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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.  A 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. 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.