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\n

iLearn, our system for classifying
human activities using the Apple iPhone‟s 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
any training by an end-user

\n
\n
\n

Activity inference may also offer significant
value to healthcare, eldercare, and fitness applications by
providing doctors, family members, and users with a better
understanding of daily physical activities

\n
\n
\n

We seek to lower the barrier to creating activity-informed
applications on mobile phones by demonstrating how the
iPhone can be used to create applications with real-time
activity classification using the iPhone‟s three-axis
accelerometer and the Nike+iPod Sport Kit.

\n
\n
\n

We
augment these capabilities by demonstrating the possibility
of very accurate classification of simple human activities
such as running, walking, bicycling, and sitting. We also
provide a set of open source tools for collecting training
data, building activity models (using the Weka toolkit [12]),
and running these models on the iPhone to provide real-
time activity classification to applications

\n
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Randall W. Kindley discusses the two types of e‐Learning – traditional
e‐learning and scenario‐based e‐learning. Scenario‐based e‐learning is
a new way of learning in which a scenario is given and students are
asked to make decisions. Scenario‐based e‐learning must involve a
real‐world situation and open‐ended learner‐instructor feedback.

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