CN111685770B - A wearable human body back curve detection method and device - Google Patents

Abstract

The invention belongs to the field of human body morphology measurement, and particularly relates to a wearable human body back curve detection method and device. The method comprises the following steps: acquiring horizontal inclination angles corresponding to a plurality of measuring nodes respectively, wherein the plurality of measuring nodes are connected to form a chain type measuring unit which is tightly attached and fixed to the position of a spine of a human body; setting original coordinates of a first end of a chain type measuring unit, wherein the first end of the chain type measuring unit is a first end of a first measuring node; determining the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes according to the horizontal inclination angles and the original coordinates corresponding to the plurality of measurement nodes; acquiring a human body back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes; the back curve can be rapidly and accurately measured continuously and can be worn.

Description

A wearable human body back curve detection method and device

technical field

The invention belongs to the field of human body morphology measurement, and in particular relates to a wearable human body back curve detection method and device.

Background technique

The measurement of the back curve is of great significance in the research and application of biomechanics and ergonomics. For example, by detecting the position and curvature of the back curve, it is possible to judge the sitting posture of students or computer operators, and to remind them of bad sitting posture to prevent low back diseases and cervical spine problems. Back curve measurement can also be used to detect the degree of low back curvature in the supine position, thereby assessing the comfort level of the sleep support system. At present, the back curve measurement method in the prior art is realized by a three-dimensional scanning system or a motion capture system on the one hand, which is costly and time-consuming, and cannot be used for portable detection, and neither can measure the back curve in a supine position. On the other hand, the movement data of scattered parts of the human body are captured by sensors, and the overall movement information of the human body is obtained according to the preset human body model parameters. The detection error is large, and the amount of information obtained is small, and the application range is relatively limited. There is no wearable human body back curve detection device on the market.

Therefore, the traditional human back curve detection method has the problems of high cost, long time consumption, inability to achieve portable and accurate detection, and inability to measure the back curve in a supine position and not being wearable.

Contents of the invention

In view of this, the embodiment of the present invention provides a wearable human body back curve detection method and device, which aims to solve the traditional human body back curve detection method that takes a long time and cannot achieve portable and accurate detection, and Unable to measure back curve in supine position and problem of not being wearable.

The first aspect of the embodiments of the present invention provides a wearable human body back curve detection method, the method includes the following steps:

Obtain the horizontal inclination corresponding to each of the multiple measurement nodes. Wherein, the plurality of measurement nodes are connected to form a chain measurement unit, and the chain measurement unit is closely fixed on the spine of the human body.

Set the original coordinates of the first end of the chain measurement unit. Wherein, the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the i-th measurement node is connected to the first end of the i+1-th measurement node.

End point coordinates corresponding to the second ends of the plurality of measurement nodes are determined according to the horizontal inclination angles corresponding to the plurality of measurement nodes and the original coordinates.

Obtaining the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes.

The second aspect of the embodiment of the present invention provides a wearable human body back curve detection device, the device comprising:

The horizontal inclination acquisition module is configured to acquire the horizontal inclinations corresponding to the plurality of measurement nodes. Wherein, the plurality of measurement nodes are connected to form a chain measurement unit, and the chain measurement unit is closely fixed on the spine of the human body.

The preset module is used to set the original coordinates of the first end of the chain measuring unit. Wherein, the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the i-th measurement node is connected to the first end of the i+1-th measurement node.

An endpoint coordinate determining module, configured to determine the corresponding endpoint coordinates of the second ends of the plurality of measurement nodes according to the horizontal inclination angles and the original coordinates corresponding to the plurality of measurement nodes.

A curve determination module, configured to obtain the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes.

The third aspect of the embodiments of the present invention provides a wearable human body back curve detection device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, the processor When the computer program is executed, the steps of the above-mentioned wearable human body back curve detection method are realized.

The fourth aspect of the embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned wearable human body back The steps of the curve detection method.

