Mohammad Faizuddin Mohd Noor

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We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that(More)
Back of device interaction is gaining popularity as an alternative input modality in mobile devices, however it is still unclear how the back of device is related to other interactions. My research explores the relationship between hand grip from the back of the device and other interactions. In order to investigate this relationship, I will use touch(More)
We show that when users make errors on mobile devices they make immediate and distinct physical responses that can be observed with standard sensors. We used three standard cognitive tasks (Flanker, Stroop and SART) to induce errors from 20 participants. Using simple low-resolution capacitive touch sensors placed around a standard mobile device and the(More)
We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified(More)
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