Abstract

Ubiquitous, context-aware computer systems may ultimately enable computer applications that naturally and usefully respond to a user's everyday activity. Although new algorithms that can automatically detect context from wearable and environmental sensor systems show promise, many of the most flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training data with labeled examples. In this paper, we describe the need for such training data and some challenges we have identified when trying to collect it while testing three contextdetection systems for ubiquitous computing and mobile applications. Author Keywords Context-aware, ubiquitous, computing, supervised learning, experience sampling, user interface design ACM Classification Keywords H5.m Information interfaces and presentation (e.g. HCI): Miscellaneous.

Citations