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The delivery of Ambient Assisted Living services, specifically relating to the smart-home paradigm, assumes that people can be provided with help, automatically and in real time, in their homes as and when required. Nevertheless, the deployment of a smart-home can lead to high levels of expense due to configuration requirements of multiple sensing and actuating technology. In addition, the vast amount of data produced leads to increased levels of computational complexity when trying to ascertain the underlying behavior of the inhabitant. This contribution presents a methodology based on feature selection which aims to reduce the number of sensors required whilst still maintaining acceptable levels of activity recognition performance. To do so, a smart-home dataset has been utilized, obtaining a configuration of sensors with the half sensors with respect to the original configuration.