System and method for identifying user

1. A method implemented on a computing device having at least one processor, at least one computer-readable storage medium, and a communication port connected to a measuring device, the method comprising:

acquiring first physiological data relating to a first subject;

extracting at least one first physiological feature from the first physiological data relating to the first subject;

determining a first model relating to at least one first reference physiological feature;

generating, based on the first model and the at least one first physiological feature, a second model, the second model relating to at least one second reference physiological feature corresponding to the second model;

determining, based on the second model and the at least one first physiological feature, at least one identification physiological feature relating to the first subject, wherein the at least one identification physiological feature corresponds to the at least one second reference physiological feature;

identifying a second subject based on the at least one identification physiological feature relating to the first subject;

wherein the identifying a second subject based on the at least one first physiological feature relating to the first subject comprises:

acquiring the at least one identification physiological feature relating to the first subject;

determining at least one second physiological feature relating to the second subject;

comparing the at least one second physiological feature relating to the second subject and the at least one identification physiological feature relating to the first subject; and

identifying the second subject based on the comparison; and

wherein the comparing the at least one second physiological feature relating to the second subject and the at least one identification physiological feature relating to the first subject comprises:

determining a first feature vector based on the at least one identification physiological feature relating to the first subject;

determining a second feature vector based on the at least one second physiological feature relating to the second subject;

determining a distance between the first feature vector relating to the first subject and the second feature vector relating to the second subject; and

comparing the distance between the first feature vector relating to the first subject and the second feature vector relating to the second subject with a threshold.

2. The method of claim 1 , wherein the first physiological data relating to the first subject includes at least one of an electrocardiogram (ECG) signal or a photoplethysmogram (PPG) signal.

3. The method of claim 1 , wherein the determining a first model relating to at least one first reference physiological feature comprises:

acquiring multiple physiological data relating to multiple subjects;

extracting physiological features relating to the multiple subjects from the multiple physiological data; and

generating the first model by training a machine learning model based on the physiological features relating to the multiple subjects.

4. The method of claim 1 , wherein the machine learning model is constructed based on at least one of a convolutional neural network, a long short-term memory network, a deep belief network, a generative adversarial network, a support vector machine, or a random forest model.

5. The method of claim 1 , wherein the determining, based on the first model and the at least one first physiological feature, a second model comprises:

determining at least one effective physiological feature based on the first model and the at least one first physiological feature, the at least one effective physiological feature corresponding to the at least one first reference physiological feature; and

generating the second model corresponding to the first subject by training the first model by the at least one effective physiological feature.

6. The method of claim 1 , wherein the determining, based on the first model and the at least one first physiological feature, a second model comprises:

acquiring second physiological data relating to the first subject, the second physiological data corresponding to the first physiological data;

generating a third model based on the first model and the at least one first physiological feature, the second model relating to at least one third reference physiological feature corresponding to the third model; and

generating the second model by training the third model based on the second physiological data.

7. The method of claim 6 , wherein

the second physiological data relating to the first subject includes at least one of blood pressure data, blood glucose data, heart rate data, or respiration rate data; and

the at least one first physiological feature includes at least one of an electrocardiogram (ECG) signal or a photoplethysmogram (PPG) signal.

8. The method of claim 1 , wherein the determining at least one second physiological feature relating to the second subject includes:

acquiring at least one of the first model or the second model; and

determining the at least one second physiological feature relating to the second subject based on the at least one of the first model or the second model.

9. The method of claim 1 , further comprising:

determining, in response to the determination that the distance is lower than or equal to the threshold, that the second subject matches with the first subject.