METHODS AND SYSTEMS OF BIOMETRIC IDENTIFICATION IN TELEMEDICINE USING REMOTE SENSING
In an aspect, a system for biometric identification in telemedicine includes a computing device configured to initiate a communication interface with a client device operated by a human subject, wherein the communication interface includes an audiovisual streaming protocol, receive, from at least a remote sensor at the human subject, a plurality of current physiological data, generate at least a biometric identification signature of the human subject, wherein generating further includes receiving subject signature training data, including a plurality of category descriptors and correlated physiological data entries, training a biometric signature model as a function of the subject signature training data and a machine-learning process, generating the biometric identification signature as a function of the biometric signature model, determining a degree of similarity between the plurality of current physiological data and the at least a biometric signature, and calculate an identity quantifier as a function of the degree of similarity.
1 . A system for authentication of physiological data for use in telemedicine, the system comprising a computing device configured to:
initiate a communication interface between the computing device and a client device, wherein the communication interface includes an audiovisual streaming protocol;
receive, using the audiovisual streaming protocol, a first physiological sample set;
generate a biometric identification signature of a human subject, wherein generating the biometric identification signature further comprises:
receiving a subject signature training data comprising a plurality physiological data entries;
training a machine-learning model as a function of a machine-learning process and the subject signature data; and
generating the biometric identification signature as a function of the machine-learning model;
determine a first degree of similarity between the plurality of the first physiological sample set and the biometric signature;
calculate an identity quantifier as a function of the first degree of similarity; and
authenticate the first physiological sample set to the human subject, as a function of the identity quantifier.
2 . The system of claim 1 , wherein the subject signature training data further comprises a plurality of category descriptors correlated to physiological entries.
3 . The system claim 1 , wherein the subject signature training data classifies physiological entries corresponding to the human subject.
4 . The system of claim 1 , wherein the computing device is further configured to:
generate a second biometric identification signature of the human subject, as a function of the machine-learning model;
determine a second degree of similarity between a second physiological sample set and the second biometric signature;
calculate the identity quantifier as a function of the first degree of similarity and the second degree of similarity.
5 . The system of claim 1 , wherein generating the biometric identification signature further comprises:
receiving a plurality of physiological data corresponding to a plurality of users;
performing a feature learning algorithm on the plurality of physiological data;
identifying, as a function of the feature learning algorithm, at least a highly divergent data category; and
generating the biometric identification signature as a function of the at least a highly divergent data category.
6 . The system of claim 1 , wherein the computing device is further configured to receive the first physiological sample set from a remote sensor.
7 . The system of claim 1 , wherein the first physiological sample set further comprises image data.
8 . The system of claim 1 , wherein the first physiological sample set further comprises audio data.
9 . The system of claim 1 , wherein the computing device is further configured to:
divide a physiological sample set from the human subject into a plurality of physiological sample subsets;
generate feature learning training data comprising the plurality of physiological sample subsets;
train feature learning model, as a function of the feature learning training data and a feature learning algorithm; and
correlate physiological subsets from the plurality of physiological subsets to one another, as a function of the feature learning model.
10 . The system of claim 1 , wherein determining the first degree of similarity further comprises:
generating a distance metric between the first physiological sample set and the at least a biometric signature; and
determining the first degree of similarity as a function of the distance metric.
11 . A method of authentication of physiological data for use in telemedicine, the method comprising:
initiating, using a computing device, a communication interface between the computing device and a client device, wherein the communication interface includes an audiovisual streaming protocol;
receiving, using the computing device and the audiovisual streaming protocol, a first physiological sample set;
generating, using the computing device, a biometric identification signature of a human subject, wherein generating the biometric identification signature further comprises:
receiving a subject signature training data, comprising a plurality physiological data entries;
training a machine-learning model as a function of a machine-learning process and the subject signature data; and
generating the biometric identification signature as a function of the machine-learning model;
determine, using the computing device, a first degree of similarity between the plurality of the first physiological samples set and the biometric signature;
calculate, using the computing device, an identity quantifier as a function of the first degree of similarity; and
authenticate, using the computing device, the first physiological sample set as belonging to the human subject, as a function of the identity quantifier.
12 . The method of claim 11 , wherein the subject signature training data further comprises a plurality of category descriptors correlated to physiological entries.
13 . The method claim 11 , wherein the subject signature training data classifies physiological entries to the human subject.
14 . The method of claim 11 , further comprising:
generating, using the computing device, a second biometric identification signature of the human subject, as a function of the machine-learning model;
determining, using the computing device, a second degree of similarity between a second physiological sample set and the second biometric signature;
calculating, using the computing device, the identity quantifier as a function of the first degree of similarity and the second degree of similarity.
15 . The method of claim 11 , wherein generating the biometric identification signature further comprises:
receiving a plurality of physiological data corresponding to a plurality of users;
performing a feature learning algorithm on the plurality of physiological data;
identifying, as a function of the feature learning algorithm, at least a highly divergent data category; and
generating the biometric identification signature as a function of the at least a highly divergent data category.
16 . The method of claim 11 , further comprising receiving the first physiological sample set from a remote sensor.
17 . The method of claim 11 , wherein the first physiological sample set further comprises image data.
18 . The method of claim 11 , wherein the first physiological sample set further comprises audio data.
19 . The method of claim 11 , further comprising:
dividing a physiological sample set from the human subject into a plurality of physiological sample subsets;
generating feature learning training data comprising the plurality of physiological sample subsets;
training a feature learning model, as a function of the feature learning training data and a feature learning process; and
correlating physiological subsets from the plurality of physiological subsets to one another, as a function of the feature learning model.
20 . The method of claim 11 , wherein determining the first degree of similarity further comprises:
generating a distance metric between the first physiological sample set and the at least a biometric signature; and
determining the first degree of similarity as a function of the distance metric.