32 results about "Behavioral assessment" patented technology

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention provides a request processing method and device, equipment and a storage medium, relates to the technical field of computers, and is used for accurately performing exception processing on a data access request. The method comprises the following steps: acquiring access behavior information corresponding to a data access request; performing quantification processing on the access behavior information based on a behavior perception model, and determining a behavior feature vector corresponding to the access behavior information; performing analysis processing on the behavior feature vector based on a risk assessment model, and determining a behavior assessment result; the behavior evaluation result is used for indicating the abnormal degree of the access behavior corresponding to the data access request; processing the data access request according to an access control strategy corresponding to the behavior evaluation result; the access control strategy is used for controlling the access authority of the data access request.

The invention relates to the technical field of nerve regulation and control, and provides a closed-loop ultrasonic brain-computer interface regulation and control method for improving post-stroke dysfunction, which comprises the following steps: S1, constructing integrated closed-loop ultrasonic brain-computer interface equipment which comprises a behavior control unit, a signal generator unit, a power amplifier unit and an electrophysiological signal monitoring unit; s2, constructing a mouse model, and embedding an electrode provided for the electrophysiological signal monitoring unit for monitoring; s3, dividing the mouse model into a random stimulation group and a brain-computer interface treatment group, and simultaneously performing closed-loop ultrasonic stimulation for preset time; s4, after closed-loop ultrasonic stimulation for a preset time, performing various behavioral test evaluations including representation of a sensory movement function and representation of an emotional emotion function; and S5, carrying out neural circuit remodeling evaluation, and observing the projection change of the high-order thalamus PO nuclei region of the mouse to the primary cortex. In the aspects of targeting, closed-loop control and integrated design, the method is expected to become a novel and effective post-stroke dysfunction treatment method.

The invention provides a medical resource utilization behavior assessment method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a to-be-assessed behavior sequence; dividing the behavior sequence into a plurality of windows with the same time length, and converting each behavior point in each window into a corresponding behavior label; generating a sample sequence corresponding to the behavior sequence based on a plurality of windows of the behavior sequence and a plurality of behavior tags in each window; calculating an abnormal score of the behavior sequence based on the sample sequence; capturing a plurality of forward dependencies and a plurality of backward dependencies of the behavior sequence; splicing the plurality of forward dependencies and the plurality of backward dependencies to generate a feature vector; reconstructing the feature vector to obtain a reconstructed behavior sequence; calculating a reconstruction error value between the reconstruction behavior sequence and the behavior sequence; and judging whether the behavior sequence is credible or not based on the abnormal score and the reconstruction error value of the behavior sequence to be evaluated. According to the invention, whether the medical resource utilization behavior is credible can be accurately evaluated.

System device for security-oriented access enforcement, comprehensive at least one processing unit, an identity capture unit for the technical recording of user- or system-related identity characteristics, a behavioral assessment unit, and an access enforcement unit, wherein the system device is designed to evaluate access requests to a computing resource system-side, characterized by that the behavioral assessment unit generates a dynamic identity assessment based on time-dependent behavioral characteristics at the hardware or firmware level, and the access enforcement unit technically grants, restricts, or prevents access depending on this assessment.

The invention relates to the technical field of teenager mental health monitoring, and discloses an intelligent monitoring system convenient for intervening teenager depression self-injury behaviors, and the system comprises a data collection module, a data analysis module, a single behavior evaluation module, a continuous behavior evaluation module, an optional data storage module, an early warning feedback module and a user interaction module. The data acquisition module acquires physiological parameters of the smart bracelet through Bluetooth and preprocesses the physiological parameters; the data analysis module identifies fluctuation characteristics and normalizes data; the single behavior evaluation module extracts key points of the physiological parameter curve and generates an abnormal waveform to judge single abnormality; the continuous behavior evaluation module generates an abnormal region based on continuous 24-hour data, and analyzes a change rate to judge continuous abnormality; the data storage module locally stores, encrypts and uploads to the cloud; the early warning feedback module sends graded early warning in real time; and the user interaction module responds to the query instruction. The system realizes multi-dimensional monitoring and accurate early warning of the depression self-injury behavior of the teenagers, and improves the mental health monitoring efficiency.

