Wearable Augmented Reality headsets are inherently mobile: they enable hands-free and immersive interaction while on the go.Despite this, research into input methods that cater to mobility issues, such as the instabilities introduced by canonical tasks such as walking, remains in its infancy.This paper addresses this omission by presenting StabilizAR, a technique to enhance head cursor input while walking.It introduces a novel cursor velocity limit activated by the mutual alignment of head and eye vectors that enhances fine-grained targeting without compromising input speed during large-scale cursor motion.It integrates this with a target scoring system that reduces the precision required during selection by accruing proximity-based estimates of a user's intended target.Two studies show these combined techniques dramatically increase targeting performance-boosting success rates from 6% to 91% while mobile-and elevate measures of usability and user preference.They show StabilizAR's potential to enable genuinely mobile HMD use.

Touchscreen gestures are attracting research attention as an authentication method. While studies have showcased their usability, it has proven more complex to determine, let alone enhance, their security. Problems stem both from the small scale of current data sets and the fact that gestures are matched imprecisely - by a distance metric. This makes it challenging to assess entropy with traditional algorithms. To address these problems, we captured a large set of gesture passwords (N=2594) from crowd workers, and developed a security assessment framework that can calculate partial guessing entropy estimates, and generate dictionaries that crack 23.13% or more gestures in online attacks (within 20 guesses). To improve the entropy of gesture passwords, we designed novel blacklist and lexical policies to, respectively, restrict and inspire gesture creation. We close by validating both our security assessment framework and policies in a new crowd-sourced study (N=4000). Our blacklists increase entropy and resistance to dictionary based guessing attacks.

We empirically explore fundamental requirements for achieving VR in-air typing by observing the unconstrained eyes-free in-air typing of touch typists. We show that unconstrained typing movements differ substantively from previously observed constrained in-air typing movements and introduce a novel binary categorization of typing strategies: typists who use finger movements alone (FINGER) and those who combine finger movement with gross hand movement (HAND). We examine properties of finger kinematics, correlated movement of fingers, interrelation in consecutive key-strokes, and 3D distribution of key-stroke movements. We report that, compared to constrained typing, unconstrained typing generates shorter (49 mm) and faster (764 mm/s) key-strokes with a high correlation of finger movement and that the HAND strategy group exhibits more dynamic key-strokes. We discuss how these findings can inform the design of future in-air typing systems.

Wearable head-mounted displays combine rich graphical output with an impoverished input space. Hand-to-face gestures have been proposed as a way to add input expressivity while keeping control movements unobtrusive. To better understand how to design such techniques, we describe an elicitation study conducted in a busy public space in which pairs of users were asked to generate unobtrusive, socially acceptable hand-to-face input actions. Based on the results, we describe five design strategies: miniaturizing, obfuscating, screening, camouflaging and re-purposing. We instantiate these strategies in two hand-to-face input prototypes, one based on touches to the ear and the other based on touches of the thumbnail to the chin or cheek. Performance assessments characterize time and error rates with these devices. The paper closes with a validation study in which pairs of users experience the prototypes in a public setting and we gather data on the social acceptability of the designs and reflect on the effectiveness of the different strategies.

The steam superheater and the steam pressure systems are time delay systems that have poles near the origin in the left half plane or a pure integrator. Smith predictor can't be applied to these systems any more, because of the steady state error for the step disturbance. In this paper, a new Smith predictor controller is proposed for the steam superheater and the steam pressure. A disturbance observer to estimate an input disturbance and a new controller to eliminate an effect of a disturbance are proposed. The simulation results verifies the efficiency of the proposed system.