METHOD AND SYSTEM FOR AUTOMATIC POLYGRAPH TESTING USING THREE ENSEMBLES OF MACHINE LEARNING MODELS Russian patent published in 2023 - IPC G06N20/00 A61B5/16 

FIELD: polygraph testing.

SUBSTANCE: invention relates to a method and system for automatically checking a subject using a polygraph using machine learning methods. In the method, records of polygraphic tests are obtained containing sensor signals with time scales on which the beginning and end of the question are marked; additional data containing at least the age of the person being checked, gender, job information are received; processing of the received signals using the first ensemble of ML models trained on one topic is performed, and during this processing the following is carried out: signal processing by the first ML model, during which the following is performed: determination of time intervals for extracting variables based on the time stamps of the beginning and end of the question and the time answer labels, and based on question type and topic; extracting variables from each signal at certain time intervals; processing of obtained variables from signals, which involves normalization and concatenation of processed variables and construction of a vector based on them; feeding said vector to the 1st ML model to obtain the output value of the 1st ML model; transferring the output value of the 1st ML model to the input of the 2nd ML model; using the second ML model, the output value of the 1st ML model and additional data are processed, and during this processing the following is carried out: division of additional data into categorical and numerical variables; processing of obtained variables from additional data, which involves vectorization of categorical variables and normalization of numerical variables; concatenation of processed additional variables, as well as the output value of the 1st ML model and construction of a vector based on them; feeding said vector to the 2nd ML model to obtain the output value of the 2nd ML model; feeding the output value of the 2nd ML model to the third ML model to form the output value of the first ensemble; carry out processing of the received signals using a second ensemble of the ML models trained on a combination of topics, and during this processing the following is carried out: signal processing by the first ML model during which the following is performed: determination of time intervals for extracting variables based on the time stamps of the beginning and end of the question and the time stamp answer, and based on the type and topic of the question; extracting variables from each signal at certain time intervals; processing of obtained variables from signals, which involves normalization and concatenation of processed variables and construction of a vector based on them; feeding said vector to the 1st ML model to obtain the output value of the 1st ML model; transferring the output value of the 1st ML model to the input of the 2nd ML model; using the second ML model, the output value of the 1st ML model and additional data are processed, and during this processing the following is carried out: division of additional data into categorical and numerical variables; processing of obtained variables from additional data, which involves vectorization of categorical variables and normalization of numerical variables; concatenation of processed additional variables, as well as the output value of the 1st ML model and construction of a vector based on them; feeding said vector to the 2nd ML model to obtain the output value of the 2nd ML model; feeding the output value of the 2nd ML model to the third ML model to form the output value of the second ensemble; carry out processing of the received signals using a third ensemble of machine learning models trained on a combination of topics, and during the specified processing the following is carried out: signal processing by the first ML model, during which the following is performed: determination of time intervals for extracting variables based on the time stamps of the beginning and end of the question and response timestamp, and based on question type and topic; extracting variables from each signal at certain time intervals; processing of obtained variables from signals, which involves normalization and concatenation of processed variables and construction of a vector based on them; feeding said vector to the 1st ML model to obtain the output value of the 1st ML model; transferring the output value of the 1st ML model to the input of the 2nd ML model; using the second ML model, the output value of the 1st ML model and additional data are processed, and during this processing the following is carried out: division of additional data into categorical and numerical variables; processing of obtained variables from additional data, which involves vectorization of categorical variables and normalization of numerical variables; concatenation of processed additional variables, as well as the output value of the 1st ML model, and construction of a vector based on them; feeding said vector to the 2nd ML model to obtain the output value of the 2nd ML model; feeding the output value of the 2nd ML model to the third ML model to form the output value of the third ensemble; using the third ML model, the output values of the first, second and third ML ensembles are processed, and during this processing the following is carried out: concatenation of the processed outputs data values of the first, second and third ensembles and construction of a vector based on them; feeding said vector to the 3rd ML model to obtain the output value of the 3rd ML model; comparison of the output value of the 3rd model with a given threshold value; and determining that the response is false if the output value is greater than or equal to the threshold value, or the response is true if the output value is below the threshold value.

EFFECT: increased accuracy of polygraph testing.

7 cl, 11 dwg, 11 tbl