CN111685770B - Wearable human body back curve detection method and device - Google Patents
Wearable human body back curve detection method and device Download PDFInfo
- Publication number
- CN111685770B CN111685770B CN201910184589.8A CN201910184589A CN111685770B CN 111685770 B CN111685770 B CN 111685770B CN 201910184589 A CN201910184589 A CN 201910184589A CN 111685770 B CN111685770 B CN 111685770B
- Authority
- CN
- China
- Prior art keywords
- node
- target
- measurement
- measuring
- back curve
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1077—Measuring of profiles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6823—Trunk, e.g., chest, back, abdomen, hip
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
Technical Field
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.
Background
The measurement of the back curve is of great significance in the research and application of biomechanics and ergonomics. For example, the sitting posture condition of students or computer operators can be judged by detecting the position and the curvature of the back curve, and the alarm can be given when the students or the computer operators are in bad sitting posture, so that the problems of lumbar and back diseases and cervical vertebra can be prevented. The back curve measurement can also be used to detect the degree of lumbar and back flexion in the supine position, so that the comfort of the sleep support system can be evaluated. At present, in the prior art, a back curve measurement mode is realized through a three-dimensional scanning system or a motion capture system, so that the cost is high, the consumed time is long, the portable detection cannot be realized, and the back curve under the supine posture cannot be measured. On the other hand, the motion data of the scattered parts of the human body are captured through the sensor, the motion information of the whole human body is obtained according to the preset human body model parameters, the detection error is large, the obtained information amount is small, and the application range is limited. The wearable human back curve detection device is not available in the market.
Therefore, the traditional method for detecting the back curve of the human body has the problems of higher cost, longer time consumption, incapability of realizing portable and accurate detection, incapability of measuring the back curve in a supine posture and incapability of wearing.
Disclosure of Invention
In view of this, embodiments of the present invention provide a wearable human back curve detection method and apparatus, which aim to solve the problems that the traditional human back curve detection method is time-consuming, cannot realize portable and accurate detection, cannot measure the back curve in the supine posture, and is not wearable.
A first aspect of an embodiment of the present invention provides a wearable human body back curve detection method, including:
and acquiring horizontal inclination angles corresponding to the plurality of measurement nodes. The plurality of measuring nodes are connected to form a chain type measuring unit, and the chain type measuring unit is tightly attached and fixed to the position of the spine of the human body.
And setting original coordinates of the first end of the chain type measuring unit. The first end of the chain type measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node.
And determining the endpoint coordinates corresponding to the second ends of the plurality of measuring nodes according to the horizontal inclination angles and the original coordinates corresponding to the measuring nodes.
And 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.
A second aspect of an embodiment of the present invention provides a wearable human back curve detection apparatus, including:
and the horizontal dip angle acquisition module is used for acquiring the horizontal dip angles corresponding to the plurality of measurement nodes. The plurality of measuring nodes are connected to form a chain type measuring unit, and the chain type measuring unit is tightly attached and fixed to the position of the spine of the human body.
And the presetting module is used for setting the original coordinates of the first end of the chain type measuring unit. The first end of the chain type measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node.
And the endpoint coordinate determination module is used for determining 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.
And the curve determining module is used for acquiring a human body back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measuring nodes.
A third aspect of the embodiments of the present invention provides a wearable human back curve detection apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the wearable human back curve detection method as described above.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a computer program that, when being executed by a processor, implements the steps of the wearable human back curve detection method as described above.
According to the wearable human body back curve detection method and device, the horizontal inclination angles corresponding to the multiple measurement nodes are obtained, the coordinate of the first end of the first measurement node is set to be an original coordinate, the endpoint coordinates corresponding to the second ends of the multiple measurement nodes are determined according to the horizontal inclination angles corresponding to the multiple measurement nodes and the original coordinate, and then the human body back curve is obtained according to the original coordinate, the endpoint coordinates corresponding to the second ends of the multiple measurement nodes and a cubic spline interpolation algorithm, so that the human body back curve can be obtained quickly, efficiently and accurately in real time, and accurate reference data are provided for correcting postures. In addition, the bending curvature and the curve inclination angle of the cervical vertebra can be obtained according to the back curve of the human body, so that the sitting posture state or the standing posture state can be obtained. And acquiring the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature according to the back curve of the human body so as to acquire the grade of the sleep support system and the like. The portable and accurate detection of the shape of the whole human spine and the parameters such as the length, the curvature and the like of each spine part in different states (sitting, standing and lying on the back) is realized, and the device is a wearable human back curve detection device.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flowchart illustrating a wearable human back curve detection method according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a principle of calculating end point coordinates of each measurement node in the wearable human back curve detection method according to an embodiment of the present invention;
fig. 3 is another schematic flow chart illustrating a wearable human back curve detection method according to an embodiment of the present invention;
fig. 4 is another schematic flow chart illustrating a wearable human back curve detection method according to an embodiment of the present invention;
fig. 5 is another schematic flow chart of a wearable human back curve detection method according to an embodiment of the invention;
fig. 6 is a schematic structural diagram of a wearable human back curve detection apparatus according to an embodiment of the present invention;
fig. 7 is another schematic structural diagram of a wearable human back curve detection apparatus according to an embodiment of the present invention;
fig. 8 is another schematic structural view of a wearable human back curve detection apparatus according to an embodiment of the invention;
fig. 9 is another schematic structural diagram of a wearable human back curve detection apparatus according to an embodiment of the present invention;
fig. 10 is another schematic structural diagram of a wearable human back curve detection apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a schematic flow chart of a wearable human back curve detection method according to an embodiment of the present invention is shown, for convenience of description, only the relevant portions of the embodiment are shown, and the following details are described:
a wearable human body back curve detection method comprises the following steps:
step S01: and acquiring horizontal inclination angles corresponding to the plurality of measurement nodes. The plurality of measuring nodes are connected to form a chain type measuring unit, and the chain type measuring unit is tightly attached to and fixed at the position of the spine of the human body.
