Human reachable volume analysis method, system, and apparatus based on optical motion capture

Abstract

This application relates to the field of optical motion capture system technology, and discloses a method, system, and device for human reachability analysis based on optical motion capture. The method includes: acquiring real-time motion trajectory data of human body points; preprocessing the real-time motion trajectory data in the scene where the target 3D model is located to obtain preprocessed point cloud data; fitting the point cloud data into one or more envelopes using a fitting algorithm; fitting the one or more envelopes into a single overall envelope and calculating the features of the overall envelope; generating multiple sets of two-dimensional envelope curves by sectioning based on the overall envelope and its features, and calculating the positional features between the overall envelope and the target 3D model; and visually storing the overall envelope features, the multiple sets of two-dimensional envelope curves, and the positional features. This application can objectively, accurately, and efficiently measure the range of motion or extension of a certain part of the human body in three-dimensional space.

Description

Technical Field

[0001] This application relates to the field of optical motion capture system technology, and more specifically, to a method, system, and device for analyzing human reachability based on optical motion capture. Background Technology

[0002] Optical motion capture technology, with its high precision and non-contact measurement capabilities, has become an important tool in the field of motion analysis. This technology involves placing reflective or luminous markers on key parts of the human body, and then simultaneously capturing two-dimensional images of these markers using multiple high-speed infrared cameras. Through triangulation, the motion trajectory data of the markers in three-dimensional space is obtained. This data has wide applications in animation production, sports biomechanical analysis, virtual reality interaction, and ergonomics evaluation.

[0003] Accessibility typically refers to the maximum range that a part or joint of the human body can reach or move within a given space. Accurate and objective assessment of human accessibility is crucial in fields such as ergonomics, product design, and rehabilitation medicine.

[0004] Existing methods for measuring reachability generally suffer from problems such as strong subjectivity, limited measurement dimensions, insufficient accuracy, safety risks, or poor data integrity. Traditional technologies cannot simultaneously meet the application requirements of three-dimensional spatial measurement, automated acquisition, and high-precision analysis. Especially in fields such as high-end industrial design and rehabilitation medicine, where the requirements for measurement results are stringent, the shortcomings of existing technologies have become a key bottleneck restricting the development and performance optimization of related products. Therefore, there is an urgent need for a technical solution that can objectively, accurately, and efficiently measure the reachability of the human body in three-dimensional space. Summary of the Invention

[0005] In view of the above situation, this application provides a method, system and device for human reachability analysis based on optical motion capture, which aims to solve the above problems or at least partially solve the above problems.

[0006] In a first aspect, embodiments of this application provide a method for analyzing the human reachability domain based on optical motion capture, the method comprising: Collect real-time motion trajectory data of human body stickers; In the scene where the target 3D model is located, the real-time motion trajectory data is preprocessed to obtain preprocessed point cloud data; The point cloud data is fitted into one or more envelopes using a fitting algorithm; Fit one or more envelopes into a single global envelope and calculate the features of the global envelope; Based on the overall envelope and its features, multiple sets of two-dimensional envelope curves are generated by sectioning, and the positional features between the overall envelope and the target three-dimensional model are calculated. The overall envelope features, multiple sets of two-dimensional envelope curves, and positional features are visualized and saved.

[0007] Secondly, embodiments of this application also provide a human reachability domain analysis system based on optical motion capture, the system comprising: Data acquisition module: used to collect real-time motion trajectory data of human body stickers; Data preprocessing module: used to preprocess the real-time motion trajectory data in the scene where the target 3D model is located, to obtain preprocessed point cloud data; Data processing module: used to fit point cloud data into one or more envelopes using a fitting algorithm; to fit one or more envelopes into a single global envelope and to calculate the features of the global envelope; Data visualization module: Based on the overall envelope and its features, it generates multiple sets of two-dimensional envelope curves by sectioning, calculates the positional features between the overall envelope and the target three-dimensional model, and visualizes and saves the overall envelope features, multiple sets of two-dimensional envelope curves, and positional features.

[0008] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps described in the first aspect.

[0009] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps described in the first aspect.

[0010] The above-mentioned technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: Human motion trajectory is collected based on optical motion capture technology, ensuring the three-dimensionality, continuity, and objectivity of the data from the source, overcoming the subjective errors and dimensional limitations of traditional manual measurement methods; by placing the motion data in the target three-dimensional model scene for coordinate unification, point loss interpolation, and other preprocessing, and using a fitting algorithm to robustly reconstruct the discrete point cloud into a continuous and smooth envelope model, the final reachable domain has clear mathematical boundaries and quantitative characteristics, significantly improving the accuracy and reliability of the reachable domain expression; furthermore, by calculating the precise distance and angle characteristics between the envelope and the target model, a leap from simple limb movement range measurement to quantitative evaluation of specific human-computer interaction performance is achieved, enabling the analysis results to be directly and quantitatively used to guide the ergonomic optimization of product design and spatial layout, enhancing the practical value and engineering guidance significance of the analysis results. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating the human reachability domain analysis method based on optical motion capture provided in an embodiment of this application is shown. Figure 2 This paper shows a structural diagram of a human reachability analysis system based on optical motion capture provided in an embodiment of this application. Figure 3 This paper illustrates a flowchart of the human reachability analysis system based on optical motion capture provided in an embodiment of this application. Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0014] The method for collecting data on reachability, i.e., range of motion and reachable area, is determined based on the assessment requirements. Currently, there are several measurement methods available, including: (1) Protractor: This device has two straight sides that can be rotated relative to a protractor to measure the angle between them. The protractor must be aligned with physiological landmarks on the subject, and the subjectivity of this alignment may lead to differences in measurement results between different experimenters or between different tests performed by the same experimenter.

[0015] (2) Photographs can provide direct data during the experiment. The subjectivity of data extraction may lead to measurement differences between extraction or testing.

[0016] (3) Inclinometer, a device used to measure deviation from the vertical direction, and can be used to measure the movement of the torso. Its accuracy may be affected by misalignment or slippage during initial installation, which occurs on the object being measured.

[0017] (4) Radiographic testing: The measurement of range of motion is achieved by taking a series of X-rays of the body and using visual inspection or computer models to determine the relative rotation of different parts of the body. This requires the subject to be exposed to a large amount of radiation and may not be able to accurately measure rotations that are not in a single plane.

[0018] (5) Lumbar spine measuring instrument to measure the range of motion of the back.

[0019] (6) Motion capture tools are more objective than angle measurement or photographs. The markers used to track motion may move or be occluded. They capture the range of motion to generate working envelope data. The disadvantage is that the markers used to track motion may move or be occluded.

[0020] In addition, the related technology can accurately measure the range of motion of human limbs in a certain plane in space. It has the characteristics of high measurement accuracy and real-time display of test trajectory. It uses a high-precision angle sensor to measure the change of the rotation axis angle to indirectly obtain the trajectory curve of the object. It can be applied to fields such as sports medicine and modern industrial design.

[0021] Existing reachability measurement systems and methods, such as protractors, photographs, inclinometers, X-ray inspection, and lumbar spine measuring instruments, require manual measurement by surveyors, which is highly subjective and cannot meet the needs of scenarios requiring higher accuracy and efficiency. While motion capture tools can measure reachability more objectively, they suffer from occlusion issues. Other related technologies only support measuring the range of motion of human limbs in a single plane in space, failing to meet the requirements for three-dimensional reachability measurement. They may also be used to determine the shortest time for each road node among multiple road nodes in a predefined road network topology, but cannot be used to measure the reachability of human limbs.

[0022] Based on this, this application proposes a human reachability domain analysis method based on optical motion capture.

[0023] Figure 1 This document illustrates a flowchart of a human reachability domain analysis method based on optical motion capture provided in an embodiment of this application. Figure 1 It can be seen that this application includes at least steps S101-S106: Step S101: Collect real-time motion trajectory data of human body stickers.

[0024] Among them, human body markers represent optical markers of key parts of the human body, and the continuous coordinate sequence of these markers in three-dimensional space is obtained.

[0025] In some embodiments, after marking and naming human target points, motion trajectory data output by the optical motion capture system is received in real time. During the acquisition process, real-time prompts are given for any points that are missed, and the original motion trajectory data is saved after the acquisition is completed.

