Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices
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Título: | Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices |
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Autor/es: | Pires, Ivan Miguel | Marques, Gonçalo | Garcia, Nuno M. | Pombo, Nuno | Flórez-Revuelta, Francisco | Spinsante, Susanna | Teixeira, Maria Canavarro | Zdravevski, Eftim |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Activities of Daily Living (ADL) | Data fusion | Environments | Feature extraction | Pattern recognition | Sensors |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 7-dic-2019 |
Editor: | MDPI |
Cita bibliográfica: | Pires IM, Marques G, Garcia NM, Pombo N, Flórez-Revuelta F, Spinsante S, Teixeira MC, Zdravevski E. Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices. Electronics. 2019; 8(12):1499. doi:10.3390/electronics8121499 |
Resumen: | The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose. |
Patrocinador/es: | This work is funded by FCT/MEC through national funds and co-funded by FEDER-PT2020 partnership agreement under the project UID/EEA/50008/2019. |
URI: | http://hdl.handle.net/10045/100069 |
ISSN: | 2079-9292 |
DOI: | 10.3390/electronics8121499 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 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/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/electronics8121499 |
Aparece en las colecciones: | INV - AmI4AHA - Artículos de Revistas INV - I2RC - Artículos de Revistas |
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