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

Published: 09 September 2019 Publication History

Abstract

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.

References

[1]
Adrian Akbar, Abdullah Khan, Francois Carrez, and Klaus Moessner. 2017. Predictive analytics for complex IoT data streams. IEEE Internet of Things Journal 4, 5 (2017), 1571--1582.
[2]
Qianjin Du, Weixi Gu, Lin Zhang, and Shao-Lun Huang. 2018. Attention-based LSTM-CNNs For Time-series Classification. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 410--411.
[3]
Weixi Gu, Yunxin Liu, Yuxun Zhou, Zimu Zhou, Costas J Spanos, and Lin Zhang. 2017. BikeSafe: bicycle behavior monitoring via smartphones. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 45--48.
[4]
Weixi Gu, Longfei Shangguan, Zheng Yang, and Yunhao Liu. 2015. Sleep hunter: Towards fine grained sleep stage tracking with smartphones. IEEE Transactions on Mobile Computing 15, 6 (2015), 1514--1527.
[5]
Weixi Gu, Zheng Yang, Longfei Shangguan, Xiaoyu Ji, and Yiyang Zhao. 2014. Toauth: Towards automatic near field authentication for smartphones. In 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications. IEEE, 229--236.
[6]
Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang. Costas J Spanos, and Lin Zhang. 2017. SugarMate: Non-intrusive Blood Glucose Monitoring with Smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017). 54.
[7]
Weixi Gu, Zimu Zhou, Yuxun Zhou, Miao He, Han Zou, and Lin Zhang. 2017. Predicting Blood Glucose Dynamics with Multi-time-series Deep Learning. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. ACM, 55.
[8]
Boris P Kovatchev, William L Clarke, Marc Breton, Kenneth Brayman, and Anthony McCall. 2005. Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. Diabetes technology & therapeutics 7, 6 (2005), 849--862.
[9]
Judea Pearl. 2003. Causality: models, reasoning, and inference. Econometric Theory 19, 675--685 (2003), 46.
[10]
Kevin Plis, Razvan Bunescu, Cindy Marling, Jay Shubrook, and Frank Schwartz. 2014. A machine learning approach to predicting blood glucose levels for diabetes management. In Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence.

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  • (2020)Relay strategy in online mobile gamesAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414595(605-615)Online publication date: 10-Sep-2020

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      cover image ACM Conferences
      UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
      September 2019
      1234 pages
      ISBN:9781450368698
      DOI:10.1145/3341162
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 09 September 2019

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      Author Tags

      1. blood glucose inference
      2. causal pattern mining
      3. multi-variate sensing data stream

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      • (2020)Relay strategy in online mobile gamesAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414595(605-615)Online publication date: 10-Sep-2020

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