Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
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IEEE Transactions on Neural Networks
Activity Recognition from Accelerometer Data on a Mobile Phone
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
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Tracking system based on accelerometry for users with restricted physical activity
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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Improving the classification accuracy of streaming data using SAX similarity features
Pattern Recognition Letters
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Pervasive and Mobile Computing
Recognition of coupling-paired activities in daily life
Proceedings of the 2012 Joint International Conference on Human-Centered Computer Environments
Survey on classifying human actions through visual sensors
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A feature selection-based framework for human activity recognition using wearable multimodal sensors
Proceedings of the 6th International Conference on Body Area Networks
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ACM Transactions on Computer-Human Interaction (TOCHI)
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ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
The Journal of Supercomputing
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This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy of our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme.