Human model measurement typing method based on human three-dimensional reconstruction

Abstract

The application provides a human body model measurement classification method based on human body three-dimensional reconstruction, S1. Data acquisition, S2. Data preprocessing, S3. Reconstructed model S4. Model segmentation S5 body key data measurement, using a matching model measurement tool, measuring body key data; S6. Automatic body type classification, using clustering algorithm to cluster analysis on the extracted features. The three-dimensional scanning technology can obtain a large amount of point cloud data in a very short time, form a highly realistic three-dimensional model, the data acquisition accuracy is high, and the subtle features of the object can be captured. This greatly improves the efficiency of data acquisition, and at the same time ensures the accuracy of the data, and provides a solid foundation for subsequent body type classification.

Description

Technical Field

[0001] The present invention relates to the technical field of human body model measurement, and in particular to a human body model measurement and typing method based on three-dimensional reconstruction of the human body. Background Art

[0002] There are two main methods for calculating body shape:

[0003] 1.BMI (Body Mass Index):

[0004] This is a body shape indicator currently used in the medical field. BMI is calculated by dividing weight (kg) by height (m) squared.

[0005] According to the World Health Organization, a BMI between 18.5 and 24.9 is considered normal weight; between 25 and 29.9 is considered overweight; and 30 and above is considered obese. However, different countries and regions may adjust this standard based on specific circumstances.

[0006] It should be noted that although the BMI index is simple and easy to use, it cannot accurately reflect a person's body shape and health status. Especially for people with large muscle mass, the BMI value may be higher.

[0007] 2. Volume index method (t = w / (h*h)):

[0008] This is another body shape calculation method based on height and weight. Where t is body mass index, w is weight in kilograms, and h is height in meters.

[0009] According to the value of body mass index, the weight type can be determined: when t<18, it is underweight; when 18≤t<25, it is normal weight; when 25≤t<27, it is overweight; when t≥27, it is obese.

[0010] Compared with the BMI index, the body index method takes into account the square relationship between height and weight and may be more accurate in some cases. Summary of the Invention

[0011] The present invention aims to address the defects of the prior art and provide a human body model measurement and typing method for three-dimensional reconstruction of the human body.

[0012] The BMI index method and the body mass index method are both simple calculations based on height and weight. Although they can preliminarily judge a person's body shape, they cannot fully and accurately reflect a person's body shape and health status. Therefore, in medical evaluation, it is necessary to combine other indicators (such as waist circumference, body fat percentage, muscle mass, etc.) for comprehensive analysis. By further dividing the human body shape into H type, A type, T type, and X type,

[0013] 1) H type (Straight cylinder type)

[0014] Characteristics: Shoulders, waist, and hips are nearly in a straight line, with minimal difference between waist and hip circumference, presenting a straight overall body shape.

[0015] Identification criteria:

[0016] Waist-Hip Ratio (WHR) close to 1 (usually 0.8-1.0).

[0017] Shoulder width and hip width have little difference, and waist circumference is not significantly narrow.

[0018] BMI index may be in the normal range, but body fat percentage may be high, especially visceral fat.

[0019] 2) A type (Pear type)

[0020] Characteristics: Hips and thigh areas are relatively plump, waist is relatively thin, upper body is narrow. Fat is mainly distributed in the lower body.

[0021] Identification criteria:

[0022] Waist-Hip Ratio (WHR) less than 0.8.

[0023] Hip circumference is significantly larger than shoulder circumference, and waist circumference is relatively thin.

[0024] Body fat percentage is high, especially in the lower body (such as thighs, hip area).

[0025] More common in women, often evaluated using Waist-Hip Ratio and Waist-Height Ratio (WHtR).

[0026] 3) T type (Inverted triangle type)

[0027] Characteristics: Shoulders are wide, chest is relatively developed, waist is narrow, hips are small. Fat is mainly distributed in the upper body.

[0028] Identification criteria:

[0029] Shoulder-Hip Ratio greater than 1.

[0030] Upper body (especially shoulders and chest) circumference is significantly larger than lower body (hips and thighs).

[0031] More common in men, usually accompanied by lower body fat percentage and higher muscle mass, especially in the upper body.

[0032] 4) X type (Hourglass type)

[0033] Characteristics: Shoulders and hips are wide, waist is significantly thin, presenting a typical "hourglass" shape.

[0034] Identification criteria:

[0035] Waist-to-hip ratio (WHR) is usually around 0.7.

[0036] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0037] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0038] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0039] These body type classifications can more accurately assess and judge a person's body type and have the following advantages:

[0040] 1) Improved diagnostic accuracy: With clear body type classifications, doctors can more accurately determine a patient's body type, which helps to more precisely diagnose body type-related health issues. For example, an H-body type may be more susceptible to certain metabolic diseases, while an A-body type may be associated with an increased risk of cardiovascular disease.

[0041] 2) Personalized treatment: Different body types may respond differently to medications, treatments, and nutritional needs. By using body type classification, doctors can develop more personalized treatment plans for patients, thereby improving treatment effectiveness and patient satisfaction.

[0042] 3) Health Risk Assessment: Body type classification helps doctors more accurately assess patients' health risks. For example, an X-shaped body type is generally considered ideal, while an A-shaped body type may be associated with an increased risk of cardiovascular disease. By understanding a patient's body type, doctors can more accurately assess health risks and develop preventive measures.

[0043] 4) Health Education and Prevention: Body type classification can serve as the basis for health education and prevention strategies. Doctors can provide patients with personalized health advice and prevention recommendations based on the characteristics of different body types, helping them establish healthier lifestyles and habits.

[0044] 5) Research Promotion: Clear body type classifications can help advance medical research. By comparing the health differences and physiological characteristics of different body types, researchers can gain a deeper understanding of human physiology and disease mechanisms, making greater contributions to medical development.

[0045] This invention uses 3D scanning to collect human body shape data, reconstructs a 3D human body model, and uses the highly restored 3D model to automatically calculate human body shape (H-type, A-type, T-type, X-type). This solves the problem that traditional body shape calculation methods cannot fully reflect individual body shapes and enables rapid classification of human body shapes.

[0046] 1. Data Collection

[0047] Choose the right scanning device: Choose the right scanning device based on your needs. For example, if you need to scan a surface, you can choose a laser scanner or a structured light scanner; if you need to scan internal structures, you can choose a CT scanner or an MRI.

[0048] Data Collection: Ensure the subject is relatively still and centered within the scanning device to obtain accurate scan data. During the acquisition process, the subject can be scanned at different angles or positions as needed to obtain more comprehensive data.

[0049] 2. Data Preprocessing

[0050] Denoising: Remove noise from scanned data through filtering and other methods to improve the accuracy of subsequent processing.

[0051] Registration: If you are using multiple scanning devices or multiple scans, you need to register the different datasets to ensure they are in the same coordinate system.

[0052] Data repair: Repair missing or damaged parts of the data. Methods such as interpolation can be used to repair data.

[0053] 3. Reconstruct the model

[0054] Select a reconstruction algorithm: Select an appropriate reconstruction algorithm based on the data type and requirements. For example, for point cloud data, you can use a surface reconstruction algorithm such as the Marching Cubes algorithm; for volume data, you can use a voxelization reconstruction algorithm.

[0055] Model reconstruction: Use the selected algorithm to convert the pre-processed data into a 3D model. This may involve steps such as mesh generation, surface fitting, and voxelization.

[0056] Point cloud data is a type of data commonly generated by 3D scanning and perception devices, and usually does not contain clear connection relationships. In order to build a 3D model, the common process of point cloud data reconstruction is:

[0057] 1. Mesh generation: For point cloud data or volume data, the core of the reconstruction process is to generate polygonal meshes or triangular patches.

[0058] Using the Marching Cubes algorithm, the specific process is as follows:

[0059] 1. Divide the three-dimensional space into a series of regular cubes and mark the vertices of each cube as "inside" or "outside", depending on where the data point is located.

[0060] 2. Within each cube, determine which vertices need to be connected by looking up the edge table.

[0061] 3. Generate triangular patches and connect them to form a mesh. These triangular patches will eventually form the surface of the entire 3D model.

[0062] 2. Surface fitting: For data that requires a smooth surface, the Poisson reconstruction algorithm is used. The basic process is:

[0063] 1. Compute the gradient field and normal vector of the point cloud.

[0064] 2. Through global fitting, these gradient field information are used to generate smooth surfaces.

[0065] 3. Generate the final triangular patch to represent the model surface.

[0066] 4. Model segmentation (optional)

[0067] If surface area and volume calculations are required for specific areas, the model can be segmented. This can be done manually or using an automated segmentation algorithm.

[0068] 1) Manual segmentation

[0069] Manual segmentation is a segmentation method that relies on manual operation and is usually performed by professional technicians using 3D modeling software (such as Blender, MeshLab, etc.).

[0070] step:

[0071] Import the complete 3D model into the modeling software.

[0072] Use the "segmentation tool" in the software to manually select the area to be segmented (such as the head, torso, limbs, etc.).

[0073] Isolate the selected area from the overall model as a separate model part.

[0074] Surface area and volume calculations are performed separately for each subregion.

[0075] 2) Automatic segmentation algorithm

[0076] Automatic segmentation algorithms use computer programs to automatically segment models based on specific rules or conditions, reducing manual operations and improving efficiency. Common automatic segmentation algorithms include grid-based segmentation, volume-based segmentation, and morphology-based segmentation.

[0077] Based on Mesh Segmentation algorithm:

[0078] Mesh preprocessing: After importing the model, preprocess its mesh, such as removing redundant points and mesh smoothing.

[0079] Cutting plane determination: Based on the topological structure of the model, the cutting plane is automatically determined by curvature changes, geometric features, etc. For example, the joints of the body (shoulders, elbows, knees) are usually natural segmentation points.

[0080] Region segmentation: Divide the model into multiple sub-regions according to the cutting plane, such as the head, torso, limbs, etc.

[0081] Data extraction: Calculate the volume and surface area of ​​each subregion.

[0082] Voxel-based Segmentation:

[0083] Voxelization: Voxelize the model and convert it into three-dimensional data consisting of a series of regular small cubes (voxels).

[0084] Region growing algorithm: By selecting a seed point, the algorithm expands to neighboring voxels to form a segmented region. For example, you can start region growing from the shoulder or knee joint.

[0085] Segmentation complete: Now that the different body regions have been segmented, the surface area and volume of each region can be measured.

[0086] Based on morphological segmentation algorithm:

[0087] Edge detection: Edge detection is performed through the geometric contours of the model (such as the curvature of the surface) to identify the key points of the model, such as shoulders, waist, knees, etc.

[0088] Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area.

[0089] Region division: Divide the model into head, torso, limbs and other parts.

[0090] Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

[0091] 5. Measure key body data using the matching model measurement tools.

[0092] After the model is segmented, use the accompanying measurement tools to measure key body data (such as surface area, volume, length, circumference, etc.). Commonly used measurement tools include:

[0093] Surface area and volume measurements:

[0094] Perform surface area and volume measurements on each segmented region using built-in tools in 3D modeling software (such as the Measure Tool add-on for Blender) or by writing custom scripts.

[0095] The surface area and volume of closed surfaces can be calculated using the Gauss-Bonnet formula or directly by integration.

[0096] Length and girth measurements:

[0097] Use the model's geometric properties to select key points and segments to calculate the length and girth of different body parts. For example, you can measure the length from shoulder to wrist or calculate the waist circumference.

[0098] Calculate precise length and girth data by setting a fixed measurement path or using an algorithm to find the shortest path along a surface.

[0099] 6. Automatic body type classification

[0100] Clustering algorithms are used to perform cluster analysis on the extracted features. Clustering algorithms include K-means clustering, hierarchical clustering, and density clustering. These algorithms can divide data points into different clusters or categories based on their similarities. During the clustering process, appropriate clustering parameters (such as the number of clusters, similarity measurement method, etc.) need to be selected to achieve the best clustering effect. The clustering results are evaluated to determine the accuracy and reliability of the typing. In addition, the clustering results are displayed through visualization techniques (such as scatter plots, heat maps, etc.) to more intuitively understand the differences between different body types.

[0101] 1.H type (straight type)

[0102] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0103] Identification criteria:

[0104] Waist-to-hip ratio (WHR) is close to 1 (usually 0.8-1.0).

[0105] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0106] Your BMI may be within the normal range, but your body fat percentage may be high, especially visceral fat.

[0107] 2. Type A (pear-shaped)

[0108] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0109] Identification criteria:

[0110] Waist-to-hip ratio (WHR) less than 0.8.

[0111] The hips are significantly larger than the shoulders, and the waist is thinner.

[0112] The body fat percentage is high, especially in the lower body (such as the thighs and buttocks).

[0113] It is more common in women and is often assessed by waist-to-hip ratio and waist-to-height ratio (WHtR).

[0114] 3. T-type (inverted triangle)

[0115] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0116] Identification criteria:

[0117] Shoulder-to-hip ratio is greater than 1.

[0118] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0119] It is more common in men and is usually accompanied by lower body fat percentage and higher muscle mass, especially in the upper body.

[0120] 4. X-shaped (hourglass-shaped)

[0121] Characteristics: Broad shoulders and hips, with a noticeably narrow waist, creating a classic "hourglass" shape.

