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Inertial motion capture systems widely use low-cost IMUs to obtain theorientation of human body segments, but these sensors alone are unable toestimate link positions. Therefore, this research used a SLAM method inconjunction with inertial data fusion to estimate link positions. SLAM is amethod that tracks a target in a reconstructed map of the environment using acamera. This paper proposes quaternion-based extended and square-root unscentedKalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filtersuse measurements based on SLAM position data, multi-link biomechanicalconstraints, and vertical referencing to correct errors. In addition to thesensor biases, the fusion algorithm is capable of estimating link geometries,allowing the imposing of biomechanical constraints without a priori knowledgeof sensor positions. An optical tracking system is used as a reference ofground-truth to experimentally evaluate the performance of the proposedalgorithm in various scenarios of human arm movements. The proposed algorithmsachieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitudeestimation. Compared to the EKF, the SRUKF algorithm presents a smoother andhigher convergence rate but is 2.4 times more computationally demanding. Afterconvergence, the SRUKF is up to 17% less and 36% more accurate than the EKF inposition and attitude estimation, respectively. Using an absolute positionmeasurement method instead of SLAM produced 80% and 40%, in the case of EKF,and 60% and 6%, in the case of SRUKF, less error in position and attitudeestimation, respectively.

Augmenting Inertial Motion Capture with SLAM Using EKF and SRUKF Data Fusion Algorithms