Heart rate estimation device and earphone system

Abstract

A heart rate estimation device is provided with: a feature point extraction unit that extracts waveform feature points from a waveform indicating pressure fluctuations in a confined space; a pulse waveform generation unit that generates a pulse waveform in which a pulse is formed at the same time as the waveform feature point; a frequency analysis unit that performs frequency analysis on the pulse waveform; and a heart rate estimation unit that estimates the heart rate by analyzing the periodic feature points obtained from the results of the frequency analysis.

Description

Technical Field

[0001] The technology of the present disclosure relates to a heart rate estimation device and a headphone system. Background Art

[0002] Attempts are actively being made to collect biological information from the human head using headphones. Among biological information, for head movements, deep body temperature, and heartbeat, information with particularly high collection accuracy is also desired.

[0003] In the process of estimating the heart rate, it becomes important to exclude false detections caused by noise generated by body movements. Therefore, in an optical heart rate estimation method, a method that combines observing the same part from multiple directions using multiple light sources or sensors and removing noise caused by body movements is known (for example, U.S. Patent No. 8,998,815). Summary of the Invention Problems to be Solved by the Invention

[0004] However, in the case of estimating the heart rate based on pressure changes in a closed space, the pressure changes in the same space are generally uniform. Therefore, even if multiple pressure sensors or microphones are used, no noise reduction effect can be expected.

[0005] Therefore, for the pressure changes sensed by the pressure sensors or microphones, it is necessary to analyze them in a state including noise to estimate the heart rate. However, in the pressure changes in a closed space, the changes caused by body movements are often significantly larger than the expansion and contraction of blood vessels accompanying blood flow.

[0006] Therefore, even if general frequency analysis (spectrum analysis) is performed on the pressure changes in a closed space, the characteristics caused by body movements, artifacts, etc. become dominant, and the periodic characteristic points caused by the expansion and contraction of blood vessels are masked, which becomes a problem.

[0007] An object of the technology of the present disclosure is to provide a heart rate estimation device and a headphone system that can accurately estimate the heart rate based on pressure changes in a closed space. Means for Solving the Problems

[0008] One aspect of the present disclosure is a heart rate estimation device configured to include: a feature point extraction unit that extracts waveform feature points from a waveform representing pressure changes in a closed space; a pulse waveform generation unit that generates a pulse waveform having pulses formed at the same times as the waveform feature points; a frequency analysis unit that performs frequency analysis on the pulse waveform; and a heart rate estimation unit that estimates the heart rate by analyzing periodic characteristic points obtained from the result of the frequency analysis.

[0009] One aspect of the present disclosure is a headphone system configured to include: the above-described heart rate estimation device; and headphones including a hollow housing worn on a user's ear, a sound signal output driver provided inside the housing, a light detection unit provided inside the housing and behind the driver, and an output unit that outputs a signal output from the light detection unit as a waveform representing pressure fluctuations in a closed space to the heart rate estimation device. Advantageous Effects of the Invention

[0010] As described above, according to the heart rate estimation device and the headphone system of the technology of the present disclosure, the heart rate can be estimated with high accuracy based on pressure fluctuations in a closed space. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1A It is a diagram showing an example of a waveform of a signal output from a microphone. Figure 1B It is a diagram showing an example of waveform feature points extracted from a waveform of a signal output from a microphone. Figure 1C It is a diagram showing an example of the same time as the waveform feature points. Figure 1D It is a diagram for explaining a method of generating a pulse waveform. Figure 1E It is a diagram showing an example of a pulse waveform. Figure 1F It is a diagram showing an example of a frequency analysis result of a pulse waveform. Figure 1G It is a diagram showing an example of period feature points obtained from the result of frequency analysis. Figure 2A It is a diagram showing an example of waveform feature points extracted from a waveform of pressure fluctuations in a closed space at rest. Figure 2B It is a diagram showing an example of period feature points obtained from the result of frequency analysis. Figure 3A It is a diagram showing an example of waveform feature points extracted from a waveform of pressure fluctuations in a closed space during body movement. Figure 3B It is a diagram showing an example of period feature points obtained from the result of frequency analysis. Figure 4A It is a diagram showing an example of a frequency analysis result based on a discrete Fourier transform. Figure 4B It is a diagram showing an example of a frequency analysis result of a fast Fourier transform. Figure 5 It is a cross-sectional view showing the overall configuration of headphones according to a first embodiment of the technology of the present disclosure. Figure 6 It is a block diagram showing the configuration of an information processing unit according to a first embodiment of the technology of the present disclosure. Figure 7 It is a diagram for explaining a method of extracting a zero crossing as a waveform feature point. Figure 8 It is a diagram showing examples of assumed peaks and assumed valleys detected from a signal waveform. Figure 9 It is a diagram showing examples of assumed peaks and assumed valleys after removing assumed peaks and assumed valleys presumed to be erroneously detected. Figure 10 It is a flowchart showing the content of a heart rate estimation process executed by an information processing unit according to a first embodiment of the technology of the present disclosure. Figure 11 It is a cross-sectional view showing the overall configuration of a headphone according to a second embodiment of the technology of the present disclosure. Figure 12 It is a cross-sectional view showing the overall structure of a headphone of the embodiment. Detailed Embodiment

[0012] Hereinafter, embodiments of the technology of the present disclosure will be described in detail with reference to the drawings.

[0013] [First Embodiment] <Overview of the First Embodiment of the Technology of the Present Disclosure> In the process of estimating the heart rate, it is important to exclude false detection of the heart rate due to noise generated by body movement, artifacts, etc.

[0014] In the present embodiment, for the waveform of the pressure change in a closed space, instead of directly performing frequency analysis, waveform feature points as the feature points of the waveform are extracted, and a pulse waveform that forms a pulse at the same time as the waveform feature points is generated. Frequency analysis is performed on the pulse waveform, and the heart rate is estimated by analyzing the period feature points as the feature points of the frequency analysis.

[0015] Specifically, obtain Figure 1A the waveform of the pressure change in the closed space shown, as Figure 1B shown, extract waveform feature points (refer to the × marks in Figure 1B ). Then, as Figure 1C , Figure 1D shown, generate a pulse waveform that forms a pulse at the same time as the waveform feature points. Then, perform frequency analysis on the Figure 1E shown pulse waveform to obtain Figure 1F the frequency analysis result shown. Then, estimate the heart rate based on the period feature points indicated by the arrow marks in Figure 1G .

[0016] Waveform feature points caused by large pressure changes in a closed space due to body movement or the like and waveform feature points caused by minute pressure changes in the closed space due to the expansion and contraction of blood vessels are converted to the same change level for frequency analysis. Therefore, in the conventional method, even in a situation such as the frequency characteristics being masked by body movement, it is easy to discriminate the periodic feature points manifested due to the expansion and contraction of blood vessels, and even in a state where body movement occurs, the heart rate can be estimated with high accuracy.

[0017] Here, Figure 2A An example of waveform feature points extracted from the waveform of the pressure change in the closed space at rest is shown. In this example, the pulse is estimated to be 64 ppm (1.07 Hz) based on the waveform interval of the expansion and contraction of blood vessels. At this time, in the present embodiment, Figure 2B the result of the frequency analysis shown is obtained, and the peak (refer to the arrow mark in Figure 2B ) clearly appears in the frequency band (band) calculated based on the waveform interval and is extracted as a periodic feature point. It can be seen that this periodic feature point corresponds to 64 ppm (1.07 Hz).

[0018] In addition, Figure 3A An example of waveform feature points extracted from the waveform of the pressure change in the closed space during body movement is shown. In this example, the pulse is estimated to be 68 ppm (1.13 Hz) based on the waveform interval of the expansion and contraction of blood vessels. At this time, in the present embodiment, Figure 3B the result of the frequency analysis shown is obtained, and the peak (refer to the arrow mark in Figure 3B ) clearly appears in the frequency band calculated based on the waveform interval and is extracted as a periodic feature point. It can be seen that this periodic feature point corresponds to 68 ppm (1.13 Hz).