The above-mentioned wearable human body back curve detection method and device obtains the respective horizontal inclination angles corresponding to multiple measurement nodes and sets the coordinates of the first end of the first measurement node as the original coordinates, and then according to the respective horizontal inclination angles of the multiple measurement nodes The inclination angle and the original coordinates determine the corresponding endpoint coordinates of the second ends of the multiple measurement nodes, and then obtain the human back curve according to the original coordinates and the corresponding endpoint coordinates of the second ends of the multiple measurement nodes and the cubic spline interpolation algorithm, so as to realize Real-time, fast, efficient and accurate acquisition of the human back curve, providing accurate reference data for posture correction. In addition, the curvature of the cervical spine and the inclination angle of the curve can also be obtained according to the curve of the back of the human body, so as to obtain the state of the sitting or standing posture. And according to the back curve of the human body, the curvature of the cervical spine, the curvature of the thoracic spine and the curvature of the lumbar spine are obtained, so as to obtain the level of the sleep support system, etc. Realize the portable and accurate detection of the shape of the entire human spine in different states (sitting, standing and supine), as well as the length and curvature of each spinal part, and the device is a wearable human back curve detection device.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

Fig. 1 is a schematic flow chart of a wearable human body back curve detection method provided by an embodiment of the present invention;

Fig. 2 is a schematic diagram of the calculation principle of the endpoint coordinates of each measurement node in a wearable human body back curve detection method provided by an embodiment of the present invention;

Fig. 3 is another schematic flowchart of a wearable human body back curve detection method provided by an embodiment of the present invention;

Fig. 4 is another schematic flowchart of a wearable human body back curve detection method provided by an embodiment of the present invention;

Fig. 5 is another schematic flowchart of a wearable human body back curve detection method provided by an embodiment of the present invention;

Fig. 6 is a schematic structural diagram of a wearable human body back curve detection device provided by an embodiment of the present invention;

Fig. 7 is another structural schematic diagram of a wearable human body back curve detection device provided by an embodiment of the present invention;

Fig. 8 is another structural schematic diagram of a wearable human body back curve detection device provided by an embodiment of the present invention;

Fig. 9 is another structural schematic diagram of a wearable human body back curve detection device provided by an embodiment of the present invention;

Fig. 10 is another structural schematic diagram of a wearable human body back curve detection device provided by an embodiment of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Please refer to Fig. 1, a schematic flowchart of a wearable human body back curve detection method provided by an embodiment of the present invention. For the convenience of description, only the parts related to this embodiment are shown, and the details are as follows:

A wearable human body back curve detection method, comprising the following steps:

Step S01: Obtain the horizontal inclination angles corresponding to each of the plurality of measurement nodes. Wherein, a plurality of measuring nodes are connected to form a chain measuring unit, and the chain measuring unit is closely fixed on the spine of the human body.

In specific implementation, the first measuring node of the chain measuring unit can be placed close to the first measured vertebra of the subject, and the measuring nodes can be placed one by one close to the central spine position of the subject's back until The last measurement node can completely cover the measured spine of the person being tested.

In a specific implementation, each measurement node includes an inertial measurement sensor, and the horizontal inclination of the corresponding measurement node is measured by the inertial measurement sensor.

Step S02: Set the original coordinates of the first end of the chain measuring unit. Wherein, the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the i-th measurement node is connected to the first end of the i+1-th measurement node.

In a specific implementation, the original coordinates of the first end of the chain measurement unit may be set to (0,0).

The entire chain measurement unit includes n measurement nodes, n is a positive integer, and n≥1, the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the first measurement node is connected to The first end of the second measurement node is connected, the second end of the second measurement node is connected to the first end of the third measurement node, and so on, the second end of the n-1th measurement node is connected to the nth The first ends of the measurement nodes are connected, and the second end of the nth measurement node is the second end of the chain measurement unit.

Step S03: Determine the corresponding endpoint coordinates of the second ends of the plurality of measurement nodes according to the horizontal inclination angles and original coordinates corresponding to the plurality of measurement nodes.

In a specific implementation, the endpoint coordinates of each measurement node and the position information relative to the first measurement node are obtained through a chain calculation formula according to the horizontal inclination angles of multiple measurement nodes.

