Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Just-for-us: a context-aware mobile information system facilitating sociality
Proceedings of the 7th international conference on Human computer interaction with mobile devices & services
Design requirements for technologies that encourage physical activity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Ageing in a networked society: social inclusion and mental stimulation
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
E-Shadow: Lubricating Social Interaction Using Mobile Phones
ICDCS '11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems
"Read" More from Business Cards: Toward a Smart Social Contact Management System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Hybrid SN: Interlinking Opportunistic and Online Communities to Augment Information Dissemination
UIC-ATC '12 Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing
Sensing the "Health State" of a Community
IEEE Pervasive Computing
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With lots of sensors built in, mobile phones become a pervasive platform for seamlessly sensing of human behaviors. In this paper, we investigate how to use location data and communication records collected from mobile phones to obtain human social interaction features and activity patterns. Social Interaction features refer to the temporal and spatial interactive information, and activity patterns include movement patterns. Meanwhile, the similarities and differences of human behaviors at different ages, as well as distinct occupations are analyzed. The results indicate that different population has a diversity of social interaction and activity patterns, and human social behaviors are highly associated with age and occupation. Furthermore, we make a correlation analysis about social temporal interaction, social spatial interaction and social activity, which lead us to conclude that the three elements are interrelated among young people but not middle-ages. Our work could be a cornerstone for research of personalized psychological health assistance based on mobile phone data.