Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

TL;DR

This paper presents a novel approach using deep learning with sensor data fusion from motion and magnetic sensors in mobile devices to accurately recognize Activities of Daily Living with nearly 90% accuracy.

Contribution

It introduces a new method employing artificial neural networks, especially deep learning, for ADL recognition using fused sensor data, improving accuracy over previous techniques.

Findings

Deep learning achieved 89.51% accuracy in ADL recognition.

Sensor data fusion enhances activity classification performance.

L2 regularization and normalization improve neural network results.

Abstract

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, reports an accuracy of 89.51%.