MagStroke: A Magnetic Based Virtual Keyboard for Off-the-Shelf Smart Devices

Abdelnasser, Heba; Harras, Khaled A.; Youssef, Moustafa

doi:10.1109/secon48991.2020.9158416

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Namely, the related researches either: (1) Present proof-of-concept prototypes without quantitative indicators, or (2) mainly report the accuracy of magnet-based gesture recognition. For example, if a magnetic ring is worn on the finger, Magstroke [ 21 ] system can detect keystroke gestures with an accuracy of

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

.…”

Section: Discussionmentioning

confidence: 99%

“…Another type of application leverages the embedded magnetometer for providing textual input to smartphone devices. Solutions such as MagBoard [ 20 ] and MagStroke [ 21 ] utilize custom-printed keyboard to detect keystrokes made while wearing specially designed ring magnets. The detected keystrokes are transferred as textual inputs to the underlying application on the mobile device.…”

Section: Introductionmentioning

confidence: 99%

“…In most of the mentioned research papers [ 13 , 17 , 21 , 23 , 24 ], permanent magnets with specific forms (e.g., ring) or within the specific accessory (e.g., pen) were used, and their movement was detected by utilizing artificial NNs. Furthermore, in many other ADI-based research efforts, the various forms of NNs are utilized to either detect interaction patterns or classify the gestures.…”

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

.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%