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Identifying causal patterns from mobile sensing data: a case study on blood glucose inference

The high-dimensional and co-evolved data streams sensed by mobile devices typically exists time delays that form the "causal-and-effect" patterns. Understanding the informative causal patterns from the multivariate time series is critical but challenging for the inference tasks with the sensing data. While a large scope of statistical learning methods has undergone great advances in the causal pattern recognition problem, most of them are still limited in the unreliable causal analysis, high computational complexity and the environmental noise interruption. To this end, we propose a novel directed information (DI)-aided approach to efficiently select the casual patterns from a set of feature streams collected from mobile devices. The proposed approach has been evaluated on a real blood glucose sensing dataset. The results demonstrate our proposed approach outperforms the traditional methods in cost efficiency and inference accuracy.