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Advisor(s)
Abstract(s)
Using the AdaBoost method may increase the accuracy and reliability of a framework for
daily activities and environment recognition. Mobile devices have several types of sensors, including
motion, magnetic, and location sensors, that allow accurate identification of daily activities and
environment. This paper focuses on the review of the studies that use the AdaBoost method with the
sensors available in mobile devices. This research identified the research works written in English
about the recognition of daily activities and environment recognition using the AdaBoost method
with the data obtained from the sensors available in mobile devices that were published between 2012
and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched
databases. The results proved the reliability of the method for daily activities and environment
recognition, highlighting the use of several features, including the mean, standard deviation, pitch,
roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the
signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than
80% in recognition of daily activities and environments with the Adaboost method.
Description
Keywords
Daily activities recognition Ensemble learning Ensemble classifiers Environments Mobile devices Sensors Systematic review