Spinal form prediction method and device for constructing driving digital human model

Abstract

The invention discloses a spine form prediction method and device for driving digital human model construction, and relates to the field of driving digital human model construction, and the method comprises the steps: obtaining first scanning data when a driver keeps a sitting posture on a seat with a hole in a backrest for three-dimensional scanning; four-limb marking points and a body marking point are arranged on the body of the driver; acquiring second scanning data when the driver keeps the standing posture to perform three-dimensional scanning; acquiring third scanning data when the driver keeps the sitting posture on the driving seat to perform three-dimensional scanning; according to the first scanning data, the second scanning data and the third scanning data, a geometrical shape alignment technology and a coordinate system conversion technology are applied, and the spine form of the driver when the driver keeps the sitting posture on the driving seat is predicted; the spine form can be accurately predicted, and the problem that the spine form cannot be accurately measured due to shielding of a driving seat is solved.

Description

Technical Field

[0001] This application relates to the field of driving digital human model construction, and particularly to a spinal morphology prediction method and device for driving digital human model construction. Background Art

[0002] With the rapid development and technological progress of the transportation and aviation industries, the design of cockpits and cabs has gradually shifted from traditional optimization to greater emphasis on driver comfort, work efficiency, and safety. Whether operating a vehicle for a long time or flying complex routes, drivers need to cope with high-intensity physical labor and complex working environments. Therefore, how to ensure driver comfort, work efficiency, and safety has always been one of the key issues urgently to be solved in the transportation and aviation industries. As an advanced technical tool based on ergonomics, the digital human model plays an important role in these fields. Introducing the digital human model in the early stage of product development can not only help designers optimize the spatial layout of cockpits and cabs, reduce the risk of operator injury and discomfort, but also improve system performance and reduce overall production costs. By digitally simulating the operator's posture, movement, field of view, and body interactions, the digital human model can predict and solve potential design problems at the design stage, thus providing a scientific basis for optimizing the working environment, enhancing the operation experience, and improving system safety.

[0003] Optical motion capture technology is widely used in the construction of digital human models, especially excellent in human pose modeling. By placing marker points on key parts of the human body and using multiple high-speed cameras to capture the dimensions and joint angles of various body parts, researchers can accurately restore the human pose. However, in a real driving environment, the spinal region of the driver is easily blocked due to being close to the seat, resulting in the inability of high-speed cameras to accurately capture the spinal morphology. In addition, the cab space is usually relatively small, which poses great challenges for arranging multiple high-speed cameras. These limitations make it quite difficult to apply optical motion capture technology to capture the driving posture of drivers in a driving environment.

[0004] Some scholars have tried to use 3D scanning technology to capture the driving postures of drivers. This technology non - contact scans the driver through a 3D scanning device, can accurately obtain the 3D morphological data of the human body's exterior, and estimate the joint positions of the human body movement linkage model based on this data, thereby reconstructing the driving posture. Compared with optical motion capture, 3D scanning technology does not need to rely on marker points and directly obtains the detailed geometric data of the driver's body surface. In addition, the scanning process is relatively fast and has less interference with the driver's normal operations. In the limited cab space, portable handheld 3D scanning devices perform particularly well due to their flexibility. However, due to the occlusion of the driving seat, the data of the spinal region is often difficult to capture completely, resulting in the spinal morphology being unable to be accurately predicted when reconstructing the driving digital human model. This limitation has become the main challenge in the application of 3D scanning technology in the driving environment and also poses higher requirements for further optimizing driving posture modeling. Summary of the Invention

[0005] The purpose of this application is to provide a spinal morphology prediction method and device for constructing a driving digital human model, which can accurately predict the spinal morphology and solve the problem that the spinal morphology cannot be accurately measured due to the occlusion of the driving seat.

[0006] To achieve the above - mentioned purpose, this application provides the following solutions:

[0007] In the first aspect, this application provides a spinal morphology prediction method for constructing a driving digital human model, including:

[0008] Obtain the first scan data when the driver maintains a sitting posture on a seat with an opening in the backrest for 3D scanning; limb marker points and body marker points are set on the driver;

[0009] Obtain the second scan data when the driver maintains a standing posture for 3D scanning;

[0010] Obtain the third scan data when the driver maintains a sitting posture on the driving seat for 3D scanning;

[0011] According to the first scan data, the second scan data, and the third scan data, apply geometric shape alignment technology and coordinate system conversion technology to predict the spinal morphology of the driver when maintaining a sitting posture on the driving seat; the spinal morphology is used to construct a driving digital human model.

[0012] In the second aspect, this application provides a spinal morphology prediction device for constructing a driving digital human model, including:

[0013] A first scan data acquisition module, configured to obtain the first scan data when the driver maintains a sitting posture on a seat with an opening in the backrest for 3D scanning; limb marker points and body marker points are set on the driver;

[0014] The second scan data acquisition module is used to acquire the second scan data when the driver maintains a standing posture for three-dimensional scanning;

[0015] The third scan data acquisition module is used to acquire the third scan data when the driver maintains a sitting posture on the driving seat for three-dimensional scanning;

[0016] The spinal morphology prediction module is used to predict the spinal morphology of the driver when maintaining a sitting posture on the driving seat according to the first scan data, the second scan data and the third scan data, by applying geometric shape alignment technology and coordinate system transformation technology; the spinal morphology is used to construct a driving digital human model.

[0017] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned spinal morphology prediction method for driving digital human model construction.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned spinal morphology prediction method for driving digital human model construction.

[0019] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above-mentioned spinal morphology prediction method for driving digital human model construction.

[0020] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0021] The present application provides a spinal morphology prediction method and device for driving digital human model construction, which acquire the first scan data when the driver maintains a sitting posture on the seat with a backrest opening for three-dimensional scanning, the second scan data when the driver maintains a standing posture for three-dimensional scanning, and the third scan data when the driver maintains a sitting posture on the driving seat for three-dimensional scanning; according to the first scan data, the second scan data and the third scan data, by applying geometric shape alignment technology and coordinate system transformation technology, predict the spinal morphology of the driver when maintaining a sitting posture on the driving seat. The present application can accurately predict the spinal morphology and solve the problem that the spinal morphology cannot be accurately measured due to the occlusion of the driving seat. Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0023] Figure 1 It is an application environment diagram of a spinal morphology prediction method for driving digital human model construction in an embodiment of the present application;

[0024] Figure 2 It is a flowchart of a spinal morphology prediction method for driving digital human model construction provided in an embodiment of the present application;

[0025] Figure 3 It is a schematic diagram of the technical concept of a spinal morphology prediction method for driving digital human model construction provided in an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of a human body motion link model provided in an embodiment of the present application;

[0027] Figure 5 It is a schematic diagram of a hard seat with a backrest opening provided in an embodiment of the present application;

[0028] Figure 6 It is a schematic diagram of the representation method of the cervical vertebra provided in an embodiment of the present application;

[0029] Figure 7 It is a schematic diagram of the representation method of the thoracic vertebra provided in an embodiment of the present application;

[0030] Figure 8 It is a schematic diagram of the calculation method of the hip joint center point and the lower lumbar joint point provided in an embodiment of the present application;

[0031] Figure 9 It is a schematic diagram of the pelvic coordinate system provided in an embodiment of the present application;

[0032] Figure 10 It is a schematic diagram of the functional modules of a spinal morphology prediction device for driving digital human model construction provided in an embodiment of the present application;

[0033] Figure 11 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0034] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0035] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] The spinal morphology prediction method for driving digital human model construction provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the first scan data when the driver is sitting upright on the seat with an opening in the backrest for three-dimensional scanning, the second scan data when the driver is standing upright for three-dimensional scanning, and the third scan data when the driver is sitting upright on the driving seat for three-dimensional scanning to the server. After receiving the data, the server applies geometric shape alignment technology and coordinate system conversion technology to predict the spinal morphology of the driver when sitting upright on the driving seat and feedback it to the terminal. In addition, in some embodiments, the spinal morphology prediction method for driving digital human model construction can also be implemented independently by the server or the terminal. For example, the terminal can directly perform spinal morphology prediction for driving digital human model construction on the collected first to third scan data, or the server can obtain the first to third scan data from the data storage system and perform spinal morphology prediction for driving digital human model construction.

[0037] Among them, the terminal can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablet computers, Internet of Things devices, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0038] The existing method for obtaining the skin landmark points of the driver's spine is to rely on experts to evaluate the skin landmark points of the spine or make an opening in the backrest of the driving seat. The accuracy of the expert evaluation method depends on the experience of the experts. It is required that the experts participating in the evaluation have rich practical experience in anthropometry. Due to the obstruction of the backrest of the driving seat, the experts are unable to estimate the skin landmark points of the driver's spine. And making an opening in the back of the driving seat to obtain the skin landmark points of the spine has a high cost. Therefore, in an exemplary embodiment, such asFigure 2 and Figure 3 As shown in Figure 1 , a spinal morphology prediction method for driving digital human model construction is provided. This method is executed by a computer device, which can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking the application of this method to the Figure 1 server in Figure 1 as an example for illustration, it includes the following steps 101 to 104.

[0039] Step 101, obtain the first scan data when the driver maintains a sitting posture on a seat with an opening in the backrest for three-dimensional scanning; limb marking points and body marking points are provided on the driver.

[0040] Preparation stage: Construct a human body motion linkage model, select human joint skin landmark points, the driver wears a tight-fitting garment, and marking points are pasted at the joint skin landmark points. The preparation stage includes: using the motion linkage model to represent the driving posture. The advantage of this representation method is that it can both retain the anatomical relationship and represent the kinematic complexity with fewer joint points. The included angle of the link in the motion linkage model represents the joint angle of the driving posture, and the link length represents the length of each segment of the limb. To define the link length, information about the bone points is required. It is difficult to obtain real human bone point information, so the information of the human joint skin points is selected to approximately represent the bone point information. To obtain the information of the human joint skin points, marking points need to be represented at the human skin joint points. Through the information of two marking points, the link length in the motion linkage model can be known, that is, the size of a certain part of the human limb. The defined human body motion linkage model is as Figure 4 shown, and the selected human joint skin marking points are shown in Table 1.

[0041] Table 1 Human joint skin marking points

[0042] Skin landmark points Definition Occiput When the head is in the Frankfurt horizontal plane, the calm skin surface point is located at the rearmost position on the midsagittal plane of the head Vertex When the head is in the Frankfurt horizontal plane, the highest point on the midsagittal plane of the head is the non-depressed skin surface Tragion The non-depressed skin surface point is obtained by palpating the foremost edge of the cartilage indentation above the auricle Lowest point of the orbital margin The non-depressed skin surface point is located at the junction of the outer sides of the upper and lower eyelids Suborbital at the pupil center When looking straight ahead, the non-compressed skin surface point located on the outer side of the pupil is obtained by touching the lower orbital margin Glabella The non-depressed skin surface point is obtained by palpating the foremost protruding part at the midline of the supraorbital ridge on the forehead Suprasternal point The non-depressed skin surface point at the upper edge of the jugular notch of the sternum on the midsternal line Infrasternal point The depressed skin surface point at the midline of the lower edge of the sternum C7 The depressed skin surface point at the rearmost of the spinous process of the seventh cervical vertebra T4 The depressed skin surface point at the rearmost of the spinous process of the fourth thoracic vertebra T8 The depressed skin surface point at the rearmost of the spinous process of the eighth thoracic vertebra T12 The depressed skin surface point at the rearmost of the spinous process of the twelfth thoracic vertebra L1 The depressed skin surface point at the rearmost of the spinous process of the first lumbar vertebra L2 The depressed skin surface point at the rearmost of the spinous process of the second lumbar vertebra L3 The depressed skin surface point at the rearmost of the spinous process of the third lumbar vertebra L4 The depressed skin surface point at the rearmost of the spinous process of the fourth lumbar vertebra L5 The depressed skin surface point at the rearmost of the spinous process of the fifth lumbar vertebra Anterior superior iliac spine The depressed skin surface point located at the anterior superior iliac spine Posterior superior iliac spine Palpate backward along the iliac crest until the rearmost protruding point near the sacrum is found to obtain the depressed skin surface point Acromion The non-compressed skin surface point obtained by touching the foremost upper part of the lateral edge of the acromion process of the scapula Lateral epicondyle of the humerus The non-depressed skin surface point at the outermost (near the elbow) of the humeral condyle Medial epicondyle of the humerus The non-depressed skin surface point at the innermost (near the elbow) of the humeral condyle Styloid process of the ulna A point on the skin surface is obtained by palpating the most distal edge of the styloid process of the ulna Styloid process of the radius A point on the skin surface is obtained by touching the most distal edge of the styloid process of the radius Lateral malleolus of the femur The non-compressed skin surface point at the outermost of the lateral condyle of the femur Medial malleolus of the femur The depressed skin surface point at the outermost of the medial condyle of the femur Above the patella The non-depressed skin surface point at the proximal end of the sagittal midline of the patella Below the patella The non-depressed skin surface point at the distal end of the sagittal midline of the patella Lateral side of the ankle joint The non-compressed skin surface point at the outermost of the fibular malleolus (lateral malleolus protrusion) of the ankle joint Medial side of the ankle joint The non-depressed skin surface point at the innermost of the tibial malleolus (medial malleolus protrusion) of the ankle joint Lateral side of the sole A point on the medial side of the fifth metatarsophalangeal joint on the lateral side of the foot Medial side of the sole A point on the medial side of the first metatarsophalangeal joint on the medial side of the foot Heel The point at the rearmost of the heel

