24457 results about "Human body" patented technology

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention provides a rehabilitation training action evaluation method and device based on multi-view vision. According to the method, a multi-view camera is adopted to synchronously collect a rehabilitation training image sequence, two-dimensional coordinates of key points of a human body are extracted by improving an HRNet deep learning model, three-dimensional reconstruction is performed in combination with a Gaussian process, motion feature data are extracted, and rehabilitation training quality is evaluated. Accurate motion capture without wearing mark points by the patient is realized, the training constraint feeling of the patient is effectively reduced, the rehabilitation evaluation accuracy is improved, and a personalized rehabilitation scheme can be generated.

Systems and methods herein generally relate to prolonging viability of bodily tissue, especially an organ such as a lung, by adjusting pressure as needed to maintain a constant pressure within the organ even during external pressure fluctuations due, for example, to transportation of the organ in an airplane. The systems and methods herein can include an electronic pump that pumps gas into an organ and a mechanical pressure regulator to release gas based on organ pressure.

The invention provides a cooperative mechanical arm self-adaptive obstacle avoidance method in a dynamic scene, which comprises the following steps of: 1, setting an initial pose and a target pose according to a work task of a cooperative robot, and performing global path planning in an initial environment; 2, an operator completes an assembly task in an assembly area, and the collaborative robot operates according to a preset route and completes the assembly task in a matched mode; 3, the depth camera obtains a human body skeleton and skeleton node information, and a human body bounding box is generated; 4, whether the distance between the arm bounding box and the robot is smaller than the safety distance or not is judged, and if yes, collision early warning is triggered; 5, a new collision-free track is re-planned based on the self-adaptive obstacle avoidance strategy of the artificial potential field method, and the mechanical arm moves according to the new track to avoid the obstacle; and 6, after the obstacle leaves the safety early warning range, the mechanical arm returns to the global path and finally arrives at the target point.

The invention provides a human body posture detection method and equipment based on machine vision, which are applied to posture recognition in physical fitness detection, and are used for generating a dynamic human body region set of a target user by acquiring a multi-frame continuous image sequence of a physical fitness training scene and performing human body region positioning processing on the multi-frame continuous image sequence. Performing posture feature extraction processing on the dynamic human body region set to obtain a space-time posture feature set of the target user, performing posture state recognition processing on the space-time posture feature set based on a preset physical fitness evaluation model to generate a posture state recognition result of the target user, and generating physical fitness evaluation parameters according to the posture state recognition result, and the physical fitness evaluation parameters are fed back to the physical fitness training guidance system. According to the method, the identification capability of fine posture change in the fitness action of the complex body can be improved, the synchronous evaluation of the action specification degree and the physiological load state is realized, and the limitation of the traditional single-dimensional action evaluation is broken through.

The invention relates to a human shape posture recognition method and system based on image analysis, and the method comprises the steps: inputting the reference human shape data of a monitored object, and collecting the multi-modal data of an RGB image, an infrared image and inertial measurement data in a target scene in real time; space-time alignment processing is carried out, and corresponding features are fused; image space features are extracted, time sequence modeling is carried out on inertial data, and dynamic weighted fusion of two paths of network outputs is realized through a gating mechanism; human body basic joint points are positioned, and refined posture vectors including joint angles and limb relative positions are generated; according to attitude data and environment information collected in real time, adaptively adjusting an attitude classification threshold value and a similarity measurement standard, and predicting abnormal behaviors in a future time period; and when the abnormal behavior is predicted, triggering to execute a preset safety measure. Multi-modal data can be efficiently fused, attitude features can be accurately extracted, an identification strategy can be adaptively adjusted, and human shape attitude identification with behavior trend prediction capability can be realized.

The invention discloses a human body motion posture evaluation method and system. Low-cost and high-precision human body posture analysis is realized through common camera equipment. The method comprises the following steps: acquiring a human body motion video through camera equipment; detecting key points of a human body by using a deep learning model, and extracting two-dimensional or three-dimensional coordinates of the human body; calculating joint angle, speed and acceleration based on the key point data; and analyzing attitude stability, motion symmetry and fluency characteristics, and generating a comprehensive evaluation result. The system evaluates the motion quality of the user through comparison between dynamic feature extraction and a standard motion template, and generates personalized improvement suggestions based on specific indexes, including direction adjustment and amplitude range adjustment. The method can be widely applied to scenes such as exercise and fitness, rehabilitation medical treatment and competitive training, and the comprehensiveness, the accuracy and the guiding effect of action evaluation are improved. By means of the method, the user can obtain motion quality feedback in real time, the posture is effectively optimized, and the exercise performance or rehabilitation effect is improved.

