Enabling Expressive Keyboard Interaction with Finger, Hand, and Hand Posture Identification
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The input space of conventional physical keyboards is largely limited by the number of keys. To enable more actions than simply entering the symbol represented by a key, standard keyboards use combinations of modifier keys such as command, alternate, or shift to re-purpose the standard text entry behaviour. To explore alternatives to conventional keyboard shortcuts and enable more expressive keyboard interaction, this thesis first presents Finger-Aware Shortcuts, which encode information from finger, hand, and hand posture identification as keyboard shortcuts. By detecting the hand and finger used to press a key, and an open or closed hand posture, a key press can have multiple command mappings. A formative study revealed the performance and preference patterns when using different fingers and postures to press a key. The results were used to develop a computer vision algorithm to identify fingers and hands on a keyboard captured by a built-in laptop camera and a reflector. This algorithm was built into a background service to enable system-wide Finger-Aware Shortcut keys in any application. A controlled experiment used the service to compare the performance of Finger-Aware Shortcuts with existing methods. The results showed that Finger-Aware Shortcuts are comparable with a common class of shortcuts using multiple modifier keys. Several application demonstrations illustrate different use cases and mappings for Finger-Aware Shortcuts. To further explore how introducing finger awareness can help foster the learning and use of keyboard shortcuts, an interview study was conducted with expert computer users to identify the likely causes that hinder the adoption of keyboard shortcuts. Based on this, the concept of Finger-Aware Shortcuts is extended and two guided keyboard shortcut techniques are proposed: FingerArc and FingerChord. The two techniques provide dynamic visual guidance on the screen when users press and hold an alphabetical key semantically related to a set of commands. FingerArc differentiates these commands by examining the angle between the thumb and index finger; FingerChord differentiates these commands by allowing users to press different key areas using a second finger. The thesis contributes comprehensive evaluations of Finger-Aware Shortcuts and proof-of-concept demonstrations of FingerArc and FingerChord. Together, they contribute a novel interaction space that expands the conventional keyboard input space with more expressivity.
Cite this work
Jingjie Zheng (2017). Enabling Expressive Keyboard Interaction with Finger, Hand, and Hand Posture Identification. UWSpace. http://hdl.handle.net/10012/12371