This application relates to a device control method, apparatus, device, and system based on behavior monitoring. The device control method based on behavior monitoring includes: extracting features from behavior monitoring data of a target object to obtain behavior features; performing a first behavior evaluation process on the behavior features to obtain a first evaluation result; performing a second behavior evaluation process on the behavior features to obtain a second evaluation result; identifying the target behavior state of the target object based on the first and second evaluation results; and adjusting the device in the space where the target object is located based on the target behavior state. This solution can improve the intelligence level of the device control process.

This invention discloses a method for assessing shrimp health status based on multi-source information fusion, which relates to the field of data processing technology. The method includes the following steps: acquiring and preprocessing shrimp images at different growth stages; generating training images using artificial labels; extracting features such as shrimp outline length, area, and shell contrast; establishing a target recognition and feature extraction model based on the training images; identifying shrimp in the monitored aquaculture environment and extracting their behavioral characteristic parameters after training; calculating the behavioral assessment influence coefficient and growth assessment influence coefficient; and obtaining water source characteristic parameters to calculate the disease incidence influence coefficient; finally, generating a comprehensive health status assessment index, comparing it with a health judgment threshold, and outputting the health status judgment result. This achieves effective assessment of shrimp health status, real-time monitoring, and early warning, helping farmers take timely measures to prevent disease occurrence and improve shrimp survival rate and economic benefits.

The invention relates to the field of rehabilitation supervision, in particular to a medical supervision system based on orthopedic rehabilitation, and the system comprises an initial evaluation module which is used for responding to a behavior judgment condition to determine a key supervision behavior of a supervision target, and responding to an evaluation condition to determine a supervision analysis strategy of the supervision target; the execution analysis module is used for responding to a set setting condition to determine a set setting strategy and determining an autonomous execution interference coefficient of each key supervision behavior based on the behavior evaluation set; the supervision matching module is used for responding to the matching analysis condition to determine a supervision matching mode of the supervision target and responding to different matching conditions to determine an autonomous interference analysis strategy of the key supervision behavior; and the rehabilitation analysis module is used for responding to the interference adjustment condition to determine whether the rehabilitation process index is adjusted or not, and the accuracy of the judgment result of the rehabilitation process of the supervision target is improved.

The present invention discloses a cognitive behavioral testing method combined with medical data analysis, which relates to the field of data analysis and processing technology. It connects to a medical database, mines cognitive behavioral maps, introduces a penalty coefficient based on specific variables, and supervises the training of a digital testing module. It builds a behavioral cognitive platform, and embeds the digital testing module into the behavioral cognitive platform. It establishes a platform connection based on account information, uploads behavioral source data, makes behavioral cognitive decisions by constructing a bio-behavioral portrait, determines behavioral cognitive results, and generates a platform test pop-up window for interface visualization. It is used to solve the technical problems existing in the prior art, such as the inability to effectively handle complex data correlations and uncertainties, the lack of flexibility and accuracy in cognitive behavioral testing, and the difficulty in providing clear and operational cognitive behavioral assessment results. It can efficiently and specifically analyze behavioral data and identify cognitive disorders.

The invention relates to the technical field of man-machine interaction data processing, in particular to a training scene difficulty level updating method and system based on user operation behavior scoring, and the method comprises the steps: collecting behavior parameters of a user, the behavior parameters including delay related parameters and psychological adaptation related parameters; calculating an initial response factor based on the delay correlation coefficient, wherein the initial response factor is used for eliminating the influence of network delay on user operation behavior evaluation; and correcting the initial response factor based on the psychological adaptation related parameters. According to the invention, user operation data is collected, network delay compensation, psychological pre-input habit correction and physiological fatigue correction are carried out in sequence, and a behavior score which can truly reflect the skill level of a user is calculated, so that the training difficulty is dynamically adjusted; the problem of behavior score distortion caused by network delay and user psychological adaptation in the prior art is effectively solved, and accurate and personalized difficulty self-adaptation is achieved.

A method for sharing data across disparate emergency provider systems is described. The method comprising: analyzing content of communications between a caller and a call taker. An identifier associated with the caller is determined. An escrow database is accessed to determine the presence of a behavioural assessment indicator (BAI). The BAI indicates that the caller has a mental health profile stored in the escrow database. Permission is received from the caller to access the mental health profile. Mental health information of the caller is retrieved from a data structure comprising a first portion storing a locked mental health history and a second portion storing accessible mental health status information. The retrieved information is presented to the call taker to assist handling the call. A device comprising a processor and a computer-readable storage medium storing instructions that cause the processor to perform the method is also described.

