Reivindicaciones
1. A method (100) for characterising and certifying cognitive activities in a non-invasive manner by means of a characterisation and certification system (1) comprising: • a detection module (2) comprising at least one gaseous component measuring element (2.1) configured for generating at least one signal (2.2) indicating the temporal evolution of at least one detected gaseous component; • a characterisation module (3) configured for characterising the at least one signal (2.2) generated by the detection module (2) based on the individual or combined sequential structure of the temporal evolution thereof and for determining whether said at least one signal (2.2) corresponds to the development of a specific cognitive activity; wherein the method (100) comprises the following steps: a) generating (110) at least one signal (2.2) indicating the temporal evolution of at least one gaseous component corresponding to a specific cognitive activity detected by means of the at least one measuring element (2.1) of the detection module (2) during a predetermined time period; b) receiving (120) the generated signal (2.2) by means of the characterisation module (3); c) characterising (130) the signal (2.2) based on the sequential structure of events in the temporal evolution thereof by means of the characterisation module (3); and d) determining (141) whether the signal (2.2) corresponds to the development of a specific cognitive activity based on the result of the characterisation. 2. The method (100) according to the preceding claim, wherein the system (1) comprises a conditioning module (4) configured for conditioning the signal (2.2) generated by the detection module (2); and wherein the method (100) further comprises, between steps (a) and (b), the steps of receiving and conditioning the signal (2.2) by means of the conditioning module (4), and wherein step (c) is performed on the generated and conditioned signal. 3. The method (100) according to the preceding claim, wherein the step of conditioning the signal (2.2) comprises filtering and/or sampling said signal (2.2) maintaining the sequential structure of the temporal evolution of the cognitive activity. 4. The method (100) according to any of the preceding claims, wherein steps (c) and (d) are performed by means of a machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity, previously trained with training signals (3.3) corresponding to at least one cognitive activity. 5. The method (100) according to any of the preceding claims, wherein the cognitive activity belongs to one of the following categories: - effective cognitive activity or cognitive activity that fulfils pre-established objectives; or - cognitive activity associated with an emotion which causes the release of substances detectable by the device, preferably the emotion of pleasantness, satisfaction, relaxation, unpleasantness, dissatisfaction, or stress; or - cognitive activity with a specific level of attention; or - cognitive activity typical of a work routine; or - cognitive activity typical of a school routine; or - cognitive activity typical of a leisure routine; or - cognitive activity typical of an examination or test; or - cognitive activity typical of a job interview; or - cognitive activity typical of a specific health condition of an individual; or - a combination of any of the above. 6. The method (100) according to any of the preceding claims, wherein the method further comprises classifying the at least one signal (2.2) into at least one subcategory of cognitive activities by means of the characterisation module (3). 7. The method (100) according to claim 6, wherein the classification of the signal (2.2) is performed by means of a machine learning algorithm previously trained with training signals (3.3) which are classified into at least one predefined subcategory of cognitive activity. 8. The method (100) according to any of the preceding claims, wherein step (c) comprises identifying (131) at least one temporal event (3.2) in the signal (2.2), and wherein the determination of step (d) is performed based on the sequentiality of the identified temporal events (3.2). 9. The method (100) according to claims 6 and 8, wherein classification of the signal (2.2) is performed based on the sequentiality of the identified temporal events (3.2). 10. The method (100) according to any of claims 8 to 9, wherein the temporal events (3.2) detected in the signal (2.2) comprise maximums, minimums, slopes, the surpassing of thresholds, and/or sequential sets of the foregoing which define a specific temporal structure. 11. The method (100) according to any of the preceding claims, wherein the at least one gaseous component measuring element (2.1) is an olfactory sensor, said olfactory sensor preferably being configured for detecting at least one substance emitted by an organism during a cognitive activity; the substance preferably being at least one of: carbon dioxide, esters, acetone, urea, amines, alcohols, hydrogen, ammonia, methane, nitrogen monoxide, carbon monoxide, and other mixtures of organic compounds, such as VOCs (volatile organic compounds). 