Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach

Abstract

Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup is important, the analysis of and reasoning about the data is what we will present in this work. Our focus in this work is the comprehensive representation of physical behaviour throughout consecutive days and allowing to find subgroups in the population with similar physical activity levels. We have a unique data set of 4628 participants wearing tri-axial accelerometers for six days and will present a case-based reasoning (CBR) system that can find and compare similar activity profiles. In this work, we focus on creating a CBR model using myCBR and do initial experiments with the resulting system. We will introduce a data-driven approach for modelling local similarity measures. Eventually, in the experiments we will show that for the given data set, the CBR system outperforms a k-Nearest Neighbor regressor in finding most similar participants.