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Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets
Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Un...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017504/ https://www.ncbi.nlm.nih.gov/pubmed/27556473 http://dx.doi.org/10.3390/s16081341 |
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author | Moschetti, Alessandra Fiorini, Laura Esposito, Dario Dario, Paolo Cavallo, Filippo |
author_facet | Moschetti, Alessandra Fiorini, Laura Esposito, Dario Dario, Paolo Cavallo, Filippo |
author_sort | Moschetti, Alessandra |
collection | PubMed |
description | Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios. |
format | Online Article Text |
id | pubmed-5017504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-50175042016-09-22 Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets Moschetti, Alessandra Fiorini, Laura Esposito, Dario Dario, Paolo Cavallo, Filippo Sensors (Basel) Article Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios. MDPI 2016-08-22 /pmc/articles/PMC5017504/ /pubmed/27556473 http://dx.doi.org/10.3390/s16081341 Text en © 2016 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 Moschetti, Alessandra Fiorini, Laura Esposito, Dario Dario, Paolo Cavallo, Filippo Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title | Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title_full | Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title_fullStr | Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title_full_unstemmed | Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title_short | Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets |
title_sort | recognition of daily gestures with wearable inertial rings and bracelets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017504/ https://www.ncbi.nlm.nih.gov/pubmed/27556473 http://dx.doi.org/10.3390/s16081341 |
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