[0019] In addition, regarding the amplitude of the pulse waveform, any value can be used to change it, but it can also be fixed to a constant value. In addition, regarding the frequency analysis, the fast Fourier transform can be used, but the discrete Fourier transform (DFT) is preferably used.

[0020] For the purpose of estimating the heart rate, as long as a strong frequency can be extracted, the discrete Fourier transform of the frequency characteristic Ff of the pulse wave X(n) can omit operations for normalizing the output such as calculating the square root of the mean square and is simplified as follows. (1)

[0022] In Equation (1), N represents the number of samples of the pulse wave X(n), f s represents the sampling frequency of X(n), and f represents the frequency to be analyzed.

[0023] In addition, since X(n) is a pulse wave and is zero except at the pulse point n where there is a pulse, no operation is required. Additionally, when the amplitude is a fixed value, the sin value / cos value is obtained only by referring to a table. Therefore, the intensity is obtained by performing N ALU / MUX operations (a composite process of referring to the table, multiplication, and addition) for each frequency f. In addition, the operation can be limited to only the frequencies f required for heart rate estimation. Therefore, compared with the case where fast Fourier transform is performed (computational complexity: N log 2 N), a significant reduction in computational complexity is achieved.

[0024] The following shows an implementation when applying Equation (1) to the above-mentioned Figure 1E pulse waveform.

[0025] Sampling frequency f s = 100 Hz Sample length N = 2048 Number of pulse points 45 points Analysis frequency 0.5 ≤ f ≤ 3.0 (corresponding to 30 ppm to 180 ppm)

[0026] The above-mentioned Figure 1E cost required for calculating the frequency characteristic Xf in the pulse waveform is as follows.

[0027] The capacity of the table memory is 2048 × 16 bits. The number of ALU / MUX operations is 4500 times (= 45 (number of pulse points) × 50 (number of analysis frequency bands) × 2 (real part + imaginary part)). Here, "45" represents the number of pulse points, "50" represents the number of analysis frequency bands, and "2" represents the two, namely the real part and the imaginary part.

[0028] The cost required when obtained by fast Fourier transform is as follows.

[0029] The capacity of the table memory is 2048 × 16 bits. The number of ALU / MUX operations is 40960 times (= 2048 × log 2 (2048) × 2).

[0030] In this way, compared with the cost required in the case of obtaining by fast Fourier transform, the effect in this implementation can be confirmed.

[0031] The obtained frequency characteristic is shown in Figure 4A , Figure 4B . Figure 4A is a diagram showing an example of the result of frequency analysis based on discrete Fourier transform, showing an example where the analysis frequency f is from 0.5 Hz to 3.0 Hz. Figure 4B is a diagram showing an example of the result of frequency analysis based on fast Fourier transform.

[0032] Regarding the result of frequency analysis based on the discrete Fourier transform, the scale on the Y-axis is different from the result of frequency analysis based on the fast Fourier transform. However, since the shapes are the same, it can be confirmed that there is no problem even when simplified by the discrete Fourier transform.

[0033] <Configuration of the headphone system according to the first embodiment of the technology of the present disclosure> The headphone system according to the first embodiment of the technology of the present disclosure has Figure 5 the headphone 100 shown.

[0034] As Figure 5 shown, the headphone 100 has a hollow housing 10 that houses various functional components inside and is worn on the user's ear. In addition, the headphone 100 has a tubular ear canal insertion portion 12, which is a part of the housing 10 and is provided at the ear canal side portion of the housing 10 when worn on the user's ear and has a hollow portion.

[0035] In addition, the headphone 100 has a driver 14 for outputting a sound signal provided inside the housing 10.

[0036] In addition, the headphone 100 includes: a microphone 16 that is provided to collect signals propagating into the hollow portion of the ear canal insertion portion 12; a reproduction unit 20 that outputs a sound signal from the driver 14; and a communication unit 23 that sends the output of the microphone 16 to the information processing unit 50.

