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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/9/3087 |
| _version_ | 1797535905078050816 |
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| author | Sandi Ljubic Franko Hržić Alen Salkanovic Ivan Štajduhar |
| author_facet | Sandi Ljubic Franko Hržić Alen Salkanovic Ivan Štajduhar |
| author_sort | Sandi Ljubic |
| collection | DOAJ |
| 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). |
| first_indexed | 2024-03-10T11:51:58Z |
| format | Article |
| id | doaj.art-4715748151084c678aa43bfd8eab9cee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:51:58Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4715748151084c678aa43bfd8eab9cee2023-11-21T17:39:02ZengMDPI AGSensors1424-82202021-04-01219308710.3390/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 |
| 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 |
| title | 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_short | Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization |
| title_sort | augmenting around device interaction by geomagnetic field built in sensor utilization |
| 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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