Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentiona...

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Main Authors: Sandi Ljubic, Franko Hržić, Alen Salkanovic, Ivan Štajduhar
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/9/3087
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spelling doaj-4715748151084c678aa43bfd8eab9cee2021-04-28T23:06:56ZengMDPI AGSensors1424-82202021-04-01213087308710.3390/s21093087Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor UtilizationSandi Ljubic0Franko Hržić1Alen Salkanovic2Ivan Štajduhar3Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaFaculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, CroatiaIn this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).https://www.mdpi.com/1424-8220/21/9/3087Around-Device Interaction (ADI)geomagnetic field sensortouchless interactionmagnetic field fingerprintingpointingneural networks
collection DOAJ
language English
format Article
sources DOAJ
author Sandi Ljubic
Franko Hržić
Alen Salkanovic
Ivan Štajduhar
spellingShingle Sandi Ljubic
Franko Hržić
Alen Salkanovic
Ivan Štajduhar
Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
Sensors
Around-Device Interaction (ADI)
geomagnetic field sensor
touchless interaction
magnetic field fingerprinting
pointing
neural networks
author_facet Sandi Ljubic
Franko Hržić
Alen Salkanovic
Ivan Štajduhar
author_sort Sandi Ljubic
title Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
title_short Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
title_full Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
title_fullStr Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
title_full_unstemmed Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization
title_sort augmenting around-device interaction by geomagnetic field built-in sensor utilization
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-04-01
description In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).
topic Around-Device Interaction (ADI)
geomagnetic field sensor
touchless interaction
magnetic field fingerprinting
pointing
neural networks
url https://www.mdpi.com/1424-8220/21/9/3087
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