[0026] Specifically, optical reflective or luminescent markers are affixed to specific anatomical locations on the human body, such as joint rotation centers and limb bone ends. Each marker is then assigned a unique and semantically meaningful identifier in the software, such as right shoulder or left wrist. This process maps the light points in physical space to specific parts of the human kinematic model, ensuring accurate identification, tracking, and analysis of the movement of specific body parts during subsequent processing.

[0027] Furthermore, based on the named markers, more complex geometric elements can be constructed in real time using software functions: Midpoint: For example, selecting two markers, the left and right shoulders, allows the software to calculate and display their midpoint in real time. This midpoint can represent a key point on the trunk's center line. Line / Vector: Connecting two markers forms a line, such as the line from the hip to the shoulder, which can be used to calculate the trunk tilt angle. Plane: Selecting three non-collinear markers defines a plane, which can represent the trunk's coronal plane and serve as a reference for angle calculations. Show / Hide: Controlling the display or hiding of these constructive elements in the 3D view allows for focusing on key content amidst complex visual information.

[0028] Furthermore, by receiving motion capture data from optical motion capture systems such as Qualisys and OptiTrack in real time via SDK or API interface, and by receiving 3D coordinate data calculated from the motion capture camera array in real time streaming, continuous and complete motion trajectories can be captured without delay, thereby truly reflecting the dynamic process of motion and avoiding the loss of motion information due to sampling delay or interval.

[0029] By utilizing predefined geometric relationships, the system calculates the midpoint position and the angle between limbs in real time the instant each frame of data arrives. Operators can observe changes in key angles and data in real time during the experiment, allowing for immediate assessment of whether actions are performed correctly and data is valid, thus achieving online monitoring of the acquisition process's quality.

[0030] During data acquisition, the system continuously monitors the visibility of each marker. If a marker disappears from the field of view of most cameras due to obstruction, poor light reflection, or other reasons, preventing the system from calculating its position, a visual or audible alert is immediately issued to the operator. This ensures that the operator can promptly identify data acquisition problems during the experiment and take immediate measures such as adjusting the subject's posture, removing obstructions, or adjusting camera parameters. This maximizes the integrity and quality of the raw data from the source, reducing the workload and uncertainty of subsequent data repair.

[0031] Step S102: In the scene where the target 3D model is located, the real-time motion trajectory data is preprocessed to obtain preprocessed point cloud data.

[0032] The target 3D model is a custom model, such as a standard human body model, or an externally imported CAD format model, which enables this solution to perform both general ergonomic analysis and precise adaptability verification for specific products.

[0033] During preprocessing, point cloud data and the target 3D model are mapped to the same world coordinate system. Through coordinate transformation, the motion point cloud data representing a person and the 3D CAD model representing a machine or environment are precisely placed in the same virtual 3D space. For example, hand motion data is accurately placed in front of a virtual car steering wheel. This creates a human-machine-environment integrated digital analysis sandbox, providing a realistic context for subsequent calculations of interactive features such as the distance and angle between the hand and the steering wheel.

[0034] In some embodiments, the original motion trajectory data is converted into point cloud data, coordinate system transformation and data rotation are performed, interpolation algorithms are used to fill in the missing point intervals, and outlier points are removed.

[0035] Specifically, the set of three-dimensional coordinate points collected over a continuous time series is reconstructed in the computer into a discrete set of three-dimensional spatial points, i.e., a point cloud. Since the optical motion capture system has its own measurement coordinate system, and the target three-dimensional model also exists in its design coordinate system, this step unifies all motion data into a common world coordinate system consistent with the target scene through rotation and translation transformations.

[0036] Furthermore, for data gaps caused by temporary occlusion of marker points, an interpolation algorithm is used to intelligently estimate and fill in the missing points based on the valid data before and after, thus ensuring the continuity of the motion trajectory and preventing holes or distortions in the subsequently generated envelope due to data interruptions. Noise points that significantly deviate from the reasonable motion trajectory are also automatically identified and removed. This ensures that the fitted envelope has clear and accurate boundaries, unaffected by erroneous data.

[0037] The interpolation algorithm can be either a layered Poisson surface reconstruction technique or a point cloud projection algorithm.

[0038] In some embodiments, the interpolation algorithm is a hierarchical Poisson surface reconstruction technique. This technique calculates the convex hull of the point cloud and iteratively processes point clouds at different levels, then uses the Poisson reconstruction algorithm for surface interpolation. This achieves both density and smoothing of the point cloud data and supports interpolation parameter tuning. The hierarchical Poisson surface reconstruction technique is suitable for processing point cloud data with complex curved surfaces and internal structures, effectively preserving the topological structure and detailed features of the point cloud.

[0039] In other embodiments, the interpolation algorithm is a point cloud projection algorithm, which projects a 3D point cloud onto a 2D plane, calculates the convex hull of the projected points, and then performs interpolation along the convex hull boundary line to increase the point density of the point cloud edge contour. This interpolation algorithm is suitable for processing point cloud data with obvious edge features and can effectively smooth point cloud boundaries.

[0040] Step S103: Fit the point cloud data into one or more envelopes using a fitting algorithm.

[0041] The fitting algorithms include convex hull algorithm, alpha shapes algorithm, or alphashapes algorithm based on quadratic filling.

[0042] In some embodiments, the fitting algorithm is a convex hull algorithm, which achieves surface fitting by calculating the convex hull of the point cloud data, generating a minimal convex polyhedron that completely encloses all points, and supports tuning of the fitting parameters. The convex hull algorithm does not consider the point cloud normal vector information, but relies purely on the geometric positional relationship of the points for fitting. Therefore, the convex hull algorithm is simple and fast, and can generate a minimal convex polyhedron that completely encloses all points. It is suitable for processing point cloud data with relatively regular shapes and no internal details.

[0043] In other embodiments, the fitting algorithm is the alpha shapes algorithm. This algorithm adjusts the fineness of the fitted surface by controlling the alpha parameter. During the fitting process, the point cloud normal vectors are estimated to ensure they point outwards. It also includes non-manifold edge repair, non-manifold vertex repair, and hole filling functions. The alpha shapes algorithm adjusts the fineness of the fitted surface by controlling the alpha parameter, effectively preserving the geometric features and concave parts of the point cloud. The fitted mesh undergoes repair processing to ensure the absence of non-manifold edges and vertices, including two repair methods: vertex segmentation repair and hybrid repair. During implementation, the algorithm estimates the point cloud normal vectors by calculating the direction from each point to the centroid of the point cloud as its initial normal vector, ensuring that the normal vectors point outwards, which helps to more accurately represent the concavity and convexity features of the model. Furthermore, it includes non-manifold edge repair, non-manifold vertex repair, and automatic hole filling functions to ensure that the generated mesh is complete and closed.

[0044] In other embodiments, the fitting algorithm is an alpha shapes algorithm based on secondary filling. Initial surface reconstruction is performed using the first alpha parameter. On the reconstructed surface, Poisson disk sampling is used to generate uniformly distributed intermediate points. Secondary reconstruction is then performed using the second alpha parameter. Finally, mesh repair processing is performed, including non-manifold edge repair, non-manifold vertex repair, hole filling, and component cleanup. This method not only inherits the normal vector processing mechanism from the alpha shapes algorithm but also uses Poisson disk sampling technology to generate uniformly distributed intermediate points, optimizing the overall point density distribution and better handling uneven or missing point cloud regions. The Poisson disk sampling process considers the surface's geometric characteristics, including normal vector consistency and curvature changes, ensuring that the generated intermediate points better preserve the original surface features. The repair stage also includes steps such as non-manifold edge repair, non-manifold vertex repair, hole filling, and component cleanup, resulting in a higher quality final model. If holes exist in the fitted mesh, the alpha series parameters can be increased.

[0045] Furthermore, a complete test typically involves multiple repetitive action loops, such as having a subject repeatedly reach out to touch a target 10 times, or multiple different test scenarios. This application does not combine all data points from all actions to generate a single envelope, but rather performs independent fitting based on a single action or trial as the basic unit. For example, the data from 10 reach-outs is divided into 10 independent point cloud subsets, and then a fitting algorithm is run on each subset separately, resulting in 10 independent envelopes, each of which is a closed 3D mesh composed of vertices and triangular facets.

