  Conventional methods for human pose estimation either require a high degree
of instrumentation, by relying on many inertial measurement units (IMUs), or
constraint the recording space, by relying on extrinsic cameras. These deficits
are tackled through the approach of human pose estimation from sparse IMU data.
We define attention-oriented adjacency adaptive graph convolutional long-short
term memory networks (A3GC-LSTM), to tackle human pose estimation based on six
IMUs, through incorporating the human body graph structure directly into the
network. The A3GC-LSTM combines both spatial and temporal dependency in a
single network operation, more memory efficiently than previous approaches. The
recurrent graph learning on arbitrarily long sequences is made possible by
equipping graph convolutions with adjacency adaptivity, which eliminates the
problem of information loss in deep or recurrent graph networks, while it also
allows for learning unknown dependencies between the human body joints. To
further boost accuracy, a spatial attention formalism is incorporated into the
recurrent LSTM cell. With our presented approach, we are able to utilize the
inherent graph nature of the human body, and thus can outperform the state of
the art for human pose estimation from sparse IMU data.
