Increasing availability of sensor-based location traces for individuals, combined with the goal of better understanding user context, has resulted in a recent emphasis on algorithms for automatically extracting users' significant places from location data. Place-finding can be characterized by two sub-problems, (1) finding significant locations, and (2) assigning semantic labels to those locations (the problem of "moving from location to place") [8]. Existing algorithms focus on the first sub-problem and on finding city-level locations. We use a principled approach in adapting Gaussian Mixture Models (GMMs) to provide a first solution for finding significant places within the home, based on the first set of long-term, precise location data collected from several homes. We also present a novel metric for quantifying the similarity between places, which has the potential to assign semantic labels to places by comparing them to a library of known places. We discuss several implications of these new techniques for the design of Ubicomp systems. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Aipperspach, R., Rattenbury, T., Woodruff, A., & Canny, J. (2006). A quantitative method for revealing and comparing places in the home. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4206 LNCS, pp. 1–18). Springer Verlag. https://doi.org/10.1007/11853565_1
Mendeley helps you to discover research relevant for your work.