331 results about "Posture recognition" patented technology

The invention relates to the technical field of multi-modal posture recognition, and relates to a multi-modal posture recognition system based on Transform fusion, which fully excavates the complementary advantages of visual information in spatial detail representation and inertial information in time sequence dynamic capture through space-time alignment of multi-modal data and deep fusion based on an attention mechanism. The accuracy and the stability of attitude estimation under the conditions of visual occlusion, rapid movement and complex illumination are obviously improved; posture optimization is carried out by introducing physical constraints such as bone length constancy and joint movement range limitation, and time domain smoothing and contact state correction are applied, so that the precision of the generated three-dimensional posture sequence is ensured, a solid technical foundation is laid for improving the naturalness, safety and intelligent level of man-machine interaction, and the method is suitable for popularization and application. And meanwhile, reliable application and deep development of the intelligent robot in key fields of service robots, virtual reality, remote cooperation and the like are powerfully promoted.

The invention provides a physical training posture correction method based on machine vision, which comprises the following steps: acquiring an athlete training image sequence through a multi-view image acquisition system and preprocessing the athlete training image sequence, extracting three-dimensional posture key points of a human body by utilizing a depth posture recognition model, constructing a skeleton model and calculating real-time kinematics characteristic parameters, and performing multi-dimensional comparison with standard parameters to generate a posture deviation evaluation report, thereby generating a personalized multi-modal correction feedback scheme, and establishing a personal athlete movement feature database to realize adaptive standard parameter optimization. According to the method, the training postures of athletes can be accurately recognized and corrected in real time, the training effect can be improved, sports injuries can be prevented, and training individuation and scientificity are improved.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The invention discloses a millimeter-wave radar sparse point cloud-based human body posture recognition method, which belongs to the technical field of human body posture recognition, and comprises the following steps of: acquiring three-dimensional point cloud data of human body actions through a millimeter-wave radar, and preprocessing the three-dimensional point cloud data; and training a lightweight neural network model by using the preprocessed point cloud data, wherein the model comprises an edge convolution module and a grouping sparse Transform encoder module. The edge convolution module extracts spatial geometric features and detects dynamics, static postures are directly classified, dynamic postures capture a time sequence dependency relationship through a grouping sparse Transform module, attention calculation is only carried out on frames with feature changes exceeding a threshold value, and mean pooling aggregation is carried out on other frames. And finally, classifying the human body postures based on the spatial geometric features and the time sequence dependency relationship to obtain a classification result. The device is simple in structure, accurate in recognition and suitable for efficient deployment of edge equipment.

The invention provides an intelligent warehouse goods posture recognition and automatic sorting method and system, and the method comprises the steps: S2, extracting the edge contour and surface features of goods from point cloud data for a preliminary value set, calculating the direction vector and shape distribution characteristics of the goods through a principal component analysis method, and obtaining a quantitative result of shape feature extraction; s6, historical sorting data and real-time sensor data are extracted from the warehousing system database according to the classification basis adapted to the complex scene, the dynamic posture change trend of the goods is predicted through a time sequence analysis method, and optimization parameters of real-time processing are obtained; and S7, the movement track and the grabbing angle of the sorting mechanical arm are adjusted through the optimization parameters subjected to real-time processing, a deep reinforcement learning algorithm is adopted to conduct iterative optimization on the sorting action sequence, and an execution scheme of sorting accuracy is determined. According to the method, the accuracy of goods posture recognition and the automatic sorting efficiency in the complex storage environment are remarkably improved.

The invention belongs to the field of power distribution network monitoring, and provides a power distribution box damage behavior monitoring method and system based on human body posture estimation, and the method comprises the steps: obtaining a monitoring video of a power distribution box terminal for preprocessing, and obtaining a preprocessed video frame; based on the preprocessed video frame, performing posture recognition by using a pre-trained human body and distribution box posture recognition model to obtain a human body and distribution box posture recognition result; key point tracking and identity association are performed by using a human body and distribution box posture recognition result, a space-time skeleton diagram is constructed, and the space-time skeleton diagram is screened to generate a skeleton space-time sequence; on the basis of the skeleton space-time sequence, a pre-trained space-time diagram convolutional network model is utilized to identify a damage behavior, and a damage behavior identification result is obtained; when the confidence coefficient in the damage behavior recognition result exceeds an alarm threshold value, alarm information is generated, and an alarm process and safety linkage control are triggered. According to the invention, accurate monitoring and real-time early warning of the damage behavior of the distribution box are realized.

