Global Kalman filter approaches to estimate absolute angles of lower limb segments

BioMedical Engineering OnLine

Marco H. Terra¹  Arlindo N. Montagnoli²  Samuel L. Nogueira²  Roberto S. Inoue²  Adriano A. G. Siqueira³  Stefan Lambrecht⁴  Eduardo Rocon⁵  Jose L. Pons⁶  Magdo Bortole⁶  Juan C. Moreno⁶ 

[1] Department of Electrical Engineering of the University of São Paulo;Department of Electrical Engineering, Federal University of São Carlos;Department of Mechanical Engineering of the University of São Paulo;Division PMA, Department of Mechanical Engineering, Katholieke Universiteit Leuven;Group of Neural and Cognitive Engineering of the Consejo Superior de Investigaciones Científicas;Neural Rehabilitation Group, Consejo Superior de Investigaciones Científicas;

关键词: Exoskeleton;    Inertial sensors;    Kalman filter;    Markovian jump systems;    Wearable robots;   

DOI  :  10.1186/s12938-017-0346-7

来源: DOAJ

【 摘 要 】

Abstract Background In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF. Results The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance. Conclusion The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.

【 授权许可】

Unknown