10.5281/zenodo.2575385
https://zenodo.org/records/2575385
oai:zenodo.org:2575385
P.J. Kieliba
P.J. Kieliba
P.H. Veltink
P.H. Veltink
T. Lisini Baldi
T. Lisini Baldi
D. Prattichizzo,
D. Prattichizzo
G. Santaera
G. Santaera
A. Bicchi
A. Bicchi
M. Bianchi
M. Bianchi
van Beijnum
van Beijnum
Comparison of Three Hand Pose Reconstruction Algorithms Using Inertial and Magnetic Measurement Units
Zenodo
2019
2019-02-24
10.5281/zenodo.2575384
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
688857
Synergy-based Open-source Foundations and Technologies for Prosthetics and RehabilitatiOn