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Conventional methods for human pose estimation either require a high degreeof instrumentation, by relying on many inertial measurement units (IMUs), orconstraint the recording space, by relying on extrinsic cameras. These deficitsare tackled through the approach of human pose estimation from sparse IMU data.We define attention-oriented adjacency adaptive graph convolutional long-shortterm memory networks (A3GC-LSTM), to tackle human pose estimation based on sixIMUs, through incorporating the human body graph structure directly into thenetwork. The A3GC-LSTM combines both spatial and temporal dependency in asingle network operation, more memory efficiently than previous approaches. Therecurrent graph learning on arbitrarily long sequences is made possible byequipping graph convolutions with adjacency adaptivity, which eliminates theproblem of information loss in deep or recurrent graph networks, while it alsoallows for learning unknown dependencies between the human body joints. Tofurther boost accuracy, a spatial attention formalism is incorporated into therecurrent LSTM cell. With our presented approach, we are able to utilize theinherent graph nature of the human body, and thus can outperform the state ofthe art for human pose estimation from sparse IMU data.

A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements