Machine Learning Based Technique for Detecting Daily Routine and Deviations

Abstract

This paper presents a technique for detecting the routine of the daily activities of a person and the deviations from this. The technique proposed has three main steps. The first step consists in identifying the daily living activities performed by a person by using two machine learning algorithms, one based on Decisions Trees and the other based on Random Forests. The second step consists in recognizing activity patterns corresponding to a daily routine by using the FP-Growth algorithm, while the third step computes the deviation from the daily activity routine of the person. The system proposed has been tested on the DaLiAc data set, which contains data collected from human subjects by using sensors based on accelerometers and gyroscopes.