Floating search methods in feature selection
Pattern Recognition Letters
Self-organizing maps
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Visual Contextual Awareness in Wearable Computing
ISWC '98 Proceedings of the 2nd IEEE International Symposium on Wearable Computers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Mobile Computing
A Comparative Analysis of Personalization Techniques for a Mobile Application
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
On-body activity recognition in a dynamic sensor network
Proceedings of the ICST 2nd international conference on Body area networks
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning situation models for providing context-aware services
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interaction
Tracking free-weight exercises
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Personalising context-aware applications
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Daily living activity recognition based on statistical feature quality group selection
Expert Systems with Applications: An International Journal
Automatic image tagging using two-layered Bayesian networks and mobile data from smart phones
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
Context Inference Engine (CiE): Inferring Context
International Journal of Advanced Pervasive and Ubiquitous Computing
A survey on smartphone-based systems for opportunistic user context recognition
ACM Computing Surveys (CSUR)
Activity recognition for creatures of habit
Personal and Ubiquitous Computing
A mobile picture tagging system using tree-structured layered Bayesian networks
Mobile Information Systems
An intelligent mobile based decision support system for retinal disease diagnosis
Decision Support Systems
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In mobile devices there exist several in-built sensor units and sources which provide data for context reasoning. More context sources can be attached via wireless network connections. Usually, the mobile devices and the context sources are battery powered and their computational and space resources are limited. This sets special requirements for the context recognition algorithms. In this paper, several classification and automatic feature selection algorithms are compared in the context recognition domain. The main goal of this study is to investigate how much advantage can be achieved by using sophisticated and complex classification methods compared with a simple method that can easily be implemented in mobile devices. The main result is that even a simple linear classification algorithm can achieve a reasonably good accuracy if the features calculated from raw data are selected in a suitable way. Usually context recognition algorithms are fitted to a particular problem instance in an off-line manner and modifying methods for on-line learning is difficult or impossible. An on-line version of the Minimum-distance classifier is presented in this paper and it is justified that it leads to considerably higher classification accuracies compared with the static off-line version of the algorithm. Moreover, we report superior performance for the Minimum-distance classifier compared to other classifiers from the view point of computational load and power consumption of a smart phone.