This work compares four mid-air target selection methods (Push, Tap, Dwell, Pinch) with two types of ultrasonic haptic feedback (Select, HoverSelect) in a Fitts’ law experiment. Results revealed that Tap is the fastest, the most accurate, and one of the least physically and cognitively demanding selection methods. Pinch is relatively fast but error prone and physically and cognitively demanding. Dwell is slowest by design, yet the most accurate and the least physically and cognitively demanding. Both haptic feedback methods improve selection performance by increasing users’ spatial awareness. Particularly, Push augmented with Hover & Select feedback is comparable to Tap. Besides, participants perceive the selection methods as faster, more accurate, and more physically and cognitively comfortable with the haptic feedback methods.

Picking numbers is arguably the most frequently performed input task on smartwatches. This paper presents three new methods for picking numbers on smartwatches by performing directional swipes, twisting the wrist, and varying contact force on the screen. Unlike the default number picker, the proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements during the input process. We evaluated these methods in two user studies. The first compared the new methods with the default input stepper method in both stationary and mobile settings. The second compared them for individual numeric values and values embedded in text. In both studies, swipe yielded a significantly faster input rate. Participants also found the method faster, more accurate, and the least mentally and physically demanding compared to the other methods. Accuracy rates were comparable between the methods.

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Mobile clients for telepresence robots are cluttered with interactive elements that either leave a little room for the camera feeds or occlude them. Many do not provide meaningful feedback on the robot's state and most require the use of both hands. These make maneuvering telepresence robots difficult with mobile devices. TiltWalker enables controlling a telepresence robot with one hand using tilt gestures with a smartphone. In a series of studies, we first justify the use of a Web platform, determine how far and fast users can tilt without compromising the comfort and the legibility of the display content, and identify a velocity-based function well-suited for control-display mapping. We refine TiltWalker based on the findings of these studies, then compare it with a default method in the final study. Results revealed that TiltWalker is significantly faster and more accurate than the default method. Besides, participants preferred TiltWalker's interaction methods and graphical feedback significantly more than those of the default method.