Thoracic motion measurement device and thoracic motion measurement program

Abstract

To provide a thoracic motion measurement device and a thoracic motion measurement program that enable comparing thoracic motion-related conditions relatively corresponding to the right and left lungs.SOLUTION: A thoracic motion measurement device 10 comprises: a measurement part 11A that continuously measures thoracic motion of a person to be measured in a non-contact manner with the person to be measured while targeting a measurement region including a thoracic region of the person to be measured; and a presentation part 11B that divides the thoracic motion measured by the measurement part 11A for each region where the measurement region is divided into at least two with respect to an arrangement direction of the right and left lungs of the person to be measured, and individually presents thoracic motion-related information related to the thoracic motion for each region.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a thoracic cage motion measurement device and a thoracic cage motion measurement program. [Background technology]

[0002] While the number of patients with chronic obstructive pulmonary disease (COPD) has been increasing in recent years, only about 5% of patients are actually receiving treatment. This is likely due to the fact that many patients are unaware that they have the disease or that they have not been properly diagnosed. Therefore, there is a need for a simple method to measure human respiratory function without using special equipment such as a spirometer, which requires a mouthpiece.

[0003] Conventionally, the following techniques have been applicable to measuring human respiratory function.

[0004] Patent Document 1 discloses a respiratory disease discrimination device that aims to enable respiratory disease discrimination without using a mouthpiece. This respiratory disease discrimination device includes a first detection means for detecting the distance from the body surface of the subject, and a second detection means, disposed at a position different from the first detection means, for detecting the distance from the body surface of the subject. This respiratory disease discrimination device also includes a respiration measurement means for measuring the respiration of the subject excluding body movement based on the detection results of the first detection means and the second detection means. This respiratory disease discrimination device also includes a disease discrimination means for discriminating the respiratory disease of the subject based on the detection results of the respiration measurement means.

[0005] Patent Document 2 discloses a respiratory measurement device that can accurately measure respiratory waveforms in real time without contact. This respiratory measurement device includes a three-dimensional image acquisition unit that acquires a three-dimensional image containing distance information and posture information of a target area of ​​a subject at each unit time, and a distance acquisition unit that acquires distance information between a measurement reference position and the target area from the three-dimensional image. The respiratory measurement device also includes a posture acquisition unit that acquires posture information of the target area from the three-dimensional image, and a main respiratory area determination unit that determines a main respiratory area within the target area using the posture information. The respiratory measurement device also includes a movement waveform generation unit that generates a movement waveform of the main respiratory area by chronologically arranging the distance information of one or more pixels of the three-dimensional image corresponding to the main respiratory area. The respiratory measurement device also includes a respiratory waveform generation unit that generates a respiratory waveform from the movement waveform.

[0006] Patent Document 3 discloses a respiratory movement monitoring device that aims to measure respiratory movement abnormalities or respiratory organ abnormalities completely non-invasively. This respiratory movement monitoring device is equipped with two or more laser sensors that can measure the distance between the sensor and the subject using a laser, and continuously measures the movement of two or more different points on the subject using the sensors.

[0007] Patent Document 4 discloses a device for contactless respiratory monitoring of a patient, which includes a distance sensor that continuously detects a time-dependent distance variation relative to the patient's chest, and a calculation unit that determines the respiratory activity based on the detected time-dependent distance variation. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Publication No. 2020-92980 [Patent Document 2] Japanese Patent Application Publication No. 2017-217298 [Patent Document 3] Japanese Patent Application Laid-Open No. 2000-217802 [Patent Document 4] Special Publication No. 2011-519657 Summary of the Invention [Problem to be solved by the invention]

[0009] Incidentally, in order to measure values ​​related to a person's respiratory function (hereinafter referred to as "respiratory function values"), such as the forced expiratory volume in one second measured by a spirometer, without using a special device such as a spirometer, a method of measuring a person's thoracic movement, such as the technology disclosed in Patent Document 1, can be considered.

[0010] On the other hand, humans have two lungs, a right lung and a left lung, and comparing the conditions related to thoracic movement corresponding to each of the right and left lungs is extremely meaningful in determining in detail the quality of a person's respiratory function.

[0011] In contrast, the technologies disclosed in Patent Documents 1 to 4 do not distinguish between the right and left lungs, and instead measure respiratory function for the entire lung as a whole, which creates the problem of not being able to compare the conditions related to thoracic movement corresponding to each of the right and left lungs.

[0012] The present disclosure has been made in consideration of the above-mentioned problems, and aims to provide a thoracic movement measurement device and a thoracic movement measurement program that can compare conditions related to thoracic movement corresponding to each of the right and left lungs. [Means for solving the problem]

[0013] A first aspect of the present disclosure is a thoracic movement measuring device comprising: a measuring unit that measures the thoracic movement of a subject continuously and without contacting the subject in a measurement area including the subject's thoracic region; and a presentation unit that divides the thoracic movement measured by the measuring unit into at least two areas that are obtained by dividing the measurement area in the direction of the subject's right and left lungs, and presents thoracic movement-related information relating to the thoracic movement for each area individually.

[0014] A second aspect of the present disclosure is a thoracic movement measurement device of the first aspect, wherein the presentation unit divides the measurement area into two parts in the placement direction, and into at least one of two and three parts in the direction intersecting the placement direction.

[0015] A third aspect of the present disclosure is the thoracic cage movement measuring device according to the first or second aspect, wherein the thoracic cage movement related information includes at least one of a respiratory curve, an FV curve, and a distance image.

[0016] A fourth aspect of the present disclosure is a thoracic cage motion measuring device according to the first or second aspect, which is used in a hand-held state.

[0017] A fifth aspect of the present disclosure is the thoracic movement measuring device of the first or second aspect, wherein the measurement unit measures the thoracic movement using a distance image sensor.

[0018] A sixth aspect of the present disclosure is a thoracic movement measuring device according to the first or second aspect, wherein the presentation unit performs the division using an estimation model trained by machine learning, with time series data of thoracic movement measured by the measurement unit as input information and thoracic movement for each divided area corresponding to the time series data as output information.

[0019] A seventh aspect of the present disclosure is a thoracic movement measuring device according to the first or second aspect, further comprising an acquisition unit that acquires position information indicating the position of each lobe of at least one of the right and left lungs of the subject, and the presentation unit performs the division using the position information acquired by the acquisition unit.

