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- W2294889774 abstract "A solution for the automated recognition of six full body motion activities is proposed. This problem is posed by the release of the Activity Recognition database [1] and forms the basis for a classification competition at the European Symposium on Artificial Neural Networks 2013. The data-set consists of motion characteristics of thirty subjects captured using a single device delivering accelerometric and gyroscopic data. Included in the released data-set are 561 processed features in both the time and frequency domains. The proposed recognition framework consists of an ensemble of linear support vector machines each trained to discriminate a single motion activity against another single activity. A majority voting rule is used to determine the final outcome. For comparison, a six winner take all multiclass support vector machine ensemble and k-Nearest Neighbour models were also implemented. Results show that the system accuracy for the one versus one ensemble is 96.4% for the competition test set. Similarly, the multiclass SVM ensemble and k-Nearest Neighbour returned accuracies of 93.7% and 90.6% respectively. The outcomes of the one versus one method were submitted to the competition resulting in the winning solution." @default.
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- W2294889774 date "2013-11-11" @default.
- W2294889774 modified "2023-09-26" @default.
- W2294889774 title "A One-Vs-One classifier ensemble with majority voting for activity recognition" @default.
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