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

The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems 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...

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Main Author:	Ngo, Courtney Anne M.
Format:	text
Language:	English
Published:	Animo Repository 2014
Online Access:	https://animorepository.dlsu.edu.ph/etd_masteral/4606
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Institution:	De La Salle University
Language:	English

id	oai:animorepository.dlsu.edu.ph:etd_masteral-11444
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:etd_masteral-114442022-07-05T02:36:25Z Using machine learning to detect pedestrian locomotion from sensor-based data Ngo, Courtney Anne M. The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems 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. 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. 2014-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/4606 Master's Theses English Animo Repository
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
language	English
description	The integration of low cost micro-electro-mechanical (MEM) sensors into smart phones have made inertial navigation systems 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. 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.
format	text
author	Ngo, Courtney Anne M.
spellingShingle	Ngo, Courtney Anne M. Using machine learning to detect pedestrian locomotion from sensor-based data
author_facet	Ngo, Courtney Anne M.
author_sort	Ngo, Courtney Anne M.
title	Using machine learning to detect pedestrian locomotion from sensor-based data
title_short	Using machine learning to detect pedestrian locomotion from sensor-based data
title_full	Using machine learning to detect pedestrian locomotion from sensor-based data
title_fullStr	Using machine learning to detect pedestrian locomotion from sensor-based data
title_full_unstemmed	Using machine learning to detect pedestrian locomotion from sensor-based data
title_sort	using machine learning to detect pedestrian locomotion from sensor-based data
publisher	Animo Repository
publishDate	2014
url	https://animorepository.dlsu.edu.ph/etd_masteral/4606
_version_	1797546058269589504

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

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