JP2023102264A - Sleeping posture data processing device, bedding manufacturing apparatus, bedding selection device, sleeping posture data processing method, computer program and bedding data processing device - Google Patents

Abstract

To provide a sleeping posture data processing device that can provide information on bedding to a user before the user acquires the bedding, and to provide a bedding manufacturing apparatus, a bedding selection device, a sleeping posture data processing method, a computer program and a bedding data processing device.SOLUTION: A sleeping posture data processing device includes: an acquisition part for acquiring imaging data concerning a body surface shape of at least a portion of a human body; a generation part for generating sleeping posture data showing the sleeping posture of the human body on the basis of the imaging data acquired in the acquisition part; and an output part for outputting information on bedding corresponding to the sleeping posture data to a display part.SELECTED DRAWING: Figure 1

Description

The present technology relates to a sleeping posture data processing device, a bedding manufacturing device, a bedding selection device, a sleeping posture data processing method, a computer program, and a bedding data processing device that output information on bedding to a display unit based on data related to the body surface shape of at least a part of the human body.

An apparatus has been proposed that measures the body surface shape of a human body, acquires measurement data, and manufactures a pillow based on the measurement data. When the body surface shape of the human body is measured, the person to be measured lies down, and the posture of the person to be measured is the sleeping posture. Therefore, it is possible to manufacture a pillow that reflects the sleeping posture.

JP 2021-69913 A

However, in the manufacturing apparatus described above, information on the pillow to be manufactured is not provided to the user before manufacturing.

The present disclosure has been made in view of such circumstances, and aims to provide a sleeping posture data processing device, a bedding manufacturing device, a bedding selection device, a sleeping posture data processing method, a computer program, and a bedding data processing device that can provide information on bedding to the user before the user acquires the bedding.

A sleeping posture data processing device according to an embodiment of the present disclosure includes an acquisition unit that acquires imaging data related to a body surface shape of at least a part of a human body, a generation unit that generates sleeping posture data indicating a sleeping posture of a human body based on the imaging data acquired by the acquisition unit, and an output unit that outputs information on bedding corresponding to the sleeping posture data to a display unit.

In one embodiment of the present disclosure, the bedding information corresponding to the sleeping posture data is output to the display unit, so the user obtains the bedding information before obtaining the bedding.

A sleeping posture data processing apparatus according to an embodiment of the present disclosure includes a similarity calculation unit that calculates a degree of similarity between the imaging data acquired by the acquisition unit and each of a plurality of different accumulated data that are stored in advance and indicate the entire body surface shape of a human body, and a selection unit that selects at least one of the accumulated data according to the similarity calculated by the similarity calculation unit.

In one embodiment of the present disclosure, imaging data is compared with a plurality of different stored data representing the entire body surface shape of a human body, at least one stored data having a high degree of similarity is selected, and data representing a sleeping posture is generated based on the selected stored data.

In the sleeping posture data processing device according to an embodiment of the present disclosure, the imaging data is data relating to at least a partial body surface shape of a human body in a standing or sitting position.

An embodiment of the present disclosure eliminates the need to lay down the human body to generate imaging data.

A sleeping posture data processing device according to an embodiment of the present disclosure, wherein the accumulated data includes a first distance between a reference line connecting a first vertex of the back of the chest and a second vertex of the back of the buttocks of a human body in a standing position and a first vertex of a curved surface of the back of the human body that curves to project forward between the first and second vertexes, and the sleeping posture data includes the first distance of the curved surface of the back of the human body that projects forward to project between the first and second vertices in the sleeping posture. The generating unit calculates the second distance based on the first distance, and the output unit outputs the bedding information corresponding to the second distance to the display unit.

In one embodiment of the present disclosure, a suitable second distance is calculated based on the first distance. The bedding corresponding to the second distance can relax the human body. That is, it is possible to provide the user with information on bedding that can relax the human body.

A sleeping posture data processing device according to an embodiment of the present disclosure, wherein the bedding includes a mattress, a second acquisition unit that acquires at least two of height, weight, and BMI, and a hardness calculation unit that calculates the hardness of the mattress based on at least two of the height, weight, and BMI obtained by the second acquisition unit, and the output unit displays information about the mattress on the display unit based on the hardness calculated by the hardness calculation unit.

In one embodiment of the present disclosure, the user is provided with comfortable firmness mattress information.

A sleeping posture data processing device according to an embodiment of the present disclosure, wherein the bedding includes a pillow, a height calculation unit for calculating the height of the pillow based on the hardness calculated by the hardness calculation unit, and the output unit displays information on the pillow on the display unit based on the height calculated by the height calculation unit.

In one embodiment of the present disclosure, the height corresponding to the hardness of the mattress is calculated, and the information of the pillow having the calculated height is provided to the user.

A sleeping posture data processing device according to an embodiment of the present disclosure includes a third acquisition unit that acquires pressure acting on a human body, the plurality of accumulated data are associated with the pressure, and the similarity calculation unit calculates the similarity between the imaged data and each of the plurality of accumulated data based on the pressure acquired by the third acquisition unit.

In one embodiment of the present disclosure, the degree of similarity between the imaging data and each of the plurality of accumulated data is calculated based on the pressure acting on the human body.

A bedding manufacturing apparatus according to an embodiment of the present disclosure manufactures bedding based on the sleeping posture data generated by the sleeping posture data processing device.

In one embodiment of the present disclosure, bedding is manufactured based on sleeping posture data.

A bedding selection device according to an embodiment of the present disclosure selects at least one bedding from a plurality of beddings based on the sleeping posture data generated by the sleeping posture data processing device.

In one embodiment of the present disclosure, at least one bedding is selected from a plurality of bedding based on sleeping posture data.

A sleeping posture data processing method according to an embodiment of the present disclosure acquires imaging data related to the body surface shape of at least a part of a human body, generates sleeping posture data indicating the sleeping posture of the human body based on the acquired imaging data, and outputs information on bedding corresponding to the sleeping posture data to a display unit.

In one embodiment of the present disclosure, the bedding information corresponding to the sleeping posture data is output to the display unit, so the user obtains the bedding information before obtaining the bedding.

A computer program according to an embodiment of the present disclosure acquires imaging data relating to a body surface shape of at least a part of a human body, generates sleeping posture data indicating a sleeping posture of a human body based on the acquired imaging data, and causes a computer to execute processing for outputting information on bedding corresponding to the sleeping posture data to a display unit.

In one embodiment of the present disclosure, the bedding information corresponding to the sleeping posture data is output to the display unit, so the user obtains the bedding information before obtaining the bedding.

A bedding data processing device according to an embodiment of the present disclosure includes: an imaging data acquisition unit that acquires imaging data related to the body surface shape of at least a part of a human body; an information acquisition unit that acquires the hardness of the mattress and the height of the pillow by inputting the imaging data acquired by the imaging data acquisition unit and the answers to the questionnaire to a learning model that outputs the hardness of the mattress and the height of the pillow when the hardness of the mattress and the height of the pillow are used as teacher data and the imaging data and answers to a questionnaire are input; and an output unit for outputting the information of the mattress and pillow corresponding to the hardness and height to a display unit.

In one embodiment of the present disclosure, the user obtains mattress and pillow information based on imaging data and questionnaire results before acquiring bedding.

In the bedding data processing device according to an embodiment of the present disclosure, the similarity calculation unit calculates the similarity based on the matching rate of contour lines on the front or left and right sides of at least a part of the human body, or the proportion of projected areas on the front or left and right sides of at least a part of the human body.

In one embodiment of the present disclosure, the degree of similarity is calculated based on the matching rate of outlines or projected areas.

In the bedding data processing device according to one embodiment of the present disclosure, the second distance is 3/4 or less of the first distance.

An embodiment of the present disclosure implements the calculation of the second distance.

In the bedding data processing device according to an embodiment of the present disclosure, the second distance is 1/3 to 1/2 of the first distance.

An embodiment of the present disclosure implements the calculation of the second distance.

In the bedding data processing device according to an embodiment of the present disclosure, the hardness calculation unit corresponds to a plurality of predetermined body types based on height and weight, calculates the ratio of the width to the height and the height by calculating the width, calculates the body surface area based on the calculated width and height, calculates the pressure acting on the human body based on the calculated body surface area and weight, and calculates the hardness of the mattress based on the calculated pressure.

An embodiment of the present disclosure implements a mattress firmness calculation.

In the bedding data processing device according to an embodiment of the present disclosure, the height calculation unit calculates so that the hardness calculated by the hardness calculation unit becomes higher as the hardness calculated by the hardness calculation unit becomes softer, and the hardness calculated by the hardness calculation unit becomes lower as the hardness calculated by the hardness calculation unit becomes harder.

An embodiment of the present disclosure implements pillow height computation.

