669 results about "Body posture" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of robot perception, in particular to an intelligent accompanying human-type robot based on multi-mode emotion interaction and a perception method, and the method comprises the following steps: collecting facial expression and body posture data of a user through a visual perception unit, collecting intonation, speed, volume and keyword data through a voice perception unit, and carrying out the recognition of the speech, speed, volume and keyword data; data preprocessing and feature extraction are carried out; secondly, carrying out time sequence modeling on the extracted visual and voice features based on a recurrent neural network, and generating visual and voice time sequence feature vectors; the method comprises the steps that firstly, time sequence features are extracted, then, a multi-mode fusion model based on an attention mechanism is adopted, the time sequence features are fused into a comprehensive emotion feature vector used for emotion state classification, and finally, personalized interaction content such as voice, expressions and limb actions matched with the emotion state of a user is generated according to an emotion classification result. According to the method, the accuracy of robot emotion perception and the naturalness of man-machine interaction are remarkably improved, and the method is suitable for multi-scene application of education, medical treatment, old-age care and the like.

The invention provides a bridge swivel attitude intelligent adjustment method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source monitoring data set generated in a bridge swivel process, carrying out the time-space alignment and fusion processing of the multi-source monitoring data set, generating fusion monitoring data, and carrying out the adjustment of the swivel attitude of a bridge. And based on the fused monitoring data and a preset target attitude parameter set, identifying attitude deviation characteristics between a bridge swivel attitude and a target attitude, generating an attitude adjustment strategy for a bridge swivel execution mechanism according to the attitude deviation characteristics, and executing the attitude adjustment strategy to adjust the attitude of the bridge swivel execution mechanism. And carrying out real-time posture adjustment operation on the bridge turning process. According to the method, the system reliability can be enhanced while the adjustment efficiency is improved, and the problems of insufficient control precision and response delay caused by data isolation and strategy staticization in a traditional method are solved.

The invention belongs to the technical field of three-dimensional reconstruction, and discloses a three-dimensional digital human generation method and system capable of voice interaction. According to the invention, brand new speaking audios in different languages are automatically generated according to different languages of the input target text and the sampled human voice audios; the sequential stability and detail reduction capability of three-dimensional human motion are guaranteed by using multi-model joint estimation and a sequential loss function, and facial expression details and hand postures in the image can be accurately estimated. After the high-precision three-dimensional human body model is obtained through estimation, human body action and expression generation is carried out based on voice driving, accurate synchronization of actions and expressions generated through voice is achieved, and facial expression movement and body posture movement, namely a whole-body three-dimensional human body model, conforming to brand-new speaking audio are accurately generated; and finally, rendering the whole-body three-dimensional human body model into a real digital human capable of voice interaction by using a three-dimensional neural rendering model. According to the invention, the realization of single person picture input, high-precision three-dimensional digital person generation and voice interaction is facilitated.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention relates to the technical field of robot joint control, and discloses a robot dog mechanical joint control method. The method comprises the following steps: acquiring a whole-body joint real-time sensing data set which covers the torque reading of a multi-axial force sensor, the angle data of a joint encoder and the body attitude data of an inertial measurement unit; motion state compensation is carried out on the sensing data based on a dynamic window mechanism, and a dynamic compensation matrix containing kinematics compensation parameters is generated; and inputting the multi-modal motion control data set into a gradient descent model under space-time constraint to complete multi-modal motion feature fusion so as to obtain a fusion motion control data set. Joint behavior abnormity is detected through a Lyapunov stability analysis algorithm, and abnormal nodes are identified; and iteratively optimizing control parameters through a fuzzy PID optimization algorithm to generate an optimized control parameter set, finally constructing a three-dimensional virtual motion space model, and establishing a dynamic mapping relationship between a virtual track and an actual mechanism.

The invention discloses a golf motion simulation method and system based on digital twinning, and the method comprises the steps: obtaining an original data set containing a timestamp and a space coordinate from the cooperative motion of a plurality of parts and equipment of an athlete body through a sensor network technology in combination with a high-speed image capture device, and obtaining a preliminary multi-source motion information record; aiming at the initial multi-source motion information record, correcting time synchronization deviation and space matching error by adopting a data preprocessing module, and determining a corrected unified data framework by comparing timestamps and coordinate mapping relationships of data sources; according to the corrected unified data framework, a feature extraction algorithm is used for separating and marking the action analysis details and key parameters at the ball hitting moment, and a feature vector set containing the body posture and the equipment state is obtained. According to the invention, a highly vivid virtual environment support is provided for exercise training and match analysis, and the simulation precision and application value of a virtual exercise scene are effectively improved.