[0020] An eighth aspect of the present disclosure is a thoracic movement measuring device according to the first or second aspect, wherein the presentation unit presents the thoracic movement-related information using attribute information of the person being measured.

[0021] A ninth aspect of the present disclosure is the thoracic cage motion measuring device of the eighth aspect, wherein the attribute information includes at least one of age, sex, height, and weight.

[0022] A tenth aspect of the present disclosure is a thoracic movement measurement program for causing a computer to execute processing to measure the thoracic movement of a subject without contacting the subject and continuously, with a measurement area including the subject's thoracic region as the target, divide the measured thoracic movement into at least two areas obtained by dividing the measurement area in the direction of the subject's right and left lungs, and individually present thoracic movement-related information relating to the thoracic movement for each area. [Effects of the Invention]

[0023] According to the present disclosure, conditions associated with thoracic movement corresponding to each of the right and left lungs can be compared. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 is a block diagram showing an example of a hardware configuration of a thoracic cage motion measuring device according to an embodiment. [Figure 2] 1 is a block diagram showing an example of the functional configuration of a thoracic cage motion measuring device according to an embodiment. FIG. [Figure 3] FIG. 2 is a schematic diagram illustrating an example of a configuration of an attribute information database according to an embodiment. [Figure 4] 1 is a schematic diagram showing an example of a state when thoracic motion is measured using a thoracic motion measurement device according to an embodiment. FIG. [Figure 5] 10 is a flowchart illustrating an example of a thoracic cage motion measurement process according to an embodiment. [Figure 6]FIG. 10 is a front view illustrating a method for detecting a measurement region in a thoracic cage motion measurement process according to an embodiment. [Figure 7] FIG. 10 is a front view showing an example of the configuration of a distance warning screen displayed when performing thoracic cage motion measurement processing according to an embodiment. [Figure 8] FIG. 10 is a front view showing an example of the configuration of an effort breathing instruction screen displayed when executing a thoracic cage motion measurement process according to one embodiment. [Figure 9] 10A to 10C are diagrams illustrating a chest movement measurement process according to an embodiment, and are diagrams illustrating an example of various states of an unnecessary component removal process. [Figure 10] 10A to 10C are diagrams illustrating a chest movement measurement process according to an embodiment, and are diagrams illustrating an example of various states of an unnecessary component removal process. [Figure 11] FIG. 10 is a diagram illustrating a thoracic cage motion measurement process according to an embodiment, and is a graph showing an example of a change in measured volume over time. [Figure 12] FIG. 10 is a diagram illustrating a thoracic movement measurement process according to an embodiment, and is a graph showing an example of a change in estimated pulmonary ventilation over time and a key region of the pulmonary ventilation. [Figure 13] 13 is a diagram illustrating the thoracic cage motion measurement process according to one embodiment, and is a graph showing an example in which the main part region in FIG. 12 is enlarged. [Figure 14] FIG. 10 is a front view showing an example of the configuration of a measurement result display screen displayed when thoracic cage motion measurement processing according to an embodiment is executed. [Figure 15] FIG. 10 is a diagram illustrating another example of the unnecessary component removal process, which is provided for explaining the thoracic cage motion measurement process according to an embodiment. [Figure 16] FIG. 10 is a front view showing another example of the configuration of the forced breathing instruction screen displayed when executing the thoracic cage motion measurement process according to one embodiment. [Figure 17] FIG. 10 is a diagram for explaining another example of a method for dividing a measurement region in a thoracic cage motion measurement process according to an embodiment, and is a schematic diagram showing the structure of a human lung. [Figure 18]FIG. 10 is a front view illustrating another method of dividing the measurement region in the thoracic cage motion measurement process according to an embodiment. [Figure 19] FIG. 10 is a front view showing another example of the configuration of the measurement result display screen displayed when the thoracic cage motion measurement process according to an embodiment is executed. [Figure 20] FIG. 10 is a front view showing another example of the configuration of the measurement result display screen displayed when the thoracic cage motion measurement process according to an embodiment is executed. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of the present disclosure will be described in detail below with reference to the drawings. Note that the same or equivalent components and parts in each drawing are denoted by the same reference numerals. Also, the dimensional proportions in the drawings are exaggerated for the sake of explanation and may differ from the actual proportions.

[0026] In this embodiment, a case where a thoracic motion measuring device according to the technology of the present disclosure is applied to a general-purpose smartphone will be described. However, the application of the technology of the present disclosure is not limited to smartphones, and it can also be applied to other portable, mobile, or stationary information processing devices such as portable game devices, tablet terminals, wearable devices, notebook personal computers, and desktop personal computers.

[0027] First, the configuration of a thoracic motion measurement device 10 according to this embodiment will be described with reference to Fig. 1 and Fig. 2. Fig. 1 is a block diagram showing an example of the hardware configuration of the thoracic motion measurement device 10 according to one embodiment. Fig. 2 is a block diagram showing an example of the functional configuration of the thoracic motion measurement device 10 according to one embodiment.

[0028] As shown in FIG. 1 , the thoracic motion measuring device 10 according to this embodiment includes a CPU (Central Processing Unit) 11, a memory 12 serving as a temporary storage area, a nonvolatile storage unit 13, and an input unit 14 such as a touch panel and various switches. The thoracic motion measuring device 10 according to this embodiment also includes a display unit 15 such as a liquid crystal display, and a medium read / write device (R / W) 16. The thoracic motion measuring device 10 according to this embodiment also includes a wireless communication unit 18 for mobile communication using a predetermined communication standard, and an audio output unit 19. The thoracic motion measuring device 10 according to this embodiment also includes an imaging unit 20 and a distance image sensor 21. The CPU 11, memory 12, storage unit 13, input unit 14, display unit 15, medium read / write device 16, wireless communication unit 18, audio output unit 19, imaging unit 20, and distance image sensor 21 are connected to one another via a bus B. The medium read / write device 16 reads information from and writes information to a recording medium 17.

[0029] On the other hand, the storage unit 13 is realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. A thoracic motion measurement program 13A is stored in the storage unit 13 as a storage medium. The thoracic motion measurement program 13A is stored (installed) in the storage unit 13 by setting a recording medium 17, on which the program 13A has been written, in the medium reading and writing device 16, and the medium reading and writing device 16 reading the program 13A from the recording medium 17. The CPU 11 reads the thoracic motion measurement program 13A from the storage unit 13, expands it in the memory 12, and sequentially executes the processes of the thoracic motion measurement program 13A.