The present invention relates to a pose recognition method and system combining environmental adaptation with estimation and classification, and a medium, the method comprising: on the basis of an application scenario, obtaining a human pose estimation model; inputting image data to be tested into the human pose estimation model to output human pose keypoints; performing data enhancement and normalization on the human pose keypoints; inputting the human pose keypoints that have undergone data enhancement and normalization and high-order pose features into a pose classification model to obtain a pose recognition result, the high-order pose features comprising bone vectors, the mean of bone vector moduli, relative lengths of bones, and an included angle between the line connecting each human pose keypoint and a trunk midpoint and the horizontal direction. Thus, according to the present invention, pose recognition is divided into two processes of pose estimation and pose classification, which facilitates algorithm updating and iteration being performed when there is better estimation and classification performance, and the structure is flexible.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention relates to the field of medical robots, and discloses a meridian point foundation construction method for precise positioning of an acupuncture robot, which comprises the following steps: scanning skin texture, muscle contour and skeleton mark features of a human body surface, and establishing a human body surface feature digital model; the method comprises the following steps: acquiring temperature distribution data of a human body surface, human body surface feature digital model data and elastic modulus data, performing weight mapping feature level fusion to generate an enhanced acupuncture point feature map, and establishing an acupuncture point-meridian association relationship matrix based on the enhanced acupuncture point feature map; compared with the prior art, the method has the advantages that three-dimensional body surface scanning, infrared thermal imaging and elastic modulus data are integrated through the multi-modal data fusion technology to generate the enhanced acupoint feature map, the dynamic meridian-acupoint twinborn model is constructed in combination with double-ellipse section fitting and implicit curved surface reconstruction, and tissue deformation is simulated in real time.

The invention relates to the technical field of clothing data processing, in particular to a digital design method of three-dimensional prototype clothing. The method comprises the following steps: obtaining human body three-dimensional scanning data and carrying out feature point labeling, reconstructing a three-dimensional human body model as basic data of costume design, carrying out contour looseness calculation on the human body model, generating a looseness data set, carrying out sample plate plane analysis to obtain a plane sample plate data set, and after collecting costume fabric data, carrying out data processing on the plane sample plate data set. The method comprises the following steps: performing physical characteristic mapping on a human body model and a planar template, reconstructing a three-dimensional garment model conforming to fabric characteristics, performing spatial positioning on the template, performing virtual sewing simulation, generating a shape data set of the garment, performing dynamic motion analysis on the shape data of the garment according to the human body three-dimensional model, and evaluating the comfort level of the garment. The garment form is locally adjusted to reconstruct a planar template. According to the invention, a more comprehensive and more reliable digital design method of the three-dimensional prototype clothing is realized.

In one implementation, a method of body pose estimation is performed at a device including one or more processors and non-transitory memory. The method includes obtaining a plurality of two-dimensional images of a body in a three-dimensional environment at a respective plurality of times. The method includes determining, for each of the plurality of two-dimensional images, the two-dimensional location in the two-dimensional image of one or more joints of the body at the respective plurality of times. The method includes determining, based on the two-dimensional locations, a plurality of three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times. The method includes determining, based on the three-dimensional locations, a plurality of updated three-dimensional locations in the three-dimensional environment of the one or more joints of the body at the respective plurality of times.

The invention relates to the technical field of laboratory safety management. The experimenter monitoring method based on behavior analysis comprises the steps of performing human body detection and tracking on video information through an RCNN algorithm to obtain experimenter position information in a laboratory, performing face recognition verification on the experimenter information through a DLIB library to obtain experimenter identity information, and performing face recognition verification on the experimenter identity information through a DLIB library. If the identity information of the experimenter is successfully matched, carrying out behavior analysis on the experimenter in the laboratory through a behavior analysis model to obtain behavior state information, and judging whether the operation behavior of the experimenter accords with a preset laboratory safety management rule or not based on the behavior state information of the experimenter; and if the operation behavior of the experimenter does not conform to the preset laboratory safety management rule, safety early warning information is generated, and early warning levels are divided according to the safety early warning information. The method has the effect of improving the safety management of the laboratory.

