Tom Lovett and Eamonn O’NeillDepartment of Computer Science

University of BathBath BA2 7AY

UK

eamonn@cs.bath.ac.uk

+44 (0)1225 383216

Social sensing: context transitions

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?

• eamonn@cs.bath.ac.uk

• http://www.cs.bath.ac.uk/pervasive

Thank you