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- W1569268180 abstract "Pattern recognition is a key element of myoelectrically controlled prostheses. Improvements in classification accuracy have been achieved using various feature extraction and classification methodologies. In this paper, it is demonstrated that using a simple and direct approach can achieve high classification accuracy, while maintaining a low computational load; important characteristics for a real-time embedded system. An average classification accuracy of 94.06% was achieved for a six class problem, using a single mixture Gaussian mixture model, along with majority vote post-processing." @default.
- W1569268180 created "2016-06-24" @default.
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- W1569268180 date "2004-06-21" @default.
- W1569268180 modified "2023-10-16" @default.
- W1569268180 title "Continuous classification of myoelectric signals for powered prostheses using gaussian mixture models" @default.
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- W1569268180 doi "https://doi.org/10.1109/iembs.2003.1280510" @default.
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