The application relates to the technical field of monitoring, and discloses a pet behavior monitoring method, equipment and medium. The method is applied to an edge device and comprises the following steps: obtaining a latest target video frame in a monitoring video; performing pet skeleton key point detection on the target video frame to obtain skeleton key point features; identifying pet posture features of a pet to be monitored based on the skeleton key point features; performing furniture semantic detection on the target video frame to obtain furniture semantic features; performing behavior intention and behavior risk assessment on the pet to be monitored based on the skeleton key point features, the pet posture features and the furniture semantic features, to obtain a behavior assessment result; and performing a corresponding monitoring operation based on the behavior assessment result. The embodiments of the application can improve the real-time performance and joint understanding performance of pet behavior monitoring.

This invention discloses a multi-view skeletal reconstruction method and system for digital evaluation of canine training behavior, relating to the fields of computer vision and canine behavior recognition. The method includes: acquiring image signals; detecting two-dimensional keypoints using a neural network integrating a canine attention mechanism; obtaining a three-dimensional skeletal sequence through three-dimensional reconstruction and bundle adjustment optimization; performing anomaly detection and interpolation repair to obtain a standardized skeletal sequence; extracting features using a spatiotemporal graph convolutional network and generating behavioral feature vectors through a cross-attention mechanism; obtaining semantic action segments through frame-level primitive classification and grammatical parsing; and performing spatiotemporal alignment and deviation analysis between the action segments and a standard digital twin model to generate phenotypic vectors and a behavior evaluation report. Through multi-view skeletal reconstruction and fusion, high-precision capture, semantic segmentation, and multi-dimensional performance quantification evaluation of canine training behavior are achieved.

Computer-implemented methods and systems for managing the collection of and access to behavior assessment data. In an embodiment, a first user having authority to act on behalf of an individual under study identifies a second and third user role, specifies behavior data, symptom measurement data, and / or medicine regimen data associated with the individual under study, and defines access permissions for the second and third user roles with respect to the behavior data and symptom measurement data. The symptoms and behaviors to be measured are specified based on a condition of the individual. Users provide behavior data and symptom measurement data observed from the individual. An analytics module performs computational analysis on the behavior data and symptom measurement data, thereby producing behavior assessment data. A reporting module presents the behavior assessment data to the users in a manner consistent with the defined access permissions.

Real-time correlation of personality traits based on multimodal interactions for action enablement is described. A method includes collecting multimodal data during venture capital multimodal interactions with an interviewee, where the data includes textual content, vocal characteristics, facial expressions, and behavioral cues of the interviewee, analyzing the collected data to determine individual personality traits and social relationship characteristics, correlating the identified personality traits and the social relationship characteristics with fundraising outcomes, leveraging natural language processing, computer vision and multimodal analysis tools to analyze the intents and behaviors of the interviewee, using a statistical model to determine a probability of funding for the interviewee based on the personality traits, the social relationship characteristics, the intents, and the behaviors, and providing real-time actionable insights and recommendations based on correlation analysis, intent and behavioral assessment, and probability of funding to facilitate decision-making in venture capital investment and fundraising processes.

To evaluate behavior of applying material.SOLUTION: A behavior evaluation method includes the steps of: acquiring sound information showing sound caused by application of material; on the basis of the sound information, estimating whether or not at least one of strength of the application and speed of the application is within a predetermined range; and outputting a result of the estimation.SELECTED DRAWING: Figure 3

The invention provides an employee physical and psychological health behavior assessment method and device based on a cloud model, and the method comprises the steps: obtaining a consideration factor of each individual and a corresponding expert score, and obtaining the weight of the corresponding consideration factor; all consideration factors of each individual are divided into at least three criterion layers according to types, and the weight of each accurate measurement layer of the corresponding individual is obtained; according to the weight of each criterion layer and the expert score and the weight of each consideration factor contained in each criterion layer, obtaining a cloud feature value of the corresponding criterion layer; obtaining a comprehensive cloud feature value of the corresponding individual according to the cloud feature values of all the criterion layers of the corresponding individual, and comparing the comprehensive cloud feature value with a standard cloud feature value to obtain an evaluation result of the corresponding individual; the physical and psychological health assessment result of the individual is obtained by obtaining a large number of consideration factors of the individual and carrying out objective judgment on the large number of consideration factors through the cloud model, subjective deviation in the assessment process is reduced, and assessment efficiency and accuracy are improved.