[0043] Measurement stage 1: The driver maintains a sitting posture on a hard seat with an opening in the backrest and conducts the first scan to collect data. Obtain the information of the limb and spine landmark points of the human body in the state of a hard seat with an opening in the backrest. The main purpose of using a hard seat with an opening in the backrest for measurement is to determine the spinal morphology in the sitting posture.

[0044] Measurement stage 1 includes:

[0045] The driver maintains a sitting posture on a hard seat with an opening in the backrest and uses a portable handheld three-dimensional scanning instrument to conduct the first scan to collect data. The hard seat with an opening in the backrest is as Figure 5As shown. The main purpose of using a hard seat with an opening in the backrest for measurement is to determine the spinal morphology in a sitting position. The information of the spinal landmark points is processed, and the spine is represented by the cervical vertebrae, thoracic vertebrae, lumbar vertebrae, and pelvis.

[0046] The cervical vertebrae are represented by the line connecting the upper cervical joint and the lower cervical joint. The upper cervical joint is determined by rotating the line connecting the tragus point and the outer orbital margin point (length A) downward by 117°, and the distance from the tragus point is 0.31A. The lower cervical joint is determined by rotating the line connecting the C7 point and the suprasternal notch point (length C) upward by 8°, and the distance from C7 is 0.55C, as Figure 6 shown.

[0047] The length of the thoracic vertebrae is represented by the line connecting the lower cervical joint and the upper lumbar joint. The upper lumbar joint is determined by rotating the line connecting T8 and T12 to the left by 94 degrees, and the distance from T12 is 0.52C, as Figure 7 shown.

[0048] The length of the lumbar vertebrae is represented by the line connecting the upper lumbar joint and the lower lumbar joint. The positions of the hip joints and the lower lumbar joints are calculated through the two anterior superior iliac spine and two posterior superior iliac spine landmark points, as Figure 8 shown.

[0049] Step 102: Obtain the second scan data when the driver maintains a standing position for three-dimensional scanning.

[0050] Measurement stage 2: The driver maintains a standing position, and the driver is scanned for the second time to collect data. Obtain the positions of the spinal landmark points and the center point of the hip joint.

[0051] Step 103: Obtain the third scan data when the driver maintains a sitting position on the driving seat for three-dimensional scanning.

[0052] Measurement stage 3: The driver maintains a sitting position on the driving seat and is scanned for the third time to collect data. Obtain the information of the human limbs and spinal landmark points in the sitting state of the driving seat. Since the driver is sitting on the driving seat, it is impossible to measure the T4, T8, T12, L1, L2, L3, L4, L5, and posterior superior iliac spine landmark points on the spine, so it is impossible to calculate the upper lumbar joint point, the lower lumbar joint point, and the center point of the hip joint.

[0053] Step 104: According to the first scan data, the second scan data, and the third scan data, apply geometric shape alignment technology and coordinate system conversion technology to predict the spinal morphology of the driver when maintaining a sitting position on the driving seat; the spinal morphology is used to construct a driving digital human model. A human motion linkage model with the spatial positions of each marker point (including spinal marker points) is the driving digital human model.

[0054] In the data processing stage, geometric alignment is performed on the spinal landmark information obtained in the measurement 1 stage and the spinal landmark information obtained in the measurement 3 stage; based on the spinal landmarks in the measurement 2 stage and the measurement 3 stage, the position of the hip joint center point obtained in the measurement 2 stage is coordinate-transformed to obtain the hip joint center point in the sitting posture state of the driver's seat.

[0055] In another exemplary embodiment of the present application, in step 104, according to the first scan data, the second scan data, and the third scan data, applying geometric shape alignment technology and coordinate system transformation technology, predicting the spinal morphology of the driver when maintaining a sitting posture on the driver's seat specifically includes:

[0056] (1) According to the first scan data and the third scan data, applying geometric shape alignment technology, obtain the position of the upper lumbar vertebral joint marker points of the driver when maintaining a sitting posture on the driver's seat.

