This paper presents an intelligent system aimed at detecting a person’s posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard–Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.
