A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition

Well-documented, large and diverse datasets are important to facilitate supervised and transferable deep learning methods for human activity recognition. The past years have seen the machine learning community strive to achieve best practices in dataset creation for various applications. This contribution is the first to provide a comprehensive tutorial on data creation for sensor-based human activity recognition with firsthand instructions. We introduce the dataset creation process consisting of three phases as a guideline for the community. Each phase explains common challenges researchers may face and suggest solutions. A case study demonstrates how the framework can be used by researchers for holistically planning and critically reflecting their approach of dataset creation.

  • Veröffentlicht in:
    International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events
  • Typ:
    Inproceedings
  • Autoren:
    Reining, Christopher; Nair, Nilah Ravi; Niemann, Friedrich; Rueda, Fernando Moya; Fink, Gernot A.
  • Jahr:
    2023

Informationen zur Zitierung

Reining, Christopher; Nair, Nilah Ravi; Niemann, Friedrich; Rueda, Fernando Moya; Fink, Gernot A.: A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition, International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, 2023, https://ieeexplore.ieee.org/document/10150401, Reining.etal.2023a,