We introduce a new framework that combines deep learning and top-down optimization to predict lower extremity joint kinematics directly from inertial data, without relying on magnetometer readings.
The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of motion-tracking sensors 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 out-of-laboratory 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.
Citation: Eric Rapp*, Soyong Shin*, Wolf Thomsen, Reed Ferber, and Eni Halilaj. "Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework." Journal of Biomechanics 116 (2021): 110229.
* equal contribution