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Sensors (Basel). 2020 Feb 12;20(4). pii: E976. doi: 10.3390/s20040976.

Unsupervised and Generic Short-Term Anticipation of Human Body Motions.

Author information

1
Visual Computing Department, University of Bonn Germany, 53115 Bonn, Germany.
2
HdM Stuttgart, 70569 Stuttgart, Germany.
3
University of Stuttgart, 70569 Stuttgart, Germany.

Abstract

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable to or even better for very short anticipation times ( < 0 . 4 sec) than a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of previous states and delays. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence it is of a generic nature.

KEYWORDS:

delay coordinates; dynamic mode decomposition; human motion anticipation; short-time future prediction

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