Resumen:
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[EN] The accurate location of the main axes of rotation (AoR) is a crucial step in many applications of human movement analysis. There are different formal methods to determine the direction and position of the AoR, whose ...[+]
[EN] The accurate location of the main axes of rotation (AoR) is a crucial step in many applications of human movement analysis. There are different formal methods to determine the direction and position of the AoR, whose performance varies across studies, depending on the pose and the source of errors. Most methods are based on minimizing squared differences between observed and modelled marker positions or rigid motion parameters, implicitly assuming independent and uncorrelated errors, but the largest error usually results from soft tissue artefacts (STA), which do not have such statistical properties and are not effectively cancelled out by such methods. However, with adequate methods it is possible to assume that STA only account for a small fraction of the observed motion and to obtain explicit formulas through differential analysis that relate STA components to the resulting errors in AoR parameters. In this paper such formulas are derived for three different functional calibration techniques (Geometric Fitting, mean Finite Helical Axis, and SARA), to explain why each technique behaves differently from the others, and to propose strategies to compensate for those errors. These techniques were tested with published data from a sit-to-stand activity, where the true axis was defined using bi-planar fluoroscopy. All the methods were able to estimate the direction of the AoR with an error of less than 5 degrees whereas there were errors in the location of the axis of 30-40 mm. Such location errors could be reduced to less than 17 mm by the methods based on equations that use rigid motion parameters (mean Finite Helical Axis, SARA) when the translation component was calculated using the three markers nearest to the axis. (C) 2017 Elsevier Ltd. All rights reserved.
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Agradecimientos:
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This work was funded by the Spanish Government and co-financed by EU FEDER funds (Grant DPI2013-44227-R). We would like to thank Prof. Tung-Wu Lu, Tsung-Yuan Tsai, Mei-Ying Kuo and Horn-Chaung Hsu from National Taiwan ...[+]
This work was funded by the Spanish Government and co-financed by EU FEDER funds (Grant DPI2013-44227-R). We would like to thank Prof. Tung-Wu Lu, Tsung-Yuan Tsai, Mei-Ying Kuo and Horn-Chaung Hsu from National Taiwan University for making the data from their studies available for further research on STA,, and Dr. Tecla Bonci from the Italian University of Sport and Movement 'Foro Italico' for providing the access to benchmark data.
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