[0057] (2) According to the second scan data and the third scan data, applying coordinate system transformation technology, obtain the position of the hip joint center marker points and the lower lumbar vertebral joint marker points of the driver when maintaining a sitting posture on the driver's seat.

[0058] (3) Determine the spinal morphology of the driver when maintaining a sitting posture on the driver's seat according to the position of the upper lumbar vertebral joint marker points, the hip joint center marker points, and the lower lumbar vertebral joint marker points of the driver when maintaining a sitting posture on the driver's seat.

[0059] In another exemplary embodiment of the present application, in the measurement 1 stage, the spinal geometric morphology of the human motion linkage model in the sitting posture state is obtained, where the upper lumbar vertebral joint points are regarded as part of the chest rigid body. Taking C7, the upper chest point, the lower chest point, and the upper lumbar vertebral joint points as a whole, the spatial position relationship between these four landmark points is established. In the measurement 3 stage, the spatial coordinates of C7, the upper chest fossa point, and the lower chest fossa point of the driver in the sitting posture state of the driver's seat are collected. Based on the spatial position relationship between the upper lumbar vertebral joint points obtained in the measurement 1 stage and the other three landmark points, the position of the upper lumbar vertebral joint points of the driver in the sitting posture state of the driver's seat is deduced. Therefore, it can be obtained that in step (1), according to the first scan data and the third scan data, applying geometric shape alignment technology, obtaining the position of the upper lumbar vertebral joint marker points of the driver when maintaining a sitting posture on the driver's seat specifically includes:

[0060] (1-1) Perform geometric alignment on the corresponding body landmark points in the first scan data and the third scan data.

[0061] (1-2) Determine the spatial positional relationship among the C7 marker point, the upper thoracic fossa marker point, the lower thoracic fossa marker point, and the upper lumbar joint marker point in the first scan data; the C7 marker point refers to the marker point corresponding to the skin surface depression at the rearmost part of the spinous process of the seventh cervical vertebra; the upper thoracic fossa marker point refers to the marker point corresponding to the non-depressed point on the skin surface at the upper edge of the jugular notch of the sternum on the midline of the sternum; the lower thoracic fossa marker point refers to the marker point corresponding to the skin surface depression at the midline of the lower edge of the sternum.

[0062] (1-3) Based on the positions of the C7 marker point, the upper thoracic fossa marker point, and the lower thoracic fossa marker point in the aligned third scan data, and in combination with the spatial positional relationship, calculate the position of the upper lumbar joint marker point when the driver maintains a sitting posture on the driver's seat.

[0063] In another exemplary embodiment of the present application, in the measurement stage 2, the spatial coordinate information of the anterior superior iliac spine, the iliac crest point, and the posterior superior iliac spine point of the driver in the standing posture is obtained. A pelvic coordinate system is constructed through the anterior superior iliac spine and the posterior superior iliac spine point, and the spatial position of the hip joint center point in the standing posture is calculated. In the measurement stage 3, the spatial coordinate information of the anterior superior iliac spine and the iliac crest point of the driver in the sitting posture on the driver's seat is collected. Based on the relative positional relationship between the anterior superior iliac spine and the iliac crest point in the standing and sitting postures, the pelvic coordinate system is transformed, and then the spatial coordinates of the hip joint center point of the driver in the sitting posture on the driver's seat are calculated, and from this, the spatial position of the lower lumbar joint is derived.

[0064] Therefore, it can be obtained that in step (2), according to the second scan data and the third scan data, by applying the coordinate system transformation technology, the positions of the hip joint center marker point and the lower lumbar joint marker point when the driver maintains a sitting posture on the driver's seat are obtained, specifically including:

[0065] (2-1) Determine the spatial coordinate information of the anterior superior iliac spine marker point and the posterior superior iliac spine marker point in the second scan data.

[0066] (2-2) Determine the pelvic coordinate system according to the spatial coordinate information of the anterior superior iliac spine marker point and the posterior superior iliac spine marker point in the second scan data.

[0067] (2-3) According to the pelvic coordinate system, calculate the spatial position of the hip joint center marker point of the driver in the standing posture.

