  Inertial motion capture systems widely use low-cost IMUs to obtain the
orientation of human body segments, but these sensors alone are unable to
estimate link positions. Therefore, this research used a SLAM method in
conjunction with inertial data fusion to estimate link positions. SLAM is a
method that tracks a target in a reconstructed map of the environment using a
camera. This paper proposes quaternion-based extended and square-root unscented
Kalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filters
use measurements based on SLAM position data, multi-link biomechanical
constraints, and vertical referencing to correct errors. In addition to the
sensor biases, the fusion algorithm is capable of estimating link geometries,
allowing the imposing of biomechanical constraints without a priori knowledge
of sensor positions. An optical tracking system is used as a reference of
ground-truth to experimentally evaluate the performance of the proposed
algorithm in various scenarios of human arm movements. The proposed algorithms
achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude
estimation. Compared to the EKF, the SRUKF algorithm presents a smoother and
higher convergence rate but is 2.4 times more computationally demanding. After
convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in
position and attitude estimation, respectively. Using an absolute position
measurement 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 attitude
estimation, respectively.