[0046] Step S104: Fit one or more envelopes into a single global envelope and calculate the global envelope features.

[0047] In some embodiments, spatial coordinate data of one or more envelopes are fused and fitted using statistical methods based on maximum, minimum, or average values ​​to generate a single closed overall envelope.

[0048] For example, using the maxima method, the extreme values ​​of the coordinates of all input envelopes in each of the X, Y, and Z dimensions of space are calculated, and the maximum range of all envelopes in these directions is taken. Then, based on these global maximum and minimum values, a new minimum convex envelope that can completely enclose all the original envelopes is reconstructed.

[0049] For example, the minimum value method can be used to analyze the internal core region of each envelope, or to calculate the spatial distribution of all envelope surface points, and then reconstruct the core region with the highest common overlap to find the spatial region commonly covered by all input envelopes.

[0050] For example, by using the average statistical method, the average spatial location and distribution density of all sampling points on the surface of the envelope are calculated by statistically analyzing the spatial distribution of all points. Then, a new envelope is generated by refitting the surface based on this average point cloud.

[0051] In addition, the overall envelope features include at least one of the following: the three-dimensional spatial extreme points of the overall envelope, the surface normal vector, the geometric dimensions, and the surface curvature distribution.

[0052] Step S105: Based on the overall envelope and its features, generate multiple sets of two-dimensional envelope curves by sectioning, and calculate the positional features between the overall envelope and the target three-dimensional model.

[0053] The location features include the shortest distance, average distance, angle between feature points and the target 3D model, and dihedral angle of the reference plane.

[0054] Specifically, a series of parallel cutting planes are used, for example, a horizontal plane every 50 millimeters along the vertical direction, or multiple sagittal planes along the front-back direction, to spatially cut the entire envelope. Each plane intersects the three-dimensional envelope, producing one or more closed two-dimensional contour curves.

[0055] Under a unified world coordinate system, spatial geometric calculations are performed on the overall envelope and the target 3D model to extract core quantitative relationship features. The shortest distance calculation employs an efficient spatial search algorithm to calculate the shortest Euclidean distance from all points on the envelope surface to the target model surface, and takes the global minimum. If the shortest distance is less than or equal to 0, it indicates penetration, which is unacceptable in the design; if the shortest distance is greater than 0, this value is the minimum safe clearance. The average distance is calculated by uniformly sampling N points on the envelope surface, calculating the distance from each sampling point to the model surface, and then taking the arithmetic mean. The average distance reflects the overall, average spatial density relationship between the envelope and the target model. A larger average distance may mean that the operator needs a larger overall reach; combined with the shortest distance analysis, it can determine whether the clearance distribution is uniform. The feature point pair angle is calculated by identifying a pair of closest points on the envelope surface and the target model surface respectively when calculating the shortest distance, obtaining the unit normal vectors of the surfaces containing these two points, and calculating the angle between them. For gripping and pressing operations, this angle reflects the alignment between the palm / fingertip surface and the control surface. A smaller angle indicates more contact, more direct force application, and greater operational comfort. For gaze analysis, it can be used to assess the deviation between the gaze direction and the display screen normal; a smaller deviation results in better visual perception. The dihedral angle of the reference plane involves higher-level geometric relationships. First, a characteristic plane representing the main extension direction of the envelope is defined or calculated, for example, the best-fit plane determined by principal component analysis, or the arm movement plane defined by key points of the shoulder, elbow, and wrist. Similarly, key reference planes, such as the ground, are defined on the target model. Then, the dihedral angle between these two planes is calculated to assess the macroscopic coordination between the overall movement direction and the device layout. For example, the dihedral angle between the arm movement plane and the center console tilt plane is calculated. If this angle is too large, it indicates that the operator needs to twist their forearm or torso to adapt to the device layout, which can easily lead to fatigue over long periods. This feature provides a quantitative basis for optimizing the overall spatial orientation and layout angles of the device.

[0056] In addition, other location features can be calculated as needed.

[0057] Step S106: Visualize and save the overall envelope features, multiple sets of two-dimensional envelope curves, and positional features.

