Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artifici...

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Bibliographic Details
Published in:PeerJ preprints 2019-02
Main Authors: Ivan Miguel Pires, Garcia, Nuno M, Pombo, Nuno, Flórez-Revuelta, Francisco, Spinsante, Susanna, Teixeira, Maria Canavarro, Zdravevski, Eftim
Format: Article
Language:English
Subjects:
Accuracy
Activities of daily living
Artificial intelligence
Data acquisition
Data processing
Learning
Neural networks
Pattern recognition
Online Access:Get full text
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Summary:This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method based on ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final model. Results of this research probes that the best accuracies are achieved with Deep Neural Networks (DNN) with an accuracy higher than 80%. The results obtained are similar with other studies, but we compared tree types of ANN in order to discover the best method in order to obtain these results with less memory, verifying that, after the generation of the model, the DNN method, when compared with others, is also the fastest to obtain the results with better accuracy.
ISSN:2167-9843
DOI:10.7287/peerj.preprints.27225v2