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Sensors 2018, 18(1), 160; https://doi.org/10.3390/s18010160

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

1
Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
2
Altranportugal, 1990-096 Lisbon, Portugal
3
ALLab—Assisted Living Computing and Telecommunications Laboratory, Computing Science Department, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
4
ECATI, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal
5
Department of Computer Technology, Universidad de Alicante, 03690 Sant Vicent del Raspeig, Alicante, Spain
6
Department of Information Engineering, Marche Polytechnic University, 60121 Ancona, Italy
7
Department of Informatics, New Bulgarian University, 1618 g.k. Ovcha kupel 2 Sofia, Bulgaria
8
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
*
Author to whom correspondence should be addressed.
Received: 28 November 2017 / Revised: 25 December 2017 / Accepted: 5 January 2018 / Published: 9 January 2018
(This article belongs to the Special Issue New Trends in Ambient Intelligence Applications)
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Abstract

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). View Full-Text
Keywords: acoustic sensors; fingerprint recognition; data processing; artificial intelligence; mobile computing; signal processing algorithms; systematic review; Activities of Daily Living (ADL) acoustic sensors; fingerprint recognition; data processing; artificial intelligence; mobile computing; signal processing algorithms; systematic review; Activities of Daily Living (ADL)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Pires, I.M.; Santos, R.; Pombo, N.; Garcia, N.M.; 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, 160.

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