[0037] The reproduction unit 20 and the communication unit 23 are mounted on a main substrate (not shown) disposed inside the housing 10.

[0038] When no sound signal is output from the driver 14, the signal output from the microphone 16 is sent to the information processing unit 50 through the communication unit 23. Here, the signal output from the microphone 16 when no sound signal is output from the driver 14 is a waveform representing the pressure change in the closed space formed by the hollow portion of the housing 10 and the user's ear canal, for example, the waveform shown above Figure 1A shown.

[0039] The headphone system according to the first embodiment of the technology of the present invention has Figure 6 the information processing unit 50 shown. In addition, the information processing unit 50 is an example of a heart rate estimation device.

[0040] The information processing unit 50 is composed of a portable terminal, a computer terminal, etc., or the information processing unit 50 is built into the headphone system. Here, the mobile terminal includes a smart phone terminal, a mobile phone, and a PDA (Personal Digital Assistant) terminal. The computer terminal includes a notebook-type, book-type computer terminal, and a desktop computer terminal. The information processing unit 50 can be provided in the housing 10 of the headphone 100, or can be provided separately from the headphone 100.

[0041] The information processing unit 50 is composed of a computer having a CPU, a RAM, and a ROM storing a program for executing a heart rate estimation processing routine described later, and is functionally configured as follows.

[0042] As Figure 6 shown, the information processing unit 50 includes an input unit 60, an arithmetic unit 70, and an output unit 80. The arithmetic unit 70 includes a feature point extraction unit 72, a pulse waveform generation unit 74, a frequency analysis unit 76, and a heart rate estimation unit 78.

[0043] The input unit 60 accepts the input of the signal output from the microphone 16 received from the headphone 100.

[0044] The feature point extraction unit 72 extracts waveform feature points from the signal output from the microphone 16 as described above Figure 1B shown.

[0045] Specifically, the feature point extraction unit 72 extracts a zero crossing having a variation amplitude equal to or greater than a threshold value as a waveform feature point from the waveform representing the pressure variation in the closed space, that is, the signal output from the microphone 16 (refer to Figure 7 the × mark in).

[0046] Figure 7 The following figure is Figure 7 an enlarged view of the rectangular frame part in the above figure. In Figure 7 the following figure, among the zero crossing points having a variation amplitude equal to or greater than the threshold value before and after the zero crossing, the zero crossing point indicated by the arrow mark is presumed to be the zero crossing point based on the heartbeat. The other zero crossing points are presumed to be the zero crossing points caused by body movement.

[0047] Here, the threshold value of the variation amplitude in zero crossing extraction can also be changed according to the situation. In addition, since the periodicity of all the zero crossing points extracted as the zero crossing points having a variation amplitude equal to or greater than the threshold value appears in the frequency characteristics, even if the zero crossing points caused by heartbeat and body movement are mixed, the periodic feature points appear in the heartbeat period. However, if there are many waveform feature points based on body movement, the periodic feature points based on body movement are also found accordingly. Therefore, it is best to have as few waveform feature points based on body movement as possible, but it is not necessary to exclude the waveform feature points based on body movement.

[0048] In addition, as long as it is a method that can extract waveform feature points including points linked to the heartbeat, it is also possible to extract points other than zero-crossing points as waveform feature points. For example, it is also possible to extract peak points and valley points as waveform feature points from the waveform representing the pressure change in the closed space. In this case, peak points and valley points are detected from the waveform representing the pressure change in the closed space ( Figure 8 ), and the peak points and valley points presumed to be misdetected among the detected peak points and valley points are removed ( Figure 9 ). More specifically, as Figure 8 shown, points exceeding the moving average of their respective upper limits are extracted from the waveform representing the pressure change in the closed space as hypothetical peak points (refer to the white circle parts). In addition, points below the moving average of their respective lower limits are extracted as hypothetical valley points (refer to the black circle parts). Then, considering the time difference and amplitude amount from the hypothetical valley points to the hypothetical peak points, the intervals of valid hypothetical peak points, and the intervals of valid hypothetical valley points, the presumed misdetected hypothetical peak points and hypothetical valley points are removed.

