Adaptive Class Association Rule Mining for Human Activity Recognition

The analysis of human activity data is an important research area in the context of ubiquitous and social environments. Using sensor data obtained by mobile devices, e. g., utilizing accelerometer sensors contained in mobile phones, behavioral patterns and models can then be obtained. However, the utilized models are often not simple to interpret by humans in order to facilitate assessment, evaluation and validation, e. g., in computational social science or in medical contexts. In this paper, we propose a novel approach for generating interpretable rule sets for classi cation: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze di erent techniques for obtaining the nal classi er. The approach is investigated in the context of human activity recognition. For our evaluation, we apply real-world activity data collected using mobile phone sensors.