US-20260128174-A1 - Computer Application for Determining a Cardiac Score and Providing Corresponding Recommendations Via a Computing Device

US20260128174A1US 20260128174 A1US20260128174 A1US 20260128174A1US-20260128174-A1

Abstract

A computer-implemented method includes obtaining demographic data of a user. The demographic data comprises an age for the user. The method also includes obtaining physiological data of the user. The physiological data comprises one or more cardiac metrics of the user. Further, the method also includes determining, via a model, a predicted cardiac age for the user using the demographic data and the physiological data as model inputs. Moreover, the method includes determining a cardiac score based on a difference between the predicted cardiac age and the age for the user. The cardiac score is configured to assess cardiac health of the user. In addition, the method includes causing a display screen of an electronic device to display the cardiac score for the user.

Inventors

Anthony Zahi Faranesh
Zeinab Esmaeilpour
Davide Valeriani
Hulya Emir-Farinas

Assignees

GOOGLE LLC

Dates

Publication Date: 20260507
Application Date: 20241107

Claims (20)

1 . A computing device, comprising: one or more processors; and one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing device to perform operations, the operations comprising: obtaining demographic data of a user, the demographic data comprising an age for the user; obtaining physiological data of the user from one or more physiological sensors, the physiological data comprising one or more cardiac metrics of the user; inputting the demographic data and the physiological data into a machine-learned model configured to output a predicted cardiac age for the user using at least the demographic data and the physiological data as model inputs; generating a cardiac score based on the predicted cardiac age output by the machine-learned model and the age for the user, the cardiac score configured to assess cardiac health of the user; obtaining at least one of historical activity data for the user and one or more activity preferences for the user; determining a recommended activity for the user based on the cardiac score and the at least one of the historical activity data for the user and the one or more activity preferences for the user, wherein the recommended activity is configured to influence the cardiac score of the user to achieve a desired cardiac score; prompting the user to perform the recommended activity in response to a trigger; determining an updated cardiac score based on inputting the demographic data and updated physiological data of the user to the machine-learned model, wherein the updated physiological data is obtained from the one or more physiological sensors while the user performs the recommended activity; and causing a display screen of the computing device to display a comparison of the cardiac score and the updated cardiac score so as to indicate to the user an influence of the recommended activity on the one or more cardiac metrics and the cardiac score.
2 . (canceled)
3 . (canceled)
4 . (canceled)
5 . The computing device of claim 1 , wherein the operations further comprise: obtaining anthropometric data for the user; and inputting the anthropometric data into the machine-learned model, the machine-learned model configured to output the predicted cardiac age for the user using the demographic data, the physiological data, and the anthropometric data as model inputs.
6 . The computing device of claim 1 , wherein the operations further comprise: categorizing the cardiac score for the user based, at least in part, on the demographic data for the user; and causing the display screen to display the categorization.
7 . (canceled)
8 . The computing device of claim 1 , wherein the operations further comprise: inputting the cardiac score and the physiological data to a second model as second model inputs; generating, via the second model, one or more explanations providing context for the cardiac score given the physiological data using the second model inputs, the second model being a machine-learned model; and causing the display screen to display the one or more explanations output by the second model.
9 . The computing device of claim 1 , wherein the one or more cardiac metrics comprise at least one of a resting heart rate, an average heart rate, a maximum heart rate, a heart rate recovery, and a maximal oxygen consumption.
10 . The computing device of claim 1 , wherein the computing device is a wearable computing device worn by the user or a mobile computing device.
11 . A computer-implemented method for determining a cardiac score for a user, the computer-implemented method comprising: obtaining, via an electronic device, demographic data of a user, the demographic data comprising an age for the user; obtaining, via the electronic device, physiological data of the user from one or more physiological sensors of the electronic device, the physiological data comprising one or more cardiac metrics of the user; inputting, via the electronic device, the demographic data and the physiological data into a machine-learned model of the electronic device, the machine-learned model configured to output a predicted cardiac age for the user using at least the demographic data and the physiological data as model inputs; generating, via the electronic device, a cardiac score based on the predicted cardiac age output by the machine-learned model and the age for the user, the cardiac score configured to assess cardiac health of the user; obtaining, via the electronic device, at least one of historical activity data for the user and one or more activity preferences for the user; determining, via the electronic device, a recommended activity for the user based on the cardiac score and the at least one of the historical activity data for the user and the one or more activity preferences for the user, wherein the recommended activity is configured to influence the cardiac score of the user to achieve a desired cardiac score of the user; prompting, via the electronic device, the user to perform the recommended activity in response to a trigger; determining, via the electronic device, an updated cardiac score based on inputting the demographic data and updated physiological data of the user to the machine-learned model, wherein the updated physiological data is obtained via the one or more physiological sensors while the user performs the recommended activity; and causing a display screen of the electronic device to display a comparison of the cardiac score and the updated cardiac score so as to indicate to the user an influence of the recommended activity on the one or more cardiac metrics and the cardiac score.
12 . (canceled)
13 . (canceled)
14 . (canceled)
15 . The computer-implemented method of claim 11 , further comprising: obtaining, via the electronic device, anthropometric data for the user; and inputting, via the electronic device, the anthropometric data into the machine-learned model, the machine-learned model configured to output the predicted cardiac age for the user using the demographic data, the physiological data, and the anthropometric data as model inputs.
16 . The computer-implemented method of claim 11 , further comprising: categorizing, via the electronic device, the cardiac score for the user based, at least in part, on the demographic data for the user; and causing the display screen to display the categorization.
17 . (canceled)
18 . The computer-implemented method of claim 11 , further comprising: determining, via a second model of the electronic device, one or more explanations providing context for the cardiac score given the physiological data based on using the cardiac score and the physiological data as second model inputs, the second model being a machine-learned model; and causing the display screen to display the one or more explanations.
19 . The computer-implemented method of claim 11 , wherein one or more cardiac metrics comprise at least one of a resting heart rate, an average heart rate, a maximum heart rate, a heart rate recovery, and a maximal oxygen consumption.
20 . A computer-implemented method for determining a cardiac score for a user, the computer-implemented method comprising: obtaining, via an electronic device, demographic data of a user, the demographic data comprising an age for the user; obtaining, via the electronic device, physiological data of the user from one or more physiological sensors, the physiological data comprising at least one of a resting heart rate, an average heart rate, a maximum heart rate, a heart rate recovery, and a maximal oxygen consumption; inputting, via the electronic device, the demographic data and the physiological data into a machine-learned model of the electronic device, the machine-learned model configured to output a predicted-cardiac age for the user using at least the demographic data and the physiological data as model inputs; generating, via the electronic device, a cardiac score based on the predicted cardiac age output by the machine-learned model and the age for the user, the cardiac score configured to assess cardiac health of the user; inputting, via the electronic device, the cardiac score and the physiological data into a second model of the electronic device as second model inputs; generating, via the second model, one or more explanations providing context for the cardiac score given the physiological data so as to permit the user to interpret the cardiac score based on using the second model inputs, the second model being a machine-learned model; and causing a display screen of the electronic device to display the cardiac score for the user and the one or more explanations so as to indicate to the user how the physiological data influences the cardiac score.

Description

FIELD OF THE INVENTION The present disclosure relates generally to a computer application implemented on a wearable computing device, mobile computing device, and/or server system that generates a cardiac score and provides recommendations to a user relating to the cardiac score. BACKGROUND Individuals are unique and their motivational and adherence patterns in striving for a behavioral goal can vary significantly. Health-related changes in response to a behavioral change can also vary between people. Advances in sensors and wearable technologies have made it increasingly possible for individuals to collect data about themselves with the goal of self-knowledge through personal data. However, gaining self-knowledge can be more challenging than only a simple task of data collection. For example, human cardiac health may be influenced by a plurality of factors. As such, cardiac health management may be delayed until after an onset of noticeable symptoms and/or reaching a certain age. Due to this reactive approach, individuals may lack knowledge and motivation to adopt and/or maintain behaviors associated with better cardiac health. Accordingly, the present disclosure is directed to a computer application that can be implemented on a wearable computing device, mobile computing device, and/or server system to allow a user to proactively receive a cardiac score configured to assess cardiac health of the user. SUMMARY OF THE INVENTION Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments. In an aspect, the present disclosure is directed to a computing device having one or more processors and one or more computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing device to perform operations. The operations include obtaining demographic data of a user, the demographic data comprises an age for the user; obtaining physiological data of the user, the physiological data comprises one or more cardiac metrics of the user; determining, via a model, a predicted cardiac age for the user using the demographic data and the physiological data as model inputs; determining a cardiac score based on a difference between the predicted cardiac age and the age for the user, the cardiac score is configured to assess cardiac health of the user; and causing a display screen of an electronic device to display the cardiac score for the user. In another aspect, the present disclosure is directed to a computer-implemented method that includes obtaining demographic data of a user, the demographic data comprises an age for the user; obtaining physiological data of the user, the physiological data comprises one or more cardiac metrics of the user; determining, via a model, a predicted cardiac age for the user using the demographic data and the physiological data as model inputs; determining a cardiac score based on a difference between the predicted cardiac age and the age for the user, the cardiac score is configured to assess cardiac health of the user; and causing a display screen of an electronic device to display the cardiac score for the user. In another aspect, the present disclosure is directed to a computer-implemented method that includes obtaining demographic data of a user, the demographic data comprising an age for the user; obtaining physiological data of the user, the physiological data comprising a resting heart rate, an average heart rate, a maximum heart rate, a heart rate recovery, and a maximal oxygen consumption; determining, via a model, a predicted cardiac age for the user using the demographic data and the physiological data as model inputs; determining a cardiac score based on a difference between the predicted cardiac age and the age for the user, the cardiac score configured to assess cardiac health of the user; and causing a display screen of an electronic device to display the cardiac score for the user. These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related principles. BRIEF DESCRIPTION OF THE DRAWINGS Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended figures, in which: FIGS. 1, 2, and 3 illustrate various perspective views of an example wearable computing device according to one or more example embodiments of the present disclosure. FIG. 4 illustrates a block diagram of an example device according to one or more example embodiments