Using machine learning to detect pedestrian locomotion from sensor-based data

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

Proceedings of the 14th Philippine Computing Science Congress (PCSC 2014)

First Page

219

Last Page

226

Publication Date

2014

Abstract

The integration of low cost microelectromagnetic (MEM) sensors into smart phones have made inertial navigation systems (INS) possible for ubiquitous use. Many research studies developed algorithms to detect a user's steps, and to calculate a user's stride to know the position displacement of the user. Subsequent research have already integrated the phone's heading to map out the user's movement across a physical area. These research, however, have not taken into account negative pedestrian locomotion, wherein the user is moving but is not exhibiting any position displacement. Current INSs are not suited to handle negative pedestrian locomotion movements, and this leads them to consider false steps as real steps. As the INS's modules depend heavily on the outputs of the other modules, a cascading error would most likely occur. This research aims to solve this problem by collecting positive and negative pedestrian locomotion with data from phone-embedded sensors positioned in the research subject's front pocket. Using these data, a model will be built to classify negative pedestrian locomotion from positive ones, and to eventually improve the INS's accuracy overall.

html

Disciplines

Databases and Information Systems

Keywords

Inertial navigation systems; Motion; Spatial data mining

Upload File

wf_no

This document is currently not available here.

Share

COinS