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

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).

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

“…According to the previously proposed structure of a framework for the recognition of ADL and environments [2,[17][18][19][20][21][22][23][24][25], the main focus of this study was related to the data classification module, taking into account the implementations of the other modules performed in previous studies. Previously, the DNN method was implemented, and it reported reliable results.…”

“…The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.Electronics 2020, 9, 180 2 of 16The automatic recognition of ADL is widely researched [11][12][13][14][15][16], where the previously proposed framework [2,[17][18][19][20][21][22][23][24][25] was tested and validated with different types of Artificial Neural Networks (ANN) [26][27][28], verifying that the best results were achieved with Deep Neural Networks (DNN). The proposed framework allows the recognition of eight ADL, i.e., walking, running, standing, going upstairs, going downstairs, watching television, sleeping, driving, and other activities without motion, and nine environments, i.e., bar, classroom, gym, hall, kitchen, library, street, bedroom, and living room.…”

Promotion of Healthy Nutrition and Physical Activity Lifestyles for Teenagers: A Systematic Literature Review of The Current Methodologies

“…Research has shown that personalized approaches and systematic feature engineering and selection can help in improving the activity recognition accuracy [22][23][24]. In addition to using inertial sensors (i.e., accelerometers and gyroscopes) embedded in mobile devices, acoustic sensors can augment and improve the activity and environment recognition [25,26]. However, these methods have several limitations that should be considered during the development of these systems, such as availability of the sensors, weather conditions, battery lifetime, limited power processing, and memory capabilities, among others [27,28].…”

Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

“…To date, and due to the increasing power processing capabilities of the different mobile devices, the Adaboost method is one of the most used methods, and it reports reliable results [24][25][26][27][28][29][30][31][32]. The motivation of this systematic review is to evaluate the reliability of the Adaboost method for daily activities and environment recognition using the sensors available in mobile devices for further implementation of a framework [33][34][35][36][37][38][39][40][41][42].…”

“…Due to the complex nature of the sensory data collected using the sensors available in mobile devices, the overfitting problem is impacts many machine learning algorithms, including multilayer perceptron neural networks (MLP), deep neural networks (DNN) and feedforward neural networks (FNN) [33][34][35][36][37][38][39][40][41][42]. Methods for parameter tuning such as grid search [45] and systematic feature selection [23] are usually applied to mitigate this problem.…”