Applying electric field sensing to human-computer interfaces
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine learning and data mining
Communications of the ACM
Radial Basis Functions
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Cognitive Technical Systems -- What Is the Role of Artificial Intelligence?
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Capacitive sensor-based hand gesture recognition in ambient intelligence scenarios
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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In Ambient Assisted Living (AAL), the context-dependent adaption of a system to a person's needs is of particular interest. In the living area, a fine-grained context may not only contain information about the occupancy of certain furniture, but also the posture of a user on the occupied furniture. This information is useful in the application area of home automation, where, for example, a lying user may effect a different system reaction than a sitting user. In this paper, we present an approach for determining contextual information from furniture, using capacitive proximity sensors. Moreover, we evaluate the performance of Naïve Bayes classifiers, decision trees and radial basis function networks, regarding the classification of user postures. Therefore, we use our generic classification framework to visualize, train and evaluate postures with up to two persons on a couch. Based on a data set collected from multiple users, we show that this approach is robust and suitable for real-time classification.