95 results about "Human skeleton" patented technology

The invention provides a radar-based human skeleton estimation method, system and product, and the method comprises the steps: carrying out the target detection of collected radar echo data, and generating a target four-dimensional point cloud containing distance information, speed information and angle information based on a target detection result; dividing the target four-dimensional point cloud into a plurality of human body topology point cloud blocks based on the velocity direction similarity of each point in the target four-dimensional point cloud; and inputting the human body topology point cloud block into a human body skeleton estimation network for human body skeleton estimation to obtain a human body skeleton estimation result. According to the human skeleton estimation method provided by the invention, human topology priori can be constructed from sparse millimeter wave radar point clouds, point cloud block features sensed by a structure are extracted, the modeling capability of a model for the spatial relationship of key parts of a human body is enhanced, diversified and natural daily human behaviors in a non-inductive monitoring scene can be adapted, and the human skeleton estimation accuracy is improved. The human body skeleton estimation with higher generalization ability is realized, and the accuracy and robustness of human body posture prediction can be effectively improved.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The application discloses a human abnormal behavior detection method combining appearance texture and motion skeleton, and belongs to the field of computer vision; first, an original video is divided at equal intervals, and human appearance key regions in each frame and skeleton key points of each human body are extracted respectively; global motion trajectories of all human bodies are calculated; then, based on STGAT, trajectories of skeleton key points of each human body in a future frame are predicted; for each human body, predicted skeleton key points are converted into dense flow heat maps as guidance information by using DFE, and the guidance information is input into CGAN to generate human appearance key regions corresponding to skeleton key point postures; in addition, pixel-by-pixel analysis is used to eliminate background deviation of the appearance key regions to improve accuracy; finally, skeleton key point prediction values and appearance key region generation values of each human body are calculated with corresponding label values to obtain error, and the error is weighted and summed to obtain an abnormal score; the application effectively reduces the false alarm rate and realizes rapid and effective video anomaly detection.

A human whole body action real-time reconstruction method and system based on three-point sensing signals, according to the collected head and double-hand three-point sensor signals, a pre-estimated waist joint and root joint are generated through a waist joint and root joint pre-estimation algorithm; the pre-estimated waist joint and root joint are combined with a human skeleton model, and an upper body reconstruction result is generated through a reverse motion algorithm; in addition, a lower body reconstruction network based on BiGRU is used to generate a lower body reconstruction result for an input action feature sequence, and a double-threshold-based action post-processing technology is used to further stabilize the lower body action, and finally the whole body action is obtained by merging and reconstructing. The application uses only the sensing signals of three body parts of the head and the hands to pre-estimate the waist joint and the root joint, increases the number of known joints, more effectively reconstructs the upper body action and the lower body action at the same time, and significantly improves the reconstruction effect of the lower body action.

The invention provides a human skeleton behavior recognition method based on adaptive graph convolution and self-attention, and relates to the technical field of behavior recognition, and the method comprises the following steps: processing an NTU RGB + D sequence containing K types of human skeleton behaviors; constructing a spatial relationship modeling module; constructing a multi-scale time convolution module; and constructing a hybrid network. According to the technical scheme, the problems that in the prior art, an identification model is poor in robustness to complex actions and non-standard attitudes, and the identification precision is low due to insufficient spatial-temporal feature extraction are solved.

The application discloses a human body skeleton generation method for behavior recognition, and steps include: 1) collecting a data set of related human body skeleton actions; 2) pre-processing the data set, selecting action category samples that need to be generated and complete action samples that need to be referenced, and dividing a training set and a test set; 3) building a skeleton style migration characteristic generation network, the generation network is composed of an encoder, a decoder and a discriminator, wherein the encoder and the decoder together constitute a generator, and the encoder is further subdivided into a content encoder and a style encoder; 4) using the training set to sequentially train the content encoder, the style encoder, the decoder and the discriminator; 5) the generated new sample is used to participate in the training of a behavior recognition network, the generalization ability of the behavior recognition network is improved, and the accuracy of the test set on the action is improved. The method has better accuracy of human body skeleton generation results and better recognition effect.

The invention discloses a human body size data determination method and device based on 3D modeling, a medium and a product. The method comprises the following steps: collecting a multi-view visible light image and a multi-view depth map of a target human body to generate multi-view point cloud data; performing alignment fusion on the multi-view point cloud data by iteratively minimizing corresponding point distance deviation among the multi-view point cloud data to generate a global point cloud model; classifying the global point cloud model to obtain a human body point cloud; performing grid reconstruction based on the human body point cloud to generate an initial three-dimensional grid model; smoothing the initial three-dimensional mesh model to obtain a final three-dimensional mesh model; and positioning a human skeleton datum point of the final three-dimensional grid model, and extracting human body size data. By implementing the technical scheme provided by the invention, the technical problems that the accuracy of the extracted key size data such as the girth and the length of the human body is insufficient and the requirement of a high-precision application scene is difficult to meet are effectively solved.

This application provides a method and apparatus for open-set skeleton action recognition based on outlier prototype learning. The method includes: constructing a neural network model; preprocessing human skeleton data by obtaining initial features through a multi-branch feature extraction network; processing the initial features using a classifier and a hypersphere feature mapper to obtain logical prediction values ​​for action categories and branch features; training the neural network model based on a training set; after the first iteration, optimizing the feature space of in-distribution samples using multi-class loss; synthesizing virtual outliers in the optimized feature space of in-distribution samples; optimizing the energy boundary by combining in-distribution samples and virtual outliers; after the second iteration, selecting the optimal model weights based on the combined performance of open-set recognition and closed-set classification on the validation set; and determining the action type and triggering the corresponding robot operation based on the energy score output by the trained neural network model and the action classification result.

The invention discloses a gait recognition method based on a Pluecker straight line, and belongs to the technical field of computer vision and biological feature recognition. The core of the method is to provide a double-layer gait feature modeling framework. The method comprises the following steps: firstly, modeling human skeletons into spatial straight lines by utilizing Pluecker coordinates, and calculating absolute rigid body motion characteristics of each skeleton between adjacent frames through dual quaternions; furthermore, by combining the absolute motion characteristics of adjacent bones, the relative motion characteristics of the joints, which can better reflect the physiological characteristics of individuals, are calculated. And finally, aggregating the two complementary features to construct a gait feature sequence, and completing identity recognition by using a time sequence deep learning model. According to the method, the overall motion information and the local joint motion information are separated and fused, so that the defects that an existing method is incomplete in feature representation and is greatly interfered by the overall motion are effectively overcome, and the accuracy, the discrimination capability and the robustness of gait recognition are remarkably improved.

The invention discloses a human body model, and relates to the technical field of teaching instruments, and the human body model comprises a first model frame which is provided with a plurality of first meshes and is used for simulating a human body skeleton; the second model frame is provided with a cavity, a first connecting part, a second connecting part and a plurality of second meshes, the first model frame is arranged in the cavity and detachably connected with the second model frame, the first connecting part is detachably connected with the second connecting part, the second model frame is used for simulating the outer contour of a human body, and the aperture of at least part of the second meshes is larger than that of the first meshes; each of the first model frame and the second model frame comprises at least one of a head and neck, a thorax, an abdomen, a pelvis and four limbs and is correspondingly arranged; the simulation structure comprises a first simulation piece and a second simulation piece, and the first simulation piece is used for simulating blood vessels, nerves and lymphatic vessels; the second simulation piece is used for simulating human muscles and organs. The problem that in the prior art, a human body model used for teaching is difficult to operate manually while the internal structure is visualized is solved.

The invention relates to the technical field of rehabilitation training, in particular to a rehabilitation training method based on multi-modal interaction and skeleton detection. The method comprises the following steps: extracting 33 key points when a human skeleton is detected through Pose model detection and a SkelegonProcessor module, calculating relative coordinates of all the key points of the human body by taking average coordinates of a left hip and a right hip as an original point of a middle hip, calculating a distance from a zero key point of an approximate nose on the top of the head to the middle hip as a trunk length, performing scaling normalization on the relative coordinates, and obtaining a trunk length; generating a human skeleton visual image; enabling the specific action to correspond to a keyboard instruction or a mouse instruction; the action and the mouse instruction or the keyboard instruction are bound and replaced through a database; two actions are set to be combined into one instruction. According to the technical scheme, whether the rehabilitation actions of the patient are standard or not and whether the joint movement range meets the requirement or not can be accurately judged in real time, instructions are sent out through action recognition, the interestingness of rehabilitation training is improved, and the rehabilitation training effect is improved.

