© 2019

Human Activity Sensing

Corpus and Applications

  • Nobuo Kawaguchi
  • Nobuhiko Nishio
  • Daniel Roggen
  • Sozo Inoue
  • Susanna Pirttikangas
  • Kristof Van Laerhoven

Part of the Springer Series in Adaptive Environments book series (SPSADENV)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Modalities and Applications

    1. Front Matter
      Pages 1-1
    2. Philipp M. Scholl, Kristof Van Laerhoven
      Pages 17-28
    3. Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, Nobuo Kawaguchi
      Pages 29-46
    4. Akhil Mathur, Anton Isopoussu, Fahim Kawsar, Robert Smith, Nadia Berthouze, Nicholas D. Lane
      Pages 47-57
    5. Shohei Harada, Kazuya Murao, Masahiro Mochizuki, Nobuhiko Nishio
      Pages 59-68
  3. Data Collection and Corpus Construction

    1. Front Matter
      Pages 69-69
    2. Mathias Ciliberto, Lin Wang, Daniel Roggen, Ruediger Zillmer
      Pages 71-89
    3. Naomi Johnson, Michael Jones, Kevin Seppi, Lawrence Thatcher
      Pages 91-110
    4. Philipp M. Scholl, Benjamin Völker, Bernd Becker, Kristof Van Laerhoven
      Pages 111-119
  4. SHL: An Activity Recognition Challenge

    1. Front Matter
      Pages 151-152
    2. Lin Wang, Hristijan Gjoreski, Mathias Ciliberto, Sami Mekki, Stefan Valentin, Daniel Roggen
      Pages 153-170
    3. Peter Widhalm, Maximilian Leodolter, Norbert Brändle
      Pages 197-211
    4. Vito Janko, Martin Gjoreski, Gašper Slapničar, Miha Mlakar, Nina Reščič, Jani Bizjak et al.
      Pages 233-250

About this book


Activity recognition has emerged as a challenging and high-impact research field, as over the past years smaller and more powerful sensors have been introduced in wide-spread consumer devices. Validation of techniques and algorithms requires large-scale human activity corpuses and improved methods to recognize activities and the contexts in which they occur. 

This book deals with the challenges of designing valid and reproducible experiments, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating activity recognition systems in the real world with real users.


Activity Recognition Benchmark Datasets Wearable Computing Wearable Sensing Inertial Measurement

Editors and affiliations

  • Nobuo Kawaguchi
    • 1
  • Nobuhiko Nishio
    • 2
  • Daniel Roggen
    • 3
  • Sozo Inoue
    • 4
  • Susanna Pirttikangas
    • 5
  • Kristof Van Laerhoven
    • 6
  1. 1.Institute of Innovation for Future SocietyNagoya UniversityNagoyaJapan
  2. 2.Department of Computer ScienceRitsumeikan UniversityKyotoJapan
  3. 3.University of SussexBrightonUK
  4. 4.Kyushu Institute of TechnologyKitakyushuJapan
  5. 5.Center for Ubiquitous ComputingUniversity of OuluOuluFinland
  6. 6.Ubiquitous ComputingUniversity of SiegenSiegenGermany

Bibliographic information