PT Journal
AU Filjar, R
   Sklebar, I
   Horvat, M
TI A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones
SO Communications - Scientific Letters of the University of Zilina
PY 2020
BP 153
EP 162
VL 22
IS 4
DI 10.26552/com.C.2020.4.153-162
WP https://komunikacie.uniza.sk/artkey/csl-202004-0019.php
DE mobility classification; smartphone; inertial sensor; statistical learning
SN 13354205
AB General mobility estimation is demanded for strategy, policy, systems and services developments and operations in transport, urban development and telecommunications. Here is proposed an individual motion readings collection with preserved privacy through loosely fit smartphones, as a novel sole inertial sensors use in commercial-grade smartphones for a wide population data collection, without the need for the new infrastructure and attaching devices. It is shown that the statistical learning-based models of individual mobility classification per means of transport are capable of overcoming the variance introduced by the proposed data collection method. The success of the proposed methodology in a small-scale experiment for the Individual Mobility Classification Model development, using selected statistical learning methods, is demonstrated.
ER