information based on the time and value of points a to e as features.

[0087] Let's return to the explanation in Figure 6. Following S113, the feature calculation unit 145 calculates the average value of each type of feature over the entire cycle (S114). Following the example of the feature calculation method described above, the feature calculation unit 145 calculates the average value for each of bx, cx, dx, ex, by / ay, cy / ay, dy / ay, and ey / ay over the entire cycle.

[0088] Next, the blood pressure estimation unit 146 estimates the blood pressure based on the set of average values ​​of the features (S115). The output unit 147 outputs the estimated blood pressure (S116). Then, the operation of the wearable device 1 of this embodiment is completed.

[0089] As described above, according to the embodiment, the wearable device 1 as a biological information measuring device comprises a photoelectric pulse wave sensor 13 that detects volume pulse waves, and a processor 14 as an estimation unit that estimates blood pressure as biological information related to blood vessels based on point a, which is the maximum value within the period of the acceleration pulse wave obtained by the second derivative of the volume pulse wave, or point b, which is the minimum value.

[0090] Therefore, since point a is identified without further differentiation of the acceleration pulse wave, highly accurate identification of point a is possible. As a result, the accuracy of biometric information estimation is improved. Even if the wearer loosens the band 12 of the wearable device 1 and the volume pulse wave data detected in this way contains a large noise component, biometric information can still be estimated with good accuracy. In other words, the wearable device 1 can suitably measure biometric information.

[0091] Furthermore, according to the embodiment, as shown in S102 to S105 of Figure 6, the processor 14 obtains an acceleration pulse wave by applying a bandpass filter to the volume pulse wave and taking the second derivative of the volume pulse wave after applying the bandpass filter. The processor 14 then performs peak detection on the acceleration pulse wave, detecting the maximum and minimum values ​​as peaks.

[0092] Because peak detection is performed after baseline fluctuations and the effects of high-frequency noise corresponding to the wearer's breathing and body movements are suppressed by a bandpass filter, it is possible to accurately identify points a to e. As a result, the accuracy of biological information estimation is improved. In other words, wearable device 1 can suitably measure biological information.

[0093] Furthermore, according to the embodiment, as shown in S108 of Figure 6, the processor 14 identifies point a based on the ratio of the values ​​of two adjacent peaks among the multiple peaks detected by peak detection.

[0094] Therefore, it is possible to identify point a without further differentiating the acceleration pulse wave.

[0095] In another embodiment, the processor 14 identifies point a based on the ratio of the values ​​of two adjacent peaks among the multiple peaks with positive values ​​detected by peak detection.

[0096] In acceleration pulse wave data, point 'a' belongs to a peak with a positive value. Therefore, by narrowing down the candidates to peaks with positive values ​​and identifying point 'a', the efficiency of specific processing can be improved.

[0097] As mentioned above, the processor 14 may be configured to narrow down the candidates to a peak with a negative value to identify point b, and then identify point a based on the identified point b.

[0098] Furthermore, according to the embodiment, as shown in S107 of Figure 6, the processor 14 identifies point a based on the ratio of the values ​​of two adjacent peaks, as well as the ratio of the value to the maximum amplitude of the acceleration pulse wave on the positive or negative side to which the peak belongs.

[0099] Therefore, it is possible to suppress the misidentification of significantly smaller peaks that are different from point a as point a, and the accuracy of identifying point a is further improved.

[0100] The processor 14 may be configured to omit the determination process in S107 of Figure 6 and to identify point a solely through the process in S108.

[0101] Furthermore, according to the embodiment, as shown in S115 of Figure 6, the processor 14 identifies point a and points b to e that follow point a, and calculates feature quantities based on the values ​​of points a to e and the intervals between their appearance timings. The processor 14 then estimates biological information based on the calculated feature quantities. In this embodiment, the processor 14 estimates blood pressure as an example of biological information, but it may also estimate the degree of progression of arteriosclerosis. In addition, it may have functions such as estimating the state of heart rate variability or abnormalities in autonomic nervous system function from the interval between two adjacent points a.

[0102] The algorithm for determining point a can be modified in various ways. Several variations are described below.

[0103] (First variation) First, as one variation of the embodiment, the first variation will be described. The wearable device of the first variation will be referred to as wearable device 1a. In the description of the first variation, matters that differ from the embodiment will be described. Matters that are the same as in the embodiment will be omitted or described in a simplified manner.

[0104] Figure 10 is a schematic diagram showing an example of the functional configuration realized by the processor 14 in the wearable device 1a of the first modified example.

[0105] The processor 14 functions as an acquisition unit 140, a filter unit 141, a differentiation unit 142, a peak detection unit 143, a feature point identification unit 144, a feature quantity calculation unit 145, a blood pressure estimation unit 146, an output unit 147, and a period identification unit 148.

[0106] The period identification unit 148 identifies the periodic boundaries of the pulse wave and identifies each period based on the periodic boundaries. For example, the period identification unit 148 identifies the minimum point of the volume pulse wave after filtering and uses the identified minimum point as the periodic boundary of the pulse wave. Then, the period identification unit 148 identifies the period divided by the two periodic boundaries as one period. If the volume pulse wave has a length of multiple periods, the period identification unit 148 identifies each period using the method described above. Note that this method of period identification is just one example. The period identification unit 148 can identify each period using any method.

[0107] Figure 11 is a flowchart showing an example of the operation of the wearable device 1a of the first modified example.

[0108] In the first modified example, following the process in S102, the period identification unit 148 identifies period boundaries from the volume pulse wave (S201). Then, the period identification unit 148 calculates the average value of the period length based on the period boundaries (S202). In other words, the period identification unit 148 averages the lengths of multiple periods separated by each period boundary over the entire period.

[0109] After the processing in S202, the processes in S103 to S108 are executed.

[0110] Note that the execution timing of processes S201 and S202 does not necessarily have to be between processes S102 and S103. Processes S201 and S202 can be executed at any time as long as it is before the execution timing of process S206, which will be described later.

[0111] If the determination process in S108 is determined to be Yes, the feature point identification unit 144 determines the target peak as a candidate point a (S203). Then, the feature point identification unit 144 determines whether or not there are any unselected peaks remaining among the positive peaks (S204).

[0112] If there are still unselected peaks (S204: Yes), the feature point identification unit 144 selects one peak from among the unselected peaks (S205), and repeats the process from S107 using the selected peak as the new target peak.

[0113] If there are no unselected peaks remaining (S204: No), the feature point identification unit 144 excludes the falsely detected a-point candidate based on a comparison between the interval between two adjacent a-point candidates and the average value of the period length calculated in S202 (S206).

[0114] For example, if the distance between two adjacent candidate points a is significantly shorter than the average value of the period length, one of the two adjacent candidate points a is considered to be a misdetected point between the two actual points a. In such a case, the feature point identification unit 144 identifies one of the two adjacent candidate points a as a misdetected point and determines that the candidate point a identified as a misdetected point is not point a.

[0115] More specifically, if the distance between two adjacent candidate points a exceeds a predetermined percentage of the average value of the period length, the feature point identification unit 144 determines that both of the adjacent candidate points a are indeed points a. If the distance between two adjacent candidate points a is less than a predetermined percentage of the average value of the period length, the feature point identification unit 144 determines that one of the two adjacent candidate points a is a falsely detected point.

[0116] In one example, the feature point identification unit 144 compares the interval between two adjacent candidate points a with the average value of the period length, in the order in which the candidate points a appeared. If it determines that the interval between two adjacent candidate points a is less than a predetermined percentage of the average value of the period length, the feature point identification unit 144 excludes the candidate point a that appeared later than the other two adjacent candidate points a.

[0117] The specified percentage can be arbitrarily determined by the designer based on experiments or calculations. For example, the designer could set the specified percentage to 70 percent.

[0118] If the distance between two adjacent candidate points a is significantly greater than the average value of the period length, it is considered that there was a missed detection of a point a between the two adjacent candidate points a. In such cases, the feature point identification unit 144 identifies the falsely detected point based on a comparison between the distance between the two adjacent candidate points a and twice the average value of the period length.

[0119] The method for excluding candidate point a described above is just one example. The feature point identification unit 144 can identify falsely detected candidate point a using any method based on a comparison between the interval at which candidate point a appeared and the average value of the period length.

[0120] The feature point identification unit 144 determines the remaining candidate point a as point a (S207). Then, the processing from S112 onwards is executed.

[0121] Thus, the processor 14 may be configured to identify and exclude falsely detected a-point candidates based on a comparison between the interval of a-point candidates and the average value of the period length.

[0122] This improves the accuracy of feature point identification.

[0123] Note that the process in S107 may be omitted. The process in S107 is performed to improve the accuracy of identifying point a. According to the process in S206, it is possible to identify point a with high accuracy even without the process in S107.

[0124] (Second variation) As another variation of the embodiment, a second variation will be described.

[0125] In the embodiment and the first modification, as an example, the target peak was selected from the positive peaks, and the determination was performed based on the ratio of values, using only the positive peaks. In the second modification, the target peak may be selected without distinguishing between positive and negative peaks. Furthermore, the ratio of values ​​for the determination may be selected from peaks whose values ​​are compared without distinguishing between positive and negative peaks.

[0126] Hereinafter, the wearable device of the second modified example will be referred to as wearable device 1b. The description of the second modified example will explain the differences from the embodiment and the first modified example. Matters that are the same as those in the embodiment or the first modified example will be omitted or explained briefly.

[0127] Figure 12 is a flowchart showing an example of the operation of the wearable device 1b, a second modified example.

[0128] In the second modified example, similar to the first modified example, following the process in S102, the periodic boundary is identified (S201) and the average value of the period length is calculated (S202).

[0129] After the processing in S202, the processes in S103 to S105 are executed.

[0130] After peak detection in S105, the feature point identification unit 144 selects one peak from among the detected peaks (S301). In S301, the feature point identification unit 144 selects one peak as the target peak from among all peaks, regardless of whether they are on the positive or negative side.

[0131] Next, the feature point identification unit 144 determines that the ratio of the absolute value of the target peak to the peak immediately preceding the target peak, and the ratio of the absolute value of the target peak to the peak immediately following the target peak, both exceed the third threshold (S302). In S302, the feature point identification unit 144 calculates the ratio of the absolute value of the target peak to the peak immediately preceding the target peak, regardless of whether the peak immediately preceding the target peak is a positive or negative peak. The feature point identification unit 144 also calculates the ratio of the absolute value of the target peak to the peak immediately following the target peak, regardless of whether the peak immediately following the target peak is a positive or negative peak. The third threshold may be the same value as the second threshold, or it may be a different value from the second threshold.

[0132] If the determination process in S302 is determined to be Yes, the feature point identification unit 144 determines the target peak as a candidate point a (S303).

[0133] Then, the feature point identification unit 144 determines whether or not there are any unselected peaks remaining (S304). In S304, the feature point identification unit 144 determines whether or not there are any unselected peaks remaining among all the peaks, regardless of whether they are on the positive or negative side.

[0134] If there are still unselected peaks remaining (S304: Yes), the feature point identification unit 144 selects one peak from among the unselected peaks (S305), and repeats the process from S302 using the selected peak as the new target peak.

[0135] If there are no unselected peaks remaining (S304: No), then processes S206-S207 and S112-S116 are executed.

[0136] Thus, the processor 14 may be configured to identify point a based on the ratio of values ​​of two adjacent peaks, without distinguishing between positive and negative peaks among the multiple peaks detected by peak detection.

[0137] The processor 14 may be configured to omit the processing steps S206 to S207 in Figure 12 and identify point a by processing step S302 alone.

[0138] (Third variation) A third variation will be described as yet another variation of the embodiment. The wearable device of the third variation will be referred to as wearable device 1c. The description of the third variation will describe the differences from the embodiment, the first variation, and the second variation. Matters that are the same as those in the embodiment, the first variation, or the second variation will be omitted or described in a simplified manner.

[0139] Figure 13 is a flowchart showing an example of the operation of the wearable device 1c, a third modified example.

[0140] In the third modified example, similar to the first modified example, following the process in S102, the periodic boundary is identified (S201) and the average value of the period length is calculated (S202).

[0141] After the processing in S202, the processes in S103 to S105 are executed.

[0142] After peak detection in S105, the feature point identification unit 144 identifies the maximum peak of the velocity pulse wave for each period (S401). The feature point identification unit 144 then determines the peak that appeared immediately before the timing of the maximum peak of the velocity pulse wave, among the acceleration pulse wave peaks detected by peak detection, as a candidate point a (S402). The peak that appeared immediately before a certain timing is the peak that appeared the latest among all the peaks that appeared before that timing.

[0143] Figure 14 is a diagram illustrating the processes S401 and S402 shown in Figure 13. This figure shows volume pulse waves, velocity pulse waves, and acceleration pulse waves for two periods, the nth period and the (n+1)th period.