Please refer to Figure 2, define the intersection line between the sagittal plane and the horizontal plane of the human body as the horizontal x-axis, and the direction perpendicular to the x-axis as the longitudinal z-axis to establish an xz coordinate system. Define the coordinates of the first end of the first measurement node (measurement node 1) as the original coordinates, for example, define the original coordinates as x ₁ =0 and z ₁ =0, the length of each measurement node is H and remains unchanged, The horizontal inclination angle of the first measuring node measured by the inertial measurement sensor of the first measuring node is θ ₁ , and the coordinates (x ₂ , z ₂ ) of the second end of the first measuring node can be calculated according to the following formula:

x ₂ ＝x ₁ +H*cosθ ₁

z ₂ =z ₁ +H*sinθ ₁ , where x ₂ is the horizontal x-axis coordinate of the second end of the first measurement node, and z ₂ is the longitudinal z-axis coordinate of the second end of the first measurement node.

Therefore, the chain calculation formula can be:

x _n+1 ＝x _n +H*cosθ _n

z _n+1 ＝z _n +H*sinθ _n

Among them, x _n is the horizontal x-axis coordinate of the first end of the nth measurement node, which is also equal to the horizontal x-axis coordinate of the second end of the n-1th measurement node; z _n is the first end of the nth measurement node The longitudinal z-axis coordinate of the n-1th measurement node is also equal to the longitudinal z-axis coordinate of the second end of the n-1th measurement node; x _n+1 is the horizontal x-axis coordinate of the second end of the nth measurement node, z _n+1 is the nth The longitudinal z-axis coordinate of the second end of the measurement node; θ _n is the horizontal inclination angle of the nth measurement node.

Since the second end of the first measurement node is the first end of the second measurement node (measurement node 2), through the horizontal inclination angle of each measurement node, each measurement can be calculated in turn according to the above chain calculation formula The coordinates of the second end of the node.

The position coordinates of the next measurement node in the chain calculation model are calculated based on the position coordinates of this measurement node and the horizontal inclination angle of the next measurement node. The adjacent measurement nodes are interlocking, and the continuity of calculation parameters and The accuracy is high, and through the continuous arrangement of multiple measurement nodes, it can be highly close to the real curved shape of the human back, so that the curve of the human back can be accurately obtained.

In a specific implementation, the number n of measurement nodes can be obtained by measuring the total length L of the back curve of the subject from the first measured vertebra to the last measured vertebra and the length H of each measurement node. Specifically, the total length L of the testee's back curve can be measured with a soft ruler, and the total length L can be the length between the first measured vertebra and the last measured vertebra. Generally, the first measured vertebra can be the fifth lumbar vertebra, and the last measured vertebra can be the first cervical vertebra. Therefore, the total length L of the subject’s back curve includes the area from the fifth lumbar vertebra to the first cervical vertebra. . Divide the total length L of the back curve by the length H of each measurement node to obtain the number n of measurement nodes required. If the total length L of the back curve cannot be divided by the length H of a single measurement node, carry to the unit to ensure that the chain The chain measuring unit can completely cover all the measured spine parts of the subject, and increase or decrease the number of measuring nodes in the chain measuring unit according to the actual length of the test subject's back curve.

In specific implementation, step S03 includes step S031 to step S037.

Step S031: Set the target flag to 1.

Step S032: Set the target coordinates as the original coordinates.

Step S033: Set the target measurement node as the first measurement node.

Step S034: Calculate the endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal inclination corresponding to the target measurement node and the target coordinates.

Among them, the target coordinates include horizontal target coordinates and vertical target coordinates, and the endpoint coordinates include horizontal endpoint coordinates and vertical endpoint coordinates,

Specifically, the endpoint coordinates corresponding to the second end of the i-th target measurement node are calculated according to the following chain calculation formula:

x _i+1 = x _i +H*cosθ _i

z _i+1 = z _i +H*sinθ _i , wherein, H is the length of the target measurement node, the value of H is generally fixed, and x _i is the horizontal target coordinate of the first end of the i-th target measurement node; z _i is the vertical target coordinate of the first end of the i-th target measurement node; x _i+1 is the horizontal end point coordinate of the second end of the i-th target measurement node; z _i+1 is the second end of the i-th target measurement node The longitudinal endpoint coordinates of the end; _θi is the horizontal inclination angle of the i-th target measurement node, and i is a positive integer greater than or equal to 1.

Step S035: Associating and storing the endpoint coordinates with the target identifier.

Step S036: Determine whether the target measurement node is the last measurement node.

