52 results about "Autism child" patented technology

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention relates to the technical field of intelligent medicine, in particular to an autism child early screening and intervention curative effect evaluation method which comprises the following steps: S1, obtaining social reaction scale data of a Chinese version; s2, acquiring and collecting data of preschool autism patients and typical development children; s3, filling missing data by adopting a mean value filling method; s4, dividing the filled data into a test set and a training set, and establishing an XGBoost model; s5, optimizing hyper-parameters in the XGBoost model by using grid search to obtain optimal model configuration; s6, Pearson correlation analysis is adopted, scale entries obtained through optimal model configuration are added to be subjected to correlation analysis with a gold standard scale capable of being used for diagnosing autism, and it is verified that the simplified scale is enough to serve as an effective tool for screening the pre-school-age autism child patients; by combining a machine learning algorithm, the screening process is optimized, and the screening efficiency and the diagnosis accuracy are improved.

The invention provides an autism child personalized emotion perception method based on tactile interaction, and the method comprises an identification stage: employing a trained autism child personalized emotion identification model to identify tactile behavior data in a man-machine interaction process of an autism child and a human-shaped accompanying robot, and obtaining an emotion classification result; in the model training stage, the personalized emotion recognition model of the autism children comprises a public feature extractor, a domain independent feature extraction and classifier, a domain general feature extraction and classifier and an aggregation prediction classifier. The training process of the personalized emotion recognition model for children with autism comprises the following steps: acquiring tactile behavior data; dividing the tactile behavior data into source domain data and target domain data; obtaining a source domain feature and a target domain feature through a common feature extractor; obtaining a domain independent classification result through a domain independent feature extraction and classifier, and obtaining a domain general classification result through a domain general feature extraction and classifier; and obtaining a classification result of the emotion category through an aggregation prediction classifier.

The invention discloses an autism engraving behavior detection method based on improved YOLOv11, belongs to the technical field of computer vision deep learning, and aims to provide an auxiliary tool for early diagnosis of autism. The improved YOLOv11 network can realize efficient and accurate detection and analysis of the engraving behaviors of the autism children by combining an ADown down-sampling module, an SCDown down-sampling module and an SCSA attention mechanism. According to the method, the advantages of ADOS-2 diagnosis basis and the YOLOv11 algorithm are combined, autism carving plate behavior characteristics in videos or pictures can be detected and analyzed, the objectivity, accuracy and efficiency of diagnosis are improved, and powerful support is provided for early intervention and treatment of autism children.

The invention discloses a method and a system for training a dialogue intervention large model for children with autism, and the system comprises a data processing module which is used for converting original dialogue data from theme dialogue intervention records of children with autism and doctors into a high-quality multi-round dialogue data set; the model training module is used for finely adjusting an existing large language model on the basis of the high-quality multi-round dialogue data set so as to learn dialogue styles and intervention strategies in the high-quality multi-round dialogue data set, and an intervention large model which can follow an ABA application behavior analysis principle to perform theme dialogues with autistic children is obtained; and the model evaluation module is used for comprehensively evaluating the intervention effect of the intervention large model from the sentence dimension and the dialogue dimension so as to feed back the model fine adjustment process. According to the method, the large model following the ABA application behavior analysis principle can be trained to perform dialogue intervention with the autism children, the intervention effect is effectively improved, and accessible, efficient and low-cost social communication rehabilitation support is provided for the autism children.

The application discloses a method, device and diagnosis and treatment machine auxiliary system for autism child behavior recognition and motion prediction, the method comprises the following steps: (1) acquiring original skeleton video data of the child from different angles; (2) inputting the acquired original skeleton video data into a view adaptive conversion unit to adaptively convert to a consistent coordinate system to obtain the optimal representation of the human skeleton; (3) inputting the optimal representation of the human skeleton into a multi-scale feature extraction unit to extract high-performance spatiotemporal features of the human skeleton; (4) inputting the high-performance spatiotemporal features into a multi-task learning unit to perform behavior recognition and motion prediction on the human body, and obtaining behavior classification results and motion prediction results. The application can predict and intervene the future motion while recognizing the behavior.