To provide a wearable device and an action evaluation system which measure and evaluate a direct relation between a wearer and a target person, and which are capable of giving awareness to the wearer regarding their action by clearly indicating the evaluation result, and to contribute to one of the Sustainable Development Goals (SDGs): "health and well-being for all", by making it possible to maintain human mental and physical health.SOLUTION: A wearable device is provided with: an imaging device which captures a subject image by imaging a target person present in front of or around a wearer; a subject image processing device which detects and performs face recognition of the target person included in the subject image, and processes the subject image so as to give an expression evaluation point to the wearer based on the facial expression of the target person obtained by the face recognition; and an output processing device which executes an evaluation process for obtaining an action evaluation point to evaluate an action of the wearer based on the expression evaluation point and an action evaluation time period being a time period during which the wearer, who is the evaluation target, performs the action, and outputs the action evaluation point.SELECTED DRAWING: Figure 4

The invention discloses a BBB repairing agent evaluation method and system based on an ischemia reperfusion injury model, and relates to the technical field of biologication.The method comprises the following specific steps that the ischemia reperfusion injury model is built, and a sample is collected, specifically, an animal ischemia reperfusion injury model is built, and experimental animals are divided into an experimental group and a control group; brain tissue and blood samples of the two groups of animals are collected at a plurality of preset time nodes, and behavioral evaluation is carried out at the same time to obtain score data; according to the method, the deep learning model fused with the dynamic attention mechanism is constructed, the weights of different time nodes and different modal data on BBB repair effect evaluation after ischemia reperfusion injury can be automatically identified, the problems that early signals are ignored and key time node evaluation is deviated due to fixed static data weights in a traditional evaluation method are solved, and the accuracy of BBB repair effect evaluation is improved. The model can flexibly adjust the weight of each modal data according to the dynamic change characteristics of the BBB repair process, and a more reliable basis is provided for research, development and application of a repair agent.

The application discloses a driving behavior specification method and system based on complex scenes, a medium and equipment, and relates to the technical field of vehicle safety management and control. The method comprises the following steps: acquiring driving data and environmental data of multiple drivers in real time; identifying and analyzing the driving data by using a behavior model to obtain driving behavior evaluation results; generating corresponding intervention decisions according to the driving behavior evaluation results, wherein, the non-standard driving behaviors are classified into self-factor influencing behaviors and environmental factor influencing behaviors, the self-factor influencing behaviors are analyzed to generate internal cause intervention decisions, the environmental data when the environmental factor influencing behaviors occur are extracted, and the environmental factor influencing behaviors are combined with the environmental data to generate external cause intervention decisions; and the intervention decisions are used to correct and continuously test the driving behaviors of the drivers. The application can identify multiple driving behaviors of the drivers in real time in complex driving scenes, and effectively correct non-standard driving behaviors through targeted and continuous intervention.

The invention discloses an interactive multi-modal artificial intelligence psychological behavior assessment method and system based on drawing analysis. The method comprises the steps that multi-modal data are synchronously collected, static drawing data, dynamic behavior data and physiological signal data of a user in the drawing process are obtained, and time alignment is conducted on the multi-modal data; analyzing the collected multi-modal data, feeding back an analysis result to the user in a suggestion or question form, then executing the data collection stage again to grasp a reaction result of the user to the suggestion or question, analyzing the collected multi-modal data, and then executing a fusion analysis stage; performing fusion analysis on data of three dimensions of static drawing, dynamic behaviors and physiological signals to generate a multi-modal feature representing a comprehensive psychological state of the user; and according to the multi-modal features representing the comprehensive psychological state of the user, in combination with psychological knowledge and a database, generating and outputting relative reports among individuals and psychological portraits in the individuals after multiple times of evaluation of the user by utilizing a natural language generation model. Starting from multiple dimensions, the defects of the current drawing psychological analysis technology can be effectively overcome, and the accuracy of psychological assessment is improved.

This application proposes an abnormal behavior warning method, device and application, which uses a progressive approach to integrate artificial intelligence analysis methods and social hazard assessment methods to monitor and track the perpetrators. First, a preset image feature model is used to compare and analyze the behavior images, and then a preset behavior factor feature model is used to compare and analyze the behavior factors. In some cases, the four-dimensional social hazard assessment method is finally used to evaluate the behavior of the perpetrators. It can timely and accurately warn of possible abnormal behaviors, reduce social conflicts and play a risk prevention role.

