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User-Independent Motion State Recognition Using Smartphone Sensors

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. Ho...

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Detalles Bibliográficos
Autores principales: Gu, Fuqiang, Kealy, Allison, Khoshelham, Kourosh, Shang, Jianga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721741/
https://www.ncbi.nlm.nih.gov/pubmed/26690163
http://dx.doi.org/10.3390/s151229821
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author Gu, Fuqiang
Kealy, Allison
Khoshelham, Kourosh
Shang, Jianga
author_facet Gu, Fuqiang
Kealy, Allison
Khoshelham, Kourosh
Shang, Jianga
author_sort Gu, Fuqiang
collection PubMed
description The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.
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spelling pubmed-47217412016-01-26 User-Independent Motion State Recognition Using Smartphone Sensors Gu, Fuqiang Kealy, Allison Khoshelham, Kourosh Shang, Jianga Sensors (Basel) Article The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy. MDPI 2015-12-04 /pmc/articles/PMC4721741/ /pubmed/26690163 http://dx.doi.org/10.3390/s151229821 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gu, Fuqiang
Kealy, Allison
Khoshelham, Kourosh
Shang, Jianga
User-Independent Motion State Recognition Using Smartphone Sensors
title User-Independent Motion State Recognition Using Smartphone Sensors
title_full User-Independent Motion State Recognition Using Smartphone Sensors
title_fullStr User-Independent Motion State Recognition Using Smartphone Sensors
title_full_unstemmed User-Independent Motion State Recognition Using Smartphone Sensors
title_short User-Independent Motion State Recognition Using Smartphone Sensors
title_sort user-independent motion state recognition using smartphone sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721741/
https://www.ncbi.nlm.nih.gov/pubmed/26690163
http://dx.doi.org/10.3390/s151229821
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