22 results about "Child autism" patented technology

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention discloses a children autism risk assessment method and system based on multi-modal collaborative reasoning and a vertical domain large model, and the method comprises the steps: collecting the audio data of a to-be-assessed child and a parent in a parent-child interaction link, and collecting the video data through collection equipment; analyzing and marking the collected audio data, and generating a sounding record and a transcription text with a timestamp; performing real-time analysis and labeling on the collected video data, and generating sight line, expression and action recognition records with timestamps; performing independent analysis on the preprocessed multi-modal data, performing feature fusion and collaborative analysis on the multi-modal data such as audio, text and video by using a multi-modal collaborative reasoning method, and generating a scientific data index for autism risk assessment; training a vertical domain large model special for autism risk assessment; and inputting the generated data indexes into a vertical domain large model, and outputting targeted assessment suggestions by the large model in combination with daily information of the children to be assessed obtained by interviews and conversations with parents.

The invention relates to the technical field of early screening of child autism, in particular to a child autism recognition method and system based on fNIRS signals and wearable equipment, and the method comprises the steps: collecting forehead multi-band cerebral blood flow signals and head movement data of a child through an fNIRS intelligent helmet; the method comprises the following steps: eliminating motion artifacts by using an edge computing chip, obtaining a noise-reduced fNIRS signal through adaptive filtering, extracting a time-domain signal slope, a frequency-domain wavelet energy entropy and an inter-channel function connection matrix, and processing time-frequency features and topological features by using a 1D convolution branch and a graph convolution branch by means of a double-current neural network, thereby obtaining a noise-reduced fNIRS signal. After features are fused, an autism risk probability value is output through channel attention weighting, a screening grade report is generated and transmitted to a mobile terminal, the system comprises a signal acquisition unit, an edge processing unit, an intelligent analysis unit and an interactive output unit, the system is adaptive to daily scenes of children, and the screening precision is improved.

The application relates to the technical field of image recognition, in particular to a children autism spectrum disorder stereotyped behavior recognition system based on video images. The system screens target pixel points from pixel points; determines a motion intensity index according to a trajectory point sequence of the target pixel points; converts the trajectory point sequence of the target pixel points into a motion vector sequence; determines direction similarity according to direction similarity between vectors; clusters the vectors based on the direction similarity, and identifies a target cluster; determines a motion regularity index according to the number of vectors in the target cluster and the direction similarity; determines a saliency index in combination with the motion intensity index; clusters the target pixel points based on the saliency index, and sets a region of interest; determines an enhancement weight according to the density of the target pixel points in the region and the saliency index; and determines a total enhancement demand value in combination with the gray scale and gradient features of the pixel points in the region; and reversely maps the total enhancement demand value into a gamma correction parameter, so that the accuracy of recognition is improved.

FIELD: neurology; psychiatry.SUBSTANCE: used as a non-pharmacological therapy for autism spectrum disorders in children. The method includes a set of procedures: interval hypoxic-hyperoxic training (HHT), hyperbaric oxygenation and ozone therapy. HHT consists of alternating inhalations of a hypoxic mixture with an oxygen content of 9 to 16 vol.% and a hyperoxic mixture with an oxygen content of 32 to 40 vol.% for a total duration of 45-50 minutes under the control of cardiovascular system parameters. Hyperbaric oxygenation is performed in a pressure chamber under a pressure of 1.3 atmospheres of 100% oxygen mixture, flow of 10 liters per minute, for 45 minutes using a mask. Ozone therapy is performed by rectal insufflation with an ozone-oxygen mixture with an ozone concentration of 85 μg / ml in a volume of 20–30 ml or by intravenous administration of a physiological solution enriched with ozone with an ozone concentration of 85 μg / ml at a rate of 200 ml per 100 ml of physiological solution. The procedures are carried out daily for 12 days.EFFECT: method is effective and has a positive impact on the patient’s condition.1 cl, 1 dwg, 3 ex

The present application relates to the technical field of early screening of children's autism, in particular, the present application relates to a child autism recognition method and system based on fNIRS signal and wearable device, through the fNIRS intelligent helmet, the multi-band brain blood flow signal of the forehead of children and the head movement data are collected, the motion artifact is eliminated by using the edge calculation chip, and the noise reduction fNIRS signal is obtained through the adaptive filtering, the time domain signal slope and the frequency domain wavelet energy entropy and the inter-channel functional connection matrix are extracted, with the aid of double-flow neural network, the time-frequency characteristics are processed by 1D convolution branch, and the topological characteristics are processed by graph convolution branch, after the fusion characteristics, the autism risk probability value is output by the channel attention weighted output, and the screening grade report is generated again and transmitted to the mobile terminal, the system contains a signal acquisition unit, an edge processing unit, an intelligent analysis unit and an interactive output unit, adapts to the daily scene of children, and improves the screening accuracy.

The present invention relates to a system for early screening of autism spectrum disorder using eye tracking. According to the present invention, autism spectrum disorder may be diagnosed early by applying response data of a user's gaze, which reacts to images or videos outputted on a display, to an autism spectrum disorder screening model. In addition, the present invention may be used to easily check a degree of suspicion of autism spectrum disorder at home, and thus may be utilized in the fields of early diagnosis, prevention, and treatment of autism in children.

The application provides a network architecture search method and system for electroencephalogram signal processing, and realizes effective migration from high-channel EEG data to low-channel EEG data. In particular, for children with autism, it is difficult for them to cooperate for a long time to collect EEG signals, which may result in incomplete data. The application searches for the optimal neural network architecture by using the multi-agent deep deterministic policy gradient algorithm to search for the neural network architecture, so that a high classification accuracy is maintained in the process of migrating from high-channel data to low-channel data.

The invention provides an infantile autism diagnosis system specially designed for young children based on multi-dimensional data analysis. The infantile autism diagnosis system is composed of wearable equipment, a data collection unit and an intelligent processing module. The wearable device is composed of a lightweight single-lead electrocardio patch, a wearable two-lead electroencephalogram, an eye tracker and a tablet device and serves as a hardware basis of the data collection unit. The data collection unit synchronously obtains physiological parameters, behavioral performance and standard scale data of the tested child. And the intelligent processing module performs feature analysis and multi-source data integration by adopting a machine learning technology, constructs a comprehensive evaluation model and outputs a detailed autism evaluation report. According to the system, the scientificity and accuracy of evaluation are remarkably enhanced, and reliable technical support is provided for clinical early intervention.

This invention discloses a feedback-based assessment system for childhood autism and ADHD, comprising: constructing a feedback-based eye-tracking fixation-dependent paradigm, divided into a static image preparation stage and a dynamic interactive task stage; collecting eye-tracking signals from subjects during eye-tracking tasks using an eye tracker, and changing the screen display content in real time based on the eye-tracking signals to obtain eye-tracking performance under feedback; subjects performing eye-tracking tasks as required, preprocessing and segmenting the eye-tracking signal data, and calculating eye-tracking features and task features for both stages; selecting multiple key features to train a classification model; subjects completing the eye-tracking task, calculating the key features for both stages and inputting them into the classification model to obtain classification results for childhood autism and ADHD. This invention incorporates a feedback mechanism into the fixation-dependent paradigm to assess children's eye-tracking ability, thereby assisting clinical diagnosis to a certain extent.

The invention discloses a children infantile autism rehabilitation training strategy generation method and system, and relates to the technical field of intelligent rehabilitation. The three core defects of high data subjectivity, strategy templating and adjustment lagging in traditional rehabilitation training are overcome. The key points of the technical scheme are as follows: a time sequence convolutional network and a gating cross-modal attention mechanism are fused to realize accurate association of symptom inducements; performing multi-step reasoning based on the dynamic knowledge graph, and identifying hidden risks; neural development logic sorting and dynamic feasibility verification are realized through a multi-level fusion algorithm; and driving continuous optimization of the knowledge graph by using the feedback data. The full-process intelligent effect from objective evaluation and precise intervention to continuous optimization is achieved, high-accuracy, high-perspectiveness, scientific and effective rehabilitation training support is provided for the autistic children, and the pertinence, predictability and long-term effectiveness of rehabilitation training are remarkably improved.

