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PKDD
2015
Springer

Adaptive Class Association Rule Mining for Human Activity Recognition

3 years 11 months ago
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 classication: We present an adaptive framework for mining class association rules using subgroup discovery, and analyze dierent techniques for obtaining the nal classier. 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.
Martin Atzmueller, Mark Kibanov, Naveed Hayat, Mat
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Martin Atzmueller, Mark Kibanov, Naveed Hayat, Matthias Trojahn, Dennis Kroll
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