The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 04, 2020

Filed:

Apr. 23, 2018
Applicant:

Whoop, Inc., Boston, MA (US);

Inventors:

Brian Anthony Todd, Boston, MA (US);

John Vincenzo Capodilupo, Boston, MA (US);

Emily Rachel Capodilupo, Boston, MA (US);

William Ahmed, Boston, MA (US);

Assignee:

Whoop, Inc., Boston, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/11 (2006.01); A61B 5/0205 (2006.01); A61B 5/00 (2006.01); G06N 3/08 (2006.01); A63B 24/00 (2006.01); G06N 3/04 (2006.01); A61B 5/024 (2006.01);
U.S. Cl.
CPC ...
A61B 5/1118 (2013.01); A61B 5/0205 (2013.01); A61B 5/1123 (2013.01); A61B 5/681 (2013.01); A61B 5/7267 (2013.01); A63B 24/0062 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); A61B 5/02438 (2013.01); A61B 2562/0219 (2013.01); A63B 2024/0071 (2013.01); A63B 2220/40 (2013.01); A63B 2230/06 (2013.01);
Abstract

A variety of techniques are used automate the collection and classification of workout data gathered by a wearable physiological monitor. The classification process is staged in order to correctly and efficiently characterize a workout type. Initially, a generalized workout event is detected using motion and heart rate data. Then a location of the monitor on a user is determined. An artificial intelligence engine can then be conditionally applied (if a workout is occurring and a suitable device location is detected) to identify the type of workout. In addition to improved speed and accuracy, a workout detection process implemented in this manner can be realized with a sufficiently small computational footprint for deployment on a wearable physiological monitor.


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