Step S037: If it is determined that the target measurement node is not the last measurement node, then update the target measurement node to the next measurement node, update the target coordinates to the endpoint coordinates, update the target ID to the sum of the target ID plus 1, and execute the step S037: S03.

In specific implementation, when step S036 is executed, if it is determined that the target measurement node is the last measurement node, then step S04 is executed. The execution step S04 here is specifically: the human back curve can be obtained according to the original coordinates, the endpoint coordinates corresponding to each of the multiple target identifiers, and the cubic spline interpolation algorithm.

By using a computer program to convert the human body back curve obtained according to the original coordinates and the corresponding end point coordinates of the second ends of the multiple measurement nodes into the corresponding end point coordinates according to the original coordinates and the multiple target identifications, first make the chain measurement unit. The endpoint coordinate polylines of multiple measurement nodes are smoothed by the cubic spline interpolation algorithm to obtain the shape of the human back curve. The method is simple and can quickly and accurately detect the state of the human back curve.

Step S04: Acquire the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes.

Connect the original coordinates and the corresponding endpoint coordinates of the second ends of multiple measurement nodes in sequence to obtain the polylines of the endpoint coordinates of multiple measurement nodes, that is, the obtained shape curve of the chain measurement unit and its relative to the first measurement node Position information, and then smooth the curve through the cubic spline interpolation algorithm to obtain the back curve of the human body.

In this embodiment, by obtaining the corresponding horizontal inclination angles of multiple measurement nodes and setting the coordinates of the first end of the first measurement node as the original coordinates, multiple measurements are determined according to the respective horizontal inclination angles and original coordinates of the multiple measurement nodes. The corresponding endpoint coordinates of the second ends of the nodes, and then obtain the back curve of the human body according to the original coordinates and the corresponding endpoint coordinates of the second ends of multiple measurement nodes combined with the cubic spline interpolation algorithm, so as to achieve real-time, fast, efficient and accurate acquisition of the human body The back curve provides accurate reference data for posture correction.

Please refer to FIG. 4 , step S05-1 and step S05-2 are also included after step S04.

Step S05-1: Obtain the curvature of the cervical spine and the inclination angle of the curve according to the curve of the back of the human body.

Step S05-2: Obtain the sitting posture state or the standing posture state according to the curvature of the cervical spine and the inclination angle of the curve.

In a specific implementation, the curvature of the cervical spine and the inclination angle of the curve can be obtained according to the back curve of the human body to obtain posture states in various situations, including sitting posture, standing posture, and back curve state during walking.

When the wearable human body back curve detection method is used to detect bad sitting or standing posture and the back curve state when walking, the endpoint coordinates of each measurement node are calculated through the horizontal inclination angle and original coordinates of the measurement nodes arranged continuously, and then combined The cubic spline interpolation algorithm can accurately obtain the curved shape of the human spine, so as to evaluate the subject's posture according to the curvature (curvature) of the back curve corresponding to the cervical spine and the inclination of the overall curve (inclination relative to the horizontal plane), Provide accurate and effective reference data for efficient correction of bad postures such as sitting or standing.

Please refer to FIG. 5 , step S06-1 and step S06-2 are also included after step S04.

Step S06-1: Obtain the curvature of the cervical spine, the curvature of the thoracic spine, and the curvature of the lumbar spine according to the curve of the back of the human body.

Step S062: Obtain the grade of the sleep support system according to the curvature of the cervical spine, the curvature of the thoracic spine, and the curvature of the lumbar spine.

When using the wearable human body back curve detection method to evaluate the sleep support system, the support level of the sleep support system to the human body can be judged according to the curvature of the lumbar spine, thoracic spine and cervical spine of the subject's back curve, for example, in a supine position, and then Provide accurate data reference for adjusting sleeping position.

Please refer to FIG. 6. FIG. 6 is a schematic structural diagram of a wearable human body back curve detection device 20 provided by an embodiment of the present invention. A wearable human body back curve detection device 20 includes a horizontal inclination acquisition module 11, a preset Module 12 , endpoint coordinate determination module 10 and curve determination module 13 .