The application discloses an interactive autistic child training system, method, electronic device and readable storage medium. The interactive autistic child training system comprises a training configuration module, a TOF radar, a multi-modal sensor fusion camera group, a gesture exercise recognition module, an entity interaction behavior recognition module and a prompt module. The training configuration module is used for configuring training actions / training elements corresponding to autistic life self-care skills. The TOF radar is used for collecting gesture exercise data of palms and knuckles. The gesture exercise recognition module is used for judging the gesture exercise data. The multi-modal sensor fusion camera group is used for collecting entity interaction behavior data of a trainee. The entity interaction behavior recognition module is used for judging the entity interaction behavior data. The prompt module is used for outputting an object according to a personalized ability model of the trainee. The application is used to improve the scientificity and systematicness of training.

The application relates to a medical engineering technology, in particular to a control method and system of a diagnosis and treatment robot for autistic children. The control method comprises the following steps: analyzing a human face video by using a deep learning model; generating a next-step intervention action recommendation list by using an LSTM state prediction model and a CPT action decision model; selecting or modifying one of the recommendation items and executing; retraining and updating the LSTM state prediction model and the CPT action decision model by using new interaction data. The control system comprises an execution module, a perception module, a memory module, an evolution module and a decision module. By introducing the quantification calculation of the concentration of the children and the valence and arousal of the facial expression, and by using the LSTM to predict the future state, the strategy can be adjusted in advance when it is predicted that the children will lose interest, so that the children's boredom or escape behavior is not easily caused, and meanwhile, the model is retrained and updated, so that the self-learning and adaptation capability is high, and the auxiliary diagnosis and treatment capability is relatively high.

The invention discloses an early risk prediction method for autistic children based on postures and facial expressions, and aims to construct a deep neural network framework fusing a facial expression image sequence and body posture motion information for the pain point that existing ASD early recognition depends on a subjective doctor observation scale and lacks objective quantitative indexes. A specific convolutional neural network (CNN) branch is designed to extract facial expression features, a time sequence modeling network (such as Transform) is utilized to analyze attitude sequence features, an attention mechanism is combined to fuse multi-modal information, and finally automatic and objective recognition of natural flowing fine abnormal expressions and engraving attitudes in social interaction of young children is realized. And an effective intelligent tool is provided for early screening and auxiliary diagnosis of ASD.

The invention discloses a social training system and method for children with autism based on multi-mode music interaction, and relates to the technical field of social training for children with autism. Visual, audio and behavior data are cooperatively integrated through the multi-modal data acquisition unit, facial micro-expressions, voice and intonation changes and body movement details of children in social interaction are comprehensively covered, and subtle emotion signals or behavior clues possibly missed by a single sensor are effectively avoided; multi-source information is weighted and integrated in real time by using an attention mechanism of a feature fusion module, a unified high-dimensional representation vector is constructed, and the recognition precision of social intent and emotional state is improved; music parameters are dynamically adjusted based on an analysis result, and children are guided to naturally practice target behaviors in the simulated social scene; music changes and story branches are seamlessly linked through the plot driving module, social rule embedding is enhanced, and it is ensured that the children gradually internalize social skills in the immersive environment.

The application relates to the technical field of machine-assisted screening, and discloses an automatic screening method, system, terminal and medium for the response ability of an autistic child based on relative semantic action segmentation, the automatic screening method for the response ability of the autistic child based on the relative semantic action segmentation comprising the following steps: acquiring video information of a doctor and a child interaction process; extracting a skeleton action sequence of the doctor from the video information; segmenting the skeleton action sequence to obtain a time window in which the doctor issues an instruction; obtaining behavior mode information of the child according to the time window; and evaluating the response ability of the child to non-verbal instructions according to the behavior mode information to obtain screening evaluation information. The application can realize automatic and objective screening of the response ability of an autistic child to non-verbal instructions, realize the automation and integration of the screening process, reduce the labor input, improve the screening efficiency, and improve the accuracy and breadth of the screening.

