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:
Oct. 14, 2025

Filed:

Jul. 01, 2022
Applicants:

Vanderbilt University, Nashville, TN (US);

North Carolina State University, Raleigh, NC (US);

Inventors:

Nilanjan Sarkar, Brentwood, TN (US);

Zhaobo Zheng, Nashville, TN (US);

Amy S. Weitlauf, Nashville, TN (US);

John Staubitz, Nashville, TN (US);

Zachary E. Warren, Nashville, TN (US);

James P. Dieffenderfer, Raleigh, NC (US);

Assignees:

Vanderbilt University, Nashville, TN (US);

North Carolina State University, Raleigh, NC (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/0205 (2006.01); A61B 5/024 (2006.01); A61B 5/0531 (2021.01); A61B 5/11 (2006.01); G16H 20/70 (2018.01); G16H 40/63 (2018.01);
U.S. Cl.
CPC ...
A61B 5/4076 (2013.01); A61B 5/02055 (2013.01); A61B 5/0531 (2013.01); A61B 5/11 (2013.01); A61B 5/4803 (2013.01); A61B 5/6804 (2013.01); A61B 5/681 (2013.01); A61B 5/7221 (2013.01); A61B 5/7267 (2013.01); A61B 5/74 (2013.01); G16H 20/70 (2018.01); G16H 40/63 (2018.01); A61B 5/02438 (2013.01); A61B 2562/0204 (2013.01); A61B 2562/0219 (2013.01); A61B 2562/04 (2013.01);
Abstract

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and/or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.


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