The application discloses a method, device and diagnosis and treatment machine auxiliary system for autism child behavior recognition and motion prediction, the method comprises the following steps: (1) acquiring original skeleton video data of the child from different angles; (2) inputting the acquired original skeleton video data into a view adaptive conversion unit to adaptively convert to a consistent coordinate system to obtain the optimal representation of the human skeleton; (3) inputting the optimal representation of the human skeleton into a multi-scale feature extraction unit to extract high-performance spatiotemporal features of the human skeleton; (4) inputting the high-performance spatiotemporal features into a multi-task learning unit to perform behavior recognition and motion prediction on the human body, and obtaining behavior classification results and motion prediction results. The application can predict and intervene the future motion while recognizing the behavior.

The application discloses a three-dimensional human skeleton fitting method and system based on a multi-camera joint association graph and combined search, and relates to the three-dimensional human skeleton fitting method, which comprises the following steps: S1, acquiring images collected by multiple cameras, detecting human two-dimensional joint nodes by using a two-dimensional pose estimation algorithm, and projecting the two-dimensional joint nodes into three-dimensional rays; S2, taking the joint nodes as nodes and the geometric consistency between three-dimensional rays of different cameras as edge weights, constructing a multi-camera joint node association graph; S3, performing group clustering on the cross-camera joint nodes with high geometric consistency on the association graph by using a breadth-first search, and forming a candidate skeleton cluster; S4, reserving the first K joint nodes with the highest geometric consistency with the candidate skeleton cluster in each camera, and obtaining candidate joint node pairs; S5, using a depth-first search (DFS) to enumerate the cross-camera joint node pairs, and screening optimal joint node pairs in combination with a pruning strategy; and S6, performing triangulation processing on the screened optimal joint node pairs, obtaining three-dimensional skeleton points, and outputting a three-dimensional human skeleton structure.

The utility model discloses a mixed fixing type joint prosthesis assembly, belongs to the technical field of joint prostheses, and is designed for solving the problems that a joint prosthesis in the prior art is loosened and even falls off and the like. The utility model discloses a mixed fixing type joint prosthesis assembly which comprises a joint prosthesis which is an arc-shaped face, and the inner concave face of the joint prosthesis comprises a posterior condyle area and a main body area. The biological material layer covers at least part of the posterior condyle area, and the biological material layer is used for forming bone ingrowth between the biological material layer and the to-be-connected bone. According to the mixed fixing type joint prosthesis assembly, the stability of connection is improved through bone ingrowth formed between the biological material layer and the skeleton to be connected, and the problem that in the prior art, bone cement in the posterior condyle area cannot reach the effective penetration depth, and consequently a joint prosthesis is loosened and even falls off from the skeleton of the human body is solved; connection between the joint prosthesis assembly and the to-be-connected skeleton is more stable, and the use experience is better.

The invention provides a low-complexity human body motion reconstruction method based on a sparse inertial measurement unit, and belongs to the technical field of virtual reality and three-dimensional human body motion reconstruction, and the method comprises the following steps: obtaining a data set required for reconstruction training based on human body postures, carrying out time modeling through a time sequence encoder, updating joint features on a human body skeleton graph, and obtaining a reconstruction result; a skeleton is divided into a trunk and four limbs according to a human anatomical structure, global rotation of a root joint and local rotation of each joint are respectively predicted by a partition kinematics regression head, low-rank decomposition is introduced into a large-scale linear layer to compress model parameters, and forward kinematics is utilized to recover three-dimensional joint positions of the whole body. In the training process, a two-stage teacher-student distillation model framework is adopted, a teacher network is trained through real labels, and then joint rotation and joint positions output by a teacher are used as soft targets to jointly restrain a student network through rotary distillation and position distillation. While the parameter quantity is reduced, the reconstruction precision and the motion smoothness of the whole body are improved.

