MagicWand: A Comparison of Gestural Affordances Between Cylindrical and Flat Display Form Factors
An affordance is the intrinsic ability of an object to allow an action. Affordances are generated by matching the fit of the body under action to the physical shape of the object. For example, a vertical door handle affords pulling, whereas a horizontal flat bar affords pushing to open. Modern smartphones with traditional flat screens offer poor affordances for gestural interactions. E.g., the flat form factor prevents a comfortable grip hindering wrist movements. Moreover, flat displays have a display area that can only be viewed from one side, and the visibility is reduced when the device is rotated. In this thesis, we present MagicWand, a cylindrical display device consisting of two 5.5” Flexible Organic Light-Emitting Diode (FOLED) screens wrapped around a 3D printed body. MagicWand features a smartphone running the Android operating system. Gesture recognition allows the use of the wand movements as a form of input. We were interested in exploring how a cylindrical form factor will offer physical affordances for actions that are quite different from those of a traditional flat form factor of a smartphone. To exhibit interactions with our prototype, an application scenario was developed where MagicWand was used as a game controller that can display a variety of 3D game elements. We believed that the physical affordance of this novel display device could affect the speed with which users learned specific gestures to operate the graphical elements on display. We conducted a user study to compare the guessability of gesture sets between flat and cylindrical device conditions. First, gestures were elicited for invoking common Graphical User Interface tasks in both device conditions, collecting over 100 unique gestures for 29 tasks. Second, a recognition engine was trained to recognize the top gestures for each task, for each device condition. Third, we measured the time it took for participants to discover these top gestures. The results of the study suggest significantly faster discovery of input gestures in the cylindrical condition over the flat device with 20 percent of gestures, specifically those involving tilt.