80 results about "Autistic child" patented technology

The invention discloses a holographic projection system based on an AI large model. According to the invention, through the nano-scale optical coating and environment self-adaptive adjustment technology, harmful blue light is effectively filtered, environment light parameters are dynamically matched, the problem that eyes are injured by a common screen is solved, and the method is especially suitable for long-time learning scenes of children. The holographic projection core module adopts a naked eye 3D light field technology, presents complex models such as dinosaur, marine organisms and the like in a three-dimensional suspension manner through high-fidelity color restoration and dynamic depth-of-field adjustment capability, and is matched with gesture touch control and voice instructions of multi-mode interaction hardware, so that a user can directly touch a virtual object, and the real-time performance of the virtual object is improved. Experience upgrading from plane observation to space interaction is achieved, and knowledge such as English learning and animal and plant cognition is converted into interactive games and story narration through virtual teacher images and scene scripts. The autistic children can complete social dialogue training under the guidance of the virtual role, the system analyzes emotion fluctuation in real time and adjusts a guidance strategy, and social fear is gradually eliminated.

The invention discloses a multi-modal evaluation method, system and device for the class participation degree of autistic children in man-machine collaborative teaching, and relates to the technical field of education artificial intelligence, and the method comprises the steps: carrying out the feature analysis of multi-modal physiological emotion data, and obtaining multi-modal features; identifying a current teaching scene, and performing weight distribution on each modal feature in the current teaching scene by using the expert intervention strategy rule base to obtain the weight of each modal feature in the current teaching scene; based on the multi-modal features and the weights thereof, utilizing the trained deep learning model to evaluate the class participation degree of the autistic children in real time; according to the multi-modal features and the classroom participation degree, an intervention strategy is retrieved through an education intervention strategy library, and the classroom participation degree and the intervention strategy are visually displayed in real time. According to the method, the accuracy, objectivity and timeliness of the participation degree identification of the autistic children are improved, the classroom participation degree and the intervention strategy are visualized, and the pertinence of the intervention strategy is improved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to an execution type intervention effect evaluation method based on deep learning, and the method specifically comprises the following steps: recording a heart rate signal of an autistic child during intervention task training through wearable heart rate monitoring equipment; the collected heart rate signals are preprocessed; constructing a data set and dividing the data set into a training set, a verification set and a test set; performing dynamic time segmentation and adaptive noise suppression on each segment of sample data in the data set; constructing an executive intervention effect evaluation model based on deep learning, inputting the denoised training set into the model for training, and verifying performance indexes of the model through a verification set; and inputting data in a test set into the trained model to obtain a final evaluation result. The heart rate signals reflecting psychological load, cognitive participation and emotional fluctuation are analyzed and processed, the situation of the intervention effect can be objectively estimated, and manual judgment is assisted.

The invention aims to solve the problem that the analysis result is affected due to the single visual angle of video acquisition in the existing autistic child classroom analysis. According to the autism child classroom behavior analysis method and system, multi-view collection, stable key point detection, high-precision multi-view three-dimensional reconstruction and deep behavior analysis modes are organically integrated through the links of projection matrix construction, weighted calculation, abnormal point filtering, algorithm optimization, error elimination and the like; a complete and efficient system is formed, the accuracy of classroom behavior analysis of children with autism is greatly improved, a solid and scientific basis is provided for personalized education intervention, and a more valuable analysis result is brought.

The invention relates to the technical field of medical information processing, in particular to a multi-modal data integrated analysis system for screening children with autism, which comprises a data acquisition module, a data processing module, a feature extraction module, a feature fusion module, a data screening module, a data analysis module and a data output module, according to the system, behavior data, brain function data and brain structure data of autism children are collected, and after preprocessing and feature extraction are carried out, fusion processing is carried out by a feature fusion module. The feature fusion module comprises a multi-scale topological feature representation sub-module, a heterogeneous feature integration sub-module and a self-adaptive weight adjustment sub-module which are respectively used for realizing multi-scale representation, heterogeneous feature integration and dynamic weight adjustment of features; the feature fusion technology solves the problems of feature scale inconsistency, inter-modal semantic gap, feature importance dynamic change and the like in multi-modal data fusion, improves the autism screening accuracy and early recognition capability, and supports personalized screening and accurate intervention.