[0144] In S401, the feature point identification unit 144 determines the peak pv nThe maximum peak of the nth period, peak pv n+1 We identify this as the maximum peak of the (n+1)th period. Peak pv n The timing of its appearance was time t1, and the peak PV n+1 The timing of its appearance is time t2.

[0145] In S402, the feature point identification unit 144 identifies peak pa, which is the peak that appeared immediately before time t1 in the nth period. n We identify point a in the nth period, and the peak pa is the peak that appeared immediately before time t2 in the (n+1)th period. n+1 We identify this as point a in the (n+1)th period.

[0146] Let's return to the explanation in Figure 13. Following S402, the feature point identification unit 144 executes the processes in S206-S207 and S112-S116.

[0147] Thus, the processor 14 may be configured to identify the maximum peak within the period of the velocity pulse wave obtained by first differentiating the volume pulse wave, and to identify point a as the peak of the acceleration pulse wave that appears immediately before the timing at which the maximum peak within the period of the velocity pulse wave appears.

[0148] In the third modification, point a is identified without further differentiation of the acceleration pulse wave, enabling highly accurate identification of point a. As a result, the accuracy of biometric information estimation is improved. In other words, the wearable device 1c can suitably measure biometric information. [Explanation of symbols]

[0149] 1,1a,1b,1c Wearable device, 10 Housing, 11 Display device, 12 Band, 13 Photoelectric pulse wave sensor, 14 Processor, 15 Memory, 16 Bus, 130 Sensor board, 131 Photodetector, 132 Light-emitting element, 133 Microcomputer unit, 134 Gain circuit, 140 Acquisition unit, 141 Filter unit, 142 Differentiation unit, 143 Peak detection unit, 144 Feature point identification unit, 145 Feature quantity calculation unit, 146 Blood pressure estimation unit, 147 Output unit, 148 Period identification unit, 200 Arm, 201 Skin, 211 Radius, 212 Ulna, 213 Radial artery, 214 Ulnar artery, 215 Tendon.

Claims

1. A photoplethysmography sensor that detects volume pulse waves, The system includes an estimation unit that estimates biological information relating to blood vessels based on a first peak, which is the point where the acceleration pulse wave obtained by taking the second derivative of the volume pulse wave takes its maximum or minimum value within the period, The estimation unit, A peak detection is performed to detect the maximum or minimum value of the acceleration pulse wave as a peak, one peak is sequentially selected from among the multiple peaks detected by the peak detection, and it is determined whether the selected peak is the first peak based on a first ratio, which is the ratio of the absolute value of the selected peak to the absolute value of the adjacent peak adjacent to the selected peak. A device for measuring biological information.

2. The estimation unit determines whether the selected peak is the first peak based on a second ratio, which is the ratio of the absolute value of the selected peak to the maximum absolute value of the peaks among the plurality of peaks, and the first ratio. A biological information measuring device according to claim 1.

3. The estimation unit detects multiple candidate points for the first peak based on the first ratio, and based on a comparison of the interval between the multiple candidate points and the length of the period of the acceleration pulse wave, it excludes the falsely detected candidate points for the first peak and identifies the first peak. A biological information measuring device according to claim 1.

4. The estimation unit identifies a second peak, which is the maximum peak within the period of the velocity pulse wave obtained by the first derivative of the volume pulse wave, and identifies the peak that appears immediately before the timing of the appearance of the second peak among the multiple peaks detected by the peak detection as the first peak. A biological information measuring device according to claim 1.

5. The estimation unit identifies a plurality of third peaks, including the first peak and a predetermined number of peaks within the period of the acceleration pulse wave following the first peak; calculates feature quantities based on the values ​​of each of the plurality of third peaks and the intervals between their appearance timings; and estimates the biological information based on the feature quantities. A biological information measuring device according to any one of claims 1 to 4.

6. The aforementioned biological information is blood pressure or the degree of progression of arteriosclerosis. The biological information measuring device according to claim 5.

7. A first peak is obtained, which is the point where the maximum or minimum value within the period of the acceleration pulse wave obtained by the second derivative of the volume pulse wave is taken. Peak detection is performed to detect the maximum or minimum value of the acceleration pulse wave as a peak. One peak is sequentially selected from among the multiple peaks detected by the peak detection. Based on a first ratio, which is the ratio of the absolute value of the selected peak to the absolute value of the adjacent peak adjacent to the selected peak, it is determined whether the selected peak is the first peak, and biological information related to blood vessels is estimated. A device for measuring biological information.

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