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

机译：建模相似性以比较体育活动资料-一种数据驱动的方法

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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.
机译：从公共卫生和计算机科学的角度来看，客观测量身体行为是一个有趣的研究领域。尽管对于公共卫生研究而言，高质量和可行设置的测量很重要，但我们将在本文中介绍对数据的分析和推理。我们在这项工作中的重点是连续几天中身体行为的全面表征，并允许在具有相似身体活动水平的人群中找到亚组。我们有4628名佩戴三轴加速度计的六天参与者的独特数据集，并将展示一个基于案例的推理（CBR）系统，该系统可以查找和比较相似的活动概况。在这项工作中，我们专注于使用myCBR创建CBR模型，并对所得系统进行初始实验。我们将介绍一种数据驱动的方法来对局部相似性度量进行建模。最终，在实验中我们将显示，对于给定的数据集，CBR系统在找到最相似的参与者方面胜过k最近邻回归器。

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