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 early diagnosis and treatment system for children with autism based on facial representation multi-modal fusion. A system collects a face video when a child watches a specific video (7 minutes, 08 seconds), first 2000 frames of images are extracted as input, expression data are extracted by adopting an improved contrast language-image pre-training model, sight line data are obtained in combination with GazeMobileNet, and the expression data are displayed on a display screen. Time sequence feature extraction and classification are carried out through a multi-mode fusion network (including a preliminary memory module, a key extraction module and a hidden learning module) of a multi-valve memory mechanism. In the intervention stage, the system provides intervention games such as whack-a-mole, pattern recognition and the like to train reaction, cognition, coordination and language abilities, and an evaluation report is generated according to training data such as reaction time and the like. The system integrates data acquisition, feature learning, multi-modal classification and intervention training, improves the diagnosis accuracy and the intervention effect, and has the characteristics of simplicity and convenience in operation and high universality.

The invention discloses an autistic child emotion regulation system integrating electroencephalogram feedback and art, and relates to the technical field of medical intelligent rehabilitation. An emotional state decoding module; a dynamic generation type art interaction guiding module; a closed-loop feedback regulation and control module; and an adaptive optimization module. According to the method, deep fusion of physiological signal monitoring and artistic emotion expression is realized, scientificity and effectiveness of emotion regulation intervention of the autistic children are remarkably improved, participation willingness and compliance of the children are effectively improved, personalized iteration and accurate regulation of the intervention process are realized, limitation of traditional single-mode intervention is broken through, and the method is suitable for popularization and application. Not only is the objectivity of emotion recognition and the pertinence of art guidance enhanced, but also an individualized rehabilitation path is formed through long-term memory map construction, and an intelligent, quantifiable and sustainable new emotion regulation normal form is provided for autistic 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 invention relates to the crossing field of target detection and emotion recognition, in particular to an autistic child facial expression recognition method based on deep learning. Comprising the following steps of: embedding three-dimensional attention (Tri-attention) behind a C3k2 module of a YOLOv11 backbone network so as to model an interactive relationship among three dimensions of height, width and channel in parallel and enhance response to key expression areas such as eyes, mouth corners and the like; three-dimensional attention (Tri-attention) is introduced into the multi-scale feature output end of the neck network again, and effective positioning and background noise suppression of a small-scale face under a complex background are achieved; respectively training and verifying the YOLOv11-TA model by using the public data set and a self-built autistic child data set, and outputting a detection frame of each face and an expression category thereof; by comparing various indexes of the original YOLOv11 and YOLOv11-TA on various data sets, the accuracy and robustness of expression recognition of the autistic children are remarkably improved by the method according to actual verification.

1. Name of the product in this design: Smart Speaker (for children with autism). 2. Purpose of this design: This design provides structured interaction for children with autism, assists in communication training and emotional regulation, and acts as a controllable social partner in repetitive learning scenarios. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The invention discloses an autistic child electroencephalogram collection cap fusing severe game intervention and a signal processing method.The autistic child electroencephalogram collection cap comprises a structure module, an electroencephalogram collection module, a wireless communication module, a power module, a signal processing module and a game and report module, and the electroencephalogram collection module collects electroencephalogram signals through five electrodes and two ear patch type electrodes; the wireless communication module is used for communication between the electroencephalogram cap and user terminal equipment, the power supply module is used for storing electric energy and supplying power to other modules, and the signal processing module is used for processing and analyzing electroencephalogram signals in the severe game intervention process, including filtering, slicing and normalization processing, and outputting standardized electroencephalogram data. And then the data is analyzed through an MCNN network model, and the condition of the children is diagnosed, so that an effective tool is provided for brain function research and diagnosis while the adaptability of the children with autism is improved.

The invention discloses an early diagnosis and intervention curative effect evaluation method for children with autism, and belongs to the technical field of medical diagnosis. The method specifically comprises the following steps: S1, obtaining historical case big data: collecting the historical case big data of an autistic truly diagnosed child and a normally developed child and corresponding diagnosis results and intervention diagnosis and treatment records, and associating to form a historical database; s2, historical data preprocessing: performing cleaning, standardization and missing value processing on historical case big data, and associating and integrating the historical case big data into a structured sample set; by collecting behavior, physiology, environment and family multi-dimensional data, mining an internal mode of the data in combination with depth features, and then utilizing a multi-modal fusion diagnosis model for analysis, dependence on subjective experience of doctors and a single scale is reduced; the classification accuracy of the model is optimized through cross validation, a diagnosis report contains core abnormal features and a judgment basis, manual recheck is supported, and the risks of missed diagnosis and misdiagnosis are further reduced.

