544 results about "Posture recognition" patented technology

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

The invention relates to the technical field of rehabilitation training detection, in particular to a rehabilitation training detection method and system based on artificial intelligence, a standard action library is constructed through standard action videos shot at multiple angles, and track and angle features of key joints are extracted for user training comparison; the skeleton key points of the user are extracted in real time through a MoveNet network, and efficient posture recognition in a home scene is achieved; analyzing position difference, angle change and acceleration characteristics by combining a space-time sequence matching algorithm, generating a dynamic matching degree index, and positioning a deviation joint to generate a correction prompt; introducing an attention mechanism model, learning the contribution degree of each joint to cycle recognition, dynamically selecting a dominant joint for action counting, recognizing starting and ending points of an action cycle through an acceleration curve, and finishing effective action statistics in combination with a dynamic threshold value, so that the counting accuracy and the self-adaptive capability are improved; therefore, the training cost is reduced, the evaluation credibility is enhanced, and accurate statistics and analysis of rehabilitation training data are realized.

The invention discloses a human motion posture recognition method and system based on multi-modal data fusion, and relates to the technical field of posture recognition, and the method comprises the steps: firstly, synchronously obtaining a human motion posture video frame and an IMU segment, then carrying out the feature extraction of the two kinds of heterogeneous data in a shunt parallel mode, and carrying out the feature extraction of the two kinds of heterogeneous data; and respectively capturing visual space attitude information and dynamic inertial characteristics of the IMU. Afterwards, specific features of the modals are uniformly expressed through a mixed Token and embedding mechanism, and a space-inertia collaborative attention fusion mechanism is further introduced to realize dynamic association and deep fusion of cross-modal information; and finally, classifying the multi-modal fusion representation vector obtained by fusion so as to realize accurate recognition of the human motion posture. In this way, the defect that traditional fusion is insufficient in capturing subtle action differences can be overcome, and the accuracy and stability of action recognition in a complex scene are improved.

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 discloses a three-dimensional human body posture estimation method and system, and relates to the technical field of computer vision, and the method comprises the steps: extracting two-dimensional human body posture key points from a monocular video picture sequence, and generating a two-dimensional human body posture key point sequence; projecting the two-dimensional key point sequence to a feature space through nonlinear high-dimensional mapping to generate a high-dimensional feature space matrix; and inputting the high-dimensional feature matrix into a three-dimensional human body posture recognition model fusing motion constraints and frequency division spatial-temporal features to obtain a three-dimensional human body posture key point sequence, and realizing three-dimensional human body posture estimation through three-dimensional coordinates. According to the method, the robustness and the detection precision of the monocular three-dimensional human body posture estimation method are improved. Error values of relative movement speed, skeleton length and skeleton direction of the key points are calculated, so that training is easier to converge, and the training process is more stable.

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 discloses a shoe tree grabbing robot track planning system and method based on multi-modal sensing. The system comprises a shoe tree storage area, a mechanical arm grabbing assembly, a multi-modal sensing device, a posture recognition module, an interference sensing module, a grabbing point optimization module, a stable window generation module, a path planning module, a fine adjustment control module and a self-learning module. The system extracts the posture of the shoe tree by fusing visual images, structural sound waves and infrared contour information, then utilizes interference fringes to analyze and judge the material and friction characteristics, combines stability to judge and screen a grabbable target, and executes posture fine adjustment and fitting at the tail end of a clamping jaw through a touch sensing film. And the grabbing failure information is used for updating the strategy database to realize self-adaptive optimization of the grabbing strategy. The method is suitable for grabbing special-shaped objects and is high in robustness and success rate.