The invention relates to the technical field of visual recognition, and particularly provides a bottle sorting-oriented three-dimensional visual posture recognition method and related equipment, and the method comprises the steps: obtaining three-dimensional point cloud data and RGB image data of bottles on a conveyor belt, carrying out the preprocessing, feature extraction and geometric analysis of the three-dimensional point cloud data, and obtaining six-degree-of-freedom posture parameters; and the six-degree-of-freedom attitude parameters are projected to RGB image data, the overlap ratio of the projection contour and the actual bottle contour is calculated, and when the overlap ratio is larger than a preset threshold value, it is confirmed that the attitude is effective, and the corresponding attitude parameters are output to a mechanical arm sorting control system. Six-degree-of-freedom posture parameters of the bottle body can be obtained through posture alignment and template point cloud registration, reliability confirmation of a posture recognition result is achieved through overlap ratio verification in an RGB image data domain, and the defect that the posture recognition result is not reliable under the conditions that the posture change of the bottle body is large, the appearance of the bottle body is diversified and the bottle body is inclined or shielded and interfered is overcome. The problems of insufficient recognition precision and incomplete attitude parameters exist.

The invention relates to the technical field of industrial automation, in particular to an industrial robot posture recognition method. Aiming at the problems of weak workpiece texture, strong surface reflection, serious stacking and shielding and the like in an industrial production line, a pixel-level dense feature fusion and self-attention mechanism is introduced, and semantic texture information of a color image and spatial geometric information of a depth image are integrated in a feature extraction stage; the problem that the posture of a rotationally symmetrical workpiece is fuzzy is solved through an asymmetric loss function, meanwhile, a comprehensive grabbing scoring model containing force sealing stability, environment collision risk probability, mechanical arm kinematics reachability and visual uncertainty is further established, and through the mode of combining off-line grabbing candidate construction and on-line real-time evaluation, the grabbing accuracy of the mechanical arm is improved. And the globally optimal grabbing pose is screened out. According to the method, the recognition precision and robustness of the industrial robot in the unstructured environment are remarkably improved, and the success rate and safety of grabbing operation are improved.

The invention discloses a target vehicle attitude recognition method based on 3D point cloud feature extraction, and the method comprises the steps: carrying out the preprocessing of the point cloud data of a target vehicle obtained through a laser radar, and the preprocessing comprises Gaussian statistical filtering, voxelization sampling, and vehicle tire point cloud extraction based on a region growing algorithm; based on the point cloud data, key points of tire steering features are extracted, and a Harris corner detection method is used for detecting the key points; calculating a fast point feature histogram (FPFH) descriptor based on the extracted key points, and performing coarse registration by adopting an SAC-IA algorithm to obtain a coarse registration matrix; and using a point-to-surface nearest point iteration ICP algorithm to perform fine registration on the point cloud after coarse registration to obtain attitude information of the target vehicle. According to the method, the three-dimensional attitude angle of the target vehicle is accurately estimated by preprocessing the laser radar point cloud, extracting tire key points and carrying out SAC-IA coarse registration and ICP fine registration. Therefore, vehicle attitude recognition with high shielding robustness and high precision is realized, and the safety of automatic driving is enhanced.

The invention relates to the technical field of posture recognition, in particular to a motion posture recognition method and system based on deep learning, and the method comprises the following steps: based on a human body motion image sequence, analyzing a skeleton center space trajectory, adjusting an acquisition visual angle, recognizing staged skeleton nodes, and generating a node time sequence trigger sequence; and optimizing joint point angle feature grouping and expression to obtain a hierarchical feature expression structure. In the invention, through detection for interference dynamic change characteristics, visual angle adjustment and node staged response in a time sequence are combined, compensation reasoning of a space trajectory is enhanced, and the hierarchical expression capability of motion capture is improved; the input features have the advantages of actively screening background disturbance, dynamically adapting the motion direction, separating action core nodes and perfecting node response association and grouping angle time sequence structures, the integrity and identification degree of action feature structure expression and hierarchical modeling are guaranteed, and higher identification accuracy and complex environment adaptability are brought.