The invention provides a rehabilitation training system for autistic children based on a virtual reality technology, and belongs to the technical field of medical rehabilitation, and the system comprises a doctor terminal which operates on a tablet device and is configured to respond to a doctor touch action to generate a scene generation instruction or a scene switching instruction; the patient end runs on the VR head-mounted display device and is configured to respond to the scene generation instruction and generate and display a mixed reality scene or a virtual reality scene; the patient end is further configured to respond to the scene switching instruction and adopt a panoramic scene gradual change technology for switching during scene switching; and the management end runs on a server or a webpage platform and is used for storing and checking the training data from the doctor end and the patient end. According to the method, switching of different scenes is linked through a panoramic scene gradual change technology, sensory stimulation and anxiety mood caused by instantaneous jumping of a picture to a child patient are avoided, the diversity of the scenes can be improved, the application scene adaptation degree in the rehabilitation training process is greatly improved, and the training safety and compliance are remarkably improved.

The invention discloses an autistic child fusion education social intervention fusion media interaction system and method, and relates to the technical field of medical rehabilitation, and the system comprises a scene design module which constructs a social intervention scene library based on the evaluation indexes of a Wenlang adaptive behavior scale VABS, the scene library comprises virtual scenes corresponding to four fields of communication, daily life skills, socialization and sports skills, and each scene is configured with an interaction task matched with the VABS sub-domain; and the interactive content generation module is integrated with a VR panoramic video acquisition unit and an unreal engine development unit. According to the social intervention fusion media interaction system and method for fusion education of children with autism provided by the invention, a personalized social intervention scene is constructed on the basis of a Wenlang adaptive behavior scale, the accuracy and pertinence of intervention content are ensured, a VR panoramic video and an unreal engine are integrated, a 3D social environment with high immersion and high interactivity is created, and the social intervention effect of children with autism is improved. The participation sense and experience sense of children are enhanced, and the fusion of digitization and traditional teaching is enhanced.

The application discloses an autism child social interaction behavior video image analysis method and system, belongs to the technical field of computer vision and medical image analysis, and comprises the following steps: a video acquisition step, wherein video images of a child and an assessor performing a structured social interaction task are acquired through a multi-angle camera; a human body posture estimation step, wherein two-person skeleton key point detection is performed and posture parameter sequences are calculated; a social signal detection step, wherein key social behavior events such as eye contact, social smile, directional gesture and body orientation response are identified; an interaction synchronism analysis step, wherein the time coupling degree of posture changes of both parties is calculated through cross-correlation analysis; and an evaluation report generation step, wherein a social interaction ability evaluation report is generated in the form of a radar chart by comprehensively considering multi-dimensional features. The application realizes objective quantitative evaluation of the social interaction ability of an autism child and improves the consistency and repeatability of evaluation.

The application discloses an autism children attention prediction method based on a Mamba-Unet structure, and comprises the following steps: firstly, a multi-modal data set for autism children attention prediction is constructed; secondly, an autism children attention prediction model based on the Mamba-Unet structure is constructed and trained; and finally, a non-typical visual attention prediction is performed on a to-be-detected image by using the trained model to obtain a prediction result. The model comprises a pretreatment module, an encoder part, a bottleneck part, a decoder part and an output part which are connected in sequence, wherein an efficient adaptive visual state space block, a frequency domain attention module, a conditional visual state space block and a conditional attention fusion module are arranged; and the output of the encoder part is further transmitted to the decoder part through a jump connection. The method can significantly improve the accuracy of autism spectrum disorder non-typical visual attention prediction.

The application discloses a system and method for autism risk assessment and early warning, and relates to the field of computer and electroencephalogram signal processing. The system comprises an electroencephalogram data acquisition module, a feature extraction module, a risk assessment module and a risk early warning module. The preset task state covers resting state, local motion state and social motion state. In the resting state, the user observes low-speed target objects in the display picture for a preset time length. In the local motion state, the user observes the first type of target person completing local limb motion. In the social motion state, the user observes the second type of target person waving hands to say hello. By collecting electroencephalogram data in each task state and extracting features, the autism risk probability and the corresponding risk level are determined, and the early warning is triggered according to the preset logic rules. The system does not need frequent language interaction with children, is suitable for young children, significantly reduces the screening manpower and time cost, and is convenient for popularization in areas with scarce medical resources.