The invention discloses a manipulator grabbing control method and equipment based on position and posture recognition. The manipulator grabbing control method comprises the following steps that S1, initial image information of a target object is acquired through image acquisition equipment; s2, performing preprocessing and feature extraction on the initial image information, and obtaining three-dimensional position coordinates and attitude parameters of the target object through a preset position and attitude recognition algorithm; and S3, according to the three-dimensional position coordinates and the posture parameters, a grabbing path of the manipulator is planned by combining a kinematic model of the manipulator. By introducing technical means such as a deep learning recognition algorithm, forward / inverse kinematics model collaborative planning, real-time posture dynamic adjustment and force sensing feedback control, the problems that a traditional mechanical arm is low in grabbing precision, poor in adaptability and insufficient in operation stability are solved, intelligent and high-precision grabbing control in a complex scene is achieved, and the grabbing precision of the mechanical arm is improved. And the requirements of the modern industry on high efficiency, reliability and flexibility of automatic equipment are met.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

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 discloses a natural growth apple three-dimensional attitude recognition method based on a real-time multi-task fusion algorithm, and the method comprises the steps: obtaining an aligned RGB-D image through a depth camera, constructing a data set in combination with an image enhancement technology, and synchronously training a classification, segmentation and key point detection model through a multi-task deep learning framework; judging the contour form of the apple through the classification model, and carrying out contour fitting by adopting a convex hull algorithm and an arc adjacency matrix ellipse detection algorithm; two-dimensional centroid positioning and three-dimensional point cloud analysis technologies are fused, and bimodal calculation in the three-dimensional fruit axis direction is realized in combination with a fruit stem and calyx key point detection result. According to the method, the precision problem of apple posture recognition in a complex natural environment is effectively solved, the anti-interference capability is remarkably improved through the three-dimensional point cloud denoising and feature value analysis technology, and reliable technical support is provided for automatic picking and growth monitoring.

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 provides a pallet position identification system and method, and relates to the technical field of image identification, and the method comprises the steps: carrying out the Gaussian filtering and Canny edge detection of image data, extracting the edge features of pallets, judging whether there is an overlapped pallet in combination with depth information, and carrying out the layered identification of an overlapped region to mark a target pallet. Calculating a contour vector of the target pallet, and mapping the contour vector to a three-dimensional space by using depth information to obtain a spatial position coordinate; when the position meets a safe distance threshold value, angular point detection is carried out, coordinates of four angular points are extracted, orientation angles are calculated, and complete position features are output. According to the method, the problems of incomplete pallet edge detection, large space positioning error and inaccurate posture recognition are effectively solved, the pallet detection precision and reliability are improved, and meanwhile, the carrying safety in a storage environment is ensured.

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 relates to the technical field of human body posture recognition, solves the technical problems that a traditional method increases calculation burden in multi-scale fusion and is difficult to realize efficient operation of a network, and particularly relates to a lightweight human body posture estimation system and method fusing channel and space activation. An input image is input into an EDCNet network, and then key point positions of human body postures in the input image are obtained through output. According to the EDCNet network, the extraction process of local and global features is fused, fusion of information with different resolutions under double scales is realized, and a space segmentation module and a double-scale cross attention module are integrated. According to the method, the precision of human body key point estimation can be improved under the condition that traditional Transformers are not stacked, and compared with a traditional stacked ViT architecture, better balance is achieved between efficiency and precision.

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 discloses a motion posture recognition detection method based on transfer learning, belongs to the field of biomedical signal processing and machine learning, and aims at improving the robustness of a model under the condition of electrode position change by constructing an adversarial training mechanism and aligning data distribution before and after electrode offset in a feature space. The problem of data distortion caused by electrode displacement in the electrical impedance tomography EIT technology is solved, and the accuracy of posture recognition is improved.

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 the technical field of lighting devices, in particular to an intelligent lighting device with a fall monitoring function and a use method thereof. According to the technical scheme, the system comprises a filtering module, a PLC module, a central control unit, a posture recognition camera, an LED driving module, an LED light source module and an alarm, the posture recognition camera collects moving images of personnel in a monitoring area in real time, the central control unit analyzes the posture of the personnel based on an AI training model and judges whether a tumble event occurs or not, and if tumble is detected, the alarm gives an alarm. If yes, an alarm is started to give an acousto-optic alarm, and alarm information is pushed to a guardian terminal device through the Internet. According to the device, the power line carrier communication technology is adopted, additional wiring is not needed, the structural integration degree is high, non-contact and high-precision recognition of a falling event is achieved through the method, and the device has the lighting and safety monitoring functions and is suitable for families, nursing homes and other places.

