Approximate Ensemble Methods for Physical Activity Recognition Applications

Abstract

The main interest of this thesis is on computational methodologies able to reduce the degree of complexity of learning algorithms and its application to physical activity recognition. Random Projections are used to reduce the learning complexity in Multiple Classier Systems. A new boosting algorithm and a new one-class classication methodology are proposed. In both cases, random projections are used for reducing the dimensionality of the problem and for generating diversity, exploiting in this way the benefits that ensemble learning provides in terms of performances and stability. The practical focus of the thesis is on physical activity recongition using wearable sensors. A new hardware platform for wearable computing application has been developed and used for gathering activity data. Based on the classication methodologies developed and the study conducted on physical activity classication, a machine learning architecture capable to provide a continuous authentication mechanism for mobile-devices users has been designed. The system, based on a personalized classifier, states on the analysis of the characteristic gait patterns typical of each individual ensuring an unobtrusive and continuous authentication mechanism.