MagController: A System for Touchless Mobile Devices Control Using the Magnetic Field

Abdelnasser, Heba; Khalafallah, Ayman; Yousef, Moustafa A.

doi:10.1109/percomw.2018.8480128

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Similarly, MagiTact [ 13 ] solution can identify in-air hand gestures with an accuracy greater than

. MagController [ 16 ] can recognize the magnetic actions with an accuracy of

in different magnet’s orientations relative to the device. However, our measurable outcome is not the gesture recognition accuracy but, in the free-move pointing case, the accuracy of magnet localization in real-time.…”

Section: Discussionmentioning

confidence: 99%

“…The formed gestures invoke different actions concerning the application they are paired with. The solutions based on this approach are: MagiTact [ 13 ] (3D gestures used for interacting with the music player, maps, and photo gallery application), MagiWrite [ 14 ] and MagiThings [ 15 ] (3D gestures used to insert digits), MagController [ 16 ] (magnet-based actions to manipulate objects in 3D space), MagiSign [ 17 ] (authentication strategy based on 3D magnetic signatures), and MagiGuitar [ 18 ] (allows in-air gestures to mimic playing the guitar).…”

Section: Introductionmentioning

confidence: 99%

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

Ljubić

Hržić

Salkanović

et al. 2021

Sensors

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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).

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“…Similarly, MagiTact [ 13 ] solution can identify in-air hand gestures with an accuracy greater than

. MagController [ 16 ] can recognize the magnetic actions with an accuracy of

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ljubić

Hržić

Salkanović

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Chen et al 6 developed a 3D input system with two magnetometers. Yoon et al, 24 Abdelnasser et al, 3 Abe et al, 4 Cheung and Girouard, 7 and Abdelnasser et al 2 developed wireless input devices for smartphones based on the permanent magnet tracking. A smartphone has an integrated magnetometer and screen, making it a compact device for detecting magnetic fields and displaying tracking results.…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Using a Smartphone Magnetometer for Assisting Needle Placement

Zhao

Wood

et al. 2019

Ann Biomed Eng

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Minimally invasive surgical procedures often require needle insertion. For these procedures, efficacy greatly depends on precise needle placement. Many methods, such as optical tracking and electromagnetic tracking, have been applied to assist needle placement by tracking the realtime position information of the needle. Compared with the optical tracking method, electromagnetic tracking is more suitable for minimally invasive surgery since it has no requirement of line-of-sight. However, the devices needed for electromagnetic tracking are usually expensive, which will increase the cost of surgery. In this study, we presented a lowcost smartphone-based permanent magnet tracking method compatible with CT imaging and designed a 3D printed operation platform to assist with needle placement prior to needle insertion during minimally invasive surgery. The needle positioning accuracy of this method was tested in an open air test and a prostate phantom test in a CT environment. For these two tests, the average radial errors were 0.47 and 2.25 mm, respectively, and the standard deviations were 0.29 and 1.63, respectively. The materials and fabrication required for the presented method are inexpensive. Thus, many image-guided therapies may benefit from the presented method as a low-cost option for needle positioning prior to needle insertion.

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TetraForce: A Magnetic-Based Interface Enabling Pressure Force and Shear Force Input Applied to Front and Back of a Smartphone

Tsuchida

Fujita

Ikematsu

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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We propose a novel phone-case-shaped interface named TetraForce, which enables four types of force input consisting of two force directions (i.e., pressure force and shear force) and two force-applied surfaces (i.e., touch surface and back surface) in a single device. Force detection is achieved using the smartphone's built-in magnetometer (and supplementary accelerometer and gyro sensor) by estimating the displacement of a magnet attached to a 3-DoF passively movable panel at the back. We conducted a user study (N=12) to investigate the fundamental user performance by our interface and demonstrated that the input was detected as intended with a success rate of 97.4% on average for all four input types. We further conducted an ideation workshop with people who were involved in human-computer interaction (N=12) to explore possible applications of this interface, and we obtained 137 ideas for applications using individual input types and 51 possible scenarios using them in combination. Organizing these ideas reveals the advantages of each input type and suggests that our interface is useful for applications that require complex operations and that it can improve intuitiveness through elastic feedback.

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MagController: A System for Touchless Mobile Devices Control Using the Magnetic Field

Cited by 3 publications

References 3 publications

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

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

The Feasibility of Using a Smartphone Magnetometer for Assisting Needle Placement

TetraForce: A Magnetic-Based Interface Enabling Pressure Force and Shear Force Input Applied to Front and Back of a Smartphone

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