31 results about "Behavioral assessment" patented technology

The current invention provides methods of enhancing a subject's attentional state. The methods comprise a series of interactive behavioral assessments and interactive behavioral training sessions, which trains the individual to be in a more optimal attentional state throughout their day-to-day life, ultimately enhancing their cognitive performance, visuomotor performance, and emotion regulation.

The invention discloses a method and a system for performing intervention according to information of chronic disease patients. The method comprises the steps that out-hospital health reminding schemes of the patients are generated according to hospital leaving advices given by a doctor; in-hospital diagnosis and treatment information, out-hospital medication information, vital sign information and healthy living mode information of the patients are collected, the collected information is processed, and labels are set; the patients are dynamically grouped according to the labels; and the doctor sends intervention information to the patients in a whole group and pushes the intervention information to each patient in the group. According to the embodiment, the collected patient information is more comprehensive in terms of dimension, contains patient diagnosis and treatment information, advice scheme information and self-examination information and also comprises patient daily behavior evaluation information, a data basis is provided for precise intervention by the doctor, precise dynamic label identification of the patients and intelligent grouping depending on the labels are realized, the doctor can quickly know the current health states of the patients by spending little time, and increase of the intervention workload of the doctor due to increase of the number of the patients managed by the doctor is avoided.

A method and system for evaluating child development activities for a preschool child. The evaluation may include scoring activities to be performed by or with the child based on a target age for the preschool child. The assessment of the preschool child's abilities and behaviors may be compared with expected levels of performance represented by development milestones. The system may include a non-transitory computer-readable medium that, when executed by a processor, will perform the method.

A surveillance system improves accuracy and robustness of abnormal behavior detection of a monitored object traversing a space includes a metadata processing module, a model building module, and a behavior assessment module. The metadata processing module generates trajectory information for a monitor object and determines attributes of the monitored object. The model building module at least one of generates and updates normal motion models based on at least one of the trajectory information, the attributes, and an abnormal behavior score. The behavior assessment module generates the abnormal behavior score based on one of a plurality of methods. A first one of the plurality of methods defines wrong direction behavior. A second one of the plurality of methods defines wandering/loitering behavior. A third one of the plurality of methods defines speeding behavior.

The invention belongs to the technical field of evaluation, and discloses a personnel ability quantitative evaluation method, which comprises the steps of obtaining target data of a to-be-evaluated person to obtain ability data and behavior data; inputting the current capability data into a preset capability evaluation model and inputting the current behavior data into a preset behavior evaluationmodel to obtain a capability value and a behavior value of the current to-be-evaluated person; according to the current ability value, the behavior value, the ability data and the behavior data, obtaining a label value of the current to-be-evaluated person; and according to the current capability value, the behavior value and the label value, obtaining and outputting a personnel portrait of the current to-be-assessed personnel. According to the invention, the personnel portrait containing personnel ability can be displayed in a three-dimensional, clear and accurate manner, the personnel capability can be effectively quantified, so that the human resource management is more standardized, the human resource management service is enabled by big data, and the method is suitable for popularization and application in enterprises and public institutions with big data basis and personnel capability quantification.

A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by a contact center, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream. In other embodiments, electronic customer communication data may include data received during a customer communication with a graphical user interface for the contact center.

A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by a contact center, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream. In other embodiments, electronic customer communication data may include data received during a customer communication with a graphical user interface for the contact center.

The invention discloses an interactive decision-making method based on key historical behaviors in a cloud computing environment. The method includes a basic idea that, before the interaction between a cloud and an entity, both interactive entities respectively send a trust query request to a trust authentication center; the trust authentication center computes the overall trust degrees according to the key historical behavior of the user entity and the service entity, and issues trusts notes to the interactive entities based on the overall trust degrees; both entities perform interactive decision-making according to the trust notes. In the overall trust degree computing process, the trust of the entity is divided into direct trust and indirect trust, and the key historical behaviors are subdivided into a plurality of evaluation indexes by using a granular approach. The method can evaluate trust according to the key historical behaviors of the entities, and the data maintenance and evaluation processes are both executed at the trust authentication center, so that the method is a lightweight class interactive decision-making method. A key historical behavior window is set for trust evaluation, so that the interactive result is more accurate and meanwhile cumulative cheating is also avoided.

The invention discloses a research method for evaluating the cognitive performance of a driver based on driving behaviors. The method comprises the following steps: step one, carrying out data preprocessing of a collected experimental database; step two, in a regression function, selecting features by using a Pearson coefficient, constructing functions of lasso regression and rid regression, and performing cross validation to improve the accuracy and obtain predicted cognitive performance; step three, predicting cognitive behaviors of the driver through two methods; and step four, analyzing behaviors of the driver, namely analyzing an MMSE target, and performing predicting in an overall road, a residence and an intersection, especially in a residence road and an intersection.

The invention discloses a teaching method based on a dangerous driving behavior evaluation model. The teaching method includes: a driving behavior evaluation model to be disposed in a vehicle, which is used for obtaining the driving behavior video information of a driver in real time, carrying out the dangerous behavior recognition of a driving video, and setting each dangerous behavior to correspond to a preset behavior value, and summing preset behavior values corresponding to all the identified dangerous behaviors to obtain a behavior risk value; and a server, which obtains a result of the driving behavior evaluation model, sets a behavior risk value threshold value, and judges the driving behavior if an evaluation value of the result breaks through the threshold value, the judgment result comprising the driving behavior needing to be reminded and the driving behavior needing to be corrected. According to the invention, the driving behavior can be supervised in real time, and information is collected, so that targeted teaching is carried out.

