MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Pervasive Computing
Privacy in mobile technology for personal healthcare
ACM Computing Surveys (CSUR)
Tapping into the Vibe of the city using VibN, a continuous sensing application for smartphones
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Proceedings of the 13th international conference on Ubiquitous computing
The social fMRI: measuring, understanding, and designing social mechanisms in the real world
Proceedings of the 13th international conference on Ubiquitous computing
A data-rich approach for investigating social mechanisms in the wild
Proceedings of the 13th international conference on Ubiquitous computing
Social fMRI: Investigating and shaping social mechanisms in the real world
Pervasive and Mobile Computing
mConverse: inferring conversation episodes from respiratory measurements collected in the field
Proceedings of the 2nd Conference on Wireless Health
Modeling the co-evolution of behaviors and social relationships using mobile phone data
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Human behavior understanding for inducing behavioral change: application perspectives
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
How many makes a crowd? on the evolution of learning as a factor of community coverage
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Putting humans in the loop: Social computing for Water Resources Management
Environmental Modelling & Software
Linking people through physical proximity in a conference
Proceedings of the 3rd international workshop on Modeling social media
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Friends don't lie: inferring personality traits from social network structure
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Mining smartphone data to classify life-facets of social relationships
Proceedings of the 2013 conference on Computer supported cooperative work
Trade-Offs in social and behavioral modeling in mobile networks
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Connecting people through physical proximity and physical resources at a conference
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Assessing contextual mood in public transport: a pilot study
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
BlueEye: a system for proximity detection using bluetooth on mobile phones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Inferring social activities with mobile sensor networks
Proceedings of the 15th ACM on International conference on multimodal interaction
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An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health.