Creating and benchmarking a new dataset for physical activity monitoring

  • Authors:
  • Attila Reiss;Didier Stricker

  • Affiliations:
  • German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany;German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

  • Venue:
  • Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
  • Year:
  • 2012

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Abstract

Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 different activities --- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes some challenges, defined by e.g. a high number of activities and personalization.