A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones
A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones
Author(s): Renato Filjar, Ivan Sklebar, Marko HorvatSubject(s): Communication studies, ICT Information and Communications Technologies, Transport / Logistics
Published by: Žilinská univerzita v Žilině
Keywords: mobility classification; smartphone; inertial sensor; statistical learning;
Summary/Abstract: 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.
Journal: Komunikácie - vedecké listy Žilinskej univerzity v Žiline
- Issue Year: 22/2020
- Issue No: 4
- Page Range: 153-162
- Page Count: 10
- Language: English