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

Sensors

Francisco Flórez-Revuelta¹  Rossitza Goleva²  Susanna Spinsante³  Eftim Zdravevski⁴  Nuno Pombo⁵  Nuno M. Garcia⁵  Ivan Miguel Pires⁵  Rui Santos⁵ 

[1] Department of Computer Technology, Universidad de Alicante, 03690 Sant Vicent del Raspeig, Alicante, Spain;Department of Informatics, New Bulgarian University, 1618 g.k. Ovcha kupel 2 Sofia, Bulgaria;Department of Information Engineering, Marche Polytechnic University, 60121 Ancona, Italy;Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia;Instituto de Telecomunicações, Universidade da Beira Interior, 6201-001 Covilhã, Portugal;

关键词: acoustic sensors;    fingerprint recognition;    data processing;    artificial intelligence;    mobile computing;    signal processing algorithms;    systematic review;    Activities of Daily Living (ADL);   

DOI  :  10.3390/s18010160

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown