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. 17, 2026

Filed:

Nov. 18, 2022
Applicant:

Orpyx Medical Technologies Inc., Calgary, CA;

Inventors:

Samuel Carl William Blades, Victoria, CA;

Eric Christian Honert, Calgary, CA;

Benno Maurus Nigg, Calgary, CA;

Assignee:
Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A63B 24/00 (2006.01); A61B 5/00 (2006.01); A61B 5/103 (2006.01); A61B 5/11 (2006.01); G06N 3/044 (2023.01); G06N 20/00 (2019.01); G16H 15/00 (2018.01); G16H 20/30 (2018.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
A63B 24/0062 (2013.01); G06N 3/044 (2023.01); A61B 5/1038 (2013.01); A61B 5/112 (2013.01); A61B 5/7267 (2013.01); A63B 2220/22 (2013.01); A63B 2220/30 (2013.01); A63B 2220/40 (2013.01); A63B 2220/51 (2013.01); A63B 2220/62 (2013.01); A63B 2220/836 (2013.01); G06N 20/00 (2019.01); G16H 15/00 (2018.01); G16H 20/30 (2018.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
Abstract

A system, method and computer program product for determining mechanical running power. A plurality of sensor readings is acquired from a plurality of force sensors positioned underfoot. Force values are determined for a plurality of strides using aggregate force data. The slope, a stance time and running speed are determined for each stride. The mechanical running power associated with the plurality of sensor readings is determined by inputting the force values, the slope, the stance time and the running speed to a machine learning model trained to predict the mechanical running power. The mechanical running power can then be provided to the user as feedback or stored for purposes such as later review and analysis. The inputs to the machine learning model can be determined entirely based off of sensor data received from a wearable device worn by the user.


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