In specific implementation, the first measuring node of the chain measuring unit can be tightly attached to the first tested spine of the tested person, and the measuring nodes are sequentially and one by one tightly attached to the central spine position of the back of the tested person until the last measuring node can completely cover the tested spine of the tested person.
In a specific implementation, each measurement node comprises an inertial measurement sensor, and the inertial measurement sensor is used for measuring the horizontal inclination angle of the corresponding measurement node.
Step S02: original coordinates of the first end of the chain type measuring unit are set. The first end of the chained measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node.
In a specific implementation, the original coordinate setting of the first end of the chain type measuring unit may be set to (0,0).
The whole chain type measuring unit comprises n measuring nodes, n is a positive integer and is larger than or equal to 1, the first end of the chain type measuring unit is the first end of a first measuring node, the second end of the first measuring node is connected with the first end of a second measuring node, the second end of the second measuring node is connected with the first end of a third measuring node, and the like are carried out in sequence, the second end of the n-1 measuring node is connected with the first end of the nth measuring node, and the second end of the nth measuring node is the second end of the chain type measuring unit.
Step S03: and determining the endpoint coordinates corresponding to the second ends of the plurality of measuring nodes according to the horizontal inclination angles and the original coordinates corresponding to the plurality of measuring nodes.
In specific implementation, the endpoint coordinates and the position information relative to the first measuring node of each measuring node are obtained through a chain calculation formula according to the horizontal inclination angles of the plurality of measuring nodes.
Referring to FIG. 2, an x-z coordinate system is established by defining the intersection line of the sagittal plane of the human body and the horizontal plane as the transverse x-axis and the direction perpendicular to the x-axis as the longitudinal z-axis. Defining the coordinates of the first end of the first measurement node (measurement node 1) as original coordinates, e.g. defining the original coordinates as x 1 =0 and z 1 =0, each measuring node has a length H and remains unchanged, and the inertial measuring sensor of the first measuring node measures the horizontal inclination angle θ of the first measuring node 1 The first measurement can be calculated according to the following equationCoordinates (x) of the second end of the node 2 ，z 2 ) Comprises the following steps:
x 2 ＝x 1 +H*cosθ 1
z 2 ＝z 1 +H*sinθ 1 wherein x is 2 Is a transverse x-axis coordinate, z, of the second end of the first measurement node 2 Is the longitudinal z-axis coordinate of the second end of the first measurement node.
Thus, the chain calculation can be:
x n+1 ＝x n +H*cosθ n
z n+1 ＝z n +H*sinθ n
wherein x is n The transverse x-axis coordinate of the first end of the nth measuring node is equal to the transverse x-axis coordinate of the second end of the (n-1) th measuring node; z is a radical of n The longitudinal z-axis coordinate of the first end of the nth measuring node is equal to the longitudinal z-axis coordinate of the second end of the (n-1) th measuring node; x is the number of n+1 Is a transverse x-axis coordinate, z, of the second end of the nth measurement node n+1 A longitudinal z-axis coordinate of the second end of the nth measurement node; theta n The horizontal inclination angle of the nth measurement node is obtained.
Since the second end of the first measurement node is the first end of the second measurement node (measurement node 2), the coordinates of the second end of each measurement node can be calculated in sequence according to the above chain calculation formula through the horizontal inclination angle of each measurement node.
The position coordinate of the next measuring node in the chain type calculation model is obtained by calculation based on the position coordinate of the measuring node and the horizontal inclination angle of the next measuring node, the adjacent measuring nodes are buckled with each other in a ring mode, the continuity and the accuracy of calculation parameters are high, the shape of the real bending of the back of the human body can be highly attached to the plurality of continuously arranged measuring nodes, and then the curve of the back of the human body can be accurately obtained.
