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Open Access Highly Accessed Research

Reconstruction of skeletal movement using skin markers: comparative assessment of bone pose estimators

Andrea Cereatti1, Ugo Della Croce23* and Aurelio Cappozzo1

Author Affiliations

1 Department of Human Movement and Sport Sciences, Istituto Universitario di Scienze Motorie, Rome, Italy

2 Department of Biomedical Sciences, University of Sassari, Sassari, Italy

3 Centro di Cura e Riabilitazione Santa Maria Bambina, Oristano, Italy

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Journal of NeuroEngineering and Rehabilitation 2006, 3:7  doi:10.1186/1743-0003-3-7

Published: 23 March 2006

Abstract

Background

The assessment of the accuracy of the pose estimation of human bones and consequent joint kinematics is of primary relevance in human movement analysis. This study evaluated the performance of selected pose estimators in reducing the effects of instrumental errors, soft tissue artifacts and anatomical landmark mislocations occurring at the thigh on the determination of the knee kinematics.

Methods

The pattern of a typical knee flexion-extension during a gait cycle was fed into a knee model which generated a six-components knee kinematics and relevant marker trajectories. The marker trajectories were corrupted with both instrumental noise and soft tissue artifacts. Two different cluster configurations (4 and 12-marker cluster) were investigated. Four selected pose estimators, a Geometrical method, a SVD-based method, and the Pointer Cluster Technique in the optimized and non optimized version, were analyzed. The estimated knee kinematics were compared to the nominal kinematics in order to evaluate the accuracy of the selected pose estimators.

Results

Results have shown that optimal pose estimators perform better than traditional geometric pose estimators when soft tissue artifacts are present. The use of redundant markers improved in some cases the estimation of the dynamics of the kinematics patterns, while it does not reduce the offsets from the nominal kinematics curves. Overall, the best performance was obtained by the SVD-based pose estimator, while the performance of the PCT pose estimator in its optimal version was not satisfactory. However, the knee kinematics errors reached 5 deg for rotations and 10 mm for translations).

Conclusion

Given the favorable experimental conditions of this study (soft tissue artifacts determined from a young, healthy and non overweight subject), the errors found in estimating the knee kinematics have to be considered unsatisfactory even if the best performing pose estimator is used. Therefore, it is the authors' opinion that the movement analysis research community should make additional efforts in the search of more subject specific error models to increase the accuracy of joint kinematics estimations.