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Another study about therapeutic games was performed by Viriyasaksathian et al. \cite{Viriyasaksathian_2011}. EMG gestures were applied to an augmented reality game for the upper-limb rehabilitation of stroke patients. The combination of music synchronization, biofeedback technology and augmented reality was employed to attract the attention of stroke patients since existing therapy methods are often boring thus results to lack of motivation.  Aside from using EMG signals as computer inputs for game control, a notable study performed by Schuuurink et al. \cite{Schuurink_2008} applied the said technology for measuring the user's engagement in the game. It has been learned that effects of sound and dynamics in serious gaming have shown a significant influence on the affective appraisal of the environment.  \subsection{Related Studies on Machine Learning in processing EMG based gestures}  Several studies related to EMG pattern recognition have been conducted over the past decades. These techniques have been used to analyze EMG signals which have been complex to recognize due to large variations in signals. In a study conducted by Liu et al. \cite{Liu_2007}, a novel EMG classifier called cascaded kernel learning machine (CKLM) was proven to be effective, achieving a high recognition rate of 93.54\%. The study employed a cascaded architecture of kernel learning machines including the General Discriminant Analysis (GDA), and the support vector machine (SVM) which offers classification performance that matches or exceeds other classifiers and does so in a computationally efficient manner \cite{Oskoei_2008}.