The invention relates to a tumble risk assessment method and system based on a multi-modal deep learning network, and relates to the technical field of human body posture recognition, and the method comprises the steps: collecting a scene motion image sequence, a plantar pressure feature sequence and personnel basic information; analyzing the scene motion image sequence by using a human body posture estimation network model to determine a human body key feature point sequence; performing feature extraction on the human body key feature point sequence and the scene motion image sequence to generate human body posture sequence features; performing feature extraction fusion on the human body key feature point sequence and the plantar pressure feature sequence to generate human body motion fusion time sequence features; performing feature extraction on the personnel basic information to generate personnel basic information features; and inputting the human body posture sequence features, the human body motion fusion time sequence features and the personnel basic information features into a multi-modal deep learning network for analysis so as to determine a fall risk score. The method and the device have the effect of improving the precision of fall risk assessment.

The invention belongs to the technical field of underground safety intelligent monitoring and early warning, and particularly relates to an underground safety posture recognition and early warning method based on self-adaptive cascade skeleton compensation, which comprises the following steps: S1, acquiring real-time video stream data of a coal mine underground hydraulic support operation area; s2, extracting key point coordinates and confidence of the skeleton frame by frame by using a lightweight attitude estimation network deployed at an edge computing terminal; s3, for each frame, counting the number of continuous missing frames from each key point to the current frame, and calculating the global missing key point proportion; s4, performing branch compensation on the current frame based on the diagnosis result of S3; s5, calling a preset operation safety risk index model, outputting a real-time risk score of the current frame, and identifying whether a violation behavior violating the violation posture rule exists or not; and S6, triggering a grading early warning mechanism. According to the method, the real-time performance of the edge terminal can be guaranteed, and meanwhile high-robustness recognition and accurate safety evaluation of the posture of the miner in the operation area of the hydraulic support are achieved.

The present disclosure provides an apparatus configured to perform gesture recognition and communicate with a smartphone, comprising one or more sensors configured to detect movement of at least one wearable component associated with a user, one or more memories configured to store gesture data, and one or more processors, coupled to the one or more memories and the one or more sensors, configured to capture, via the one or more sensors, motion data corresponding to movement of the at least one wearable component, input the motion data into a machine learning model trained to predict gestures, output, by the machine learning model, a gesture identifier based on the motion data, and transmit, via a wireless communication interface, the gesture identifier to a smartphone device.

The invention discloses a multifunctional automatic comprehensive optometry combined operation table and a use method thereof. The operating platform comprises a mechanical main body structure, a posture sensing system and a central processing and control unit. The mechanical main body structure comprises an optometry chair driven by a rotating disc, a lifting mechanism and an electric rotating shaft, and the posture sensing system is composed of a sitting and leaning posture recognition mechanism used for recognizing body postures and a head posture recognition mechanism used for capturing head movement. The central processing and control unit judges the posture of the optometry person according to the sensing data, and if the posture does not meet the preset standard, the seat height, the backrest angle and the horizontal position are adjusted through voice prompt and an automatic driving mechanism; when the posture reaches the standard, the optometry equipment is automatically controlled to complete operation, automatic and accurate correction of the posture before optometry and intelligentization of the optometry process are achieved, and the accuracy and efficiency of optometry results are effectively improved.

The invention discloses a shoulder joint rehabilitation action image recognition and real-time posture correction feedback method and system, and belongs to the technical field of medical image processing and computer vision, and the method comprises the steps: collecting a rehabilitation training image frame sequence through an RGB camera; inputting the image into a human body analysis model, extracting skeleton key point coordinates and calculating a joint angle sequence; constructing the skeleton key points into a space-time diagram structure, and inputting the space-time diagram structure into a space-time diagram convolutional network for action classification and recognition; aligning and comparing the actual joint angle with the standard action template by adopting a dynamic time warping algorithm to calculate a deviation vector and an action compliance score; a standard posture skeleton line and an angle deviation indicating arrow are overlaid and rendered on an image, the compliance degree of each joint is displayed in a color gradient mode, an action compliance degree score and personalized correction suggestions are output, the error posture recognition accuracy is larger than 92%, the feedback delay is smaller than 100 ms, and the technical problem that home rehabilitation lacks professional action guidance is solved.