[0068] For the prediction of the hip joint center point, through the spatial coordinate system transformation based on the landmark points on the driver's pelvis, the position of the hip joint center point of the driver in the environment of the sitting posture on the driver's seat is obtained. Compared with the expert estimation and the opening of the driver's seat backrest, it can provide more reliable results and save costs, thus smoothly constructing the driving digital human model.

[0069] (2-4) Determine the spatial coordinate information of the anterior superior iliac spine marking point and the posterior superior iliac spine marking point in the third scan data.

[0070] (2-5) According to the relative position relationship between the anterior superior iliac spine marking point and the posterior superior iliac spine marking point in the second scan data and the third scan data, transform the pelvic coordinate system, and deduce the spatial position of the lower lumbar joint marking point when the driver maintains a sitting posture on the driver's seat.

[0071] Spatial coordinate system transformation involves operations such as translation and rotation.

[0072] Translation transformation: The new coordinates (x′, y′, z′) after translating the point (x, y, z) by (t x , t y , t z ) are as follows.

[0073]

[0074] Rotation transformation: The transformation matrices for rotation around the x, y, and z axes are as follows.

[0075] Rotation around the x-axis by θ angle:

[0076]

[0077] Rotation around the y-axis by θ angle:

[0078]

[0079] Rotation around the z-axis by θ angle:

[0080]

[0081] In another exemplary embodiment of the present application, in step (2-3), according to the pelvic coordinate system, the spatial position of the hip joint center marking point in the driver's standing state is deduced, specifically including:

[0082] Determine the pelvic width according to the pelvic coordinate system; determine the spatial position of the hip joint center marking point according to the pelvic width.

[0083] The driver maintains a standing posture, and the driver is scanned for the second time to collect landmark data. Establish a pelvic coordinate system through the anterior superior iliac spine and the posterior superior iliac spine points. As Figure 9 shown, use the Bell prediction method to predict the hip joint center point in the standing state. The regression equation of the Bell prediction method is as follows:

[0084] x = -0.19PW, y = -0.30PW, Z = 0.36PW (6)

[0085] where PW is the width of the pelvis.

[0086] In another exemplary embodiment of the present application, the first scan data, the second scan data, and the third scan data are all data obtained by three-dimensional scanning using a handheld three-dimensional scanning instrument. Compared with using a three-dimensional motion capture system to capture the driving posture, the portable three-dimensional scanner is more flexible, can capture the driving posture within a limited driving space, causes less interference to the driver, and avoids arranging multiple cameras inside the cockpit.

[0087] In the present application, human joint landmark points are selected to establish a human motion linkage model (limbs, spine); the driver wears a tight-fitting suit, and landmark points are pasted on the driver's body according to the human motion linkage model; the driver sits on a hard seat with an opening in the back and hands on the thighs; a portable three-dimensional scanner is used to perform the first scan on the driver to reconstruct the posture of the driver in the sitting position; the driver stands, and the portable three-dimensional scanner is used to perform the second scan on the driver, and the position of the hip joint center point is obtained using the prediction method; the driver sits on the driving seat and maintains the sitting position, and the portable three-dimensional scanning instrument is used to perform the third scan on the driver again; the geometric alignment method is used to align the spine landmark points of the first scan and the third scan to obtain the morphology of the upper thoracic vertebra in the driving seat state; the coordinate transformation method is used to transform the position of the hip joint center point predicted during the second scan, so as to obtain the hip joint position and the morphology of the lower lumbar vertebra of the driver in the sitting position on the driving seat; finally, the human motion linkage model of the driver on the driving seat is obtained. The spine morphology prediction method provided by the present invention can estimate the morphology of the driver's spine when the joint landmark points of the spine cannot be measured due to the occlusion of the driving seat, and solve the problem of describing the driver's driving posture when constructing a digital driving human model.

[0088] The present application has the following technical effects:

