A Comparison of Machine Learning-Based Individual Mobility Classification Models Developed on Sensor Readings from Loosely Attached Smartphones

Renato Filjar,Marko Horvat,Ivan Sklebar

doi:10.26552/com.c.2020.4.153-162

Renato Filjar, Marko Horvat + Show 1 more

Open Access

https://doi.org/10.26552/com.c.2020.4.153-162

Copy DOI

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.

Highlights

Mobility may be seen as the level of one’s ability to move physically, and without restrictions in a given framework of the individual and collective transportation infrastructure, that involves different means of transport and walk
With the loose smartphone attachment to user in a common pose of a smartphone usage, the correspondence of the all the reference frames involved was accomplished. This accomplishment allows for utilisation of the smartphone inertial sensor readings for the individual mobility classification
The proposed methodology does not rely upon the high-precision absolute position determination, often unavailable in a number of high-population scenarios where the individual mobility assessment is needed

Summary

Introduction

Mobility may be seen as the level of one’s ability to move physically, and without restrictions in a given framework of the individual and collective transportation infrastructure, that involves different means (modes) of transport and walk. The mobility estimation is a mathematical process of the evidence-based estimation of the level of mobility within the given spatio-temporal constraints. The mobile telecommunications Location-Based Services (LBS) started to exploit the case [1] This trend expanded to other disciplines including Intelligent Transport Systems, general human activity recognition [2,3], mobile health, medical diagnostics and convalescence [4]. Authors of [1] proposed the accepted LBS model that facilitate the position-location duality, through recognition of position as a place of existence determined in the physical world, and location as a place of existence in the world of context (the world of information). The term localisation will comprise a set of methods for determination of location relation classes This may include identification of the mobility patterns (classes of location-related behaviour). Formal description of human mobility has been addressed from biological and medical [7], to mathematical [8] perspectives

Objectives

Methods

Findings

Conclusion