26 results about "Attention Concentration" patented technology

A system and method relating to a brain-computer interface in which a visual stimulus overlaying one or more objects is provided, at least a portion of the visual stimulus having a characteristic modulation. The brain computer interface measures neural response to objects viewed by a user. The neural response to the visual stimulus is correlated to the modulation, the correlation being stronger when attention is concentrated upon the visual stimulus. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

The invention provides an attention training method and device based on dynamic sensory stimulation. The method comprises: generating and presenting a game interaction interface to a user, the game interaction interface comprising at least one game task for training an attention level of the user, the game task being presented by at least one sensory stimulus, at least one variation dimension of the at least one sensory stimulus is composed of at least one variable having a plurality of values; obtaining performance information when the user performs the at least one game task, wherein the performance information is used for indicating the attention concentration degree of the user under the condition of different values of at least one variable; and determining the attention level of the user according to the performance information. Through the scheme, the diversity and interestingness of the attention training process are improved.

The invention discloses a psychological health screening method based on immersive game tasks and EEG (electroencephalogram) analysis, and relates to the technical field of psychological health screening, comprising the following steps: determining a psychological health dimension to be evaluated, and integrating VR (virtual reality) and EEG equipment; extracting corresponding data from different psychological dimension game data of VR to obtain emotion stability, cognitive ability and social skill evaluation values; and constructing a psychological health evaluation model based on linear regression through the EEG feature data, outputting score results on different psychological dimensions, and automatically generating a psychological health evaluation report in combination with corresponding evaluation values. The method solves the problems that it is difficult to establish a player attention state classification model to determine the attention concentration duration of players in different game stages, it is difficult to quantify evaluation values constructed by feature data in different psychological dimensions, and it is lack of automatically generating a psychological health evaluation report by combining VR games and EEG electroencephalogram signals.

The invention belongs to the technical field of video management, and particularly relates to a video management method based on artificial intelligence. The method comprises the following steps: firstly, extracting an interest value of a user in a video watching process through facial recognition and expression analysis, and judging an attention concentration time frame; and then text features and scene features of the time frame and the advertisement video key frame are extracted, semantic alignment and matching scoring are performed on the video and the advertisement by using a cross-modal matching model, and the advertisement with high matching degree is screened and inserted into the video. The system further collects interaction behavior data generated after the user watches the advertisement, calculates the advertisement adhesion score, dynamically adjusts the advertisement putting strategy, and achieves the continuous optimization of the advertisement effect. The method improves the correlation between the advertisement and the video content and the user experience, and has the advantages of high precision, high adaptability and the like.

The invention provides a life scene assisting method based on electroencephalogram-eye movement information, a storage medium and equipment. The life scene assisting method comprises the following steps: acquiring first electroencephalogram data; if it is judged that the user is in the Alzheimer's disease morbidity state according to the first electroencephalogram data, the electroencephalogram data are obtained again according to a preset credible mode and recorded as reliable electroencephalogram data; if it is judged that the user is in an attention concentration state according to the reliable electroencephalogram data and the preorder electroencephalogram data, real-time eye movement data are obtained according to a preset reliable mode; inputting the real-time eye movement data into the eye movement data extraction model to obtain reliable eye movement data; if it is determined that the user intends to control the living scene object according to the reliable electroencephalogram data, the preorder electroencephalogram data and the reliable eye movement data, determining a target control object of the user according to the reliable eye movement data and a map of the living scene where the user is located; collecting limb parameters of the user according to a second preset frequency; and if the limb parameters indicate that the user does not act within the preset time threshold, triggering object operation reminding.

A heart rate measurement and mental state detection system and a heart rate measurement and mental state detection method for measuring heart interbeat interval and detecting a direction of attention of an individual engaged in an activity are disclosed. The heart rate measurement and mental state detection and method are configured to instantaneously detect and measure focused attention of an individual during a pre-action phase before the individual performs an action involved in an activity while the individual engages in the activity.

The invention provides a touch head structure of a palm device for improving concentration and a concentration reminder, the touch head structure is used for being arranged on a holding body, the touch head structure comprises a rotating part, the rotating part is provided with a mounting part, and the mounting part is used for being connected with the holding body and is driven by the holding body to rotate around the mounting part; the first elastic piece is arranged at the first end of the rotating piece; the first contact pressure head is connected with the first elastic piece and is far away from the rotating piece through the pushing force of the first elastic piece; the second contact pressure head is arranged at the second end of the rotating piece; and through the rotation of the rotating piece, the first touch pressure head and the second touch pressure head are matched with each other and respectively abut against the palm of a user to move. The problems that a user cannot keep attention concentrated for a long time and is prone to distraction or sleepiness are solved.

The invention discloses an attention state detection device, and relates to the technical field of computer vision. In the device, eye movement characteristics are acquired through a first acquisition module, then behavior characteristics are determined based on the eye movement characteristics in a first determination module, and finally, the attention state is determined based on a pre-established attention stage, a pre-established mapping relation between the behavior characteristics and a threshold value and the current behavior characteristics in a second determination module. In the attention detection device, the attention is divided into a plurality of stages in advance, such as a visual search characterization stage, a sight orientation characterization stage, an attention maintenance characterization stage and an attention concentration characterization stage, and a mapping relationship among the attention stages, behavior characteristics and threshold values is pre-established, so that the attention detection accuracy is improved. According to the invention, the existing attention stage and the missing attention stage can be determined based on the mapping relation and the current behavior characteristics, so that the user can know the specific stage where the attention is missing, and the accurate judgment of the attention state is realized.

The invention discloses a mental improvement algorithm based on human-computer interaction, and the algorithm comprises the following steps: S1, collecting the behavior data, cognitive ability indexes and learning preferences of a user through a human-computer interaction interface, and constructing an initial cognitive feature vector containing the attention concentration degree and cognitive style classification; and S2, based on a cognitive challenge module, dynamically generating a multi-dimensional interaction task according to the current cognitive level of the user. According to the method, fusion of cross-domain task complexity and a neuroscience incentive mechanism is dynamically adjusted through the fuzzy logic controller, and accurate balance between cognitive challenge and individual adaptive learning is achieved. Compared with a traditional mental training algorithm, a semantic network analysis technology is introduced into cross-domain knowledge association task generation, and the problems of task staticization, excitation simplification and path linearization in a traditional algorithm are effectively solved in combination with a three-dimensional cognitive map constructed by a map attention network and a deep reinforcement learning optimization strategy.

The present disclosure provides an information prediction method, apparatus, device, and medium, which can be applied to the fields of artificial intelligence technology and financial technology. The information prediction method includes: collecting the time-series signal of brain waves, where the time-series signal of brain waves is generated when the target object watches the video image of the target product; the time-series signal of brain waves includes band signals of different frequencies; according to the frequency information of the band signals, extracting the time-series signal of the target band from the time-series signal of brain waves, and the time-series signal of the target band includes the power information of the target band and the duration information of the occurrence of the target band; generating the concentration degree of the target object on the target product according to the power information and the duration information, where the concentration degree represents the degree of attention concentration of the target object when watching the video image of the target product; and generating the preference prediction information of the target object on the target product according to the concentration degree.

The invention discloses a multimedia teaching interaction optimization method and device, a medium and a program product, and the method comprises the steps: obtaining a monitoring time period based on the time of a multimedia teaching course; multimedia teaching content and teacher guidance content in all monitoring time periods are obtained, and teaching interaction measures are obtained; obtaining the attention concentration degree based on the student attention concentration time of the monitoring time period; comparing the attention concentration degree with a preset attention concentration degree, and when the attention concentration degree is not higher than the preset attention concentration degree, determining the monitoring time period corresponding to the attention concentration degree as a low-attention time period; based on the low-attention time period, changing the teaching interaction measure of the low-attention time period to obtain an adjusted teaching interaction measure; and further adjusting the adjusted teaching interaction measures. The problem that the current widely existing teaching interaction adjustment is large in resource investment and poor in effect is solved. Reasonable, timely and flexible optimization of the teaching interaction scheme is realized.

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

The present disclosure relates to the technical field of computers, in particular, to a word-based video recommendation method and device, medium and electronic equipment. The method comprises: determining a plurality of words to be learned and a word order of the words to be learned according to the words in a word book; determining a target definition of the words to be learned, and determining a learning video corresponding to each word to be learned according to the target definition; and displaying the learning video corresponding to each word to be learned according to the word order. In this way, for each word to be learned, the user can focus on understanding and learning the application of the word to be learned under the target definition, thereby improving the mastery and application of the word to be learned. In addition, through the display of the learning video, the real usage scenario of the word to be learned can be restored, effectively improving the user's attention concentration in learning the word, improving the user's learning pleasure, reducing the boredom of word learning, and further accelerating the speed of mastering the word.

The invention relates to a brain computer interface. A system and method involving a brain computer interface in which a visual stimulus overlying one or more subjects is provided, at least a portion of the visual stimulus having a characteristic modulation. A brain computer interface measures a neural response to a subject viewed by a user. Neural response to visual stimulation is related to modulation, and when attention is focused on visual stimulation, correlation is stronger. The visual stimulus includes a feedback element that varies according to a measurement of attention of the or each overlying object.