The invention relates to an algorithm and a system for evaluating and detecting agile strength movement postures of special children. The system comprises a user information initialization module, a detection task guide module, a posture recognition module, an index extraction and algorithm analysis module, an auxiliary equipment module and a detection result output module. The system recognizes skeleton key points by collecting video images, extracts multi-dimensional motion indexes such as rhythm synchronization, attitude stability, reaction time delay and motion symmetry, and performs comprehensive scoring by using normalization processing and weight superposition algorithms. In the detection process, a guiding interface is matched with voice prompt to achieve standardized operation, and a structured image-text evaluation report and personalized training suggestions are output. The system is suitable for individualized action ability detection in education, rehabilitation and family scenes, has the characteristics of convenient deployment, accurate recognition, quantifiable result, strong adaptability and the like, and effectively solves the problems of scattered evaluation process, subjective scoring, functional dimension deficiency, poor individual adaptability and the like in the prior art.

The invention provides a human body posture recognition and rehabilitation training evaluation method and system based on deep learning, and relates to the technical field of human body posture recognition. According to the method, multiple human body key points are extracted from a pre-acquired user image by using a deep learning attitude estimation model, then measurement data of human body key angles are acquired based on the human body key points, and then dynamic smoothing processing is performed on the human body key angle data based on an adaptive filtering algorithm introduced with a multi-level smoothing factor switching mechanism. And finally, outputting the smoothed key angle data of the human body in real time, and performing human body posture recognition and rehabilitation training evaluation according to the smoothed key angle data of the human body. Compared with the prior art, different dimensions such as real-time performance, measurement precision, equipment portability, functional integrity and user experience are remarkably improved.

The invention provides an unmanned plate sorting area dynamic safety protection system based on multi-modal fusion perception, relates to the field of plate sorting protection, and solves the technical problem that adaptive protection cannot be performed on dynamic complex risks by depending on static rules and a single data source. The method comprises the following steps: a multi-sensor data acquisition and fusion module obtains an environment perception data set; and the personnel posture recognition and trajectory tracking module is used for analyzing the score of the correlation degree between the working personnel posture and the environment and outputting the personnel posture category, the position coordinate and the movement speed in real time. And the robot trajectory analysis and multi-channel prediction module outputs multi-channel trajectory prediction data containing short-term and long-term prediction results in real time through a kinetic model or a learning predictor. And the collision risk fusion judgment module is used for determining a historical high-frequency close-range event occurrence area and calculating a collision risk probability. And the grading early warning module generates a corresponding early warning instruction and an obstacle avoidance strategy. The plate sorting device is used in the plate sorting process.

The invention relates to a sleeping posture recognition and self-adaptive pillow height adjustment system and method and a storage medium, and the sleeping posture recognition and self-adaptive pillow height adjustment system comprises a data collection module, a sleeping posture recognition module, a pillow height adjustment module and a user interaction module; the user interaction module is used for acquiring personalized prior data and historical sleep comfort score of a user; the data acquisition module is used for acquiring a pressure data set of the pressure sensor array in real time; the sleeping posture recognition module is used for constructing a plurality of groups of time domain data slices for the pressure data set according to a preset time window, inputting the time domain data slices into a time domain feature model to obtain a plurality of groups of low-dimensional pressure data feature parameters, and further recognizing an effective sleeping posture state; and the pillow height adjusting module is used for calculating and calibrating the pillow height based on the personalized prior data of the user, the effective sleeping posture state and the historical sleeping comfort score, and adjusting the pillow surface. According to the method and the device, the problems of high computing power consumption and difficulty in meeting precise and personalized sleep requirements of the user in related technologies are solved.

The invention relates to the technical field of action recognition, in particular to an AI-based motion posture recognition system, which comprises a key point extraction module, a graph structure construction module, a node trajectory modeling module, an action segmentation analysis module and a posture recognition output module. According to the method, the spatial position of each node of the human body in the continuous image under the time sequence structure is extracted, and the graph structure sequence based on the topological relation between the nodes and the motion trend is established, so that the dynamic modeling and the structure expression of the skeleton motion can be realized, and the trajectory characteristics of node position change, speed fluctuation, direction change and the like are analyzed; according to the method, key paragraphs with inversion and abnormity in actions are effectively identified, action segmentation classification is carried out in combination with frame sequence spacing and key node activity states, accurate analysis of complex action processes is realized under the condition of not depending on external hardware, and the accuracy and timeliness of posture identification are improved in a mode of matching with a standard template.

The present disclosure provides a multi-dimensional overload information-based attitude recognition method, which continuously samples three-axis acceleration sensor overload signals and converts them into one-axis overload data and two-path overload data; determines the entry into the target and the exit from the target based on the axis overload data; calculates the real-time attitude angles of two radial directions according to the two-path overload data, and synthesizes the real-time attitude angle of the penetrator; and recognizes the attitude of the penetrator according to the attitude angle at the moment when the penetrator exits the target. The present application uses a three-axis overload sensor to collect axis and two-path multi-dimensional overload information, breaks through the limitation of single-dimensional monitoring, can comprehensively capture the subtle changes of the projectile stress and attitude in the penetration of long-stroke and complex medium, significantly improves the accuracy and integrity of attitude recognition, and avoids the attitude deviation and missed judgment caused by insufficient monitoring dimensions.

The application provides a posture recognition method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: obtaining target data related to the posture of a target object in a detection area based on positioning of the target object; performing time-frequency domain feature extraction on the target data to obtain first target features and second target features; the first target features are frequency domain expressions of the posture of the target object in the detection area, and the second target features are time domain expressions of the posture of the target object in the detection area; and the posture of the target object is recognized according to the first target features and the second target features to obtain a posture recognition result. The application solves the problem of low accuracy of posture recognition in related technologies.

This invention relates to the field of posture recognition technology and provides a deep learning-based, efficient 3D human posture recognition method, comprising the following steps: recording video data; performing 3D posture estimation on all images in the video using BlazePose to extract 3D keypoint coordinates; then performing data augmentation by randomly jittering and mirroring the obtained keypoint coordinates to expand the dataset; constructing a KpointNet model based on a point cloud model; training the KpointNet model to generate a training model; and finally testing the trained model and applying it in practice. This invention can obtain richer spatial information from RGB images, increasing the information dimensionality; the KpointNet model based on a point cloud model improves the inference utilization of the network model and has better accuracy; directly learning from 3D keypoint data results in more efficient data utilization; this invention can efficiently identify human posture categories in 3D, correct poor posture in a timely manner, and prevent potential health risks.

The invention discloses a display height adaptive adjustment method and system based on user posture recognition, and the method comprises the steps: processing a user posture image through a lightweight MTCNN model, dynamically adjusting the image contrast based on a Retinex theory, and obtaining an enhanced user posture image; detecting eye pixel coordinates and shoulder key point coordinates in the enhanced user posture image by using a lightweight HRNet model, and converting the eye pixel coordinates into three-dimensional space coordinates with the center of a display as an original point in combination with depth map auxiliary distance data; acquiring a comfortable visual angle established by initial calibration of the user, calculating a sitting posture compensation amount based on the shoulder key point coordinates, and calculating a target height according to the sitting posture compensation amount; and performing Kalman filtering on the three-dimensional space coordinates, predicting an attitude change trend through a lightweight LSTM network, and generating an adaptive adjustment instruction according to the attitude change trend and the target height. And the height self-adaptive adjustment efficiency and accuracy of the display are improved.

The application discloses a kind of continuous production line product automatic tray placing control system and method, tray placing demonstration, preset standard posture threshold and product identification area, after obtaining current product real-time posture image, whether the real-time posture of current product meets the requirement of tray placing according to standard posture threshold comparison judgment, again, the product that meets the requirement of tray placing is grabbed, and is rotated to the angle that meets the requirement of tray placing, finally, according to planned path, it is placed in turn to tray;Solve the problems of poor metal part posture recognition, insufficient versatility and low efficiency in the prior art, can meet the requirements of automatic, high precision and high adaptability tray placing operation of precision parts after continuous production line punching, shearing and bending forming.

