136 results about "Multimodal data" patented technology

Methods, systems, and apparatus for detecting an epileptic event, for example, a seizure in a patient using a medical device. The determination is performed by providing an autonomic signal indicative of the patient's autonomic activity; providing a neurologic signal indicative of the patient's neurological activity; and detecting an epileptic event based upon the autonomic signal and the neurologic signal.

Methods and apparatuses of the present invention generally relate to generating actionable data based on multimodal data from unsynchronized data sources. In an exemplary embodiment, the method comprises receiving multimodal data from one or more unsynchronized data sources, extracting concepts from the multimodal data, the concepts comprising at least one of objects, actions, scenes and emotions, indexing the concepts for searchability; and generating actionable data based on the concepts.

In an approach to incident prediction and response, one or more computer processors receive one or more alerts corresponding to an incident. The one or more computer processors aggregate the one or more alerts with additional data corresponding to the incident. The one or more computer processors feed the aggregated data into a prediction model, where training of the prediction model uses associated independent stacked Restricted Boltzmann Machines utilizing one or more supervised methods and one or more unsupervised methods. The one or more computer processors determine, based, at least in part, on one or more calculations by the prediction model, at least one probability of the incident. The one or more computer processors determine whether the at least one probability exceeds a pre-defined threshold. In response to determining the at least one probability exceeds a pre-defined threshold, the one or more computer processors send at least one notification.

The present invention provides a system and method for analysis of multimodal imaging and non-imaging biomedical data, using a multi-parametric data representation and integration framework. The present invention makes use of (1) dimensionality reduction to account for differing dimensionalities and scale in multimodal biomedical data, and (2) a supervised ensemble of embeddings to accurately capture maximum available class information from the data.

A method, comprising receiving at least one of a signal relating to a first cardiac activity and a signal relating to a first body movement from a patient; triggering at least one of a test of the patient's responsiveness, awareness, a second cardiac activity, a second body movement, a spectral analysis test of the second cardiac activity, and a spectral analysis test of the second body movement, based on at least one of the signal relating to the first cardiac activity and the signal relating to the first body movement; determining an occurrence of an epileptic event based at least in part on said one or more triggered tests; and performing a further action in response to said determination of said occurrence of said epileptic event. Further methods allow classification of epileptic events. Apparatus and systems capable of implementing the method.

The invention discloses a supervoxel sequence lung image 3D pulmonary nodule segmentation method based on multimodal data. The method comprises the following steps: A) extracting sequence lung parenchyma images through superpixel segmentation and self-generating neuronal forest clustering; B) registering the sequence lung parenchyma images through PET/CT multimodal data based on mutual information; C) marking and extracting an accurate sequence pulmonary nodule region through a multi-scale variable circular template matching algorithm; and D) carrying out three-dimensional reconstruction on the sequence pulmonary nodule images through a supervoxel 3D region growth algorithm to obtain a final three-dimensional shape of pulmonary nodules. The method forms a 3D reconstruction area of the pulmonary nodules through the supervoxel 3D region growth algorithm, and can reflect dynamic relation between pulmonary lesions and surrounding tissues, so that features of shape, size and appearance of the pulmonary nodules as well as adhesion conditions of the pulmonary nodules with surrounding pleura or blood vessels can be known visually.

A neuromorphic parallel image processing approach that has five (5) functional layers. The first performs a frequency domain transform on the image data generating multiple scales and feature based representations which are independent of orientation. The second layer is populated with feature based representations. The third layer, an object class recognizer layer, are fused using a neuromorphic parallel processor. Fusion of multimodal data can achieve high confidence, biometric recognition.

The invention relates to an analysis and prediction system for cooperative correlative tracking and global situation of network social events. The system comprises an information fusion unit, a tracking unit, and an analysis and prediction unit. The information fusion unit is used for fusing multimodal features of network social event data to obtain fused information of the multimodal data of social events, thus establishing a semantic description model for the multimodal data of cross-social events. The tracking unit is connected with the information fusion unit and is used for acquiring sematic correlative tracking data of the social events on each aspect according to cross-modal property, cross-platform property and trans-time-and-space property of the network social events in face of network contents including rich multimedia information on the basis of the semantic description model for the multimodal data of the cross-social events. The analysis and prediction unit connected with the tracking unit is used for acquiring social-event-based global situation analysis and prediction data on the basis of the semantic correlative tracking data.