[0030] In this way, in the thoracic motion measurement device 10 according to this embodiment, the thoracic motion measurement program 13A is installed in the thoracic motion measurement device 10 via the recording medium 17, but this is not limitative. For example, the thoracic motion measurement program 13A may be installed in the thoracic motion measurement device 10 by downloading it via the wireless communication unit 18.

[0031] Furthermore, an attribute information database 13B is stored in the storage unit 13. The attribute information database 13B will be described in detail later.

[0032] Furthermore, the storage unit 13 stores a first estimation model 13C, a second estimation model 13D, and a third estimation model 13E.

[0033] The first estimation model 13C according to this embodiment receives as input information attribute information indicating the attributes of the subject measured by the thoracic motion measurement device 10 and information indicating the volume change of the measurement region during thoracic motion (described in detail below), and outputs as output information information indicating the pulmonary ventilation volume of the subject's entire lungs. In this embodiment, information indicating gender and age is used as the attribute information, but this is not limited thereto. For example, in addition to gender and age, any one or a combination of four types of information, namely, height and weight, may be used as the attribute information. Furthermore, in this embodiment, the output information of the first estimation model 13C is information indicating the pulmonary ventilation volume of the entire lungs, but this is not limited thereto. Information indicating an FV curve (flow-volume curve) of the entire lungs may also be used as the output information.

[0034] Furthermore, the second estimation model 13D according to this embodiment receives the attribute information and time-series data of distance image data of the entire lungs (described later) as input information, and receives time-series data of distance image data for each divided region (described later) as output information. Thus, in this embodiment, the input information of the second estimation model 13D is the attribute information of the subject and time-series data of distance image data of the entire lungs, and the output information is time-series data of distance image data for each divided region. However, this is not limiting. For example, the input information may be the attribute information of the subject and information indicating the amount of volume change of the entire lungs, and the output information may be information indicating the amount of volume change of each divided region.

[0035] Furthermore, in the third estimation model 13E according to this embodiment, information indicating the volume change amount for each divided region during chest movement of the subject is used as input information, and information indicating the pulmonary ventilation volume of the subject for each divided region is used as output information. In this embodiment, the output information of the third estimation model 13E is information indicating the pulmonary ventilation volume, but this is not limited thereto, and information indicating the FV curve for each divided region may be used as output information.

[0036] In this embodiment, the first estimation model 13C, the second estimation model 13D, and the third estimation model 13E are based on a recurrent neural network (RNN), which is an estimation model using machine learning. However, the present invention is not limited to this. Other estimation models using machine learning, such as a multilayer perceptron or a convolutional neural network (CNN), may be used as the first estimation model 13C, the second estimation model 13D, and the third estimation model 13E. The first estimation model 13C, the second estimation model 13D, and the third estimation model 13E may be estimation models such as statistical models that do not use machine learning. Since a conventionally known method (e.g., a method using supervised learning or reinforcement learning) can be used as a learning method for the first estimation model 13C, the second estimation model 13D, and the third estimation model 13E using machine learning, detailed description thereof will be omitted here.

[0037] On the other hand, the photographing unit 20 functions as a photographing device that photographs moving images and is capable of photographing the user of the thoracic motion measuring device 10. The photographing unit 20 is often installed in general-purpose portable information processing devices, and in this case, the photographing unit 20 does not increase costs.

[0038] Furthermore, the distance image sensor 21 functions to detect distance image data, which is data indicating distance for each pixel of a size corresponding to the resolution. The distance image sensor 21 according to this embodiment is configured so that the area from which the distance image data is acquired coincides with the angle of view of the image captured by the image capture unit 20. While a ToF (Time of Flight) type sensor is used as the distance image sensor 21 in this embodiment, the present invention is not limited thereto. For example, a bright spot projection type sensor, a stereo (multi-lens) camera, or a displacement sensor may also be used as the distance image sensor 21. Furthermore, optical signals are often used for the distance image sensor 21, but radio wave or ultrasonic signals may also be used. When a ToF type or bright spot projection type sensor is used as the distance image sensor 21, these sensors are often installed in general-purpose portable information processing devices, and in this case, the distance image sensor 21 does not increase costs.

[0039] Next, the functional configuration of the thoracic motion measurement device 10 according to this embodiment will be described with reference to Fig. 2. As shown in Fig. 2, the thoracic motion measurement device 10 according to this embodiment includes a measurement unit 11A and a presentation unit 11B. The CPU 11 of the thoracic motion measurement device 10 executes a thoracic motion measurement program 13A, thereby functioning as the measurement unit 11A and the presentation unit 11B.

[0040] The thoracic motion measuring device 10 according to this embodiment is handheld by the subject and measures the thoracic motion of the subject using distance image data obtained by the mounted distance image sensor 21. Note that the thoracic motion measuring device 10 according to this embodiment may be configured to measure the thoracic motion of the subject while being handheld by a person other than the subject.

[0041] That is, the measurement unit 11A of this embodiment uses distance image data obtained by the distance image sensor 21 to measure the thoracic movement of the person being measured (in this embodiment, the thoracic movement associated with forced breathing) in a measurement area (hereinafter simply referred to as the "measurement area") including the thoracic region of the person being measured, without contacting the person being measured and continuously.

[0042] The presentation unit 11B according to this embodiment divides the thoracic cage motion measured by the measurement unit 11A into at least two regions (two in this embodiment) obtained by dividing the measurement region in the direction of the arrangement of the right and left lungs of the subject (hereinafter referred to as the "lung arrangement direction"), and presents thoracic cage motion-related information relating to the thoracic cage motion for each region individually. Note that the "thoracic cage motion-related information" here refers to measurement values ​​such as distance image data obtained by measuring the thoracic cage motion, and information relating to respiratory function derived using the measurement values.

[0043] In this embodiment, the subject's respiratory curve is used as the thoracic movement-related information, but this is not limiting. For example, in addition to the respiratory curve, at least one of four types of information, namely, a rate-of-respiration-in-one-second curve, an FV curve, and a distance image, may be used as the thoracic movement-related information. Also, in this embodiment, as described above, the time-series data (or the amount of volume change) of the distance image data of the measurement region is divided into divided regions using the second estimation model 13D. Hereinafter, the divided regions will be referred to as "divided regions."