The invention discloses a domain adaptive space-time electroencephalogram modeling emotion recognition method and system for autism. The method comprises the steps that typical development child electroencephalogram signal fragments with labels serve as source domain data, autism child electroencephalogram signal fragments without labels serve as target domain data, and the source domain data and the target domain data serve as training data together; training data are input into the emotion recognition model for training optimization, a graph is dynamically constructed on the basis of physical coordinates of a channel, a graph structure is dynamically updated on the basis of the graph, and spatial topological structure features of the data are learned through graph convolution; meanwhile, time sequence features are obtained through a time sequence feature extraction module, and then time-space features are fused through a multi-head attention mechanism; and after fusion features are obtained, domain invariant features of electroencephalogram fragments are fully learned in a feature space through a transfer learning method. According to the method, the emotion recognition task of the autism children can be completed only by using the labels of the typical development children.

The invention provides essential oil capable of reducing glutamic acid concentration and relieving autism anxiety mood behaviors. The essential oil comprises bergamot essential oil. According to the method, the titer of the essential oil smell is creatively corresponding to the titer of the social information, the autism children are stimulated through the essential oil smell to generate corresponding titer emotion experience in the intervention course, and the social information is helped to be further received and processed. Meanwhile, in consideration of the problem behaviors that the anxiety rate of autism is high and anxiety is easily caused to an intervention environment, clinical tests prove that the bergamot essential oil capable of relieving anxiety is used for relieving the anxiety emotion of autism when an intervention course starts.

The application provides a kind of autism children classroom behavior analysis method and analysis system, and the system will multi-view collection, stable key point detection, high-precision multi-view three-dimensional reconstruction and deep behavior analysis mode, through the construction of projection matrix, weighted calculation, filtering abnormal points, algorithm optimization and error elimination etc. Link realizes organic integration, forms a complete and efficient system, greatly improves the accuracy of autism children classroom behavior analysis, provides solid and scientific basis for individualized education intervention, and brings more valuable analysis results.

The interesting rehabilitation training device comprises a bottom plate, vertical plates are fixedly mounted on the left side and the right side of the top of the bottom plate correspondingly, connecting frames are fixedly mounted on the left side and the right side of the top of the bottom plate and located on the inner sides of the vertical plates correspondingly, and a turnover mechanism is arranged at the top of the bottom plate; the turnover mechanism comprises a double-shaft motor, an output shaft, a transmission wheel, a transmission belt, a driven wheel and a sliding sleeve, through the arrangement of the turnover mechanism, richer and more personalized training experience can be provided for autistic children, the children are helped to make contact with various different toy teaching aids on one device, the game experience of the children is enriched, and the interestingness of the children is improved. And secondly, the all-in-one design can promote the sensory and cognitive development of the children, so that the children can understand and accept different types of stimulation more easily, and the development of social skills of the children is also facilitated because the children can be used together with other children to play interactive games.

The invention discloses an autistic child evaluation system and device based on an electroencephalogram large model, the system comprises an electroencephalogram collection module, a preprocessing module, a model training module and a control module, the electroencephalogram collection module is used for collecting a first electroencephalogram data set of a non-autistic child group and a second electroencephalogram data set of an autistic child group; the preprocessing module is used for preprocessing the first electroencephalogram data set and the second electroencephalogram data set to obtain a third electroencephalogram data set and a fourth electroencephalogram data set; the model training module is used for determining a target pedestal model according to the third electroencephalogram data set, the fourth electroencephalogram data set and a preset pedestal model; the electroencephalogram acquisition module is also used for acquiring to-be-evaluated electroencephalogram data corresponding to the target child; and the control module is used for determining a target evaluation result corresponding to the to-be-evaluated electroencephalogram data according to the target base model. By adopting the embodiment of the invention, the reliability of the autism evaluation result can be improved.

The utility model belongs to the technical field of identity recognition of children with autism, particularly relates to an identity recognition instrument for children with autism, and aims to solve the problems that the conventional identity recognition mode is not good enough, and the conventional electronic information equipment is single in function and inconvenient to use by children with autism. The identity recognition instrument body is used for storing electronic identity information of the autistic children, an NFC induction area is arranged on one side of the identity recognition instrument body, an NFC chip is arranged in the NFC induction area, and the identity recognition instrument for the autistic children integrates the functions of identity recognition, emergency help seeking, fixed carrying, multifunctional reminding and the like, so that the identity recognition instrument for the autistic children has the advantages of being convenient to carry, safe and reliable. The safety of the autistic children is effectively guaranteed, the NFC technology and the emergency button design are utilized, children information is rapidly transmitted, the searching efficiency is improved, meanwhile, personalized protection is provided for the children through the fixing assembly and personalized information storage, and the requirements of families of the autistic children can be better met.

The application discloses an autism child stereotyped repetitive behavior image recognition method and system, and belongs to the technical field of artificial intelligence and image recognition. The method comprises the following steps: acquiring whole body movement video image data of an autism child, and extracting a movement trajectory sequence through a human body skeleton key point detection network; detecting a periodic repetition mode in the movement trajectory through a time sequence analysis algorithm, and recognizing five stereotyped behavior types, i.e., hand slapping, body shaking, rotating behavior, arranging articles and repeatedly opening and closing a door; establishing a stereotyped behavior feature library, and extracting quantitative parameters of a movement period, a movement amplitude, a continuous time length and a triggering situation; calculating a stereotyped behavior comprehensive score, supporting multi-period comparison and analysis to evaluate intervention effects; and generating an analysis report to assist clinical diagnosis and rehabilitation training scheme formulation.

The present invention discloses a rehabilitation training device for children with autism, and relates to the technical field of training equipment. The present invention comprises a base, a box body is arranged on the upper side of the base, a rotating plate is rotatably matched in the box opening of the box body, a motor connected to the rotating plate is arranged on one side of the box body, a first cushion is arranged on one side of the rotating plate, a lifting assembly is arranged between the first cushion and the rotating plate, and a rocking chair is arranged on the other side of the rotating plate. The present invention controls the rotation speed of the fan blades by the speed at which the child steps on two pedals to rotate, and controls the wind force to blow the distance that the plastic ball moves in the transparent tube. When the plastic ball squeezes the pressure sensor, the motor starts to drive the rotating plate to flip 180 degrees, so that the rocking chair is above the box body, and autistic children obtain entertainment equipment through physical training, which further attracts autistic children and improves the fun of training.

This invention discloses a method for screening autism children based on eye movement detection using ordinary video. The method comprises a video acquisition unit, a preprocessing unit, an eye movement estimation unit, a scene analysis unit, a feature quantization unit, a screening inference unit, and a display unit. It uses an ordinary camera to capture facial video of children, integrates head posture compensation to achieve label-free eye movement tracking, automatically segments dynamic social regions and extracts autism-specific eye movement features, completes risk screening through a lightweight model, and outputs a visual report. This invention requires no professional eye tracker, no head fixation, is suitable for children with low cooperation, and is fully automated. It has the advantages of low cost, strong robustness, good interpretability, and ease of implementation at the grassroots level, making it suitable for large-scale early screening of autism children.

The invention relates to the technical field of autism child behavior analysis, and provides an autism child daily behavior data acquisition method and system, and the method comprises the steps: collecting the three-axis acceleration and three-axis angular velocity of an inertial measurement unit in real time, and taking the three-axis acceleration and three-axis angular velocity as original data; extracting feature data of the original data; according to the feature data, judging the physical consistency of the original data, and distinguishing a real limb movement signal and a sensor relative movement signal; decomposing the original data into a first information stream and a second information stream; based on the first information flow, identifying a behavior pattern of limb movement of the autism child; extracting features of the second information flow to obtain second information features; the second information feature is used for representing the intensity of the relative motion of the sensor corresponding to the relative motion signal of the sensor; calculating a behavior intensity index according to the second information feature; and outputting the behavior pattern and the behavior intensity index. The method has the effect of improving the accuracy of collecting the daily behavior data of the autism children.

