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In the game industry, EMG technology has been widely used to replace physical components, such as the traditional joysticks and keyboards with something virtual. In a study conducted by Wheeler and Jorgensen \cite{Wheeler_2003}, it has been understood that there are two forms of most used gesture-recognition systems for receiving inputs. First is through image processing with an external camera as the source of input. Second is through muscle sensors such as the wearable dry-electrode sleeve device they have developed to sense EMG signal as computer inputs. These EMG electrodes work by detecting skin currents with a very low-impendence connection with the skin. It receives the currents that travel in the muscle fiber from the innervation point to the end of the muscle. This device was tested using a virtual number pad and in their case, the participant had to be extra careful and precise with each movement because of the sensitivity and difficulty of distinguishing the keys that were hit.  As mentioned above, another gesture-recognition system is image processing through an external camera. In the study performed by Rautaray et al. \cite{Rautaray_2011}, various image processing techniques were applied for hand tracking and gesture recognition in a virtual gaming environment such as Camshift, Lucas Kanade, Haar etc. The different gestures used for the game interactions were grab, punch and go.  \subsubsection{hi}  According to Jayarathne et al. \cite{Jayarathne_2015}, a surface EMG (sEMG) application that can indicate muscle efforts has been created. After developing, the muscle effort indicator application was then implemented in the Flappy Bird game. It has been demonstrated that with the use of fast Fourier transform (FFT) and several other computer algorithms that sEMG can be used to provide biofeedback in a gaming environment. Another muscle contraction research was carried out by Gao et al. \cite{Gao_2006}in which the force of the muscles was also calculated with the implementation of sEMG and several computing techniques such as black-propagation neural network, 3-D accelerometers and more for an Arm Wrestling Robot game.  Several studies have been conducted about the implementation of EMG based gestures on therapeutic games for rehabilitation purposes. An alternate interface has been developed in the study conducted by Armiger et al. \cite{Armiger_2008} for Guitar Hero® using surface EMG to train and assess the performance of upper-extremity amputees. Instead of using the guitar, EMG electrodes were used to record the myoelectric activity. After recording, the acquired data is processed in real-time using pattern recognition algorithms to classify the gestures and then use them to control the game. The scores obtained by the amputees were relatively lower than those of the non-amputees.