Measurement device

Abstract

To provide a measurement device with which it is possible to heighten the accuracy of decrease of environment noise components in a data partitioning factor analysis (PFA).SOLUTION: Provided is a measurement device comprising: a signal sensor unit having a function to measure a measurement signal in which a target signal and a first noise coexist and a function to detect a first noise signal including the first noise, with the component level of the target signal being less than a prescribed value, and including one or more signal sensors; and a processing unit for performing processing to reduce the first noise component included in the measurement data by a data partitioning factor analysis, on the basis of measurement data which is the data of the measurement signal measured by the signal sensor unit and control data which is the data of the first noise signal measured by the signal sensor unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a measurement device. [Background technology]

[0002] In order to obtain good analytical results in various measurements, it is necessary to remove various environmental noises that are superimposed on the desired signal. For example, data partitioning factor analysis (PFA) has been disclosed as a method for removing environmental noise of biological origin in magnetoencephalography (see Non-Patent Document 1 and Non-Patent Document 2, especially p. 96-97).

[0003] In PFA, the signal measurement period is divided into two sections (temporal sections): a control section (e.g., a section that does not include the target signal and is therefore only noise) and a measurement section (a section that includes the target signal).The environmental noise components are estimated from the control section and then removed from the entire measurement data. As a specific example, in PFA, the section of the signal portion in the center (or near the center) of the signal measurement period is set as the section of the target signal (measurement section), and a section sufficiently distant from that in the past (for example, the section on the left side of the graph) or a section sufficiently distant from that in the future (for example, the section on the right side of the graph) is set as the control section, and control data is obtained.

[0004] Patent Document 1 describes noise removal by active cancellation, or signal processing by SSP (Signal Space Projection) using environmental noise data acquired separately from the measurement signal (see Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2008-538956 [Non-patent literature]

[0006] [Non-Patent Document 1] SS Nagarajan, HT Attias, KE Hild II and K. Sekihara, “A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data”, Stat. Med., vol. 26, no. 21, pp. 3886-3910, September 20, 2007. [Non-Patent Document 2] K. Sekihara, and SS Nagarajan, “Electromagnetic Brain Imaging: A Bayesian Perspective”, Springer, 2015 / 3 / 20, p.75-82, p.96-97 [Non-Patent Document 3] K. Sekihara, and SS Nagarajan, “Subspace-based interference removal methods for a multichannel biomagnetic sensor array”, Journal of Neural Engineering 14 (5) (2017) 051001 [Non-Patent Document 4] Kensuke Sekihara, “Bayesian Signal Processing”, Kyoritsu Shuppan, 2015 / 04 / 10, p.65-72 Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the conventional techniques described above, environmental noise components may not be completely removed when PFA is applied, and may remain, resulting in insufficient accuracy in reducing the environmental noise components. For example, in PFA, periodic waveform undulations may exist even after environmental noise components are removed using control data. Here, periodic waveform undulations may be undulations caused by, for example, periodic noises due to the inherent vibration of the sensor jig and the body movements of the subject (e.g., heartbeats), noises due to the power source, or magnetic noises from surrounding motors. In conventional technology, control data (control interval) is selected by human visual inspection from a narrow area (signal measurement period), so it was sometimes unclear whether the target signal was completely contained within the control interval.

[0008] The present disclosure has been made in consideration of the above circumstances, and has an object to provide a measurement device that can improve the accuracy of reducing environmental noise components in PFA. [Means for solving the problem]

[0009] One aspect is a measurement device having a function of measuring a measurement signal in which a target signal and a first noise are mixed, and a function of detecting a first noise signal in which the level of a component of the target signal is less than a predetermined value and which contains the first noise, and comprising a signal sensor unit including one or more signal sensors, and a processing unit that performs processing to reduce the first noise component contained in the measurement data by data partitioning factor analysis based on measurement data, which is data of the measurement signal measured by the signal sensor unit, and control data, which is data of the first noise signal measured by the signal sensor unit. Effect of the Invention

[0010] According to the present disclosure, in a measurement device, it is possible to improve the accuracy of reducing environmental noise components in a PFA. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram showing a schematic configuration of a measurement device including an information processing device according to an embodiment. [Diagram 2]1 is a diagram showing a schematic configuration of an information processing apparatus according to an embodiment; [Diagram 3] FIG. 2 is a diagram illustrating a process flow according to a first configuration example of the embodiment. [Figure 4] FIG. 11 is a diagram illustrating a process flow according to a second configuration example of the embodiment. [Diagram 5] FIG. 11 is a diagram illustrating a process flow according to a third configuration example of the embodiment. [Figure 6] FIG. 13 is a diagram illustrating a process flow according to a fourth configuration example of the embodiment. [Figure 7] 5A and 5B are diagrams illustrating an example of a group of waveforms of measurement data before noise removal according to the embodiment. [Figure 8] 6A and 6B are diagrams illustrating an example of a group of waveforms of measurement data after noise removal according to the embodiment. [Figure 9] 11A and 11B are diagrams showing an example of a group of waveforms of control data before noise removal according to the embodiment. [Figure 10] 11A and 11B are diagrams showing an example of a group of waveforms of control data after noise removal according to the embodiment. [Figure 11] FIG. 13 is a diagram showing an example of a waveform group of pre-processed control data according to the embodiment. [Figure 12] FIG. 11 is a diagram showing an example of a waveform group resulting from PFA processing according to the embodiment. [Figure 13] FIG. 13 is a diagram showing an example of a waveform of one channel before PFA treatment according to the embodiment. [Figure 14] FIG. 13 is a diagram showing an example of a waveform of one channel resulting from PFA processing according to the embodiment. [Figure 15] FIG. 1 shows examples of control sections and measurement sections in PFA. [Figure 16] FIG. 1 is a diagram showing an outline of the PFA algorithm. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0013] [Measuring equipment] FIG. 1 is a diagram showing a schematic configuration of a measurement device 1 including an information processing device 21 according to an embodiment. For convenience of explanation, FIG. 1 shows an XYZ orthogonal coordinate system, which is a three-dimensional orthogonal coordinate system. The measurement device 1 includes a plurality of L signal sensors A1 to AL, a plurality of M reference sensors B1 to BM, and an information processing device . In the example of FIG. 1, the area in which the signal sensors A1 to AL are arranged is shown as a signal sensor arrangement portion 11 in a schematic manner. The measurement device may be called, for example, a measurement system.

[0014] In this embodiment, a case where L=16 and M=20 will be described as an example. The number of the signal sensors A1 to AL may be any number equal to or greater than 2. Furthermore, although the present embodiment shows a case in which the number of the signal sensors A1 to AL is plural, the number of the signal sensor may be one. Furthermore, the number of reference sensors B1 to BM may be any number equal to or greater than 2. Furthermore, although the present embodiment shows a case in which there are a plurality of reference sensors B1 to BM, the number of reference sensors may be one.

[0015] 1 also shows a measurement object 31. The measurement object may also be called a measurement object or the like. Any object may be used as the measurement target 31, for example, the whole or a part of a living body. As a specific example, the measurement target 31 may be a human heart, a human brain, or the like.

[0016] When a plurality of signal sensors A1 to AL are present, for example, a device (such as a signal sensor unit) that integrally includes the plurality of signal sensors A1 to AL may be configured. Furthermore, when a plurality of reference sensors B1 to BM are present, for example, a device (for example, a reference sensor unit) that integrally includes the plurality of reference sensors B1 to BM may be configured. As another example, a device (for example, a sensor unit) may be configured that integrally includes both the signal sensors A1 to AL and the reference sensors B1 to BM.

[0017] <Signal sensor placement> In the example of FIG. 1, the signal sensor arrangement section 11 is a square planar area parallel to the XY plane. In the example of Figure 1, 16 signal sensors A1 to A16 are arranged inside the signal sensor arrangement section 11 at equal intervals in a direction parallel to one side of the square (direction parallel to the X-axis) and at equal intervals in a direction perpendicular to that direction (direction parallel to the Y-axis). Specifically, four signal sensors A1 to A4 are arranged at equal intervals in a direction parallel to the Y axis, four signal sensors A5 to A8 are arranged at equal intervals in a direction parallel to the Y axis, four signal sensors A9 to A12 are arranged at equal intervals in a direction parallel to the Y axis, and four signal sensors A13 to A16 are arranged at equal intervals in a direction parallel to the Y axis. In addition, four signal sensors A1, A5, A9, A13 are arranged at equal intervals in a direction parallel to the X-axis, four signal sensors A2, A6, A10, A14 are arranged at equal intervals in a direction parallel to the X-axis, four signal sensors A3, A7, A11, A15 are arranged at equal intervals in a direction parallel to the X-axis, and four signal sensors A4, A8, A12, A16 are arranged at equal intervals in a direction parallel to the X-axis. In the example of Fig. 1, the equal intervals in the direction parallel to the X-axis and the equal intervals in the direction parallel to the Y-axis are the same. In the example of Fig. 1, a plurality of signal sensors A1 to AL are arranged in an array. The plurality of signal sensors A1 to AL may be called, for example, a sensor array.

[0018] Here, the arrangement (overall arrangement) of the signal sensors A1 to AL is not particularly limited, and other arrangements may be used. The arrangement (e.g., position, direction) of each of the signal sensors A1 to AL is not particularly limited, and various modes may be used. For example, in the example of Fig. 1, the signal sensors A1 to AL are shown to have the same direction, but the direction of each of the signal sensors A1 to AL may be arbitrary. It should be noted that the name "signal sensor" is a name for explanatory purposes, and the sensor may be called by other names, such as a measurement sensor or (simply) a sensor.

[0019] <Measurement target placement> In the example of FIG. 1, when projected onto the XY plane while ignoring the positional deviation parallel to the Z axis, the measurement target 31 is placed at the center (or in the vicinity) of the signal sensor arrangement section 11. Here, the arrangement of the measurement target 31 and the signal sensors A1 to AL is not particularly limited, and other arrangements may be used.

[0020] <Target signal> Each of the signal sensors A1 to AL measures (detects) a desired signal (for ease of explanation, also referred to as a target signal) resulting from the measurement object 31. At this time, noise is superimposed on the target signal. Therefore, each of the signal sensors A1 to AL measures (detects) a signal in which the target signal and the noise are mixed (for ease of explanation, also referred to as a mixed signal). Then, each of the signal sensors A1 to AL obtains the measurement result (signal measurement result). In this embodiment, for convenience of explanation, the mixed signal may be called a measurement signal.

[0021] The target signal may be a signal of any physical quantity, such as a magnetic signal, a current signal, a voltage signal, a sound signal, or a light signal. Furthermore, as the target signal, for example, a signal originating from a living body (biological signal) may be used, or a signal originating from an object other than a living body may be used. As a specific example, when the measurement target 31 is a human heart, a magnetic signal (a magnetocardiogram signal) or an electric signal (an electrocardiogram signal) may be used as the target signal. As another specific example, when the measurement target 31 is a human brain, a magnetic signal may be used as the target signal. As another example, the target signal may be a sound and applied to a noise canceller of a speaker.

[0022] <Noise> Here, the noise is, for example, noise caused by a signal (interfering signal) other than the target signal, and may be called, for example, environmental noise. As a specific example, the environmental noise may include noise due to inherent vibration of a jig of a sensor array (e.g., an array consisting of a plurality of signal sensors A1 to AL). Such noise may be accentuated by, for example, a person's body movement (e.g., heartbeat, etc.). As a specific example, the environmental noise may include noise originating from the power supply. As a specific example, the environmental noise may include noise caused by the rotation of an air conditioning fan installed near the measuring device 1 (for example, the operation of a motor).

[0023] In this embodiment, the information processing device 21 acquires control data including such environmental noise, and uses the control data to reduce the environmental noise components from the measurement signal (measurement data) by PFA.

[0024] <Reference sensor placement> In the example of FIG. 1, the reference sensors B1 to B20 are arranged so as to surround the periphery of the signal sensor arrangement section 11. Specifically, six reference sensors B1 to B6 are arranged at equal intervals in a direction parallel to the Y axis on the negative side of the signal sensor arrangement section 11 in a direction parallel to the X axis. Moreover, six reference sensors B6 to B11 are arranged at equal intervals in a direction parallel to the X-axis on the positive side of the signal sensor arrangement section 11 in a direction parallel to the Y-axis. Moreover, six reference sensors B11 to B16 are arranged at equal intervals in a direction parallel to the Y axis on the positive side of the signal sensor arrangement section 11 in a direction parallel to the X axis. Moreover, six reference sensors B16 to B20, B1 are arranged at equal intervals in a direction parallel to the X-axis on the negative side of the signal sensor arrangement section 11 in a direction parallel to the Y-axis. In the example of Fig. 1, the equal intervals in the direction parallel to the X-axis and the equal intervals in the direction parallel to the Y-axis are the same. In the example of Fig. 1, a plurality of reference sensors B1 to BM are arranged around the signal sensors A1 to AL.

[0025] In the example of FIG. 1, four reference sensors B2 to B5 parallel to the Y axis are arranged at the same positions on the Y axis as four signal sensors A1 to A4 parallel to the Y axis, respectively. Similarly, four reference sensors B12 to B15 parallel to the Y axis are disposed at the same positions on the Y axis as four signal sensors A4, A3, A2, and A1 parallel to the Y axis, respectively. In the example of FIG. 1, the four reference sensors B7 to B10 parallel to the X-axis are disposed at the same positions on the X-axis as the four signal sensors A1, A5, A9, and A13 parallel to the X-axis, respectively. Similarly, the four reference sensors B17 to B20 parallel to the X-axis are disposed at the same positions on the X-axis as the four signal sensors A13, A9, A5, and A1 parallel to the X-axis, respectively.

[0026] Here, the arrangement (overall arrangement) of the plurality of reference sensors B1 to BM is not particularly limited, and other arrangements may be used. The arrangement (e.g., position, direction) of each of the reference sensors B1 to BM is not particularly limited, and various modes may be used. For example, in the example of Fig. 1, the reference sensors B1 to BM are shown to have the same direction, but the direction of each of the reference sensors B1 to BM may be arbitrary. It should be noted that the name "reference sensor" is a name for explanatory purposes, and the sensor may be called by other names, such as a noise sensor or (simply) a sensor.

[0027] <Reference noise> Each of the reference sensors B1 to BM measures (detects) noise (noise signal) superimposed on the target signal, and obtains the measurement result (noise measurement result). In this embodiment, a case will be described in which each of the reference sensors B1 to BM measures noise without measuring the components of the target signal. However, as another example, a configuration may be used in which the components of the target signal are included in the results of measuring noise by each of the reference sensors B1 to BM to the extent that this does not cause any practical problems.

[0028] In this embodiment, for convenience of explanation, the noise measured by the reference sensors B1 to BM is also called reference noise, and the signals measured by the reference sensors B1 to BM are also called reference signals. The reference noise may, for example, have a noise component common to the above-mentioned environmental noise, or may have a noise component different from the above-mentioned environmental noise. As a specific example, when the target signal is a magnetic signal, the reference noise may be a noise caused by a geomagnetic signal. In this case, the reference noise may be a noise caused by a magnetic signal caused by a person or other object (e.g., a train, etc.) other than the measurement target. As a specific example, when the target signal is an electrical signal, the reference noise may be electrical noise emitted from other circuits or the like in the vicinity of the measurement target 31 . Furthermore, the electric signal and the magnetic signal may be collectively treated as an electromagnetic signal (electromagnetic wave), for example.

[0029] <Signal sensor type and reference sensor type> When a plurality of signal sensors A1 to AL are used, for example, all of the sensors may be the same type (sensors that measure the same physical quantity), or different types of sensors (sensors that measure different physical quantities) may be included. When a plurality of reference sensors B1 to BM are used, for example, all of the sensors may be the same type (sensors that measure the same physical quantity), or different types of sensors (sensors that measure different physical quantities) may be included.

[0030] Furthermore, as each of the reference sensors B1 to BM, for example, a sensor of the same type as any of the signal sensors A1 to AL may be used, or a sensor of a different type from the signal sensors A1 to AL may be used. As a specific example, when magnetic sensors are used as the signal sensors A1 to AL, magnetic sensors may be used as the reference sensors B1 to BM, or a combination of a magnetic sensor and an acceleration sensor may be used as the reference sensors B1 to BM.

[0031] <Connection between information processing device and each sensor> 1, the signal sensors A1 to AL are communicatively connected to the information processing device 21 via wire or wirelessly. The information processing device 21 can acquire the measurement results (detection results) of the signal sensors A1 to AL. Further, each of the reference sensors B1 to BM is communicably connected by wire or wirelessly to the information processing device 21. The information processing device 21 can acquire the measurement results (detection results) of each of the reference sensors B1 to BM. In the example of FIG. 1, detailed illustration of the connection between the information processing device 21 and each sensor (signal sensors A1 to AL, reference sensors B1 to BM) is omitted.

[0032] Here, in this embodiment, the information processing device 21 communicates with each sensor (signal sensors A1 to AL, reference sensors B1 to BM) to acquire the measurement results from each sensor. However, as another example, a configuration may be used in which the measurement results from each sensor are temporarily stored in a portable storage medium, and then the measurement results are output from the storage medium to the information processing device 21, so that the information processing device 21 acquires the measurement results.

[0033] <Information processing device> FIG. 2 is a diagram showing a schematic configuration of an information processing device 21 according to the embodiment. The information processing device 21 includes an input unit 111, an output unit 112, a storage unit 113, and a control unit 114. The input unit 111 includes an acquisition unit 131 . The output unit 112 includes a display unit 141 . The control unit 114 includes a processing unit 151 and a display control unit 152 .

[0034] The input unit 111 receives input from the outside. In this embodiment, the input unit 111 inputs signals (signals of measurement results) output from each sensor (signal sensors A1 to AL, reference sensors B1 to BM). As a specific example, the input unit 111 may input the signals by receiving the signals transmitted from each sensor (signal sensors A1 to AL, reference sensors B1 to BM), or may input signals stored in a portable storage device from the storage device. Furthermore, the input unit 111 may have, for example, an operation unit that is operated by a user, and may input information corresponding to the content of an operation performed by the user to the operation unit.

[0035] The acquisition unit 131 acquires the signal input by the input unit 111 . The acquiring unit 131 may store the acquired signal in the storage unit 113 . Here, when the signal input by the input unit 111 is an analog signal, for example, the acquisition unit 131 may have an analog to digital (A / D) conversion function and convert the signal from an analog signal to a digital signal. Furthermore, when the information processing device 21 is applied to real-time processing, the acquisition unit 131 acquires signals in real time. Note that even when the information processing device 21 is not applied to real-time processing, the acquisition unit 131 may acquire signals in real time.

[0036] Here, in this embodiment, a case is shown in which the acquisition unit 131 has a function of acquiring signals measured by each of the signal sensors A1 to AL and a function of acquiring signals measured by each of the reference sensors B1 to BM, but as another example, these functions may be provided separately.

[0037] The output unit 112 performs output to the outside. The display unit 141 displays and outputs information relating to the signal processing results. The display unit 141 has a screen such as a liquid crystal display (LCD) and displays information related to the signal processing result on the screen. As another configuration example, the display unit 141 may print out information related to the signal processing result on paper. The output unit 112 may have a function of outputting in other modes, such as audio output.

[0038] The storage unit 113 has a storage device such as a memory, and stores information. The storage unit 113 stores information such as, for example, an input signal and a processing result of the signal. Furthermore, the storage unit 113 stores information such as a control program, for example.

[0039] The control unit 114 performs various types of processing and control. In this embodiment, the control unit 114 has a processor such as a CPU (Central Processing Unit), and the processor executes a control program stored in the storage unit 113 to perform various types of processing and various types of control. The processor includes an arithmetic unit that performs various calculations.

[0040] The processing unit 151 performs a predetermined process based on the signals of the measurement results from the signal sensors A1 to AL. In this embodiment, the predetermined process is a process for reducing the components of the environmental noise contained in the mixed signal (measurement signal) of the target signal and the environmental noise. Furthermore, the processing unit 151 may perform a predetermined process based on the signals of the measurement results from the reference sensors B1 to BM as well as the signals of the measurement results from the signal sensors A1 to AL.

[0041] In this embodiment, the reduction of a predetermined target component may be called, for example, removal. In this case, the degree of reduction (removal) may be, for example, a complete removal of the target component, or a practically effective removal of the target component, although not complete. For example, the degree to which the environmental noise components are reduced from the mixed signal (measurement signal) may be any degree that is practically effective, that is, a mode in which the environmental noise components are completely eliminated from the mixed signal may be used, or a mode in which the environmental noise components are eliminated from the mixed signal to an unnecessary degree may be used. The display control unit 152 outputs various types of information to the screen of the display unit 141 for display.

[0042] In this embodiment, the processing unit 151 removes the environmental noise component from a mixed signal (measurement signal) of the target signal and the environmental noise by performing processing of a PFA algorithm. In this embodiment, for convenience of explanation, data including the environmental noise component may be called control data.

[0043] <Timing and duration of control data> In this embodiment, the timing for measuring the measurement signal and the timing for measuring the environmental noise (control data) are different from each other (different timings). In this embodiment, such another timing means that the environmental noise (control data) is measured during a period when the measurement signal is not being measured, and more specifically, a mode is used in which the environmental noise (control data) is measured during a period when the target signal is not measured (or a period when the level of the target signal is less than a predetermined value). The predetermined value may be set to a practically appropriate value. Furthermore, among these other timing aspects, the effect of reducing the environmental noise component can be enhanced by measuring the same (or similar) environmental noise as the environmental noise contained in the measurement signal as control data. Therefore, for example, the timing for measuring the environmental noise (control data) may be immediately before or after the timing for measuring the measurement signal.

[0044] As a specific example, when measuring a magnetic signal caused by the heart of a person (subject), environmental noise (control data) may be measured at a timing when the person's heart is at a predetermined distance or more from the measurement position (non-measurement state). At this timing, the target signal (in this example, the magnetic signal caused by the heart) does not exist, or the level of the target signal is less than a predetermined value, for example, the target signal is smaller than the environmental noise. In this embodiment, the subject represents a person (human) who is the subject of measurement, and may be called, for example, a subject or a test subject.

[0045] In addition, the length of the period for measuring the environmental noise (control data) is not particularly limited, and may be, for example, the same length as the period for measuring the measurement signal, or may be a different length.

[0046] <Pretreatment> The processing unit 151 may have a function of performing a predetermined pre-processing on the control data. The pre-processing may be, for example, a process for emphasizing a component (environmental noise component) to be removed from the measurement signal (measurement data) by the PFA. As a specific example, the pre-processing may be a filtering process using a band pass filter having a frequency band that passes the component, that is, a filtering process for extracting the component.

[0047] <Noise reduction processing> The processing unit 151 may have a function of performing a predetermined noise removal process on the measurement signals (measurement data) using the measurement results of the reference sensors B1 to BM. Furthermore, the processing unit 151 may have a function of performing a predetermined noise removal process on the control data using the measurement results of the reference sensors B1 to BM. Here, as the noise removal process, for example, a process such as adaptive noise canceling (ANC) may be used (for example, see Non-Patent Document 3).

[0048] [Example of environmental noise removal processing] A specific example of the environmental noise removal process performed by the information processing device 21 will be described with reference to FIGS. In this embodiment, for ease of explanation, the part combining L signal sensors A1 to AL will be referred to as signal sensor unit 331, the part combining M reference sensors B1 to BM will be referred to as reference sensor unit 332, and the part combining signal sensor unit 331 and reference sensor unit 332 will be referred to as sensor unit 311. In a configuration example in which the reference sensors B1 to BM are not used, the reference sensor unit 332 (reference sensors B1 to BM) does not need to be provided.

[0049] In this example, the signal sensor unit 331 (signal sensors A1 to AL) is common to the measurement signals and the environmental noise signals, but as another example, some or all of the signal sensors may be separate for the measurement signals and the environmental noise signals. In this example, the reference sensor section 332 (reference sensors B1 to BM) is common to the measurement signals and the environmental noise signals, but as another example, some or all of the reference sensors may be separate for the measurement signals and the environmental noise signals. In this example, the signal sensor section 331 and the reference sensor section 332 are provided separately.

[0050] <Specific example of processing related to the first configuration example> FIG. 3 is a diagram illustrating a process flow according to the first configuration example of the embodiment. FIG. 3 shows a sensor section 311 including a signal sensor section 331 and a processing section 151 a including a signal processing section 411 . In this example, the sensor section 311 does not necessarily have to include the reference sensor section 332 (reference sensors B1 to BM). Here, the processing unit 151a is an example of the processing unit 151 shown in FIG.

[0051] A specific example of the process in this example will be described. The measurement signal a1 is measured by the signal sensor unit 331, whereby measurement data a11, which is data on the measurement signal a1, is obtained. Furthermore, the environmental noise signal a2 is measured by the signal sensor unit 331 at a timing different from that of measuring the measurement signal a1, whereby control data a12, which is data on the environmental noise signal a2, is obtained.

[0052] In this example, when the measurement signal a1 is measured by the signal sensor unit 331, the measurement target 31 is maintained in a state where it is present at a predetermined measurement position. On the other hand, in this example, when the environmental noise signal a2 is measured by the signal sensor unit 331, a state in which the measurement target 31 is not present at a predetermined measurement position is maintained. The state in which the measurement object 31 is not present at the specified measurement position may be, for example, a state in which the signal component (target signal component) attributable to the measurement object 31 is not included in the environmental noise signal a2 (control data a12), or, even if the signal component is included in the environmental noise signal a2 (control data a12), the level of the signal component is below a specified value.

[0053] The signal processing unit 411 reduces the environmental noise components contained in the measurement data a11 by performing PFA processing using the measurement data a11 and the control data a12. As a result, a measurement signal (measurement data) in which the environmental noise components have been reduced is obtained as a processed signal.

[0054] <Specific example of processing related to the second configuration example> FIG. 4 is a diagram illustrating a process flow according to the second configuration example of the embodiment. FIG. 4 shows a sensor section 311 including a signal sensor section 331, and a processing section 151b including a signal pre-processing section 431 and a signal processing section 432. In this example, the sensor section 311 does not necessarily have to include the reference sensor section 332 (reference sensors B1 to BM). Here, the processing unit 151b is an example of the processing unit 151 shown in FIG.

[0055] A specific example of the process in this example will be described. This example is similar to the first configuration example except that the configuration of the processing unit 151b is different, and the same parts as in the first configuration example are denoted by the same reference numerals. In this example, similarly to the first configuration example, measurement data a11 and control data a12 are obtained.

[0056] The signal pre-processing unit 431 performs a predetermined pre-processing on the control data a12. The signal processing unit 432 performs PFA processing using the measurement data a11 and the pre-processed control data a12 to reduce the environmental noise components contained in the measurement data a11. As a result, a measurement signal (measurement data) in which the environmental noise components have been reduced is obtained as a processed signal.

[0057] In the second configuration example shown in FIG. 4, the signal pre-processing unit 431 performs a predetermined pre-processing on the control data a12, thereby emphasizing the environmental noise components contained in the control data a12. Therefore, it is possible to improve the accuracy of reducing the environmental noise components by PFA compared to the first configuration example shown in FIG. 3, for example.

[0058] <Specific example of processing related to the third configuration example> FIG. 5 is a diagram illustrating a process flow according to the third configuration example of the embodiment. FIG. 5 shows a sensor section 311 including a signal sensor section 331 and a reference sensor section 332, and a processing section 151c including a signal pre-processing section 431, a noise elimination section 451 and a signal processing section 452. Here, the processing unit 151c is an example of the processing unit 151 shown in FIG.

[0059] A specific example of the process in this example will be described. In this example, a reference sensor unit 332 is used, and the configuration of the processing unit 151c is different (mainly in that it has a noise removal unit 451 for the measurement signal a1), except that the present example is the same as the second configuration example, and parts similar to the second configuration example are given the same symbols. In this example, similarly to the second configuration example, the control data a12 is pre-processed by the signal pre-processing unit 431.

[0060] A reference signal a3 related to the measurement signal a1 is measured by the reference sensor unit 332. Here, in this example, the reference signal a3 represents a signal measured by the reference sensor section 332 when the measurement signal a1 is measured by the signal sensor section 331.

[0061] The noise elimination unit 451 performs noise elimination processing on the measurement data (in this embodiment, removal of the reference noise component) based on the result (measurement data) of the measurement signal a1 measured by the signal sensor unit 331 and the result (reference data) of the reference signal a3 measured by the reference sensor unit 332, thereby obtaining measurement data c11 after noise elimination. Here, various methods may be used as a noise removal method, and for example, an ANC method may be used.

[0062] The signal processing unit 452 performs PFA processing using the noise-removed measurement data c11 and the pre-processed control data a12 to reduce the environmental noise components contained in the measurement data c11. As a result, a measurement signal (measurement data) in which the environmental noise components have been reduced is obtained as a processed signal.

[0063] In the third configuration example shown in FIG. 5, the signal pre-processing unit 431 performs a predetermined pre-processing on the control data a12, so that the environmental noise components contained in the control data a12 become closer to the environmental noise components contained in the measurement data c11. Therefore, for example, compared to the first configuration example shown in FIG. 3, it is possible to improve the accuracy of reduction of the environmental noise components by PFA. In addition, in the third configuration example shown in FIG. 5, noise elimination is performed on the measurement data by the noise elimination unit 451, so that it is possible to improve the accuracy of the reduction of environmental noise components by PFA compared to, for example, the second configuration example shown in FIG.

[0064] <Specific example of processing related to the fourth configuration example> FIG. 6 is a diagram illustrating a process flow according to the fourth configuration example of the embodiment. FIG. 6 shows a sensor section 311 including a signal sensor section 331 and a reference sensor section 332, and a processing section 151d including a noise elimination section 451, a noise elimination section 471, a signal pre-processing section 472, and a signal processing section 473. Here, the processing unit 151d is an example of the processing unit 151 shown in FIG.

[0065] A specific example of the process in this example will be described. In this example, the configuration of the processing unit 151d is the same as that of the third configuration example, except for differences (mainly in that it has a noise removal unit 471 for the environmental noise signal a2), and parts that are similar to those of the third configuration example are given the same symbols. In this example, similarly to the third configuration example, measurement data c11 after noise removal is obtained.

[0066] A reference signal a4 related to the environmental noise signal a2 is measured by the reference sensor unit 332. Here, in this example, the reference signal a4 represents a signal measured by the reference sensor unit 332 when the environmental noise signal a2 is measured by the signal sensor unit 331.

[0067] The noise elimination unit 471 performs noise elimination processing on the control data (removal of the reference noise components in this embodiment) based on the result (control data) of the environmental noise signal a2 measured by the signal sensor unit 331 and the result (reference data) of the reference signal a4 measured by the reference sensor unit 332, thereby obtaining control data d11 after noise elimination. Here, various methods may be used as a noise removal method, and for example, an ANC method may be used.

[0068] The signal pre-processing unit 472 performs predetermined pre-processing on the control data d11 after noise removal. Here, as the pre-processing, for example, the same processing as in the second configuration example (and the third configuration example) may be performed, or a different processing may be performed.

[0069] The signal processing unit 473 performs PFA processing using the measurement data c11 after noise removal and the control data d11 after noise removal and preprocessing to reduce the environmental noise components contained in the measurement data c11. As a result, a measurement signal (measurement data) in which the environmental noise components have been reduced is obtained as a processed signal.

[0070] In the fourth configuration example shown in FIG. 6, noise elimination is performed on the control data by the noise elimination unit 471, so that it is possible to improve the accuracy of the reduction of environmental noise components by PFA compared to, for example, the third configuration example shown in FIG.

[0071] [Waveform example] 7 to 14, examples of waveforms of signals processed by the information processing device 21 are shown. In each of the graphs shown in FIG. 7 to FIG. 14, the horizontal axis represents time, and the vertical axis represents level (for example, B [pT], which is the unit of magnetic flux density). It should be noted that each graph shown in these figures shows the tendency of each waveform, but is not necessarily precise. This example shows an example of a waveform in the fourth configuration example shown in Fig. 6. Note that the same tendency is observed in the waveforms in the first to third configuration examples shown in Figs.

[0072] FIG. 7 is a diagram showing an example of a waveform group 2011 of measurement data before noise removal according to the embodiment. FIG. 8 is a diagram showing an example of a waveform group 2021 of measurement data after noise elimination according to the embodiment. FIG. 9 is a diagram showing an example of a waveform group 2111 of the control data before noise removal according to the embodiment. FIG. 10 is a diagram showing an example of a waveform group 2121 of the control data after noise removal according to the embodiment. FIG. 11 is a diagram showing an example of a waveform group 2131 of the pre-processed control data according to the embodiment. FIG. 12 is a diagram showing an example of a waveform group 2211 resulting from the PFA processing according to the embodiment.

[0073] 7 shows a waveform group 2011 of the measurement signal a1 measured by the signal sensor unit 331 (measurement data). The waveform group 2011 is a waveform group before noise elimination is performed by the noise elimination unit 451. The waveform group 2011 includes a plurality of (multiple channels) waveforms measured by a plurality of signal sensors A1 to AL, and the average results of these plurality of waveforms. FIG. 8 shows a waveform group 2021 (measurement data c11) resulting from noise elimination by the noise elimination unit 451. In this example, the waveform of the measurement data before noise removal contains large noise (for example, noise due to the influence of geomagnetism, etc.), but such noise components are reduced by the noise removal.

[0074] 9 shows a waveform group 2111 of the result (control data) of the environmental noise signal a2 measured by the signal sensor unit 331. The waveform group 2111 is a waveform group before noise elimination is performed by the noise elimination unit 471. The waveform group 2111 includes a plurality of (multiple channels) waveforms measured by a plurality of signal sensors A1 to AL, and the average results of these plurality of waveforms. 10 shows a waveform group 2121 (control data d11) resulting from noise elimination by the noise elimination section 471. The waveform group 2121 is a waveform group before pre-processing by the signal pre-processing section 472 is performed. In this example, the waveform of the control data before noise removal contains large noise due to, for example, the influence of geomagnetism, but such noise components are reduced by the noise removal.

[0075] FIG. 11 shows a waveform group 2131 resulting from pre-processing by the signal pre-processing unit 472 (control data). Here, in this example, the waveform of the control data before preprocessing includes noise components that are not the target of reduction by, for example, PFA, but such noise components are reduced by preprocessing, that is, the noise components that are the target of reduction by PFA (environmental noise components) are emphasized. FIG. 12 shows a waveform group 2211 resulting from PFA performed by the signal processor 473 (processed signals).

[0076] Here, FIG. 13 and FIG. 14 show an example of the waveform of one channel. FIG. 13 is a diagram showing an example of a waveform 3011 of one channel before the PFA process according to the embodiment. FIG. 14 is a diagram showing an example of a waveform 3021 of one channel resulting from the PFA processing according to the embodiment. In a waveform 3021 resulting from the PFA processing shown in FIG. 14, the environmental noise components are reduced compared to the waveform 3011 before the PFA processing shown in FIG. As shown in the examples of FIGS. 13 and 14, a waveform 3011 before the PFA processing contains undulation components, but such undulation components are reduced in a waveform 3021 after the PFA processing.

[0077] <Control section related to background art> In this embodiment, the control section and the measurement section are sections with different timings, but as a comparative example, in the background art, PFA is performed by setting control sections on the left and right sides of the waveform of the measurement signal (here, the waveform of the measurement signal before PFA) as shown in Fig. 12, that is, signal data in a part of the measurement section (ideally a section other than the target signal) is used as control data. However, in such background art, there is a possibility that measurement data is included in the control data, and there are cases in which sufficient accuracy of PFA cannot be obtained. In contrast to this, in this embodiment, the accuracy of the PFA process can be improved.

[0078] [Outline of PFA] Here, an overview of data partitioning factor analysis (PFA) will be described with reference to Figures 15 and 16. Details of PFA are described in, for example, Non-Patent Document 1 and Non-Patent Document 2, especially pages 96-97. PFA is an algorithm that uses the Bayes Factor Analysis (BFA) algorithm twice to remove interference signal components (environmental noise components in this embodiment) from a target signal (measurement signal in this embodiment) and estimate a measurement target signal (target signal in this embodiment). Details of Bayes Factor Analysis are described, for example, in Non-Patent Document 2, especially on pages 75-82, and in Non-Patent Document 4, especially on pages 65-72.

[0079] FIG. 15 shows an example of a control section and a measurement section in PFA. In the graph shown in FIG. 15, the horizontal axis represents time, and the vertical axis represents level (for example, B [pT], which is the unit of magnetic flux density). FIG. 15 shows a waveform group 4011 of a signal (target signal) that is to be subjected to PFA processing. Waveform group 4011 includes waveforms for multiple channels. An example of a control section and an example of a measurement section used in PFA processing are shown in Fig. 15. The control section means, for example, a data section that includes only interference signals (environmental noise) and does not include a measurement target signal (target signal), but as another example, it may include a measurement target signal (target signal) at a level greater than 0 and less than a predetermined value.

[0080] 1 shows an example of a formula representing the PFA data model.

[0081] [Number 1] y k =Au k +Bv k +ε :Measurement data y k =Bv k +ε :Control data Where: Au k : Modeling of the signal to be measured (vector) Bv k : Modeling of interference signal (vector) ε: Sensor noise (vector) y k : Multi-channel sensor data (vector) k: Sensor channel number (=1, 2, ...) A : Mixing matrix B : Mixing matrix

[0082] FIG. 16 is a diagram showing an outline of the PFA algorithm. In PFA, generally speaking, the processes of (step S1) to (step S3) are carried out in order.

[0083] In step S1, the BFA algorithm is applied to the data in the control section to obtain a mixing matrix B for the interference signal (environmental noise in this embodiment). In step S2, the BFA algorithm is applied to the data from the measurement section to obtain the confusion matrix A and the factor activity u k (Here, this u k is a symbol with a bar above it. In step S3, Au k (Here, this u k is a symbol with a bar on the upper side.) is obtained as the signal estimation result.

[0084] A comparison between PFA and SSP will now be made. Details of SSP are described in, for example, Non-Patent Document 3. In SSP, noise and signal are separated using only spatial information. In addition, SSP separates each component (noise, signal) as orthogonal components (orthogonal complement space).

[0085] On the other hand, PFA separates noise from signals by using both time and space information, which makes it effective for signals whose noise source and signal source are close to each other (signals in the same spatial domain), such as noise caused by jig vibration. In addition, PFA can separate noise and signal using an oblique basis, which allows for more precise separation of each component (noise, signal).

[0086] [About the above embodiment] As described above, the measuring device 1 according to this embodiment includes the signal sensor unit 331 and a processing unit (the processing unit 151 in the example of FIG. 2). The signal sensor unit 331 has one or more signal sensors (signal sensors A1 to AL in this embodiment) that acquire signals, and measures a signal (measurement signal) that is measured in a state in which a target signal and environmental noise are mixed. The processing unit regards the measurement signal acquired by the signal sensor unit 331 as measurement data, and separately regards the environmental noise signal acquired by the signal sensor unit 331 as control data, and executes a process of removing the environmental noise component by PFA.

[0087] As an example configuration, in the measuring device 1 according to this embodiment, the signal sensor unit 331 includes one or more signal sensors (signal sensors A1 to AL in this embodiment). The signal sensor unit 331 has a function of measuring a measurement signal in which a target signal and a first noise (environmental noise in this embodiment) are mixed, and a function of detecting a first noise signal in which the level of the target signal component is less than a predetermined value and which includes the first noise. Furthermore, in the measuring device 1 according to this embodiment, the processing unit performs processing to reduce the first noise component contained in the measurement data by data partitioning factor analysis (PFA) based on the measurement data, which is data of the measurement signal measured by the signal sensor unit 331, and the control data, which is data of the first noise signal measured by the signal sensor unit 331. Therefore, in the measuring apparatus 1 according to this embodiment, it is possible to improve the accuracy of reducing the environmental noise component (here, the first noise component) in the PFA.

[0088] In the measurement device 1 according to this embodiment, the processing unit performs pre-processing on the acquired control data to emphasize the environmental noise components to be removed. As one configuration example, in the measurement device 1 according to this embodiment, the processing unit performs preprocessing on the control data to emphasize the components to be reduced, prior to data partitioning factor analysis (PFA). Therefore, in the measuring device 1 according to this embodiment, the accuracy of reducing the environmental noise components in the PFA can be further improved.

[0089] The measuring device 1 of this embodiment is provided with a reference sensor unit 332 having one or more reference sensors (in this embodiment, reference sensors B1 to BM) that acquire a reference noise (predetermined noise), in addition to a signal sensor unit 331 having one or more signal sensors (in this embodiment, signal sensors A1 to AL) that acquire a signal. The processing unit executes a preliminary noise removal process (noise removal process) on the measurement signal (measurement data) acquired by the signal sensor unit 331 based on the data acquired by the reference sensor unit 332 .

[0090] As an example configuration, in the measurement device 1 according to this embodiment, the reference sensor unit 332 includes one or more reference sensors (in this embodiment, reference sensors B1 to BM). The reference sensor unit 332 has a function of measuring a second noise signal including a second noise (in this embodiment, a reference signal at the time of measuring the measurement signal). Furthermore, in the measurement device 1 according to this embodiment, the processing unit performs processing to reduce the second noise component contained in the measurement data based on the second noise signal measured by the reference sensor unit 332 prior to data partitioning factor analysis (PFA). Therefore, in the measuring device 1 according to this embodiment, the accuracy of reducing the environmental noise components in the PFA can be further improved.

[0091] In the measuring device 1 according to this embodiment, the processing unit executes a pre-stage noise removal process (noise removal process) on the control data acquired by the signal sensor unit 331 based on the data acquired by the reference sensor unit 332 .

[0092] As an example configuration, in the measurement device 1 according to this embodiment, the reference sensor unit 332 has a function of measuring a third noise signal including a third noise (in this embodiment, a reference signal when measuring an environmental noise signal). Furthermore, in the measuring device 1 according to this embodiment, prior to data partitioning factor analysis (PFA), the processing unit performs processing to reduce the third noise component contained in the control data based on the third noise signal measured by the reference sensor unit 332. Therefore, in the measuring device 1 according to this embodiment, the accuracy of reducing the environmental noise components in the PFA can be further improved.

[0093] In the measurement device 1 according to this embodiment, the reference sensor section 332 includes at least one of a sensor of the same type as the signal sensor section 331 or a sensor of a different type. As an example configuration, in the measurement device 1 according to this embodiment, at least one reference sensor included in the reference sensor unit 332 is a sensor of the same type as at least one signal sensor included in the signal sensor unit 331 . Therefore, in the measurement device 1 according to this embodiment, it is possible to use any appropriate type of sensor as the signal sensor and the reference sensor.

[0094] In this way, in the measuring apparatus 1 according to this embodiment, it is possible to accurately remove various environmental noise components (components of interference signals other than the target signal) in the PFA. In the measuring device 1 of this embodiment, for example, with regard to noise that is difficult to remove using PFA, by using data acquired separately from the measurement signal (for example, data that does not contain the target signal, such as empty-room noise data, or data that contains the target signal but at a small level) as control data, it is possible to accurately remove environmental noise components from the measurement signal (high-precision noise removal). Here, in this embodiment, for example, the measurement data and the control data are separated more clearly than in conventional techniques, which is believed to result in higher accuracy in removing environmental noise components in PFA.

[0095] Furthermore, in the measurement device 1 according to this embodiment, for example, by performing pre-processing on the control data to emphasize the environmental noise components to be removed, it is possible to achieve more accurate removal of the environmental noise components. As a specific example, in this embodiment, by performing PFA using the result of performing a predetermined pre-processing on the environmental noise signal as control data, it is possible to achieve more efficient removal of the environmental noise components, including periodic noise.

[0096] Furthermore, in the measuring device 1 according to this embodiment, for example, by providing a reference sensor that measures reference noise (predetermined noise) and combining a noise removal technique such as ANC with PFA, environmental noise can be removed efficiently, enabling high-precision measurements.

[0097] In this embodiment, for example, a measurement mode in which the subject is not covered with a magnetic shield may be used, or a measurement mode in which the subject is covered with a magnetic shield may be used. For example, in an environment where the external magnetic field is strong, it may be effective to reduce the external magnetic field by using a magnetic shield. In this embodiment, for example, by installing reference sensors B1 to BM and using noise removal such as ANC in combination, PFA can also be applied to magnetic measurement in an environment outside the magnetic shield. Furthermore, in this embodiment, the case where the measurement target is a person (subject) has been described, but as another example, the measurement target may be an object other than a person.