Techniques for multimodal data fusion having a multimodal hierarchical dictionary learning framework that learns latent subspaces with hierarchical overlaps are provided. In one aspect, a method for multi-view data fusion with hierarchical multi-view dictionary learning is provided which includes the steps of: extracting multi-view features from input data; defining feature groups that group together the multi-view features that are related; defining a hierarchical structure of the feature groups; and learning a dictionary using the feature groups and the hierarchy of the feature groups. A system for multi-view data fusion with hierarchical multi-view dictionary learning is also provided.

A data structure, method, storage medium and logic arrangement are provided for use in collecting and analyzing multilinear data describing various characteristics of different objects. In particular it is possible to recognize an unknown object or an unknown viewpoint of an object, as well as synthesize a known viewpoint never before recorded of an object, and reduce the amount of stored data describing an object or viewpoint by using dimensionality reduction techniques, and the like.

The invention discloses an interactive method and device for an intelligent robot, belongs to the technical field of robots, and solves the technical problem that a conventional interactive method for the intelligent robot cannot realize active interaction. The method comprises the following steps: in a process of interaction with a user, acquiring multimodal interactive information input by the user; analyzing the multimodal interactive information, judging that active interaction is suitably performed currently, and acquiring current interactive scene information;, determining current active interactive topic information by combining the current interactive scene information; and generating active interactive behavior multimodal data according to the current active interactive topic information and outputting the active interactive behavior multimodal data.

The invention discloses an early-stage autism screening system based on a joint attention ability test and an audio-video behavior analysis. Audio-video multimodal data of an evaluator and a testee iscollected and analyzed so as to evaluate and predict autistic spectrum disorders. The system includes a data acquisition module, a preprocessing module, a feature extraction module, a training classification module and a prediction module. The data acquisition module is used for multi-view and multi-channel collection of the audio-video multimodal data of the evaluator and the testee in a test; the preprocessing module synchronously collects the audio-video data, detects and marks the time when the evaluator issues a command through voice recognition, and extracts and analyzes an audio videoafter the point in time; the feature extraction module is used for feature extraction of the pre-processed audio-video data, and speech content, facial emotions and other features are obtained; the training classification module takes the extracted combination features as an input of a machine learning classifier for training, and a classifier model for predicting the autism is obtained; by usingthe classifier model obtained through training, the prediction module performs autism classification and prediction on the testee whose data is collected.

The invention discloses a virtual human visual processing method and system based on multimodal interaction. A virtual human runs in intelligent equipment. The method comprises the steps that when thevirtual human is in an awakened state, the virtual human is displayed in a preset display region, and the virtual human has specific personalities and properties; multimodal data is acquired, whereinthe multimodal data comprises data from surroundings and multimodal input data performing interaction with a user; a virtual human ability interface is called to analyze the multimodal input data, and multimodal output data is decided; relative position information is utilized to calculate execution parameters for turning of the head of the virtual human towards a target face; and the execution parameters are displayed in the preset display region. Through the embodiment, when the virtual human performs interaction with the user, it is guaranteed that the virtual human tracks the face of theuser all the time to perform face-to-face multimodal interaction with the user, the user demand is met, and user experience is improved.

A data structure, method, storage medium and logic arrangement are provided for use in collecting and analyzing multilinear data describing various characteristics of different objects. In particular it is possible to recognize an unknown individual, an unknown object, an unknown action being performed by an individual, an unknown expression being formed by an individual, as well as synthesize a known action never before recorded as being performed by an individual, synthesize an expression never before recorded as being formed by an individual, an reduce the amount of stored data describing an object or action by using dimensionality reduction techniques, and the like.

The invention discloses a multimodal-medical-image-based auxiliary diagnosis system and method for the Parkinson's disease. The system is composed of an input module, a feature extraction module, a feature fusion module, and a diagnosis module. In addition, the method includes: after an ultrasonic image, an MRI image and a PET image are obtained, feature extraction is carried out on the data of three channels by using different methods, and feature fusion is carried out on the extracted features by using different fusion methods; the diagnostic module processes the features of three modes by means of integrated learning to obtain a diagnosis result, so that an auxiliary reference is provided for the diagnosis by the doctor. For the exiting early-stage diagnosis of the Parkinson's disease,the multimodal data are analyzed quantitatively, so that the system and method have the important significance in the early diagnosis of the Parkinson's disease; and the accuracy of diagnosis is improved and the subjective determination error of the operator is reduced. Moreover, the early clinical intervention is realized and thus the post-stage disability is reduced, so that a certain auxiliaryguidance effect is realized.

The invention discloses a cross-media topic detection method and device based on multimodal information fusion and graph clustering. The method includes the steps that first, text information and video content are preprocessed; second, text fusion and high-layer characteristic extraction are performed to obtain text information similarity; third, similar clips of video are detected, and vision information similarity is obtained; fourth, according to the text information similarity, the vision information similarity and time information of fused data, the final data similarity is calculated; fifth, according to the final data similarity, graph fusion and graph clustering are performed, and topic detection is completed. The method effectively avoids the over-segmentation and the over-generation problem caused by hard quantification on a time axis and the problem that an existing topic detection method can not be transplanted to topic detection on multimodal data from different multimedia sources.

The invention discloses an expert service robot cloud platform, a brain-like cloud platform, an expert terminal and a user terminal. The brain cloud-like platform is connected with the user terminal through the human-computer interaction interface, and is used for receiving the multimodal data sent by the user terminal, and completing the session with the user according to various preset knowledge; conversations include: greetings, a question-and-answer session or multiple rounds of conversation; the user terminal is used for sending multimodal data to the brain cloud-like platform through thehuman-computer interaction interface, and for receiving the data returned by the brain cloud-like platform through the human-computer interaction interface; the expert terminal is used for experts toinject relevant expert knowledge data into the brain-like cloud platform. The technical proposal of the invention can provide efficient and accurate intelligent session for users and improve user experience.

The invention relates to the technical field of recognition of objects in audiovisual documents. The method uses multimodal data that is collected and stored in a similarity matrix. A level of similarity is determined for each matrix cell. Then a clustering algorithm is applied to cluster the information comprised in the similarity matrix. Clusters are identified, each identified cell cluster identifying an object in the audiovisual document.

The invention discloses a multi-modal data analysis method and system based on high Laplacian regularization and low-rank representation and belongs to the field of multimodal data analysis. The invention aims to capture the global linear structure and nonlinear geometric structure of multimodal data. The method includes the following steps that: multi-modal data are processed, so that a pluralityof data matrices are obtained; low-rank representation and Laplacian regularization term are combined so as to construct a non-negative sparse hyper-laplacian regularization and low-rank representation model, the non-negative sparse hyper-laplacian regularization and low-rank representation model is made to learn each data matrix, so that a high Laplacian regularization and low-rank subspace is obtained; learning is performed on the basis of the high Laplacian regularization and low-rank subspace and a support vector machine, so that a plurality of classifiers can be obtained; and voting is performed for the classifiers, so that a final classifier is obtained. The structure of the system includes a data processing module, a data analysis module, a classification module, and a voting module. With the method adopted, the global linear structure and nonlinear geometry of the multimodal data can be captured.

The invention provides a multimodal data classification method based on feature selection, The method comprises the following steps of: collecting and processing multimodal data, expanding and representing the data by nonlinear kernel explicit expansion, obtaining combined high-order disease features, quickly identifying key features in high-dimensional feature space by feature selection method, constructing ensemble learning model, and performing image classification. The method provided by the invention can fully utilize the data information in each mode, and improves the classification accuracy.

A method includes transforming textual material data into a multimodal data structure including a plurality of classes selected from the group consisting of output, procedural information, and contextual information to produce transformed textual data, storing the transformed textual data on a memory device, retrieving, in response to a user request via a multimodal interface, requested transformed textual data and presenting the retrieved transformed textual data to the user via the multimodal interface.

The present invention relates to a CNN-based multimodal nasopharyngeal tumor joint segmentation method. The method comprises: segmenting a multimodal nasopharyngeal tumor image into image blocks of the same size; inputting the segmented image blocks into a rough segmentation CNN for rough segmentation to obtain rough segmented images, and obtaining an image block determined as a tumor area by rough segmentation processing; inputting the rough segmented images into the fine CNN for fine segmentation; and carrying out fine segmentation on each pixel in the image blocks determined as a tumor area, determining the category of the pixel, and obtaining pixel-level finely segmented images finally. The segmentation method of the present invention does not need to determine the category of all thepixels in the nasopharyngeal tumor image, and only needs to determine a few lesion areas, thereby greatly improving the efficiency of the segmentation algorithm and shortening the segmentation time;and in addition, both multimodal data and CNN are used for image segmentation, and richer information of the to-be-segmented tumor image is preserved, so that the segmentation accuracy of the algorithm is improved.