This invention relates to the field of enterprise human resource management, and discloses an employee assessment method, apparatus, equipment, and medium, comprising: collecting indicator data of quantitative indicator-type employees and behavioral data of behavioral performance-type employees in a team; calculating the indicator achievement rate as the performance score of the quantitative indicator-type employees based on the indicator data of the quantitative indicator-type employees; analyzing the behavioral performance-type employees' behavioral data using a large model based on behavioral assessment rules to obtain their performance scores; and calculating the assessment results of all employees in the team based on team assessment rules, using a large model, and the performance scores of the quantitative indicator-type employees and behavioral performance-type employees. This solves the problems of strong subjectivity, delayed feedback, and limited dimensions in existing employee assessment technologies.

According to the tobacco dependence prediction method and device based on omics and brain cognition, early screening of high-risk groups can be achieved, an objective basis is provided for individualized treatment, the real-time monitoring intervention effect is improved, and finally the smoking cessation success rate is increased. The method comprises the following steps: (1) data acquisition: performing standardized multi-modal data acquisition, including whole genome sequencing, electroencephalogram characteristic recording and computerized cognitive behavior evaluation; and (2) data analysis: designing a multi-modal deep learning algorithm based on a Transform architecture, and realizing deep interaction among different modal features through a cross attention mechanism.

The application discloses a kind of nuclear emergency under the condition of quantification decision-making behavior evaluation method and system, it is related to emergency management technical field, this method includes: through receiving the decision-making behavior scheme under the condition of nuclear emergency, and carry out prediction, output multiple accompanying emergency events.Construct decision-making behavior prediction tree, the risk assessment of decision-making behavior scheme, output decision-making behavior risk index.Risk assessment is carried out to multiple accompanying emergency events respectively, output multiple accompanying risk index.According to decision-making behavior risk index and multiple accompanying risk index, decision-making behavior prediction tree is quantified and identified, and output the decision-making behavior prediction tree after identification.Through risk fitting to the decision-making behavior prediction tree after identification, generate decision-making behavior evaluation report.The technical problem that nuclear emergency decision-making evaluation method in prior art is difficult to carry out dynamic risk assessment to decision-making behavior, leading to emergency decision-making behavior evaluation accuracy is difficult to improve, influence the reliability of emergency decision-making.

The invention discloses a community correction object social risk dynamic evaluation system and method, and the method comprises the steps: obtaining a static file and dynamic interaction data of a correction object through a social information collection subsystem, judging an abnormal social behavior through a social behavior evaluation subsystem, carrying out the risk keyword weighted scoring of a communication text through a social language analysis subsystem, and carrying out the evaluation of the risk of the correction object. The social network analysis subsystem constructs a weighted incidence matrix, and the risk assessment subsystem calculates risk values in real time based on a dynamic iterative model by fusing historical risk values and multi-dimensional factors and maps the risk values into risk levels. The system supports periodic or triggered updating and intelligent early warning, can quantitatively analyze social influence and realize closed-loop management compared with traditional static assessment, significantly improves assessment precision and timeliness, and provides a scientific decision basis for community correction work.

A system and method for automatically determining non-compliance from an initial assessment input describing an event in a natural language are disclosed. The method involves applying an event assessment data model to the initial assessment input to identify one or more actions observed and to derive one or more behaviors related to the observed one or more actions; applying a behavioral assessment data model to select a set of misconducts from the one or more actions, the set of misconducts comprising the one or more actions likely to be non-compliant with a set of rules; identifying one or more alternative misconducts semantically similar to the set of misconducts within a context of the event associated with the one or more behaviors; and providing one or more rules violated by the set of misconducts and the one or more alternative misconducts.

The invention relates to the technical field of intelligent driving assistance, in particular to an ADHD driving support system based on attention monitoring, which comprises a driving ability evaluation system, an emotion monitoring and intelligent driving assistance cooperation system, a natural environment emotion healing module, a traffic light waiting interval concentration training module and a driving behavior evaluation and feedback system. According to the system, the driving safety and comfort of the ADHD patient are improved through a'detection-intervention-healing 'closed-loop mechanism in combination with multi-stage intervention, emotion healing and behavior evaluation. According to the method, the problems of distraction and emotion fluctuation of the ADHD patient in the driving process can be effectively relieved, personalized support is provided, and the long-term driving habit is improved.