65 results about "Mimetic Muscles" patented technology

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention relates to the field of human-computer interaction, and discloses a human-computer interaction robot facial expression implementation control method, which comprises the following steps: dynamically capturing the face, sound and posture of a user in a human-computer interaction scene, and generating an initial emotion perception sequence; performing hierarchical cross mapping on the initial emotion perception sequence, and performing combinatorial analysis on facial expressions, voices and intonations and body movement features to form an emotion matching vector; based on the emotion matching vector, utilizing a priority regulation model to predict the emotion trend of the user at the next moment, and identifying an expression enhancement point and an emotion attenuation area; mapping the generated facial micro-expression adjusting instruction to a robot facial driving unit, and performing real-time correction and conflict resolution on an expression action sequence; and reversely fusing a user fixation point, facial muscle micro-motion and voice emotion which are acquired in real time into an emotion matching vector, and dynamically updating a perception weight and expression regulation and control parameters. The method has the advantage of improving the emotion matching degree of the robot expressions.

The invention belongs to the technical field of image restoration and computer vision, and particularly relates to a multi-mode face restoration and expression recognition system and method based on semantic guidance of a facial action unit. According to the system, multi-scale features are extracted through visual coding, and an AU activation probability is detected by using a graph neural network; the semantic conversion module converts the numerical probability into an interpretable biomechanical structured text; the multi-modal reasoning module fuses vision and text information, introduces the common sense reasoning ability of a multi-modal large language model, and improves the student network performance through knowledge distillation; and finally, the conditional generation module realizes image restoration by taking the semantic features as guidance. The facial action unit is used as a biomechanical medium, the multi-modal reasoning ability is converted into restoration constraint, the defects that in the prior art, restoration of physiology is distorted, recognition depends on image quality, and two tasks are isolated are overcome, collaborative enhancement of face restoration and expression recognition is achieved, and it is ensured that the restoration result is clear in vision and conforms to the physiological law of facial muscles.

The invention relates to the technical field of English pronunciation learning, and discloses an AI (artificial intelligence) English learning system capable of correcting pronunciation. The system comprises a multi-source voice acquisition layer, an acoustic feature analysis layer, a pronunciation quality evaluation layer, a correction decision generation layer and a biological feedback execution layer. The multi-source voice acquisition layer captures a multi-channel sound wave signal and a maxillofacial electromyographic signal in real time when a user pronounces, and generates an original pronunciation data stream; the acoustic feature analysis layer performs time-frequency domain conversion on original data, separates multi-scale acoustic features and constructs a three-dimensional feature tensor; the pronunciation quality evaluation layer compares the standard template library to output scores and phoneme offset vectors; the correction decision generation layer constructs a pronunciation error topological graph and generates a correction instruction set containing tongue position, airflow and vocal cord parameters; and the biological feedback execution layer synchronously monitors the physiological response and updates the weight of the template library. According to the system, accurate acquisition, analysis, evaluation, correction and dynamic optimization of pronunciation learning are realized, and the English pronunciation learning effect is improved.

The invention provides a smokeless intelligent accurate control method for prevention and control of juvenile myopia. The smokeless intelligent accurate control method comprises the steps of eye movement and facial muscle multi-mode acquisition and noise filtering, dynamic graph convolutional network and spatial-temporal feature fusion analysis, blink and head movement prediction and intelligent moxibustion path adjustment. By constructing a resting state eye movement behavior mode library, coupling face and head dynamic data, utilizing multi-mode feature fusion and AI hierarchical control, accurately recognizing blinking and head disturbance, and generating an avoidance track and a dynamic moxibustion therapy power regulation strategy in advance, the method can effectively avoid periorbital sensitive areas, keep the temperature in the moxibustion therapy process constant, and improve the moxibustion therapy effect. The response speed and path stability are improved, and moxibustion safety and continuity are guaranteed.

The utility model discloses an oral and facial muscle function training device which can be worn between dentition and labial muscle or labial and buccal muscle of a patient, is used for blocking contact between the labial muscle or the labial and buccal muscle and the dentition, and comprises an approximately C-shaped blocking screen and a first expandable part, the first expandable part is at least arranged at the corresponding position, corresponding to the deepest position of the bottom of the vestibular sulcus of the patient, of the outer contour of the blocking screen, and after the oral and facial muscle function training device is worn, the first expandable part expands through saliva infiltration and abuts against the bottom of the vestibular sulcus. Thus, according to the size of the oral cavity space of a patient, the first expandable part expands after being infiltrated by saliva, does not continue to expand after meeting the bottom of the vestibular groove of the patient, and can stably abut against the position, so that the oral and facial muscle function training device can be stably worn between the dentition of the patient and the lip muscle or the lip-buccal muscle; the contact between the labial muscle or the labial cheek muscle and the dentition is blocked.

The present application relates to a kind of based on two-way optical flow to realize the method for fast micro-expression recognition processing, comprising the following steps: according to visual system acquisition tester face micro-expression video segment information;Extract the emotional video segment in emotional memory library, and the facial muscle movement situation of micro-expression in emotional video segment is captured by positive and negative two-way optical flow;Extract method extracts key frame in emotional video segment, and the redundant frame in continuous sequence image is eliminated;Call the optical flow information between key frame in optical flow information memory library.The present application also relates to a kind of two-way optical flow to realize fast micro-expression recognition device, processor and storage medium.The method for fast micro-expression recognition processing based on two-way optical flow of the present application, device, processor and its computer readable storage medium, the facial muscle movement situation of micro-expression is captured by positive and negative two-way optical flow, the micro-expression of tester is identified using muscle movement trend, and the micro-expression recognition accuracy is improved.

The invention provides a peripheral facial paralysis rehabilitation device. The peripheral facial paralysis rehabilitation device comprises a host and at least one stimulation patch electrically connected with the host, the stimulation patch is prefabricated into a special-shaped geometric structure matched with the anatomical direction of facial muscles, and at least integrates a myoelectricity acquisition part, an electrical stimulation part and a heating part; the host is configured to collect a surface electromyographic signal sequence of a user under a specified facial action through the electromyographic collection part of the worn stimulation patch, and the stimulation patch worn by the user is in one-to-one correspondence with target facial muscles involved in the specified facial action; determining at least one muscle function state index according to the surface electromyogram signal sequence; determining a working mode according to the muscle function state index; and controlling the electrical stimulation part and / or the heating part of the stimulation patch worn by the user to work according to the working parameters corresponding to the determined working mode.

The invention discloses a facial muscle convulsion monitoring system based on multi-sensor fusion. The facial muscle convulsion monitoring system comprises a detection device and a visualization device. The detection device comprises a main control unit, a multi-channel sensor unit and a wireless communication unit. The multi-channel sensor unit collects muscle movement signals of multiple groups of symmetrical facial muscle monitoring areas of a target person in a preset posture; the main control unit carries out filtering processing and comparison processing on the muscle movement signals of each group of symmetrical facial muscle monitoring areas to obtain target detection data after noise suppression; the wireless communication unit transmits the target detection data to visual equipment; and the visualization device generates a multi-dimensional display chart according to the target detection data and displays the multi-dimensional display chart. Interference data are eliminated through filtering processing and symmetric region data comparison processing, so that data with a high signal-to-noise ratio is obtained, and the detection accuracy is improved; and the cost is low, and the requirements of home autonomous monitoring and long-term illness state tracking of common users can be met.

The invention relates to an expression line resisting composition containing cell signal factors and plant active ingredients and a skin care product of the expression line resisting composition. The expression line resisting composition is prepared from the following components in percentage by mass: 10 to 50 percent of cell signal factors, 10 to 50 percent of flos chrysanthemi extract and 10 to 50 percent of camellia seed oil. By adopting a composite strategy of metabolism regulation and neuromuscular regulation, nerve-muscular signal conduction is regulated, facial muscle tension is weakened, and dynamic formation of expression lines is reduced; the AMPK energy metabolism pathway is activated, and the cell activity is improved; rOS accumulation in cells is reduced, and the mitochondrial function is improved; strong fat-soluble anti-oxidation protection is provided, the action time is prolonged, barrier repair, moistening and anti-inflammation are facilitated, and therefore the more efficient expression line resisting effect is achieved.

The disclosure provides a digital human generation method based on a multi-modal large model, comprising: constructing a digital human basic model; generating a structured training set; generating a question and answer model supporting multi-channel interaction; outputting a semantic answer to a user question, extracting a text emotional tendency of the semantic answer, and outputting an emotional intensity parameter; generating facial muscle movement trajectory data, and performing real-time rendering on the digital human basic model according to the facial muscle movement trajectory data to output a digital human three-dimensional image with emotional expression. Through cross-modal alignment of text, image and audio data, and by using a joint training method to optimize a multi-modal large model containing a visual, speech and knowledge model, a more natural and smooth multi-channel interaction experience is realized. In addition, by introducing an emotional recognition model and a facial interaction model, the emotional tendency implied in the semantic answer can be more accurately captured and reflected, thereby outputting a digital human three-dimensional image with real emotional expression.

