The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings.

CODE: https://github.com/CMU-MBL/JointAnglePrediction_JOB

To cite this work:
@article{rappshin2021,
title={Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework},
author={Rapp, Eric and Shin, Soyong and Thomsen, Wolf and Ferber, Reed and Halilaj, Eni},
journal={Journal of Biomechanics},
year={2021},
}

Link to paper: https://www.sciencedirect.com/science/article/pii/S0021929021000099