[0058] For example, the entire envelope, the target 3D model, and the key positional relationships between them can be displayed simultaneously in the same window. Users can rotate and zoom to view it from any angle. The location of the cross-section of the 2D envelope curve and its generated contour map are displayed synchronously. Clicking on any cross-section automatically positions the main 3D view at that cross-section and highlights the distance and angle features at that point. Next to the view, all calculated quantitative features are clearly listed in a table or list format, such as the envelope volume, surface area, a list of minimum distances to each target point, and key angle values. The data is often linked to visualization elements; for example, clicking on the minimum distance between a row in the table and button A will highlight the corresponding nearest point in the 3D view.

[0059] from Figure 1 As can be seen from the method described, this application acquires human motion trajectories based on optical motion capture technology, ensuring the three-dimensionality, continuity, and objectivity of the data from the source, overcoming the subjective errors and dimensional limitations of traditional manual measurement methods. By placing the motion data in the target three-dimensional model scene for coordinate unification, point loss interpolation, and other preprocessing, and using a fitting algorithm to robustly reconstruct the discrete point cloud into a continuous and smooth envelope model, the final reachable domain has clear mathematical boundaries and quantitative characteristics, significantly improving the accuracy and reliability of the reachable domain expression. Furthermore, by calculating the precise distance and angle characteristics between the envelope and the target model, a leap from simple limb motion range measurement to quantitative evaluation of specific human-computer interaction performance is achieved, enabling the analysis results to be directly and quantitatively used to guide the ergonomic optimization of product design and spatial layout, enhancing the practical value and engineering guidance significance of the analysis results.

[0060] Figure 2 This diagram illustrates a system architecture diagram of a human reachability domain analysis system based on optical motion capture according to an embodiment of this application. Figure 2 It can be seen that it includes data acquisition module 1, data preprocessing module 2, data processing module 3, and data visualization module 4.

[0061] Data acquisition module 1: Used to collect real-time motion trajectory data of human body stickers; Data preprocessing module 2: used to preprocess the real-time motion trajectory data in the scene where the target 3D model is located, to obtain preprocessed point cloud data; Data processing module 3: Used to fit point cloud data into one or more envelopes using a fitting algorithm; to fit one or more envelopes into a single overall envelope and to calculate the features of the overall envelope; Data visualization module 4: Based on the overall envelope and its features, it generates multiple sets of two-dimensional envelope curves by sectioning, calculates the positional features between the overall envelope and the target three-dimensional model, and visualizes and saves the overall envelope features, multiple sets of two-dimensional envelope curves, and positional features.

[0062] In some embodiments, the data acquisition module 1 is specifically used to mark and name human target points, receive motion trajectory data output by the optical motion capture system in real time, provide real-time prompts for point loss during the acquisition process, and save the original motion trajectory data after the acquisition is completed.

[0063] In some embodiments, the data preprocessing module 2 is specifically used to convert the original motion trajectory data into point cloud data, perform coordinate system transformation and data rotation, use interpolation algorithms to fill in the missing point intervals, and remove abnormal points; the target 3D model is a custom model or an externally imported CAD format model, and during preprocessing, the point cloud data and the target 3D model are mapped to the same world coordinate system.

[0064] In some embodiments, the interpolation algorithm is a hierarchical Poisson surface reconstruction technique, which calculates the convex hull of the point cloud and iteratively processes point clouds at different levels, uses the Poisson reconstruction algorithm for surface interpolation, achieves densification and smoothing of point cloud data, and supports interpolation parameter optimization; or the interpolation algorithm is a point cloud projection algorithm, which projects the three-dimensional point cloud onto a two-dimensional plane, calculates the convex hull of the projected points, and then performs interpolation on the convex hull boundary line to increase the point density of the point cloud edge contour.

[0065] In some embodiments, the fitting algorithm is a convex hull algorithm, which achieves surface fitting by calculating the convex hull of the point cloud data, generating a minimum convex polyhedron that completely encloses all points, and supports fitting parameter tuning operations; or the fitting algorithm is an alpha shapes algorithm, which adjusts the fineness of the fitted surface by controlling the alpha parameter, estimates the point cloud normal vectors during the fitting process to ensure that the normal vectors point outward, and includes non-manifold edge repair, non-manifold vertex repair, and hole filling functions; or the fitting algorithm is an alpha shapes algorithm based on secondary filling, including: performing preliminary surface reconstruction using the first alpha parameter, generating uniformly distributed intermediate points on the reconstructed surface using Poisson disk sampling, performing secondary reconstruction using the second alpha parameter, and finally performing mesh repair processing, the repair processing including non-manifold edge repair, non-manifold vertex repair, hole filling, and component cleanup.

[0066] In some embodiments, the data processing module 3 is specifically used to fuse and fit the spatial coordinate data of one or more envelopes using statistical methods of maximum, minimum or average values ​​to generate a single closed overall envelope.

[0067] In some embodiments, the overall envelope features include the three-dimensional spatial extrema of the overall envelope, surface normal vectors, geometric dimensions, and surface curvature distribution.

[0068] In some embodiments, the positional features include the shortest distance, average distance, feature point pair angle, and reference plane dihedral angle between the overall envelope and the target 3D model.

[0069] It should be noted that any of the above-mentioned human reachability analysis systems based on optical motion capture can be implemented one-to-one with the aforementioned human reachability analysis methods based on optical motion capture, which will not be elaborated here.

[0070] Figure 3 This demonstrates the working mode of a human reachability domain analysis system based on optical motion capture: The user opens the experimental specimen to create a project, enters the data acquisition module to set relevant experimental parameters, selects motion capture software such as Qualisys and OptiTrack for integration, marks and names target points, and clicks "Acquire" to obtain real-time motion trajectory data of the human body's contact points. During acquisition, the user observes whether the data is normal; if points are lost, the software prompts a pop-up window to reselect points. After data acquisition is complete, the data is saved. The user then enters the data preprocessing module to import the acquired data. Users can customize models or import external CAD models according to their needs, setting parameters for operations such as coordinate axis rotation and data rotation. Interpolation can be performed on lost point intervals, outliers are deleted, and the data is exported. In the data preprocessing module, the point cloud data is processed using various interpolation and fitting algorithms to construct a high-precision envelope and calculate the fitting error. Next, multiple envelopes are statistically fitted into a single envelope, and envelope features are calculated and saved. In the data visualization module, the user imports the envelope and sets its attributes. Multiple sets of two-dimensional curves are obtained through sectioning, and features such as the envelope's extreme points and the distance and angle between the envelope and the model are calculated. Finally, the experimental software is closed.

[0071] This application acquires human motion trajectories using optical motion capture technology, ensuring the three-dimensionality, continuity, and objectivity of the data from the source, overcoming the subjective errors and dimensional limitations of traditional manual measurement methods. By placing the motion data in a target 3D model scene for coordinate unification, point loss interpolation, and other preprocessing, and using a fitting algorithm to robustly reconstruct the discrete point cloud into a continuous and smooth envelope model, the final reachable domain has clear mathematical boundaries and quantitative characteristics, significantly improving the accuracy and reliability of the reachable domain representation. Furthermore, by calculating the precise distance and angle characteristics between the envelope and the target model, a leap from simply measuring the range of limb movement to quantitatively evaluating specific human-computer interaction performance is achieved. This allows the analysis results to be directly and quantitatively used to guide the ergonomic optimization of product design and spatial layout, enhancing the practical value and engineering guidance significance of the analysis results.

[0072] Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 4 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0073] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0074] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0075] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a human reachability analysis system based on optical motion capture at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0076] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0077] This electronic device can execute the human reachability domain analysis method based on optical motion capture provided in several embodiments of this application, and implement it as a human reachability domain analysis system based on optical motion capture. Figure 2 The functions of the embodiments shown are not described in detail here.

[0078] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the human reachability analysis method based on optical motion capture provided in several embodiments of this application.

[0079] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0084] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0085] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0086] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for analyzing the human reachability domain based on optical motion capture, characterized in that, The method includes: Collect real-time motion trajectory data of human body stickers; In the scene where the target 3D model is located, the real-time motion trajectory data is preprocessed to obtain preprocessed point cloud data; The point cloud data is fitted into one or more envelopes using a fitting algorithm; The one or more envelopes are fitted into a single global envelope, and the features of the global envelope are calculated. Based on the overall envelope and its features, multiple sets of two-dimensional envelope curves are generated by sectioning, and the positional features between the overall envelope and the target three-dimensional model are calculated. The overall envelope features, multiple sets of two-dimensional envelope curves, and positional features are visualized and saved.

