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- W2083303090 abstract "An understanding of muscle activities can reveal the mechanism of producing locomotion, helping to improve the performance of assistive robots in supporting the users' movement. This paper proposes a framework to recognize the human walking movement by investigating electromyography (EMG), which has not been widely approached yet. The framework is a combination of a Hidden Markov Model (HMM) and Locally Linear Embedding (LLE) technique. First, we show that using LLE algorithm we could reduce the dimensionality of the high-dimensional EMG dataset. The extracted primitive components gave a meaningful representation of the EMG. Second, we demonstrate that the HMMs trained by these components could recognize the movement intention at a high rate of accuracy." @default.
- W2083303090 created "2016-06-24" @default.
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- W2083303090 date "2014-12-01" @default.
- W2083303090 modified "2023-09-24" @default.
- W2083303090 title "Recognition of walking movement from EMG using a framework combining LLE and HMM" @default.
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- W2083303090 doi "https://doi.org/10.1109/sii.2014.7028089" @default.
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