[0049] In addition, machine learning can also be used to extract peak points and valley points from the waveform representing the pressure change in the closed space as waveform feature points. For example, the method described in Reference 1 can be used to extract peak points and valley points.

[0050] [Reference 1]: "Robust ECG R-peak Detection Using LSTM", Juho Laitala et al., SAC‘20: Proceedings of the 35 th Annual ACM Symposium on Applied Computing. March 2020 Pages 1104-1111, https: / / doi.org / 10.1145 / 3341105.3373945

[0051] The pulse waveform generation unit 74 generates a pulse waveform in which pulses with a specified amplitude are formed at the same time as the waveform feature points. For example, as described above Figure 1D shown, a pulse waveform in which pulses with a certain amplitude are formed at the same time as the waveform feature points is generated. In addition, the pulses of the pulse waveform may not have a constant amplitude, and any value can be used to make it change.

[0052] The frequency analysis unit 76 performs frequency analysis on the pulse waveform. Specifically, the frequency analysis unit 76 performs frequency analysis by discrete Fourier transform to obtain Figure 1FFrequency characteristics as shown. Additionally, instead of using the discrete Fourier transform, the fast Fourier transform can also be used.

[0053] The heart rate estimation unit 78 estimates the heart rate by analyzing the periodic feature points obtained from the result of the frequency analysis.

[0054] Specifically, as Figure 1G shown, the heart rate estimation unit 78 extracts the period corresponding to the frequency with a frequency component greater than the threshold value from the result of the frequency analysis as the period feature point. The heart rate estimation unit 78 identifies the period feature point corresponding to the heart beat interval among the extracted period feature points, and estimates the heart rate per unit time based on the identified period feature points.

[0055] <Operation of the headphone system according to the first embodiment of the technology of the present disclosure> When the housing 10 of the headphone 100 is worn on the user's ear, an instruction for estimating the heart rate is received from the user's information processing unit 50 through wireless communication. At this time, the headphone 100 transmits the signal output from the microphone 16 when no sound signal is output from the driver 14 to the information processing unit 50 through the communication unit 23.

[0056] Then, when the information processing unit 50 receives the signal output from the microphone 16, it performs the heart rate estimation process as Figure 10 shown.

[0057] First, in step S100, the input unit 60 obtains the signal output from the microphone 16 received from the headphone 100.

[0058] In step S102, the feature point extraction unit 72 extracts waveform feature points from the signal output from the microphone 16.

[0059] In step S104, the pulse waveform generation unit 74 generates a pulse waveform having pulses with a specified amplitude formed at the same time as the waveform feature points.

[0060] In step S106, the frequency analysis unit 76 performs a frequency analysis on the pulse waveform.

[0061] In step S108, the heart rate estimation unit 78 estimates the heart rate by analyzing the periodic feature points obtained from the result of the frequency analysis, displays the estimated heart rate through the output unit 80, and ends the heart rate estimation process.

[0062] As described above, in the headphone system according to the first embodiment of the technology of the present disclosure, the heart rate estimation device extracts waveform feature points from a signal representing the pressure change of the closed space output from the microphone of the headphone. The heart rate estimation device generates a pulse waveform that forms a pulse at the same time as the waveform feature points, and estimates the heart rate by analyzing the periodic feature points obtained from the result of performing frequency analysis on the pulse waveform. Thus, the heart rate can be accurately estimated based on the pressure change of the closed space. In particular, even in a state where body movement occurs, the heart rate can be estimated.

[0063] In addition, zero-crossing points having a variation amplitude above a threshold are extracted from the signal representing the pressure change of the closed space as waveform feature points, thereby improving the accuracy of heart rate estimation.