In the bedding data processing device according to one embodiment of the present disclosure, the hardness calculation unit has a specific part calculation unit that calculates the hardness of the first part of the mattress, a table indicating the difference between the hardness of the first part and the hardness of the second part of the mattress other than the first part is stored in advance, the hardness of the first part calculated by the specific part calculation part is applied to the table to obtain the hardness of the second part, and the output part calculates the hardness of the first part and the second part. Based on this, the information of the mattress is output to the display unit.

In one embodiment of the present disclosure, the firmness of each of a plurality of parts of the mattress can be calculated, and mattress information reflecting the calculated firmness can be provided to the user.

In the sleeping posture data processing device, the bedding manufacturing device, the bedding selection device, the sleeping posture data processing method, the computer program, and the bedding data processing device according to an embodiment of the present disclosure, the user obtains the bedding information before acquiring the bedding. Therefore, the user can confirm in advance whether or not the bedding is to his liking.

1 is a block diagram of a bedding selection device according to Embodiment 1; FIG. FIG. 4 is an explanatory diagram for explaining acquisition of imaging data by an imaging unit; FIG. 10 is an explanatory diagram illustrating calculation of similarity between captured data and a plurality of accumulated data; It is an explanatory view explaining the distance Y in the sleeping posture. 1 is a schematic partial enlarged side view of a mattress; FIG. It is a graph which shows the relationship between body pressure and the hardness of a mattress. It is a graph which shows the relationship between the firmness of a mattress, and the height of a pillow. It is a flow chart explaining bedding selection processing by CPU. FIG. 11 is a block diagram of a bedding selection device according to Embodiment 2; It is a schematic side view of a body pressure measuring part. It is a flow chart explaining bedding selection processing by CPU. FIG. 11 is a block diagram of a bedding manufacturing apparatus according to Embodiment 3; FIG. 14 is a flowchart for explaining bedding selection processing by a CPU according to Embodiment 4; FIG. FIG. 13 is a schematic diagram showing an example of a learning model according to Embodiment 5; FIG. 11 is a block diagram of a bedding selection device according to Embodiment 6; FIG. 11 is a schematic diagram showing parts of a mattress according to Embodiment 7; It is a table which shows the hardness of each part of the mattress in each posture.

(Embodiment 1)
BEST MODE FOR CARRYING OUT THE INVENTION The present invention will be described below with reference to the drawings showing a bedding selection device according to Embodiment 1. FIG. FIG. 1 is a block diagram of a bedding selection device. The bedding selection device includes a control device 1 , an operation section 2 , an imaging section 3 , a search section 4 and a display section 5 . The control device 1 includes a CPU 1a, a RAM 1b, a storage section 1c, and an interface section (I/F section) 1d. The CPU 1a, RAM 1b, storage section 1c and I/F section 1d are interconnected via a bus.

The CPU 1a is an example of a control unit, and a logic circuit such as an FPGA may be used instead of the CPU 1a. The operation unit 2 has a keyboard, mouse, switches, or the like. The display unit 5 has a display screen. Information is input to the control device 1 from the operation unit 2, the imaging unit 3, and the search unit 4 via the I/F unit 1d.

FIG. 2 is an explanatory diagram for explaining acquisition of imaging data by the imaging unit 3. As shown in FIG. The imaging unit 3 includes, for example, a 3D scanner or camera. It is desirable to use a 3D scanner for imaging. This is because the imaging accuracy can be improved as compared with the case of using a camera. At least part of the user (human body) is imaged by the imaging unit 3 . For example, the upper body, torso, lower body, whole body, or the like is imaged. The imaging unit 3 acquires imaging data related to at least a part of the body surface shape of the user in a standing position. In FIG. 2, the posture of the user is a standing position, but the user may be in a sitting position, and the imaging unit 3 may acquire imaging data related to at least a part of the body surface shape in the sitting position. In the case of imaging by a camera, for example, the user is imaged from each of the front, back, left, and right directions. For example, the CPU 1a acquires imaging data from the imaging section 3 via the I/F section 1d and stores it in the storage section 1c. A CPU 1a that acquires imaging data from the imaging unit 3 via the I/F unit 1d constitutes an acquisition unit.

Before or after taking an image, the user inputs part information indicating an imaged part into the control device 1 via the operation unit 2 . For example, "whole body", "upper body", "buttocks", etc. are input. The input region information is associated with the imaging data and stored in the storage unit 1c together with the imaging data.

As for the method of inputting the part information, accumulated data described later may be utilized. For example, the distance may be calculated for each part based on each accumulated data, and the part may be input based on the calculated distance and imaging data obtained by imaging the entire body. For example, if the distance from the top of the head to the base of the neck is defined as the length of the "head", the ratio of the distance of the "head" to the total height is calculated in each accumulated data, and the average value of each calculated ratio is calculated. The average value of the above ratios is applied to the imaging data obtained by imaging the entire area with respect to the distance from the top of the head. The part of the imaging data corresponding to the distance obtained by applying is stored as the imaging data of the "head". The same applies to the "upper body". For the "buttocks", for example, in each accumulated data, the ratio of the distance from the top of the head to the upper end of the buttocks to the total height is calculated, and the average value (first average value) of the calculated ratios is calculated. In each accumulated data, the ratio of the distance from the toes to the bottom of the buttocks to the total height is calculated, and the average value (second average value) of the calculated ratios is calculated. The first average value is applied to the distance from the top of the head to the imaging data obtained by imaging the whole. Further, the second average value is applied to the distance from the tip of the foot to the imaging data obtained by imaging the whole. The distance obtained by applying the first average value and the distance obtained by applying the second average value are subtracted from the overall height. The part of the imaged data corresponding to the distance obtained by the subtraction is stored as the imaged data of the "waist". Also, each body part may be estimated and stored according to body type data standards acquired for each race and country, such as JISL0111, ISO3635, and ISO7250.

Before or after taking an image, the user inputs biological sex, height, weight, BMI, posture at the start of sleeping (back, side, prone), etc. to the control device 1 via the operation unit 2. The input sex, height, weight, BMI, and posture at the start of sleeping are linked to the imaging data and stored in the storage unit 1c together with the imaging data. Any two of height, weight, and BMI may be input to the control device 1 via the operation unit 2, and the CPU 1a may calculate the remaining one from any two of the input height, weight, and BMI, and store it in the storage unit 1c.

The storage unit 1c stores a plurality of different data (accumulated data) representing the entire body surface shape of the human body in a standing position, and a program (program product) for generating sleeping posture data and selecting bedding. The distance X (first distance), height and weight are associated with each accumulated data and stored in the storage unit 1c. Various threshold values are stored in the storage unit 1c. The CPU 1a reads the program into the RAM 1b and executes processing for generating sleeping posture data and processing for selecting bedding.

The distance X will be explained. As shown in FIG. 2, let S be a line connecting the vertex A of the back of the chest and the vertex B of the back of the buttocks of the human body in a standing position. On the back of the human body, the surface of the portion between the vertex A and the vertex B curves in an arc shape so as to protrude toward the front. X is the distance between the vertex C1 of the arc-shaped curved surface and the line S. As shown in FIG.

The search unit 4 stores a plurality of pieces of bedding information indicating bedding such as mats and pillows. The bedding information includes bedding images, dimensions, manufacturing numbers, and the like. When the manufacturing number is received from the control device 1 , the searching unit 4 transmits to the control device 1 an image, dimensions, etc. of the bedding corresponding to the received manufacturing number. The control device 1 causes the display unit 5 to display the manufacturing number, the image of the bedding, the dimensions, and the like.

A description will be given of processing for generating sleeping posture data. FIG. 3 is an explanatory diagram for explaining calculation of the degree of similarity between imaging data and a plurality of accumulated data. In FIG. 3, the hatched portion indicates the upper body portion of the stored data and indicates the portion corresponding to the imaging data.

For example, as shown in FIG. 3, when the CPU 1a acquires imaging data obtained by imaging the upper body of a human body from the imaging unit 3, the CPU 1a refers to the part information ("upper body" in this embodiment) linked to the imaging data. Note that the upper half of the body is an example, and imaging data obtained by imaging the trunk, the lower half of the body, the buttocks, the whole body, or the like may be used. The upper body part in each of the plurality of stored data is compared with the captured data to calculate the degree of similarity. The degree of similarity includes, for example, the matching rate of outlines of the front or left and right sides of the upper body, or the matching rate of the projected areas of the front or left and right sides of the upper body. The CPU 1a extracts one or a plurality of accumulated data having a similarity of 80% or higher, preferably 90% or higher, more preferably 95% or higher. If there are a plurality of selected accumulated data, the accumulated data with the highest degree of similarity is further extracted. For example, as shown in FIG. 3, the CPU 1a extracts one stored data 101 having the highest degree of similarity from the three selected stored data 101-103.

The weight and height associated with the accumulated data may be compared with the weight and/or height associated with the imaging data, and the degree of similarity may be corrected so that the similarity increases as the difference in weight and/or height decreases. Alternatively, the BMI may be calculated from the weight and height of the accumulated data, and the degree of similarity may be corrected so that the similarity increases as the difference between the BMI calculated from the weight and height of the imaging data and the BMI calculated from the weight and height of the accumulated data decreases.

