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Video processing for motor task analysis

A motion task, video technology, applied in image analysis, image data processing, electrical digital data processing and other directions, can solve problems such as observer changes

Pending Publication Date: 2017-05-10
MICROSOFT TECH LICENSING LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing methods of analysis of motor task performance typically involve manual observation and are therefore subjective, with the potential for variations in making the analysis depending on the skill of the observer

Method used

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  • Video processing for motor task analysis
  • Video processing for motor task analysis
  • Video processing for motor task analysis

Examples

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Embodiment Construction

[0017] The detailed description provided below in connection with the accompanying drawings is intended as a description of the present example and is not intended to represent the only forms in which the present example may be constructed or utilized. The description sets forth the functionality of the example and the sequence of steps used to build and operate the example. However, the same or equivalent functions and sequences can be accomplished by different examples.

[0018] figure 1 is a schematic diagram of a person 101 performing a motor task such as placing the index finger on the nose (as indicated by arm position 112 ) after starting from a position where the arm is extended horizontally (as indicated by arm position 114 ) . This is just one example of a motion task and is referred to below figure 2 Give more examples.

[0019] The task of analyzing motion, which is affected by variability such as observer bias, observer experience level, and observer environm...

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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.

Description

Background technique [0001] Performance of motor tasks (such as raising arms, standing upright, raising legs, and others) often varies considerably between individuals. For example, depending on anatomy, weight and other factors such as the individual's expertise and skills, experience, strength and physical ability. Existing methods of analysis of motor task performance typically involve manual observation and are thus subjective, with the potential for variations in making the analysis depending on the skill of the observer. [0002] The embodiments described below are not limited to implementations that address any or all of the disadvantages of known motor task analysis systems. Contents of the invention [0003] A brief summary of the disclosure is presented below in order to provide the reader with a basic understanding. This summary is not an extensive overview of the disclosure and does not identify key / critical elements or delineate the scope of the specification....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N20/10G06N20/20G06V10/764
CPCG06F16/783G06F16/9566G06N20/10G06N20/20G06N5/025G06N5/00G06T7/20G16H50/20G06N20/00G16H20/30G06V40/23G06V10/764G06F18/24155G16H50/30G06V20/46G06V20/44G06F18/24G06T7/0012G06T2207/10016G06T2207/20081G16H20/40
Inventor P·康特斯希德J·多恩D·奇基克A·克里米尼西
Owner MICROSOFT TECH LICENSING LLC