The invention provides a curing cabin for autistic children and a control method, and relates to the technical field of medical technical equipment. According to the technical scheme, the curing cabin for the autism children comprises a cabin body; the cabin door is connected with the cabin body; the seat is located in the cabin body, an expansion air bag is arranged on the seat, and the expansion air bag is arranged in the seat; the interactive display screen is positioned in front of the seat in the cabin body; the sound equipment is positioned on the cabin body on the rear side above the seat; the fragrance mechanism is located on the inner side of the top of the cabin body; and the illumination mechanism comprises a main illumination part located on the inner side of the top of the cabin body, and an auxiliary atmosphere lamp strip located on the inner side of the cabin body. The invention aims to provide a relatively closed treatment environment through the treatment and healing cabin and combine vision, hearing, smell and touch to treat and cure autism symptoms of children.

A computer-implemented therapy method for training conversation and dialogue interaction skills as well as pragmatic language skills in children with ASD is provided. The method includes: obtaining a robot having a non-humanoid, non-animal-like shape, the robot including at least one processor and memory coupled to the processor for controlling one or more actions of the robot; and configuring the processor to control the robot: (i) to monitor audible and visual expressions of a child with Autism Spectrum Disorder in proximity of the robot; and (ii) to audibly generate sentences pronounced by the robot in order to elicit a verbal response from the child and to trigger a conversation, the sentences being automatically generated as a function of the audible and visual expressions of the child to thereby initiate and maintain a conversation with the child.

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 belongs to the technical field of medical health monitoring and behavior intervention, and discloses a pre-school autism sleep personalized intervention system based on AI behavior analysis, which comprises a behavior acquisition unit, an environment monitoring unit, a living habit management unit, a fusion analysis unit, an intervention execution unit, a self-adaptive optimization unit and a family interaction unit, according to the system, body movement, heart rate, skin electricity and sleeping posture information of children and environment parameters such as illumination, noise and temperature and humidity of a bedroom are synchronously collected, diet and exercise balance indexes are combined for fusion analysis, and behavior risk indexes are generated; a personalized intervention scheme is automatically generated according to the risk level, and intervention is implemented through light, sound and visual guidance modes. The system dynamically adjusts analysis parameters according to an intervention result, realizes closed-loop correlation analysis and continuous regulation and control of sleep behaviors, environment changes and parent feedback, and is suitable for individualized sleep management of children with autism.

The present invention relates to the field of artificial intelligence and data processing technology, and in particular to a method for evaluating the effect of an executive intervention based on deep learning, which is as follows: recording the heart rate signals of autistic children during intervention task training through a wearable heart rate monitoring device; preprocessing the collected heart rate signals; constructing a data set and dividing it into a training set, a validation set, and a test set; performing dynamic time segmentation and adaptive noise suppression on each sample data in the data set; constructing an executive intervention effect evaluation model based on deep learning, inputting the denoised training set into the model for training, and verifying the performance indicators of the model through the validation set; inputting the data in the test set into the trained model to obtain the final evaluation results. The present invention can objectively estimate the effect of the intervention and assist manual judgment by analyzing and processing the heart rate signals reflecting psychological load, cognitive engagement, and emotional fluctuations.

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 present application relates to the cross field of target detection and emotion recognition, and particularly relates to a facial expression recognition method for autistic children based on deep learning. The method comprises the following steps: embedding a three-dimensional attention (Tri-Attention) after a C3k2 module of a YOLOv11 backbone network to model the interaction relationship among height, width and channel dimensions in parallel, and enhancing the response to key expression regions such as eyes and corners of the mouth; introducing the three-dimensional attention (Tri-Attention) again at the multi-scale feature output end of the neck network to realize effective positioning of small-scale faces in a complex background and suppression of background noise; training and verifying the YOLOv11-TA model by using a public data set and a self-built autistic children data set respectively, and outputting the detection frame and expression category of each face; and comparing various indexes of the original YOLOv11 and the YOLOv11-TA on multiple data sets, and actually verifying that the method significantly improves the accuracy and robustness of the expression recognition of autistic children.

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 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 invention aims to solve the problem that existing autistic children are difficult to effectively pacify in time in monitoring. According to the multi-mode monitoring method for the autistic children, the action features, the facial features and the voice features are fused, the comprehensive features are finally obtained, and compared with a single-mode data processing mode in the prior art, behavior information and emotion information of the autistic children can be captured more comprehensively and meticulously; the accuracy and reliability of behavior analysis are remarkably improved, the reliability of voice interaction of the large language model is also improved, the voice interaction of the large language model is combined, the blank that only warning can be given out and real-time interactive pacifying is lacked when the abnormal behavior occurs is effectively filled, and the voice interaction of the large language model is improved. Primary emotion pacification and behavior guidance can be carried out on children before guardians arrive, and harm possibly brought by abnormal behaviors is reduced.

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 present invention belongs to the field of medical image processing and analysis, and particularly relates to a prediction system for sulcal abnormalities, comprising: acquiring MRI brain image data of autistic children from multiple acquisition sites; reconstructing the white matter surface of the children's brain MRI image data; extracting sulcal depressions based on the reconstructed white matter surface of the brain, and projecting them onto a standard sphere space to construct a sulcal map; extracting sulcal features based on the constructed sulcal map to reflect the geometric characteristics and topological properties of the sulci; an abnormal sulcal pattern analysis unit constructs a sulcal abnormality value based on the sulcal abnormality based on the obtained geometric characteristics and topological properties, and sets an alarm threshold, and makes a prediction based on the relationship between the sulcal abnormality value and the alarm threshold. The present invention derives the sulcal pattern abnormality of autistic children based on the geometric characteristics and topological properties of the abnormal sulci of the autistic children, and the present invention still has a good recognition effect when the sulcal pattern changes and fluctuations in the early stages of autism are small.

The invention relates to the technical field of image interpolation enhancement, in particular to a social intervention training method and system for autistic children. The method comprises the following steps: in a current child region image, according to the gray difference between each pixel point of the child region image and a neighborhood pixel point, and in combination with the position change of each pixel point in a continuous frame image, obtaining the interpolation necessity of each pixel point; further obtaining a dimension to be interpolated according to the overall features of the interpolation necessity of all the pixel points of each dimension in combination with the difference features of the interpolation necessity of the pixel points of adjacent dimensions; and finally, interpolating the current child region image according to the gray level change characteristics of the pixel points of the dimension to be interpolated and the adjacent dimension to obtain a child region enhanced image. According to the method, the region needing interpolation in the child region image is accurately positioned, detail enhancement is carried out on the key part of the child region image, the accuracy of auxiliary recognition is ensured, and the effect of intervention training on autistic children is improved.

The present invention relates to the field of auxiliary diagnosis and treatment, and in particular to an analysis method for auxiliary diagnosis and treatment of autistic children. The present invention performs pre-detection on a target object and stores detection data to analyze the target object's motion feature representation, determine the target object's motion feature feedback degree, and perform auxiliary diagnosis and treatment analysis, including: adjusting the image acquisition accuracy of the image acquisition device for the target object, calling the video record in the target object's review case, identifying emotional gaze features, limb movement amplitude, and the change ratio of feature matching parameters relative to the pre-detection based on the video record, and comparing them with expected control data determined based on a pre-stored intervention diagnosis and treatment plan to determine whether the feature matching standard is met; generating a recommended number of re-examinations based on the motion feature representation, and outputting the results of the auxiliary diagnosis and treatment analysis through an output end. The data analysis results are accurate and reliable, can provide doctors with auxiliary diagnosis and treatment data support, and improve diagnosis and treatment efficiency.

A targeted acupuncture treatment method for children with autism belongs to the field of medical care, and comprises a sound induction ear acupuncture method, a long-acting eye stimulation opposite pressing acupuncture method and a liver-spleen-kidney simultaneous acupuncture method. The system can improve the core symptoms of the autistic children (3-12 years old), such as not listening to instructions, eye contact parallax, inattention, diet singleness, emotion awareness, hyperactivity and the like, realizes targeted intervention through multi-module collaborative acupuncture, and is suitable for pediatrics of medical institutions, children rehabilitation centers and professional acupuncture clinics.