[0122] Identification criteria:

[0123] Waist-to-hip ratio (WHR) is usually around 0.7.

[0124] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0125] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0126] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0127] 7. Visual display

[0128] Use 3D rendering technology to visualize the results for easier understanding and sharing. Use 3D rendering software or libraries to visualize the distribution of surface area and volume, and generate reports or animations.

[0129] A computer-readable medium storing software, wherein the software includes instructions executable by one or more computers, wherein the instructions, when executed, cause the one or more computers to perform operations, wherein the operations include the process of the above-mentioned system.

[0130] A computer system comprising:

[0131] one or more processors;

[0132] The memory stores operable instructions, which, when executed by the one or more processors, enable the one or more processors to perform operations, wherein the operations include the process of the above-mentioned system.

[0133] Compared with the prior art, the advantages of the present invention are:

[0134] 1. High Precision and Efficiency: 3D scanning technology can acquire large amounts of point cloud data in a very short time, creating highly realistic 3D models. Its high data acquisition accuracy allows it to capture even the subtlest features of an object. This significantly improves data acquisition efficiency while ensuring data accuracy, providing a solid foundation for subsequent body type classification.

[0135] 2. Non-contact measurement: 3D scanning technology uses non-contact measurement, eliminating the need for physical contact with the target object. Therefore, measurements can be taken without damaging or contaminating the object. This avoids errors caused by human factors and improves measurement accuracy and reliability.

[0136] 3. Strong adaptability: 3D scanning technology can operate in a variety of environmental conditions, including indoors, outdoors, and in dark environments, making it widely applicable in multiple fields. In human body typing, this adaptability enables the technology to cope with various complex environments and ensure smooth data collection.

[0137] 4. Intuitiveness and Diverse Results: The collected point cloud data contains not only spatial information but also color information and reflectivity values, enabling a realistic reproduction of the object scene. In body type classification, this rich information provides a more comprehensive understanding of the body's morphology and structure, improving classification accuracy. Furthermore, a single measurement can generate multiple outputs, eliminating the need for repeated measurements and improving work efficiency.

[0138] 5. High degree of automation: By combining advanced computer vision and machine learning algorithms, 3D scan data can be automatically processed and analyzed, allowing for automatic body type classification. This highly automated processing significantly reduces the need for human intervention and improves the efficiency and accuracy of body type classification.

[0139] 6. Personalized services: Body type classification based on 3D scanning data can provide personalized services based on an individual's specific morphological structure. For example, in the clothing industry, tailored clothing can be created for different body types; in the fitness industry, personalized fitness plans and recommendations can be provided for different body types. BRIEF DESCRIPTION OF THE DRAWINGS

[0140] Figure 1 It is the data collection interface;

[0141] Figure 2 Rebuild the interface for the data;

[0142] Figure 3 Split the interface for the model;

[0143] Figure 4 It is the measurement typing interface. DETAILED DESCRIPTION

[0144] like Figures 1 to 4 As shown, a human body model measurement and classification method for three-dimensional reconstruction of the human body.

[0145] The BMI index method and the body mass index method are both simple calculations based on height and weight. Although they can preliminarily judge a person's body shape, they cannot fully and accurately reflect a person's body shape and health status. Therefore, in medical evaluation, it is necessary to combine other indicators (such as waist circumference, body fat percentage, muscle mass, etc.) for comprehensive analysis. By further dividing the human body shape into H type, A type, T type, and X type,

[0146] 1) H type (straight type)

[0147] Features: The shoulders, waist, and hips are almost on the same straight line, the difference between waist and hip circumference is small, and the overall body shape is straight.

[0148] Identification criteria:

[0149] Waist-to-hip ratio (WHR) is close to 1 (usually 0.8-1.0).

[0150] There is little difference in shoulder and hip width, and the waist is obviously not narrow.

[0151] Your BMI may be within the normal range, but your body fat percentage may be high, especially visceral fat.

[0152] 2) Type A (pear-shaped)

[0153] Characteristics: Fuller hips and thighs, relatively thin waist, narrow upper body. Fat is mainly distributed in the lower body.

[0154] Identification criteria:

[0155] Waist-to-hip ratio (WHR) less than 0.8.

[0156] The hips are significantly larger than the shoulders, and the waist is thinner.

[0157] The body fat percentage is high, especially in the lower body (such as the thighs and buttocks).

[0158] It is more common in women and is often assessed by waist-to-hip ratio and waist-to-height ratio (WHtR).

[0159] 3) T-type (inverted triangle)

[0160] Characteristics: Broad shoulders, well-developed chest, narrow waist, small hips. Fat is mainly distributed in the upper body.