[0044] Here, the presenting unit 11B according to this embodiment presents the thoracic movement-related information using attribute information of the subject. As described above, in this embodiment, information indicating the gender and age of the subject is used as the attribute information.

[0045] Next, the attribute information database 13B according to this embodiment will be described with reference to Fig. 3. Fig. 3 is a schematic diagram showing an example of the configuration of the attribute information database 13B according to one embodiment.

[0046] The attribute information database 13B according to this embodiment is for storing information according to the attributes of the subject. As shown in Fig. 3, the attribute information database 13B according to this embodiment stores information on the gender, age, and dimensions.

[0047] The gender is information indicating the gender of the subject, and the age is information indicating a predetermined age group for the subject of the corresponding gender. In this embodiment, the age groups are 60 years or older and under 60 years old in 10-year increments, but it goes without saying that the age groups are not limited to this.

[0048] The above dimensions are information indicating the dimensions of the measurement areas described above that are suitable for the combination of the corresponding gender and age group. Note that the attribute information database 13B may further include information indicating the dimensions, shape, etc. of the divided areas as attribute information.

[0049] In the example shown in FIG. 3, the dimensions of the measurement area corresponding to a male aged 60 or older are stored as 35.5 cm in width and 22.0 cm in height.

[0050] Next, a method for measuring the thoracic movement of a person to be measured using the thoracic movement measurement device 10 will be described with reference to Fig. 4. Fig. 4 is a schematic diagram showing an example of a state when measuring thoracic movement using the thoracic movement measurement device 10 according to one embodiment.

[0051] 4, when measuring thoracic motion, the subject M holds the thoracic motion measurement device 10 and positions the thoracic motion measurement device 10 so that at least a part of his / her abdomen and thoracic region are included in the angle of view of the imaging unit 20 of the thoracic motion measurement device 10. At this time, the subject M positions the thoracic motion measurement device 10 so that the distance between the thoracic motion measurement device 10 and his / her thoracic region is within a predetermined reference range (in this embodiment, a range from 50 (cm) to 55 (cm)). Note that the reference range is not limited to this range and may be set appropriately depending on the performance of the distance image sensor 21 in the thoracic motion measurement device 10, the attributes of the subject M, etc.

[0052] Next, the operation of the thoracic cage motion measurement device 10 according to this embodiment will be described with reference to Figs. 5 to 15. Fig. 5 is a flowchart showing an example of thoracic cage motion measurement processing according to one embodiment. Note that, here, a case where an attribute information database 13B has already been constructed will be described in order to avoid confusion. Also, here, a case where attribute information indicating the attributes of the subject M (here, gender and age) is set in advance in order to avoid confusion will be described. Furthermore, here, a case where a first estimation model 13C, a second estimation model 13D, and a third estimation model 13E have been trained in advance in order to avoid confusion will be described.

[0053] The CPU 11 of the thoracic motion measurement device 10 executes the thoracic motion measurement program 13A, thereby executing the thoracic motion measurement process shown in Fig. 5. The thoracic motion measurement process shown in Fig. 5 is executed when the subject M inputs an instruction to start execution of the thoracic motion measurement program 13A via the input unit 14. At this time, the subject M positions the thoracic motion measurement device 10 as described with reference to Fig. 4, for example.

[0054] 5, the CPU 11 controls the imaging unit 20 and the distance image sensor 21 to start driving. In step 102, the CPU 11 reads out dimensions corresponding to the preset attribute information of the subject M from the attribute information database 13B.

[0055] In step 104, the CPU 11 acquires distance image data for one image from the distance image sensor 21.

[0056] In step 106, the CPU 11 detects the measurement area of ​​the subject M as follows.

[0057] That is, as shown in the left diagram of Fig. 6 as an example, at this point, the thoracic motion measurement device 10 is positioned so that at least a part of the abdomen and the thoracic region of the subject M are included within the angle of view of the imaging unit 20 of the thoracic motion measurement device 10. Therefore, in this embodiment, as shown in the right diagram of Fig. 6, only the region of the thoracic region is determined as the measurement region A based on the difference in the movement of the abdomen and the thoracic region during breathing.

[0058] In step 108, the CPU 11 uses the acquired distance image data to detect the distance F between the thoracic motion measurement device 10 and the person being measured M at this time. In this embodiment, the distance F is the distance indicated by the pixel data corresponding to the pixel at the center of the measurement area A in the distance image data, but this is not limited to this. For example, the distance F may be the average value of the distances indicated by the pixel data of pixels in a predetermined area (e.g., 10 pixels x 10 pixels) including the pixel at the center of the measurement area A in the distance image data.

[0059] In step 110, the CPU 11 determines whether the distance F is within the above-mentioned reference range (in this embodiment, a range from 50 (cm) to 55 (cm)), and if the determination is affirmative, the process proceeds to step 114, whereas if the determination is negative, the process proceeds to step 112. Note that in this embodiment, the reference range is a range obtained in advance by experiments using an actual device, computer simulation, or the like, as a range within which it is considered that measurement by the thoracic motion measurement device 10 can be performed with high accuracy when the distance F falls within the range. However, the present invention is not limited to this, and the reference range may be set appropriately depending on, for example, the measurement accuracy required of the thoracic motion measurement device 10, the attributes of the person M to be measured, and the like.

[0060] In step 112, the CPU 11 controls the display unit 15 to display a distance warning screen having a predetermined configuration, and then returns to step 104.

[0061] Fig. 7 shows an example of a distance warning screen according to this embodiment. As shown in Fig. 7, the distance warning screen according to this embodiment displays a message instructing the subject M to set the distance between the thoracic motion measurement device 10 and the subject's own thoracic cage to approximately within the above-mentioned reference range. Therefore, the subject M can understand that the distance between the thoracic motion measurement device 10 that the subject M is holding and the thoracic cage is not within the reference range. Then, when the subject M follows the instruction, the process proceeds to step 114 with the distance between the thoracic motion measurement device 10 and the thoracic cage within the reference range.

[0062] In step 114, the CPU 11 controls the display unit 15 to display an effort breathing instruction screen having a predetermined configuration.