The horizontal inclination acquisition module 11 is configured to acquire the horizontal inclinations corresponding to each of the plurality of measurement nodes. Wherein, a plurality of measuring nodes are connected to form a chain measuring unit, and the chain measuring unit is closely fixed on the spine of the human body.

The preset module 12 is configured to set the original coordinates of the first end of the chain measuring unit. Wherein, the first end of the chain measurement unit is the first end of the first measurement node.

The end point coordinate determination module 10 is configured to determine the corresponding end point coordinates of the second ends of the plurality of measurement nodes according to the respective horizontal inclination angles and original coordinates of the plurality of measurement nodes.

The curve determination module 13 is configured to obtain the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes.

In the specific implementation, a plurality of measurement nodes are arranged continuously and connected detachably (such as hinges, etc.) to form a chain measurement unit, each measurement node includes an inertial measurement sensor, and the chain measurement unit can use double-sided tape or other adhesives. The adhesives make the chain measurement unit close to the spine position in the center of the subject’s back, ensuring that the first to the last measurement node can completely cover the back spine that the subject needs to measure, and the chain measurement unit can As the shape of the spine changes, the back curve that is closest to the true curvature of the human back can be obtained through the chain measuring unit.

Please refer to FIG. 7 , in one embodiment, the endpoint coordinate determination module 10 includes a target identification setting unit 101, a target coordinate setting unit 102, a target measurement node setting unit 103, an endpoint coordinate calculation unit 104, a storage unit 105, A judging unit 106 and an endpoint coordinate updating unit 107 .

The target flag setting unit 101 is configured to set the target flag as 1.

The target coordinate setting unit 102 is configured to set the target coordinates as original coordinates.

The target measurement node setting unit 103 is configured to set the target measurement node as the first measurement node.

The endpoint coordinate calculation unit 104 is configured to calculate the endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal inclination corresponding to the target measurement node and the target coordinates.

The storage unit 105 is used for associating and storing the endpoint coordinates and the target identification.

The judging unit 106 is configured to judge whether the target measurement node is the last measurement node.

The endpoint coordinate update unit 107 is used to update the target measurement node to the next measurement node if it is judged that the target measurement node is not the last measurement node, update the target coordinates to the endpoint coordinates, and update the target identification to the target identification plus 1 and, and trigger the endpoint coordinate calculation unit 104.

In specific implementation, if the judgment unit 106 judges that the target measurement node is the last measurement node, the curve determination module 13 obtains the human back curve according to the original coordinates and the endpoint coordinates of multiple measurement nodes combined with the cubic spline interpolation algorithm.

In this embodiment, through specific and meticulous division of functional units in the end point coordinate determination module, starting from the target identification setting unit, starting from the target coordinates of the first measurement node, and gradually calculating the first position of all measurement nodes in the chain measurement unit. The endpoint coordinates of one end and the second end are superimposed and progressive, realizing the precise and continuous measurement of the human back curve.

Please refer to FIG. 8 , in one embodiment, the wearable human body back curve detection device 20 further includes a curve inclination angle acquisition module 14 and a posture state acquisition module 15 .

Curve inclination angle acquisition module 14, configured to acquire the curvature of the cervical spine and the inclination angle of the curve according to the back curve of the human body.

The posture state acquisition module 15 is configured to obtain the sitting posture state or the standing posture state according to the curvature of the cervical spine and the inclination angle of the curve.

According to the degree of curvature (curvature) of the cervical spine corresponding to the back curve and the inclination of the overall curve (inclination relative to the horizontal plane), the subject’s posture is evaluated, providing accurate and effective reference data for efficiently correcting bad postures such as sitting or standing.

Please refer to FIG. 9 , in one embodiment, the wearable human body back curve detection device 20 further includes a bending curvature acquisition module 16 and a support level determination module 17 .

The bending curvature acquisition module 16 is used to acquire the curvature of the cervical spine, the curvature of the thoracic spine and the curvature of the lumbar spine according to the curve of the back of the human body,

The support level determining module 17 is configured to obtain the level of the sleep support system according to the curvature of the cervical spine, the curvature of the thoracic spine and the curvature of the lumbar spine.

By obtaining the level of the sleep support system, it provides accurate data reference for adjusting the sleeping position.

