Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Resolving uncertainty in context integration and abstraction: context integration and abstraction
Proceedings of the 5th international conference on Pervasive services
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Using situation lattices in sensor analysis
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
A top-level ontology for smart environments
Pervasive and Mobile Computing
Exploring semantics in activity recognition using context lattices
Journal of Ambient Intelligence and Smart Environments
| Hi-index | 0.00 |
Most research in the area of smart environments focuses on improving the accuracy with which human activities can be recognised. Relatively little research has been done into how designers can gain insights into the behaviours their systems are observing, and feed these insights back into improving systems design. We describe a mathematical structure, the situation lattice, and show how it can be used to discover knowledge about activities and the way in which they can be sensed. We show how this knowledge can be used to improve activity recognition, using the example of a real-world smart home data set.