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US 12,667,313 B2Granted
Non-invasive method and system for characterising and certifying cognitive activities
AssigneeUniversidad Autonoma De Madrid
InventorsCarlos García Saura, Irene Rodríguez Luján, Eduardo Serrano Jerez, Francisco De Borja Rodríguez Ortiz, Pablo Varona Martínez
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Abstract
The present invention relates to non-invasive method and system for characterising and certifying cognitive activities by detecting gaseous substances emitted by an organism, by means of the respiration, perspiration, and/or secretion, and changes measureable by sensors during said cognitive activities. Substance detection makes it possible to characterise the olfactory signal in order to determine and certify whether or not a cognitive activity has occurred and to classify said signals into different categories of cognitive activities.
Claims21
A method for characterising and certifying cognitive activities in a non-invasive manner by means of a characterisation and certification system, comprising:
- a detection module comprising at least one gaseous component measuring element configured for generating at least one signal indicating a temporal evolution of at least one detected gaseous component;
- a characterisation module configured for characterising the at least one signal generated by the detection module based on an individual or combined sequential structure of the temporal evolution thereof and for determining whether said at least one signal corresponds to a development of a specific cognitive activity;
- and
- a machine learning algorithm that is trained with a method comprising acquiring the at least one detected gaseous component over time in the form of the at least one signal and classifying known activities associated with the acquired at least one signal as a result label, wherein the known activities are associated with one or more cognitive activities;
- wherein the method comprises the following steps:
- a) generating at least one signal indicating the temporal evolution of at least one gaseous component corresponding to the specific cognitive activity detected by means of the at least one measuring element of the detection module during a predetermined time period;
- b) receiving the generated signal by means of the characterisation module;
- c) characterising the generated signal based on the sequential structure of events in the temporal evolution thereof, by means of the characterisation module;
- and
- d) determining whether the generated signal corresponds to the development of the specific cognitive activity based on a result of the characterisation;
- wherein step (c) comprises identifying at least one temporal event in the signal, and wherein the determination of step (d) is performed based on the sequentiality of the identified temporal events;
- and
- wherein steps (c) and (d) are performed using the trained machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity;
- wherein the method further comprises classifying the at least one signal into at least one subcategory of cognitive activities by means of the characterisation module;
- and
- wherein the classification of the signal is performed by means of the machine learning algorithm previously trained with training signals which are classified into at least one predefined subcategory of cognitive activity, and wherein the result label is fed back into the machine learning algorithm to improve precision thereof.
13 more elements
A non-invasive system for characterising and certifying cognitive activities, comprising:
- a detection module comprising at least one gaseous component measuring element configured for generating at least one signal indicating a temporal evolution of at least one detected gaseous component;
- a characterisation module configured for characterising the at least one signal generated by the detection module based on an individual or combined sequential structure of the temporal evolution thereof, for determining whether said at least one signal corresponds to a development of a specific cognitive activity;
- a machine learning algorithm that is trained with a method comprising acquiring the at least one detected gaseous component over time in the form of the at least one signal and classifying known activities associated with the acquired at least one signal as a result label, wherein the known activities are associated with one or more cognitive activities;
- wherein the characterisation module is configured to:
- a) receive the generated signal by means of the characterisation module;
- b) characterise the generated signal based on the sequential structure of events in the temporal evolution thereof, by means of the characterisation module;
- and
- c) determine whether the generated signal corresponds to the development of a specific cognitive activity based on the result of the characterisation;
- wherein step (b) comprises identifying at least one temporal event in the generated signal, and wherein the determination of step (c) is performed based on the sequentiality of the identified temporal events;
- and
- wherein steps (b) and (c) are performed using the trained machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity;
- wherein the method further comprises classifying the at least one signal into at least one subcategory of cognitive activities by means of the characterisation module;
- and
- wherein the classification of the signal is performed by means of the machine learning algorithm previously trained with training signals which are classified into at least one predefined subcategory of cognitive activity, and wherein the result label is fed back into the machine learning algorithm to improve precision thereof.
11 more elements
A system for characterising and certifying cognitive activities in a non-invasive manner by means of a characterisation and certification system, comprising:
- a detection module comprising at least one gaseous component measuring element configured for generating at least one signal indicating a temporal evolution of at least one detected gaseous component;
- a characterisation module configured for characterising the at least one signal generated by the detection module based on an individual or combined sequential structure of the temporal evolution thereof and for determining whether said at least one signal corresponds to a development of a specific cognitive activity;
- and
- a machine learning algorithm that is trained with a method comprising acquiring the at least one detected gaseous component over time in the form of the at least one signal and classifying known activities associated with the acquired at least one signal as a result label, wherein the known activities are associated with one or more cognitive activities;
- a data processing system comprising means to:
- a) receive the generated signal by means of the characterisation module;
- b) characterise the generated signal based on the sequential structure of events in the temporal evolution thereof, by means of the characterisation module;
- and
- c) determine whether the generated signal corresponds to the development of a specific cognitive activity based on the result of the characterisation;
- wherein step (b) comprises identifying at least one temporal event in the generated signal, and wherein the determination of step (c) is performed based on the sequentiality of the identified temporal events;
- and
- wherein steps (b) and (c) are performed using the trained machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity;
- wherein the method further comprises classifying the at least one signal into at least one subcategory of cognitive activities by means of the characterisation module;
- and
- wherein the classification of the signal is performed by means of the machine learning algorithm previously trained with training signals which are classified into at least one predefined subcategory of cognitive activity, and wherein the result label is fed back into the machine learning algorithm to improve precision thereof.
12 more elements
A system for characterising and certifying cognitive activities in a non-invasive manner by means of a characterisation and certification system, comprising:
- a detection module comprising at least one gaseous component measuring element configured for generating at least one signal indicating a temporal evolution of at least one detected gaseous component;
- a characterisation module configured for characterising the at least one signal generated by the detection module based on an individual or combined sequential structure of the temporal evolution thereof and for determining whether said at least one signal corresponds to a development of a specific cognitive activity;
- and
- a machine learning algorithm that is trained with a method comprising acquiring the at least one detected gaseous component over time in the form of the at least one signal and classifying known activities associated with the acquired at least one signal as a result label, wherein the known activities are associated with one or more cognitive activities;
- a tangible, non-transitory computer readable medium storing a computer program comprising instructions which, when the program is run by a computer, causes the computer to:
- a) receive the generated signal by means of the characterisation module;
- b) characterise the generated signal based on the sequential structure of events in the temporal evolution thereof, by means of the characterisation module;
- and
- c) determine whether the generated signal corresponds to the development of a specific cognitive activity based on the result of the characterisation;
- wherein step (b) comprises identifying at least one temporal event in the signal, and wherein the determination of step (c) is performed based on the sequentiality of the identified temporal events;
- and
- wherein steps (b) and (c) are performed using the trained machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity;
- wherein the method further comprises classifying the at least one signal into at least one subcategory of cognitive activities by means of the characterisation module;
- and
- wherein the classification of the signal is performed by means of the machine learning algorithm previously trained with training signals which are classified into at least one predefined subcategory of cognitive activity, and wherein the result label is fed back into the machine learning algorithm to improve precision thereof.
12 more elements
A system for characterising and certifying cognitive activities in a non-invasive manner by means of a characterisation and certification system, comprising:
- a detection module comprising at least one gaseous component measuring element configured for generating at least one signal indicating a temporal evolution of at least one detected gaseous component;
- a characterisation module configured for characterising the at least one signal generated by the detection module based on an individual or combined sequential structure of the temporal evolution thereof and for determining whether said at least one signal corresponds to a development of a specific cognitive activity;
- and
- a machine learning algorithm that is trained with a method comprising acquiring the at least one detected gaseous component over time in the form of the at least one signal and classifying known activities associated with the acquired at least one signal as a result label, wherein the known activities are associated with one or more cognitive activities;
- a tangible, non-transitory computer-readable medium comprising instructions which, when run by a computer, causes the computer to:
- a) receive the generated signal by means of the characterisation module;
- b) characterise the generated signal based on the sequential structure of events in the temporal evolution thereof, by means of the characterisation module;
- and
- c) determine whether the generated signal corresponds to the development of a specific cognitive activity based on the result of the characterisation;
- wherein step (b) comprises identifying at least one temporal event in the signal, and wherein the determination of step (c) is performed based on the sequentiality of the identified temporal events;
- and
- wherein steps (b) and (c) are performed using the trained machine learning algorithm, which uses the sequential structure of the temporal evolution of the cognitive activity;
- wherein the method further comprises classifying the at least one signal into at least one subcategory of cognitive activities by means of the characterisation module;
- and
- wherein the classification of the signal is performed by means of the machine learning algorithm previously trained with training signals which are classified into at least one predefined subcategory of cognitive activity, and wherein the result label is fed back into the machine learning algorithm to improve precision thereof.
12 more elements
Dependencies are inferred from the claim text.