[0098] A program for implementing the functions of any of the components in any of the above-described devices may be recorded in a computer-readable recording medium, and the program may be read into a computer system and executed. The term "computer system" as used herein includes hardware such as an operating system or peripheral devices. The term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CDs (Compact Discs)-ROMs (Read Only Memory), and storage devices such as hard disks built into computer systems. The term "computer-readable recording medium" also refers to storage devices that hold a program for a certain period of time, such as volatile memory in a computer system that is a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. The volatile memory may be, for example, a RAM (Random Access Memory). The recording medium may be, for example, a non-transitory recording medium.

[0099] The above-mentioned program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network such as the Internet or a communication line such as a telephone line. The above program may be for implementing some of the above functions. Furthermore, the above program may be a so-called differential file that can implement the above functions in combination with a program already recorded in the computer system. The differential file may be called a differential program.

[0100] In addition, the function of any of the components in any of the above-described devices may be realized by a processor. For example, each process in the embodiment may be realized by a processor that operates based on information such as a program and a computer-readable recording medium that stores information such as a program. Here, the functions of each part of the processor may be realized by individual hardware, or the functions of each part may be realized by integrated hardware. For example, the processor may include hardware, and the hardware may include at least one of a circuit that processes digital signals and a circuit that processes analog signals. For example, the processor may be configured using one or more circuit devices mounted on a circuit board, or one or both of one or more circuit elements. An IC (Integrated Circuit) or the like may be used as the circuit device, and a resistor or a capacitor or the like may be used as the circuit element.

[0101] Here, the processor may be, for example, a CPU. However, the processor is not limited to a CPU, and various processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) may be used. The processor may be, for example, a hardware circuit using an ASIC (Application Specific Integrated Circuit). The processor may be, for example, a plurality of CPUs, or a hardware circuit using a plurality of ASICs. The processor may be, for example, a combination of a plurality of CPUs and a hardware circuit using a plurality of ASICs. The processor may include, for example, one or more of an amplifier circuit or a filter circuit that processes an analog signal.

[0102] Although the embodiments of this disclosure have been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and designs that do not deviate from the gist of this disclosure are also included.

[0103] [Note] Configuration examples 1 to 5 are shown below.

[0104] (Configuration example 1) a signal sensor unit including one or more signal sensors, the signal sensor unit having a function of measuring a measurement signal in which a target signal and a first noise are mixed, and a function of detecting a first noise signal in which a level of a component of the target signal is less than a predetermined value and which contains the first noise; a processing unit that performs a process of reducing the first noise component included in the measurement data by a data division factor analysis based on measurement data, which is data of the measurement signal measured by the signal sensor unit, and control data, which is data of the first noise signal measured by the signal sensor unit; A measuring device comprising:

[0105] (Configuration example 2) The processing unit performs a pre-processing on the control data prior to the data division factor analysis to emphasize a component to be reduced from the measurement signal. The measuring device described in (Configuration Example 1).

[0106] (Configuration example 3) a reference sensor unit having a function of measuring a second noise signal including a second noise and including one or more reference sensors; the processing unit performs a process of reducing the second noise component included in the measurement data based on the second noise signal measured by the reference sensor unit before the data division factor analysis. The measuring device according to (Configuration Example 1) or (Configuration Example 2).

[0107] (Configuration Example 4) the reference sensor unit has a function of measuring a third noise signal including a third noise, the processing unit performs a process of reducing the third noise component included in the control data based on the third noise signal measured by the reference sensor unit before the data division factor analysis. The measuring device described in (Configuration Example 3).

[0108] Here, the following configuration may be used. (Configuration Example 4A) A reference sensor unit having a function of measuring a third noise signal including a third noise and including one or more reference sensors; the processing unit performs a process of reducing the third noise component included in the control data based on the third noise signal measured by the reference sensor unit before the data division factor analysis. The measuring device according to (Configuration Example 1) or (Configuration Example 2).

[0109] (Configuration Example 5) At least one of the reference sensors included in the reference sensor unit is a sensor of the same type as at least one of the signal sensors included in the signal sensor unit. The measuring device according to (Configuration Example 3) or (Configuration Example 4).

[0110] The following configuration may also be used. (Configuration example 5A) At least one of the reference sensors included in the reference sensor unit is a sensor of a different type from at least one of the signal sensors included in the signal sensor unit. The measuring device according to (Configuration Example 3) or (Configuration Example 4). [Explanation of symbols]

[0111] 1...measuring device, 11...signal sensor arrangement section, 21...information processing device, 31...measurement target, 111...input section, 112...output section, 113...storage section, 114...control section, 131...acquisition section, 141...display section, 151, 151a to 151d...processing section, 152...display control section, 311...sensor section, 331...signal sensor section, 332...reference sensor section, 411, 432, 452, 473...signal processing section, 431, 472...signal pre-processing unit, 451, 471...noise elimination unit, 2011, 2021, 2111, 2121, 2131, 2211, 4011...waveform group, 3011, 3021...waveform, A1 to AL...signal sensor, B1 to BM...reference sensor, a1...measurement signal, a2...environmental noise signal, a3, a4...reference signal, a11, c11...measurement data, a12, d11...control data

Claims

1. a signal sensor unit including one or more signal sensors, the signal sensor unit having a function of measuring a measurement signal in which a target signal and a first noise are mixed, and a function of detecting a first noise signal in which a level of a component of the target signal is less than a predetermined value and which contains the first noise; a processing unit that performs a process of reducing the first noise component included in the measurement data by a data division factor analysis based on measurement data, which is data of the measurement signal measured by the signal sensor unit, and control data, which is data of the first noise signal measured by the signal sensor unit; A measuring device comprising:

2. The processing unit performs a pre-processing on the control data prior to the data division factor analysis to emphasize a component to be reduced from the measurement signal. The measurement device according to claim 1 .

3. a reference sensor unit having a function of measuring a second noise signal including a second noise and including one or more reference sensors; the processing unit performs a process of reducing the second noise component included in the measurement data based on the second noise signal measured by the reference sensor unit before the data division factor analysis. The measuring device according to claim 1 or 2.

4. the reference sensor unit has a function of measuring a third noise signal including a third noise, the processing unit performs a process of reducing the third noise component included in the control data based on the third noise signal measured by the reference sensor unit before the data division factor analysis. The measurement device according to claim 3.

5. At least one of the reference sensors included in the reference sensor unit is a sensor of the same type as at least one of the signal sensors included in the signal sensor unit. The measurement device according to claim 3.

Images