Quantifies-Self Data Mining

=== Funded by JSPS KAKENHI ===

Objective

This project aimed to identify significant rules from large quantified-self dataset, which can then be used to generate tailored health advice.

Method

Association rules mining; contrast set mining

Contributors

• Dr. Zilu Liang

• Ms. Nhung Hoang

• Ms. Alena Lukovnikova (exchange student in 2022 from Worcester Polytechnic Institute, USA)

• Ms. Hannah Jayne (exchange student in 2022 from Worcester Polytechnic Institute, USA)

Selected Publications

• Nhung H. H., Liang Z. (2023) Contrast set mining for actionable insights into associations between sleep and glucose in a normoglycemic population. [Scopus] 

• Liang Z. (2022). Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults. Frontiers in Medical Technology (Section: Medtech Data Analytics).  [PubMed] 

• Liang Z. (2022) Correlation analysis of nested consumer health data: a new look at an old problem. In Proceedings of the IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022), Osaka, Japan.  [SCI/Scopus] 

• Liang Z. (2022) Association rules mining on multimodal quantified-self data. In Proceedings of the International Seminar on Machine Learning, Optimization, and Data Science, Jakarta, Indonesia.  [Scopus] 

• Liang Z, Chapa-Martell MA, Nishimura T. (2016) Mining hidden dependencies between sleep and lifestyle factors from quantified-self data. In Proceedings of the New Frontiers of Quantified Self 2 Workshop (QS Frontier 2016) @ UbiComp '16. Heidelberg, Germany.  [SCI/Scopus]

Objective

This project aimed to develop algorithms for automatically detection of abnormal physical/mental/behaviroal patterns from quantified-self datasets.

Method

Time series analysis; anomaly detection

Contributors

Dr. Zilu Liang

Selected Publications

• ​Liang Z, Chapa-Martell MA, Nishimura T. (2016) A personalized approach for detecting unusual sleep from time series sleep-tracking data. In Proceedings of IEEE Int Conf on Healthcare Informatics (ICHI '16). Chicago, US.  [SCI/Scopus] 

=== Funded by Australian Government Endeavour Research Fellowship ===

Objective

This project aimed to develop computational methods and software to faciliate automatic discovery of interesting relationships in sleep-tracking data. 

Method

Correlation analysis; data visualization

Contributors

• Dr. Zilu Liang

• Prof. Bernd Ploderer (Queensland University of Technology, Australia)

• Prof. James Bailey (University of Melbourne, Australia)

• Prof. Lars Kulik (University of Melbourne, Australia)

• Dr. Wanyu Liu (IRCAM Centre Pompidou, France)

• Dr. Yuxuan Li (University of Melbourne, Australia)

Selected Publications

• Liang Z., Nhung H. H., Bertrand L., Cleyet-Marrel N. (2022) Context-aware sleep analysis with intraday steps and heart rate time series data from consumer activity trackers. In Proceedings of the 15th International Conference on Health Informatics (HEALTHINF 2022), Cyberspace. (Acceptance rate of full papers = 23%, Q1) 

• Liang Z, Ploderer B, Liu W, Nagata Y, Bailey J, Kulik L, Li Y. (2016). SleepExplorer: A visualization tool to make sense of correlations between personal sleep data and contextual factors. Personal and Ubiquitous Computing 20(6): 985-1000. (Impact factor: 3.00, Q1/Q2)  [SCI/Scopus] 

• Liang Z, Ploderer B, Chapa-Martell MA, Nishimura T.(2016). A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining. In Proc. of ISICS 2016, Merida, Mexico.   [SCI/Scopus]