Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer

TL;DR

This paper presents a new ANN-based method for recognizing Activities of Daily Living using mobile device accelerometer data, achieving over 80% accuracy with deep learning techniques.

Contribution

It introduces a novel ANN-based approach for ADL recognition on mobile devices and compares different ANN types to optimize accuracy.

Findings

Deep learning techniques achieved over 80% accuracy.

ANN-based methods are effective for ADL recognition.

Comparison of ANN types informs optimal model selection.

Abstract

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 implemented with 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 method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.