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We demonstrate a novel deep neural network capable of reconstructing humanfull body pose in real-time from 6 Inertial Measurement Units (IMUs) worn onthe user's body. In doing so, we address several difficult challenges. First,the problem is severely under-constrained as multiple pose parameters producethe same IMU orientations. Second, capturing IMU data in conjunction withground-truth poses is expensive and difficult to do in many target applicationscenarios (e.g., outdoors). Third, modeling temporal dependencies throughnon-linear optimization has proven effective in prior work but makes real-timeprediction infeasible. To address this important limitation, we learn thetemporal pose priors using deep learning. To learn from sufficient data, wesynthesize IMU data from motion capture datasets. A bi-directional RNNarchitecture leverages past and future information that is available attraining time. At test time, we deploy the network in a sliding window fashion,retaining real time capabilities. To evaluate our method, we recorded DIP-IMU,a dataset consisting of $10$ subjects wearing 17 IMUs for validation in $64$sequences with $330\,000$ time instants; this constitutes the largest IMUdataset publicly available. We quantitatively evaluate our approach on multipledatasets and show results from a real-time implementation. DIP-IMU and the codeare available for research purposes.

Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time