In a specific implementation, the number n of the measurement nodes can be obtained by measuring the total length L of the back curve of the tested person from the first tested vertebra to the last tested vertebra and the length H of each measurement node. Specifically, the total length L of the back curve of the tested person can be measured by using a flexible rule, and the total length L can be the length from the first tested vertebra to the last tested vertebra. Generally, the first tested vertebra can be selected as the fifth lumbar vertebra, and the last tested vertebra can be selected as the first cervical vertebra, so that the total length L of the back curve of the tested person comprises the area from the fifth lumbar vertebra to the first cervical vertebra. And if the total length L of the back curve cannot divide the length H of a single measuring node completely, carrying to one position to ensure that the chain type measuring unit can completely cover all tested spinal parts of the tested person, and increasing or reducing the number of the measuring nodes in the chain type measuring unit according to the actual length of the back curve of the tested person.
In a specific implementation, step S03 includes steps S031 through S037.
Step S031: the target flag is set to 1.
Step S032: and setting the target coordinates as original coordinates.
Step S033: and setting the target measurement node as a first measurement node.
Step S034: and calculating the endpoint coordinate corresponding to the second end of the target measurement node according to the horizontal inclination angle and the target coordinate corresponding to the target measurement node.
Wherein the target coordinates comprise transverse target coordinates and longitudinal target coordinates, the endpoint coordinates comprise transverse endpoint coordinates and longitudinal endpoint coordinates,
specifically, the endpoint coordinate corresponding to the second end of the ith target measurement node is calculated according to the following chain calculation formula:
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 generally fixed, and x i Measuring a lateral target coordinate of the first end of the node for the ith target; z is a radical of i Measuring a longitudinal target coordinate of the first end of the node for the ith target; x is the number of i+1 Measuring the lateral end point of the second end of the node for the ith targetCoordinate, z i+1 Measuring the longitudinal endpoint coordinates of the second end of the node for the ith target; theta i And i is a positive integer which is greater than or equal to 1 and is the horizontal inclination angle of the ith target measurement node.
Step S035: the endpoint coordinates and the target identification are associated and stored.
Step S036: and judging whether the target measurement node is the last measurement node or not.
Step S037: and if the target measurement node is not the last measurement node, updating the target measurement node to be the next measurement node, updating the target coordinate to be the endpoint coordinate, updating the target identification to be the sum of the target identification plus 1, and executing the step S03.
In a specific implementation, when step S036 is executed, if it is determined that the target measurement node is the last measurement node, step S04 is executed. Here, the step S04 is specifically executed as follows: the human body back curve can be obtained according to the original coordinates, the endpoint coordinates corresponding to the target identifications and the cubic spline interpolation algorithm.
The method comprises the steps of converting an obtained human body back curve according to original coordinates and endpoint coordinates corresponding to second ends of a plurality of measuring nodes into endpoint coordinates corresponding to the original coordinates and a plurality of target identifications by using a computer program, making endpoint coordinate broken lines of the plurality of measuring nodes in a chain type measuring unit, and smoothing the endpoint coordinate broken lines of the plurality of measuring nodes by a cubic spline interpolation algorithm to obtain a human body back curve shape.
Step S04: and 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.
And sequentially connecting the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes to obtain endpoint coordinate broken lines of the plurality of measurement nodes, namely the shape curve of the chain type measurement unit and the position information of the chain type measurement unit relative to the first measurement node, and smoothing the curve by a cubic spline interpolation algorithm to obtain the human body back curve.
In this embodiment, the horizontal inclination angles corresponding to the plurality of measurement nodes are acquired, the coordinate of the first end of the first measurement node is set as an original coordinate, the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes are determined according to the horizontal inclination angles and the original coordinate corresponding to the plurality of measurement nodes, and then the back curve of the human body is acquired according to the endpoint coordinates corresponding to the original coordinate and the second ends of the plurality of measurement nodes and by combining a cubic spline interpolation algorithm, so that the back curve of the human body is acquired quickly, efficiently and accurately in real time, and accurate reference data is provided for correcting postures.
Referring to fig. 4, step S05-1 and step S05-2 are also included after step S04.
Step S05-1: and acquiring the bending curvature and the curve inclination angle of the cervical vertebra according to the back curve of the human body.
Step S05-2: and acquiring a sitting posture state or a standing posture state according to the bending curvature and the curve inclination angle of the cervical vertebra.
In specific implementation, the cervical vertebra bending curvature and the curve inclination angle can be obtained according to the back curve of the human body, so that the posture states under various conditions including sitting posture, standing posture, back curve state during walking and the like can be obtained.
When the wearable human body back curve detection method is used for detecting the bad sitting posture or standing posture and the back curve state during walking, the endpoint coordinates of each measurement node are calculated through the horizontal inclination angles and the original coordinates of the measurement nodes which are continuously arranged, and then the cubic spline interpolation algorithm is combined, so that the bending shape of the human body spine can be accurately obtained, the posture condition of a tested person can be evaluated according to the bending degree (curvature) of the cervical vertebra part corresponding to the back curve and the inclination degree (inclination angle relative to the horizontal plane) of the whole curve, and accurate and effective reference data are provided for efficiently correcting the bad postures such as the sitting posture or the standing posture.
Referring to fig. 5, step S06-1 and step S06-2 are also included after step S04.
Step S06-1: and acquiring the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature according to the back curve of the human body.
Step S062: and acquiring the grade of the sleep support system according to the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature.
When the wearable human body back curve detection method is used for evaluating the sleep support system, the support level of the sleep support system to the human body can be judged according to the bending curvatures of the lumbar vertebra part, the thoracic vertebra part and the cervical vertebra part of the back curve of the testee in the supine posture, and accurate data reference is further provided for adjusting the sleeping posture.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a wearable human back curve detection apparatus 20 according to an embodiment of the present invention, in which the wearable human back curve detection apparatus 20 includes a horizontal tilt angle acquisition module 11, a presetting module 12, an endpoint coordinate determination module 10, and a curve determination module 13.
And the horizontal inclination angle acquisition module 11 is configured to acquire horizontal inclination angles corresponding to the plurality of measurement nodes. The plurality of measuring nodes are connected to form a chain type measuring unit, and the chain type measuring unit is tightly attached to and fixed at the position of the spine of the human body.
And the presetting module 12 is used for setting original coordinates of the first end of the chain type measuring unit. And the first end of the chain type measuring unit is the first end of the first measuring node.
And an endpoint coordinate determination module 10, configured to determine, according to the horizontal tilt angle and the original coordinate corresponding to each of the plurality of measurement nodes, an endpoint coordinate corresponding to each of the second ends of the plurality of measurement nodes.
And a curve determining module 13, configured to obtain a human body back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the multiple measurement nodes.
In specific implementation, a plurality of measurement nodes are continuously arranged and detachably connected (for example, hinged and the like) to form a chain type measurement unit, each measurement node comprises an inertial measurement sensor, the chain type measurement unit can be tightly attached and fixed to the spine position in the center of the back of a tested person by using double faced adhesive tape or other adhesive substances which can be adhered, the first measurement node to the last measurement node can be ensured to completely cover the back spine to be measured of the tested person, and the chain type measurement unit can change along with the shape of the spine, so that the back curve which is closest to the true bending degree of the back of a human body can be obtained through the chain type measurement unit.
Referring to fig. 7, in one embodiment, the endpoint coordinate determination module 10 includes a target identifier 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 determination unit 106, and an endpoint coordinate updating unit 107.
A target flag setting unit 101 for setting a target flag to 1.
And a target coordinate setting unit 102, configured to set the target coordinates as original coordinates.
A target measurement node setting unit 103, configured to set a target measurement node as a first measurement node.
And the endpoint coordinate calculation unit 104 is configured to calculate endpoint coordinates corresponding to the second end of the target measurement node according to the horizontal tilt angle and the target coordinates corresponding to the target measurement node.
And the storage unit 105 is used for associating and storing the endpoint coordinates and the target identification.
A determining unit 106, configured to determine whether the target measurement node is the last measurement node.
An endpoint coordinate updating unit 107, configured to update the target measurement node to a next measurement node if it is determined that the target measurement node is not the last measurement node, update the target coordinate to an endpoint coordinate, update the target identifier to a sum of the target identifier and 1, and trigger the endpoint coordinate calculating unit 104.
In a specific implementation, if the determination unit 106 determines that the target measurement node is the last measurement node, the curve determination module 13 obtains the human body back curve according to the original coordinates and the endpoint coordinates of the plurality of measurement nodes by combining a cubic spline interpolation algorithm.
In the embodiment, the endpoint coordinate determination module is divided by specific and careful functional units, starting from the target identifier setting unit and according to the target coordinate of the first measurement node, the endpoint coordinates of the first end and the second end of all measurement nodes in the chain measurement unit are gradually and sequentially calculated, and the endpoint coordinates are gradually overlapped and advanced layer by layer, so that the accurate continuity measurement of the back curve of the human body is realized.
Referring to fig. 8, in an embodiment of the invention, the wearable human back curve detecting device 20 further includes a curve inclination angle acquiring module 14 and a posture state acquiring module 15.
And the curve inclination angle acquisition module 14 is used for acquiring the cervical vertebra bending curvature and the curve inclination angle according to the back curve of the human body.
And the posture state acquisition module 15 is used for acquiring a sitting posture state or a standing posture state according to the cervical vertebra bending curvature and the curve inclination angle.
The posture condition of the tested person is evaluated according to the bending degree (curvature) of the cervical vertebra part corresponding to the back curve and the inclination degree (inclination angle relative to the horizontal plane) of the whole curve, and accurate and effective reference data are provided for efficiently correcting bad postures such as sitting posture or standing posture.
Referring to fig. 9, in an embodiment, the wearable human back curve detection apparatus 20 further includes a bending curvature obtaining module 16 and a support level determining module 17.
A bending curvature obtaining module 16 for obtaining the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature according to the back curve of the human body,
and the support grade determining module 17 is used for acquiring the grade of the sleep support system according to the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature.
By acquiring the grade of the sleep support system, accurate data reference is provided for adjusting the sleeping posture.
In the embodiment, a plurality of measurement nodes are hinged to form a chain type measurement unit, a horizontal inclination angle of each measurement node in the chain type measurement unit is obtained through an inertial measurement sensor, a coordinate of a first measurement node in the chain type measurement unit is set as an original coordinate, endpoint coordinates corresponding to second ends of the plurality of measurement nodes are determined according to the horizontal inclination angle and the original coordinate corresponding to the plurality of measurement nodes, a shape curve of the chain type measurement unit is reconstructed by using the original coordinate and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes, the shape curve is smoothed through a cubic spline interpolation algorithm, and finally a human back curve shape and position information of the human back curve relative to the first measurement node are obtained. Therefore, the portable and accurate measurement of parameters such as the shape of the whole human spine and the length and curvature of each spine part in different states (sitting, standing and lying on the back) is realized, so that accurate reference data can be provided for correcting bad postures, and the device is a wearable human back curve detection device.
Fig. 10 is another schematic view of a wearable human back curve detection apparatus 20 according to an embodiment of the present invention. As shown in fig. 10, the wearable human back curve detection apparatus 20 of the embodiment includes: a processor 21, a memory 22 and a computer program 23, such as a program of a wearable human back curve detection method, stored in the memory 22 and executable on the processor 21. The processor 21 executes the computer program 23 to implement the steps in the above-mentioned embodiments of the wearable human back curve detection method, such as the steps S01 to S06-2 and the steps S031 to S037 shown in fig. 1, fig. 3, fig. 4 and fig. 5. Alternatively, the processor 21 implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 10 to 17 and the units 101 to 107 shown in fig. 6 to 9, when executing the computer program 23.
Illustratively, the computer program 23 may be partitioned into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 23 in the wearable human back curve detecting device 20. For example, the computer program 23 may be divided into a horizontal tilt angle obtaining module 11, a presetting module 12, an endpoint coordinate determining module 10 and a curve determining module 13, and the specific functions of each module are as follows:
and the horizontal inclination angle acquisition module 11 is configured to acquire horizontal inclination angles corresponding to the plurality of measurement nodes. The plurality of measuring nodes are connected to form a chain type measuring unit, and the chain type measuring unit is tightly attached to and fixed at the position of the spine of the human body.
And the presetting module 12 is used for setting original coordinates of the first end of the chain type measuring unit. The first end of the chained measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node.
And an endpoint coordinate determination module 10, configured to determine endpoint coordinates corresponding to the second ends of the multiple measurement nodes according to the horizontal tilt angles and the original coordinates corresponding to the multiple measurement nodes, respectively.
And the curve determining module 13 is used for 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.
A wearable human back curve detection apparatus 20 may be a smart television or other display device. The wearable human back curve detection device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that fig. 10 is merely an example of the wearable human back curve detecting apparatus 20, and does not constitute a limitation of the wearable human back curve detecting apparatus 20, and may include more or less components than those shown, or combine some components, or different components, for example, the apparatus for associated application mining may further include an input-output device, a network access device, a bus, etc.
The Processor 21 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, 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 can be an internal storage unit of the wearable human back curve detection device 20, such as a hard disk or an internal memory of the wearable human back curve detection device 20. The memory 22 may also be an external storage device of the wearable human back curve detection apparatus 20, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like equipped on the wearable human back curve detection apparatus 20. Further, the memory 22 may also include both an internal storage unit and an external storage device of the wearable human back curve detection apparatus 20. The memory 22 is used for storing the computer program and other programs and data required by the wearable human back curve detecting device 20. The memory 22 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The invention is not to be considered as limited to the particular examples shown, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed.
Claims (10)
1. A wearable human body back curve detection method is characterized by comprising the following steps:
acquiring horizontal dip angles corresponding to the plurality of measurement nodes respectively; wherein, a plurality of measurement node connect in order to form chain measuring unit, chain measuring unit hugs closely and is fixed in human backbone position, specifically is: the first test node of the chain type measurement unit is closely attached to the first tested vertebra of the tested person, and the measurement nodes are sequentially and one by one closely attached to the central vertebra position of the back of the tested person until the last measurement node can completely cover the tested spine of the tested person; the length of each measuring node forming the chain type measuring unit is H;
setting an original coordinate of the first end of the chain type measuring unit and establishing a coordinate system; wherein, an intersecting line of a sagittal plane of a human body and a horizontal plane is defined as a transverse x axis, and a direction which is vertically intersected with the x axis is defined as a longitudinal z axis, and an x-z coordinate system is established; the first end of the chain type measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node;
determining, according to the horizontal inclination angle and the original coordinate corresponding to each of the plurality of measurement nodes, an endpoint coordinate corresponding to each of second ends of the plurality of measurement nodes by a chain calculation formula, specifically: x is the number of 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 constant, and x i Measuring a lateral target coordinate of the first end of the node for the ith target; z is a radical of formula i Measuring a longitudinal target coordinate of the first end of the node for the ith target; x is the number of i+1 For the transverse end point coordinate of the second end of the ith target measurement node, z i+1 Measuring the longitudinal endpoint coordinates of the second end of the node for the ith target; theta i The horizontal inclination angle of the ith target measurement node is set, wherein i is a positive integer greater than or equal to 1;
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 number n of the measurement nodes is obtained by measuring the total length L of the back curve of the tested person from the first tested vertebra to the last tested vertebra and the length H of each measurement node, and specifically comprises the following steps: and measuring the total length L of the back curve of the tested person by using a flexible rule, wherein the first tested vertebra is a fifth lumbar vertebra, and the last tested vertebra is a first cervical vertebra, the total length L of the back curve of the tested person comprises an area from the fifth lumbar vertebra to the first cervical vertebra, dividing the total length L of the back curve by the length H of each measuring node to obtain the number n of required measuring nodes, and if the total length L of the back curve cannot divide the length H of a single measuring node completely, carrying to one position.
2. The wearable human back curve detection method of claim 1, wherein the determining the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes according to the horizontal tilt angles and the original coordinates corresponding to the measurement nodes comprises:
setting a target identifier as 1;
setting a target coordinate as the original coordinate;
setting a target measurement node as the first measurement node;
calculating an endpoint coordinate corresponding to a second end of the target measurement node according to the horizontal inclination angle corresponding to the target measurement node and the target coordinate;
associating and storing the endpoint coordinates and the target identification;
judging whether the target measurement node is the last measurement node or not;
and if the target measurement node is judged not to be the last measurement node, updating the target measurement node to be the next measurement node, updating the target coordinate to be the endpoint coordinate, updating the target identification to be the sum of the target identification plus 1, and executing the step of calculating the endpoint coordinate 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 coordinate.
3. The wearable human back curve detection method according to claim 2, wherein the obtaining of the human back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measurement nodes specifically comprises:
and acquiring a human body back curve according to the original coordinates, the endpoint coordinates corresponding to the target identifications and a cubic spline interpolation algorithm.
4. The wearable human back curve detection method according to claim 1, wherein after acquiring 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 method further comprises:
acquiring the bending curvature and the curve inclination angle of the cervical vertebra according to the human back curve;
and acquiring a sitting posture state or a standing posture state according to the cervical vertebra bending curvature and the curve inclination angle.
5. The wearable human back curve detection method according to claim 1, wherein after acquiring 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 method further comprises:
acquiring cervical vertebra bending curvature, thoracic vertebra bending curvature and lumbar vertebra bending curvature according to the human back curve;
and acquiring the grade of a sleep support system according to the cervical vertebra bending curvature, the thoracic vertebra bending curvature and the lumbar vertebra bending curvature.
