Apparatus and methods for monitoring human trustworthiness
View Patent ↗An apparatus for monitoring human trustworthiness, comprising at least an interface configured to facilitate telecommunication for a participant, a plurality of sensors configured to detect a plurality of signals from the participant, and at least a computing device configured to receive the plurality of signals from the plurality of sensors, generate a participant trustworthiness classifier using at least a training sample by training the participant trustworthiness classifier using the at least a training sample, determine a participant trustworthiness as a function of the plurality of signals using the trained participant trustworthiness classifier, and generate a confidence metric associated with the determined participant trustworthiness.
1. An apparatus for monitoring human trustworthiness, wherein the apparatus comprises:
at least an interface configured to facilitate telecommunication for a participant;
a plurality of sensors configured to detect a plurality of signals from the participant; and
at least a computing device comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive the plurality of signals from the plurality of sensors;
generate a participant trustworthiness classifier using at least a training sample, wherein generating the participant trustworthiness classifier comprises:
training the participant trustworthiness classifier using the at least a training sample, wherein the at least a training sample comprises a plurality of visible feedback signals and auditory feedback signals as input correlated to a plurality of responses of known trustworthiness as output;
determine a participant trustworthiness as a function of the plurality of signals using the trained participant trustworthiness classifier; and
generate a confidence metric associated with the determined participant trustworthiness.
2. The apparatus of claim 1 , wherein the plurality of signals comprises one or more biofeedback signals.
3. The apparatus of claim 1 , wherein the plurality of signals comprises a geographic location signal associated with the participant.
4. The apparatus of claim 1 , wherein the plurality of sensors comprising at least a sensor selected from a group consisting a near-infrared (NIR) spectroscopy sensor, an exhalation sensor, an inhalation sensor, a cutaneous sensor, a speech sensor, an eye movement sensor, and a body movement sensor.
5. The apparatus of claim 1 , wherein receiving the plurality of signals comprises:
receiving the plurality of signals from the plurality of sensors during a teleconference between the participant and an interlocutor.
6. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to:
train at least a machine learning model using eye pattern training data, wherein the eye pattern training data comprises a plurality of eye parameters as input correlated to a plurality of eye patterns as output; and
determine at least an eye pattern as a function of the plurality of signals using the at least a machine learning model.
7. The apparatus of claim 6 , wherein generating the confidence metric comprises:
generating the confidence metric associated with the participant trustworthiness as a function of the at least an eye pattern, wherein the at least an eye pattern comprises a frequency of gaze at the at least an interface associated with the participant.
8. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to:
train at least a machine learning model using speech pattern training data, wherein the speech pattern training data comprises a plurality of speech parameters as input correlated to a plurality of speech patterns as output; and
determine at least a speech pattern as a function of the plurality of signals using the at least a machine learning model.
9. The apparatus of claim 8 , wherein determining the participant trustworthiness comprises:
determining the participant trustworthiness as a function of the at least a speech pattern, wherein the at least a speech pattern comprises a plurality of prosodic variables.
10. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to:
modify at least a display parameter associated with the at least an interface as a function of the participant trustworthiness and the confidence metric.
11. A method for monitoring human trustworthiness, the method comprising:
facilitating, by at least an interface, telecommunication for a participant;
detecting, by a plurality of sensors, a plurality of signals from the participant;
receiving, by at least a processor, the plurality of signals from the plurality of sensors;
generating, by the at least a processor, a participant trustworthiness classifier using at least a training sample, wherein generating the participant trustworthiness classifier comprises:
training the participant trustworthiness classifier using the at least a training sample, wherein the at least a training sample comprises a plurality of visible feedback signals and auditory feedback signals as input correlated to a plurality of responses of known trustworthiness as output;
determining, by the at least a processor, a participant trustworthiness as a function of the plurality of signals using the trained participant trustworthiness classifier; and
generating, by the at least a processor, a confidence metric associated with the determined participant trustworthiness.
12. The method of claim 11 , wherein the plurality of signals comprises one or more biofeedback signals.
13. The method of claim 11 , wherein the plurality of signals comprises a geographic location signal associated with the participant.
14. The method of claim 11 , wherein the plurality of sensors comprising at least a sensor selected from a group consisting a near-infrared (NIR) spectroscopy sensor, an exhalation sensor, an inhalation sensor, a cutaneous sensor, a speech sensor, an eye movement sensor, and a body movement sensor.
15. The method of claim 11 , wherein receiving the plurality of signals comprises:
receiving the plurality of signals from the plurality of sensors during a teleconference between the participant and an interlocutor.
16. The method of claim 11 , further comprises:
training, by the at least a processor, at least a machine learning model using eye pattern training data, wherein the eye pattern training data comprises a plurality of eye parameters as input correlated to a plurality of eye patterns as output; and
determining, by the at least a processor, at least an eye pattern as a function of the plurality of signals using the at least a machine learning model.
17. The method of claim 16 , wherein generating the confidence metric comprises:
generating the confidence metric associated with the participant trustworthiness as a function of the at least an eye pattern, wherein the at least an eye pattern comprises a frequency of gaze at the at least an interface associated with the participant.
18. The method of claim 11 , further comprises:
training, by the at least a processor, at least a machine learning model using speech pattern training data, wherein the speech pattern training data comprises a plurality of speech parameters as input correlated to a plurality of speech patterns as output; and
determining, by the at least a processor, at least a speech pattern as a function of the plurality of signals using the at least a machine learning model.
19. The method of claim 18 , wherein determining the participant trustworthiness comprises:
determining the participant trustworthiness as a function of the at least a speech pattern, wherein the at least a speech pattern comprises a plurality of prosodic variables.
20. The method of claim 11 , further comprises:
modifying, by the at least a processor, at least a display parameter associated with the at least an interface as a function of the participant trustworthiness and the confidence metric.