Comparison of Three Hand Pose Reconstruction Algorithms Using Inertial and Magnetic Measurement Units

The correct estimation of human hand kinematics has received a lot of attention in many research fields of neuroscience and robotics. Not surprisingly, many works have addressed hand pose reconstruction (HPR) problem and several technological solutions have been proposed. Among them, Inertial and Magnetic Measurement Unit (IMMU) based systems offer some elegant characteristics (including cost-effectiveness) that make these especially suited for wearable and ambulatory HPR. However, what still lacks is an exhaustive characterization of IMMU-based orientation tracking algorithms performance for hand tracking purposes. In this work, we have developed an experimental protocol to compare the performance of three of the most widely adopted HPR computational techniques, i.e. extended Kalman filter (EKF), Gauss-Newton with Complementary filter (CF) and Madgwick filter (MF), on the same dataset acquired through an IMMU-based sensing glove. The quality of the algorithms has been benchmarked against the ground truth measurement provided by an optical motion tracking system. Results suggest that performance of the three algorithms is similar, though the MF algorithm appears to be slightly more accurate in reconstructing the individual joint angles during static trials and to be the fastest one to run.