The present invention relates to an adaptive active information interaction education method, which adaptively changes the playback mode according to the learner's environment and attention situation, such as converting a video into audio through content recognition and automatic voice generation, or automatically generating subtitles and pictures from the audio to generate a video. Specifically, the device can identify environmental interference characteristics based on sound signals, vibration signals, acceleration sensors, and light sensors, and judge the level of environmental noise, sound scenes, ambient light conditions, and the shaking state of the device; at the same time, the device can identify the learner's attention state and automatically push appropriate content formats to maximize the degree of attention concentration. In addition, the device can automatically generate text describing the video and image content by recognizing the video and image content, and further synthesize audio playback; the device can also recognize the audio content and automatically generate text and pictures for visual presentation.

The present invention aims to create the ideal conditions for a user to consume and interact with digital content in a simple, effective and distraction-free manner. In order to achieve this aim, it provides the possibility of accessing the content by various means, with particular emphasis on physical media such as content discs or release discs. After accessing the content, the user experiences simplified navigation, free from distractions or interruptions. In addition to providing access to the content, the device has mechanisms to measure the user's level of attention and focus, as well as their stress and emotional state, while they are using the device. Some of these mechanisms are embedded and operate by means of an infrared camera mounted on the main device. Other mechanisms can be included by means of connecting peripherals such as an EEG, a heart rate sensor and a galvanic skin response sensor. The data resulting from these measurements are combined and used to provide feedback to the user and to personalise their content consumption experience. Finally, the user's focus and sense of immersion can be enhanced by means of using a pair of virtual reality glasses as a peripheral. These glasses may or may not have an integrated EEG and when connected to the device, provide new possibilities for viewing and interacting with the content.

The invention discloses an advertising effect analysis method and system based on artificial intelligence, and relates to the technical field of commercial advertising prediction, the advertising effect analysis method based on artificial intelligence can significantly improve the accuracy and effectiveness of advertisement putting, and through multi-modal biological feature collection, the advertising effect is improved. Data of brain waves and eye movement tracks when consumers watch advertisements are obtained in real time, the nerve excitement degree, the attention concentration degree and the emotional response of the consumers are deeply known, a nerve behavior decoding engine constructs a three-layer cognitive mapping model based on the data, the real-time transformation probability of the consumers is accurately predicted, and the conversion efficiency of the consumers is improved. The dynamic advertisement gene pool specifically adjusts visual, auditory and narrative elements of the advertisement according to the prediction result, personalized customization of the advertisement content is achieved, the accurate advertisement putting mode can better meet the requirements and interests of consumers, the matching degree of the advertisement and the consumers is improved, and therefore the conversion rate of the advertisement is effectively improved.

The invention relates to an audio-visual language audience response evaluation system and method based on multi-view synchronous perception and data fusion. The system comprises an adjustable viewing angle watching platform, an audio-visual content playing module, a synchronous perception acquisition module, a subjective feedback acquisition module, a multi-modal data fusion analysis module and an evaluation result output module, eye movement data, physiological signals and behavior responses of audiences can be synchronously acquired in real time at different watching angles, and subjective feedback information is combined, so that the audiences can be evaluated in real time. And the perception intensity, the attention concentration degree, the cognitive load and the emotion titer of the audience are comprehensively evaluated. The system can be widely applied to the fields of audio-visual language content optimization, immersive medium design and audience experience research, and has the advantages of being flexible in view angle switching, high in data fusion degree, high in analysis precision and the like.

The present invention relates to a method, device, equipment, and medium for analyzing head attention in outdoor queue training. The method comprises the following steps: obtaining a student's facial image region at three consecutive moments; calculating the facial spatial feature vector and the orientation posture 6D matrix of each moment frame based on the image region; obtaining the orientation posture Euler angle based on the orientation posture 6D matrix; calculating the average Euclidean distance of the facial spatial feature vector and the average difference of the orientation posture Euler angle for a consecutive time period frame; and determining whether attention is focused. When the average Euclidean distance of the facial spatial feature vector exceeds a preconfigured first threshold, or the average difference of the orientation posture Euler angle exceeds a preconfigured second threshold, the student is judged to be inattentive; otherwise, the student is judged to be focused. Compared with the prior art, the present invention has the advantages of continuous regression results and simple calculation.

The invention discloses an attention monitoring method. The method comprises the following steps: S1, acquiring synchronous voltage data and electroencephalogram signals of an intelligent cushion; s2, performing data preprocessing operation on the voltage data to obtain pressure matrix data in a two-dimensional form; s3, performing preprocessing operation on the electroencephalogram signals to obtain electroencephalogram data in a two-dimensional form; s4, performing attention state labeling on the electroencephalogram data by adopting an attention detection equation to obtain an attention state corresponding to the electroencephalogram data; s5, enabling the attention state as a sample label to correspond to a pressure matrix data sample, and carrying out the training, and obtaining an attention monitoring neural network model; and S6, collecting intelligent cushion pressure data of the target object in a preset state, inputting the intelligent cushion pressure data into the attention monitoring neural network model, and predicting the attention concentration condition of the target object by using the attention monitoring neural network model.

The invention relates to the technical field of intelligent analysis, regulation and control of resources, in particular to an intelligent analysis, regulation and control system for anesthesiology department operating room resources. Eye movement, physiology and operation behaviors of an operator are collected through a multimode parameter collection device group to obtain eye movement signal parameters, pulse wave signal parameters and operation behavior signal parameters, and then the attention concentration degree, the working memory load and the pressure index are analyzed and output through a multi-task deep learning model. Accurate recognition of the cognitive state of the operator is realized, and data support is provided for subsequent cognitive scene division; according to the method, the multi-dimensional cognitive state vector is analyzed through the cognitive scene division mechanism to obtain the corresponding cognitive decision scene, the cognitive decision scene is analyzed through the rendering control mechanism to output the rendering control strategy, invalid information interference and operation interaction cost are reduced, cognitive burden is reduced, decision efficiency and accuracy are improved, and user experience is improved. And the safety and the high efficiency of operating room resource regulation and control are ensured.

The present application relates to the field of intelligent lighting, and an embodiment of the present application provides a self-study room healthy lighting method based on dynamic adjustment of illumination parameters, which comprises the following steps: acquiring reservation information and human body sensing information in a self-study room; generating a self-study time planning table according to reservation duration information and attention concentration time range; acquiring learning behavior information of a learner at a preset time before the end of a self-study time period; acquiring an attention level according to the learning behavior information and the human body sensing information; determining an illumination parameter change rate according to the attention level and a difference between light environment regulation parameters and rest illumination parameters; and controlling a light source to switch from the light environment regulation parameters to preset rest illumination parameters according to the illumination parameter change rate during a rest time period. The present application solves the problem that the light in a self-study room cannot be dynamically switched to a suitable lighting mode and cannot provide a healthy and comfortable lighting light environment for learners.

A system and method relating to a brain-computer interface in which a visual stimulus overlaying one or more objects is provided, at least a portion of the visual stimulus having a characteristic modulation. The brain computer interface measures neural response to objects viewed by a user. The neural response to the visual stimulus is correlated to the modulation, the correlation being stronger when attention is concentrated upon the visual stimulus. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

The present application belongs to the technical field of video management, and particularly relates to a video management method based on artificial intelligence. The method first extracts the interest value of the user during the process of watching the video through facial recognition and expression analysis, and determines the time frame of attention concentration. Then, the text features and scene features of the time frame and the key frames of the advertisement video are extracted, the cross-modal matching model is used for semantic alignment and matching score of the video and the advertisement, and the advertisements with high matching degree are screened and inserted into the video. The system further collects the interactive behavior data generated after the user watches, calculates the advertisement sticking score, dynamically adjusts the advertisement delivery strategy, and realizes the continuous optimization of the advertisement effect. The method improves the relevance of the advertisement and the video content and the user experience, and has the advantages of high precision and high adaptability.

The invention relates to the technical field of artificial intelligence, education robots and state perception, in particular to an interactive education robot system based on artificial intelligence. The system comprises an acquisition judgment module, an efficiency analysis module and an interaction guide module. The sitting posture state data and the micro-expression feature data of the learner are collected in real time through the collection and judgment module, the learning state of the learner is judged, if the learner is in an attention concentration state, the current learning state is maintained, and if the learner is in an inattention state, the efficiency analysis module dynamically adjusts the difficulty of the current knowledge point according to the matching degree. And the interaction guiding module generates an interaction problem associated with the current knowledge point by using a multi-dimensional data association method, and guides the learner to gradually enter a learning state, so that accurate recognition of an efficient / inefficient learning state is realized, and the matching degree of teaching content and the ability of the learner is effectively improved.

The application belongs to the field of special education assistance and rehabilitation evaluation, and particularly relates to an immersive sensory training and evaluation method for special education needs. A method named "an immersive sensory training and evaluation method for special education needs" is disclosed, aiming to solve the problems of strong subjectivity and coarse data granularity of existing evaluation. The steps include: constructing a virtual scene and defining a standard trajectory; capturing data such as eye movement visual line, head pose and limb end in real time; comparing the actual trajectory with the standard trajectory by using the DTW algorithm, and extracting the offset and lag features; calculating the attention concentration and motion smoothness through visual line collision detection and Jerk value quantification; and finally automatically generating a digital evaluation report containing a space behavior heat map. Through the above scheme, the application realizes the transformation of evaluation from qualitative observation to quantitative analysis, significantly improving the objectivity, accuracy and scientificity of rehabilitation evaluation.