62 results about "Behavioral assessment" patented technology

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

The invention discloses an adaptive driving behavior evaluation and training system based on a multi-modal large model. Comprising a multi-mode driving data acquisition and situation awareness module, a data storage module, a driving behavior real-time evaluation and feedback module, an adaptive training strategy generation module, a cloud large model driven self-learning module and a driver psychological state monitoring and adjusting module. The driving behavior real-time evaluation and feedback module is used for carrying out real-time evaluation on the driving behavior of the driver through a machine learning evaluation large model and providing instant feedback; according to the invention, a multi-modal large model technology is combined, multi-modal data of a driving vehicle and a driver are collected in real time, personalized training tasks and learning strategies are generated according to changes of driving situations and real-time evaluation of driver performance, the training tasks are dynamically adjusted in different complex situations, and the training efficiency is improved. Training tasks are adjusted by analyzing the psychological state of the driver, and the system can provide detailed feedback for behaviors of the driver.

The invention discloses a software stability evaluation system for software development, which comprises a data acquisition and preprocessing module used for acquiring user behavior data and system performance indexes; the user behavior modeling module is used for modeling the user behavior data based on a psychological model; the abnormal behavior detection module is used for detecting abnormal user behaviors and evaluating potential influences on system stability; the pressure test generation and execution module is used for generating simulation data and executing a pressure test; the causal relationship optimization module is used for optimizing the edge weight of the causal relationship graph through a genetic algorithm; the module ecological modeling and analysis module is used for establishing an inter-module resource competition and cooperation model; the multi-objective optimization module is used for optimizing a resource allocation strategy based on the module health index and the balance demand; and the interpretability and decision-making module is used for analyzing the decision-making process of the evaluation model and generating a decision-making report. According to the method, the stability and fairness of software resource allocation in a complex scene are optimized.

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 discloses an abnormal behavior evaluation method and system based on data driving. The method comprises the steps of data acquisition, data preprocessing, user behavior detection model establishment, model parameter optimization and abnormal behavior evaluation. The invention belongs to the field of data processing, and particularly relates to an abnormal behavior evaluation method and system based on data driving. According to the scheme, through enhanced noise suppression and discretization processing and high-fidelity data restoration, information loss in data feature restoration is reduced, and the accuracy of abnormal behavior detection is enhanced; accurate abnormal behavior recognition capability is provided based on probability model design; on the basis of the dynamic adaptive capacity of the design activation function, the response capacity to complex user behavior data is improved, and then the accuracy of abnormal behavior evaluation is ensured; individual positions are initialized based on the dynamic offset, and fine search is carried out by designing a vertical learning strategy, so that the user abnormal behavior evaluation effect is improved.

The invention discloses a driving behavior specification method and system based on a complex scene, 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 environment data of various drivers in real time; identifying and analyzing the driving data by using the behavior model to obtain a driving behavior evaluation result; generating a corresponding intervention decision according to a driving behavior evaluation result, classifying non-standard driving behaviors into self-factor influence behaviors and environmental factor influence behaviors, performing correction analysis on the self-factor influence behaviors to generate an internal cause intervention decision, extracting environmental data when the environmental factor influence behaviors occur, and determining the internal cause intervention decision according to the extracted environmental data. Generating an external cause intervention decision by combining the environment data on the environment factor influence behavior; and performing behavioral standard correction and continuous test on the driver by using the intervention decision. According to the invention, various driving behaviors of the driver can be identified in real time in a complex driving scene, and non-standard driving behaviors are effectively corrected through targeted continuous intervention.

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.

A method for assessing pain experienced by a patient (3) comprises: receiving a sensor signal (8) which has been generated in a sequence of epochs (10) by a sensor (2) configured for measuring a physical activity of the patient (3), wherein each epoch (10) comprises a sequence of measurement periods (11), wherein the sensor signal (8) has been generated in each measurement period (11); determining a signal intensity value (12) for each measurement period (11) from the sensor signal (8); classifying the signal intensity values (12) of each epoch (10) with different physical activity classes (13a, 13b, 13c, 13d) to obtain a classification result (15) for the epoch (10); and determining a behavior assessment score (16) indicative of the pain experienced by the patient (3) from the classification results (15) of different epochs (10).

The present invention discloses a method and system for behavioral assessment based on machine learning in recruitment applications, which specifically relates to the field of machine learning technology, and comprises the following steps: generating an assessment data set by collecting and preprocessing candidate assessment behavior data; calculating an assessment task complexity index and an assessment behavior regularity index based on the data set to quantify the complexity and regularity of candidate behavior characteristics; determining the most suitable machine learning strategy through fuzzy reasoning; constructing and training a machine learning model according to the strategy, the trained model outputs the candidate's assessment score, and all scores are summarized and sorted to generate an assessment score form; the present invention can dynamically match the model depth to ensure resource utilization efficiency when a recruitment company makes optimization requirements for a pre-provided general model, so that a shallow model can quickly complete candidate scoring in a simple scenario, and a deep model can accurately extract features in a complex scenario.

A system and method for processing third party behavioral assessments that receives data for a first and second behavioral assessment, the first behavioral assessment assessing a first behavioral characteristic of a first person and the second behavioral assessment assessing a second behavioral characteristic of the first person; utilizes the first and second behavioral characteristics to calculate a behavioral parameter for the first person; compares the behavioral parameter against corresponding behavioral parameters for respective persons for a team; provides a user interface to a user device that is configured to receive a team dynamic selection; receives a hypothetical scenario dataset that describes how the first person would work with the respective persons of the team; determines an impact of including the first person in the team, based on the desired dynamic for the team and the hypothetical scenario dataset; and displays the impact via the user interface.

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 present invention relates to the technical field of storage cabinet anti-theft alarm, and specifically discloses a door opening alarm system for storage cabinets, which obtains a variety of behavior monitoring data when the target storage cabinet is opened through a behavior monitoring module, and then uses a behavior analysis module to analyze the behavior deviation degree, and then performs weighted summation of multiple behavior deviation degrees and corresponding evaluation weights to obtain a behavior abnormality index, and finally updates the behavior evaluation standard or issues an alarm based on the behavior abnormality index, and at the same time updates the evaluation weight based on the cumulative number of times the behavior abnormality index does not exceed a preset abnormal threshold. Compared with the prior art, the evaluation weight and evaluation standard corresponding to each behavior monitoring data in the present invention will be dynamically adjusted with the number of times the target storage cabinet door is opened, solving the problem in the prior art that the storage cabinet door opening warning scheme cannot be combined with the storage cabinet usage frequency to dynamically adjust the behavior recognition strategy.

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.

The present invention discloses a behavior evaluation system and method based on cloud computing, which relates to the technical field of behavior evaluation data analysis. The system includes: a data monitoring and processing module, a behavior data analysis module, and a behavior evaluation and feedback module. The data monitoring and processing module performs real-time monitoring on an evaluation object by setting a non-contact acquisition sensor in the current activity environment of the evaluation object, and is also used to collect and obtain the situation information of the evaluation object. The behavior data analysis module trains a situation-behavior reference deep learning model, calculates the reference deviation of the evaluation parameters of each behavior state of the evaluation object, and is also used to construct a behavior state evaluation knowledge graph and update and iterate the historical data of the reference deviation of the evaluation parameters of the behavior state. The behavior evaluation and feedback module performs real-time update and feedback of the evaluation results on the evaluation object according to the behavior state evaluation knowledge graph data.

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.

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 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.

A system and method for processing third party behavioral assessments that receives data for a first and second behavioral assessment, the first behavioral assessment assessing a first behavioral characteristic of a first person and the second behavioral assessment assessing a second behavioral characteristic of the first person; utilizes the first and second behavioral characteristics to calculate a behavioral parameter for the first person; compares the behavioral parameter against corresponding behavioral parameters for respective persons for a team; provides a user interface to a user device that is configured to receive a team dynamic selection; receives a hypothetical scenario dataset that describes how the first person would work with the respective persons of the team; determines an impact of including the first person in the team, based on the desired dynamic for the team and the hypothetical scenario dataset; and displays the impact via the user interface.

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