FIG. 4 is an explanatory diagram for explaining the distance Y in the sleeping posture. The surface of the portion between the apex A of the back of the chest and the apex B of the back of the buttocks of the human body in the sleeping posture (upright in FIG. 4) is arcuately curved so as to protrude toward the front. The distance Y (second distance) between the vertex C2 of the arc-shaped curved surface and the line S is defined. By using a mattress that makes the distance Y an appropriate distance, the human body in the sleeping posture can be relaxed. In other words, a deeper sleep can be given to the human body. An appropriate distance Y that can relax the human body in the sleeping posture is smaller than the distance X. For example, a suitable distance Y is (3/4)*X or less, preferably between (1/3)*X and (1/2)*X. That is, Y can be obtained from X by setting Y=aX and setting the coefficient a to an appropriate numerical value of 3/4 or less. The control device 1 calculates the distance Y from the distance X associated with the extracted accumulated data.

The firmness of the mattress is important in order to maintain the proper distance Y when using the mattress while sleeping. FIG. 5 is a schematic partial enlarged side view of the mattress. The mattress 10 comprises, for example, a first layer 11 , a second layer 12 arranged below the first layer 11 , and a third layer 13 arranged below the second layer 12 . The first layer 11 is the part that comes into contact with the human body. The hardness of the first layer 11 hardly affects the depth of human sleep. Therefore, the hardness of the first layer 11 may be selected to satisfy the user's sensuality. The third layer has a structure that allows it to move up and down while receiving the second layer flat. The hardness of the third layer 13 hardly affects the depth of human sleep. Therefore, it is sufficient for the third layer to adopt a structure that allows it to move up and down while receiving the second layer flat.

Compared to the hardness of the first layer 11 and the third layer 13, the hardness of the second layer 12 has a greater effect on the depth of sleep of the human body. The hardness of the mattress 10 described below mainly refers to the hardness of the second layer 12 . It is preferable to use a mattress 10 whose hardness becomes softer as the distance Y increases. As the distance Y increases, a larger upward force from the mattress 10 acts on the vertices A and B on the back of the human body. Therefore, by using a softer mattress 10, the pressure acting on apex A and apex B can be reduced and the comfort of the human body can be improved.

Each of the first layer 11, the second layer 12 and the third layer 13 may be composed of a plurality of layers. Alternatively, the human body may be divided into parts based on the body shape data standard described above, and the material may be determined for each part. Even if the same material is used for a plurality of parts, the hardness and surface shape may be changed for each part. That is, it may be a custom-made product made by combining parts corresponding to each part.

Also, by keeping the distance D between the line S and the back of the head at an appropriate distance, the human body in the sleeping posture can be relaxed (see FIG. 4). Here, the distance D is the distance between the line S and the back of the head when the human body is lying on the mattress 10 and the mattress 10 is sunk and the human body is in a calm state. A suitable distance D is about 6-8 cm. In order to keep the distance D at an appropriate distance, it is necessary to use pillows of appropriate height.

FIG. 6 is a graph G1 showing the relationship between the body pressure and the hardness of the mattress 10. As shown in FIG. In the graph G1, the hardness of the mattress 10 becomes harder toward the right side. In the graph G1, body pressure increases toward the upper side.

As shown in graph G1, it is necessary to use a mattress 10 whose hardness becomes softer as body pressure increases. By using a softer mattress 10, the body pressure on the back of the body is distributed and the comfort of the body can be improved. The body pressure is a value obtained by dividing the body weight of the human body by the body surface area on the back side. The body surface area is calculated, for example, by approximating the back surface of the human body to a rectangle. That is, the body surface area is the product of the height and width of the human body. The width is calculated based on at least two of height, weight and BMI. For example, when height and weight are set, three types of obese type, normal type, and thin type can be found. The ratio of width to height is set in advance according to each type of obese type, normal type, and thin type. The width is calculated by calculating the product of the height and the ratio. Note that the width may be calculated using the body type data standard described above.

Since BMI is determined based on height and weight, weight can be obtained if BMI and height are set in advance. Height can be determined if BMI and weight are set in advance. That is, body pressure can be obtained in the same way as when height and weight are set.

The body pressure acting on a part of the human body may be obtained, and the hardness of the mattress 10 may be obtained based on the obtained body pressure. For example, the body pressure of the waist may be determined, and the hardness of the mattress 10 may be determined based on the body pressure of the waist. The ratio of the weight of each part of the human body to the weight of the human body in the sleeping posture (body weight ratio) is obtained in advance by measurement. For example, the waist weight ratio is about 44% of body weight. The weight of the waist is calculated by multiplying the weight of the human body by the weight ratio of the waist. Also, the back side area of the waist is obtained in advance by measurement. By dividing the calculated weight of the waist by the measured area, the body pressure of the waist can be calculated. The hardness of the mattress 10 is obtained based on the calculated body pressure. The same is true when obtaining the body pressure of a portion other than the waist.

The determined body pressure may be corrected according to the shape of the mattress 10 . For example, as the weight-bearing area of the mattress 10 decreases, the mattress 10 is made softer, and as the area increases, the mattress 10 is made harder.

When the body weight is divided by the area, for example, the area of each part of the body may be determined manually one by one, or the average value of the areas of the men and women may be calculated. In addition, the average value of the area may be obtained in chronological order, or may be calculated for each generation. Similarly, the body weight ratio for each part of the human body may be determined manually, or may be determined by average value for men and women, or by age or generation. Alternatively, the weight may be divided by the surface area of each part of the photographed image to obtain the body pressure of each part, and the mattress 10 suitable for supporting the body pressure may be determined.

Depending on the material, the relationship between the body pressure and the hardness of the mattress 10 may be the opposite relationship of the graph G1. For example, when the mattress 10 is made of a latex (rubber-based) material, that is, a material with high elasticity, the relationship of graph G1 is adopted for body pressure distribution. On the other hand, when the mattress 10 is made of, for example, a urethane material, that is, a material having a small elastic force, the opposite relationship of the graph G1 is adopted. This is because if the relationship of the graph G1 is adopted, the portion between the vertex A and the vertex B sinks into the mattress 10 and the distance Y does not become an appropriate distance.

FIG. 7 is a graph G2 showing the relationship between the hardness of the mattress 10 and the height of the pillow. As shown in graph G2, the height of the pillow increases as the hardness of the mattress 10 increases. If the mattress 10 is hard, it is difficult for the chest and buttocks to sink deeply into the mattress 10 . That is, the position of line S is located on the upper side. Therefore, in order to keep the appropriate distance D, the height of the pillow needs to be increased. On the other hand, if the mattress is soft, the chest and buttocks tend to sink deeply as a whole. That is, the position of line S is lowered. Therefore, in order to keep the appropriate distance D, the height of the pillow needs to be low. The height can also be adjusted by adjusting the material or hardness of the pillow. Also, the surface shape of the mattress 10 or pillow insert may be adjusted to produce the same effect as changing the softness/hardness. For example, the smaller the area of the surface of the inner material that contacts the human body, the higher the body pressure acting on the human body, and the softer the user feels. That is, the surface of the mattress 10 made of the same material may be processed to form a portion where the human body easily sinks and a portion where the human body does not easily sink, thereby producing the same effect as changing the softness and hardness. For example, the more cuts formed on the surface of the mattress 10, the easier it is for the human body to sink, and the fewer the cuts, the harder it is for the human body to sink.

FIG. 8 is a flowchart for explaining bedding selection processing by the CPU 1a. The CPU 1a determines whether or not a start instruction has been input from the operation unit 2 (S1). If the start instruction has not been input (S1: NO), the CPU 1a returns the process to step S1. When a start instruction is input (S1: YES), the CPU 1a acquires imaging data from the imaging unit 3 (S2), and selects one stored data from the storage unit 1c (S3).

The CPU 1a extracts the body surface shape of the accumulated data corresponding to the body surface shape of the region indicated by the imaging data (S4), calculates the similarity between the extracted accumulated data body surface shape and the imaging data body surface shape, and determines whether or not the similarity is equal to or greater than a threshold value (S5). As described above, the degree of similarity may be corrected based on weight, height, or BMI. If the degree of similarity is equal to or greater than the threshold (S5: YES), the CPU 1a stores the selected accumulated data in the storage unit 1c (S6), and determines whether or not all the accumulated data and the imaging data have been compared (S7). In step S5, if the degree of similarity is not equal to or greater than the threshold (S5: NO), that is, if the degree of similarity is less than the threshold, the CPU 1a proceeds to step S7.

In step S7, if all the accumulated data and the imaging data have not been compared (S7: NO), the CPU 1a returns to step S3 and selects the next accumulated data (S3). The CPU 1a sequentially selects a plurality of accumulated data. In step S7, when all the accumulated data and the captured data are compared (S7: YES), the CPU 1a selects the accumulated data with the highest degree of similarity from the accumulated data whose degree of similarity is equal to or higher than the threshold (S8).

The CPU 1a calculates the distance Y based on the distance X linked to the accumulated data with the highest degree of similarity (S9). The distance Y is calculated by, for example, the aforementioned formula Y=aX. The CPU 1a acquires at least two of the height, weight, and BMI linked to the imaging data (S10). The CPU 1a calculates the body pressure based on at least two of height, weight and BMI, applies the body pressure to the graph G1, and calculates the hardness of the mattress 10 (S11).

The CPU 1a transmits the hardness of the mattress 10 to the search unit 4 (S12). Upon receiving the hardness of the mattress 10 , the search unit 4 transmits the manufacturing numbers, images, dimensions, etc. of the three mattresses 10 corresponding to the hardness of the mattress 10 to the control device 1 . The three mattresses 10 are, for example, a mattress 10 with a firmness that is closest to the firmness of the received mattress 10, a mattress 10 with a firmness that is second closest, and a mattress 10 with a firmness that is third closest. The search unit 4 also transmits the production numbers, images, dimensions, etc. of the three pillows corresponding to the three mattresses 10 to the control device 1 . That is, the search unit 4 selects at least one bedding from a plurality of bedding based on the sleeping posture data.

The CPU 1a determines whether or not it has received the production numbers, images, dimensions, etc. of the three mattresses 10 and the three pillows, that is, the bedding information, from the search unit 4 (S13). If the bedding information has not been received (S13: NO), the CPU 1a returns the process to step S13. If the bedding information has been received (S13: YES), the CPU 1a causes the display unit 5 to display the manufacturing numbers, images, dimensions, etc. of the three mattresses 10 and the three pillows (S14). The CPU 1a determines whether or not any one of the three mattresses 10 and any one of the three pillows has been selected, that is, whether or not bedding has been selected (S15). The user selects one of the three mattresses 10 and one of the three pillows. The user, for example, sleeps on a sample of three mattresses 10 and selects the most comfortable mattress 10 .

The control device 1 calculates an appropriate pillow height based on the hardness of the mattress 10, as described above. The control device determines three pillows, for example, the first pillow with the height closest to the calculated pillow height, the second pillow with the second closest height, and the third pillow with the third closest height, and causes the display unit 5 to display the first pillow, the second pillow, and the third pillow. The user selects any one from the first pillow, the second pillow and the third pillow. The user tries samples of the first, second and third pillows and selects the most comfortable pillow.

Note that the control device 1 may automatically select a pillow. For example, if the first pillow is the first pillow, the second pillow is the second pillow, and the third pillow is the third pillow, the first pillow is automatically selected when the first mattress is selected, the second pillow is automatically selected when the second mattress is selected, and the third pillow is automatically selected when the third mattress is selected. That is, if the user selects either the first, second or third mattress, either the first, second or third pillow is automatically selected.

When bedding is not selected (S15: NO), the CPU 1a returns the process to step S15. When bedding is selected (S15: YES), the CPU 1a determines whether the first layer 11 of the selected mattress 10 is selected (S16). As mentioned above, the selection of the first layer 11 is performed by the user choosing what is sensually pleasing. If the first layer 11 is not selected (S16: NO), the CPU 1a returns the process to step S16. If the first layer 11 is selected (S16: YES), the CPU 1a determines the mattress 10 and pillow, displays the determined mattress 10 and pillow on the display unit 5 (S17), and ends the process. The CPU 1a may display the determined mattress 10 and a sheet cover or a bed pad for adjusting the height of the pillow together with the mattress 10 and the pillow on the display unit 5 based on the results of the questionnaire.

For example, a bedding vendor may have the determined mattress 10 and pillow shipped from a warehouse to the user. When selecting the first layer 11 in step S16, the surface layer of the pillow may also be selected.

In Embodiment 1, the distance X is calculated based on the imaging data of the user in the standing or sitting position, the distance Y is calculated based on the distance X, and bedding is selected. For example, the above technology may be applied to a device for selecting a chair, calculating the distance X based on the image data of the user in the standing position, calculating the distance Z between the back of the user in the sitting position and the back of the chair, and selecting the chair based on the distance Z. That is, the distance X, the distance Y, and the distance Z may be mutually convertible and used in selecting bedding or a chair.

Further, the selection of bedding is not limited to the case where the bedding is selected based on the distance Y, and the bedding may be selected based on the distance between an arbitrary point on the back of the human body and the line S in the sleeping posture. For example, the bedding may be selected based on the distance between the line S and the location corresponding to the cervical, thoracic, lumbar, or sacral vertebrae. The distance between the point corresponding to the cervical, thoracic, lumbar or sacral vertebrae and the line S is about half the distance X.

Alternatively, a questionnaire may be taken from the user in advance, and the bedding displayed on the display unit may be changed based on the questionnaire results. The questionnaire may be taken by using a questionnaire form, and the user may fill in the form, or the contents of the questionnaire may be displayed on a display unit having a liquid crystal screen or a touch panel, and the user may respond by operating a keyboard or touch panel. For example, if the user answers in a questionnaire that he or she has a large amount of muscle mass, the display unit may display the mattress 10 in which the portion corresponding to the shoulder is softer than the other portions. Users who answered that they have a large amount of muscle tend to have a larger amount of muscle in their shoulders than other users. By making the portion corresponding to the shoulder softer than other portions, the pressure acting on the shoulder is distributed and the comfort of the user is improved.

For example, when the user answers in a questionnaire that he/she wants to cure a stooped back, the display part may display the mattress 10 in which the portions corresponding to the shoulders and the waist are made harder than the other portions, and the other portions become softer as the distance from the portions corresponding to the shoulders and the waist becomes softer. By making the portions corresponding to the shoulders and the waist harder than the other portions, the back tends to curve in the sleeping posture. That is, improvement of stooped back can be expected.

For example, if a joint such as a neck or waist is damaged due to an accident or deterioration over time, the user may be asked to answer a questionnaire in advance. For example, a human body image is displayed on a touch panel, and a user with a neck injury is asked to tap a button displayed on the neck of the human body image or near the neck, and a user with a waist injury is asked to tap a button displayed on the waist of the human body image or near the waist. Based on the results of the questionnaire, the sleeping posture data processing device may select bedding that matches the shape of the user's body surface from among bedding that is less likely to put a load on the neck if it is a neck or on the waist if it is a waist, and display information on the selected bedding on the display unit. Specifically, the sleeping posture data processing device may select bedding that applies body pressure to parts of the body other than the joints to which the load is not desired to be applied, such as the neck and waist, or may select a plurality of beddings that are combined so that body pressure can be distributed and received at all parts other than the parts to which the load is not desired to be applied.

Also, the bedding displayed on the display unit may be changed according to the user's preference. For example, when the user answers a questionnaire about the degree of breathability, the feel of the skin, the presence or absence of allergies to materials, the height of the pillow, the hardness of the pillow, etc., the mattress 10 or pillow corresponding to the user's preference may be displayed on the display unit.

Alternatively, the body fat percentage may be estimated from the user's BMI, and bedding displayed on the display unit may be changed based on the estimated body fat percentage. For example, when the body fat percentage is higher than the reference value, the user tends to be less likely to turn over than when the body fat percentage is lower than the reference value. Therefore, when the body fat percentage is higher than the reference value, a harder mattress 10 is displayed on the display. If the mattress 10 becomes harder, that is, if the elastic force becomes greater, the user will be more likely to turn over, which can be expected to improve the comfort and health of the user. If the body fat percentage is higher than the reference value, only the shoulders and hips may be made softer than the other parts. If the body fat percentage is higher than the reference value, it is assumed that the user is relatively heavy, and the body pressure acting on the shoulders and hips is likely to increase. In this case, by making only the shoulder and waist areas softer than the other areas, the pressure is distributed and the comfort of the user is improved.

In addition, when the user's fat percentage is lower than the reference value and the muscle mass is large, the user's back tends to curve, and the body pressure acting on the waist and shoulders in the sleeping posture tends to increase. In this case, by making only the shoulder and waist areas softer than the other areas, the pressure is distributed and the comfort of the user is improved.

In the bedding selection apparatus according to Embodiment 1, since the bedding information corresponding to the sleeping posture data is output to the display unit 5, the user obtains the bedding information before obtaining the bedding. Therefore, the user can confirm in advance whether or not the bedding is to his liking.

Also, the imaging data is compared with a plurality of different stored data representing the entire body surface shape of the human body, at least one stored data having a high degree of similarity is selected, and data representing the sleeping posture is generated based on the selected stored data.

In addition, since the imaging data is data relating to at least a part of the body surface shape of the human body in a standing or sitting position, it is not necessary to lay down the human body to generate the imaging data.

Also, based on the distance X, an appropriate distance Y is calculated. The bedding corresponding to the distance Y can relax the human body. That is, it is possible to provide the user with information on bedding that can relax the human body.

It also provides the user with information on the mattress 10 with a comfortable hardness.