The invention discloses a three-dimensional human body reconstruction method based on an implicit neural network and a diffusion model, and belongs to the field of three-dimensional reconstruction. According to the invention, a diffusible density volume representation is constructed, and identity-based rendering is realized by using a diffusion model. By adopting parameterized three-dimensional distribution representation, Pareto optimum, balanced realistic rendering and feasible prediction are realized in an unbalanced optimization framework. In order to increase the rendering training speed without sacrificing the quality, mixed multi-resolution hash grid features are adopted to enhance the directional feature representation capability. Meanwhile, a multi-scale structural similarity constraint (MSSC) is provided to maintain identity distinguishability and ensure geometric consistency, so that synthesis with high calculation efficiency and satisfactory visual effect is realized. According to the method, the three-dimensional human body model can be reconstructed from a single image based on limited geometric and appearance information, the method is excellent in topology credibility and visual fidelity, and a more flexible and efficient solution is provided for an actual application scene.

The invention discloses an emotional understanding and feedback method for a full-duplex interactive question-answering digital person, and the method comprises the steps: S1, arousing interaction: enabling a digital person of a digital person system to enter an interaction state in a human body detection arousing or voice instruction arousing manner by a user; s2, state analysis: the digital human system judges the state of the user in real time by collecting image data and audio data of the user, and identifies and analyzes the direction, emotion and speech content of the user in real time; and S3, feedback output: the digital human system drives the digital human in voice, expression and body movement through a screen in real time according to the analysis result of the direction, emotion and speech content of the user, and feeds back the result to the user in a manner of adding auxiliary characters, images and videos. The method has the advantages that the emotional state of the user is accurately judged through an advanced algorithm, digital human expressions, body actions and voice feedback are driven in real time according to the emotion and speech content of the user, response is made in time, and a real-person-like real-time interaction scene is achieved.

The invention discloses a patient gait abnormity evaluation auxiliary system based on human body posture detection, and relates to the technical field of intelligent medical treatment, and the system comprises the steps: collecting patient gait video data, preprocessing the patient gait video data, and marking patient skeleton key points; based on the patient skeleton data set, calculating position coordinates of patient skeleton key points, and according to the position coordinates of the patient skeleton key points, identifying motion tracks of the patient skeleton key points; on the basis of the patient skeleton key point motion trail, patient gait features are extracted, and a comprehensive gait feature vector is formed; training a gait prediction model by using the patient skeleton data set, inputting the comprehensive gait feature vector into the gait prediction model, and predicting a gait anomaly index of the patient; and evaluating the prediction result, and generating an evaluation report. The gait abnormity index of the patient is predicted by recognizing the motion trail of the skeleton key points of the patient, comprehensive evaluation of the gait state of the patient is achieved, and clinical diagnosis accuracy and efficiency are improved.

The invention provides a 3D posture estimation method and system based on space-time double-flow intersection in the technical field of monocular video 3D human body posture estimation, and the method comprises the steps: S1, obtaining a large number of historical human body videos, carrying out the frame-by-frame processing of each historical human body video through a 2D human body posture estimation model, obtaining a 2D coordinate position, and carrying out the frame-by-frame processing of each historical human body video; constructing a joint space-time sequence data set; s2, creating a 3D human body posture estimation model based on a joint semantic coding module, a dynamic graph convolution module, a space-time double-flow cross fusion module and a 3D posture decoding module; s3, training the 3D human body posture estimation model through the joint space-time sequence data set and the loss function; and S4, acquiring a real-time human body video, and reasoning the real-time human body video through the 2D human body posture estimation model and the 3D human body posture estimation model to obtain a 3D human body posture estimation result. The method has the advantages that the precision and robustness of 3D human body posture estimation are greatly improved.

The invention relates to the technical field of human body posture detection and gait analysis, and discloses a portable posture detection and gait analysis method and system. The method comprises the steps that a hardware system is built, the hardware system comprises an embedded visual module, a data processing module and an interaction module, and human body motion video streams are collected through the embedded visual module; performing dynamic foreground extraction on the acquired video stream by adopting a background difference method and a self-adaptive illumination compensation algorithm, and outputting an illumination compensation image; according to the output illumination compensation image, the data processing module is used for increasing time sequence constraint loss on the basis of a YoLoV8 model, positioning key points of a human body and constructing a three-dimensional skeleton motion model; according to the method, a dynamic shielding processing engine and a multi-target decoupling mechanism are utilized, the constructed three-dimensional skeleton motion model is tracked based on a hierarchical association degree measurement function, human body motion data can be collected in real time, and accurate evaluation of postures and accurate extraction of gait features in multiple scenes are achieved.

