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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 uncontroll


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.

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