The application discloses a rehabilitation action recognition and classification method, system, device and storage medium, the method comprising: collecting a three-dimensional coordinate sequence of a human skeleton point corresponding to a target rehabilitation action performed by a user; performing normalization and time alignment processing on the three-dimensional coordinate sequence to generate a standardized posture matrix; extracting principal component features for representing action patterns from the standardized posture matrix; performing unsupervised clustering analysis on the principal component features, and dividing a plurality of action pattern categories according to a clustering analysis result; performing similarity matching on the principal component features and features corresponding to the plurality of different action pattern categories in a typical action pattern database; and determining a specific action pattern category to which the target rehabilitation action belongs according to a similarity matching result. The application improves the objectivity, repeatability and clinical interpretability of rehabilitation action recognition and classification, and expands the applicable scenarios.

This invention provides a method, device, and system for abnormal behavior recognition based on multimodal interaction. It simultaneously extracts RGB sequences and human skeleton sequences from surveillance videos, performing feature extraction and alignment respectively. Second-order interaction features between the two modalities are calculated using compact bilinear pooling, and a lightweight Transformer encoder is used for global spatiotemporal correlation modeling to achieve two-stage deep fusion. Simultaneously, a feature pyramid module with a three-level structure (local, regional, and global) is designed to extract multi-scale spatiotemporal features, which are then dynamically fused using an adaptive attention mechanism. Finally, multi-level features are aggregated for classification decisions. This invention integrates the complementary advantages of appearance and structural information, achieving deep cross-modal interaction and multi-scale understanding, significantly improving the accuracy and robustness of behavior recognition in complex scenarios, and is particularly suitable for public security monitoring scenarios such as rail transit.

The present application provides a single-view three-dimensional human skeleton key point detection method, device, equipment and medium, comprising: acquiring a single-view human image sequence, the single-view human image sequence comprising a target image and a related image; acquiring a first skeleton key point of the target image and a second skeleton key point of the related image respectively, extracting spatial feature of the first skeleton key point to obtain spatial semantic feature, and extracting time sequence feature of the first skeleton key point and the second skeleton key point; fusing the supervision feature with the spatial semantic feature and the time sequence feature to obtain three-dimensional human skeleton key point feature information. In the present application, the spatial semantic feature and the time sequence feature are extracted to obtain the plane information and the depth information of the skeleton key point; the supervision feature is fused with the spatial semantic feature and the time sequence feature, which can effectively reduce the depth ambiguity and the ill-posedness of the two-dimensional human skeleton key point mapping three-dimensional human skeleton key point of the single-view image.

The invention discloses a skeleton action recognition method based on motion center space-time diagram convolution. The method comprises the following steps: acquiring a human body skeleton diagram sequence of a moving object; performing time sequence feature extraction on the human body skeleton graph sequence to obtain a time sequence feature graph used for representing time features of each moving object; extracting the time sequence feature map to obtain a motion center of the moving object; based on the motion center and the three-dimensional space position information, obtaining skeleton characteristics including spatio-temporal information; establishing a skeleton action recognition model, and training the skeleton action recognition model based on the skeleton features to obtain a trained skeleton action recognition model; the skeleton action recognition model comprises a feature extraction module and a classification module; and recognizing a skeleton action based on the trained skeleton action recognition model to obtain an action classification result of the moving object. According to the method, the space structure information and the time change characteristics of each moving object are fully utilized, and the motion center is introduced to express the characteristics of each moving object in different motions, so that the motion recognition accuracy is improved.

The application provides a kind of based on visual analysis's practical operation process stability evaluation method and device, specifically related to stability evaluation field, this method includes: extracting each frame image from teacher practical operation video, generating multiple channel heat map for preset key point, based on preset limb connection, the two-dimensional human skeleton posture of each frame is constructed, three-dimensional reconstruction is carried out to continuous two-dimensional posture sequence, obtains the three-dimensional space coordinate sequence of key point, based on three-dimensional space coordinate sequence, calculate comprehensive stability evaluation score, obtain comprehensive stability evaluation score.The application enhances the stability of limb connection under complex occlusion environment, ensures the accurate construction of two-dimensional posture, can provide an objective and accurate comprehensive evaluation result of practical operation process stability.