The present application relates to the technical field of functional woven fabric, in particular to a face-slimming beauty headgear and a weaving process for weaving the face-slimming beauty headgear. The face-slimming beauty headgear is an elastic fabric structure woven by an integrated weaving process. The face-slimming beauty headgear comprises a body matched with the head, the bottom of the body has a neck opening for wearing, the face of the body has a mouth and nose opening and two eye openings, the back of the body has a back opening, and the two sides of the body are provided with elastic stretch parts extending to the back of the brain. The face-slimming beauty headgear can form a covering effect on the whole head and face, prevent the irradiation of sunlight, protect the facial skin from external interference, and achieve the purpose of face slimming. The face-slimming beauty headgear utilizes the elastic stretch parts to stretch the facial muscles backward, so that the facial muscles can be relaxed and contracted backward at the same time, thereby achieving the purpose of face slimming. The face-slimming beauty headgear is convenient to wear and can be adjusted after being worn, and is suitable for different head shapes.

This technology efficiently improves the accuracy of musculoskeletal model simulations that take into account variations in muscle development among individuals. [Solution] The simulation device 1 of a musculoskeletal model including multiple muscle parts arranged along the skeleton includes a measurement result acquisition unit 11 that acquires a first measurement result, which is the measurement result of muscle strength measurement using multiple muscle parts belonging to the subject's SFL, and a second measurement result, which is the measurement result of muscle strength measurement using multiple muscle parts belonging to the subject's SBL, and an adjustment unit 12 that performs an adjustment process to adjust the muscle strength of at least one of the multiple muscle parts belonging to the SFL and the multiple muscle parts belonging to the SBL in the musculoskeletal model based on a comparison result of the first measurement result and the second measurement result.

The utility model discloses a line sculpture wire which comprises a wire body, a plurality of continuous barb portions are arranged on the wire body, barbs are arranged on the barb portions, projections of the barbs on the adjacent barb portions on the horizontal plane are staggered, a wrapping net is further wrapped outside the wire body, a plurality of grids are formed on the wrapping net, and the barbs are arranged on the wrapping net. Connecting points are arranged at the positions, corresponding to the barb parts, of the wrapping net, the wrapping net is fixedly connected with the line body through the connecting points, the connecting points are arranged at the edges of the grids, the grids are arranged in a rhombus shape, and the inverted pierces penetrate out of the grids. The facial mask is reasonable in structural layout, can be fixed with facial muscles more firmly, is not easy to rotate and loosen, and has a good use effect.

A system includes at least one wearable device having a housing, at least one sensor disposed within the housing, at least one output device disposed within the housing, and at least one processor operatively connected to the sensors and output devices, wherein one or more sensors are configured to detect electrical activity from a user's facial muscles and to transmit a data signal concerning the electrical activity of the user's facial muscles to one of more of the processors. A method of controlling a wearable device includes determining facial muscular electrical data of a facial gesture made by a user, interpreting the facial muscular electrical data to determine a user response, and performing an action based on the user response.

This invention relates to the field of health data analysis technology, and particularly to a method and application for analyzing the correlation between eating micro-expressions and food satisfaction based on deep learning. The method includes the following steps: S1, capturing facial video images of users during the eating process using a camera device, and extracting eating facial feature sequences from them; S2, separating chewing actions and facial muscle movements from the eating facial feature sequences based on the spatial displacement differences of facial key points, and obtaining target micro-expression feature vectors. In this invention, by cross-evaluating the captured eating micro-expression emotional state with the user's physiological health indicators, nutritional threshold constraints and weight corrections are applied to the initial food preferences generated based on emotions. This allows for strict control of the intake of core risk nutrients while catering to the user's personal taste satisfaction, ultimately generating personalized recommended recipes that balance emotional experience and medical health standards.

The utility model relates to an auxiliary device for monitoring facial muscle movement, the auxiliary device comprises a head-mounted mechanism and a measuring mechanism, the head-mounted mechanism is used for being worn on the head of a user, the measuring mechanism comprises a measuring arm, a sensor and a trigger springback assembly, the measuring arm is adjustably connected to the side part of the head-mounted mechanism, the sensor is arranged in the measuring arm, and the trigger springback assembly is arranged in the measuring arm. The trigger springback assembly is mounted on the measuring arm in a springback manner; the trigger rebound assembly comprises a contact piece and a touch pressing piece arranged on the contact piece, the contact piece protrudes out of the measuring arm and is used for being attached to the face, and the touch pressing piece is located between the contact piece and the sensor and is used for touching and pressing the sensor. The device is convenient to wear and carry about, the measuring arm can be flexibly adjusted according to the rehabilitation training requirement, the sensor can be protected, the trigger rebound assembly is designed into the structural form of the contact piece and the touch pressing piece, the sensor can more sensitively sense the pressure change, and the monitoring sensitivity is improved.

This invention discloses an artificial intelligence-based method for simulating facial expressions in an interactive humanoid robot, belonging to the field of facial expression simulation technology. The method includes: acquiring and preprocessing user brainwave signals to generate power spectral density features; inputting the power spectral density features into a pre-trained emotion classification model to output the user's emotion category and intensity value; mapping the emotion category and intensity value to an emotional expression and outputting biomimetic muscle contraction commands; adjusting the facial muscle fibers of the humanoid robot according to the biomimetic muscle contraction commands to dynamically adjust the surface texture details of the humanoid robot and generate simulated micro-expressions; capturing user facial feedback data through camera eyes, evaluating the user's acceptance of the simulated micro-expressions based on the user's facial feedback data, and outputting an expression optimization strategy. This invention achieves bio-realistic micro-expressions in a humanoid robot through a dual closed-loop modeling approach of deformation matching and user feedback-driven expression optimization.

This invention relates to the field of biomedical information technology, and more particularly to a method for constructing an autism spectrum disorder (ASD) identification model based on deep learning. The method includes the following steps: acquiring videos of conversations with autistic patients; continuously monitoring the eye fixation frequency in the videos; when the eye fixation frequency is detected to be below a threshold, determining it as a reduced eye fixation state and recording the duration of eye fixation; performing sociolinguistic analysis on the conversation videos to obtain sociolinguistic data; and identifying facial reaction features of the autistic patients in the conversation videos based on the sociolinguistic data and recording facial muscle reaction data. This invention, through data processing and deep learning technologies, assesses communication and social interaction impairments in autistic patients and constructs an ASD identification model, thereby improving the efficiency and accuracy of ASD identification.

The present application relates to a kind of facial rehabilitation action training method and computer program product, belong to medical rehabilitation technical field.The method is by obtaining user video stream, extracts facial key point and generates facial expression parameter;From facial expression parameter, determine the target parameter associated with preset rehabilitation action, and the target parameter is compared with preset evaluation standard, obtain evaluation result;According to evaluation result, execute feedback operation and / or adjust training target.The method can be for closed-lip action, cheek-puffing action, tongue muscle stretch action, tongue muscle around action and neck massage action etc. many kinds of rehabilitation training are accurately evaluated, by monitoring the duration of facial expression parameter, repeat frequency, facial key point displacement etc. index, combined with training management model generates individualized training target and evaluation threshold.The present application provides a kind of intelligent, individualized facial rehabilitation training solution, can effectively improve the accuracy and effectiveness of facial muscle function rehabilitation training.

It suppresses estimation errors caused by individual differences, even in the range of high intensity facial muscle movement. [Solution] The information processing device inputs each image of a pair of face images, each assigned a ground truth value for the magnitude relationship of AU intensity, into the relative intensity model to calculate the relative intensity magnitude relationship obtained, and calculates the loss function of the relative intensity model based on the ground truth value for the magnitude relationship. Based on the multiple relative intensities obtained by inputting each of the multiple face images into the relative intensity model, it determines the lower limit of the relative intensity. Based on the relative intensity obtained by inputting the intensity learning face images, each assigned a ground truth value for AU intensity, into the relative intensity model, and the estimated value of AU intensity obtained by inputting the determined lower limit of relative intensity into the transformation model, it calculates the loss function of the transformation model based on the ground truth value for AU intensity. The relative intensity model and the transformation model are then trained to minimize the overall loss function, which is an integration of the loss functions of the relative intensity model and the transformation model.

The invention relates to the technical field of medical robots, in particular to a method, a system and equipment for autonomously collecting questionnaire information of a robot with a body in a medical scene and a storage medium. Receiving the questionnaire collection task, planning a navigation path and controlling the robot to move to the position of the target patient; collecting state information of the patient; calculating an attention concentration degree value of the patient based on the eye features and the trunk posture features, and judging an emotional state category of the patient based on the facial muscle motion features; when the attention concentration degree numerical value and the emotional state category meet the interaction condition, playing a voice inquiry of the current question; receiving a voice response of the patient, converting the voice response into a response text, judging whether the response text is consistent with a preset answer format of the question, if so, storing the response text and switching to the next question, and if not, playing the guide voice and replaying the current question; after all questions are collected, a structured questionnaire record is generated, and the robot with the body is controlled to return to the charging station.

A control system for an intelligent biomimetic expression robot based on a facial muscle neural network, including a support body, a power module, a control module, a muscle neural network, and an expression generation body. Second sensors are connected to the grid conductor. Drive units are distributed and connected at the intersection points within the grid conductor, and the ends of the grid conductor are electrically connected to first output ports. Under the control of control units, the drive units generate driving forces. The first sensor is configured to detect the longitudinal forces generated by the drive units, while the second sensor is configured to detect the horizontal force generated by the drive units. When the first output port applies the power difference through the grid conductor to the drive units, the power difference between adjacent drive units continuously changes, thereby causing the deformation zone to undergo continuous deformation.

This product is designed to stimulate the contraction and relaxation of facial muscles when placed on the face. The product is impregnated with a beauty serum and has slits formed in relation to the corresponding areas of the facial muscles. When the product is placed on the face, the slits are opened, creating surface tension in the serum within the slit openings, and this surface tension acts on the skin surface of the face. This force based on surface tension on the skin surface of the face creates a motor illusion in the facial muscles, thereby stimulating the contraction and relaxation of the facial muscles.

1. The name of the design product: facial muscle stick. 2. The use of the design product: for facial beauty massage. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

1. Name of the product in this design: Fascia Gun (CJ90). 2. Purpose of this product design: A fascia gun for facial muscle massage and relaxation. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design highlights: 3D view of component 1.

A system having a skull tracking bracket; a mandible motion tracking bracket; a motion detecting camera; an electromyography sensors adapted to detect and measure facial muscle activation data and transmit the data to the computerized controller; an audio sensor carried by the subject and adapted to detect and measure sound data associated with jaw (and joint) movement data and transmit the data to the computerized controller; a bite force sensor adapted to detect and measure static and dynamic bite force data; a pain push-button switch (or other sensor) adapted to be activated by the subject when experiencing pain; and a computerized controller is adapted to receive motion data, facial muscle data, joint sound data, static and dynamic bite force data, pain data, and received a set of three-dimensional images of the subject, overlay the detected motion with the three-dimensional image and display the overlay.

The application discloses an expression positioning method and system based on action unit priori and micro-expression enhancement, relates to the technical field of computer vision and emotion computing, and adopts MDMO optical flow features to capture the main motion direction of a face, combines with the modeling of a backbone network to associate space and time, and improves the effectiveness of feature extraction; introduces AU priori knowledge to construct a target AU set, forcibly makes the model learn the essential law of facial muscle movement through an AU-guided alignment mechanism, makes the features extracted by the model more discriminative and generalizable, and solves the problem that the features are not closely related to the essential movement of expressions in the prior art; and designs a micro-expression perception enhancement mechanism, explicitly expands the difference between the micro-expression vertex probability and the background probability through a special perception loss function, effectively solves the problem of low positioning accuracy of micro-expressions caused by low intensity and easy to be submerged in the background, and significantly improves the accuracy of micro-expression positioning.

The invention relates to the field of medical equipment, in particular to ophthalmological patient rehabilitation treatment equipment which comprises a helmet, an eyeshade, a pull rod and the like. The helmet is connected with an adjusting assembly; the adjusting assembly is connected with an eyeshade for atomization treatment; a plurality of rubber sheets are mounted on the eyeshade; each rubber sheet is fixedly connected with a connecting ring; and each connecting ring is connected with a pull rod in a sliding manner. Parts for atomization, liquid storage and treatment are integrated on the helmet, a patient can freely move in the treatment process when wearing the helmet, the experience feeling and comfort are improved, the defects that according to existing equipment, the patient needs to keep the same posture for a long time, and the patient is prone to fatigue are overcome, the water absorption strip is moved to make contact with the eyelids of the patient, and the treatment effect is improved. Water drops condensed on the eyelids are sucked dry, so that discomfort of the eyelids is eliminated, a patient is prevented from blinking frequently due to discomfort in the treatment process, and the situation that the attaching sealing performance of the eyeshade sealing strip and the skin is affected due to frequent movement of facial muscles is avoided.

The present disclosure provides a method and device for dynamic recognition of emotion based on facial muscle movement monitoring, including: obtaining muscle movement data on one side and continuous frame images on the other side; obtaining multiple emotional states related to emotions, associating each emotional state with its corresponding continuous frame images, associating each emotional state with the muscle movement data corresponding to the continuous frame images at the same location and at the same time, to form a training set, building an emotion recognition model; inputting the muscle movement data and continuous frame images obtained in real time into the emotion recognition model to obtain a corresponding emotional state. This method builds an emotion recognition model through muscle movement data and continuous frame image, and uses motion data to make up for the parts of small action images that cannot be collected, thereby obtaining accurate emotion detection results.