318 results about "Human skeleton" patented technology

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 provides a human body skeleton point positioning identification method and system based on OpenPose, and the method comprises the steps: carrying out the preprocessing of an input human body image, and obtaining the preprocessing image data; performing skeleton point detection on the preprocessed image data by adopting a double-domain multi-path self-supervised diffusion model in combination with a convolutional neural network to obtain two-dimensional human skeleton point coordinate data; performing signal-to-noise ratio evaluation and noise reduction processing on the two-dimensional skeleton point coordinate data to obtain two-dimensional skeleton point data after noise reduction; performing two-dimensional to three-dimensional conversion through a triangulation principle and a feature matching technology to obtain preliminary three-dimensional skeleton point coordinate data; performing skeleton point optimization through rate perception analysis and a three-dimensional Gaussian compression algorithm; and carrying out shielding prediction and completion processing on the optimized three-dimensional skeleton points to obtain a complete human skeleton point positioning identification result. According to the invention, the problems of accuracy and stability of skeleton point positioning in a complex dynamic scene are solved.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention relates to a multi-person motion capture real-time digital human driving method based on a skeleton-muscle interaction model, and belongs to the field of artificial intelligence and ubiquitous universe. According to the method, the key points of the human body are detected through YOLO-Pose, the positions of the shielding points are obtained through a double-stage dynamic shielding point compensation method, and the accuracy of the key points is guaranteed. And outputting the three-dimensional coordinates of the key points of the human body by adopting a three-dimensional key point depth extractor based on multi-dimensional exclusive-shared feature reasoning in combination with an MT ransPose model. A real person-skeleton model-digital person real-time mapping method is provided, three-dimensional key points are mapped to a standard human body skeleton model, and a digital person role and the skeleton model are bound through a weight mapping algorithm. Based on a skeleton-muscle interaction model, the digital person accurately reflects a real motion posture by introducing the mechanical action of a skeleton rigid body dynamic model and a muscle force line model, and the process of driving the digital person in real time through multi-person motion capture is achieved.

The invention relates to the technical field of millimeter-wave radars, in particular to a millimeter-wave radar action evaluation method, system and equipment based on a human skeleton model, and the method comprises the following steps: S1, millimeter-wave radar point cloud sampling: employing millimeter-wave radar equipment to collect three-dimensional point cloud data of a human body, and extracting target points related to human skeleton nodes; s2, skeleton node estimation: predicting three-dimensional coordinates of each joint of the human body by adjusting an output structure of a PointNet neural network model, so as to reconstruct and fit a skeleton structure of the human body; and S3, human body action evaluation: constructing the obtained three-dimensional coordinate data of the multiple frames of human body skeleton nodes into time sequence data, and analyzing a motion track by using an evaluation system based on an ST-GCN network. According to the invention, complex dynamic changes and behavior modes in human motion can be accurately captured, and high-precision real-time evaluation and feedback are provided for the fields of motion monitoring, rehabilitation training, health evaluation and the like.

The invention relates to a forklift examination anti-cheating method and system based on artificial intelligence and a quantification method. The anti-cheating method for the forklift examination comprises the following steps: S1, acquiring a limb action video V (t) of an examinee during the examination and pressure data P (t) of a forklift driving system; s2, extracting a human body skeleton graph Gt at the t-th moment from the V (t); extracting the Gt to obtain a spatial structure feature set St, and extracting a time sequence dynamic feature set Dt of the St; and fusing the St and Dt features to obtain a pose feature Zt. Visual sense and hydraulic sensing data are fused, association analysis is carried out on examinee actions and vehicle responses, and abnormities which are difficult to find through a single video or sensing means are detected. Through conjoint analysis of images and pressure data, hidden cheating means such as remote control and dangerous violation operation can be identified, and three-dimensional monitoring of the examination process is realized.

The invention relates to the technical field of computer vision, in particular to a human body behavior recognition method and system. According to the method, a multi-head space hypergraph convolution module is arranged before each time graph convolution of an ST-GCN model, and a human body behavior recognition model is constructed; the multi-head space hypergraph convolution module constructs a non-uniform hypergraph to represent the topological relation of the human skeleton through the maximum number, which can be contained by hyperedges, of each node, and generates the output of the module in combination with a physical adjacency matrix reflecting the relation of each joint; virtual connection is also added, virtual features which are the same as the input channel dimension and the time dimension of the multi-head space hypergraph convolution module are constructed, and the virtual features and modal data features are spliced along the joint dimension and then serve as module input again, so that global semantic information of real nodes is enriched, generalization information of human body behavior modes is supplemented, and the real-time performance of the multi-head space hypergraph convolution module is improved. And stage dense connection is introduced between different layers to smooth the change degree of the features, so that the human behavior recognition precision is improved.