6. A wearable human back curve detection device, the device comprising:
the horizontal dip angle acquisition module is used for acquiring the horizontal dip angles corresponding to the plurality of measurement nodes; wherein, a plurality of measurement node connect in order to form chain measuring unit, chain measuring unit hugs closely and is fixed in human backbone position, specifically is: the first test node of the chain type measurement unit is closely attached to the first tested vertebra of the tested person, and the measurement nodes are sequentially and one by one closely attached to the central vertebra position of the back of the tested person until the last measurement node can completely cover the tested spine of the tested person; the length of each measuring node forming the chain type measuring unit is H;
the preset module is used for setting an original coordinate of the first end of the chain type measuring unit and establishing a coordinate system; defining the intersecting line of a sagittal plane of a human body and a horizontal plane as a transverse x-axis, and establishing an x-z coordinate system by taking the direction which is vertically intersected with the x-axis as a longitudinal z-axis; the first end of the chained measuring unit is the first end of a first measuring node, and the second end of the ith measuring node is connected with the first end of the (i + 1) th measuring node;
an endpoint coordinate determination module, configured to determine, according to the horizontal tilt angle and the original coordinate corresponding to each of the multiple measurement nodes, an endpoint coordinate corresponding to each of second ends of the multiple measurement nodes through a chain calculation formula, where the determining includes: x is the number of 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 constant, and x i Measuring a lateral target coordinate of the first end of the node for the ith target; z is a radical of i Measuring a longitudinal target coordinate of the first end of the node for the ith target; x is the number of i+1 Measuring for the i-th target the transverse end point coordinates of the second end of the node, z i+1 Measuring the longitudinal endpoint coordinates of the second end of the node for the ith target; theta.theta. i The horizontal inclination angle of the ith target measurement node is shown, wherein i is a positive integer greater than or equal to 1;
the curve determining module is used for acquiring a human body back curve according to the original coordinates and the endpoint coordinates corresponding to the second ends of the plurality of measuring nodes;
the number n of the measurement nodes is obtained by measuring the total length L of the back curve of the tested person from the first tested vertebra to the last tested vertebra and the length H of each measurement node, and specifically comprises the following steps: and measuring the total length L of the back curve of the tested person by using a flexible rule, wherein the first tested vertebra is a fifth lumbar vertebra, and the last tested vertebra is a first cervical vertebra, the total length L of the back curve of the tested person comprises an area from the fifth lumbar vertebra to the first cervical vertebra, dividing the total length L of the back curve by the length H of each measuring node to obtain the number n of required measuring nodes, and if the total length L of the back curve cannot completely divide the length H of a single measuring node, carrying to one position.
7. The wearable human back curve detection device of claim 6, wherein the endpoint coordinate determination module comprises:
a target identifier setting unit for setting a target identifier to 1;
a target coordinate setting unit for setting a target coordinate as the original coordinate;
a target measurement node setting unit, configured to set a target measurement node as the first measurement node;
the end point coordinate calculation unit is used for calculating the end point coordinate 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 coordinate;
the storage unit is used for associating and storing the endpoint coordinates and the target identification;
a judging unit, configured to judge whether the target measurement node is a last measurement node;
and the endpoint coordinate updating unit is used for updating the target measurement node to be the next measurement node, updating the target coordinate to be the endpoint coordinate, updating the target identifier to be the sum of the target identifier and 1 and triggering the endpoint coordinate calculating unit if the target measurement node is judged not to be the last measurement node.
8. The wearable human back curve detection device of claim 6, further comprising:
the curve inclination angle acquisition module is used for acquiring the cervical vertebra bending curvature and the curve inclination angle according to the human back curve;
and the posture state acquisition module is used for acquiring a sitting posture state or a standing posture state according to the cervical vertebra bending curvature and the curve inclination angle.
9. A wearable human back curve detection apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the wearable human back curve detection method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the wearable human back curve detection method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910184589.8A CN111685770B (en) | 2019-03-12 | 2019-03-12 | Wearable human body back curve detection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910184589.8A CN111685770B (en) | 2019-03-12 | 2019-03-12 | Wearable human body back curve detection method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111685770A CN111685770A (en) | 2020-09-22 |
| CN111685770B true CN111685770B (en) | 2023-03-10 |
Family
ID=72474743
