Adaptive online sensing of spatio-temporal social graphs through wearable sensors

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About Adaptive online sensing of spatio-temporal social graphs through wearable sensors


Description

Wearable sensing and computing devices can be used to overcome the limitations of self-reported data in the study of complex social systems. Automatically collected fine-grained proximity data can be used to investigate social behavior, e.g. physical social networks, face-to-face interactions and crowd dynamics, through quantitative modelling.

In this project we will investigate the usage of wearable sensors to track physical proximity in real-time, through an ad hoc wireless network. Current techniques collect the data during the event, to later analyse it offline after the event, relaying on a global view over the data.

We will devise an algorithm that is able to adapt to the environmental conditions and utilises only local information available to each device.