The invention discloses a figure gait recognition method based on a dynamic space-time diagram convolutional neural network, and the method comprises the following steps: carrying out the human body posture estimation of a CASIA-B data set, and generating a thermodynamic diagram; inputting the thermodynamic diagram into an HM-C module to generate human skeleton sequence information; extracting joint, skeleton and speed features; inputting into a local attention module to obtain feature vectors containing weight information of different body parts in the whole action sequence; inputting the data into a backbone network dynamic GCN module to generate a spatial feature map; inputting the human skeleton sequence information and the spatial features into a dynamic TCN module to generate a rich time feature map; and inputting the time feature map into a joint and skeleton dynamic fusion module, fusing joint-level and skeleton-level motion modes in different time ranges, and generating joint-skeleton motion features which are richer and stronger in discrimination. According to the method, the problem that a baseline GCN method seriously depends on a manually defined skeleton topology is solved.

The invention relates to the technical field of knee joint rehabilitation evaluation, in particular to a knee joint rehabilitation evaluation method based on a human skeleton by using a space-time diagram convolutional network, and the method comprises the following steps: obtaining a rehabilitation evaluation gait video set; extracting human body key point skeleton data, and slicing the human body key point skeleton data into gait samples according to the number of time frames; preprocessing the gait sample, and extracting joint, skeleton and joint angle features; a gait-link method is adopted to divide a space gait skeleton diagram, a space-time attention mechanism is added, and an improved CTR-GCN network model is constructed; training the improved CTR-GCN network model by using the gait sample to obtain a gait evaluation model; and processing a gait video to be evaluated, and inputting the processed gait video into the gait evaluation model to obtain a rehabilitation evaluation result of the patient. According to the method, the human body key point skeleton in the walking video of the patient is extracted and input into the space-time diagram convolutional network for training, the rehabilitation evaluation model is obtained, the rehabilitation condition of the patient is evaluated by using the model, and the requirement of rehabilitation evaluation is met.

The invention discloses a deep learning-based human behavior recognition method and recognition system, and the method is characterized in that the method comprises the following steps: S1, data collection: collecting video data containing a plurality of human behaviors through a high-definition camera or a video database, and covering different scenes, illumination conditions and human postures; s2, data preprocessing: firstly extracting a three-dimensional coordinate sequence of human skeleton key points by using a Medape pre-training model, and then performing mean filtering and normalization preprocessing on the collected data to improve the generalization ability of the model; according to the method, the advantages of the DCN and the LSTM are combined, the improved double-flow convolution model is added with the space-time soft attention mechanism, the problems that space-time feature extraction is insufficient and feature design is complex in human body action recognition are effectively solved, the lightweight degree of the model is remarkably improved, and the human body action recognition accuracy is improved. And a new method is provided for research and application of human behavior recognition.

The invention belongs to the technical field of human body tumble detection, and particularly relates to a human body tumble detection method and device based on multi-feature fusion. According to the method, firstly, human skeleton key point features are extracted through an RGB camera in combination with an OpenPose model, then human contour features are extracted through an infrared camera in combination with an OpenCV library, then human three-dimensional point cloud features are obtained through a millimeter wave radar in combination with a data preprocessing method, and finally, the three features are fused and input into a GNN neural network model, so that the human body three-dimensional point cloud features are obtained. And obtaining a human body falling detection result. Through multi-mode feature complementation, the human body tumble detection system can adapt to different illumination, shielding and environmental interference, and is efficient and reliable.

The invention discloses a swimming pool drowning detection method based on human skeleton key points, and the method comprises the steps: firstly carrying out the real-time analysis of a video stream through employing an advanced human body key point detection model, and positioning the human body key points; and then a plurality of swimmers are distinguished by means of a multi-target tracking model, continuous tracking is carried out, and the dynamic change of each swimmer in the time sequence is captured, so that the motion trail of the human body is obtained. And finally, carrying out deep analysis on the extracted human body key point features through an action recognition model to judge whether drowning signs exist or not. According to the drowning detection scheme, the drowning situation can be monitored in real time and accurately recognized, the detection efficiency and accuracy and the generalization performance of the model are remarkably improved, and more reliable technical support is provided for swimming pool safety monitoring.