[0161] Identification criteria:

[0162] Shoulder-to-hip ratio is greater than 1.

[0163] The circumference of the upper body (especially the shoulders and chest) is significantly larger than that of the lower body (hips and thighs).

[0164] It is more common in men and is usually accompanied by lower body fat percentage and higher muscle mass, especially in the upper body.

[0165] 4) X-shaped (hourglass-shaped)

[0166] Characteristics: Broad shoulders and hips, with a noticeably narrow waist, creating a classic "hourglass" shape.

[0167] Identification criteria:

[0168] Waist-to-hip ratio (WHR) is usually around 0.7.

[0169] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0170] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0171] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0172] These body type classifications can more accurately assess and judge a person's body type and have the following advantages:

[0173] 1) Improved diagnostic accuracy: With clear body type classifications, doctors can more accurately determine a patient's body type, which helps to more precisely diagnose body type-related health issues. For example, an H-body type may be more susceptible to certain metabolic diseases, while an A-body type may be associated with an increased risk of cardiovascular disease.

[0174] 2) Personalized treatment: Different body types may respond differently to medications, treatments, and nutritional needs. By using body type classification, doctors can develop more personalized treatment plans for patients, thereby improving treatment effectiveness and patient satisfaction.

[0175] 3) Health Risk Assessment: Body type classification helps doctors more accurately assess patients' health risks. For example, an X-shaped body type is generally considered ideal, while an A-shaped body type may be associated with an increased risk of cardiovascular disease. By understanding a patient's body type, doctors can more accurately assess health risks and develop preventive measures.

[0176] 4) Health Education and Prevention: Body type classification can serve as the basis for health education and prevention strategies. Doctors can provide patients with personalized health advice and prevention recommendations based on the characteristics of different body types, helping them establish healthier lifestyles and habits.

[0177] 5) Research Promotion: Clear body type classifications can help advance medical research. By comparing the health differences and physiological characteristics of different body types, researchers can gain a deeper understanding of human physiology and disease mechanisms, making greater contributions to medical development.

[0178] This invention uses 3D scanning to collect human body shape data, reconstructs a 3D human body model, and uses the highly restored 3D model to automatically calculate human body shape (H-type, A-type, T-type, X-type). This solves the problem that traditional body shape calculation methods cannot fully reflect individual body shapes and enables rapid classification of human body shapes.

[0179] 1. Data Collection

[0180] Choose the right scanning device: Choose the right scanning device based on your needs. For example, if you need to scan a surface, you can choose a laser scanner or a structured light scanner; if you need to scan internal structures, you can choose a CT scanner or an MRI.

[0181] Data Collection: Ensure the subject is relatively still and centered within the scanning device to obtain accurate scan data. During the acquisition process, the subject can be scanned at different angles or positions as needed to obtain more comprehensive data.

[0182] 2. Data Preprocessing

[0183] Denoising: Remove noise from scanned data through filtering and other methods to improve the accuracy of subsequent processing.

[0184] Registration: If you are using multiple scanning devices or multiple scans, you need to register the different datasets to ensure they are in the same coordinate system.

[0185] Data repair: Repair missing or damaged parts of the data. Methods such as interpolation can be used to repair data.

[0186] 3. Reconstruct the model

[0187] Select reconstruction algorithm: Choose the appropriate reconstruction algorithm based on the data type and requirements. For example, for point cloud data, surface reconstruction algorithms such as Marching Cubes can be used; for volume data, voxelization reconstruction algorithms can be used.

[0188] Model reconstruction: Convert preprocessed data into a three-dimensional model using the selected algorithm. This may involve steps such as mesh generation, surface fitting, voxelization, etc.

[0189] Point cloud data is a common data generated by three-dimensional scanning and perception devices, usually without explicit connection relationships. In order to construct a three-dimensional model, the common process of point cloud data reconstruction is as follows:

[0190] I. Mesh generation: For point cloud data or volume data, the core of the reconstruction process is to generate a polygon mesh or triangular patch.

[0191] Using the Marching Cubes algorithm, the specific process is as follows:

[0192] 1. Divide the three-dimensional space into a series of regular cubes (Cube), and mark the vertices of each cube as "inside" or "outside", depending on the position of the data points.

[0193] 2. Within each cube, find the boundary table to determine which vertices need to be connected.

[0194] 3. Generate triangular patches and connect them to form a mesh. These triangular patches will eventually make up the surface of the entire three-dimensional model.

