Oppugning the assumptions of spatial averaging of segment and joint orientations Academic Article uri icon

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abstract

  • Movement scientists frequently calculate "arithmetic averages" when examining body segment or joint orientations. Such calculations appear routinely, yet are fundamentally flawed. Three-dimensional orientation data are computed as matrices, yet three-ordered Euler/Cardan/Bryant angle parameters are frequently used for interpretation. These parameters are not geometrically independent; thus, the conventional process of averaging each parameter is incorrect. The process of arithmetic averaging also assumes that the distances between data are linear (Euclidean); however, for the orientation data these distances are geodesically curved (Riemannian). Therefore we question (oppugn) whether use of the conventional averaging approach is an appropriate statistic. Fortunately, exact methods of averaging orientation data have been developed which both circumvent the parameterization issue, and explicitly acknowledge the Euclidean or Riemannian distance measures. The details of these matrix-based averaging methods are presented and their theoretical advantages discussed. The Euclidian and Riemannian approaches offer appealing advantages over the conventional technique. With respect to practical biomechanical relevancy, examinations of simulated data suggest that for sets of orientation data possessing characteristics of low dispersion, an isotropic distribution, and less than 30 degrees second and third angle parameters, discrepancies with the conventional approach are less than 1.1 degrees . However, beyond these limits, arithmetic averaging can have substantive non-linear inaccuracies in all three parameterized angles. The biomechanics community is encouraged to recognize that limitations exist with the use of the conventional method of averaging orientations. Investigations requiring more robust spatial averaging over a broader range of orientations may benefit from the use of matrix-based Euclidean or Riemannian calculations.

publication date

  • February 2009