[0063] An example of the forced breathing instruction screen according to this embodiment is shown in Fig. 8. As shown in Fig. 8, the forced breathing instruction screen according to this embodiment displays a message indicating that the positional relationship between the subject M and the thoracic motion measurement device 10 is appropriate, and a message instructing the subject M to perform forced breathing. Therefore, the subject M performs forced breathing at this timing.

[0064] Therefore, in step 116, the CPU 11 acquires range image data from the range image sensor 21.

[0065] In step 118, CPU 11 stores the acquired distance image data in memory unit 13. In step 120, CPU 11 determines whether a predetermined amount of distance image data (equivalent to 3 seconds' worth in this embodiment) has been stored, and if the determination is negative, the process returns to step 116, whereas if the determination is positive, the process proceeds to step 122. Note that while repeatedly executing the processes of steps 116 to 120, CPU 11 also executes processes similar to those of steps 104 to 112 in parallel. This allows distance image data corresponding to measurement area A to be obtained with distance F within the reference range.

[0066] In step 122, CPU 11 reads all distance image data (hereinafter referred to as "target distance image data") from storage unit 13, and performs a predetermined unnecessary component removal process on the read target distance image data as follows.

[0067] First, CPU 11 derives a histogram of each pixel data (data indicating distance) in the object distance image data for each piece of object distance image data, as shown in the graph in the lower left diagram of Fig. 9. This histogram is broadly divided into three types of regions: the arm region of subject M, the body surface region excluding the arm region of subject M, and the background region of subject M, with the maximum frequency of the body surface region being the largest. Furthermore, the body surface region is located intermediate between the arm region and the background region, and is relatively closer to the arm region than the background region.

[0068] Therefore, the CPU 11 extracts, as pixels to be processed (hereinafter referred to as "processing target pixels"), pixels of the target distance image data whose distances fall within a predetermined range (in this embodiment, a range of ±10 (cm)) centered on the distance at which the frequency is greatest in the derived histogram, as shown in the graph in the lower right diagram of Fig. 9. Note that in this embodiment, the predetermined range is a fixed value, but the present invention is not limited to this and may be set appropriately depending on the attributes of the subject M, etc.

[0069] Then, the CPU 11 sets the pixel data of the extracted target pixel to 1 and the pixel data of pixels other than the target pixel (hereinafter referred to as "non-processing pixels") to 0 (zero), and stores the binarized mask image data in the memory unit 13 in association with the corresponding target distance image data.

[0070] Through the above processing, for each piece of object distance image data, mask image data shown in the upper right diagram of FIG. 9 can be derived from the object distance image data showing the distance image shown in the upper left diagram of FIG. 9 as an example.

[0071] Next, CPU 11 targets pixels in the mask image data whose pixel data is set to 1, and extracts pixels whose maximum value of the difference (displacement) between corresponding pixel data in each of the target distance image data is greater than the difference (displacement) between the maximum and minimum values ​​of the corresponding distances when labored breathing is occurring, as shown in Fig. 10. CPU 11 then masks (non-processes) pixels in the portion where there is movement greater than that due to labored breathing by setting the pixel data of the mask image data corresponding to the extracted pixels to 0 (zero).

[0072] In step 124, the CPU 11 calculates the volume change amount by calculating the sum of the differences in pixel data between corresponding pixels in chronologically adjacent target distance image data for pixels whose pixel data is set to 1 in the mask image data obtained by the above processing. This allows the volume change amount that changes over time to be obtained, as shown in FIG. 11 as an example.

[0073] In step 126, the CPU 11 derives an estimated value of the pulmonary ventilation volume of the subject M by inputting information indicating the derived volume change and pre-set attribute information of the subject M into the first estimation model 13C.

[0074] In step 128, the CPU 11 calculates the FEV1% using the derived estimated value of the pulmonary ventilation. Hereinafter, a method for calculating the FEV1% will be described with reference to Figures 12 and 13. Note that Figure 12 is a graph showing the change in the estimated pulmonary ventilation over time and a key region X of the pulmonary ventilation, and Figure 13 is a graph showing an enlarged view of the key region.

[0075] First, the CPU 11 derives the starting point t0 of the forced expiratory volume in one second FEV1 in the main region X of the pulmonary ventilation volume by inverse extrapolation, as shown in FIG. 13 as an example.

[0076] That is, t in FIG. max is the maximum inspiration time, which is the maximum value (peak) of the waveform V(t). PFis the time of maximum airflow, which is the point at which the slope of the waveform V(t) is maximum, and is calculated by the following equation (1).

[0077]

number

[0078] Point(t PF ,V(t PF )) and point (t0,V(t0)), the equation for calculating the gradient a of the steep point tangent can be transformed to calculate the starting point t0 using the following equation (2).

[0079]

number

[0080] Then, the forced expiratory volume in one second FEV1 is calculated using the following formula (3A), and the fractional expiratory volume in one second FEV1% is calculated using formula (3B) from the forced expiratory volume in one second FEV1 and the estimated value of pulmonary ventilation FVC derived by the first estimation model 13C.

[0081]

number

[0082] In step 130, CPU 11 derives information indicating a respiratory curve of the entire lungs by chronologically connecting the estimated values ​​of pulmonary ventilation derived by the processing of step 126. Also in step 130, CPU 11 accumulates (integrates) the estimated values ​​of pulmonary ventilation derived by the processing of step 126 to derive information indicating an FV curve of the entire lungs (hereinafter referred to as "FV curve information").

[0083] In step 132, the CPU 11 executes the region division process as follows.

[0084] That is, first, the CPU 11 inputs the target distance image data and the predetermined attribute information of the subject M into the second estimation model 13D, thereby deriving time series data of the distance image data for each of the divided areas, i.e., for each of the right lung and left lung.

[0085] In this manner, in this embodiment, time-series data of distance image data for each divided region is acquired using the second estimation model 13D, but the present invention is not limited to this. For example, as shown in the left diagram of Fig. 18, division in the lung arrangement direction may be performed using a line connecting the center point of the measurement region A and the center point of the abdominal region, and time-series data of distance image data for each divided region may be acquired by extracting portions corresponding to each divided region from the target distance image data.

[0086] Next, the CPU 11 performs unnecessary component removal processing for each divided region on the time series data of the distance image data for each divided region obtained by the above processing, similar to the processing in step 122 described above, and then derives the volume change amount for each divided region, similar to the processing in step 124 described above.