[0064] In addition, in the frequency analysis of the pulse waveform, by using the discrete Fourier transform, a significant reduction in the amount of calculation is achieved compared to the case of using the fast Fourier transform.

[0065] According to the technology of the present invention, the waveform feature points are extracted from the waveform representing the pressure change of the closed space by the feature point extraction unit. The pulse waveform generation unit generates a pulse waveform that forms a pulse at the same time as the waveform feature points. The frequency analysis unit performs frequency analysis on the pulse waveform. The heart rate estimation unit estimates the heart rate by analyzing the periodic feature points obtained from the result of the frequency analysis.

[0066] In this way, the waveform feature points are extracted from the waveform representing the pressure change of the closed space, a pulse waveform that forms a pulse at the same time as the waveform feature points is generated, and the heart rate is estimated by analyzing the periodic feature points obtained from the result of performing frequency analysis on the pulse waveform. Thus, the heart rate can be accurately estimated based on the pressure change of the closed space.

[0067] The feature point extraction unit according to the technology of the present invention can extract zero-crossing points, peak points, or valley points having a variation amplitude above a threshold from the waveform representing the pressure change of the closed space as the waveform feature points.

[0068] The pulse waveform generation unit according to the technology of the present invention can generate the above pulse waveform that forms a pulse with a specified amplitude.

[0069] The frequency analysis unit according to the technology of the present invention can perform the above frequency analysis by the discrete Fourier transform.

[0070] The headphone system related to the technology of the present invention is configured to include: the above-mentioned heart rate estimation device; and headphones, including a hollow housing worn on the user's ear, a driver for outputting a sound signal provided inside the housing, a microphone provided inside the housing and behind the driver, and an output unit that outputs a signal output from the microphone to the heart rate estimation device as a waveform representing a pressure change in a closed space.

[0071] According to the technology of the present disclosure, in the headphones, a signal output from the microphone is output to the heart rate estimation device as a waveform representing a pressure change in a closed space through the output unit.

[0072] Moreover, the heart rate is estimated by the heart rate estimation device based on the signal output from the microphone.

[0073] In this way, a signal output from the microphone can be used as a waveform representing a pressure change in a closed space, and the heart rate can be accurately estimated based on the pressure change in the closed space.

[0074] According to the technology of the present disclosure, in the headphones, a signal output from the light detection unit is output to the heart rate estimation device as a waveform representing a pressure change in a closed space through the output unit.

[0075] Moreover, the heart rate is estimated by the heart rate estimation device based on the signal output from the light detection unit.

[0076] In this way, a signal output from the light detection unit can be used as a waveform representing a pressure change in a closed space, and the heart rate can be accurately estimated based on the pressure change in the closed space.

[0077] [Second Embodiment] Next, a headphone system according to the second embodiment will be described. Parts having the same structure as those in the first embodiment are denoted by the same reference numerals and description thereof is omitted.

[0078] In the second embodiment, it is different from the first embodiment in that photoelectric plethysmography is used to obtain biological information.

[0079] <Configuration of the Headphone System According to the Second Embodiment of the Technology of the Present Disclosure> The headphone system according to the second embodiment of the technology of the present disclosure includes Figure 11 the headphones 200 shown. In addition to a configuration similar to that of the headphones 100 according to the above first embodiment, the headphones 200 further include a photoelectric plethysmograph 216 that measures photoelectric plethysmography. The photoelectric plethysmograph 216 is an example of a light detection unit.

[0080] The photoelectric pulse wave measuring instrument 216 includes a light emitting part such as a near-infrared LED and a light detecting part such as a phototransistor, measures the photoelectric pulse wave in the external auditory canal, and outputs a signal representing the measured photoelectric pulse wave. The signal output from the photoelectric pulse wave measuring instrument 216 is sent to the information processing part 50 through the communication part 23. Here, the signal output from the photoelectric pulse wave measuring instrument 216 is a waveform representing the pressure change of the closed space formed by the hollow part of the housing 10 and the user's ear, for example, is the same waveform as the Figure 1A waveform shown above.

