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Abstract-Activity classification have been used in different fields such as energy expenditure measurement or health monitoring. Many combinations of different sensors and machine learning techniques have been proposed in order to do this kind of classification. The aim of this paper is to introduce an activity classification approach for Climbing/Descending stairs detection divided in two phases. In the first phase the signals from accelerometer and gyroscope are filtered, then implementing step detection allows us to extract the relevant features from these signals. The second phase consists of a principal component analysis (PCA) for reducing dimensionality, and a support vector machines (SVM) classifier to identify the motion. Using this methodology, an accuracy of 98.76% is achieved. The data used for classification were taken from an inertial measurement unit carried by three users in their ankles, which was provided by a database from the UCI machine learning repository.

This paper introduces an Automatic Target Recognition (ATR) method based on X Band Radar image processing. A software which implements this method was developed following four principal stages: digital image formation, image preprocessing, feature selection through a combination of C4.5 Decision Tree and PCA and classification using SVM. The automatic process was validated using two images sets, one of them containing real images with natural noise levels and the other with different degrees of impulsive noise contamination. The method achieves a very nice computation behavior and effectiveness, high accuracy and robustness in noise environments with a low storage memory and high decision speed.