[0089] By means of geometric shape alignment, hip center point prediction, coordinate system transformation and other methods, the problem that the spinal morphology cannot be completely obtained due to the occlusion of the driving seat when using 3D scanning technology to reconstruct the driving posture is effectively solved, and the reconstruction accuracy of the spinal morphology in the driving posture is greatly improved, providing reliable data support for the construction of the driving digital human model; the present invention can be applied to posture capture and modeling in complex driving environments, especially in limited spaces or occlusion conditions, and can still achieve accurate reconstruction of the spinal morphology, thereby improving the wide applicability of the digital human model; by accurately predicting the spinal morphology of the driver, the digital human model can provide more accurate ergonomic support for cab design, solve the problem of insufficient design optimization caused by the lack or inaccuracy of spinal morphology data in the driving posture during the cab design stage, and provide a scientific basis for the ergonomic design, space layout optimization of the cab, and evaluation of driver comfort and operability by accurately predicting the spinal morphology of the driver, thereby improving the rationality and safety of cab design and enhancing driver comfort and operation efficiency. From the perspective of constructing a driving digital human model, a method for predicting spinal morphology is designed. To further improve the applicability of the method, the characteristics of different types of vehicles can be combined, the driving task background can be introduced, and the experimental design can be optimized, so as to enhance the objectivity and accuracy of the results.

[0090] This application also provides an application scenario, which applies the above-mentioned method for predicting spinal morphology for constructing a driving digital human model. Specifically: The method for predicting spinal morphology for constructing a driving digital human model provided in this embodiment can be applied in the cab design scenario. This includes a data acquisition link, a spinal morphology prediction link, a driving digital human model construction link, and a cab design optimization link; the data acquisition link is used to collect the first scan data when the driver maintains a sitting posture on a seat with a backrest opening for 3D scanning, the second scan data when the driver maintains a standing posture for 3D scanning, and the third scan data when the driver maintains a sitting posture on the driving seat for 3D scanning; the spinal morphology prediction link is used to predict the spinal morphology according to the collected scan data; the driving digital human model construction link is used to construct a driving digital human model according to the predicted spinal morphology; the cab design optimization link is used to optimize the ergonomic design and space layout of the cab according to the driving digital human model. The method for predicting spinal morphology for constructing a driving digital human model provided in this embodiment belongs to the spinal morphology prediction link.

[0091] Based on the same inventive concept, an embodiment of the present application further provides a spinal morphology prediction device for driving digital human model construction, which is used to implement the above-mentioned spinal morphology prediction method for driving digital human model construction. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following spinal morphology prediction device for driving digital human model construction can refer to the limitations on the spinal morphology prediction method for driving digital human model construction in the above text, and will not be repeated here.

[0092] In an exemplary embodiment, as Figure 10 shown, a spinal morphology prediction device for driving digital human model construction is provided, including:

[0093] The first scan data acquisition module M1 is used to acquire the first scan data when the driver maintains a sitting posture on a seat with a backrest opening for three-dimensional scanning; limb marking points and body marking points are provided on the driver.

[0094] The second scan data acquisition module M2 is used to acquire the second scan data when the driver maintains a standing posture for three-dimensional scanning.

[0095] The third scan data acquisition module M3 is used to acquire the third scan data when the driver maintains a sitting posture on the driving seat for three-dimensional scanning.

[0096] The spinal morphology prediction module M4 is used to predict the spinal morphology of the driver when maintaining a sitting posture on the driving seat according to the first scan data, the second scan data, and the third scan data, and applying geometric shape alignment technology and coordinate system transformation technology; the spinal morphology is used to construct a driving digital human model.

[0097] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 11As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store spine morphology prediction data for driving digital human model construction. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a spine morphology prediction method for driving digital human model construction.

[0098] Those skilled in the art can understand that Figure 11 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0099] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0100] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0101] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0102] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0103] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0104] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0105] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A spine morphology prediction method for building a driving digital human model, characterized in that: The spine morphology prediction method for building a driving digital human model includes: Acquire first scanning data when a driver maintains a sitting position on a seat with a backrest opening for three-dimensional scanning; the driver is provided with limbs marking points and body marking points; Acquire second scanning data when the driver maintains a standing position for three-dimensional scanning; Acquiring third scanning data when the driver remains in a sitting position on the driving seat for three-dimensional scanning; According to the first scanning data, the second scanning data and the third scanning data, the spinal morphology of the driver when sitting in the driving seat is predicted by applying the geometric shape alignment technology and the coordinate system conversion technology; the spinal morphology is used to construct the driving digital human model.

2. The spine morphology prediction method for building a driving digital human model according to claim 1, characterized in that: According to the first scanning data, the second scanning data and the third scanning data, the geometric shape alignment technology and the coordinate system conversion technology are applied to predict the spinal morphology of the driver when he / she maintains a sitting posture in the driving seat, specifically including: According to the first scanning data and the third scanning data, a geometric shape alignment technology is applied to obtain the position of the upper lumbar vertebra joint marker point when the driver maintains a sitting position on the driving seat; According to the second scanning data and the third scanning data, the coordinate system conversion technology is applied to obtain the position of the hip joint center mark point and the position of the lower lumbar vertebra joint mark point when the driver maintains a sitting position on the driving seat; The spinal morphology of the driver when the driver maintains a sitting position on the driver's seat is determined according to the position of the upper lumbar vertebrae joint marking point, the position of the hip joint center marking point and the position of the lower lumbar vertebrae joint marking point when the driver maintains a sitting position on the driver's seat.

3. The spine morphology prediction method for building a driving digital human model according to claim 2, characterized in that: According to the first scanning data and the third scanning data, the geometric shape alignment technology is applied to obtain the position of the upper lumbar vertebra joint marker points when the driver maintains a sitting position on the driving seat, specifically including: Geometrically aligning corresponding body landmarks in the first scan data and the third scan data; Determine the spatial position relationship between the C7 marker point, the upper chest fossa marker point, the lower chest fossa marker point and the upper lumbar joint marker point in the first scan data; the C7 marker point refers to the marker point corresponding to the skin surface depression point at the rearmost of the seventh cervical spinous process; the upper chest fossa marker point refers to the marker point corresponding to the non-depressed skin surface point at the upper edge of the sternojugular notch on the midline of the sternum; the lower chest fossa marker point refers to the marker point corresponding to the skin surface depression point at the midline of the lower edge of the sternum; According to the positions of the C7 marker point, the superior thoracic fossa marker point and the inferior thoracic fossa marker point in the aligned third scan data, combined with the spatial position relationship, the position of the upper lumbar joint marker point when the driver maintains a sitting position in the driving seat is calculated.

4. The spine morphology prediction method for building a driving digital human model according to claim 2, characterized in that: Based on the second scan data and the third scan data, the coordinate system conversion technology is applied to obtain the position of the hip joint center mark point and the position of the lower lumbar vertebrae joint mark point when the driver maintains a sitting position on the driver's seat, specifically including: Determine spatial coordinate information of anterior superior iliac spine marker point and posterior superior iliac spine marker point in the second scan data; Determine the pelvic coordinate system according to the spatial coordinate information of the anterior superior iliac spine marker point and the posterior superior iliac spine marker point in the second scanning data; Determine and calculate the spatial position of the center mark point of the driver's hip joint in a standing state according to the pelvic coordinate system; Determine the spatial coordinate information of the anterior superior iliac spine marking point and the posterior superior iliac spine marking point in the third scanning data; According to the relative position relationship between the anterior superior iliac spine marker point and the posterior superior iliac spine marker point in the second scan data and the third scan data, the pelvic coordinate system is transformed to calculate the spatial position of the lower lumbar joint marker point when the driver maintains a sitting position in the driver's seat.

5. The spine morphology prediction method for building a driving digital human model according to claim 1, characterized in that: The spatial position of the driver's hip joint center mark point in the standing state is determined and calculated based on the pelvic coordinate system, specifically including: Determine the pelvic width based on the pelvic coordinate system; The spatial position of the hip joint center marker point is determined according to the pelvic width.

6. The spine morphology prediction method for building a driving digital human model according to claim 1, characterized in that: The first scanning data, the second scanning data and the third scanning data are all data obtained by performing three-dimensional scanning using a handheld three-dimensional scanning device.

7. A spinal morphology prediction device for building a driving digital human model, characterized in that: The spine morphology prediction device for building a driving digital human model comprises: A first scanning data acquisition module is used to acquire first scanning data when a driver maintains a sitting position on a seat with a backrest opening for three-dimensional scanning; the driver is provided with limbs marking points and body marking points; A second scanning data acquisition module, used to acquire second scanning data when the driver maintains a standing position for three-dimensional scanning; A third scanning data acquisition module is used to acquire third scanning data when the driver maintains a sitting position on the driving seat for three-dimensional scanning; The spine morphology prediction module is used to predict the spine morphology of the driver when he / she maintains a sitting position in the driving seat based on the first scanning data, the second scanning data and the third scanning data by applying geometric shape alignment technology and coordinate system conversion technology; the spine morphology is used to construct a driving digital human model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the spine morphology prediction method for constructing a driving digital human model according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the spine morphology prediction method for constructing a driving digital human model described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the spine morphology prediction method for constructing a driving digital human model described in any one of claims 1 to 6 is implemented.

Images