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\section{Background of the Study} \section{The Problem and Its Background}    \subsection{Statement of the Problem} \subsection{Introduction}  With the advancement of technology and the decline of manual labor, humans try to improve their quality of life using any innovation that they can think of. Technology makes our lives easier and more efficient; in turn, efficiency implies that we can allot the saved time for other tasks. For example, the evolution of buttons to touch screen. This evolution saved precious time by introducing dynamic menus, faster input, and a lot of flexibility; however, evolution does not stop there. With the introduction of electromyography(EMG), or the technique of evaluating and recording muscle activity through either a needle(intramuscular) or electrodes on muscles(surface), another form of input was made. It's unclear if it provides a substantial increase in efficiency compared to traditional touch screens; however, having different options for certain situations are favorable. Other forms of input are: speech recognition, eye gaze trackers, and computer vision. These three other forms of input however, are unfavorable in certain situations; electromyography may be used for those situations. Surface EMG may be preferred over intramuscular EMG because it's too professional and expensive. One device that's able to conduct surface EMG is the Myo armband.  \subsection{Background of the Study}  \subsection{Theoretical Framework}  \subsection{Conceptual Framework}  \subsection{Statement of the Problem}  The Myo armband is a wearable gesture control device that has been developed for the detection of arm muscle movements through a sensor placed on the upper arm. This device has been used for various things like controlling a flying drone, gaming, and touch-free control of mobile and computer devices; however, the problem with EMG signals is the complex recognition of gestures due to large variations in the signals. This study will aim to find a solution in order to simultaneously process multiple gestures from two separate devices and to minimize the lag time of inputs.  Specifically, this study aims to answer the following questions:  \begin{enumerate}