The invention provides a physical training posture correction system and method, and the method comprises the steps: obtaining a physical training posture image, carrying out the human posture estimation of the physical training posture image based on an improved HRNet network structure, obtaining a thermodynamic diagram of key points of a human skeleton, and carrying out the decoding operation of the obtained thermodynamic diagram of the key points of the human skeleton, human body posture features are extracted based on the human body skeleton key point coordinates; comparison evaluation is carried out on the extracted human body posture features and standard action posture features based on a DTW evaluation model; non-standard actions are extracted; the non-standard actions are displayed to trainees for posture correction. The posture detection of physical training can be realized, whether a nonstandard training posture exists or not is judged according to the posture detection, if yes, the nonstandard training posture is displayed to trainees for correction, and use is convenient.

The invention discloses a field personnel behavior identification method based on human skeleton key points. The method is suitable for behavior monitoring of operators in complex production environments such as hydraulic power plants. The method comprises the following steps: performing feature extraction and modeling on skeleton data acquired by camera equipment, and performing multi-scale space-time modeling by adopting a time sequence guide space topology modeling module and a space-time motion modeling network; according to the method, the capturing capability of the dynamic relation between human skeleton nodes is enhanced, the behavior recognition precision of long time span and complex postures is improved, and the robustness and generalization capability of the model in a complex environment are improved; on the whole, the risk behavior of the operating personnel can be effectively identified, the production safety is guaranteed, and the risk caused by human errors is reduced.

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 an action error prevention method and system based on AI identification. The method comprises the following steps: acquiring a standard action video and a to-be-detected action video; inputting the standard action video into a dynamic serialization analysis model for training, and extracting movement track features of standard human skeleton points and standard key objects; inputting the to-be-detected action video into the trained dynamic serialization analysis model to extract motion track features of to-be-detected human skeleton points and to-be-detected key objects; and comparing the motion trail characteristics of the standard motion with the motion trail characteristics of the to-be-detected motion to obtain an error value, and comparing the error value with a preset threshold to judge whether the motion is wrong or not. The method can overcome the defects that a traditional detection method is low in efficiency, difficult to capture motion dynamic characteristics, poor in adaptability and the like, manual operation errors can be found in time, and the product quality and the production efficiency are effectively guaranteed.

The invention discloses a dance movement auxiliary generation method and system based on three-dimensional modeling. The method comprises the following steps: constructing a three-dimensional human skeleton model; generating a joint action set of each joint node in the dance action; constructing a dance movement auxiliary model based on fusion of a time sequence convolutional network and a graph convolutional neural network, obtaining three-dimensional joint movement trajectory data in continuous frames, and obtaining relevance among dance movements by analyzing time sequence features and collaborative movement rules in the dance movements; acquiring joint actions associated with the current dance action in the joint action set, and generating a candidate action set; and calculating to obtain a connection fluency score and a posture aesthetics score with the current dance movement, and weighting to obtain the candidate movement with the highest total score as the next dance movement. The method has the advantages that through precise skeleton modeling and kinematics constraint, the deep learning technology is combined, smooth and attractive dance actions are efficiently generated, and the method is suitable for dance creation and virtual performance.

The invention relates to the technical field of body posture recognition, and particularly discloses an automatic body posture recognition system based on video analysis, which comprises a video acquisition module, a preprocessing module, a key point detection module, a skeleton tracking module, an action recognition module and a cross-view fusion module. According to the scheme, a parallel detection and information fusion mode is adopted, and the detection precision of the key points of the human body is remarkably improved by utilizing the accurate position of the high-resolution feature and the rich semantics of the low-resolution feature; the target is tracked according to the topological structure and the key point position of the human skeleton, the method has extremely high robustness for illumination and dressing changes, and a more stable and continuous human skeleton movement track is generated; the time sequence convolution and the space-time diagram convolution are combined to carry out joint modeling on the space and time double-domain features, complete action representation is obtained, and the stability, the accuracy and the adaptability of body posture recognition are effectively improved.

The invention provides a 3D human body posture estimation method based on a diffusion model and continuous frame time-frequency information fusion, and the method comprises the steps: inputting a 2D posture into an initial estimator for posture extraction, and obtaining an initial 3D human body posture; inputting the initial 3D human body posture into a diffusion model in an iteration form, and training in combination with noise to obtain an optimized 3D human body posture; performing mechanism processing on the long sequence data of the 3D human body skeleton by using an encoder of a space Transform module and a time frequency domain fusion module to obtain encoding features, and performing mechanism processing on the long sequence data of the 3D human body skeleton by using the time frequency domain fusion module; and the coding features are input into the time-frequency feature fusion module for mechanism processing, so that the accuracy of attitude estimation is improved. According to the method, the initial estimator is used for integrating constraints such as the bone length consistency and the joint connection relation, and fuzziness and ambiguity in 2D-3D inference can be reduced.

The invention discloses a video-based digital human motion capture and motion generation method and device and a medium, and relates to the technical field of machine vision, and the method comprises the steps: obtaining a video containing human motion; extracting a body area image frame and a hand area image frame corresponding to the figure based on the video; performing human body reconstruction based on the body area image frame and the hand area image frame to obtain a 3D human body model; and acquiring human skeleton information based on the 3D human body model, and generating a skeleton action file based on the human skeleton information. According to the scheme disclosed by the invention, the digital human body motion capture cost is reduced, and the problem that animation needs to be made again due to skeleton adjustment is solved.

The invention provides a skeleton point data-based sit-up counting method and system, a medium and a product, and relates to the technical field of electric digital data processing. According to the method, video data are acquired by using a camera, skeleton point information is determined by means of a human skeleton point recognition model constructed by deep learning, and skeleton point sequence information is obtained by arranging according to a time sequence. And on the basis, standard degree scoring information is determined by using a sit-up standard model constructed through deep learning, so that whether the sit-up is qualified or not is judged, and the number of times is counted. By means of the process, automatic and intelligent sit-up counting is achieved, errors and subjectivity of manual counting are reduced, and counting accuracy and efficiency are improved.

The invention provides a human skeleton behavior recognition method based on a multi-modal enhancement converter and a hypergraph structure. The method comprises the following steps: S1, acquiring original skeleton data representing human behavior recognition; preprocessing the skeleton data to obtain four skeleton modal data in total; s2, carrying out single-mode feature extraction on the four types of skeleton modal data, and then carrying out multi-mode feature fusion to form unified skeleton features; s3, performing feature refining and three-dimensional dynamic feature reinforcement on the skeleton features of the unified mode in time and space; and S4, the refined skeleton features are subjected to a global average pooling layer and a full connection layer to obtain a behavior identification prediction result, and the result is a discrete action classification label. According to the method, a lightweight multi-modal converter network and a double hypergraph topological structure are constructed to enhance the multi-modal skeleton data fusion performance and capture the cooperative motion relationship between joint groups, so that the recognition accuracy and recognition efficiency of double interaction behaviors are improved.

The invention discloses a burn patient rehabilitation training method and system based on virtual reality. The method comprises the steps that information of a to-be-trained patient is acquired; the information of the patient to be trained is input into a rehabilitation training model, a rehabilitation training result is obtained, the rehabilitation training model is constructed through a Kinect V2 module and a Leap Motion module, the Kinect V2 module is used for capturing human skeleton points, and the Leap Motion module is used for recognizing hand postures; and in combination with a historical training result, evaluating the rehabilitation training result, obtaining stage rehabilitation suggestions, and completing the current training. The limb rehabilitation training system has the advantages of improving the rehabilitation training efficiency, effect, safety, individuation and the like, and has important significance and application value for limb rehabilitation training.

The present invention discloses an intelligent rehabilitation assistance training system for spinal degenerative diseases based on deep learning. The system of the present invention includes a real-time classification module for traditional Chinese medicine guiding techniques videos based on deep learning and a video sequence division and evaluation module based on human skeleton representation; the former obtains two-dimensional human skeleton data as the training data of the learning model, conducts deep learning training to obtain a generalized deep learning model, and finally obtains the real-time frame classification result; the latter, according to the frame classification result, segments and corrects the skeleton sequences of the same category in real time, and compares and scores the segmented sequence segments with the skeleton sequence segments of the expert group videos of the corresponding category. The system of the present invention does not require the guidance and intervention of medical staff, enables patients to perform traditional Chinese medicine guiding techniques training by themselves at any time, is applicable to families and primary medical and health institutions, can relieve the pressure of medical staff, and improve the flexibility and accuracy of patients' rehabilitation training.

