Time Series Analysis for Long Term Prediction of Human Movement Trajectories

This paper's intention is to adapt prediction algorithms well known in the field of time series analysis to problems being faced in the field of mobile robotics and Human-Robot-Interaction (HRI). The idea is to predict movement data by understanding it as time series. The prediction takes place with a black box model, which means that no further knowledge on motion dynamics is used then the past of the trajectory itself. This means, the suggested approaches are able to adapt to different situations. Several state-of-the-art algorithms such as Local Modeling, Cluster Weighted Modeling, Echo State Networks and Autoregressive Models are evaluated and compared. For experiments, real movement trajectories of a human are used. Since mobile robots highly depend on real-time application, computing time is also considered. Experiments show that Echo State Networks and Local Model show impressive results for long term motion prediction.

• Type: Conference
• Year: 2008
• Where: ICONIP
• Authors: Sven Hellbach, Julian Eggert, Edgar Körner, Horst-Michael Gross
• Keywords: Echo State Networks, ICONIP 2008, Information Technology, Prediction Takes Place, Time Series