In this embodiment, a plurality of measurement nodes are hingedly connected to form a chain measurement unit, and the horizontal inclination angle of each measurement node in the chain measurement unit is acquired by an inertial measurement sensor, and the coordinates of the first measurement node in the chain measurement unit are set is the original coordinates, according to the corresponding horizontal inclination angles and original coordinates of the plurality of measurement nodes, determine the corresponding end point coordinates of the second ends of the plurality of measurement nodes, and use the original coordinates and the corresponding end point coordinates of the second ends of the plurality of measurement nodes to reconstruct The shape curve of the chain measurement unit is smoothed by the cubic spline interpolation algorithm, and finally the shape of the human back curve and its position information relative to the first measurement node are obtained. In this way, the portable and accurate measurement of the shape of the entire human spine in different states (sitting, standing and supine) and the length and curvature of each spinal part can be achieved, so as to provide accurate reference data for correcting bad postures, and the device is Wearable human back curve detection device.

FIG. 10 is another schematic diagram of a wearable human body back curve detection device 20 provided by an embodiment of the present invention. As shown in Figure 10, the wearable human body back curve detection device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and operable on the processor 21, such as a wearable Program of the human back curve detection method. When the processor 21 executes the computer program 23, it realizes the steps in the embodiments of the above-mentioned wearable human body back curve detection method, such as steps S01 to S06-2 and steps S031 to S031 shown in Fig. 1, Fig. 3, Fig. 4 and Fig. 5 . S037. Alternatively, when the processor 21 executes the computer program 23, it realizes the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 10 to 17 and the units 101 to 107 shown in FIG. 6 to FIG. 9 .

Exemplarily, the computer program 23 can be divided into one or more modules/units, and one or more modules/units are stored in the memory 22 and executed by the processor 21 to implement the present invention. One or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 23 in the wearable human body back curve detection device 20 . For example, the computer program 23 can be divided into a horizontal inclination acquisition module 11, a preset module 12, an endpoint coordinate determination module 10 and a curve determination module 13, and the specific functions of each module are as follows:

The horizontal inclination acquisition module 11 is configured to acquire the horizontal inclinations corresponding to each of the plurality of measurement nodes. Wherein, a plurality of measuring nodes are connected to form a chain measuring unit, and the chain measuring unit is closely fixed on the spine of the human body.

The preset module 12 is configured to set the original coordinates of the first end of the chain measuring unit. Wherein, the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the i-th measurement node is connected to the first end of the i+1-th measurement node.

The end point coordinate determination module 10 is configured to determine the corresponding end point coordinates of the second ends of the plurality of measurement nodes according to the respective horizontal inclination angles and original coordinates of the plurality of measurement nodes.

The curve determining module 13 obtains the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes.

A wearable human body back curve detection device 20 may be a smart TV or other display devices. The wearable body back curve detection device 20 may include, but not limited to, a processor 21 and a memory 22 . Those skilled in the art can understand that Fig. 10 is only an example of the wearable human body back curve detection device 20, and does not constitute a limitation to the wearable human body back curve detection device 20, and may include more or less components, or a combination of certain components, or different components, for example, the apparatus for mining associated applications may also include input and output devices, network access devices, buses, and the like.

The so-called processor 21 can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The memory 22 may be an internal storage unit of the wearable human body back curve detection device 20 , such as a hard disk or memory of the wearable human body back curve detection device 20 . The memory 22 can also be an external storage device of the wearable human body back curve detection device 20, such as a plug-in hard disk equipped on the wearable human body back curve detection device 20, a smart memory card (Smart Media Card , SMC), Secure Digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Further, the memory 22 may also include both an internal storage unit of the wearable human body back curve detection device 20 and an external storage device. The memory 22 is used to store the computer program and other programs and data required by the wearable human body back curve detection device 20 . The memory 22 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Completion of modules means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit, and the above-mentioned integrated units may adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above system, reference may be made to the corresponding process in the foregoing method embodiments, and details will not be repeated here.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

The above are only optional embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention. Inside.