The invention discloses an automobile performance display method and system based on VR, and relates to the technical field of automobile VR. The system is composed of a plurality of functional modules, and comprises a multi-modal data acquisition module for acquiring multi-modal data; the multi-modal data comprises human body posture capture, seat mechanics monitoring data and vehicle state synchronization data; the dynamic fusion module is used for extracting modal features based on the multi-modal data; the modal features comprise attitude features, pressure features and performance features, a joint feature vector is obtained through splicing fusion, a multi-head attention mechanism is introduced, and each modal weight is dynamically allocated; a Transform + CNN hybrid architecture is used, modal features are input, a generative adversarial network is used, somatosensory simulation control signals are output, and a vehicle performance mapping model is constructed; and vehicle performance display: obtaining the body posture data of the experiencer, and carrying out self-adaptive performance adjustment on the experiencer according to the vehicle performance mapping model in combination with the current vehicle performance state.

The invention relates to a multi-modal virtual human real-time generation method based on an expression control embedding space, and belongs to the field of artificial intelligence. According to the method, an expression control embedding space is constructed and used for fusing voice semantics, a rhythm structure and multi-dimensional emotion information, and continuous and controllable multi-modal driving vectors are generated. The whole system has an end-to-end linkage mechanism from audio input to expression and action output. Semantic features, rhythm structures and emotional states jointly act on generation paths of lip and upper body postures and expression modalities, and all modal features are fused and expressed in a unified control space through a collaborative coding and time sequence alignment mechanism. And finally, a high-consistency and high-fidelity virtual human video is generated in real time through an output scheduling mechanism. The method has remarkable advantages in the aspects of modal fusion consistency, generation expression naturalness and emotion control flexibility, and can be widely applied to key scenes such as virtual human broadcasting, voice interaction agency and meta-universe digital identity construction.

The invention discloses a bedsore prevention and body position management system and method.The system comprises a mattress, a multi-mode sensing unit, a central processing unit and a body position adjusting unit, the mattress is used for providing lying support for a patient, the multi-mode sensing unit is arranged on the mattress, the central processing unit is arranged on the mattress, and the body position adjusting unit is arranged on the central processing unit. The multi-mode sensing unit is used for collecting pressure distribution data, temperature and humidity data and body posture data of a bedridden patient in real time, and the central processing unit is in communication connection with the multi-mode sensing unit and used for receiving, fusing and processing the data collected by the multi-mode sensing unit and conducting bedsore risk assessment based on a preset bedsore risk model. The central processing unit is used for generating a bedsore risk level and a body position management instruction, and the body position adjusting unit is arranged in the mattress, connected with the central processing unit and used for receiving and executing the body position management instruction so as to adjust the distribution of pressure borne by the body surface of a patient. According to the system, real-time monitoring of bedsore risks and automatic body position adjustment are achieved, the nursing load is relieved, and the nursing efficiency and quality are improved.

The invention provides an intelligent control system and method for a massage device, and relates to the technical field of massage devices, and the system comprises an identity authentication module which is used for obtaining the identity authentication information of a target user; the intelligent sensing module is used for sensing the body posture and massage requirements of a target user in real time, including pressure distribution and muscle tension, so as to automatically adjust the massage strength, speed and position parameters of the massage device; and the personalized setting module is used for performing intelligent analysis by using a wolf pack algorithm according to user preferences and historical data, and formulating a massage scheme for a target user, including massage duration, massage intensity upper limit values and massage modes of all parts. By integrating the functions of identity authentication, intelligent induction, personalized setting, execution control, fatigue degree determination, health monitoring and the like, customized, safe and efficient massage service is provided for a user, meanwhile, the health condition is monitored in real time, the user experience is improved, and the user safety is guaranteed.

The invention relates to the technical field of pressure sore nursing, discloses an intelligent nursing system for pressure sore nursing of a patient suffering from cerebral infarction and a back raising optimization method, and is particularly used for solving the problem that the risk of pressure sore deterioration is caused due to the fact that the patient suffering from cerebral infarction cannot sense local pressure and abnormal shearing force in the back raising process in time due to feeling and dyskinesia. The system comprises a data acquisition module, a feature extraction module, a feature fusion module, a feature dimension reduction module, a pressure sore risk prediction module, a feedback control module and a back-up execution module, and the method is based on a target function, and dynamically adjusts back-up execution parameters in combination with acquired data and pressure sore risk scores and grades obtained through analysis. According to the method, multi-modal data are collected in real time, the body posture, motion trail, local stress, shearing force and pressure of the patient in the back raising process are comprehensively considered, closed-loop control is optimized through an objective function, the back raising speed, angle and supporting force are dynamically adjusted, and pressure sore aggravation or secondary injury of the patient after cerebral infarction in the back raising process is prevented.

The invention relates to the technical field of 3D modeling, and discloses a 3D modeling-based teenager body posture intelligent evaluation method, which comprises the following steps of: 1, performing spatial modeling on teenager body postures by using a topology method, and representing the teenager body postures as a high-dimensional manifold space, the topological relation between postures in the posture space is captured by conducting homology group analysis on the high-dimensional manifold space; and 2, after the topological structure of the body posture of the teenager is obtained, carrying out dimension reduction on high-dimensional posture data by applying a manifold learning technology, and optimizing the expression of the data by using a local linear embedding method. Through combination of a topology method and homology group analysis, modeling of teenager body postures in a high-dimensional manifold space is realized, topological relations among postures in a posture space can be effectively captured, a 3D posture model is obtained, and the problem that complex posture changes cannot be accurately described in the prior art is solved.

The invention provides an anti-rollover control method and system for an electric automobile, and relates to the field of automobile body posture control. The control method comprises the following steps: collecting vehicle driving data; calculating a transverse load transfer rate based on the vehicle driving data, and calculating a comprehensive risk index in combination with the transverse load transfer rate and a sprung mass roll angle; the method comprises the following steps: presetting a comprehensive risk index early warning threshold value and a comprehensive risk index danger threshold value, judging the rollover working condition and the comprehensive risk index of the automobile, and executing a corresponding rollover prevention control strategy: when the rigidity of the suspension is changed, the comprehensive risk index still increases and exceeds the comprehensive risk index danger threshold value; the anti-rollover moment is changed by changing the control force of the suspensions on the two sides. After the suspension control force suppresses roll for a certain period of time, the comprehensive risk index is still not reduced to be below the early warning threshold value, the active steering system intervenes in combined control, the active steering system has two steering modes including front wheel steering and four-wheel steering, and different steering modes are used under different working conditions.

The invention relates to the technical field of heart rate detection, in particular to a wearable underwear heart rate detection method and system.The method comprises the following steps that motion information of a user is obtained, and specific body posture changes are recognized based on the motion information of the user; generating a physiological artifact signal related to the specific body posture change based on the specific body posture change; removing a physiological artifact signal from the original physiological signal to obtain a purified physiological signal; heart rate abnormity judgment is conducted on the purified physiological signals. According to the scheme, the physiological artifact signals can be effectively removed, and the accuracy of heart rate detection is improved.

The invention relates to the technical field of piano teaching and computers, discloses a multi-dimensional scoring auxiliary system and method in piano teaching, and aims to solve the problems that existing piano teaching is single in evaluation dimension, one-sided in data acquisition and lagged in feedback. According to the method, a five-dimensional scoring model covering rhythm, strength, timbre, posture and fingering is constructed by synchronously collecting a key triggering time sequence, an audio frequency spectrum, limb posture and finger joint tracks, and quantitative scores are output through time alignment, feature extraction and neural network fusion calculation. The system comprises a key sensing module, an audio acquisition module, a posture capture module, a score calculation module and an AR visual feedback module, and supports teaching suggestion generation, historical trend analysis, difficulty self-adaption and multi-person comparison functions. Through multi-modal data closed-loop evaluation and intelligent intervention, the teaching accuracy and individuation level are remarkably improved, and scientific quantification and efficient improvement of the piano playing ability are achieved.

The invention provides a patient state auxiliary judgment and posture adjustment method, device and system, and relates to the technical field of medical equipment. The method comprises the following steps: acquiring real-time state data of a patient; generating a three-dimensional posture model and emotion parameters of the current body posture of the patient based on the real-time state data; according to the difference degree of the preset target posture and the three-dimensional posture model, a posture adjustment instruction is generated in combination with a manual adjustment instruction of a doctor; and based on the posture adjustment instruction, prompting the patient to adjust the posture until the difference degree is smaller than a preset threshold value and the emotion parameters are in a target emotion range and lasts for a preset duration, and confirming that the patient completes examination preparation. Through the technical scheme provided by the invention, the problems that accurate state judgment is difficult to obtain and posture auxiliary adjustment cannot be performed on the patient before the patient is examined are solved, the accuracy of state perception of the patient and the efficiency of posture adjustment are improved, and the examination experience of the patient is improved.

