Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Pose Estimation from Sparse Inertial Measurements through
  Recurrent Graph Convolution   We propose the adjacency adaptive graph convolutional long-short term memory
network (AAGC-LSTM) for human pose estimation from sparse inertial
measurements, obtained from only 6 measurement units. The AAGC-LSTM combines
both spatial and temporal dependency in a single network operation. This is
made possible by equipping graph convolutions with adjacency adaptivity, which
also allows for learning unknown dependencies of the human body joints. To
further boost accuracy, we propose longitudinal loss weighting to consider
natural movement patterns, as well as body-aware contralateral data
augmentation. By combining these contributions, we are able to utilize the
inherent graph nature of the human body, and can thus outperform the state of
the art for human pose estimation from sparse inertial measurements.

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The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex