Learn More
—We propose a lightweight, and temporally and spatially aware user behaviour modelling technique for sensor-based authentication. Operating in the background, our data driven technique compares current behaviour with a user profile. If the behaviour deviates sufficiently from the established norm, actions such as explicit authentication can be triggered. To(More)
One of the main reasons why smartphone users do not adopt screen locking mechanisms is due to the inefficiency of entering a PIN/pattern each time they use their phone. To address this problem we designed a context-sensitive screen locking application which asked participants to enter a PIN/pattern only when necessary, and evaluated its impact on efficiency(More)
General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every(More)
Current methods of behavioral data collection from mobile devices either require significant involvement from participants to verify the 'ground truth' of the data, or approximations that involve post-experiment comparisons to seed data. In this paper we argue that user involvement can be gracefully reduced by performing more intelligent seed comparisons.(More)
A majority of Stroke survivors have an arm impairment (up to 80%), which persists over the long term (>12 months). Physiotherapy experts believe that a rehabilitation Aide-Memoire could help these patients [25]. Hence, we designed, with the input of physiotherapists, Stroke experts and former Stroke patients, the Aide-Memoire Stroke (AIMS) App to help(More)
  • 1