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iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones
 

Summary: 1
iLearn on the iPhone: Real-Time Human Activity
Classification on Commodity Mobile Phones
T. Scott Saponas, Jonathan Lester, Jon Froehlich, James Fogarty, James Landay
University of Washington
Seattle, WA 98115
{ssaponas, jlester, jfroehli, jfogarty, landay}@cs.washington.edu
ABSTRACT
As computing moves beyond the desktop, human activity
becomes an essential component of many applications.
Activity classification is an active research area and several
research systems have been constructed. Most have focused
on fragile custom hardware only available in limited
quantities. We instead seek to use commodity hardware to
lower the barrier to creating activity-informed mobile
applications. We describe iLearn, our system for classifying
human activities using the Apple iPhones three-axis
accelerometer and the Nike+iPod Sport Kit. Our results
suggest activities including running, walking, bicycling,
and sitting can be recognized at accuracies of 97% without

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences