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WH '10 Wireless Health 2010
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BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
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ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Demo: AD-Sense: activity-driven sensing for mobile devices
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Proceedings of the 6th International Conference on Body Area Networks
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
iSleep: unobtrusive sleep quality monitoring using smartphones
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Can you form healthy habit?: predicting habit forming states through mobile phone
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Understanding the relationship between sleep and daily life can provide insights into a healthy life style since the sleep quality is one of the most important indicators of people's health status. This paper studies the extent to which a person's sleep quality can be predicted by his/her daily context information. A combination of the machine learning technology and medical knowledge is used to study the relation between context and sleep quality, so that sleep quality can be predicted in real time according to the relation. We propose a novel sleep quality predicting framework from user context data, without requiring users to wear special devices. We develop a data collecting and analyzing prototype system called SleepMiner, which uses on-phone data such as mobile sensor data and communication data to extract human contexts. Then the relationship between context data and sleep quality is analyzed and a learning model based on factor graph model is proposed to predict sleep quality. From experimental results we demonstrate that it is possible to accurately infer sleep quality (around 78%) from user context information. A set of solutions are proposed to address the practical problems of Android phone in data collection, making SleepMiner work with minimal impact on the phone's resources. We finally carry out experiments to evaluate our design in effectiveness and efficiency.