Claims (10)

1. a wearable human body back curve detection method, is characterized in that, described method comprises the following steps: Obtain the respective horizontal inclination angles corresponding to a plurality of measurement nodes; wherein, the plurality of measurement nodes are connected to form a chain measurement unit, and the chain measurement unit is closely fixed to the position of the human spine, specifically: the chain measurement unit The first test node is placed close to the first measured spine of the subject, and the measurement nodes are placed one by one close to the central spine of the subject's back until the last measurement node can completely cover the subject's spine. Measured spine; Wherein, the length of each measuring node forming a chain measuring unit is H; Set the original coordinates of the first end of the chain measuring unit, and establish a coordinate system; wherein, define the intersection line of the sagittal plane of the human body and the horizontal plane as the horizontal x-axis, and the direction perpendicular to the x-axis is the longitudinal z-axis, and establish x-z coordinate system; the first end of the chain measurement unit is the first end of the first measurement node, and the second end of the i measurement node is connected to the first end of the i+1 measurement node; According to the horizontal inclination angles and the original coordinates corresponding to each of the plurality of measurement nodes, the corresponding end point coordinates of the second ends of the plurality of measurement nodes are determined through a chain calculation formula, specifically: x _i+1 = x _i +H*cosθ _i , z _{i +1} ＝zi +H*sinθ _i , where H is the length of the target measurement node, the value of H is fixed, and x _i is the first The horizontal target coordinates of the end; z ⁱ is the vertical target coordinates of the first end of the i-th target measurement node; x _i+1 is the horizontal end point coordinates of the second end of the i-th target measurement node, z _i+1 is the i-th The longitudinal endpoint coordinates of the second end of the target measurement node; _θi is the horizontal inclination angle of the i-th target measurement node, and i is a positive integer greater than or equal to 1; Obtaining a human body back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes; Wherein, the quantity n of measuring nodes is obtained by measuring the total length L of the back curve of the subject from the first measured vertebra to the last measured vertebra and the length H of each measuring node, specifically: measure with a soft ruler The total length L of the testee's back curve, the first measured vertebra is the fifth lumbar vertebra, and the last measured vertebra is the first cervical vertebra, then the total length L of the testee's back curve includes the fifth lumbar vertebra to the first cervical vertebra. In the region between the cervical vertebrae, the total length L of the back curve is divided by the length H of each measurement node to obtain the number n of the required measurement nodes. If the total length L of the back curve cannot be divisible by the length H of a single measurement node, the bit carry. 2. the wearable human body back curve detection method as claimed in claim 1, is characterized in that, described according to the horizontal inclination angle corresponding to each of the plurality of measurement nodes and the original coordinates to determine the plurality of measurement nodes The corresponding endpoint coordinates of the second end of each include: Set the target flag to 1; Setting the target coordinates as the original coordinates; setting the target measurement node as the first measurement node; calculating the endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal inclination corresponding to the target measurement node and the target coordinates; Associating and storing the coordinates of the endpoint with the target identifier; judging whether the target measurement node is the last measurement node; If it is judged that the target measurement node is not the last measurement node, update the target measurement node to the next measurement node, update the target coordinates to the endpoint coordinates, and update the target identifier to the The sum of the target identifier plus 1, and performing the step of calculating the endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal inclination angle corresponding to the target measurement node and the target coordinates. 3. the wearable human body back curve detection method as claimed in claim 2, is characterized in that, according to the endpoint coordinates corresponding to the second ends of the described original coordinates and the plurality of measurement nodes respectively, obtain the body back curve specific for: The back curve of the human body is obtained according to the original coordinates, the endpoint coordinates corresponding to each of the plurality of target identifiers, and a cubic spline interpolation algorithm. 4. the wearable human body back curve detection method as claimed in claim 1, is characterized in that, after described according to the endpoint coordinates corresponding to the second end of described original coordinates and the second end of described plurality of measurement nodes respectively obtains human body back curve Also includes: Obtaining the curvature of the cervical spine and the inclination angle of the curve according to the curve of the back of the human body; A sitting posture state or a standing posture state is acquired according to the curvature of the cervical spine and the inclination angle of the curve. 