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GroupUs: Smartphone Proximity Data and Human Interaction Type Mining

66 citations0/0 files storedInternational Symposium on Wearable Computers

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Human Mobility and Location-Based Analysis Context-Aware Activity Recognition Systems Opportunistic and Delay-Tolerant Networks

Abstract

There is an increasing interest in analyzing social interaction from mobile sensor data, and smart phones are rapidly becoming the most attractive sensing option. We propose a new probabilistic relational model to analyze long-term dynamic social networks created by physical proximity of people. Our model can infer different interaction types from the network, revealing the participants of a given group interaction, and discovering a variety of social contexts. Our analysis is conducted on Bluetooth data sensed with smart phones for over one year on the life of 40 individuals related by professional or personal links. We objectively validate our model by studying its predictive performance, showing a significant advantage over a recently proposed model.

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2005Reality mining: sensing complex social systems 2009Inferring friendship network structure by using mobile phone data 2003Using GPS to learn significant locations and predict movement across multiple users 2010Towards rich mobile phone datasets: Lausanne data collection campaign 2002Social net 2008What did you do today?

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