The embodiment of the invention discloses a user credit behavior evaluation method and device and electronic equipment. The method comprises the steps of acquiring behavior data used for representinguser credit behavior characteristics; determining a first credit energy value corresponding to first behavior data belonging to performance credit behaviors in the behavior data based on a first credit risk assessment model; determining a second credit energy value corresponding to second behavior data belonging to non-performance credit behaviors in the behavior data based on a second credit riskassessment model, wherein the first credit energy value and/or the second credit energy value are/is used for evaluating the credit behavior of the user.

The invention discloses a student learning habit analysis system based on big data, which relates to the technical field of online teaching and comprises a behavior analysis module, a behavior evaluation module, a course auxiliary module and a teaching analysis module. The behavior analysis module is used for carrying out bad learning behavior identification through a learning behavior detection model; the behavior evaluation module is used for performing learning deviation coefficient analysis on the student according to the identification result and reminding the student to concentrate on learning in time; the teaching analysis module is used for acquiring learning deviation coefficients of all students in real time and performing teaching deviation analysis; if the teaching deviation value is larger than or equal to the teaching threshold value, a teacher is reminded to change the teaching content or the teaching mode of the current teaching course, so that the teaching quality and efficiency are improved; and the course auxiliary module is used for analyzing course preferences of the students according to the course learning records, judging whether the students need to be subjected to extracurricular tutoring or not, providing correct guidance for healthy development of the students, and preventing the students from deviating from the course.

A system and method for determining the level of behavioral concern and displaying the determination in a behavioral assessment matrix is disclosed. Determining the level of behavioral concern is based on the presence or absence of predetermined deceptive behaviors. The deceptive behaviors are identified within a transcript segment, categorized and indicators, representing the level of behavioral concern, are displayed in a behavioral assessment matrix.

The invention discloses an abnormal driving behavior online identification method based on an Encoder-Decoder attention network and an LSTM (Long Short Term Memory). The method is composed of three main modules, namely an encoder-decoder based on LSTM, an attention mechanism and a classifier based on SVM, and comprises the steps of input encoding, attention learning, feature decoding, sequence reconstruction, residual calculation and driving behavior classification. According to the method, on the basis of mobile phone multi-sensor fusion data, on the basis of driving behavior data characteristics and behavior pattern analysis, an Encoder-Decoder deep learning model, an Attention attention mechanism and an SVM classification model are fused to recognize abnormal driving behaviors. The method has the advantages of being easy in data acquisition, non-intrusive, low in cost and the like, not only considers the time correlation of the driving behaviors, but also considers the difference of different moments, can perform online identification on abnormal driving behaviors in an end-to-end mode, can provide a method basis for driving behavior evaluation and safety early warning, and has a good application prospect. The method has significant meaning for intelligent driving system design and traffic safety decision making.

The invention is suitable for the technical field of nuclear power station informatization, and provides a method and system for monitoring and analyzing behavioral habits of a nuclear power station operator, and the method comprises the steps: obtaining an image of first visual angle operation information of an operator; acquiring an image of the third visual angle control information of the operator; generating a video matrix according to the image of the first visual angle control information and the image of the third visual angle control information of the operator; displaying operation record parameters of an operator through a virtual master control room panel; and in combination with the video matrix and the operation record parameters, carrying out human factor analysis on an operator, and carrying out full-closed-loop control. Through comprehensive monitoring and evaluation of the first visual angle and the third visual angle, a multi-visual-angle operator behavior evaluationscheme is formed, operation record parameters of an operator are displayed through the virtual master control room console, weak matters of the operator can be analyzed more accurately, and full-closed-loop HMI design and human factor evaluation schemes and strategies are realized.

The invention discloses an autism auxiliary diagnosis method, system and device and a medium, and relates to the technical field of autism diagnosis, and the method comprises the steps: obtaining the video data of a target person; preprocessing the video data to obtain a feature data set of the target person, the feature data set including behavior data and face data; inputting the behavior data and the face data into an auxiliary diagnosis analysis model to obtain a behavior evaluation index and an emotion evaluation index; and obtaining a comprehensive index according to the behavior evaluation index and the emotion evaluation index, evaluating the degree of infantile autism of the target user according to the comprehensive index, and outputting a diagnosis suggestion. According to the method, the behavior evaluation index, the emotion evaluation index and the comprehensive evaluation index are introduced while the behavior action and facial expression abnormity of the autism patient are considered, rapid and accurate diagnosis of the autism is realized, and the method has a wide application prospect.

The invention provides a restrictive random-based behavioral assessment content accurate pushing method, system and device and a storage medium, and belongs to the technical field of big data application. According to assessment index labels, questions from a source database are divided into a plurality of groups, under the common limitation of specific character labels and specific question characteristic labels, behavioral assessment questions are randomly extracted from the source database, behavioral assessment content is formed, and the assessment goal of knowing things and recognizing people is achieved. On the premise that the randomness of the extracted behavioral examination questions is ensured, the behavioral examination questions with a predetermined number of established character tags are reserved firstly by judging the character tags of the behavioral examination questions, so that questionnaires of behavioral examination contents are randomly extracted on the premise that the questionnaires have conditional limitations, and the questionnaires of the behavioral examination contents are randomly extracted. The accuracy of pushing the behavioral assessment content is improved, so that the questionnaire of the behavioral assessment content has higher pertinence, and the scientificity of an investigation result is improved.