A constrained extended Kalman filter for the optimal estimate of kinematics and kinetics of a sagittal symmetric exercise

V. Bonnet, R. Dumas, A. Cappozzo, V. Joukov, G. Daune, D. Kulić, P. Fraisse, S. Andary, G. Venture

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11 Citations (Scopus)

Abstract

This paper presents a method for real-time estimation of the kinematics and kinetics of a human body performing a sagittal symmetric motor task, which would minimize the impact of the stereophotogrammetric soft tissue artefacts (STA). The method is based on a bi-dimensional mechanical model of the locomotor apparatus the state variables of which (joint angles, velocities and accelerations, and the segments lengths and inertial parameters) are estimated by a constrained extended Kalman filter (CEKF) that fuses input information made of both stereophotogrammetric and dynamometric measurement data. Filter gains are made to saturate in order to obtain plausible state variables and the measurement covariance matrix of the filter accounts for the expected STA maximal amplitudes. We hypothesised that the ensemble of constraints and input redundant information would allow the method to attenuate the STA propagation to the end results. The method was evaluated in ten human subjects performing a squat exercise. The CEKF estimated and measured skin marker trajectories exhibited a RMS difference lower than 4 mm, thus in the range of STAs. The RMS differences between the measured ground reaction force and moment and those estimated using the proposed method (9 N and 10 N m) were much lower than obtained using a classical inverse dynamics approach (22 N and 30 N m). From the latter results it may be inferred that the presented method allows for a significant improvement of the accuracy with which kinematic variables and relevant time derivatives, model parameters and, therefore, intersegmental moments are estimated.

Original languageEnglish
Pages (from-to)140-147
Number of pages8
JournalJournal of Biomechanics
Volume62
DOIs
Publication statusPublished - 6 Sep 2017
Externally publishedYes

Keywords

  • Extended Kalman filter
  • Inertial parameters identification
  • Inverse dynamics
  • Inverse kinematics

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