Aetna Inc.

Dwayne Kurfirst

Robert E Bates, Iii

In some instances, a user device for determining whether an individual is sick is provided. The user device is configured to obtain a facial image of an individual; obtain an audio file comprising a voice recording of the individual; determine a facial recognition confidence value associated with whether the individual is sick based on inputting the facial image into a facial recognition machine learning dataset that is individualized for the individual; determine a voice recognition confidence value associated with whether the individual is sick based on inputting the audio file into a voice recognition machine learning dataset that is individualized for the individual; determine whether the individual is sick based on the facial recognition confidence value and the voice recognition confidence value; and causing display of a prompt indicating whether the individual is sick.

Systems and methods for determining whether an individual is sick based on machine learning algorithms and individualized data

In some instances, the disclosure provides a method performed by a smart vital device. The method comprises receiving sensor information indicating one or more health characteristics associated with an individual, wherein the sensor information comprises audio information indicating audio signals from a surrounding environment and temperature information indicating temperature readings from the surrounding environment, determining one or more health audio characteristics of the individual based on inputting the audio signals into one or more health condition machine learning datasets, determining one or more health temperature characteristics of the individual based on the temperature readings from the surrounding environment, determining one or more health conditions of the individual based on the one or more health audio characteristics and the one or more health temperature characteristics, and outputting the one or more health conditions of the individual.

Systems and methods for determining and using health conditions based on machine learning algorithms and a smart vital device

In some instances, a fall detection system comprising a first fall detection device and a user device is provided. The fall detection device is configured to: detect an occurrence of a fall event associated with an individual based on sensor information from the one or more sensors and a fall detection model; and provide a first indication indicating the occurrence of the fall event. The user device is configured to: receive the first indication; cause display of a prompt requesting user feedback as to whether the individual fell based on the first indication and a second indication from a second fall detection device; provide update information indicating for the first fall detection device to update the fall detection model based on the user feedback; and provide user fall information associated with the occurrence of the fall event based on the user feedback.

Systems and methods for using primary and redundant devices for detecting falls

In some instances, a method performed by a smart pill bottle delivery device is provided. The method comprises determining one or more prescription timing intervals indicating times for the user to take one or more medication units of the medication based on retrieving a prescription for the user from memory, providing one or more visual notifications indicating for the user to take the one or more medication units based on the one or more prescription timing intervals, subsequent to providing the one or more visual notifications, detecting position information indicating an action performed by the user, and causing display of a second display screen based on the position information indicating the action performed by the user.

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Hartford, CT, United States of America

Dwayne Kurfirst