2. The method according to claim 1, characterized in that, The real-time motion trajectory data of the human body stickers collected includes: After marking and naming the human target points, the system receives motion trajectory data output by the optical motion capture system in real time. During the acquisition process, it provides real-time prompts for any points that are missed, and saves the original motion trajectory data after the acquisition is completed.

3. The method according to claim 1, characterized in that, The process of preprocessing the real-time motion trajectory data within the scene where the target 3D model is located to obtain preprocessed point cloud data includes: The original motion trajectory data is converted into point cloud data, coordinate system transformation and data rotation are performed, interpolation algorithm is used to fill in the missing point intervals, and outlier points are removed. The target 3D model is a custom model or an externally imported CAD format model. During preprocessing, the point cloud data and the target 3D model are mapped to the same world coordinate system.

4. The method according to claim 3, characterized in that, The interpolation algorithm is a hierarchical Poisson surface reconstruction technique. It calculates the convex hull of the point cloud and iteratively processes point clouds at different levels, then uses the Poisson reconstruction algorithm for surface interpolation to achieve both density and smoothing of the point cloud data; or The interpolation algorithm is a point cloud projection algorithm, which projects a three-dimensional point cloud onto a two-dimensional plane, calculates the convex hull of the projected points, and then performs interpolation on the boundary line of the convex hull to increase the point density of the edge contour of the point cloud.

5. The method according to claim 1, characterized in that, The fitting algorithm is a convex hull algorithm, which achieves surface fitting by calculating the convex hull of the point cloud data, generating the smallest convex polyhedron that encloses all points; or The fitting algorithm is the alpha shapes algorithm, which adjusts the fineness of the fitted surface by controlling the alpha parameter. During the fitting process, the normal vector of the point cloud is estimated to ensure that the normal vector points outward, and includes non-manifold edge repair, non-manifold vertex repair and small hole filling functions. or The fitting algorithm is an alpha shapes algorithm based on secondary filling, which includes: performing preliminary surface reconstruction using the first alpha parameter, generating uniformly distributed midpoints on the reconstructed surface using Poisson disk sampling, performing secondary reconstruction using the second alpha parameter, and performing mesh repair processing, which includes non-manifold edge repair, non-manifold vertex repair, hole filling, and component cleanup.

6. The method according to claim 1, characterized in that, The process of fitting one or more envelopes into a single overall envelope includes: By using statistical methods based on maximum, minimum, or average values, the spatial coordinate data of one or more envelopes are fused and fitted to generate a single, closed, overall envelope.

7. The method according to any one of claims 1-6, characterized in that, The overall envelope features include the three-dimensional spatial extrema of the overall envelope, surface normal vectors, geometric dimensions, and surface curvature distribution.

8. The method according to any one of claims 1-6, characterized in that, The positional features include the shortest distance, average distance, feature point angle, and dihedral angle between the overall envelope and the target 3D model.

9. A human reachability domain analysis system based on optical motion capture, characterized in that, The system includes: Data acquisition module: used to collect real-time motion trajectory data of human body stickers; Data preprocessing module: used to preprocess the real-time motion trajectory data in the scene where the target 3D model is located, to obtain preprocessed point cloud data; Data processing module: used to fit point cloud data into one or more envelopes using a fitting algorithm; to fit one or more envelopes into a single global envelope and to calculate the features of the global envelope; Data visualization module: Based on the overall envelope and its features, it generates multiple sets of two-dimensional envelope curves by sectioning, calculates the positional features between the overall envelope and the target three-dimensional model, and visualizes and saves the overall envelope features, multiple sets of two-dimensional envelope curves, and positional features.

10. An electronic device, comprising: processor; And a memory arranged to store computer-executable instructions, characterized in that, when executed, the executable instructions cause the processor to perform the steps of the human reachability analysis method based on optical motion capture as described in any one of claims 1-8.

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