  In this study, a novel method to obtain user-dependent human activity
recognition models unobtrusively by exploiting the sensors of a smartphone is
presented. The recognition consists of two models: sensor fusion-based
user-independent model for data labeling and single sensor-based user-dependent
model for final recognition. The functioning of the presented method is tested
with human activity data set, including data from accelerometer and
magnetometer, and with two classifiers. Comparison of the detection accuracies
of the proposed method to traditional user-independent model shows that the
presented method has potential, in nine cases out of ten it is better than the
traditional method, but more experiments using different sensor combinations
should be made to show the full potential of the method.
