METHOD OF GENERATING A CLASSIFICATION MODEL

1 . A method for creating a classification model, the method comprising:

obtaining at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter;

creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter;

creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model;

assessing the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model;

obtaining at least one group of final time-series signals associated with at least one final acquisition parameter selected from an analysis of the assessed performances of each test classification model; and

creating the classification model from the at least one group of final time-series signals.

2 . The method according to claim 1 , wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency and of an initial amount of data from initial time-series signals.

3 . The method according to claim 2 , wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals.

4 . The method according to claim 2 , wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals.

5 . The method according to claim 2 , wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data.

6 . The method according to claim 1 , wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals.

7 . The method according to claim 6 , wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals from which this group of simulated time-series signals is created.

8 . The method according to claim 7 , further comprising extracting the feature values of the initial and simulated time-series signals, each test classification model being created from an analysis of the extracted feature values and of the class associated with each group of initial or simulated time-series signals used to create this test classification model.

9 . The method according to claim 8 , further comprising extracting the feature values of the final time-series signals, the classification model being created from an analysis of the extracted feature values and of the class associated with each group of final time-series signals.

10 . The method according to claim 1 , further comprising creating a computer program product comprising instructions which, when the program is executed by a computer, result in the latter implementing the classification model.

11 . A method for creating a classification model, the method comprising:

obtaining at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter;

creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter;

creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model;

assessing the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model; and

indicating the performances of each test classification model in relation with the at least one acquisition parameter associated with this test classification model.

12 . The method according to claim 11 , wherein the indication of performances of each classification model comprises displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter.

13 . The method according to claim 11 , wherein the assessed performances of each test classification model comprise an accuracy, an acquisition time of a time-series signal, and an amount of acquired data for this time-series signal.

14 . A computer system comprising:

a memory comprising a computer program, the computer program comprising instructions to:

obtain at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter;

create at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter;

create various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model;

assess the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model;

obtain at least one group of final time-series signals associated with at least one final acquisition parameter selected from an analysis of the assessed performances of each test classification model; and

create the classification model from the at least one group of final time-series signals; and

a processor configured to execute the computer program.

15 . The computer system according to claim 14 , wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency and of an initial amount of data from initial time-series signals.

16 . The computer system according to claim 15 , wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals.

17 . The computer system according to claim 15 , wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals.

18 . The computer system according to claim 15 , wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data.

19 . The computer system according to claim 14 , wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals.

20 . The computer system according to claim 14 , further comprising indicating the performances of each test classification model by displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter, the screen being coupled with the computer system.