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Multi-sensor Acceleration-Based Action Recognition

  • Florian Baumann
  • Irina Schulz
  • Bodo Rosenhahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8815)

Abstract

In this paper, a framework to recognize human actions from acceleration data is proposed. An important step for an accurate recognition is the pre-processing of input data and the following classification by the machine learning algorithm. In this paper, we suggest to combine Dynamic Time Warping (DTW) with Random Forest. The intention of using DTW is to pre-process the data to eliminate outliers and to align the time series. Many applications require more than one inertial sensor for an accurate prediction of actions. In this paper, nine inertial sensors are deployed to ensure an accurate recognition of actions. Further, sensor fusion approaches are introduced and the most promising strategy is shown. The proposed framework is evaluated on a self-recorded dataset consisting of six human actions. Each action was performed three times by 20 subjects. The dataset is publicly available for download.

Keywords

Majority Vote Dynamic Time Warping Inertial Sensor Sensor Fusion Acceleration Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institut für Informationsverarbeitung (TNT)Leibniz Universität HannoverHannoverGermany
  2. 2.Institute for Systems Engineering (RTS)Leibniz Universität HannoverHannoverGermany

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