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:
Mar. 10, 2026

Filed:

Nov. 24, 2021
Applicant:

The Regents of the University of Michigan, Ann Arbor, MI (US);

Inventors:

Elliott J. Rouse, Ann Arbor, MI (US);

Ung Hee Lee, Ann Arbor, MI (US);

Varun Shetty, Ann Arbor, MI (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61F 2/70 (2006.01); A61F 2/66 (2006.01); G05B 13/02 (2006.01);
U.S. Cl.
CPC ...
A61F 2/70 (2013.01); G05B 13/0265 (2013.01); A61F 2002/6614 (2013.01); A61F 2002/704 (2013.01);
Abstract

Techniques are provided for using machine learning methods to predict user preferences with respect to robotic assistive prostheses and/or custom tune the robotic assistive prostheses for individual users based on the predicted user preferences. A training biomechanical dataset, including historical biomechanical sensor data for a robotic assistive prosthetic device operating using a plurality of tuning settings at a plurality of speeds, and a training user preference dataset including historical user tuning preference data for the robotic assistive prosthetic device for each respective tuning setting and speed, are generated. A machine learning model is trained using the training biomechanical dataset and the training user preference dataset. The trained machine learning model is applied to new biomechanical sensor data associated with a particular user to predict the user's tuning preferences for the robotic assistive prosthetic device, and the settings of the device are automatically modified based on the predicted tuning preferences.


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