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Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts o...

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Detalles Bibliográficos
Autores principales: Li, Frédéric, Shirahama, Kimiaki, Nisar, Muhammad Adeel, Köping, Lukas, Grzegorzek, Marcin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855052/
https://www.ncbi.nlm.nih.gov/pubmed/29495310
http://dx.doi.org/10.3390/s18020679
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author Li, Frédéric
Shirahama, Kimiaki
Nisar, Muhammad Adeel
Köping, Lukas
Grzegorzek, Marcin
author_facet Li, Frédéric
Shirahama, Kimiaki
Nisar, Muhammad Adeel
Köping, Lukas
Grzegorzek, Marcin
author_sort Li, Frédéric
collection PubMed
description Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.
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spelling pubmed-58550522018-03-20 Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors Li, Frédéric Shirahama, Kimiaki Nisar, Muhammad Adeel Köping, Lukas Grzegorzek, Marcin Sensors (Basel) Article Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data. MDPI 2018-02-24 /pmc/articles/PMC5855052/ /pubmed/29495310 http://dx.doi.org/10.3390/s18020679 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Frédéric
Shirahama, Kimiaki
Nisar, Muhammad Adeel
Köping, Lukas
Grzegorzek, Marcin
Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title_full Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title_fullStr Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title_full_unstemmed Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title_short Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
title_sort comparison of feature learning methods for human activity recognition using wearable sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855052/
https://www.ncbi.nlm.nih.gov/pubmed/29495310
http://dx.doi.org/10.3390/s18020679
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