Abstract
Gathered kinematic data usually requires post-processing in order to handle noise. There a three different approaches frequently used: local regression & moving average algorithms, and Butterworth filters. In order to examine the most appropriate post-processing approach and its optimal settings to human upper limb movements, we examined how far the approaches were able to reproduce a simulated movement signal with overlaid noise. We overlaid a simulated movement signal (movement amplitude 80 cm) with normal distributed noise (standard deviation of 0.5 cm). The resulting signal was post-processed with local regression and moving average algorithms as well as Butterworth filters with different settings (spans/orders). The deviation from the original simulated signal in four kinematic parameters (path length, maximum velocity, relative activity, and spectral arc length) was calculated and checked for a minimum. The unprocessed noisy signal showed absolute mean deviations of 54.78% ± 12.16% in the four kinematic parameters. The local regression algorithm revealed the best performance at a span of 420 ms with an absolute mean deviation of 2.00% ± 0.86%. For spans between 280–690 ms the local regression algorithm still revealed deviations below 5%. Based on our results we suggest a local regression algorithm with a span of 420 ms for smoothing noisy kinematic data in upper limb performance, e.g., activities of daily living. This suggestion applies to kinematic data of human movements.
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Gulde, P., Hermsdörfer, J. (2018). A Comparison of Smoothing and Filtering Approaches Using Simulated Kinematic Data of Human Movements. In: Lames, M., Saupe, D., Wiemeyer, J. (eds) Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017). IACSS 2017. Advances in Intelligent Systems and Computing, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-67846-7_10
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