This invention relates to the field of intelligent sports training equipment technology. It discloses an intelligent sports training machine with motion posture recognition, comprising: an equipment frame; a drive assembly mounted on the equipment frame, the drive assembly including: a motor; a reel connected to the output shaft of the motor; and a rope wound around the reel, the free end of which extends to the outside of the equipment frame. This invention offers significant advantages over traditional training machines. It achieves stepless resistance adjustment through motor drive, and with closed-loop control, ensures precise and stable resistance that can be dynamically adjusted according to posture. It achieves three-dimensional recognition of key points of the human skeleton through visual acquisition and posture analysis, and combined with audio-visual feedback, allows users to correct their movements in real time, improving training effectiveness. It automatically reduces resistance in abnormal postures and provides emergency braking in dangerous situations, significantly reducing the risk of injury. It can also record training data for digital management, providing support for personalized training.

1. The name of the design product: dual-energy X-ray bone densitometer (INNO S). 2. The use of the design product: for measuring the bone density and bone mineral content of human skeleton. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application relates to the field of computer-aided medical technology, and discloses a rehabilitation plan recommendation method for multiple fractures, which comprises the following steps: acquiring a first image for representing multiple fractures, the first image being an image in which multiple fracture position bounding boxes are marked on a preset human skeleton image; determining position information of a concurrent injury position according to the first image; and performing rehabilitation plan recommendation according to the position information of the concurrent injury position. The image in which multiple fracture position bounding boxes are marked on the preset human skeleton image is acquired, then the position information of the concurrent injury position is determined according to the image, and thus the rehabilitation plan recommendation can be performed according to the position information of the concurrent injury position. In this way, the rehabilitation training plan can be recommended for the multiple fracture user. The application further discloses a rehabilitation plan recommendation device for multiple fractures, an electronic device and a storage medium.

The invention discloses a quantitative interpretation method for abnormal gait detection. The method comprises the following steps: generating contribution degree thermodynamic diagrams corresponding to two sub-networks respectively based on a space-time diagram convolutional network model; fusing the contribution degree thermodynamic diagrams corresponding to the two sub-networks to generate a fused contribution degree thermodynamic diagram; and based on the fused contribution degree thermodynamic diagram, performing quantitative evaluation on contribution conditions of all joints in the human skeleton point sequence input into the space-time diagram convolutional network model to the output result, and generating and outputting a quantitative evaluation result. According to the method, the contribution degree thermodynamic diagram is generated based on the feature extraction result of the sub-network, and the contribution condition of each joint in the human skeleton point sequence to the model output result is quantitatively calculated based on the contribution degree thermodynamic diagram, so that the deep learning model is no longer completely in a black box mode during gait anomaly analysis; and the reliability of a deep learning model, especially a space-time diagram convolutional network model, in abnormal gait analysis application is greatly improved.

This invention provides an automatic ultrasound detection method and system based on human posture. The method includes the following steps: acquiring three-dimensional coordinate data of human skeletal joints and their rotation direction positioning points; establishing a three-dimensional human skeleton model; obtaining the rotation angle of each joint through rotation registration; using the qualified detection posture of the detection unit as coarse positioning data; moving the robotic arm above the detection unit to complete coarse positioning; acquiring an RGB image of the detection unit for physicians to delineate the detection area and draw the detection path; acquiring a depth map of the detection unit and aligning it with the RGB image; generating seed points on the corresponding detection area of ​​the depth map; dividing the detection area into multiple test areas using a region segmentation algorithm and mapping them onto a point cloud; and calculating the point cloud normal vector of the test area corresponding to the detection path; adjusting the robotic arm to fit the ultrasound probe along the normal vector to the human skin until the first detection feedback is within a preset range, thereby aligning the ultrasound probe with the corresponding human posture to obtain a high-quality ultrasound detection image.

