Continuous monitoring of a user's health with a mobile device

Patent metadata

Field	Value
Publication number	US-12453482-B2
Application number	US-201816186244-A
Country	US
Kind code	B2
Filing date	Nov 9, 2018
Priority date	Dec 12, 2013
Publication date	Oct 28, 2025
Grant date	Oct 28, 2025

Abstract

Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

First claim

What is claimed is:

1 . An apparatus, comprising:
a processing device; a low-fidelity heath-indicator data sensor operatively coupled to the processing device; a high-fidelity health-indicator data sensor operatively coupled to the processing device; and a memory having instructions stored thereon that, when executed by the processing device, cause the processing device to:
continuously receive measured low-fidelity health-indicator data at a first time, wherein the measured low-fidelity health-indicator data is obtained by the low-fidelity health-indicator data sensor;
input a set of data comprising the measured low-fidelity health-indicator data into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model is configured to utilize the measured low-fidelity health-indicator data to predict health-indicator data of the user at a future time, based on a low-fidelity health-indicator threshold and a first time threshold;
in response to the predicted health-indicator data of the user at the future time being outside a normal range: receive measured high-fidelity health-indicator data obtained by the high-fidelity health-indicator data sensor at the future time; and
in response to a determination that the measured high-fidelity health-indicator data obtained at the future time is inside the normal range: modify the low-fidelity health-indicator threshold in real-time to decrease a notification sensitivity.

2 . The apparatus of claim 1 , wherein the low-fidelity health-indicator threshold corresponds to a first sensitivity threshold, and wherein to modify the low-fidelity health-indicator threshold the processing device is to modify the first sensitivity threshold to a second sensitivity threshold.

3 . The apparatus of claim 2 , wherein the processing device is further to: modify the second sensitivity threshold to the first sensitivity threshold in response to an expiration of a time interval.

4 . The apparatus of claim 2 , wherein to predict the health-indicator data of the user at the future time, the processing device is to: determine whether the measured low-fidelity health-indicator data is outside the low-fidelity health-indicator threshold longer than the first time threshold.

5 . The apparatus of claim 4 , wherein the low-fidelity health-indicator threshold corresponds to the first time threshold, and wherein to modify the low-fidelity health-indicator threshold the processing device is to modify the first time threshold to a second time threshold.

6 . The apparatus of claim 1 , wherein the high-fidelity health-indicator data sensor comprises an electrocardiogram (ECG) sensor and wherein the health condition is an arrhythmia.

7 . The apparatus of claim 1 , wherein the low-fidelity health-indicator data sensor comprises a photoplethysmography (PPG) sensor.

8 . The apparatus of claim 1 , wherein the apparatus is one of: a smartwatch, a fitness band, a computer tablet, or a laptop computer.

9 . The apparatus of claim 1 , wherein the trained high-fidelity machine learning model comprises one or more of: a generative neural network, a recurrent neural network (RNN), or a feed-forward neural network.

10 . The apparatus of claim 1 , wherein the set of data further comprises a record of activity level of the user.

11 . A method, comprising:
continuously receiving measured low-fidelity health-indicator data at a first time, wherein the measured low-fidelity health-indicator data is obtained by a low-fidelity health-indicator data sensor; inputting a set of data comprising the measured low-fidelity health-indicator data into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model is configured to utilize the measured low-fidelity health-indicator data to predict health-indicator data of a user at a future time, based on a low-fidelity health-indicator threshold and a first time threshold; in response to the predicted health-indicator data of the user at the future time being outside a normal range: receiving measured high-fidelity health-indicator data obtained by a high-fidelity health-indicator data sensor at the future time; and in response to determining that the measured high-fidelity health-indicator data obtained at the future time is inside the normal range: modifying, by a processing device, the low-fidelity health-indicator threshold in real-time to decrease a notification sensitivity.

12 . The method of claim 11 , wherein the low-fidelity health-indicator threshold corresponds to a first sensitivity threshold, and wherein to modify the low-fidelity health-indicator threshold the method further comprises: modifying the first sensitivity threshold to a second sensitivity threshold.

13 . The method of claim 12 , further comprising: modifying the second sensitivity threshold to the first sensitivity threshold in response to an expiration of a time interval.

14 . The method of claim 12 , wherein predicting the health-indicator data of the user at the future time comprises: determining whether the measured low-fidelity health-indicator data is outside the low-fidelity health-indicator threshold longer than the first time threshold.

15 . The method of claim 14 , wherein the low-fidelity health-indicator threshold corresponds to the first time threshold, and wherein modifying the low-fidelity health-indicator threshold comprises: modifying the first time threshold to a second time threshold.

16 . The method of claim 11 , wherein the high-fidelity health-indicator data sensor comprises an electrocardiogram (ECG) sensor and wherein the health condition is an arrhythmia.

17 . The method of claim 11 , wherein the low-fidelity health-indicator data sensor comprises a photoplethysmography (PPG) sensor.

18 . The method of claim 11 , wherein the processing device corresponds to one of:
a smartwatch, a fitness band, a computer tablet, or a laptop computer.

19 . The method of claim 11 , wherein the trained high-fidelity machine learning model comprises one or more of: a generative neural network, a recurrent neural network (RNN), or a feed-forward neural network.

20 . The method of claim 11 , wherein the set of data further comprises a record of activity level of the user.

Assignees

• Alivecor Inc

Inventors

• Valys Alexander Vainius
• Petterson Frank Losasso
• Galloway Conner Daniel Cross
• Albert David E
• Gopalakrishnan Ravi
• Korzinov Lev
• Wang Fei
• Thomson Euan
• Srivastava Nupur
• Dawood Omar
• Abuzeid Iman

Classifications

• A61B5/349

  Detecting specific parameters of the electrocardiograph cycle · CPC title

• G16Z99/00

  Subject matter not provided for in other main groups of this subclass · CPC title

• A61B5/361

  Detecting fibrillation · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H15/00

  ICT specially adapted for medical reports, e.g. generation or transmission thereof · CPC title