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910184589.8A Active CN111685770B (en) | 2019-03-12 | 2019-03-12 | Wearable human body back curve detection method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111685770B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115153509B (en) * | 2022-08-05 | 2023-05-05 | 芙索特(上海)医疗科技有限公司 | Spine space curve generation method, system and spine measuring instrument |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009053671A1 (en) * | 2007-10-25 | 2009-04-30 | University College Cardiff Consultants Limited | Device for measuring spinal curvature |
| JP2010214098A (en) * | 2009-02-23 | 2010-09-30 | Kochi Univ Of Technology | Measuring instrument and measuring method of load on intervertebral disk |
| CN102137623A (en) * | 2008-07-11 | 2011-07-27 | 麦德托尼克公司 | Posture state classification for a medical device |
| CN104856686A (en) * | 2015-05-08 | 2015-08-26 | 南通大学 | Real-time in-vivo measuring method for right and left lateral flexion of thoracic and lumbar vertebrae |
| CN206534622U (en) * | 2016-11-18 | 2017-10-03 | 经纬泰和健康产业投资控股（北京）有限公司 | Scoliosis degree various dimensions detecting system |
| CN107693020A (en) * | 2017-10-23 | 2018-02-16 | 黄鹏 | Backbone physiological camber monitoring device and backbone physiological camber monitoring method |
| CN108030495A (en) * | 2017-11-10 | 2018-05-15 | 深圳纳富特科技有限公司 | Measuring method, device, care appliance and the computer-readable medium of body gesture |
| CN108764089A (en) * | 2018-05-18 | 2018-11-06 | 四川效率未来科技有限公司 | Human body back midspinal line recognition methods based on depth camera |
| CN108836350A (en) * | 2018-06-24 | 2018-11-20 | 西宁泰里霍利智能科技有限公司 | Wearable trunk attitude monitoring system and production method |
| CN109147285A (en) * | 2018-07-10 | 2019-01-04 | 江苏柔世电子科技有限公司 | Wearable Intelligent sitting posture monitors system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201814695U (en) * | 2010-08-04 | 2011-05-04 | 杨德昭 | Base plate of three-curved-surface natural-physiological-curvature lumbar rehabilitation bed |
| JP2015061579A (en) * | 2013-07-01 | 2015-04-02 | 株式会社東芝 | Motion information processing apparatus |
-
2019
- 2019-03-12 CN CN201910184589.8A patent/CN111685770B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009053671A1 (en) * | 2007-10-25 | 2009-04-30 | University College Cardiff Consultants Limited | Device for measuring spinal curvature |
| CN102137623A (en) * | 2008-07-11 | 2011-07-27 | 麦德托尼克公司 | Posture state classification for a medical device |
| JP2010214098A (en) * | 2009-02-23 | 2010-09-30 | Kochi Univ Of Technology | Measuring instrument and measuring method of load on intervertebral disk |
| CN104856686A (en) * | 2015-05-08 | 2015-08-26 | 南通大学 | Real-time in-vivo measuring method for right and left lateral flexion of thoracic and lumbar vertebrae |
| CN206534622U (en) * | 2016-11-18 | 2017-10-03 | 经纬泰和健康产业投资控股（北京）有限公司 | Scoliosis degree various dimensions detecting system |
| CN107693020A (en) * | 2017-10-23 | 2018-02-16 | 黄鹏 | Backbone physiological camber monitoring device and backbone physiological camber monitoring method |
| CN108030495A (en) * | 2017-11-10 | 2018-05-15 | 深圳纳富特科技有限公司 | Measuring method, device, care appliance and the computer-readable medium of body gesture |
| CN108764089A (en) * | 2018-05-18 | 2018-11-06 | 四川效率未来科技有限公司 | Human body back midspinal line recognition methods based on depth camera |
| CN108836350A (en) * | 2018-06-24 | 2018-11-20 | 西宁泰里霍利智能科技有限公司 | Wearable trunk attitude monitoring system and production method |
| CN109147285A (en) * | 2018-07-10 | 2019-01-04 | 江苏柔世电子科技有限公司 | Wearable Intelligent sitting posture monitors system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111685770A (en) | 2020-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109893110B (en) | Method and device for calibrating dynamic blood pressure | |
| CN105608737B (en) | A kind of human foot three-dimensional rebuilding method based on machine learning | |
| CN109785297A (en) | A kind of detection method and device of scoliosis | |
| US11779242B2 (en) | Systems and methods to estimate human length | |
| CN108805859A (en) | A kind of image detecting method, image detection device and terminal device | |
| JP6741305B2 (en) | Vertebral posture estimation device | |
| US20210374452A1 (en) | Method and device for image processing, and elecrtonic equipment | |
| CN109893111B (en) | Dynamic blood pressure measurement mode selection method and device | |
| CN111685770B (en) | Wearable human body back curve detection method and device | |
| US10699480B2 (en) | System and method for providing reconstruction of human surfaces from orientation data | |
| US20240049985A1 (en) | Methods of selecting surgical implants and related devices | |
| CN113146638A (en) | Centroid pose estimation method and device, computer readable storage medium and robot | |
| KR20110073000A (en) | Method and apparatus for measuring body size using image | |
| Hong et al. | Measurement of covered curvature based on a tape of integrated accelerometers | |
| US10028697B2 (en) | System and method for measuring skin movement and strain and related techniques | |
| JP2006515422A (en) | Calibration software for surface reconstruction of small objects | |
| WO2023135625A1 (en) | Evaluation device, evaluation method, and recording medium | |
| CN116077053A (en) | Spinal activity measuring method, device, computer equipment and storage medium | |
| JP2015195913A (en) | Posture estimation method, posture estimation program and posture estimation device | |
| CN212281355U (en) | Measuring device for measuring cervical vertebra mobility | |
| CN115705635A (en) | Spine bending angle calculation method and device and computer equipment | |
| CN112933579A (en) | Motion quality evaluation method and device and storage medium | |
| CN113554609B (en) | Neck dystonia identification system based on vision | |
| Boisvert et al. | 3D anatomical variability assessment of the scoliotic spine using statistics on Lie groups | |
| Shah | 3D Stereovision for Quantification of Skin Diseases |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |