US 12,440,147 B2
Systems and methods for evaluating and mitigating problem behavior by detecting precursors
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); and James P. Dieffenderfer, Raleigh, NC (US)
Assigned to Vanderbilt University, Nashville, TN (US); and North Carolina State University, Raleigh, NC (US)
Filed by Vanderbilt University, Nashville, TN (US); and North Carolina State University, Raleigh, NC (US)
Filed on Jul. 1, 2022, as Appl. No. 17/856,528.
Claims priority of provisional application 63/217,585, filed on Jul. 1, 2021.
Prior Publication US 2023/0000423 A1, Jan. 5, 2023
Int. Cl. 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)
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)] 19 Claims
OG exemplary drawing
 
1. A system for predicting problem behavior in individuals with developmental and behavior disabilities, the system comprising:
a wearable upper body motion sensing device including a plurality of inertial measurement units positioned at known positions relative to each other; and
an electronic controller configured to
receive output signals from each of the plurality of inertial measurement units while the wearable upper body motion sensing device is worn by a subject individual,
model an upper body position of the subject individual by calculating one or more angles for body joints based on the output signals from the plurality of inertial measurement units, wherein the one or more angles include at least one selected from the group consisting of a roll angle, a pitch angle, and a yaw angle,
apply a trained machine-learning model by providing, as input to the trained machine-learning model, an input data set that includes at least one selected from the group consisting of signal data from at least one inertial measurement unit, the upper body position information, and a sequence of upper body position information,
wherein the trained machine-learning model is trained to produce as output, in response to receiving the input data set, an indication of whether a precursor to the problem behavior is detected, and
generate a notification indicating that the precursor to the problem behavior is detected in response to the trained machine-learning model producing an output indicating that the precursor to the problem behavior has been detected.