The invention discloses a human body behavior recognition method, system and device based on a space-time shift attention mechanism and a medium. The recognition method comprises the following steps: firstly, performing position coding on human skeleton data as input of the spatial feature extraction module; secondly, a spatial feature extraction module extracts spatial features through global cyclic shift operation, a physical topology matrix and a spatial self-attention mechanism; then, taking the output of the spatial feature extraction module as the input of a time feature extraction module, and extracting time features by the time feature extraction module through a multi-scale time convolution module and a time transformer module; then, the classification module performs classification according to the features extracted by the space and time module to obtain the output of the single-joint flow; similarly, obtaining the output of the single skeleton flow; and finally, carrying out weighted fusion on the single-joint flow output and the single-bone flow output to obtain final recognition precision. According to the method, richer local and global space-time feature information can be captured, the human skeleton topological relation is effectively supplemented, and the method has the advantage of effectively extracting the local and global space-time dependency relation between the nodes.

The application discloses a kind of spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask, comprising: extracting human skeleton sequence data from original video and carrying out data enhancement;Based on dynamic semantic guide mask mechanism, the skeleton sequence after data enhancement is masked in operation in two dimensions of space and time;Skeleton sequence data after masking operation is sent into query encoder and momentum encoder respectively, spatial representation and time representation are obtained respectively, cross-domain contrast loss is constructed, contrast learning training is carried out to obtain human behavior recognition model;The video to be identified is input into human behavior recognition model, and the prediction result of human behavior in the video to be identified is obtained.The spatio-temporal decoupling human behavior recognition method, device and equipment based on dynamic semantic guide mask of the application significantly improve human behavior recognition precision and environmental adaptability without manual label.

The invention provides a human activity fine-grained identification method based on a human body RFID skeleton, and belongs to the field of deep learning. According to the method, an advanced identification method and existing problems are considered, firstly, 16 RFID flexible tags are arranged at main joints of a human body by using an RFID technology and distribution of a human skeleton, a new human activity data set is acquired by reading the tags, the data set is an activity data set with most human activity types in the HA R field of bound RFID so far, and the data set is a new human activity data set with most human activity types in the HA R field of bound RFID. The problems that at present, the HA R based on the binding type RFID mainly utilizes human body trunk movement information, four-limb movement information related to human activities is lacked, human activity characteristics cannot be effectively captured, and the types and the number of human activities are small are solved. Then, based on the sampling data, a space-time skeleton diagram of human body activities is designed, and the problem that at present, HAR based on binding type RFID cannot utilize a graphic interaction network to classify graphs is solved. And finally, designing a space-time skeleton diagram convolutional network for human activity identification, and classifying the space-time skeleton diagram to realize human activity identification, thereby solving the problems that the HAR based on binding type RFID uses the traditional machine learning as an identification method at present, the HAR is limited in modeling capability for complex activities and low in accuracy. Experiments show that the method is a simple and feasible fine-grained human activity identification method.

This invention belongs to the field of computer vision technology, specifically to a method for extracting a human skeleton from point cloud data. To make the human skeleton extracted in complex environments more stable and robust, this method uses a trained deep learning network to extract features from the original point cloud. This method then aggregates these features using a feature pooling operation, deriving joint points from the aggregated features. Finally, these joint points are connected in a specific order to form a three-dimensional human skeleton, enabling applications such as behavior recognition and other human-computer interaction applications.

The invention discloses an attitude estimation method based on graph convolution and double-branch fusion, and the method comprises the steps: employing a double-branch structure, a Mama branch and a Transform branch to work in parallel, enabling the Mama branch to process long-distance time dependence information through employing an efficient state space model, capturing a long-time-span correlation mode, and enabling the Transform branch to carry out the parallel processing of the long-distance time dependence information; the Transform branch strengthens modeling of local and global attention through a self-attention mechanism and pays attention to interaction relations of different time steps, and after the two branches are output and fused, dynamic changes of human body joint points on a time sequence can be more accurately expressed, a complex time sequence mode can be flexibly and effectively processed, different human body actions can be better adapted, and posture estimation accuracy is improved. The GCN is used for extracting the spatial topological features of the human skeleton, generating the preliminary feature representation, subsequently performing spatial structure optimization on the fused time features by using the GCN, and generating the structured associated features, so that the mode of combining the spatial information and the time sequence information can more comprehensively understand the posture of the human body and consider the dynamic change of the time and the relative position and the connection relationship of the space.