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\subsection{Statement of the Problem}
With the advance of technology and the decline of manual labor, humans try to improve their quality of life using any innovation they make. 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 touch screens; however, having different options for certain situations are favorable. Because of the expensive and professional implications of intramuscular EMG, surface EMG is better suited for undergrad studies. One device that's able to conduct surface EMG is the Myo armband.
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
can be has been used for various things like controlling a flying drone, gaming, and touch-free control of mobile and computer
devices. The devices; however, the problem with EMG signals is the complex recognition of
the 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. devices and to minimize the lag time of inputs.
Specifically, this study aims to answer the following questions:
\begin{enumerate}
\item What approach can be used in order to asynchronously process the electromyographic signals from the two gesture control devices?
\item What machine learning approach is the most appropriate method of making the device adjust to the user?
\item Would the gestures be fully recognized?
\item If lag from the recognition of the signals were to exist, would there be a way to
totally completely eliminate the said problem?
\end{enumerate}
\subsection{Objectives}
\subsubsection{General Objectives}
The main objective of the study is to develop
a rhythm game prototype
and enable the which enables asynchronous and seamless gesture processing
of through two Myo armbands.
\subsubsection{Specific Objectives}
\begin{enumerate}
\item Identify the gestures that will be needed in the game and configure
the Myo
armband to detect only these gestures.
\item Use machine learning in processing gestures performed in different ways or styles.
\item Configure two armbands to work asynchronously with each other.
\end{enumerate}
...
\end{enumerate}
\subsection{Significance of the Study}
The game industry is clearly one of the fastest growing economic sectors in our world today. With this,
game the gaming experience has been continuously innovated from
the game control up to user interface techniques. Within the past few years, various
computer human-computer interfaces have been developed for games and one of these is electromyography.
This study will be of great significance in the
game gaming industry because with the enhanced recognition of gestures as the system adjusts itself to the person, it will be much
more convenient
to for the users to play the game.
Furthermore, this study will benefit the following:
\begin{itemize}
\item Game developers – this would urge more game developers to develop new games in the said technology.
\item Doctors and Physicians – the prototype can be used for rehabilitation purposes such as recovering from stroke and other similar diseases.
\item Trainers - the prototype can help with reaction time which may improve the performance of athletes.
\item Music teachers – the prototype could help them teach music to their students in a very different way.
\item Future researchers - the study can be of help to future researchers that would like to conduct a study related to the application of machine learning in muscle-based gesture recognition.
\end{itemize}