[0195] II. Surface fitting: For data that requires smooth surfaces, use the Poisson reconstruction algorithm. The basic process is as follows:

[0196] 1. Calculate the gradient field and normal vector for the point cloud.

[0197] 2. Use this gradient field information to generate smooth surfaces through global fitting.

[0198] 3. Generate the final triangular patches to represent the model surface.

[0199] 4. Model segmentation (optional)

[0200] If you need to calculate the surface area and volume of a specific part, you can perform model segmentation. This can be done through manual segmentation or automatic segmentation algorithms.

[0201] 1) Manual segmentation

[0202] Manual segmentation is a segmentation method that relies on manual operation, usually performed by professional technicians using three-dimensional modeling software (such as Blender, MeshLab, etc.).

[0203] step:

[0204] Import the complete 3D model into the modeling software.

[0205] Use the "segmentation tool" in the software to manually select the area to be segmented (such as the head, torso, limbs, etc.).

[0206] Isolate the selected area from the overall model as a separate model part.

[0207] Surface area and volume calculations are performed separately for each subregion.

[0208] 2) Automatic segmentation algorithm

[0209] Automatic segmentation algorithms use computer programs to automatically segment models based on specific rules or conditions, reducing manual operations and improving efficiency. Common automatic segmentation algorithms include grid-based segmentation, volume-based segmentation, and morphology-based segmentation.

[0210] Based on Mesh Segmentation algorithm:

[0211] Mesh preprocessing: After importing the model, preprocess its mesh, such as removing redundant points and mesh smoothing.

[0212] Cutting plane determination: Based on the topological structure of the model, the cutting plane is automatically determined by curvature changes, geometric features, etc. For example, the joints of the body (shoulders, elbows, knees) are usually natural segmentation points.

[0213] Region segmentation: Divide the model into multiple sub-regions according to the cutting plane, such as the head, torso, limbs, etc.

[0214] Data extraction: Calculate the volume and surface area of ​​each subregion.

[0215] Voxel-based Segmentation:

[0216] Voxelization: Voxelize the model and convert it into three-dimensional data consisting of a series of regular small cubes (voxels).

[0217] Region growing algorithm: By selecting a seed point, the algorithm expands to neighboring voxels to form a segmented region. For example, you can start region growing from the shoulder or knee joint.

[0218] Segmentation complete: Now that the different body regions have been segmented, the surface area and volume of each region can be measured.

[0219] Based on morphological segmentation algorithm:

[0220] Edge detection: Edge detection is performed through the geometric contours of the model (such as the curvature of the surface) to identify the key points of the model, such as shoulders, waist, knees, etc.

[0221] Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area.

[0222] Region division: Divide the model into head, torso, limbs and other parts.

[0223] Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

[0224] 5. Measure key body data using the matching model measurement tools.

[0225] After the model is segmented, use the accompanying measurement tools to measure key body data (such as surface area, volume, length, circumference, etc.). Commonly used measurement tools include:

[0226] Surface area and volume measurements:

[0227] Perform surface area and volume measurements on each segmented region using built-in tools in 3D modeling software (such as the Measure Tool add-on for Blender) or by writing custom scripts.

[0228] The surface area and volume of closed surfaces can be calculated using the Gauss-Bonnet formula or directly by integration.

[0229] Length and girth measurements:

[0230] Use the model's geometric properties to select key points and segments to calculate the length and girth of different body parts. For example, you can measure the length from shoulder to wrist or calculate the waist circumference.

[0231] Calculate precise length and girth data by setting a fixed measurement path or using an algorithm to find the shortest path along a surface.

[0232] 6. Automatic body type classification

[0233] Clustering algorithms are used to perform cluster analysis on the extracted features. Clustering algorithms include K-means clustering, hierarchical clustering, and density clustering. These algorithms can divide data points into different clusters or categories based on their similarities. During the clustering process, appropriate clustering parameters (such as the number of clusters, similarity measurement method, etc.) need to be selected to achieve the best clustering effect. The clustering results are evaluated to determine the accuracy and reliability of the typing. In addition, the clustering results are displayed through visualization techniques (such as scatter plots, heat maps, etc.) to more intuitively understand the differences between different body types.

[0234] 1. H-type (Straight Cylinder)

[0235] Characteristics: Shoulders, waist, and hips are nearly in a straight line, with minimal difference between waist and hip circumference. The body appears linear overall.

[0236] Identification Criteria:

[0237] Waist-Hip Ratio (WHR) is close to 1 (usually 0.8-1.0).

[0238] Shoulder width and hip width are not significantly different, and the waist is not significantly narrow.

