Instance-Based Learning Algorithms
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
IEEE Pervasive Computing
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Using acceleration signatures from everyday activities for on-body device location
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Rapid Speaker Adaptation Using Clustered Maximum-Likelihood Linear Basis With Sparse Training Data
IEEE Transactions on Audio, Speech, and Language Processing
Personal and Ubiquitous Computing
Sharing training data among different activity classes
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Activity and context recognition in pervasive and wearable computing ought to continuously adapt to changes typical of open-ended scenarios, such as changing users, sensor characteristics, user expectations, or user motor patterns due to learning or aging. System performance inherently relates to the user's perception of the system behavior. Thus, the user should be guiding the adaptation process. This should be automatic, transparent, and unconscious. We capitalize on advances in electroencephalography (EEG) signal processing that allow for error related potentials (ErrP) recognition. ErrP are emitted when a human observes an unexpected behavior in a system. We propose and evaluate a hand gesture recognition system from wearable motion sensors that adapts online by taking advantage of ErrP. Thus the gesture recognition system becomes self-aware of its performance, and can self-improve through re-occurring detection of ErrP signals. Results show that our adaptation technique can improve the accuracy of a user independent gesture recognition system by 13.9% when ErrP recognition is perfect. When ErrP recognition errors are factored in, recognition accuracy increases by 4.9%. We characterize the boundary conditions of ErrP recognition guaranteeing beneficial adaptation. The adaptive algorithms are applicable to other forms of activity recognition, and can also use explicit user feedback rather than ErrP.