Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A-MAL: Automatic Movement Assessment Learning from Properly Performed
  Movements in 3D Skeleton Videos   The task of assessing movement quality has recently gained high demand in a
variety of domains. The ability to automatically assess subject movement in
videos that were captured by affordable devices, such as Kinect cameras, is
essential for monitoring clinical rehabilitation processes, for improving motor
skills and for movement learning tasks. The need to pay attention to low-level
details while accurately tracking the movement stages, makes this task very
challenging. In this work, we introduce A-MAL, an automatic, strong movement
assessment learning algorithm that only learns from properly-performed movement
videos without further annotations, powered by a deviation time-segmentation
algorithm, a parameter relevance detection algorithm, a novel time-warping
algorithm that is based on automatic detection of common temporal
points-of-interest and a textual-feedback generation mechanism. We demonstrate
our method on movements from the Fugl-Meyer Assessment (FMA) test, which is
typically held by occupational therapists in order to monitor patients'
recovery processes after strokes.

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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org