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 relates to the technical field of image recognition, in particular to an aviation field-oriented image recognition tool control system, which comprises an illumination posture recognition module, a contour coherence screening module, a time response extraction module, a confidence fluctuation marking module and an acquisition frame retention module. According to the method, a stable region is screened according to the posture and illumination change of an image frame, clear image content is extracted in combination with edge gray aggregation features, and continuously appearing key entities are further recognized based on multi-frame target repeated response frequency by eliminating structural fracture and distortion paths, retaining coherent closed regions and improving image structure expression integrity. Scattered interference content is eliminated, fluctuation targets are screened out by using confidence section distribution stability, credibility and consistency of recognition results are ensured, finally, frame image recognition value is judged by combining image definition and target concentration degree, an image sequence with time sequence continuity and structure focusing characteristics is formed, and recognition accuracy and output stability are enhanced.

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 invention relates to the technical field of video communication, in particular to an intelligent AI glasses video call auxiliary system which comprises a visual adjustment module, a voice interaction module, a communication scheduling module, a posture recognition module and a feedback prompt module. According to the invention, by acquiring the eye movement track and the pupil diameter change, adjusting the image brightness and the color contrast in combination with the ambient light, relieving visual fatigue, enhancing the call comfort, analyzing the voice frequency band and matching the keywords, the accurate recognition of the operation intention is realized, the call response efficiency is improved, and the mistaken touch and delay are avoided; the call priority is judged according to the action state, the acceleration trend and the contact person weight, the task access accuracy is improved, the user intention is judged according to the head posture and the sight line trend, misoperation and repeated confirmation are avoided, interaction convenience is enhanced, meanwhile, the audio intensity and image prompt are regulated and controlled according to the task type and the prompt level, the multi-environment prompt requirement is met, and the user experience is improved. Information omission is reduced, and call experience is optimized.

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 relates to a monitoring method for an express sorting stage. The monitoring method comprises the following steps: S1, acquiring video stream data of a sorting area in real time; s2, performing image analysis on the video stream data, and identifying express number information; s3, the sorting action of the operator is detected based on a human body posture recognition algorithm; s4, judging whether the current sorting action is illegal or not according to a preset illegal sorting behavior rule; the illegal sorting behavior rule comprises an action specification type illegal behavior, an operation process type illegal behavior and a health and safety type illegal behavior; if it is judged that the current operation of the courier conforms to the violation behavior, recording the violation operation time, operator information, a corresponding express number and violation image evidence; and S5, generating a sorting violation statistical report and outputting alarm information.

The invention discloses an image generation method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial model image; performing human body posture recognition and mask segmentation processing on the initial model image to obtain a mask image; generating a clothing text cue word according to the basic quality constraint parameter, the semantic enhancement parameter and the attribute combination generation parameter; performing image generation processing on the mask image through a generation model according to the clothing text cue word to obtain a target generated image; and carrying out anti-detection edge optimization processing on the target generation image to obtain a target model image. The image generation efficiency can be improved, and the method can be widely applied to the technical field of image processing.

The invention discloses a health monitoring and correction method and device based on posture recognition, a medium and a product, and relates to the field of computer vision and health management cross technologies, and the method comprises the steps: obtaining RGB image data, depth map data and radar observation data in real time; respectively carrying out normalization processing on the RGB image data and the depth map data; the RGB image data after normalization processing and the depth map data after normalization processing are fused into RGB-D image data; an HRNet-Spine model is constructed, and the HRNet-Spine The RGB-D image data and the radar observation data are input into an HRNet-Spine model, and a spine key point detection result is obtained; obtaining a health score of the current posture based on a spine key point detection result; a gesture correction suggestion is generated based on the health score for the current gesture. According to the invention, efficient and accurate personnel health monitoring and real-time posture correction can be realized.