Neural Networks for Indoor Localization based on Electric Field Sensing

Florian Kirchbuchner, Moritz Andres, Julian von Wilmsdorff, Arjan Kuijper

2022

Abstract

In this paper, we will demonstrate a novel approach using artificial neural networks to enhance signal processing for indoor localization based on electric field measurement systems Up to this point, there exist a variety of approaches to localize persons by using wearables, optical sensors, acoustic methods and by using Smart Floors. All capacitive approaches use, to the best of our knowledge, analytic signal processing techniques to calculate the position of a user. While analytic methods can be more transparent in their functionality, they often come with a variety of drawbacks such as delay times, the inability to compensate defect sensor inputs or missing accuracy. We will demonstrate machine learning approaches especially made for capacitive systems resolving these challenges. To train these models, we propose a data labeling system for person localization and the resulting dataset for the supervised machine learning approaches. Our findings show that the novel approach based on artificial neural networks with a time convolutional neural network (TCNN) architecture reduces the Euclidean error by 40% (34.8cm Euclidean error) in respect to the presented analytical approach (57.3cm Euclidean error). This means a more precise determination of the user position of 22.5cm centimeter on average.

Paper Citation

in Harvard Style

Kirchbuchner F., Andres M., von Wilmsdorff J. and Kuijper A. (2022). Neural Networks for Indoor Localization based on Electric Field Sensing. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 25-33. DOI: 10.5220/0011266300003277

in Bibtex Style

@conference{delta22,
author={Florian Kirchbuchner and Moritz Andres and Julian von Wilmsdorff and Arjan Kuijper},
title={Neural Networks for Indoor Localization based on Electric Field Sensing},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={25-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011266300003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Neural Networks for Indoor Localization based on Electric Field Sensing
SN - 978-989-758-584-5
AU - Kirchbuchner F.
AU - Andres M.
AU - von Wilmsdorff J.
AU - Kuijper A.
PY - 2022
SP - 25
EP - 33
DO - 10.5220/0011266300003277