Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783307023232794624 |
---|---|
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. |
format | Online Article Text |
id | pubmed-5855052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lifrederic comparisonoffeaturelearningmethodsforhumanactivityrecognitionusingwearablesensors AT shirahamakimiaki comparisonoffeaturelearningmethodsforhumanactivityrecognitionusingwearablesensors AT nisarmuhammadadeel comparisonoffeaturelearningmethodsforhumanactivityrecognitionusingwearablesensors AT kopinglukas comparisonoffeaturelearningmethodsforhumanactivityrecognitionusingwearablesensors AT grzegorzekmarcin comparisonoffeaturelearningmethodsforhumanactivityrecognitionusingwearablesensors |