12. The method (100) according to claim 11, wherein the olfactory sensor is of any of the following types: chemoresistive, chemocapacitive, potentiometric, gravimetric, optical, acoustic, thermal, polymer, amperometric, chromatographic, spectrometric, or field effect sensor. 13. The method (100) according to any of the preceding claims, wherein: - the detection module (2) further comprises at least one environmental condition detecting element (2.3) for detecting environmental conditions, preferably humidity, temperature, atmospheric pressure, brightness, noise, and/or ventilation; - the method (100) further comprises a step of obtaining measurements of at least one magnitude by means of the at least one environmental condition detecting element (2.3) and of identifying temporal events (3.2) of the signal (2.2) which are associated with said at least one magnitude; and - wherein said identified temporal events (3.2) are used as additional context information during the step of characterising (130) the signal (2.2). 14. The method (100) according to any of the preceding claims, wherein: - the detection module (2) further comprises at least one external event recording element (2.4), preferably for recording the opening of doors or windows, for recording the activation or deactivation of a temperature control system, for recording the activation or deactivation of ventilation, and/or for recording times; - the method (100) further comprises a step of identifying temporal events (3.2) of the signal (2.2) which are associated with the presence of external events; and - wherein said identified temporal events (3.2) are used as additional context information during the step of characterising (130) the signal (2.2). 15. The method (100) according to any of the preceding claims, wherein step (c) of the method (100) comprises comparing the signal (2.2) with at least one reference signal of the cognitive activity. 16. The method (100) according to any of the preceding claims, wherein the steps of the method are repeated periodically, where the repetition period is a predefined value, in order to monitor the detected and/or classified cognitive activities. 17. A non-invasive system (1) for characterising and certifying cognitive activities, comprising: - a detection module (2) comprising at least one gaseous component measuring element (2.1) configured for generating at least one signal (2.2) indicating the temporal evolution of at least one detected gaseous component; - a characterisation module (3) configured for characterising the at least one signal (2.2) generated by the detection module (2) based on the individual or combined sequential structure of the temporal evolution thereof, for determining whether said at least one signal (2.2) corresponds to the development of a specific cognitive activity; wherein the characterisation module (3) is configured for carrying out steps (b) to (d) of the method according to any of the preceding claims. 18. The system (1) according to claim 17, further comprising a conditioning module (4) configured for conditioning the signal (2.2) originating from a specific cognitive activity generated by the detection module (2). 19. The system (1) according to any of claims 17 or 18, wherein the characterisation module (3) of the system (1) is further configured for classifying the at least one signal (2.2) into at least one subcategory of cognitive activities. 20. The system (1) according to any of claims 17 to 19, comprising contextualization elements of cognitive activity: - at least one environmental condition detecting element (2.3) for detecting environmental conditions, preferably humidity, temperature, atmospheric pressure, brightness, noise, and/or ventilation; and/or - at least one external event recording element (2.4), preferably for recording the opening of doors or windows, for recording the activation or deactivation of a temperature control system, for recording the activation or deactivation of ventilation, and/or for recording times. 21. The system (1) according to any of claims 17 to 20, wherein the at least one gaseous component measuring element (2.1) is an olfactory sensor, said olfactory sensor being configured for detecting at least one substance emitted by an organism during a cognitive activity; the substance preferably being at least one of: carbon dioxide, esters, acetone, urea, amines, alcohols, hydrogen, ammonia, methane, nitrogen monoxide, carbon monoxide, and other mixtures of organic compounds, such as VOCs (volatile organic compounds). 22. The system (1) according to claim 21, wherein the olfactory sensor is of any of the following types: chemoresistive, chemocapacitive, potentiometric, gravimetric, optical, acoustic, thermal, polymer, amperometric, chromatographic, spectrometric, or field effect sensor. 23. A data processing system comprising means for carrying out steps (b) to (d) of the method (100) according to any of claims 1 to 16. 24. A computer program comprising instructions which, when the program is run by a computer, causes the computer to carry out steps (b) to (d) of the method (100) according to any of claims 1 to 16. 25. A computer-readable medium comprising instructions which, when run by a computer, causes the computer to carry out steps (b) to (d) of the method (100) according to any of claims 1 to 16.
