Abstract:
Mobile phone based activity recognition uses data
obtained from embedded sensors to infer user’s
physical activities. Therefore, many mobile phones
have been equipped with sensors to enable the
delivery of advanced features to the users.
Accelerometer and gyroscope are the sensors that
embedded to several types of mobiles devices. In this
paper, we apply 17 classifier algorithms to select the
best performance ones using UCI data sets. These
dataset are labeled twelve human activities. To test
the performance accuracy of these algorithms, the
10-fold cross validation is done using Weka 3.6.11
data mining tool. The overall accuracy rates for
classifiers are exceeded 85% and nearly 96% which
are encouraged results. Thus, we select the
appropriate classifier algorithms based on these
accuracy results to be used for online human activity recognition.
