Title:
Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing
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
Abowd, Gregory D.
Essa, Irfan
Bettadapura, Vinay
Reyes, Gabriel
Sandesh, Megha
Schindler, Grant
Plötz, Thomas
Abowd, Gregory D.
Essa, Irfan
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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 Issued
2012-09
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