[0081] The input part 60 of the information processing part 50 receives the input of the signal output from the photoelectric pulse wave measuring instrument 216 received from the earphone 200.

[0082] The feature point extraction part 72 extracts waveform feature points from the signal output from the photoelectric pulse wave measuring instrument 216.

[0083] In addition, regarding the other structures and functions of the earphone system according to the second embodiment, since they are the same as those of the first embodiment, the description thereof is omitted.

[0084] As described above, according to the earphone system of the second embodiment of the technology of the present disclosure, the heart rate estimation device extracts waveform feature points from the signal representing the pressure change of the closed space output from the photoelectric pulse wave measuring instrument of the earphone. The heart rate estimation device generates a pulse waveform that forms a pulse at the same time as the waveform feature point, and estimates the heart rate by analyzing the period feature points obtained from the result of performing frequency analysis on the pulse waveform. Thus, the heart rate can be estimated with high accuracy based on the pressure change of the closed space. In particular, even in a state where body movement occurs, the heart rate can be estimated.

[0085] <Example> An example of the earphone of the first embodiment described above will be described. As Figure 12 shown, the housing 10 of the earphone of this embodiment is formed by fitting the main housing 1a and the front housing 1b.

[0086] The main housing 1a is a hollow member having a generally cylindrical shape, and the rear opening is closed by the cover 2. Inside the main housing 1a, a main substrate 3 is disposed opposite to its opening. The main substrate 3 is a substrate on which electronic components that function as the reproduction part 20 and the communication part 23 are mounted.

[0087] A battery 6 is disposed in front of the main substrate 3 with a battery buffer pad 7 and a battery cover 8 interposed therebetween.

[0088] A housing rubber 9 is provided on the outer periphery of the main housing 1a. The housing rubber 9 is a cylindrical elastic member embedded in the outer periphery of the main housing 1a, alleviates contact with the ear, and prevents water from entering the housing 10.

[0089] The front case 1b is arranged to close the front opening of the cylindrical main case 1a. The front case 1b is in the shape of an oblique circular truncated cone as a whole, and a part of the peripheral edge is slightly raised toward the eardrum side.

[0090] An external auditory canal insertion portion 12 protruding from the top of an oblique circular cone toward the eardrum side is provided in front of the front shell 1b. The external auditory canal insertion portion 12 is a cylindrical shape provided in a part of the front shell 1b, and is open at the front and the rear, so that the inside and the outside of the front shell 1b are connected. A driver 14 having a cylindrical shell is provided inside the external auditory canal insertion portion 12. Therefore, a positioning portion 11 of the driver 14 is provided near the front opening of the external auditory canal insertion portion 12, and the front end of the driver 14 is engaged with the positioning portion 11, so that the driver 14 is fixed to the inner side surface of the external auditory canal insertion portion 12. The rear end of the driver 14 is configured to reach near the front end of the front shell 1b. The driver 14 has a magnetic circuit, a vibration plate, etc. for generating an output signal in the cylindrical shell, using an appropriate known structure.

[0091] The earphone of this embodiment includes a microphone 16. The microphone 16 is provided in the vicinity of the external auditory canal insertion portion 12 in the front housing 1b, that is, behind the driver 14.

[0092] The microphone 16 is mounted on the microphone substrate 15. The microphone substrate 15 is fixed to the block 416. The block 416 is a block-shaped component that supports the microphone 16 and the microphone substrate 15. The microphone substrate 15 and the block 416 are provided with openings 15a and 16a so that the sound signal in the external auditory canal can reach the microphone 16.

[0093] The inner surface of the external auditory canal insertion portion 12 is provided with a groove, i.e., a hollow portion 16A, formed along the axial direction of the external auditory canal insertion portion 12. The hollow portion 16A is a square groove-shaped (angular groove-shaped) space formed between the side surface of the driver 14, and is connected from the front end portion of the external auditory canal insertion portion 12 to the opening portion 16a of the block 416 fixed to the front shell 1b.

[0094] An earpiece 13 is fixed to the outer periphery of the external auditory canal insertion portion 12. The earpiece 13 is also called an earplug, an ear pad, or an ear cap, and is made of, for example, an elastic member such as silicone rubber. The earpiece 13 has a hemispherical, close-fitting portion 13a that is in close contact with the wall of the external auditory canal at the front end of the cylindrical portion 13b embedded in the outer periphery of the external auditory canal insertion portion 12. An earpiece mounting groove 412 is provided on the outer periphery of the external auditory canal insertion portion 12, and on the other hand, a fitting portion 13c is provided on the inner periphery of the cylindrical portion 13b of the earpiece 13, and the fitting portion 13c is engaged with the earpiece mounting groove 412, whereby the earpiece 13 is fixed to the external auditory canal insertion portion 12.

[0095] As described in the above embodiment, a configuration is adopted in which the microphone is arranged behind the driver. Thus, compared with the case where the microphone is arranged in front of the driver, the volume of the closed space formed by the housing of the earphone incorporating necessary components and the external auditory canal can be reduced.

[0096] In addition, regarding the closed space formed by the housing of the earphone and the external auditory canal, the smaller the volume of the closed space, the greater the pressure variation accompanying the expansion and contraction of blood vessels with blood flow (see Reference 2). Therefore, by reducing the volume of the closed space using the configuration described in the above embodiment, the pressure variation in the closed space can be obtained more accurately, and the heart rate can be estimated with high accuracy.

[0097] [Reference 2] Patent Document 1: Japanese Patent No. 6082131

[0098] Furthermore, the technology of the present disclosure is not limited to the above embodiments, and various modifications and applications can be made without departing from the gist of the technology of the present disclosure.

[0099] For example, in the above embodiments, the case where the technology of the present disclosure is applied to an earphone is taken as an example for description, but it is not limited thereto. The technology of the present disclosure can also be applied to other than earphones. For example, the technology of the present disclosure can also be applied to an electronic stethoscope.

[0100] The entire disclosure of Japanese Application 2022-171796 is incorporated herein by reference.

[0101] All references, patent applications, and technical standards described in this specification are incorporated herein by reference to the same extent as if each reference, patent application, and technical standard was specifically and individually stated to be incorporated by reference.

Claims

1. A heart rate estimation device, comprising: A feature point extraction unit that extracts waveform feature points from a waveform representing pressure changes in a closed space; A pulse waveform generation unit that generates a pulse waveform having a pulse formed at the same time as the waveform feature points; A frequency analysis unit that performs frequency analysis on the pulse waveform; and A heart rate estimation unit that estimates the heart rate by analyzing period feature points obtained from the result of the frequency analysis.

2. The heart rate estimation device according to claim 1, wherein the feature point extraction unit extracts a zero crossing, a peak point, or a valley point having a change amplitude equal to or greater than a threshold value from the waveform representing pressure changes in the closed space as the waveform feature points.

3. The heart rate estimation device according to claim 1, wherein the pulse waveform generation unit generates the pulse waveform having a prescribed amplitude.

4. The heart rate estimation device according to claim 1, wherein the frequency analysis unit performs the frequency analysis by discrete Fourier transform.

5. A headphone system, which Comprises: The heart rate estimation device according to any one of claims 1-4 and headphones; The headphones include: A hollow housing worn on the user's ear; A driver for outputting a sound signal provided inside the housing; A microphone provided inside the housing and behind the driver; and An output unit that outputs a signal output from the microphone as a waveform representing pressure changes in a closed space to the heart rate estimation device.

6. A headphone system, which Comprises: The heart rate estimation device according to any one of claims 1-4 and headphones; The headphones include: A hollow housing worn on the user's ear; A driver for outputting a sound signal provided inside the housing; A light detection unit provided inside the housing and behind the driver; and An output unit that outputs a signal output from the light detection unit as a waveform representing pressure changes in a closed space to the heart rate estimation device.

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