An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition

Authors Pattreeya Tanisaro, Gunther Heidemann

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Pattreeya Tanisaro
  • Institute of Cognitive Science, University of Osnabrück, Germany
Gunther Heidemann
  • Institute of Cognitive Science, University of Osnabrück, Germany

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Pattreeya Tanisaro and Gunther Heidemann. An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition. In 25th International Symposium on Temporal Representation and Reasoning (TIME 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 120, pp. 21:1-21:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short-Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to speech recognition and language modeling. Despite this, amongst current researches, there are limited studies on the deep RNNs architectures and their effects being applied to other application domains. In this paper, we evaluated the different strategies available to construct bidirectional recurrent neural networks (BRNNs) applying Gated Recurrent Units (GRUs), as well as investigating a reservoir computing RNNs, i.e., Echo state networks (ESN) and a few other conventional machine learning techniques for skeleton-based human motion recognition. The evaluation of tasks focuses on the generalization of different approaches by employing arbitrary untrained viewpoints, combined together with previously unseen subjects. Moreover, we extended the test by lowering the subsampling frame rates to examine the robustness of the algorithms being employed against the varying of movement speed.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Time series analysis
  • Recurrent Neural Networks
  • Human Motion Classification
  • Echo State Networks
  • Motion Capture
  • Bidirectional Recurrent Neural Networks


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