Online Identification and Visualization of the Statically Equivalent Serial Chain via Constrained Kalman Filter - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2013

Online Identification and Visualization of the Statically Equivalent Serial Chain via Constrained Kalman Filter

Abstract

A human's center of mass (CoM) trajectory is useful to evaluate the dynamic stability during daily life activities such as walking and standing up. To estimate the subjectspecific CoM position in the home environment, we make use of a statically equivalent serial chain (SESC) developed with a portable measurement system. In this paper we implement a constrained Kalman filter to achieve an online estimation of the SESC parameters while accounting for the human body's bilateral symmetry. This results in constraining SESC parameters to be consistent with the human skeletal model used. The proposed identification method can inform the subject or the therapist, in real-time, about the quality of the on-going CoM estimation. This information can be helpful to reduce the identification time and establish a personalized protocol. A Kinect is used as a markerless motion capture system for measuring limb orientations while the Wii board is used to measure the subject's center of pressure (CoP) during the identification phase. CoP measurements and Kinect data were recorded for four able-bodied subjects. The recorded data was then given to the proposed recursive algorithm to identify the parameters of the SESC online. A cross-validation test was performed to verify the identification performance. The results for these subjects are shown and discussed.
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Dates and versions

lirmm-00830245 , version 1 (04-06-2013)

Identifiers

  • HAL Id : lirmm-00830245 , version 1

Cite

Alejandro González, Mitsuhiro Hayashibe, Philippe Fraisse. Online Identification and Visualization of the Statically Equivalent Serial Chain via Constrained Kalman Filter. ICRA: International Conference on Robotics and Automation, May 2013, Karlsruhe, Germany. pp.5303-5308. ⟨lirmm-00830245⟩
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