Floating search methods in feature selection
Pattern Recognition Letters
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
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The Knowledge Engineering Review
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Recognizing Human Actions Using Silhouette-based HMM
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
HMM machine learning and inference for Activities of Daily Living recognition
The Journal of Supercomputing
A Control Approach Based on Passive Behavior to Enhance User Interaction
IEEE Transactions on Robotics
IEEE Transactions on Information Technology in Biomedicine
Human action learning via hidden Markov model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
MaskIt: privately releasing user context streams for personalized mobile applications
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Walk detection and step counting on unconstrained smartphones
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to performthe classification task. HiddenMarkovModel (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.