Addressing Human Behavior Inference via Pervasive Sensing Platforms (original) (raw)
—Mobile sensing has been gaining ground due to the increasing capabilities of mobile, personal devices that are carried around by citizens, giving access to a large variety of data and services, all based on the way humans interact. Nevertheless, pervasive platforms used to capture and to infer human interaction are still designed in a simplistic way, aspect which prevents them to adequately scale. This paper contributes by bringing awareness into the challenges faced by mobile sensing platforms that capture and perform learning based on human interaction; how current solutions perform activity recognition, which classification models they consider, and which type of behavior inference can be seamlessly provided as of today. The paper contributes to raise awareness to current challenges faced by these platforms, and provides a set of guidelines towards a better functional design.
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