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:
Dec. 15, 2020

Filed:

Jan. 24, 2020
Applicant:

Zibrio Inc., Houston, TX (US);

Inventors:

Katharine Forth, Houston, TX (US);

Erez Lieberman Aiden, Houston, TX (US);

Assignee:

Zibrio Inc., Houston, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/11 (2006.01); G06N 20/00 (2019.01); A61B 5/103 (2006.01); A61B 5/00 (2006.01); G16H 50/30 (2018.01); G06N 7/00 (2006.01); G16H 50/20 (2018.01); G16H 20/10 (2018.01); G16H 20/30 (2018.01);
U.S. Cl.
CPC ...
A61B 5/1117 (2013.01); A61B 5/1036 (2013.01); A61B 5/4023 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); A61B 5/0022 (2013.01); A61B 5/1116 (2013.01); A61B 5/6887 (2013.01); A61B 2503/08 (2013.01); A61B 2562/0252 (2013.01); G16H 20/10 (2018.01); G16H 20/30 (2018.01);
Abstract

A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.


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