US 2026/0245733 A1 · PREDICTING SUBJECTIVE RECOVERY FROM ACUTE EVENTS USING CONSUMER WEARABLES

Abstract

In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.

Claims21

Dependent claims

-19. (canceled)

A system for capturing wearable health data comprising:

(A) a sleep sensor configured to physically couple to a body of a user, wherein said sleep sensor is further configured to monitor sleep of said user while said sleep sensor is physically coupled to said body of said user;
(B) an activity sensor configured to monitor activity of said user;
and
(C) A mobile device comprising one or more processors and one or more memories storing computer-executable instructions that, when executed, cause said one or more processors to perform operations comprising:
(a) receiving a recovery trajectory of said user from a surgery, wherein said recovery trajectory is generated using operations comprising:
(i) obtaining sleep data of said user, wherein said sleep data is collected by said sleep sensor over a first time period, wherein at least part of said first time period is at least two weeks after said surgery,
(ii) processing said sleep data to generate sleep efficiency data corresponding to said user over said first time period,
(iii) obtaining activity data of said user, wherein said activity data is collected by said activity sensor over a second time period that is after said surgery,
(iv) processing said activity data to generate step count data corresponding to said user over said second time period, and
(v) predicting, via a machine learning model, said recovery trajectory from said surgery for said user based at least in part on processing said sleep efficiency data and said step count data;
and
(b) display a representation of said recovery trajectory to said user via a graphical user interface of said mobile device.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application

19/330,065Filed 16 Sep 2025

Priority

17 Sep 2021

Published

20 Aug 2026

Term ends

Not estimatedThe source records don’t support an estimate.

Classification21 codes

G16H 50/30
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…
A61B 5/4806
Other medical applications: sleep evaluation
A61B 5/4815
Sleep evaluation: sleep quality
A61B 5/681
Sensor mounted on worn items: wristwatch-type devices

Predicting subjective recovery from acute events using consumer wearables

Life of this patent

Predicting subjective recovery from acute events using consumer wearables

Life of this patent

Abstract

Claims21