The invention discloses an intelligent screening method for autism of children, a storage medium and a system. Belongs to the field of machine screening. The method comprises the following steps: acquiring eye movement video data of left and right eyes generated when a screening object watches a three-dimensional video; processing the eye movement video data to generate an eye movement parameter set; wherein the eye movement parameter set comprises a three-dimensional gaze point sequence, a convergence angle sequence and a parallax sequence of left and right eyes; determining multi-dimensional features for screening based on the eye movement parameter set; wherein the multi-dimensional features comprise spatial statistical features, spatio-temporal dynamic features and convergence stability features, inputting the multi-dimensional features into a pre-trained screening prediction model, and outputting a screening prediction result indicating whether the screened object suffers from autism. Therefore, the specific tiny eye movement difference can be identified more comprehensively and accurately, and the accuracy of the output screening prediction result is guaranteed.

The invention discloses a child autism auxiliary diagnosis method based on a large model and fNIRS signals. The method comprises three logic chains: (1) a voice-driven large model inquiry link; (2) a brain function collection and semantic coding link based on fNIRS; and (3) reasoning a link based on a large language model and a medical knowledge base. According to the method, a large model inquiry module driven by voice interaction, an fNIRS signal semantic coding module optimized for an autism scene and a diagnosis and intervention suggestion generation module based on RAG (Retrieval Enhanced Generation) are constructed, so that the operation ability of a child guardian is limited, and an fNIRS signal is easily interfered; objective quantitative evaluation and interpretable auxiliary diagnosis of ASD risks are realized, the use threshold is reduced, and the evaluation efficiency and reliability are improved.

The application discloses a kind of multimodal AI algorithm and block chain data security system, it is related to the technical field of children autism rehabilitation intervention, including the following steps: S1 data information acquisition;S2 data preprocessing;S3 behavior recognition and quantitative mapping;S4 emotional state quantitative score;S5 emotion guidance and rehabilitation training matching;S6 gamification guidance training;S7 algorithm self-optimization iteration.The application realizes non-contact multimodal behavior data acquisition, relies on CNN+Transformer fusion model to complete the joint extraction of static features and dynamic time series features, realizes the high accuracy of behavior recognition and the quantitative docking of professional rehabilitation evaluation, effectively improves the objectivity and reference value of behavior analysis;Through emotional state quantitative score and personalized rehabilitation adaptation algorithm, dynamically match exclusive emotion guidance scheme and gamification rehabilitation task, and adjust task difficulty according to age, symptom degree, rehabilitation stage, significantly improve the pertinence of rehabilitation training and child compliance.

The invention discloses a kit and a system for auxiliary diagnosis of children autism, and relates to the technical field of medical diagnosis. The kit and the system disclosed by the invention take arachidonic acid, tetracosanoic acid, S-adenosylmethionine, oxidized glutathione, glutathione, S-adenosylhomocysteine, choline, glycine, L-serine, cystathionine, L-cysteine, taurine, folic acid or vitamin B12 as biomarkers, and are used for auxiliary diagnosis of children autism; the specificity and the sensitivity are relatively high.

The invention discloses an interactive text generation method for children autism risk assessment. The method comprises the following steps: step 1, acquiring autism domain knowledge and initializing a knowledge database; step 2, capturing expressions and voices of a user in real time from a multi-modal sensor, converting the expressions and the voices into text features, and performing feature fusion; and step 3, loading the fused features into a large language model, and in combination with a knowledge base retrieval enhancement technology, completing one or more rounds of interaction through an analysis chain, an interview chain and a question chain to generate an interactive text. The interactive text generation strategy is iterated and optimized according to the dialogue context and the multi-modal features, and according to the dialogue context, a scale score is summarized through a large language model, and a user portrait and an interview result are output. The invention further provides a generation system for implementing the interactive text generation method and related applications, and the interactive text generation method has wide application value.

Provided herein are metabolites and methods of using the metabolites for diagnosing and treating Autism Spectrum Disorder (ASD) in a subject with a gastrointestinal problem, for determining a personalized treatment protocol for the subject, and for monitoring the therapeutic effect of an ASD treatment protocol in the subject being treated with the ASD treatment protocol.

This application relates to the field of data processing technology, and discloses a method, system, and medium for identifying autistic behaviors in children based on data analysis. The method includes: collecting and preprocessing multimodal behavioral data of children; extracting nonlinear features to obtain a time-series feature matrix; identifying repetitive behavioral patterns using a three-dimensional convolutional network; analyzing temporal changes in behavior using a temporal convolutional network; fusing multiple feature representations to obtain a comprehensive feature vector; and applying a multi-classifier system to identify autistic behavior types and assess severity. By extracting nonlinear time-series features, constructing a deep spatiotemporal feature extraction network, designing a feature fusion mechanism, and integrating a multi-classifier system, this invention overcomes the limitations of existing technologies involving single modality, linear features, and single algorithms, thereby improving the accuracy and interpretability of autistic behavior identification.

The present invention discloses a kind of child autism social training intervention system based on large model and VB-MAPP, including perception module, performance module and control module; Perception module is used to collect information of testee, including content and audio of speech; Performance module is used to generate corresponding feedback content according to the information of testee and its own configuration; Control module is used to control the workflow of whole system, complete synchronization and record; Performance module builds system prompt words based on VB-MAPP paradigm, and uses large model to carry out dialogue generation according to system prompt words; System prompt words include three parts, the first part is the personal information of testee; The second part is that the current large model needs to play virtual image, including role description and ability limitation; The third part is the description of current dialogue topic. The present invention can generate personalized dialogue content for different autistic children, make dialogue richer, more easily attract children's attention, and enhance intervention effect.

The application discloses a hand action mode training method for autism child rehabilitation training and belongs to the technical field of reinforcement learning. The application uses a reinforcement learning model to perform real-time analysis on the hand action mode recognition result in the rehabilitation training of a child autism patient, and recommends different training contents to train the child autism patient, so that the effect of guiding and optimizing the hand action mode of the child autism patient is achieved. The experience of the reinforcement learning model is used to replace artificial experience, the hand action rule of the autism child is found, and through an optimal training content recommendation strategy, the child is guided to efficiently master the training content and make correct action modes.

The invention provides a traditional Chinese medicine composition for treating autism. The traditional Chinese medicine composition is prepared from the following components in parts by weight: 280 to 320 parts of broccoli seed aqueous extract, 180 to 220 parts of astaxanthin powder, 80 to 120 parts of epigallocatechin gallate, 80 to 120 parts of grape seed extract, 2 to 8 parts of spirulina platensis, 28 to 32 parts of purple perilla, 5 to 12 parts of herba cistanche, 8 to 12 parts of rhizoma polygonati and 18 to 22 parts of herba taraxaci. The composition can well reduce scores of a children autism rating scale (CARS), an autism children behavior scale (ABC scale) and a treatment evaluation scale (ATEC), the total effective rate is high, and the longer the persistence time is, the better the curative effect is.

The embodiment of the present application provides a kind of autism intelligence auxiliary analysis method and system based on typical development behavior alignment, it is related to the technical field of autism auxiliary analysis technology.The method includes: obtaining the facial video data of subject under multi-paradigm experiment;With the preset prior action space as the benchmark, the alignment degree of the multi-modal behavior of the subject and the prior action space is determined, and the first feature sequence is generated based on the alignment degree;The first feature sequence is subjected to timing feature extraction and global integration to obtain global embedding representation;According to the global embedding representation, the probability of autism spectrum disorder is determined.Through the present application, the problem of low precision of autism auxiliary analysis is solved, and the effect of improving the precision of auxiliary analysis is achieved.