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Miguel Valencia edited Aside_from_using_EMG_signals__.tex
about 8 years ago
Commit id: b3dd598322306bb0c154630c427d592ccd33d142
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In a study conducted by Yoshikawa et al. \cite{Yoshikawa_2006}, a real-time hand motion estimation was conducted by using EMG signals with SVMs. The study involved an experiment wherein a subject was required to perform seven hand motions sequentially for 10 sessions. High accuracy is maintained through nine of the sessions, one exhibiting a performance of 94.17\% accuracy.
As stated in a study by Han et al. \cite{Han_Pang_Huang}, two issues exist concerning the classification of EMG signals. One of these is selecting a feature subset with the best discrimination ability, which they were able to solve by using a supervised feature mining (SFM) method, which is an intelligent approach based on genetic algorithms, fuzzy measure, and domain knowledge on pattern recognition. Experimental results shown in their paper exhibit that the optimal EMG feature subset obtained from SFM can obtain higher classification rates compared with using all feature candidates by K-NN method. The second issue on classification of EMG signal mentioned in the study is the design of the classifier. On a review paper by
\cite{ahsan2009emg}
In a similar study of real-time muscle estimation was conducted by Lozito et al. \cite{Lozito_2015}. In this work, a real-time embedded implementation for a biomechanical model of the leg was created. The neural estimator implemented in the model allowed two configurations of the model: Precision oriented and Performance oriented. According to Hiraiwa et al. \cite{Hiraiwa}, neural networks make EMG pattern recognition much easier and more efficient. Their study was able to successfully accomplish the classification of the 5 finger movements based on the recognition of their patterns.