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:
Sep. 20, 2022

Filed:

Oct. 12, 2020
Applicants:

Jakob Balslev, Copenhagen, DK;

Anders Kullmann Klok, Copenhagen, DK;

Maziar Taghiyar-zamani, Copenhagen, DK;

Matias Søndergaard, Copenhagen, DK;

Lasse Petersen, Copenhagen, DK;

Peter Jensen, Copenhagen, DK;

Inventors:

Jakob Balslev, Copenhagen, DK;

Anders Kullmann Klok, Copenhagen, DK;

Maziar Taghiyar-Zamani, Copenhagen, DK;

Matias Søndergaard, Copenhagen, DK;

Lasse Petersen, Copenhagen, DK;

Peter Jensen, Copenhagen, DK;

Assignee:

Other;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 20/10 (2019.01); G06F 17/14 (2006.01); G06F 3/01 (2006.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6269 (2013.01); G06F 3/011 (2013.01); G06F 17/142 (2013.01); G06N 20/10 (2019.01); G06V 40/23 (2022.01);
Abstract

In one aspect, a computerized process useful for movement classification using a motion capture suit includes the step of providing the motion capture suit worn by a user. The motion capture suit comprises a set of position sensors and a Wi-Fi system configured to communicate a set of position sensor data to a computing system. The process includes the step of providing the computing system to: receive a set of position data from the motion capture suit for a specified time window of data comprising X, Y and Z axis positions and a joints-angle data for each position sensor of the set of position sensors, transforming each joints-angle data to a corresponding frequency domain using a fast Fourier transformation to remove any time dependency value, after the fast Fourier data transformation, train a support vector machine using the X, Y and Z axis positions data and the frequency domain data as input, using the support vector machine to predict a set of body positions and movements.


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