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The model produced was able to know which activity type the given feature sets and sequences would lead to. Additionally, the specific kinds of hand gesture acquired from the data collection were used as labels to help the model classify the given activity types. \\    The modeling approach did not need a specialized machine learning tool for the kind of technology given in the study since it was already present in the Kinect sensor, thus gestures would be easily recognized. The Kinect sensor was used as a classifier for the skeletal tracking and hand events like grip and grip release, hand stabilization, and gesture recognition. The skeletal tracking found the user's hand. A window of NxN pixels around each hand was then extracted. Machine learning executes and generates all the hand events. Taking into consideration the research presented by the study studies  conducted by \cite{zhang2012microsoft} \cite{zhang2012microsoft},  \cite{Gonz_lez_Ortega_2014} and  \cite{yeung2014evaluation}, this process arrived with a standard basis for every hand data as a result of the training and was used in the implementation stage of the study.