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In this paper we present a context-sensing component that recognizes
meetings in a typical office environment. Our prototype detects the
meeting start and end by combining outputs from pressure and motion
sensors installed on the chairs. We developed a telephone controller
application that transfers incoming calls to voice-mail when the user
is in a meeting. Our experiments show that it is feasible to detect
high-level context changes with ``good enough'' accuracy, using
low-cost, off-the-shelf hardware, and simple algorithms without
complex training. We also note the need for better metrics to measure
context detection performance, other than just accuracy. We propose
several metrics appropriate for our application in this paper. It may
be useful, however, for the community to define a set of general
metrics as a basis to compare different approaches of context
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
Or copy and paste:
Jue Wang, Guanling Chen, and David Kotz, "A meeting detector and its applications." Dartmouth Computer Science Technical Report TR2004-486, March 2004.
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