Also, the height corresponding to the hardness of the mattress is calculated, and the information of the pillow having the calculated height is provided to the user.

In Embodiment 1, data relating to at least a part of the body surface shape of the human body in the standing or sitting position is used as the imaging data, but at least a part of the body surface shape of the human body in the sleeping position may be imaged.

(Embodiment 2)
The present invention will be described below with reference to the drawings showing a bedding selection device according to Embodiment 2. FIG. Among the configurations according to the second embodiment, the configurations similar to those of the first embodiment are denoted by the same reference numerals, and detailed description thereof will be omitted. FIG. 9 is a block diagram of the bedding selection device. The bedding selection device according to Embodiment 2 includes a body pressure measurement unit 6 .

10 is a schematic side view of the body pressure measuring section 6. FIG. The body pressure measurement unit 6 is a bed-type device, and the user can lie on the body pressure measurement unit 6 . The body pressure measurement unit 6 has a plurality of pressure sensors (not shown). A plurality of pressure sensors measure pressure acting on a plurality of points of the user in the sleeping posture, that is, body pressure. The body pressure data of the user is input from the body pressure measurement unit 6 to the control device 1, and stored in the storage unit 1c in association with the imaging data. In addition, body pressure data acting on a plurality of parts of the human body (for example, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, and sacral vertebrae in the spine) are stored in the storage unit 1c in association with the accumulated data.

The CPU 1a calculates the degree of similarity between the body pressure data of the accumulated data and the body pressure data of the imaging data. For example, the degree of coincidence of body pressure data is calculated for each of a plurality of locations, and the average value of each degree of coincidence is calculated. The CPU 1a compares the average value with a threshold value stored in advance in the storage unit 1c, and stores the accumulated data as a target candidate for the bedding selection process when the average value is equal to or greater than the threshold value.

Further, for example, when the matching degree of body pressure data in the cervical spine portion is equal to or greater than a first threshold, the matching degree of body pressure data in the thoracic spine portion is equal to or greater than a second threshold, the matching degree of body pressure data in the lumbar vertebrae is equal to or greater than a third threshold, and the matching degree of body pressure data in the sacrum portion is equal to or greater than a fourth threshold, the accumulated data may be stored as candidates for correction processing. Note that the first to fourth threshold values are stored in advance in the storage unit 1c.

FIG. 11 is a flowchart for explaining bedding selection processing by the CPU 1a. Steps S41 to S45 and S47 to S58 in FIG. 11 are the same as steps S1 to S17 in FIG. 8, so detailed description thereof will be omitted. Here, step S46 in FIG. 11 will be described.

In step S45, if the degree of similarity is equal to or greater than the threshold (S45: YES), the CPU 1a calculates the degree of similarity between the body pressure data of the selected accumulated data and the body pressure data of the imaging data, and determines whether or not the degree of similarity is equal to or greater than the threshold (S46). If the similarity is equal to or greater than the threshold (S46: YES), the CPU 1a stores the selected accumulated data in the storage unit 1c (S47). If the similarity is not equal to or greater than the threshold (S46: NO), the CPU 1a proceeds to step S48.

The bedding selection device according to Embodiment 2 calculates the degree of similarity between the imaging data and each of the plurality of accumulated data based on the pressure acting on the human body.

(Embodiment 3)
The present invention will be described below based on the drawings showing a bedding manufacturing apparatus according to Embodiment 3. FIG. Among the configurations of Embodiment 3, the configurations similar to those of Embodiments 1 and 2 are denoted by the same reference numerals, and detailed description thereof will be omitted. FIG. 12 is a block diagram of the bedding manufacturing apparatus. Unlike Embodiments 1 and 2, the bedding manufacturing apparatus according to Embodiment 3 includes a manufacturing unit 7 . The manufacturing unit 7 has a CPU, a storage unit, a RAM, a storage unit that stores materials for pillows or mattresses, and a processing unit that processes the materials (all not shown).

The CPU 1a transmits the determined manufacturing numbers of the mattress 10 and the pillow to the manufacturing department 7. FIG. The storage unit 1c of the manufacturing unit 7 preliminarily stores a manufacturing program corresponding to each manufacturing number, reads the manufacturing program corresponding to the received manufacturing number to the RAM 1b, takes out the material from the storage unit, processes it in the processing unit, and manufactures the pillow or mattress 10 indicated by the manufacturing number. Processing units include machine tools and 3D printers.

The bedding manufacturing apparatus according to Embodiment 3 can manufacture bedding based on sleeping posture data.

(Embodiment 4)
The present invention will be described below based on the drawings showing a bedding manufacturing apparatus according to Embodiment 4. FIG. Among the configurations of the fourth embodiment, the configurations similar to those of the first to third embodiments are denoted by the same reference numerals, and detailed description thereof will be omitted. In the first embodiment, the distance Y is obtained using the accumulated data, but in the fourth embodiment, the distance Y is obtained without using the accumulated data.

When imaging a user, the user is imaged from the left or right direction. It is desirable to use a 3D scanner for imaging. This is because the imaging accuracy can be improved as compared with the case of using a camera. The control device 1 calculates the distance X based on the imaging data of the user imaged from the left or right direction (see FIG. 2). The control device 1 calculates the distance Y from the distance X. For example, the distance Y is calculated by the formula Y=aX.

FIG. 13 is a flowchart for explaining bedding selection processing by the CPU 1a. The CPU 1a determines whether or not a start instruction has been input from the operation unit 2 (S61). If the start instruction has not been input (S61: NO), the CPU 1a returns the process to step S61. When the start instruction is input (S61: YES), the CPU 1a acquires image data from the imaging unit 3 (S62) and calculates the distance X (S63). The CPU 1a calculates the distance Y based on the distance X (S64). Steps S65 to S72 are the same as steps S10 to S17, and therefore will be omitted.

Note that the distance Y may be obtained directly by measuring the user's body surface shape using a 3D scanner while the user is in a sleeping posture.

(Embodiment 5)
The present invention will be described below based on the drawings showing a bedding manufacturing apparatus according to Embodiment 5. FIG. Among the configurations of the fifth embodiment, the configurations similar to those of the first to fourth embodiments are denoted by the same reference numerals, and detailed description thereof will be omitted. The storage unit 1 c of the control device 1 stores the learning model 100 . FIG. 14 is a schematic diagram showing an example of the learning model 100. As shown in FIG. The learning model 100 is a learning model that is assumed to be used as a program module that is part of artificial intelligence software, and can use a multilayer neural network (deep learning). For example, a convolutional neural network (CNN) can be used. Note that other machine learning may be used. The CPU 1a reads the learning model 100 into the RAM 1b, and according to instructions from the learning model 100, performs calculations on the input data input to the input layer of the learning model 100, that is, the imaging data and the responses to the questionnaire, and operates to output the hardness of the mattress 10 and the height of the pillow from the output layer.

For CNN, the intermediate layers include convolution layers, pooling layers, and fully connected layers. The number of nodes (neurons) is not limited to the case of FIG. One or more nodes exist in the input layer, the output layer, and the intermediate layer, and the nodes in each layer are unidirectionally coupled with the nodes in the preceding and succeeding layers with a desired weight.

Using teacher data, the CPU 1a generates in advance a learning model 100 that outputs the hardness of the mattress 10 and the height of the pillow when imaging data and answers to questionnaires are input. Specifically, the CPU 1a inputs teacher data to the input layer, performs arithmetic processing in the intermediate layer, and obtains the hardness of the mattress 10 and the height of the pillow from the output layer.

The CPU 1a compares the hardness of the mattress 10 and the height of the pillow output from the output layer with the hardness of the mattress 10 and the height of the pillow labeled for the imaging data and the answers to the questionnaire in the teacher data, i.e., the correct values, and optimizes the parameters used for arithmetic processing in the intermediate layer so that the output values from the output layer approach the correct values. The parameters are, for example, the above-mentioned weights (coupling coefficients), coefficients of activation functions, and the like. Although the parameter optimization method is not particularly limited, for example, the CPU 1a optimizes various parameters using the error backpropagation method. The CPU 1a stores the generated learning model 100 in the storage unit 1c, and terminates a series of processes for the learning model 100. FIG. Note that the CPU 1a may re-learn the learning model 100. FIG.

The CPU 1a inputs the imaging data and the answers to the questionnaire to the learned learning model 100, and obtains the hardness of the mattress 10 and its probability, and the height of the pillow and its probability. The CPU 1a transmits the hardness of the mattress 10 and its probability and the height of the pillow and its probability to the search unit 4. FIG. The search unit 4 transmits information on, for example, three mattresses 10 and three pillows to the control device 1 based on the received hardness of the mattress 10 and its probability and the height of the pillow and its probability. For example, the most probable firmness mattress 10, the second probable firmness mattress 10, and the third probable firmness mattress 10, the highest probable height pillow, the second probable height pillow, and the third probable height pillow information are transmitted. The CPU 1a receives the information of the three mattresses 10 and the three pillows from the search unit 4, and causes the display unit 5 to display the manufacturing numbers, images, dimensions, etc. of the three mattresses 10 and the three pillows. Deep learning is an example of machine learning, and other machine learning such as decision tree learning, clustering, or Bayesian learning may be used instead of deep learning.

(Embodiment 6)
15 is a block diagram of a bedding selection device according to Embodiment 6. FIG. The bedding selection device includes a control device 20 , a terminal 30 and a search section 4 . Control device 20 corresponds to control device 1 in the above-described embodiment. The control device 20 includes a control section 21 , a RAM 22 , a storage section 23 and a communication section 24 . The control unit 21, RAM 22, storage unit 23 and communication unit 24 are interconnected via a bus.

The control unit 21 has, for example, a CPU. A logic circuit such as an FPGA may be used instead of the CPU. Wired or wireless communication is performed between the control device 20 , the terminal device 30 and the search unit 4 via the communication unit 24 .

The terminal device 30 is, for example, a personal computer, a smart phone, or a tablet terminal. The terminal 30 may be owned by the user. The terminal device 30 includes a control section 31 , an operation section 32 , an imaging section 33 , a storage section 34 , a display section 35 and a communication section 36 . The control unit 31 has, for example, a CPU. A logic circuit such as an FPGA may be used instead of the CPU. An operation unit 32, an imaging unit 33, and a display unit 35 correspond to the operation unit 2, the imaging unit 3, and the display unit 5 in the first embodiment.

The operation unit 32 has a keyboard, mouse, touch panel, or the like. The imaging unit 33 has, for example, a 3D scanner or a camera. The storage unit 34 stores a program for displaying an interface on the display unit 35, for example. The program may be pre-installed or may be downloaded from a server via a network. The display unit 35 has a display screen. The control unit 31 can execute the program and display an interface for accepting operations on the display unit 35 . The user can operate the interface displayed on the display unit 35, that is, the touch panel (operation unit 32) to input imaging data and various types of information. Wired or wireless communication is performed between the terminal device 30 and the control device 20 via the communication unit 36 .

The imaging unit 33 acquires user imaging data. Before or after imaging, the user inputs biological sex, height, weight, BMI, region information indicating the region imaged, and the like via the operation unit 32 . Each piece of input information is associated with the imaging data, transmitted to the control device 20 together with the imaging data, and stored in the storage unit 23 . The control unit 21 executes sleep posture data generation processing, bedding selection processing, and the like, based on the imaging data, various information, accumulated data, and the like stored in the storage unit 23 . The control unit 21 transmits to the terminal device 30 information regarding the mattress 10 and the pillow received from the search unit 4 , such as manufacturing numbers, images, dimensions, and the like. The terminal 30 displays information such as the manufacturing number, image, and dimensions on the display section 35 .

(Embodiment 7)
The present invention will be described below with reference to the drawings showing a bedding selection device according to Embodiment 7. FIG. Among the configurations of Embodiment 7, the configurations similar to those of Embodiments 1 to 6 are denoted by the same reference numerals, and detailed description thereof will be omitted. When the hardness of each part of the mattress 10 can be changed, in the bedding selection process, the hardness of a specific part of the mattress 10, for example, the part corresponding to the waist may be calculated, and the hardness of the parts other than the part corresponding to the waist may be obtained based on the hardness of the part corresponding to the waist. For example, when the user captures an image of the waist, the user inputs site information indicating the waist to the control devices 1 and 20 via the operation units 2 and 32 before or after capturing the image. In addition, when the user takes an image of a part other than the waist or a part including the waist, for example, the whole body, and inputs the part information indicating the whole to the control device 1, 20 via the operation unit 2, 32, and the control device 1, 20 calculates the hardness of the mattress 10, the calculated hardness may be the hardness of the part corresponding to the waist. In this case, the hardness of the parts other than the part corresponding to the waist is obtained based on the calculated hardness.

FIG. 16 is a schematic diagram showing parts of the mattress 10. As shown in FIG. In FIG. 16, 1 indicates a portion (part 1) of the mattress 10 corresponding to the neck, 2 indicates a portion (part 2) of the mattress 10 corresponding to the shoulders, 3 indicates a portion (part 3) of the mattress 10 corresponding to the waist, 4 indicates a portion (part 4) of the mattress 10 corresponding to the buttocks, and 5 indicates a portion (part 5) of the mattress 10 corresponding to the legs. The mattress 10 is a mattress in which the hardness of parts 1 to 5 can be changed. The number of parts and the method of dividing the parts shown in FIG. 16 are only examples, and the number of parts may be 2 to 4, or may be 6 or more, and the division may be performed not only in the height direction (vertical direction in FIG. 16) but also in the shoulder width direction (horizontal direction in FIG. 16).

FIG. 17 is a table showing the hardness of each part of the mattress in each posture. The control devices 1 and 20 store the table shown in FIG. 17 in advance. In the table of FIG. 17, postures 1 to 4 are postures of the user at the time of imaging. For example, the postures are standing postures, posture 1 is a posture with less unevenness of the back, posture 2 is a stooped posture, posture 3 is a posture with an S-shaped back, and posture 4 is a posture with a curved waist. The postures 1 to 4 may be sitting postures, or may represent postures focused on other than the back.

In the table of FIG. 17, 1 to 5 in the leftmost column indicate regions 1 to 5 of the mattress 10 . "X1" shown in the column corresponding to part 3 in the column of posture 1 is the hardness calculated in the bedding selection process and indicates the hardness of part 3 (the part of mattress 10 corresponding to the waist). In the column of Posture 1, "-1" shown in the column corresponding to parts 1, 4 and 5 indicates the hardness obtained by subtracting 1 from the hardness "X1" of part 3, and "±0" shown in the column corresponding to part 2 indicates the same hardness as the hardness "X1" of part 3.

In the column for Posture 2, “±0” shown in the column corresponding to parts 1 and 5 indicates the same hardness as the hardness “X2” of part 3, “+1” shown in the column corresponding to part 2 indicates the hardness obtained by adding 1 to the hardness “X2” of part 3, and “−1” indicated in the column corresponding to part 4 indicates the hardness obtained by subtracting 1 from the hardness “X2” of part 3. As with the columns for poses 1 and 2, the columns for poses 3 and 4 also show the hardness of parts 1, 2, 4, and 5 with reference to the calculated "X3" and "X4." A detailed description of the Attitude 3 and Attitude 4 columns is omitted. The hardness of the table in FIG. 17 is expressed, for example, as the amount of sinking when a predetermined load is applied, and the larger the numerical value, the softer the table. The unit of the sinking amount is, for example, cm, but other units such as mm may be used. The numerical values in FIG. 17 are only examples, and other numerical values may be used. A physical quantity other than the amount of sinking may be used as the quantity indicating the hardness of the mattress 10 . When the amount of sinking is used, as the numerical value of the table increases, the back portion of the user corresponding to the numerical value tends to protrude further to the back side, and as the numerical value of the table decreases, the back portion of the user corresponding to the numerical value tends to protrude further to the front side.

The control devices 1 and 20 calculate the hardness of the part 3 and acquire information indicating the posture of the user. Information indicating the user's posture may be input to the control devices 1 and 20 via the operation units 2 and 32, or may be obtained by the control devices 1 and 20 based on the captured data when whole-body or upper-body imaging data is acquired. The control devices 1 and 20 apply the information indicating the hardness of the part 3 and the user's posture to the table of FIG. The displayed information of the mattress 10 may be, for example, the information (serial number, image, dimensions, etc.) of the finished product of the mattress 10 searched by the search unit 4, or the information of each part constituting each part 1 to 5 of the mattress 10. May be information.

Instead of, or in addition to, postures 1-4, the body part where the user is injured may be used. For example, when the neck and shoulders are used as the injured parts, in the table of FIG. That is, based on the calculated stiffness of site 3, a column showing the stiffness of sites 1, 2, 4 and 5 when the neck and shoulders are injured is created and stored. The control devices 1 and 20, for example, acquire the calculated hardness of the site 3 and information indicating the damaged location, apply it to the table in FIG. Information indicating the location of damage is input to the control devices 1 and 20 via the operation units 2 and 32, for example. The hardness of the parts 1, 2, 4 and 5 is such that the burden on the damaged parts is suppressed.

Further, when the control devices 1 and 20 acquire the information indicating the user's posture and the information indicating the damaged location, after applying the user's posture and the calculated hardness to the table, based on the information indicating the damaged location, the hardness of the parts 1 to 5 may be corrected. For example, if the information indicating the user's posture indicates the posture 2 and the information indicating the injured part indicates the neck, the hardness of the part 1 in the column of the posture 2 may be corrected to a softer value, and the part corresponding to the part of the user's body that is closer to the neck and protrudes to the back side than the neck, namely the part 2, may be corrected to a harder value, the load acting on the injured neck may be reduced, and the body may be more firmly supported by the shoulder instead.

(Experimental result)
The applicant conducted the following experiment and measured the effect when the user used the mattress selected by the bedding selection device according to the first embodiment. First, the bedding selection device was used to select the bedding corresponding to the subject's sleeping posture data, here the mattress. The subjects were a total of three, two men and one woman. Next, the subject was asked to sleep on the mattress selected by the bedding selection device (hereinafter referred to as mattress A) and the mattress not selected by the bedding selection device (hereinafter referred to as mattress B), and the subject's sleep data was acquired and analyzed. The mattress A is a mattress mainly made of urethane foam, for example. The mattress B is a mattress mainly made of polyethylene, for example.

"Silmee Bar type Lite Analysis System" (hereinafter referred to as "experiment system") manufactured by TDK Corporation was used for data acquisition and calculation, and data acquisition and calculation were performed. The experimental system includes a measuring device and an arithmetic device that calculates heartbeats, body movements, and the like measured by the measuring device. The subject wears the measuring device and goes to bed. The measuring device measures the heart rate, body movement, etc. during the sleeping period. The measurement results are transmitted from the measuring device to the computing device by wireless communication, or stored in a memory of the measuring device and transmitted from the memory to the computing device by wired or wireless communication.

Based on the measurement results, the arithmetic device calculates "number of times of mid-wake up", "mid-wake time", "sleep time", "total bedtime", "total sleep time", "PS deep sleep time", "S sleep time", "act total", "HF total amount", "heartbeat/pulse wave interval", "average of LF for one night", "average of HF for one night", "body movement frequency", "PS deep sleep ratio", and "S sleep ratio". ", "Relaxation level", "Sleep efficiency", "Rhythm", "Sleep cycle", etc. (hereinafter also referred to as items) were calculated.

A total of 25 data were obtained from 3 subjects for each of these items. There are 13 data for mattress A and 12 data for mattress B. Data for one of the 13 mattresses A were not calculated well and were excluded from subsequent analyzes such as t-test and analysis of variance.

The “number of times of awakening” is the number of times of awakening during sleep time. "Mid-awakening time" is the sum of wakefulness during sleep time. "Sleep time" is the time from falling asleep to waking up. "Total bedtime" is the time from bedtime to wakeup. The "total sleeping time" is the time obtained by subtracting the halfway awakening time from the sleeping time. "PS deep sleep time" is the time determined as deep sleep of PS sleep. PS sleep is sleep corresponding to a state generally called non-REM sleep. There are degrees of depth in PS sleep. "S sleep time" is the time determined as S sleep. Note that S sleep is sleep corresponding to a state generally called non-REM sleep. S-sleep and PS-sleep alternate in approximately 90-minute cycles.

The "actigram total" is the sum of the measurement range of the amount of body movement (actigram). "HF total amount" is the total amount [msec] of the HF measurement range. HF represents the degree of activity of the parasympathetic nerves, and when HF is predominant, it can be said that the person is in a relaxed state. “Heartbeat/pulse wave interval” is the average value [msec] of the measurement range of the heartbeat interval or pulse wave interval. "Average of LF for one night" is an average of LF for one night [msec]. Note that LF is a state in which the activity of the sympathetic nerve representing tension/activity and the activity of the parasympathetic nerve representing relaxation are mixed.

"HF overnight average" is the HF overnight average [msec]. "Body movement frequency" is the amount of body movement/sleep time. "PS deep sleep ratio" is PS deep sleep time/sleep time. "S sleep ratio" is S sleep time/sleep time. The “relaxation degree” is the time when the autonomic nerve index HF is greater than LF. "Sleep efficiency" is total sleep time/sleep time. "Rhythmicity" is the degree of matching of LF/HF to the 90-minute pre- and post-sleep cycles. The “sleep cycle” is a sleep cycle of around 90 minutes obtained when calculating the rhythmicity.

Then, the calculated "number of midway awakenings", "midway awakening time", "sleep time", "total bedtime", "total sleep time", "PS deep sleep time", "S sleep time", "act total", "HF total", "heart rate/pulse wave interval", "LF average for one night", "HF average for one night", "body movement frequency", "PS deep sleep ratio", "S sleep ratio", "relaxation level", "sleep efficiency", "rhythm" and "sleep cycle", a t-test was performed.

As a result, for "sleep time", "total bedtime", "total sleep time", "PS deep sleep time", "HF total amount" and "PS deep sleep ratio", the average value when using mattress A and the average value when using mattress B were significantly different. Specifically, it is as follows.

Regarding "sleep hours" The average "sleep hours" for Mattress A was 389.917 minutes, and the average "sleep hours" for Mattress B was 324.000 minutes. For "sleep hours", a t-test (two-tailed test) was performed assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "there is no difference in mean sleep duration between mattresses A and B". The significance level is 0.05. As a result of the t-test, the p-value was 0.0025 (see Table 1 showing the results of the t-test below). Therefore, the null hypothesis that "there is no difference in the average sleeping time between mattresses A and B" was rejected, and the average value for "sleep time" when using mattress A and mattress B. A significant difference was observed between the average values when using.

なお確認のため、「マットレスＡ及びＢにおける睡眠時間の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。なお分散分析においては、標本の正規性に囚われない結果を得ることができる。分散分析においてもｐ値は０．００２５であった（下記分散分析の結果を示す表２参照）。したがって、分散分析においても、帰無仮説は棄却され、「睡眠時間」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average sleeping hours between mattresses A and B", and the p-value was calculated. In the analysis of variance, it is possible to obtain results that are not bound by the normality of the sample. Also in the analysis of variance, the p-value was 0.0025 (see Table 2 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and there was a significant difference between the average value for "sleep time" when using mattress A and the average value when using mattress B.

Regarding "total hours in bed" The average "total hours in bed" for mattress A was 403.167 minutes, and the average "total hours in bed" for mattress B was 335.750 minutes. For "total hours in bed", a t-test (two-tailed test) was performed assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "no difference in mean total hours in bed for mattresses A and B". The significance level is 0.05. As a result of the t-test, the p-value was 0.0019 (see Table 3 showing the results of the t-test below). Therefore, the null hypothesis that "there is no difference in the average total hours in bed between mattresses A and B" was rejected, and there was a significant difference between the average values for "total hours in bed" when using mattress A and the average when using mattress B.

なお確認のため、「マットレスＡ及びＢにおける総就床時間の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。分散分析においてもｐ値は０．００１９であった（下記分散分析の結果を示す表４参照）。したがって、分散分析においても、帰無仮説は棄却され、「総就床時間」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average total bedtime between mattresses A and B", and the p-value was calculated. The p-value was also 0.0019 in the analysis of variance (see Table 4 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and there was a significant difference between the average value when using mattress A and the average value when using mattress B for "total bedtime".

Regarding "total sleep time" The average "total sleep time" for mattress A was 352.250 minutes, and the average "total sleep time" for mattress B was 289.583 minutes. For "total sleep time", a t-test (two-tailed test) was performed assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "no difference in mean total sleep time between mattresses A and B". The significance level is 0.05. As a result of the t-test, the p-value was 0.0009 (see Table 5 showing the results of the t-test below). Therefore, the null hypothesis that "there is no difference in the average total sleep time between mattresses A and B" was rejected, and the average value for "total sleep time" when using mattress A and mattress B. A significant difference was observed between the average values when using.

なお確認のため、「マットレスＡ及びＢにおける総睡眠時間の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。分散分析においてもｐ値は０．０００９であった（下記分散分析の結果を示す表６参照）。したがって、分散分析においても、帰無仮説は棄却され、「総睡眠時間」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average total sleep time between mattresses A and B", and the p-value was calculated. The p-value was also 0.0009 in the analysis of variance (see Table 6 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and there was a significant difference between the average value when using mattress A and the average value when using mattress B for "total sleep time".

Regarding "PS deep sleep time" The average "PS deep sleep time" for mattress A was 75.5000 minutes, and the average "PS deep sleep time" for mattress B was 41.9167 minutes. For "PS deep sleep time", a t-test (two-tailed test) was performed assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "there is no difference in mean PS deep sleep duration between mattresses A and B". The significance level is 0.05. The t-test resulted in a p-value of less than 0.0001 (see Table 7 showing the t-test results below). Therefore, the null hypothesis that "there is no difference in the average PS deep sleep time between mattresses A and B" was rejected, and the average value for "PS deep sleep time" when using mattress A and mattress B. A significant difference was observed between the average values when using.

なお確認のため、「マットレスＡ及びＢにおけるＰＳ深睡眠時間の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。分散分析においてもｐ値は０．０００１未満であった（下記分散分析の結果を示す表８参照）。したがって、分散分析においても、帰無仮説は棄却され、「ＰＳ深睡眠時間」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average PS deep sleep time between mattresses A and B", and the p-value was calculated. The p-value was also less than 0.0001 in the analysis of variance (see Table 8 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and for "PS deep sleep time", there was a significant difference between the average value when using mattress A and the average value when using mattress B.

Regarding "total amount of HF" The average amount of "total amount of HF" for mattress A was 8097.27 msec, and the average amount of "total amount of HF" for mattress B was 6391.43 msec. For "Total HF", a t-test (two-tailed test) was performed assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "no difference in mean total HF in mattresses A and B". The significance level is 0.05. A t-test gave a p-value of 0.008 (see Table 9 showing the t-test results below). Therefore, the null hypothesis that "there is no difference in the average HF total amount between mattresses A and B" was rejected, and the average value for "total HF" when using mattress A and mattress B. A significant difference was observed between the average values when using.

なお確認のため、「マットレスＡ及びＢにおけるＨＦ総量の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。分散分析においてもｐ値は０．００８であった（下記分散分析の結果を示す表１０参照）。したがって、分散分析においても、帰無仮説は棄却され、「ＨＦ総量」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average total amount of HF between mattresses A and B", and the p-value was calculated. The p-value was also 0.008 in the analysis of variance (see Table 10 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and there was a significant difference between the average value when using mattress A and the average value when using mattress B for "total amount of HF".

About "PS deep sleep ratio" The average of "PS deep sleep ratio" in mattress A was 0.192874, and the average of "PS deep sleep ratio" in mattress B was 0.128649. A t-test (two-tailed test) was performed on the "percentage of PS deep sleep" assuming that the population follows a normal distribution and that the variances are equal. The null hypothesis is "there is no difference in mean PS deep sleep percentages between mattresses A and B". The significance level is 0.05. The t-test resulted in a p-value of less than 0.0001 (see Table 11 showing the t-test results below). Therefore, the null hypothesis that "there is no difference in the average PS deep sleep ratio between mattresses A and B" was rejected, and the average value for "PS deep sleep ratio" when using mattress A and mattress B. A significant difference was observed between the average values when using.

なお確認のため、「マットレスＡ及びＢにおけるＰＳ深睡眠割合の平均値に差が無い」を帰無仮説とする分散分析を行い、ｐ値を演算した。分散分析においてもｐ値は０．０００１未満であった（下記分散分析の結果を示す表１２参照）。したがって、分散分析においても、帰無仮説は棄却され、「ＰＳ深睡眠割合」について、マットレスＡを使用した場合の平均値と、マットレスＢを使用した場合の平均値との間に有意な差が認められた。

For confirmation, analysis of variance was performed with the null hypothesis that "there is no difference in the average PS deep sleep ratio between mattresses A and B", and the p-value was calculated. The p-value was also less than 0.0001 in the analysis of variance (see Table 12 showing the results of the analysis of variance below). Therefore, even in the analysis of variance, the null hypothesis was rejected, and for the "PS deep sleep ratio", there was a significant difference between the average value when using mattress A and the average value when using mattress B.

From the above analysis results, sleep time, total sleep time, total bedtime, total HF amount (parasympathetic nerve index), PS deep sleep (equivalent to non-REM sleep stages 3 and 4) time and PS deep sleep ratio (total sleep time PS deep sleep time ratio) showed a significant difference at the 5% level. In other words, it was revealed that when mattress A was used, PS deep sleep was significantly greater (=sleeping soundly) than when mattress B was used. In addition, no difference was observed in LF (sympathetic nerve index), and a difference was observed only in HF. Therefore, when mattress A was used, parasympathetic nerve activity was promoted more than when mattress B was used, making it easier to sleep soundly.

The embodiments disclosed this time should be considered as examples in all respects and not restrictive. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope of claims and equivalents.

1 control device 1a CPU
1b RAM
1c storage unit 1d I/F unit 2 operation unit 3 imaging unit 4 search unit 5 display unit 6 body pressure measurement unit 7 manufacturing unit

Claims (18)

an acquisition unit that acquires imaging data related to a body surface shape of at least a part of a human body;
a generating unit that generates sleeping posture data indicating a sleeping posture of a human body based on the imaging data acquired by the acquiring unit;
A sleeping posture data processing apparatus comprising: an output unit that outputs information on bedding corresponding to the sleeping posture data to a display unit. a similarity calculation unit that calculates a similarity between the imaging data acquired by the acquisition unit and each of a plurality of different accumulated data that are stored in advance and indicate an entire body surface shape of a human body;
a selection unit that selects at least one of the accumulated data according to the degree of similarity calculated by the degree of similarity calculation unit;
2. The sleeping posture data processing device according to claim 1, wherein the generating unit generates sleeping posture data indicating a sleeping posture of a human body based on the selected accumulated data. 3. The sleeping posture data processing apparatus according to claim 1, wherein the imaging data is data relating to at least a partial body surface shape of a human body in a standing or sitting position. The accumulated data includes a reference line connecting the first vertex of the back of the chest and the second vertex of the back of the buttocks of the human body in a standing position, and a first distance between the first vertex of the curved surface of the back of the human body that projects forward between the first vertex and the second vertex,
The sleeping posture data includes a second distance between the reference line and the second vertex of the curved surface of the back surface of the human body that is curved so as to protrude toward the front, between the first vertex and the second vertex in the sleeping posture,
The generator calculates the second distance based on the first distance,
3. The sleeping posture data processing device according to claim 2, wherein the output unit outputs information on the bedding corresponding to the second distance to the display unit. the bedding includes a mattress;
a second acquiring unit that acquires at least two of height, weight and BMI;
a hardness calculation unit that calculates the hardness of the mattress based on at least two of the height, weight, and BMI obtained by the second obtaining unit;
3. The sleeping posture data processing device according to claim 1, wherein the output unit displays information of the mattress on the display unit based on the hardness calculated by the hardness calculation unit. the bedding includes a pillow;
a height calculation unit that calculates the height of the pillow based on the hardness calculated by the hardness calculation unit;
6. The sleeping posture data processing device according to claim 5, wherein the output unit displays the information of the pillow on the display unit based on the height calculated by the height calculation unit. A third acquisition unit that acquires the pressure acting on the human body,
The plurality of accumulated data are linked to the pressure,
3. The sleeping posture data processing device according to claim 2, wherein the degree of similarity calculation section calculates the degree of similarity between the imaging data and each of the plurality of accumulated data based on the pressure obtained by the third obtaining section. A bedding manufacturing apparatus for manufacturing bedding based on the sleeping posture data generated by the sleeping posture data processing device according to claim 1 or 2. A bedding selection device that selects at least one of a plurality of beddings based on the sleeping posture data generated by the sleeping posture data processing device according to claim 1 or 2. Acquiring imaging data related to the body surface shape of at least a part of the human body,
generating sleeping posture data indicating a sleeping posture of a human body based on the acquired imaging data;
A sleeping posture data processing method, comprising: outputting information on bedding corresponding to the sleeping posture data to a display unit. Acquiring imaging data related to the body surface shape of at least a part of the human body,
generating sleeping posture data indicating a sleeping posture of a human body based on the acquired imaging data;
A computer program that causes a computer to execute a process of outputting information on bedding corresponding to the sleeping posture data to a display unit. an imaging data acquisition unit that acquires imaging data related to the body surface shape of at least a part of a human body; an information acquisition unit that acquires the hardness of the mattress and the height of the pillow by inputting the imaging data acquired by the imaging data acquisition unit and the answers to the questionnaire to a learning model that outputs the hardness of the mattress and the height of the pillow when the hardness of the mattress and the height of the pillow are used as training data and the imaging data and the answers to the questionnaire are input;
A bedding data processing device comprising: an output unit configured to output information on the mattress and the pillow corresponding to the hardness and height acquired by the information acquisition unit to a display unit. 3. The sleeping posture data processing device according to claim 2, wherein the similarity calculation unit calculates the similarity based on the matching ratio of the front or left and right side outlines of at least a part of the human body, or the matching ratio of the front or left and right side projected areas of at least a part of the human body. 5. The sleeping posture data processing device according to claim 4, wherein the second distance is 3/4 or less of the first distance. 15. The sleeping posture data processing device according to claim 14, wherein the second distance is 1/3 to 1/2 of the first distance. The hardness calculation unit
calculating the width by multiplying the ratio of the width to the height and the height, corresponding to a plurality of body types determined in advance based on height and weight;
Calculate the body surface area based on the calculated width and height,
Calculate the pressure acting on the human body based on the calculated body surface area and weight,
6. The sleeping posture data processing device according to claim 5, wherein the firmness of the mattress is calculated based on the calculated pressure. 7. The sleeping posture data processing device according to claim 6, wherein the height calculation unit increases the height as the hardness calculated by the hardness calculation unit decreases, and decreases as the hardness calculated by the hardness calculation unit increases. The hardness calculation unit has a specific part calculation unit that calculates the hardness of the first part of the mattress,
storing in advance a table indicating a difference between the hardness of the first portion and the hardness of a second portion of the mattress other than the first portion;
Applying the hardness of the first part calculated by the specific part calculation unit to the table to obtain the hardness of the second part,
6. The sleeping posture data processing device according to claim 5, wherein the output unit outputs information of the mattress to the display unit based on the hardness of the first part and the second part.

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