Heba Abdelnasser

Heba Abdelnasser, Moustafa Youssef, Khaled A. Harras

One of the main methods for interacting with mobile devices today is the error-prone and inflexible touch-screen keyboard. This paper proposes MagBoard: a homomorphic ubiquitous keyboard for mobile devices. MagBoard allows application developers and users to design and print different custom keyboards for the same applications to fit different user's needs. The core idea is to leverage the triaxial magnetometer embedded in standard mobile phones to accurately localize the location of a magnet on a virtual grid superimposed on the printed keyboard. This is achieved through a once in a lifetime fingerprint. MagBoard also provides a number of modules that allow it to cope with background magnetic noise, heterogeneous devices, different magnet shapes, sizes, and strengths, as well as changes in magnet polarity. Our implementation of MagBoard on Android phones with extensive evaluation in different scenarios demonstrates that it can achieve a key detection accuracy of more than 91% for keys as small as 2cm*2cm, reaching 100% for 4cm*4cm keys. This accuracy is robust with different phones and magnets, highlighting MagBoard promise as a homomorphic ubiquitous keyboard for mobile devices.

Heba Abdelnasser, Moustafa Youssef, Khaled A. Harras

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modi-fications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy in-creases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.