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

Ivan Pires,Francisco Flórez-Revuelta,Eftim Zdravevski,Susanna Spinsante,Nuno Pombo,Nuno Garcia,Rui Santos,Rossitza Goleva

doi:10.3390/s18010160

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).

Highlights

The identification of Activities of Daily Living (ADL) [1] is of utmost importance to build EnhancedLiving Environment and Ambient Assisted Living solutions [2,3], or to allow the development of Personal Digital Life Coaching systems [4]
We suggest that interested readers refer to the original cited works to find relevant information about the details of the methods analyzed in this review
Only one of the reviewed studies [38] can achieve reliable performance with reduced computational cost and memory usage. It utilizes the Fast Fourier Transform (FFT) implementation in the CUFFT library, divide and locate (DAL) audio fingerprint method, and sub-fingerprint masking based on the predominant pitch extraction methods

Summary

Introduction

Living Environment and Ambient Assisted Living solutions [2,3], or to allow the development of Personal Digital Life Coaching systems [4]. Several authors have proposed the development of solutions based on mobile devices (e.g., smartphones) [5,6,7,8] for several reasons, the most prominent being the adoption ratios of these devices, its increasing computing power and memory, and the fact that these devices already come equipped with a plethora of sensors that can be used to sense and feed data to ADL identification systems. Despite the increasing complexity of ADL identification systems, the recognition of the surrounding environment is limited because of the restrictions of some location sensors. As proposed in previous works [9,10,11], an ADL identification framework should be able to integrate data from the sound of the environment into the ADL identification module in order to allow the system to sense the environmental sounds, to determine the type of environment, and to increase the accuracy of the overall ADL identification solution

Methods

Results

Discussion

Conclusion