Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing
Author(s)
Thomaz, Edison
Bettadapura, Vinay
Reyes, Gabriel
Sandesh, Megha
Schindler, Grant
Plötz, Thomas
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Abstract
Activity recognition in the home has been long recognized as
the foundation for many desirable applications in fields such
as home automation, sustainability, and healthcare. However,
building a practical home activity monitoring system
remains a challenge. Striking a balance between cost, privacy,
ease of installation and scalability continues to be an
elusive goal. In this paper, we explore infrastructure-mediated
sensing combined with a vector space model learning approach
as the basis of an activity recognition system for the
home. We examine the performance of our single-sensor
water-based system in recognizing eleven high-level activities
in the kitchen and bathroom, such as cooking and shaving.
Results from two studies show that our system can estimate
activities with overall accuracy of 82.69% for one individual
and 70.11% for a group of 23 participants. As far
as we know, our work is the first to employ infrastructure-mediated
sensing for inferring high-level human activities in
a home setting.
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Date
2012-09
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