Recognition of activities of daily living and environments using acoustic sensors embedded on mobile devices
Journal
Electronics
Date Issued
2019-12-07
Author(s)
Pires, Ivan Miguel
Marques, Gonçalo
Garcia, Nuno M
Pombo, Nuno
Flórez-Revuelta, Francisco
Spinsante, Susanna
Canavarro Teixeira, Maria
Abstract
The identification of Activities of Daily Living (ADL) is intrinsic with the user’s
environment recognition. This detection can be executed through standard sensors present in
every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment
and standing activities. On the other hand, these features are included in a framework for the ADL
and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic
sensors are used for the collection of data towards the recognition of the environment and, secondly,
the information of the environment recognized is fused with the information gathered by motion
and magnetic sensors. The environment and ADL recognition are performed by pattern recognition
techniques that aim for the development of a system, including data collection, processing, fusion
and classification procedures. These classification techniques include distinctive types of Artificial
Neural Networks (ANN), analyzing various implementations of ANN and choosing the most
suitable for further inclusion in the following different stages of the developed system. The results
present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL
recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized
data for environment recognition. Furthermore, the tests conducted present 100% accuracy for
standing activities recognition using DNN with normalized data, which is the most suited for the
intended purpose.
environment recognition. This detection can be executed through standard sensors present in
every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment
and standing activities. On the other hand, these features are included in a framework for the ADL
and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic
sensors are used for the collection of data towards the recognition of the environment and, secondly,
the information of the environment recognized is fused with the information gathered by motion
and magnetic sensors. The environment and ADL recognition are performed by pattern recognition
techniques that aim for the development of a system, including data collection, processing, fusion
and classification procedures. These classification techniques include distinctive types of Artificial
Neural Networks (ANN), analyzing various implementations of ANN and choosing the most
suitable for further inclusion in the following different stages of the developed system. The results
present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL
recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized
data for environment recognition. Furthermore, the tests conducted present 100% accuracy for
standing activities recognition using DNN with normalized data, which is the most suited for the
intended purpose.
Subjects
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