[0239] BMI index may be within the normal range, but body fat percentage may be high, especially visceral fat.

[0240] 2. A-type (Pear Shape)

[0241] Characteristics: Hips and thigh areas are relatively full, waist is relatively thin, and upper body is narrow. Fat is mainly distributed in the lower body.

[0242] Identification Criteria:

[0243] Waist-Hip Ratio (WHR) is less than 0.8.

[0244] Hip circumference is significantly larger than shoulder circumference, and waist circumference is relatively thin.

[0245] Body fat percentage is high, especially in the lower body (such as thighs and hip areas).

[0246] More common in women, often evaluated using Waist-Hip Ratio and Waist-Height Ratio (WHtR).

[0247] 3. T-type (Inverted Triangle)

[0248] Characteristics: Shoulders are wide, chest is relatively developed, waist is narrow, and hips are small. Fat is mainly distributed in the upper body.

[0249] Identification Criteria:

[0250] Shoulder-Hip Ratio is greater than 1.

[0251] Upper body (especially shoulders and chest) circumference is significantly larger than lower body (hips and thighs).

[0252] More common in men, often accompanied by lower body fat percentage and higher muscle mass, especially in the upper body.

[0253] 4. X-type (Hourglass Shape)

[0254] Characteristics: Broad shoulders and hips, with a noticeably narrow waist, creating a classic "hourglass" shape.

[0255] Identification criteria:

[0256] Waist-to-hip ratio (WHR) is usually around 0.7.

[0257] The waist circumference is significantly smaller than the shoulder circumference and hip circumference.

[0258] The body fat percentage is moderate or slightly high, and the fat is evenly distributed.

[0259] It is common in women and is usually determined by the ratio of waist to hip circumference.

[0260] 7. Visual display

[0261] Use 3D rendering technology to visualize the results for easier understanding and sharing. Use 3D rendering software or libraries to visualize the distribution of surface area and volume, and generate reports or animations.

[0262] A computer-readable medium storing software, wherein the software includes instructions executable by one or more computers, wherein the instructions, when executed, cause the one or more computers to perform operations, wherein the operations include the process of the above-mentioned system.

[0263] A computer system comprising:

[0264] one or more processors;

[0265] The memory stores operable instructions, which, when executed by the one or more processors, enable the one or more processors to perform operations, wherein the operations include the process of the above-mentioned system.

[0266] In this embodiment,

[0267] 1. High Precision and Efficiency: 3D scanning technology can acquire large amounts of point cloud data in a very short time, creating highly realistic 3D models. Its high data acquisition accuracy allows it to capture even the subtlest features of an object. This significantly improves data acquisition efficiency while ensuring data accuracy, providing a solid foundation for subsequent body type classification.

[0268] 2. Non-contact measurement: 3D scanning technology uses non-contact measurement, eliminating the need for physical contact with the target object. Therefore, measurements can be taken without damaging or contaminating the object. This avoids errors caused by human factors and improves measurement accuracy and reliability.

[0269] 3. Strong adaptability: 3D scanning technology can operate in a variety of environmental conditions, including indoors, outdoors, and in dark environments, making it widely applicable in multiple fields. In human body typing, this adaptability enables the technology to cope with various complex environments and ensure smooth data collection.

[0270] 4. Intuitiveness and Diverse Results: The collected point cloud data contains not only spatial information but also color information and reflectivity values, enabling a realistic reproduction of the object scene. In body type classification, this rich information provides a more comprehensive understanding of the body's morphology and structure, improving classification accuracy. Furthermore, a single measurement can generate multiple outputs, eliminating the need for repeated measurements and improving work efficiency.

[0271] 5. High degree of automation: By combining advanced computer vision and machine learning algorithms, 3D scan data can be automatically processed and analyzed, allowing for automatic body type classification. This highly automated processing significantly reduces the need for human intervention and improves the efficiency and accuracy of body type classification.

[0272] 6. Personalized services: Body type classification based on 3D scanning data can provide personalized services based on an individual's specific morphological structure. For example, in the clothing industry, tailored clothing can be tailored to suit different body types; in the fitness industry, personalized fitness plans and recommendations can be provided for different body types.

[0273] While the present invention has been disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for measuring and typing a human body model based on three-dimensional reconstruction of the human body, characterized by: The following steps are involved: S1. Data Collection Data collection: Ensure that the subject is relatively still and placed in the center of the scanning device. During the data collection process, scan the subject at different angles or positions as needed; S2. Data Preprocessing Denoising: Remove noise from scanned data through filtering to improve the accuracy of subsequent processing; Registration: When using multiple scanning devices or multiple scans, different data sets need to be registered to ensure that they are in the same coordinate system; Data repair: Repair missing or damaged parts of the data and use interpolation to repair the data; S3. Reconstructing the model Select reconstruction algorithm: Select an appropriate reconstruction algorithm based on the data type and requirements; Model reconstruction: converting pre-processed data into a three-dimensional model using the selected algorithm; Building a 3D model requires point cloud data reconstruction. The specific method is as follows: S3.

1. Grid generation: S3.

11. Divide the three-dimensional space into a series of regular cubes and label the vertices of each cube as "inside" or "outside" depending on the location of the data point. S3.

12. Within each cube, determine which vertices need to be connected by looking up the edge table; S3.

13. Generate triangular facets and connect them to form a mesh. These triangular facets will eventually form the surface of the entire 3D model. S3.

2. Surface fitting: For data that requires a smooth surface, use the Poisson reconstruction algorithm: S3.

21. Compute the gradient field and normal vector of the point cloud; S3.

22. Use this gradient field information to generate a smooth surface through global fitting; S3.

23. Generate the final triangular patch to represent the model surface; S4. Model Segmentation Calculate the surface area and volume of specific parts and perform model segmentation, which can be completed by manual segmentation or automatic segmentation algorithm: S5. Key body measurements: Use the included model measurement tool to measure key body measurements, including surface area, volume, length, and circumference. S6. Automatic body type classification, including H-type, A-type, T-type, and X-type; automatically calculate body type-related data based on waist-to-hip ratio, shoulder width, hip width, waist circumference, shoulder circumference, hip circumference, height, waist-to-height ratio, and shoulder-to-hip ratio, and perform cluster analysis on the relevant data using a clustering algorithm.

2. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, wherein: The manual segmentation method in S4. is as follows: Import the complete 3D model into the modeling software; use the "segmentation tool" in the software to manually select the area to be segmented; the selected area is separated from the overall model as a separate model part; Surface area and volume calculations are performed separately for each subregion.

3. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, wherein: The automatic segmentation algorithm method in S4. is as follows: Based on Mesh Segmentation algorithm: Mesh preprocessing: After importing the model, preprocess its mesh. Cutting plane determination: Based on the topological structure of the model, the cutting plane is automatically determined by curvature changes and geometric features; Region segmentation: Divide the model into multiple sub-regions according to the cutting plane. Data extraction: Calculate the volume and surface area of ​​each subregion; Or based on volume segmentation algorithm Voxel-based Segmentation: Voxelization: convert the model into three-dimensional data consisting of a series of regular small cubes; Region growing algorithm: By selecting seed points, the region growing algorithm is used to expand to adjacent voxels to form a segmentation region; Segmentation is complete: the different body regions are segmented, and the surface area and volume of each region can be measured; Or based on morphological segmentation algorithm: Edge detection: perform edge detection based on the geometric contours of the model to identify the key points of the model; Morphological operations: Use morphological operations such as dilation and erosion to process the model and clearly segment each area; Region segmentation: divide the model into head, torso, and limbs; Surface area and volume calculations: Further surface area and volume calculations are performed on each area using specialized measurement algorithms.

4. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, wherein: After the model is segmented, use the accompanying measurement tools to measure the key data of the body: Surface area and volume measurements: Perform surface area and volume measurements on each segmented region using built-in tools in the 3D modeling software or by writing custom scripts. Calculate the surface area and volume of closed surfaces using the Gauss-Bonet formula or directly by integration; Length and girth measurements: Use the model's geometric properties to select key points and line segments to calculate the length and girth of different body parts. Calculate accurate length and girth data by setting a fixed measurement path or using algorithms to find the shortest path along a surface.

5. The method for measuring and typing a human body model based on three-dimensional reconstruction of the human body according to claim 1, wherein: The clustering algorithm divides data points into different clusters or categories based on their similarities. During the clustering process, it is necessary to select appropriate clustering parameters to obtain the best clustering effect and evaluate the clustering results.

6. A computer-readable medium storing software, the software comprising instructions executable by one or more computers, wherein the execution of the instructions causes the one or more computers to perform operations, the operations comprising the process of the human body model measurement and typing method according to claims 1 to 5.

7. A computer system comprising: one or more processors; A memory stores operable instructions, wherein when the instructions are executed by the one or more processors, the one or more processors are caused to perform operations, wherein the operations include the processes of the human body model measurement and typing method according to claims 1 to 5.

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