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A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition
Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the at...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375815/ https://www.ncbi.nlm.nih.gov/pubmed/28272362 http://dx.doi.org/10.3390/s17030529 |
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author | Janidarmian, Majid Roshan Fekr, Atena Radecka, Katarzyna Zilic, Zeljko |
author_facet | Janidarmian, Majid Roshan Fekr, Atena Radecka, Katarzyna Zilic, Zeljko |
author_sort | Janidarmian, Majid |
collection | PubMed |
description | Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. |
format | Online Article Text |
id | pubmed-5375815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53758152017-04-10 A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition Janidarmian, Majid Roshan Fekr, Atena Radecka, Katarzyna Zilic, Zeljko Sensors (Basel) Article Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results. MDPI 2017-03-07 /pmc/articles/PMC5375815/ /pubmed/28272362 http://dx.doi.org/10.3390/s17030529 Text en © 2017 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 Janidarmian, Majid Roshan Fekr, Atena Radecka, Katarzyna Zilic, Zeljko A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title_full | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title_fullStr | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title_full_unstemmed | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title_short | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
title_sort | comprehensive analysis on wearable acceleration sensors in human activity recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375815/ https://www.ncbi.nlm.nih.gov/pubmed/28272362 http://dx.doi.org/10.3390/s17030529 |
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