5. the wearable human body back curve detection method as claimed in claim 1, is characterized in that, after the endpoint coordinates corresponding to the second ends of the described original coordinates and the plurality of measurement nodes respectively obtain the human body back curve Also includes: Obtaining the curvature of the cervical spine, the curvature of the thoracic spine and the curvature of the lumbar spine according to the curve of the back of the human body; The grade of the sleep support system is obtained according to the curvature of the cervical spine, the curvature of the thoracic spine and the curvature of the lumbar spine. 6. A wearable human body back curve detection device, characterized in that the device comprises: The horizontal inclination acquisition module is used to obtain the corresponding horizontal inclinations of multiple measurement nodes; wherein, the multiple measurement nodes are connected to form a chain measurement unit, and the chain measurement unit is closely fixed to the position of the human spine, specifically : Place the first test node of the chain measuring unit close to the first measured vertebra of the subject, and place the measurement nodes close to the central spine of the test subject one by one until the last measurement node Can completely cover the measured spine of the subject; wherein, the length of each measurement node forming a chain measurement unit is H; The preset module is used to set the original coordinates of the first end of the chain measurement unit and establish a coordinate system; wherein, the intersection line between the sagittal plane and the horizontal plane of the human body is defined as the horizontal x-axis, and the direction perpendicular to the x-axis For the longitudinal z-axis, establish an x-z coordinate system; the first end of the chain measurement unit is the first end of the first measurement node, the second end of the i-th measurement node and the i+1-th measurement node One end connection; The endpoint coordinate determination module is used to determine the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes through chain calculation formulas according to the horizontal inclination angles and the original coordinates corresponding to the plurality of measurement nodes, specifically It is: x _i+1 = x _i +H*cosθ _i , z _i+1 = z _i +H*sinθ _i , where H is the length of the target measurement node, the value of H is fixed, and x _i is the ith The horizontal target coordinates of the first end of the target measurement node; z _i is the vertical target coordinates of the first end of the i target measurement node; x _i+1 is the horizontal end point coordinates of the second end of the i target measurement node, z _i+1 is the longitudinal endpoint coordinates of the second end of the i-th target measurement node; θ _i is the horizontal inclination of the i-th target measurement node, and i is a positive integer greater than or equal to 1; A curve determination module, configured to obtain the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes; Wherein, the quantity n of measuring nodes is obtained by measuring the total length L of the back curve of the subject from the first measured vertebra to the last measured vertebra and the length H of each measuring node, specifically: measure with a soft ruler The total length L of the testee's back curve, the first measured vertebra is the fifth lumbar vertebra, and the last measured vertebra is the first cervical vertebra, then the total length L of the testee's back curve includes the fifth lumbar vertebra to the first cervical vertebra. In the region between the cervical vertebrae, the total length L of the back curve is divided by the length H of each measurement node to obtain the number n of the required measurement nodes. If the total length L of the back curve cannot be divisible by the length H of a single measurement node, the bit carry. 7. wearable human body back curve detection device as claimed in claim 6, is characterized in that, described end point coordinate determination module comprises: A target identification setting unit, configured to set the target identification as 1; a target coordinate setting unit, configured to set the target coordinates as the original coordinates; a target measurement node setting unit, configured to set the target measurement node as the first measurement node; An endpoint coordinate calculation unit, configured to calculate the endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal inclination corresponding to the target measurement node and the target coordinates; a storage unit, configured to associate and store the endpoint coordinates and the target identifier; a judging unit, configured to judge whether the target measurement node is the last measurement node; An endpoint coordinate updating unit, configured to update the target measurement node to the next measurement node if it is judged that the target measurement node is not the last measurement node, update the target coordinates to the endpoint coordinates, and The target identifier is updated as the sum of the target identifier plus 1, and an endpoint coordinate calculation unit is triggered. 8. wearable human body back curve detection device as claimed in claim 6, is characterized in that, described device also comprises: Curve inclination angle acquisition module, used to acquire cervical curvature and curve inclination angle according to the human back curve; A posture state acquiring module, configured to acquire a sitting posture state or a standing posture state according to the curvature of the cervical spine and the inclination angle of the curve. 9. A wearable human body back curve detection device, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program The procedure realizes the steps of the wearable human body back curve detection method as described in any one of claims 1 to 5. 10. A computer-readable storage medium, the computer-readable storage medium stores a computer program, characterized in that, when the computer program is executed by a processor, the wearable device according to any one of claims 1 to 5 is realized. The steps of the human body back curve detection method.

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