[0087] In this way, in this embodiment, after acquiring time-series data of the distance image data for each divided region, unnecessary component removal processing is performed to derive the volume change amount, but this is not limited to this. For example, in step 124, after deriving the volume change amount for the entire measurement region, the volume change amount for each divided region may be derived by extracting the volume change amount of the portion corresponding to the divided region.

[0088] Next, the CPU 11 inputs the volume change amount for each divided area obtained by the above processing into the third estimation model 13E created for each divided area, thereby deriving an estimated value of the pulmonary ventilation volume of the subject M for each divided area.

[0089] Then, CPU 11 derives FV curve information for each divided region by performing the same process as that of step 130 for the estimated value of pulmonary ventilation for each divided region. Furthermore, CPU 11 performs the same process as that of step 130 to connect the time series data of pulmonary ventilation for each divided region for each divided region (for each of the right lung and the left lung), thereby deriving information indicating the respiratory curves for each of the right lung and the left lung.

[0090] In step 134, CPU 11 determines one of the target distance image data as the distance image data to be presented (hereinafter referred to as "presentation target distance image data"). Note that in this embodiment, the last data in chronological order in the target distance image data is applied as the presentation target distance image data, but this is not limited to this. For example, the first data in chronological order or the central data in the target distance image data may be applied as the presentation target distance image data. Furthermore, the average value of pixel data for each corresponding pixel of multiple data in the target distance image data may be applied as the presentation target distance image data.

[0091] Then, in step 134, the CPU 11 controls the display unit 15 to display a measurement result display screen of a predetermined configuration using the one-second FEV1% obtained by the above processing, the presentation target distance image data, information indicating the respiratory curve of the entire lung, the FV curve information, and information indicating the respiratory curves of the right lung and the left lung separately.

[0092] An example of a measurement result display screen according to this embodiment is shown in Fig. 14. As shown in Fig. 14, the measurement result display screen according to this embodiment displays the FEV1% obtained by the above processing, the distance image indicated by the presentation target distance image data, the respiratory curve of the entire lung, and the FV curve, as well as respiratory curves classified separately for the right and left lungs. Therefore, by referring to the measurement result display screen, the subject M can grasp this information regarding his or her own thoracic movement.

[0093] In step 136, the CPU 11 stops the driving of the photographing unit 20 and the distance image sensor 21 that was started in the processing of step 100, and then ends this thoracic cage motion measurement processing.

[0094] As described above, according to one embodiment, the measurement area including the thoracic region of the subject is targeted, and the thoracic movement of the subject is measured continuously and without contacting the subject, and the measured thoracic movement is divided into at least two regions (in this embodiment, two regions) in the direction of arrangement of the subject's right and left lungs (lung arrangement direction), and thoracic movement-related information relating to the thoracic movement for each region is presented individually. Therefore, it is possible to compare the conditions related to the thoracic movement corresponding to each of the right and left lungs.

[0095] According to one embodiment, at least one of a respiratory curve, an FV curve, and a distance image is applied as thoracic movement-related information, and therefore, the states of the right and left lungs can be compared with respect to the applied information.

[0096] According to one embodiment, the thoracic movement measuring device is used in a handheld state, so that the subject can measure the thoracic movement by himself.

[0097] Furthermore, according to one embodiment, the division is performed using a machine-learned estimation model in which time-series data of measured thoracic cage motion is used as input information and thoracic cage motion for each of the divided regions corresponding to the time-series data is used as output information. Therefore, since the division can be performed automatically, it is possible to more easily compare the conditions related to the thoracic cage motion of each of the right and left lungs compared to when the subject or the like is required to specify the divided regions.

[0098] According to one embodiment, the thoracic movement-related information is presented using the subject's attribute information, which allows for a more accurate comparison of the thoracic movement-related conditions of the right and left lungs compared to when the subject's attribute information is not used.

[0099] According to one embodiment, when the relative positional relationship with the subject is determined to be appropriate in advance, the user is notified that the relative positional relationship is appropriate, thereby further preventing a decrease in measurement accuracy.

[0100] Furthermore, according to one embodiment, if the positional relationship is inappropriate, a notification to that effect is further provided, thereby making it possible to further suppress a decrease in measurement accuracy.

[0101] According to one embodiment, the dimensions of the measurement area are determined according to the attribute. Therefore, the dimensions of the measurement area can be set accurately for each attribute, thereby further suppressing a decrease in measurement accuracy.

[0102] According to one embodiment, a distance image sensor is used to measure thoracic movement in the measurement region. Since distance image sensors are installed in many devices, using the distance image sensor allows for accurate measurement of thoracic movement without increasing costs.

[0103] According to one embodiment, the attribute is at least one of sex, age, height, and weight, so that more accurate measurements can be performed according to the applied attribute.

[0104] According to one embodiment, the chest movement of the subject is determined as the chest movement associated with labored breathing of the subject, so that even when measuring chest movement associated with labored breathing, the chest movement can be measured with high accuracy.

[0105] In the above embodiment, the unnecessary component removal process in step 122 of the thoracic cage motion measurement process removes overlapping regions of the arms, etc., background regions, and regions with movements greater than breathing in the range image data, but the present invention is not limited to this. For example, in addition to these regions, the unnecessary component removal process may also include a process for removing regions with obvious abnormal values.

[0106] When performing the process of removing this abnormal value area, for example, CPU 11 targets pixels whose pixel data in the mask image data is set to 1 and sets the pixel data of the mask image data corresponding to pixels that do not satisfy the condition shown in the following equation (4) to 0 (zero). In this way, pixels that are considered to be abnormal values ​​are masked (non-processed pixels). Note that FEV1%(i,j) in equation (4) represents the 1-second rate of the corresponding pixel (i,j) shown in FIG. 15 as an example. Also, AVE(FEV1%) in equation (4) represents the average value of the 1-second rates FEV1%, and σ represents the standard deviation of the 1-second rates FEV1%.

[0107]

number

[0108] Furthermore, in the above embodiment, a case has been described in which a notification is given when the relative positional relationship with the subject is appropriate or inappropriate, but the present invention is not limited to this.

[0109] 16, the specified measurement area A may be displayed on the display unit 15 to further notify the subject of the measurement area A. This allows the subject to understand the specified measurement area, thereby allowing the subject to set the measurement area more accurately.

[0110] In this embodiment, the notification of the measurement area A may be made when the positional relationship becomes the predetermined relationship. According to this embodiment, the subject can know whether the positional relationship is appropriate or not based on whether the measurement area A is displayed or not, which further improves convenience for the subject.

[0111] Furthermore, in this embodiment, the display form of the measurement area A may be switched depending on whether the positional relationship is the predetermined relationship, i.e., whether the positional relationship is appropriate or not. According to this embodiment, the subject can understand whether the positional relationship is appropriate or not based on the display form of the measurement area A, thereby further improving convenience for the subject.

[0112] A specific example of the display form to be switched in this case is, for example, a form in which the outer periphery of the measurement area A is made thicker when the positional relationship is appropriate than when the positional relationship is inappropriate. Also, specific examples of the display form to be switched in this case are a form in which the line type (solid line, dashed line, chain line, etc.) of the outer periphery line is changed, and a form in which the color of the outer periphery line is changed.

[0113] Furthermore, in the above embodiment, the thoracic motion measuring device of the present invention has been described as being configured as a standalone device (in the above embodiment, a smartphone), but this is not limiting. For example, the thoracic motion measuring device of the present invention may be configured as a system using multiple devices, such as a server device such as a cloud server and a terminal device. In this case, for example, the terminal device may transfer the measurement values ​​of the thoracic motion of the subject by the distance image sensor 21 to the server device, and the server device may use the measurement values ​​received to derive the rate-per-second, respiratory curve, etc. and transmit them to the terminal device, where the rate-per-second, respiratory curve, etc. are displayed.

[0114] In the above embodiment, the measurement region A is divided into two in the lung arrangement direction to accommodate two lungs, the right lung and the left lung, and information related to thoracic movement is presented. However, this is not limiting. As an example, as shown in FIG. 17, the human lungs are composed of three lobes, the right lung, the middle lobe, and the lower lobe, and the left lung is composed of two lobes, the upper lobe and the lower lobe. Therefore, the divided region on the right lung side of the measurement region A may be further divided into three in a direction intersecting the lung arrangement direction (hereinafter referred to as the "intersecting direction"), and the divided region on the left lung side may be further divided into two in the intersecting direction. In this case, it is preferable that the divided region in the intersecting direction has dimensions and shapes corresponding to the dimensions and shapes of each lobe in a standard lung for each attribute of the subject M.

[0115] Furthermore, the number of divisions of the measurement region A is not limited to two or five as described above. Needless to say, any number of divisions can be applied, such as a form in which both the divided regions on the right lung side and the left lung side are divided into two in the intersecting direction (a total of four divisions), a form in which both the divided regions on the right lung side and the left lung side are divided into three in the intersecting direction (a total of six divisions), or even a form in which the lung arrangement direction is divided into four. The more divisions there are, the more detailed the state of the lungs related to thoracic cage movement can be confirmed, but the calculation load increases.

[0116] In addition, in the above embodiment, as an example, as shown in the left diagram of Figure 18, a form has been described in which division in the lung arrangement direction is performed by a straight line connecting the center point of the measurement area A and the center point of the abdominal area. However, in this form, when dividing into two in the intersecting direction, it is also possible to divide into two in the intersecting direction with the center point of the measurement area A as the center, as shown in the right diagram of Figure 18.

[0117] In addition, since the abdominal region can also be specified in the above-described configuration in which the abdominal region and the thoracic region are included in the imaging angle of view of the imaging unit 20, a curve corresponding to the above-mentioned respiratory curve in the abdominal region (hereinafter referred to as "abdominal curve") may be derived in the same manner as the respiratory curve. In this case, as an example, as shown in Fig. 19, the abdominal curve may be derived by dividing the abdominal region into two in the lung arrangement direction, and displayed on the measurement result display screen.

[0118] In the above embodiment, the disclosed technology is applied to a portable device, but the present invention is not limited to this. For example, the disclosed technology may be applied to a stationary device. In this case, the subject M moves relative to the stationary device so that the thoracic region is positioned within the imaging range of the imaging unit 20.

[0119] In the above embodiment, the case where the measurement area is always divided and the thoracic movement-related information is presented has been described, but the present invention is not limited to this. For example, as shown in Fig. 20, by allowing the user to specify whether or not to divide the measurement area, it may be possible to select whether to divide the measurement area and present the thoracic movement-related information according to the specification, or to present the thoracic movement-related information without dividing the measurement area. Note that the example shown in Fig. 20 shows an example of a configuration where the split button 15A is selected when dividing the measurement area, and the unified button 15B is selected when not dividing the measurement area.

[0120] In the above embodiment, the second estimation model 13D is described as being one that uses attribute information as input information in addition to time-series data of thoracic cage motion, but is not limited to this. For example, the second estimation model 13D may be a model that does not use attribute information as input information but uses only time-series data of thoracic cage motion as input information.

[0121] In the above embodiment, the second estimation model 13D is used to divide the time-series data of thoracic movement, but this is not limiting. For example, the CPU 11 may acquire position information indicating the position of each lobe of at least one of the subject's right and left lungs from the subject, etc., as an acquisition unit of the technology disclosed herein, and the presentation unit 11B may perform the division using the acquired position information. In this embodiment, the second estimation model 13D is not necessary.

[0122] Although not specifically mentioned in the above embodiment, in order to prevent a decrease in measurement accuracy due to hand shake when the subject holds the thoracic movement measuring device in their hands, the device may be equipped with at least one of a function to correct hand shake and a function to track the measurement area.

[0123] In addition, in the above embodiment, an example was given of a form in which continuously acquired distance image data (e.g., target distance image data) is applied as input information for the second estimation model 13D, and information indicating time series data of distance image data for each divided area corresponding to the right lung and left lung, respectively, is applied as output information.However, in this case, since the input information is a type of planar image data, it is preferable to apply a CNN-based model as the second estimation model 13D.

[0124] In the above embodiment, the distance image data is divided into segments corresponding to the right and left lungs, respectively, and then the distance image data for each segment is individually used to derive time-series data of pulmonary ventilation for each segment or information indicating a respiratory curve for each segment, using a method similar to that described above. However, this is not limiting. For example, time-series data of pulmonary ventilation for the entire lung may be derived, and then the time-series data of pulmonary ventilation may be divided into segments corresponding to the right and left lungs, respectively. Such division of the time-series data of pulmonary ventilation for the entire lung into segments can be achieved, for example, by using an estimation model using machine learning or a statistical model.

[0125] Furthermore, it goes without saying that the various formulas (Equations (1) to (4)) described in the above embodiment are merely examples and may be changed as appropriate depending on the implementation mode, etc.

[0126] Furthermore, the attribute information database 13B described in the above embodiment is also an example, and it goes without saying that it may be modified as appropriate depending on the implementation mode, etc.

[0127] Furthermore, in the above embodiment, for example, the following various processors can be used as the hardware structure of the processing unit that executes each process of the measurement unit 11A and the presentation unit 11B. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as a processing unit, as well as dedicated electrical circuits that are processors having a circuit configuration specifically designed to execute specific processes, such as a programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit).

[0128] The processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA).The processing unit may also be configured with a single processor.

[0129] Examples of configuring a processing unit with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as the processing unit, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of the entire system, including the processing unit, on a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, the processing unit is configured using one or more of the above-mentioned various processors as a hardware structure.

[0130] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0131] The following additional notes are provided regarding the above-described embodiments.

[0132] (Appendix 1) a measurement unit that continuously measures chest movement of a subject in a measurement region including the subject's chest region without contacting the subject; a presentation unit that divides the thoracic movement measured by the measurement unit into at least two regions obtained by dividing the measurement region into two regions in the direction of arrangement of the right and left lungs of the subject, and presents thoracic movement-related information relating to each thoracic movement individually; A thoracic movement measurement device equipped with

[0133] (Appendix 2) the presentation unit divides the measurement area into two parts in the arrangement direction and into at least one of two parts and three parts in a direction intersecting the arrangement direction; 10. A thoracic motion measuring device as described in Appendix 1.

[0134] (Appendix 3) The thoracic movement related information includes at least one of a respiratory curve, an FV curve, and a distance image. 3. A thoracic movement measuring device according to claim 1 or 2.

[0135] (Appendix 4) Used in handheld mode, 4. The thoracic cage motion measuring device according to any one of Supplementary notes 1 to 3.

[0136] (Appendix 5) the measurement unit measures the thoracic movement using a distance image sensor; 10. The chest cage motion measuring device according to claim 1, wherein the chest cage motion measuring device is a thoracic cage motion measuring device.

[0137] (Appendix 6) the presentation unit performs the segmentation using an estimation model trained by machine learning, with time-series data of thoracic cage movement measured by the measurement unit as input information and thoracic cage movement for each of the segmented regions corresponding to the time-series data as output information. 10. The chest cage motion measuring device according to claim 1, wherein the chest cage motion measuring device is a thoracic cage motion measuring device.

[0138] (Appendix 7) an acquisition unit that acquires position information indicating the position of each lobe of at least one of the right lung and the left lung of the subject; the presentation unit performs the division using the position information acquired by the acquisition unit. 10. The chest cage motion measuring device according to claim 1, wherein the chest cage motion measuring device is a thoracic cage motion measuring device.

[0139] (Appendix 8) the presenting unit presents the thoracic movement-related information by using attribute information of the subject. 10. The thoracic cage motion measuring device according to claim 1, wherein the thoracic cage motion measuring device is a thoracic cage motion measuring device.

[0140] (Appendix 9) The attribute information includes at least one of age, sex, height, and weight. 9. A thoracic motion measuring device as described in Appendix 8.

[0141] (Appendix 10) measuring the chest movement of the subject in a measurement area including the chest area of ​​the subject without contacting the subject and continuously; The measured thoracic movement is divided into at least two regions obtained by dividing the measurement region into two regions in the direction of arrangement of the right and left lungs of the subject, and thoracic movement-related information relating to each thoracic movement is individually presented. A chest movement measurement program for executing processing on a computer. [Explanation of symbols]

[0142] 10. Thoracic movement measuring device 11 CPU 11A Measurement section 11B Presentation section 12 Memory 13 Storage section 13A Chest Movement Measurement Program 13B Attribute Information Database 13C First estimation model 13D Second Estimation Model 13E Third Estimation Model 14 Input section 15 Display 16 Media reading and writing device 17 Recording Media 18 Radio Communication Department 19 Audio output section 20 Photography Department 21 Range image sensor A Measurement area D Attribute-based distance E Reference position M Measured person

Claims

1. a measurement unit that continuously measures chest movement of a subject in a measurement region including the subject's chest region without contacting the subject; a presentation unit that divides the thoracic movement measured by the measurement unit into at least two regions obtained by dividing the measurement region into two regions in the direction of arrangement of the right and left lungs of the subject, and presents thoracic movement-related information relating to the thoracic movement for each region; A thoracic movement measurement device equipped with

2. the presentation unit divides the measurement area into two parts in the arrangement direction and into at least one of two parts and three parts in a direction intersecting the arrangement direction; The thoracic cage motion measuring device according to claim 1 .

3. The thoracic movement related information includes at least one of a respiratory curve, an FV curve, and a distance image. The thoracic cage movement measuring device according to claim 1 or 2.

4. Used in handheld mode, The thoracic cage movement measuring device according to claim 1 or 2.

5. the measurement unit measures the thoracic movement using a distance image sensor; The thoracic cage movement measuring device according to claim 1 or 2.

6. the presentation unit performs the segmentation using an estimation model trained by machine learning, with time-series data of thoracic cage movement measured by the measurement unit as input information and thoracic cage movement for each of the segmented regions corresponding to the time-series data as output information. The thoracic cage movement measuring device according to claim 1 or 2.

7. an acquisition unit that acquires position information indicating the position of each lobe of at least one of the right lung and the left lung of the subject; the presentation unit performs the division using the position information acquired by the acquisition unit. The thoracic cage movement measuring device according to claim 1 or 2.

8. the presenting unit presents the thoracic movement-related information by using attribute information of the subject. The thoracic cage movement measuring device according to claim 1 or 2.

9. The attribute information includes at least one of age, sex, height, and weight. The thoracic cage motion measuring device according to claim 8.

10. measuring the chest movement of the subject in a measurement area including the chest area of ​​the subject without contacting the subject and continuously; The measured thoracic movement is divided into at least two regions obtained by dividing the measurement region into two regions in the direction of arrangement of the right and left lungs of the subject, and thoracic movement-related information relating to the thoracic movement is individually presented for each region. A chest movement measurement program for executing processing on a computer.

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