The invention relates to the technical field of autism child behavior analysis, and provides an autism child daily behavior data acquisition method and system, and the method comprises the steps: collecting the three-axis acceleration and three-axis angular velocity of an inertial measurement unit in real time, and taking the three-axis acceleration and three-axis angular velocity as original data; extracting feature data of the original data; according to the feature data, judging the physical consistency of the original data, and distinguishing a real limb movement signal and a sensor relative movement signal; decomposing the original data into a first information stream and a second information stream; based on the first information flow, identifying a behavior pattern of limb movement of the autism child; extracting features of the second information flow to obtain second information features; the second information feature is used for representing the intensity of the relative motion of the sensor corresponding to the relative motion signal of the sensor; calculating a behavior intensity index according to the second information feature; and outputting the behavior pattern and the behavior intensity index. The method has the effect of improving the accuracy of collecting the daily behavior data of the autism children.

The invention is suitable for the technical field of behavior recognition, and provides a deep learning algorithm-based autism child carving behavior recognition method and system, and the method comprises the steps: collecting multi-view child carving behavior data, and carrying out the standardization processing; selecting representative carving behaviors in the multi-view data set to obtain video clips; the data set is processed based on a frequency-Colmogov-Arnod network, and frequency characteristics of the depicted behavior children are extracted; generating a feature map through algebraic normalization layer compression coding; relative entropy is introduced into cross entropy loss to serve as a regular term, the difference between output probability distribution and prior distribution is restrained, and different types of gradients are distinguished through asymmetry of the relative entropy. According to the method, frequency information is introduced into the KAN network, frequency components in autism behaviors are extracted, and the prediction precision is improved; an algebraic normalization layer is used to relieve the condition of complex calculation in an autism scene; the method solves the problem that the inscription behaviors in the autism scene are many and miscellaneous and are not easy to identify.

The invention relates to an AI interaction system and interaction method for nursing and social contact of autistic children, the system comprises a robot terminal, a cloud AI platform and a parent / therapist terminal which are connected in sequence through a communication module, and the robot terminal is used for collecting multi-modal data of the autistic children and sending the multi-modal data to the cloud AI platform. The data are uploaded to a cloud AI platform after being subjected to multi-modal data processing; the parent / therapist terminal is used for receiving the interactive abstract and the key data and configuring personalized parameters to carry out personalized adjustment and optimization on an AI model in the cloud AI platform; and the cloud AI platform is used for analyzing the processed multi-modal data by adopting the personalized and optimized AI model, generating an interaction abstract and key data, generating an interaction script, transmitting the interaction abstract and the key data to the parent / therapist terminal, transmitting the interaction script to the robot terminal, converting the interaction script into voice through the robot terminal, and outputting the voice. Compared with the prior art, the method has the advantages of individuation, interaction diversity and the like.

The present application provides an intelligent interaction-based auxiliary system and method for the intervention and treatment of autistic children, which relates to the field of intelligent data analysis. It uses AI-based data processing technology to extract audio features from the audio data of autistic children, and at the same time extracts facial features from the facial expression images of autistic children. Based on this, it can intelligently judge whether the behavior state of the autistic children is an attempt to communicate based on the multi-modal fine-grained joint representation between the audio feature encoding features and the facial image feature encoding features. In this way, through continuous data collection, it is possible to more accurately capture the extremely subtle changes of autistic children and ensure that no behavior occurring at any moment is missed. Moreover, by combining facial expression analysis and audio feature extraction, the judgment of the behavior state of autistic children can be made more accurate, and then more targeted personalized intervention feedback can be generated.