The invention discloses an age estimation network and method based on an elastic law and a non-uniformly distributed X-ray human skeleton image, and mainly solves the problem of insufficient optimization effect on a sample distribution imbalance task in medical image age estimation in the prior art. The network construction comprises the following steps: establishing a feature extractor for extracting batch features from an input image; introducing a feature queue, and using supervision force, distance elasticity and midpoint elasticity to do work on the samples so as to carry out comparative learning between the samples; defining the basic age estimation loss by using the supervision force, and obtaining the total loss of network iteration training; establishing a monotonicity constraint residual block for mapping the sample pair feature distance into a partial monotone increasing distance feature between samples; and establishing mutually independent age estimation regression heads for outputting an estimation result. According to the method, the performance of the automatic age estimation model on few sample data is improved, and the method has a good aging change characterization effect and can be used for disease risk assessment, individualized health management and medical research.

The application belongs to the technical field of virtual reality, and relates to a virtual human action generation method. The method comprises the following steps: constructing a human skeleton model, calibrating a pose zero point of the human skeleton model, and establishing a pose constraint relationship of the human skeleton model; acquiring pose data of a target person by using a sensor, and defining the pose data as first data; according to the first data and the pose constraint relationship, solving unknown actions of virtual upper limbs of the human skeleton model to obtain virtual upper limb predicted pose data; constructing a virtual lower limb action library, and acquiring virtual lower limb predicted pose data in the virtual lower limb action library according to the first data; and generating and outputting a virtual human body action according to the first data, the virtual upper limb predicted pose data and the virtual lower limb predicted pose data. In the application, only a small amount of end sensors are relied on, and virtual human body whole body actions can be accurately generated, thereby solving technical pain points in the prior art, such as high dependence on sensors, high use cost and difficulty in reproducing complex actions.

The invention discloses a system and a method for realizing three-dimensional human body reconstruction based on a single RGB picture. The system comprises a front-end data acquisition and preprocessing module, an edge calculation and data compression module, a rear-end SMPL model reconstruction and rendering display module and a front-rear-end cooperative work mechanism module. According to the method, human skeleton, joint and shape features are efficiently extracted from a single RGB picture, the high-precision three-dimensional SMPL model is generated, the compression ratio can be adaptively adjusted according to the network bandwidth and delay, the data volume is remarkably reduced, and low delay is kept; the rear end adopts incremental rendering and parameter differential updating to realize efficient real-time three-dimensional display; through front and back end cooperation and a dynamic feedback closed loop mechanism, the system is ensured to stably operate in different network and computing environments, and parameter setting is adaptively optimized according to real-time performance indexes.

The application discloses a model training method and a motion quality evaluation method. The model training method comprises the following steps: pre-training a variational autoencoder based on human skeleton motion sequences obtained by a plurality of users performing a plurality of motions; inputting a plurality of standard human skeleton motion sequences into the pre-trained encoder to output a mean vector and a log variance of a Gaussian distribution of each standard human skeleton motion sequence in a hidden space; determining parameters of a motion quality evaluation model based on the mean vector and the log variance of the Gaussian distribution of each standard human skeleton motion sequence in the hidden space; inputting a historical human skeleton motion sequence obtained by a target user performing a target motion into the pre-trained encoder; and updating the parameters of the motion quality evaluation model based on the mean vector of the Gaussian distribution of the historical human skeleton motion sequence in the hidden space output by the encoder and interaction data of the target user and a virtual coach about the target motion. The application can improve the accuracy of the evaluation result.

The application relates to a fall behavior recognition method and device based on images. The method comprises the following steps: acquiring a human skeleton point, a human contour and / or a definition of an image to be recognized; detecting whether a complete and clear human image with a definition greater than a preset value exists in the image to be recognized according to the human skeleton point, the human contour and / or the definition of the image to be recognized; and recognizing whether the human in the image to be recognized is in a falling state according to the width of the human image, the height of the human image and / or the position coordinates of the human skeleton point. The application can avoid the misrecognition caused by the image recognition when the human in the image is blocked or the definition of the image is low, and improve the recognition accuracy of the falling behavior.