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Social sensing: context transitions. Tom Lovett and Eamonn O ’ Neill Department of Computer Science University of Bath Bath BA2 7AY UK eamonn@cs.bath.ac.uk +44 (0)1225 383216. Social sensing: context transitions.
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Social sensing: context transitions Tom Lovett and Eamonn O’Neill Department of Computer Science University of Bath Bath BA2 7AY UK eamonn@cs.bath.ac.uk +44 (0)1225 383216
Social sensing: context transitions • What? Detecting the occurrence of a context change, e.g. location or activity change • Why? Improving user self-reporting tools, notification delivery, bootstrapping context-aware systems • How? Inferring context from motion sensing on a mobile device
Context transitions: challenges • What constitutes a ‘significant’ transition? • What are the limitations of a mobile device? • Power can limit sensor sample frequency • CPU (and power) can limit ‘online’ local processing • What sensors/sensor combinations are good indicators of a transition? • Can we detect transitions without expensive processing?
Context transitions: benefits • User self-reporting tools • Improve on current systems that use ‘random beeping’ or rely on user remembering to report • Bootstrapping • Lightweight detection can trigger context dependent processes • Context driven notifications and services • Beyond a research tool
Context transitions: how • Mobile device motion sensor fusion (beyond the accelerometer) • Binary yes/no – has a transition occurred? Not what has occurred • Tuning parameters, e.g. sensor weightings, to capture significant transitions and ignore the insignificant • Tradeoffs: power vs accuracy; spam vs information loss
Issues • The challenge of “social context” • e.g. several meetings in the same place (same activity, same location, different social context) • Are virtual sensors better social sensors? • e.g. users calendars, social networks • How may we legitimately sense social data in a privacy conscious world?
Thank you • eamonn@cs.bath.ac.uk • http://www.cs.bath.ac.uk/pervasive