Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Daily Mood Assessment Based on Mobile Phone Sensing
BSN '12 Proceedings of the 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Will you have a good sleep tonight?: sleep quality prediction with mobile phone
Proceedings of the 7th International Conference on Body Area Networks
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Health-compromising behaviors are difficult to change since people do not behave in accordance with their intention. This paper aims at studying the extent to which a person's healthy habit forming process can be affected by mobile phone usage. We propose a novel healthy habit forming states predicting framework using mobile phone platform. First we present a definition for the healthy habit forming process consisting of several states. We define the social intervention types and user context data which are extracted from mobile phone sensor data. Then we make use of machine learning methods to study the correlation between these data and healthy habit forming states. Specifically, a predicting model called Habits Factor Graph(HaFG) is proposed to predict the habit forming states. To evaluate our work, an Android based prototype system is implemented. Experimental results show that the healthy habit forming states are predicted possibly from user context information with a fairly good accuracy (around 67%).