Accurate estimation of lower body pose during gait is useful in a wide variety of applications, including design of bipedal walking strategies, active prosthetics, exoskeletons, and physical rehabilitation. In this paper an algorithm is developed to estimate joint kinematics during rhythmic motion such as walking, using inertial measurement units attached at the waist, knees, and ankles. The proposed approach combines the extended Kalman filter with a canonical dynamical system to estimate joint angles, positions, and velocities for 3 dimensional rhythmic lower body movement. The system incrementally learns the rhythmic motion over time, improving the estimate over a regular extended Kalman filter, and segmenting the motion into repetitions. The algorithm is validated in simulation and on real human walking data. It is shown to improve joint acceleration and velocity estimates over regular extended Kalman Filter by 40% and 37% respectively.
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