7th International Conference on Body Area Networks

Research Article

Will You Have a Good Sleep Tonight? Sleep Quality Prediction with Mobile Phone

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  • @INPROCEEDINGS{10.4108/icst.bodynets.2012.250091,
        author={yin bai and Bin Xu and Yuanchao Ma and Guodong Sun and Yu Zhao},
        title={Will You Have a Good Sleep Tonight? Sleep Quality Prediction with Mobile Phone},
        proceedings={7th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2012},
        month={11},
        keywords={mobile phone sensing sleep quality prediction factor graph model android system},
        doi={10.4108/icst.bodynets.2012.250091}
    }
    
  • yin bai
    Bin Xu
    Yuanchao Ma
    Guodong Sun
    Yu Zhao
    Year: 2012
    Will You Have a Good Sleep Tonight? Sleep Quality Prediction with Mobile Phone
    BODYNETS
    ICST
    DOI: 10.4108/icst.bodynets.2012.250091
yin bai1,*, Bin Xu2, Yuanchao Ma2, Guodong Sun3, Yu Zhao2
  • 1: Department of Computer Science and Technology, Tsinghua University
  • 2: Department of Computer Science and Technology, Tsinghua Univerisity
  • 3: Information School, Beijing Forestry University, Beijing, China
*Contact email: baiyin0429@gmail.com

Abstract

In this paper, we propose a novel sleep quality predicting framework from user context information extracted from mobile phone data. 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. We develop a prototype system called SleepMiner, which uses mobile phone data such as mobile sensor data and communication data to extract human context information. 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. Experimental result shows that it is possible to accurately infer sleep quality (around 78%) from user context information. A set of solutions are proposed to make the SleepMiner system setup suitable for daily usage with minimal impact on the phone's resources. Experiments are also carried out to evaluate our design in effectiveness and efficiency.