The embodiment of the invention discloses a sleeping posture recognition method and device, electronic equipment and a storage medium, and relates to the technical field of sleeping posture recognized.The method comprises the steps that a reset gate and an update gate in a gate control loop unit GRU are designed to share an activation function, the activation function is combined into a reset update gate, and a WCGRU model is obtained; combining a root-mean-square error, a mean absolute error, a recall rate and an accuracy rate to construct a comprehensive weighted quality assessment index WEM; constructing an optimization algorithm taking the WEM as a target function, and finding out optimal hyper-parameter configuration for the WCGRU model according to the target function; collecting a data set of BCG three-channel signals, inputting the data set into the WCGRU model to obtain model output, and training the WCGRU model by reducing an error between the model output and target output and updating the weight of the data set; when the error reaches a threshold value, a trained WCGRU model is obtained, and sleeping posture recognition is carried out on collected signals through the WCGRU model. The problems that in the prior art, model memory consumption is large, and high accuracy and low resource consumption efficiency cannot be considered at the same time are solved.

An object of a system according to an embodiment is to accurately recognize a posture during training and provide guidance in real time.SOLUTION: A system includes a posture recognition unit, a difference calculation unit, an instruction unit, a superimposition unit, a motivation improvement unit, a moving image generation unit, and a nutrition value analysis unit. The posture recognition unit recognizes a posture during training using an image recognition technique on a moving image captured by a camera of a smartphone. The difference calculation unit calculates a difference between the posture recognized by the posture recognition unit and the video of the trainer. The instructor instructs the posture in a natural language in real time on the basis of the difference calculated by the difference calculator. The superimposition unit superimposes the posture of the user on the moving image of the trainer. The motivation improving unit provides a cheering message in a natural language. The moving image generation unit automatically generates an optimal training moving image. The nutrition value analysis unit analyzes the photograph of the meal to analyze the nutrition value.SELECTED DRAWING: Figure 1

The application discloses a posture recognition method and device, a posture recognition model training method and device, equipment and a medium. The embodiments of the application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving. The method comprises the following steps: acquiring a first image of a target object; acquiring image position information of a first key point based on the first image, the image position information being used for indicating a position of the first key point in the first image, the first key point being a key point used for describing a two-dimensional posture of the target object; performing feature extraction on the image position information to obtain posture representation features of the first image; and identifying a three-dimensional posture of the target object based on the posture representation features of the first image to obtain target three-dimensional posture parameters. In this way, the three-dimensional posture of the target object is identified based on the posture representation features, the process is relatively simple, the calculation amount is saved, and the efficiency of posture recognition is improved.

The invention discloses a human body posture recognition method, which comprises the following steps of: 1, acquiring the number of people to be recognized and file data of human body posture key points corresponding to each person by utilizing a posture key point classification model; 2, screening key points of the human body posture, and screening six main key nodes of a left elbow, a right elbow, a left hip, a right hip, a left knee and a right knee; 3, judging whether the coordinates of the three auxiliary key points of each main key node are complete or not; 4, recognizing a human body posture; and 5, determining a human body shielding condition. According to the method, the human body posture is judged by determining the angle of each key point, learning the position of each joint of the human body, the angle of limbs and other complex features, calculating the angles of six body key nodes of left and right elbows, left and right hips and left and right knees and refining different conditions, and the problems that a traditional human body posture recognition technology is few in recognizable posture type and high in recognition efficiency are solved. And the precision and the speed of human body posture recognition are improved.

The present application relates to a kind of based on gyroscope three-axis angular velocity signal's cattle posture recognition method, belong to the technical field of the cross of animal behavior recognition, sensor signal processing and artificial intelligence application.The method includes the following steps:1) data acquisition and feature construction;2) unsupervised clustering label generation;3) supervised signal processing and classifier construction;4) posture discrimination.This method is by collecting the three-axis angular velocity data generated by gyroscope sensor in cattle body, and is combined with feature extraction, unsupervised clustering and supervised classification etc., realizes the automatic identification to cattle posture state.The data relied on by the present application all come from in-vivo sensor signal, is almost not disturbed by temperature change, topography fluctuation and breeding environment etc.external factors, therefore has data acquisition stable, low computing overhead, deployment flexible etc.significant advantages, can be widely applied to captive, grazing etc.various breeding scenarios, can effectively improve the practicability and adaptability of cattle behavior monitoring system.