Abstract
When analysing and evaluating human motion, two mathematical problems frequently occur: the determination of derivatives (velocities and accelerations) from displacement data and the computation of Fouriercoefficients. In this paper spline functions are used to solve these problems. The noise inherent in the raw data will be reduced by smoothing them with a spline approximation. Beside the explanation of the used algorithm and its advantages, the technique is illustrated by two applications: the computation of accelerations from the displacement data used by Pezzack et al. (1977) and the calculation of the Fourierseries of knee rotation of a test person, recorded by stroboscopic light photography in our gait laboratory. The method proved to be a rapid one and to provide accurate results, even if the data are perceptibly influenced by errors.
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