Smart device-based context and activity recognition

Updated March 2015
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validated by Lancaster University), or PhD degree at Sunway University, investigating the below research topic? Email me at (replace -at- with @). 

With the rise of new technologies, particularly mobile devices, wearables and almost-ubiquitous connectivity, we are now swamp with exciting techniques that aim to help improve and automate many tasks in our everyday living. One particular area that looks into ways to enable machines to understand situations, needs and human better is context-awareness. Since the introduction of Ubiquitous Computing (or Pervasive Computing), researchers have been investigating various approaches that put computers into the background. As Mark Weiser proposed, the best technologies are those who stays in the background.

This is where acquisition of implicit information may contribute well - imagine the different devices around us, may it be computers in a room, or the smartphone you are carrying to even little sensors placed "everywhere" - they provide all sort of information in almost real time. By applying some clever mechanism, or what we technically call algorithms, we build systems that will make sense out of these seemingly "senseless" bulks of information. This is where context acquisition can play an important role. Starting from a few years back, this field of study has a new sexy name - big data. Though big data covers a much larger scope, but the gist of the approaches is still the same - how to make sense out of no(n)-sense.

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