The invention discloses a human body tumble identification method based on millimeter wave radar perception. The method comprises the following steps: acquiring echo data of various human body actions by using a millimeter wave radar; processing the echo data to respectively obtain point cloud data of human body actions; a DBSCAN clustering method is used for clustering point cloud data, point clouds are divided into different clusters based on a central point, then the obtained point cloud data are input into a deep neural network, space structure and dependency relationship information of the point cloud data is captured, the category of human body actions is recognized according to the space structure and dependency relationship information, and a human body tumble recognition result is output. According to the invention, a deep neural network model fusing a PointNet network and a Transform module is designed, and the model can enhance the description of the spatial relationship of point clouds, thereby improving the judgment of human body tumble recognition, and reducing the false alarm and false alarm rate. According to the invention, human body postures and actions are sensed through millimeter wave radar signals, human body tumble recognition is realized, and the system has the advantages of good anti-interference performance, high recognition accuracy and the like.

The present invention relates to the field of medical equipment, and in particular to a body position reversal device and a method of using the same. The body position reversal device comprises a support structure and a retaining device for fixing a human body, wherein the retaining device is rotatably mounted on the support structure, and the retaining device comprises a front support mechanism for supporting the front side of the human body and a rear support mechanism for supporting the rear side of the human body, wherein a retaining space for accommodating the human body is formed between the rear support mechanism and the front support mechanism; the body position reversal device also comprises an operating mechanism or a driving device for driving the retaining device to reverse so that the human body can switch between a supine position and a prone position. The beneficial effects of the present invention are: it is convenient for the reversal and adjustment of the human body position, the device has a simple structure, the reversal is flexible and labor-saving, and the safety of the human body during the reversal process is ensured.

The invention provides a space interaction accurate recognition method and system based on multi-modal fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting human skeleton, visual image and voice information, generating a space-time attention map through employing a feature extraction network, enhancing visual features, and executing feature complementary correction. Fusing the three types of modal information to obtain unified feature representation; when the recognition confidence is low, the space-time convolution generative adversarial network guided based on the action causal relationship graph complements the missing image frame, and the accuracy and robustness of space interaction recognition are improved.

A human body posture estimation method based on a dynamic graph convolutional network belongs to the field of human body posture estimation, and comprises the following steps: step 1, simplifying a human body structure in a video, and constructing a human body skeleton key point structure chart; 2, defining a graph structure to represent a spatial relationship between human joints; thirdly, a graph structure is defined to represent the motion relevance between the joints of the human body; 4, constructing a dynamic graph convolutional network by using dynamic space-time graph convolution; 5, 3D human body posture behavior data enhancement is carried out; and 6, constructing human body behavior data in the virtual scene by using the 3D human body posture data. According to the method, the modeling capability of the complex relation between the joints is improved, the robustness of the model is improved, and complex behaviors are recognized more accurately.

The invention discloses a human skeleton action recognition method for electric power field operation. The method comprises the following steps: acquiring a video of an actual electric power field operation scene; preprocessing the video and then building a three-dimensional human body posture estimation network model; performing feature extraction in the three-dimensional human body posture estimation network model by using a self-attention mechanism to obtain three-dimensional skeleton joint point sequence data of a human body; and building a human skeleton action recognition network model to capture the motion information of the skeleton sequence and the correlation between time and space, and decoding through a full connection layer to obtain a final action recognition result. According to the method, the three-dimensional human body posture estimation technology and the human body skeleton action recognition technology are utilized, the defects that the robustness is poor, the background is complex and illumination changes when action recognition is directly carried out through video images are overcome, the accuracy of action recognition is improved, and therefore the accuracy of action risk judgment of field operation personnel is improved, and the working efficiency is improved. And the field operation efficiency and the safety management level are improved, and possible risks are reduced.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention relates to the technical field of medical simulation training, in particular to an anesthesia puncture virtual simulation operation training and analysis system which comprises a force feedback calibration unit, a force feedback adjustment unit and a force feedback output unit. A theoretical force feedback value is calculated based on a human tissue mechanical model library, tissue levels and characteristics are accurately considered, in a deviation correction link, a force perception deviation value is periodically recalculated, data are processed by applying a sliding window averaging method, overall deviation and local deviation are distinguished and correspondingly adjusted, and the overall deviation is uniformly corrected; functional fine adjustment is locally set according to puncture depth sections, time-dependent force feedback of tissue viscoelasticity is considered, components are calculated through convolution integral and synthesized into final values with elasticity and inertia components, force feedback accuracy is improved comprehensively, individual differences and operation details of trainees are fitted, and anesthesia puncture training quality and effect are improved efficiently.

The invention discloses a public transport passenger flow monitoring method and system based on human body detection, and the method comprises the steps: synchronously collecting skeleton key points, heat distribution and contour features of passengers through a vehicle-mounted multi-view camera and an infrared sensor, and generating a passenger space-time feature matrix; outputting a passenger movement track set based on the passenger space-time characteristic matrix; generating a dynamic congestion degree index according to the staying duration and the spatial distribution density of the passengers in the passenger movement track set; calculating an abnormal behavior probability based on the dynamic congestion degree index, and outputting an abnormal event early warning signal; and fusing the dynamic congestion degree index, the abnormal event early warning signal and the real-time passenger flow data, and generating a bus passenger flow monitoring result through space-time correlation analysis. According to the embodiment of the invention, high-precision, low-delay and intelligent passenger flow state monitoring and abnormity early warning can be realized.

The invention relates to the technical field of human body health assessment, in particular to a health assessment method and system based on a 3D human body posture image model. The method comprises the following steps: acquiring human body annular scanning data; extracting a dynamic posture sequence of a vertical position, a forward bending position and a lateral bending position of the human body annular scanning data, and performing phase shift fringe projection on the human body annular scanning data according to the dynamic posture sequence to obtain human body three-dimensional point cloud data; a sagittal plane, a coronal plane and a cross section of the human body three-dimensional point cloud data are analyzed for orthogonal projection, and a human body multi-angle projection image group is generated; and identifying skeleton contour features of the human body multi-angle projection image group so as to establish a dynamic marking system of the anatomical mark points. Through high-precision dynamic acquisition and multi-dimensional space analysis, precision, standardization and individuation of 3D human body posture health assessment are realized, and the accuracy and comprehensiveness of human body posture health assessment are improved.

The invention provides a rescue robot autonomous identification and positioning system and method based on industrial vision, and relates to the technical field of robots, and the system comprises a cooperative scanning module which is used for constructing a robot coordinate map; the data fusion module is used for establishing a data fusion frame with a unified time sequence to obtain multi-modal image data; the human body recognition module is used for mapping the thermal imaging image to a visible light image and executing human body local recognition to obtain human body structure features; the target marking module is used for determining a rescue target and marking the rescue target in the robot coordinate map; and the path planning module is used for performing path planning, constructing an updated path and performing rescue through the rescue robot. According to the method and the device, the technical problem of relatively low rescue efficiency caused by difficulty in accurately positioning the rescue target due to shielding and interference in a complex environment in the prior art can be solved, and the precision of positioning the rescue target is improved by combining industrial vision, so that the rescue efficiency is improved.

The invention discloses a three-dimensional attitude high-precision acquisition and reconstruction system for exercise training, which relates to the technical field of three-dimensional attitude acquisition and reconstruction and comprises a camera calibration module, an image acquisition module, a key point extraction module, a three-dimensional reconstruction module, an attitude optimization module, a data fusion module and a feedback generation module. The camera calibration module is used for performing internal and external parameter calibration on a multi-view camera assembly by adopting a self-adaptive joint calibration method to obtain a projection matrix parameter and a transformation matrix of each camera; the image acquisition module is used for synchronously acquiring multi-view video images of the athlete in the training process based on the calibration parameters, and performing timestamp alignment processing to obtain an original image sequence; and the key point extraction module is used for detecting and identifying a human body image by adopting a deep learning model, extracting two-dimensional coordinates of key human body joint points of each frame from an original image sequence, and obtaining a two-dimensional key point set under each view angle.

The invention discloses a human body posture key point recognition method based on feature enhancement high resolution, and the method comprises the steps: firstly introducing a Res2Net module into a backbone network, constructing a layered similar residual connection structure, achieving the fine-grained multi-scale feature representation, effectively expanding the network receptive field range, and improving the recognition precision of a human body posture key point. According to the structure, multi-scale feature extraction and fusion are achieved in a single residual block, the adaptability of a model to different scale targets is enhanced, meanwhile, the calculation complexity is reduced, then a multi-scale convolution attention MSCA module is embedded, space context information of different scales is captured through multi-branch depth separable convolution, and the multi-scale feature fusion is achieved. According to the method, the key features are adaptively enhanced in combination with a channel attention mechanism, the positioning capability of the key points of the human body is remarkably improved, and finally, richer and more accurate key point information of the human body posture is acquired by fusing multi-scale and deep feature representation, so that accurate recognition of the human body posture is realized.