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. 25, 2018

Filed:

Nov. 09, 2014
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Peter Kontschieder, Cambridge, GB;

Jonas Dorn, Reinach BL, CH;

Darko Zikic, Cambridge, GB;

Antonio Criminisi, Cambridge, GB;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06F 17/30 (2006.01); G16H 50/20 (2018.01); G06F 19/00 (2018.01); G06N 5/02 (2006.01); G06N 99/00 (2010.01); G06K 9/62 (2006.01); G06N 5/00 (2006.01); G06T 7/20 (2017.01);
U.S. Cl.
CPC ...
G06F 17/30784 (2013.01); G06F 17/30887 (2013.01); G06F 19/345 (2013.01); G06K 9/00342 (2013.01); G06K 9/6267 (2013.01); G06K 9/6278 (2013.01); G06N 5/00 (2013.01); G06N 5/025 (2013.01); G06N 99/005 (2013.01); G06T 7/20 (2013.01); G16H 50/20 (2018.01); G06F 19/3481 (2013.01);
Abstract

Video processing for motor task analysis is described. In various examples, a video of at least part of a person or animal carrying out a motor task, such as placing the forefinger on the nose, is input to a trained machine learning system to classify the motor task into one of a plurality of classes. In an example, motion descriptors such as optical flow are computed from pairs of frames of the video and the motion descriptors are input to the machine learning system. For example, during training the machine learning system identifies time-dependent and/or location-dependent acceleration or velocity features which discriminate between the classes of the motor task. In examples, the trained machine learning system computes, from the motion descriptors, the location dependent acceleration or velocity features which it has learned as being good discriminators. In various examples, a feature is computed using sub-volumes of the video.


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