Jeremiah Smith

Learn More
Sterility restrictions in surgical settings make touch-less interaction an interesting solution for surgeons to interact directly with digital images. The HCI community has already explored several methods for touch-less interaction including those based on camera-based gesture tracking and voice control. In this paper, we present a system for gesture-based(More)
Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive(More)
This paper is prompted by the overall question 'what is the most effective way to recognise disruptive smartphone interruptions?'. We design our experiments to answer 3 questions: 'Do users revise what they perceive as disruptive incoming calls as time goes by?', 'How do different types of machine-learners (lazy, eager, evolutionary, ensemble) perform on(More)
Sterility restrictions in surgical settings make touch-less interaction an interesting solution for surgeons to interact directly with digital images. In this demo, we present a system for gesture-based interaction with medical images based on a wristband inertial sensor and capacitive floor sensors, allowing for hand and foot gesture input. Hand gesture(More)
—In machine learning, concept drift can cause the optimal solution to a given problem to change as time passes, leading to less accurate predictions. Concept drift can be sudden, gradual or reoccuring. Understanding the consequences of concept drift is particularly important in human-centric applications where changes in the underlying data and environment(More)
  • 1