Subject: acoustic sensors; fingerprint recognition; data processing; artificial intelligence; mobile computing; signal processing algorithms; systematic review; Activities of Daily Living (ADL)
Year: 2018
Type: Article
Title: Recognition of activities of daily living based on environmental analyses using audio fingerprinting techniques: A systematic review
Author: Pires, Ivan Miguel
Author: Santos, Rui
Author: Pombo, Nuno
Author: M Garcia, Nuno
Author: Flórez-Revuelta, Francisco
Author: Spinsante, Susanna
Author: Goleva, Rossitza
Author: Zdravevski, Eftim
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).
Publisher: MDPI
Relation: Sensors
Identifier: oai:repository.ukim.mk:20.500.12188/20783
Identifier: http://hdl.handle.net/20.500.12188/20783
