A comprehensive filter to reduce drift from Euler angles, velocity, and position using an IMU

A comprehensive filter is developed to practically remove errors on position estimation derived from inertial measurement unit (IMU) raw signals. The comprehensive filter is applied to a bicep-curl exercise where the initial and end positions of the host (the forearm of the subject) is known. The comprehensive filter benefits from an explicit complementary filter, a zero-rate updating algorithm, Kalman filter, and a kinematic constraint. The sequence of two Kalman filters provides a significant reduction in the velocity and position drift.

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