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Article

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

1
Faculty of Engineering, University of Rijeka, Vukovarska 58, HR-51000 Rijeka, Croatia
2
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia
*
Author to whom correspondence should be addressed.
Academic Editor: Primož Podržaj
Sensors 2021, 21(9), 3087; https://doi.org/10.3390/s21093087
Received: 31 March 2021 / Revised: 20 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
(This article belongs to the Special Issue Advanced Systems for Human Machine Interactions)
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). View Full-Text
Keywords: Around-Device Interaction (ADI); geomagnetic field sensor; touchless interaction; magnetic field fingerprinting; pointing; neural networks Around-Device Interaction (ADI); geomagnetic field sensor; touchless interaction; magnetic field fingerprinting; pointing; neural networks
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MDPI and ACS Style

Ljubic, S.; Hržić, F.; Salkanovic, A.; Štajduhar, I. Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization. Sensors 2021, 21, 3087. https://doi.org/10.3390/s21093087

AMA Style

Ljubic S, Hržić F, Salkanovic A, Štajduhar I. Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization. Sensors. 2021; 21(9):3087. https://doi.org/10.3390/s21093087

Chicago/Turabian Style

Ljubic, Sandi, Franko Hržić, Alen Salkanovic, and Ivan Štajduhar. 2021. "Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization" Sensors 21, no. 9: 3087. https://doi.org/10.3390/s21093087

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