Analysis of accuracy in optical motion capture - A protocol for laboratory setup evaluation.

Eichelberger, Patric; Ferraro, Matteo; Minder, Ursina; Denton, Trevor; Blasimann, Angela; Krause, F; Baur, Heiner (2016). Analysis of accuracy in optical motion capture - A protocol for laboratory setup evaluation. Journal of biomechanics, 49(10), pp. 2085-2088. Elsevier 10.1016/j.jbiomech.2016.05.007

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Validity and reliability as scientific quality criteria have to be considered when using optical motion capture (OMC) for research purposes. Literature and standards recommend individual laboratory setup evaluation. However, system characteristics such as trueness, precision and uncertainty are often not addressed in scientific reports on 3D human movement analysis. One reason may be the lack of simple and practical methods for evaluating accuracy parameters of OMC. A protocol was developed for investigating the accuracy of an OMC system (Vicon, volume 5.5×1.2×2.0m(3)) with standard laboratory equipment and by means of trueness and uncertainty of marker distances. The study investigated the effects of number of cameras (6, 8 and 10), measurement height (foot, knee and hip) and movement condition (static and dynamic) on accuracy. Number of cameras, height and movement condition affected system accuracy significantly. For lower body assessment during level walking, the most favorable setting (10 cameras, foot region) revealed mean trueness and uncertainty to be -0.08 and 0.33mm, respectively. Dynamic accuracy cannot be predicted based on static error assessments. Dynamic procedures have to be used instead. The significant influence of the number of cameras and the measurement location suggests that instrumental errors should be evaluated in a laboratory- and task-specific manner. The use of standard laboratory equipment makes the proposed procedure widely applicable and it supports the setup process of OCM by simple functional error assessment. Careful system configuration and thorough measurement process control are needed to produce high-quality data.

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