deletions | additions       diff --git a/Aside_from_using_EMG_signals__.tex b/Aside_from_using_EMG_signals__.tex  index 4453a8e..8bde710 100644  --- a/Aside_from_using_EMG_signals__.tex  +++ b/Aside_from_using_EMG_signals__.tex 
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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.  A notable method in decoding individual flexion and extension movements of each finger has been presented by Tenore et al. \cite{Tenore_2009}. The study was able to decode the finger movements performed by a transradial amputee with greater then 90\% accuracy. This can be achieved by using a neural network classifier trained on a waveform length feature. Further decoding accuracy from a transradial amputee and able-bodied subjects show no statistically significant difference.