Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review
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Título: | Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review |
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Autor/es: | Pires, Ivan Miguel | Santos, Rui | Pombo, Nuno | Garcia, Nuno M. | Flórez-Revuelta, Francisco | Spinsante, Susanna | Goleva, Rossitza | 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: | Acoustic sensors | Fingerprint recognition | Data processing | Artificial intelligence | Mobile computing | Signal processing algorithms | Systematic review | Activities of Daily Living (ADL) |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 9-ene-2018 |
Editor: | MDPI |
Cita bibliográfica: | Pires IM, Santos R, Pombo N, Garcia NM, Flórez-Revuelta F, Spinsante S, Goleva R, Zdravevski E. Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review. Sensors. 2018; 18(1):160. doi:10.3390/s18010160 |
Resumen: | An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT). |
Patrocinador/es: | This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303—AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments. |
URI: | http://hdl.handle.net/10045/72592 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s18010160 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 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/). |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.3390/s18010160 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AmI4AHA - Artículos de Revistas |
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