The invention provides an autonomous navigation and intelligent obstacle avoidance method of a biomimetic robot, and relates to the technical field of biomimetic robots, which realizes real-time estimation of six-degree-of-freedom poses by collecting environmental data; visual features are extracted by using a convolutional network, an enhanced memory unit is constructed in combination with pose information, a triple index path memory library is maintained through a dynamic memory updating mechanism, and feature similarity retrieval and navigation instruction generation in a backtracking mode are supported; defining a state vector based on a resultant force of a robot pose, a target direction and an artificial potential field, training a strategy network through deep reinforcement learning, and outputting a motion instruction according to Q value maximization; fusing an artificial potential field and a dynamic window method, and optimizing an objective function to obtain optimal linear velocity and angular velocity; and solving a joint angle through inverse kinematics, and combining attitude error feedback to realize body attitude maintenance and foot end trajectory tracking.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention relates to the technical field of health state monitoring, in particular to a real-time health state monitoring system and method based on a biosensor. According to the scheme, through multi-mode data fusion, physiological data such as postures, heart rates and blood oxygen collected by the biosensor are combined with postures and environment videos of image collection, the limitation of single data source monitoring is solved, and the situation that traditional falling early warning is misreported due to normal actions such as stooping is avoided; or altitude stress early warning only prompts oxygen deficit but cannot be associated with the falling risk. A pre-trained risk assessment model is combined to output grading risk levels, targeted early warning such as low-risk prompt deceleration and high-risk sound-light alarm is triggered, more accurate and timely health monitoring service is provided for the elderly in the plateau area, and the falling risk caused by interaction of the environment and the physiological state is effectively reduced.

The present application relates to a kind of vehicle steering body posture and control method of handling stability, comprising the following steps: the signal input required for control is collected, vehicle total anti-lateral moment is calculated by body posture feedforward-feedback controller, front and rear axle anti-roll moment distribution ratio is calculated by handling stability feedforward-feedback controller, total anti-roll moment and its front and rear axle distribution ratio are calculated, revised by control comprehensive module, finally output each suspension angle vertical force control instruction of vehicle.The present application has the following advantages:1) vehicle body posture control and handling stability control are comprehensively considered, and both are simultaneously optimized;2) vehicle body posture control and handling stability control both use feedback-feedforward, avoid the problem of pure feedforward control or feedback control, so that the overall performance of vehicle is more "natural";3) steering input and road input are decoupled, and four high sensors are saved.

Various embodiments of the teachings herein include systems and methods for monitoring the posture of a user during use of an electronic device. An example includes: reading an image from a camera associated with the electronic device; determining a value representing the posture shown in the image; and transmitting a warning signal if the value falls in a warning range.

The utility model discloses a body posture measuring device which comprises a walking guide table used for allowing a measurer to walk and displaying a guide mark for guiding the measurer to walk; the posture capturing assembly is arranged in front of the walking guide table and is configured to be suitable for obtaining the walking posture of a measurer walking on the walking guide table in a visual detection mode; the foot measuring assembly is arranged at the front side part of the walking guide table and is used for measuring the shape of the foot of the measurer when the measurer stands above the foot measuring assembly; and the processing module is in communication connection with the posture capturing assembly and the foot measuring assembly and is used for processing the information which is obtained by the posture capturing assembly and is related to the walking posture of the measurer and the information which is obtained by the foot measuring assembly and is related to the foot shape of the measurer. According to the foot data measured by the body posture measuring equipment, the adaptability between the shoes recommended or manufactured for the user and the feet of the user can be improved.

The invention provides a neural network-based unmanned walking type excavator body posture stability control method. The method comprises the following steps: scanning the terrain in front of a walking type excavator through a laser radar and a camera to generate terrain point cloud data, and processing the terrain point cloud data to obtain terrain excitation data; the terrain excitation data and the walking leg included angle of the walking excavator serve as input, a neural network attitude prediction model of the walking excavator is constructed, and the pitch angle, the roll angle and the vertical displacement of a walking excavator body are output; based on the vehicle body posture change of the walking excavator, a vehicle body stability optimal control model of the walking excavator is constructed and solved, so that the included angle between a walking leg and a chassis is optimized, and stable control over the vehicle body posture under the complex terrain condition is achieved. The system can efficiently adapt to the complex terrain environment, the stability and safety of the unmanned walking type excavator in autonomous operation are improved, and the system has important significance in improving the operation efficiency and quality.

The invention provides a monocular vision and sparse IMU rehabilitation action whole body posture estimation method and system, and the method comprises the steps: synchronously collecting video data and inertial data of human body rehabilitation actions through a monocular RGB camera and a plurality of IMUs, and cutting and zooming an image to a preset resolution; extracting a key point thermodynamic diagram from continuous N frames of images by using a sliding window and a residual neural network, and calculating 2D key point pixel coordinates of each frame; splicing the N frames of 2D key point pixel coordinates, the rotation matrix of the IMU and the acceleration signal into an input sequence; cross-modal time sequence modeling is carried out on an input sequence through time Transform, and after high-dimensional features are extracted, weighted average is carried out through a convolutional layer, and 3D relative key point coordinates of the last frame are output through a regression head. According to the method, by fusing monocular vision and sparse IMU cross-modal data, the problem of visual information loss caused by limb self-shielding is effectively solved, and the defect that a traditional pure vision method is insufficient in precision in rehabilitation actions is overcome.

An avatar animation generation method includes obtaining video data of a physical object; extracting posture information of the object based on the video data, wherein the posture information indicates a body posture and an expression posture presented by the object in the video data; performing 3D reconstruction on the object based on the posture information to obtain motion data representing a body motion of an avatar and expression data representing a facial expression of the avatar, wherein the motion data is obtained through reconstruction based on the body posture, and wherein the expression data is obtained through reconstruction based on the expression posture; and obtaining animation data of the avatar through synthesis based on an appearance resource of the avatar, the motion data, and the expression data, wherein the animation data indicates that the avatar wears the appearance resource, presents the facial expression, and performs the body motion.

The invention discloses an advertisement putting monitoring method and system based on image AI analysis. The method comprises the following steps: integrating shooting equipment on advertisement putting equipment to obtain video data; identifying figures in the video data by using the target detection model, judging whether the eyes of the figures watch the advertisement area, and counting the number of the figures watching the advertisement area; obtaining human face features and human body posture features through a feature extraction model; classifying the figures according to ages and genders; identifying and acquiring interaction behaviors when the user watches the advertisement, and recording the number of times of the interaction behaviors; and calculating advertisement scores of different ages and different genders according to the number of people watching the advertisement, the number of interactive behaviors and the advertisement watching duration data. The advertisement putting effect can be accurately monitored, detailed audience behavior data can be provided for advertisers, the advertisement content, putting channels and strategies can be optimized, the attraction and putting efficiency of advertisements are effectively improved, and the return on investment and the propagation value of the advertisements are improved.

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 discloses a remote 3D clothing intelligent customization system based on virtual reality, which comprises a customization primary selection module, a try-on experience simulation analysis module and a try-on experience simulation module, and is characterized in that the customization primary selection module is used for primarily selecting and screening customized clothing styles and categories according to the body type of a customer and customer selection; the try-on experience simulation analysis module is used for analyzing the stress condition of the body parts of the customer after try-on, and the try-on experience simulation module is used for simulating the garment wearing experience feelings under different body postures after real try-on according to the analysis result of the try-on experience simulation analysis module. The customization primary selection module is electrically connected with the try-on experience simulation analysis module, the try-on experience simulation analysis module is electrically connected with the try-on experience simulation module, and the system has the characteristics of real try-on experience and personalized optimization design.

The invention relates to the technical field of voice analysis, and particularly discloses a vocal music training method and system based on artificial intelligence, and the method comprises the steps: collecting and analyzing the body posture parameters of a target vocal music training person, carrying out the preprocessing, detecting the posture abnormality, forming a first abnormal training set, capturing and analyzing the pronunciation feature parameters, and recognizing the pronunciation abnormality, thereby obtaining a vocal music training result; and integrating the first abnormal training set and the second abnormal training set to generate a total abnormal parameter training set, matching a correction set, and visually displaying the body posture abnormal degree and the abnormal reason and the pronunciation abnormal degree and the reason, so that the target vocal music training personnel can visually see own posture and pronunciation problems, and self-adjustment and correction are facilitated.