Feature Learning for Activity Recognition in Ubiquitous Computing

Plötz, Thomas; Hammerla, Nils Y.; Olivier, Patrick

doi:10.5591/978-1-57735-516-8/IJCAI11-290

Feature Learning for Activity Recognition in Ubiquitous Computing

Thomas Plötz, Nils Y. Hammerla, Patrick Olivier

Download BibTeX Cite

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1729-1733.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-290

Abstract

Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.

Thomas Plötz, Nils Y. Hammerla, and Patrick Olivier. 2011. Feature Learning for Activity Recognition in Ubiquitous Computing. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1729–1733. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-290

@inproceedings{ijcai2011p290,
  title     = {Feature Learning for Activity Recognition in Ubiquitous Computing},
  author    = {Plötz, Thomas and Hammerla, Nils Y. and Olivier, Patrick},
  booktitle = {Proceedings of the Twenty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-11}},
  publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
  editor    = {Toby Walsh},
  pages     = {1729--1733},
  year      = {2011},
  month     = {7},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-290},
  url       = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-290},
}

TY  - CPAPER
TI  - Feature Learning for Activity Recognition in Ubiquitous Computing
AU  - Plötz, Thomas
AU  - Hammerla, Nils Y.
AU  - Olivier, Patrick
T2  - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11
PB  - AAAI Press/International Joint Conferences on Artificial Intelligence
PY  - 2011
SP  - 1729
EP  - 1733
DO  - 10.5591/978-1-57735-516-8/IJCAI11-290
UR  - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-290
ER  -